# Copyright 2024 The HuggingFace Team. All rights reserved.
# Copyright (c) Alibaba, Inc. and its affiliates.
#
# 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.
#
# Based on [AnyText: Multilingual Visual Text Generation And Editing](https://huggingface.co/papers/2311.03054).
# Authors: Yuxiang Tuo, Wangmeng Xiang, Jun-Yan He, Yifeng Geng, Xuansong Xie
# Code: https://github.com/tyxsspa/AnyText with Apache-2.0 license
#
# Adapted to Diffusers by [M. Tolga Cangöz](https://github.com/tolgacangoz).


import inspect
import math
import os
import re
import sys
from functools import partial
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import cv2
import numpy as np
import PIL.Image
import torch
import torch.nn.functional as F
from bert_tokenizer import BasicTokenizer
from easydict import EasyDict as edict
from frozen_clip_embedder_t3 import FrozenCLIPEmbedderT3
from ocr_recog.RecModel import RecModel
from PIL import Image, ImageDraw, ImageFont
from safetensors.torch import load_file
from skimage.transform._geometric import _umeyama as get_sym_mat
from torch import nn
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection

from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
from diffusers.loaders import (
    FromSingleFileMixin,
    IPAdapterMixin,
    StableDiffusionLoraLoaderMixin,
    TextualInversionLoaderMixin,
)
from diffusers.models import AutoencoderKL, ControlNetModel, ImageProjection, UNet2DConditionModel
from diffusers.models.lora import adjust_lora_scale_text_encoder
from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
from diffusers.pipelines.stable_diffusion.pipeline_output import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import (
    USE_PEFT_BACKEND,
    deprecate,
    logging,
    replace_example_docstring,
    scale_lora_layers,
    unscale_lora_layers,
)
from diffusers.utils.torch_utils import is_compiled_module, is_torch_version, randn_tensor
from diffusers.configuration_utils import register_to_config, ConfigMixin
from diffusers.models.modeling_utils import ModelMixin


checker = BasicTokenizer()


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


EXAMPLE_DOC_STRING = """
    Examples:
        ```py
        >>> from pipeline_anytext import AnyTextPipeline
        >>> from anytext_controlnet import AnyTextControlNetModel
        >>> from diffusers import DDIMScheduler
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> # load control net and stable diffusion v1-5
        >>> text_controlnet = AnyTextControlNetModel.from_pretrained("tolgacangoz/anytext-controlnet", torch_dtype=torch.float16,
        ...                                                        variant="fp16",)
        >>> pipe = AnyTextPipeline.from_pretrained("tolgacangoz/anytext", controlnet=text_controlnet,
        ...                                          torch_dtype=torch.float16, variant="fp16",
        ...                                          ).to("cuda")

        >>> pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
        >>> # uncomment following line if PyTorch>=2.0 is not installed for memory optimization
        >>> #pipe.enable_xformers_memory_efficient_attention()

        >>> # uncomment following line if you want to offload the model to CPU for memory optimization
        >>> # also remove the `.to("cuda")` part
        >>> #pipe.enable_model_cpu_offload()

        >>> # generate image
        >>> generator = torch.Generator("cpu").manual_seed(66273235)
        >>> prompt = 'photo of caramel macchiato coffee on the table, top-down perspective, with "Any" "Text" written on it using cream'
        >>> draw_pos = load_image("www.huggingface.co/a/AnyText/tree/main/examples/gen9.png")
        >>> image = pipe(prompt, num_inference_steps=20, generator=generator, mode="generate",
        ...              draw_pos=draw_pos,
        ...              ).images[0]
        >>> image
        ```
"""


def get_clip_token_for_string(tokenizer, string):
    batch_encoding = tokenizer(
        string,
        truncation=True,
        max_length=77,
        return_length=True,
        return_overflowing_tokens=False,
        padding="max_length",
        return_tensors="pt",
    )
    tokens = batch_encoding["input_ids"]
    assert (
        torch.count_nonzero(tokens - 49407) == 2
    ), f"String '{string}' maps to more than a single token. Please use another string"
    return tokens[0, 1]


def get_recog_emb(encoder, img_list):
    _img_list = [(img.repeat(1, 3, 1, 1) * 255)[0] for img in img_list]
    encoder.predictor.eval()
    _, preds_neck = encoder.pred_imglist(_img_list, show_debug=False)
    return preds_neck


class EmbeddingManager(nn.Module):
    def __init__(
        self,
        embedder,
        placeholder_string="*",
        use_fp16=False,
    ):
        super().__init__()
        get_token_for_string = partial(get_clip_token_for_string, embedder.tokenizer)
        token_dim = 768
        self.get_recog_emb = None
        self.token_dim = token_dim

        self.proj = nn.Linear(40 * 64, token_dim)
        # self.proj.load_state_dict(load_file("proj.safetensors", device=str(embedder.device)))
        if use_fp16:
            self.proj = self.proj.to(dtype=torch.float16)

        self.placeholder_token = get_token_for_string(placeholder_string)

    @torch.no_grad()
    def encode_text(self, text_info):
        if self.get_recog_emb is None:
            self.get_recog_emb = partial(get_recog_emb, self.recog)

        gline_list = []
        for i in range(len(text_info["n_lines"])):  # sample index in a batch
            n_lines = text_info["n_lines"][i]
            for j in range(n_lines):  # line
                gline_list += [text_info["gly_line"][j][i : i + 1]]

        if len(gline_list) > 0:
            recog_emb = self.get_recog_emb(gline_list)
            enc_glyph = self.proj(recog_emb.reshape(recog_emb.shape[0], -1).to(self.proj.weight.dtype))

        self.text_embs_all = []
        n_idx = 0
        for i in range(len(text_info["n_lines"])):  # sample index in a batch
            n_lines = text_info["n_lines"][i]
            text_embs = []
            for j in range(n_lines):  # line
                text_embs += [enc_glyph[n_idx : n_idx + 1]]
                n_idx += 1
            self.text_embs_all += [text_embs]

    @torch.no_grad()
    def forward(
        self,
        tokenized_text,
        embedded_text,
    ):
        b, device = tokenized_text.shape[0], tokenized_text.device
        for i in range(b):
            idx = tokenized_text[i] == self.placeholder_token.to(device)
            if sum(idx) > 0:
                if i >= len(self.text_embs_all):
                    print("truncation for log images...")
                    break
                text_emb = torch.cat(self.text_embs_all[i], dim=0)
                if sum(idx) != len(text_emb):
                    print("truncation for long caption...")
                text_emb = text_emb.to(embedded_text.device)
                embedded_text[i][idx] = text_emb[: sum(idx)]
        return embedded_text

    def embedding_parameters(self):
        return self.parameters()


sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))


def min_bounding_rect(img):
    ret, thresh = cv2.threshold(img, 127, 255, 0)
    contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    if len(contours) == 0:
        print("Bad contours, using fake bbox...")
        return np.array([[0, 0], [100, 0], [100, 100], [0, 100]])
    max_contour = max(contours, key=cv2.contourArea)
    rect = cv2.minAreaRect(max_contour)
    box = cv2.boxPoints(rect)
    box = np.int0(box)
    # sort
    x_sorted = sorted(box, key=lambda x: x[0])
    left = x_sorted[:2]
    right = x_sorted[2:]
    left = sorted(left, key=lambda x: x[1])
    (tl, bl) = left
    right = sorted(right, key=lambda x: x[1])
    (tr, br) = right
    if tl[1] > bl[1]:
        (tl, bl) = (bl, tl)
    if tr[1] > br[1]:
        (tr, br) = (br, tr)
    return np.array([tl, tr, br, bl])


def adjust_image(box, img):
    pts1 = np.float32([box[0], box[1], box[2], box[3]])
    width = max(np.linalg.norm(pts1[0] - pts1[1]), np.linalg.norm(pts1[2] - pts1[3]))
    height = max(np.linalg.norm(pts1[0] - pts1[3]), np.linalg.norm(pts1[1] - pts1[2]))
    pts2 = np.float32([[0, 0], [width, 0], [width, height], [0, height]])
    # get transform matrix
    M = get_sym_mat(pts1, pts2, estimate_scale=True)
    C, H, W = img.shape
    T = np.array([[2 / W, 0, -1], [0, 2 / H, -1], [0, 0, 1]])
    theta = np.linalg.inv(T @ M @ np.linalg.inv(T))
    theta = torch.from_numpy(theta[:2, :]).unsqueeze(0).type(torch.float32).to(img.device)
    grid = F.affine_grid(theta, torch.Size([1, C, H, W]), align_corners=True)
    result = F.grid_sample(img.unsqueeze(0), grid, align_corners=True)
    result = torch.clamp(result.squeeze(0), 0, 255)
    # crop
    result = result[:, : int(height), : int(width)]
    return result


"""
mask: numpy.ndarray, mask of textual, HWC
src_img: torch.Tensor, source image, CHW
"""


def crop_image(src_img, mask):
    box = min_bounding_rect(mask)
    result = adjust_image(box, src_img)
    if len(result.shape) == 2:
        result = torch.stack([result] * 3, axis=-1)
    return result


def create_predictor(model_dir=None, model_lang="ch", device="cpu", use_fp16=False):
    model_file_path = model_dir
    if model_file_path is not None and not os.path.exists(model_file_path):
        raise ValueError("not find model file path {}".format(model_file_path))

    if model_lang == "ch":
        n_class = 6625
    elif model_lang == "en":
        n_class = 97
    else:
        raise ValueError(f"Unsupported OCR recog model_lang: {model_lang}")
    rec_config = edict(
        in_channels=3,
        backbone=edict(type="MobileNetV1Enhance", scale=0.5, last_conv_stride=[1, 2], last_pool_type="avg"),
        neck=edict(type="SequenceEncoder", encoder_type="svtr", dims=64, depth=2, hidden_dims=120, use_guide=True),
        head=edict(type="CTCHead", fc_decay=0.00001, out_channels=n_class, return_feats=True),
    )

    rec_model = RecModel(rec_config)
    if model_file_path is not None:
        rec_model.load_state_dict(torch.load(model_file_path, map_location=device))
    return rec_model


def _check_image_file(path):
    img_end = ("tiff", "tif", "bmp", "rgb", "jpg", "png", "jpeg")
    return path.lower().endswith(tuple(img_end))


def get_image_file_list(img_file):
    imgs_lists = []
    if img_file is None or not os.path.exists(img_file):
        raise Exception("not found any img file in {}".format(img_file))
    if os.path.isfile(img_file) and _check_image_file(img_file):
        imgs_lists.append(img_file)
    elif os.path.isdir(img_file):
        for single_file in os.listdir(img_file):
            file_path = os.path.join(img_file, single_file)
            if os.path.isfile(file_path) and _check_image_file(file_path):
                imgs_lists.append(file_path)
    if len(imgs_lists) == 0:
        raise Exception("not found any img file in {}".format(img_file))
    imgs_lists = sorted(imgs_lists)
    return imgs_lists


class TextRecognizer(object):
    def __init__(self, args, predictor):
        self.rec_image_shape = [int(v) for v in args["rec_image_shape"].split(",")]
        self.rec_batch_num = args["rec_batch_num"]
        self.predictor = predictor
        self.chars = self.get_char_dict(args["rec_char_dict_path"])
        self.char2id = {x: i for i, x in enumerate(self.chars)}
        self.is_onnx = not isinstance(self.predictor, torch.nn.Module)
        self.use_fp16 = args["use_fp16"]

    # img: CHW
    def resize_norm_img(self, img, max_wh_ratio):
        imgC, imgH, imgW = self.rec_image_shape
        assert imgC == img.shape[0]
        imgW = int((imgH * max_wh_ratio))

        h, w = img.shape[1:]
        ratio = w / float(h)
        if math.ceil(imgH * ratio) > imgW:
            resized_w = imgW
        else:
            resized_w = int(math.ceil(imgH * ratio))
        resized_image = torch.nn.functional.interpolate(
            img.unsqueeze(0),
            size=(imgH, resized_w),
            mode="bilinear",
            align_corners=True,
        )
        resized_image /= 255.0
        resized_image -= 0.5
        resized_image /= 0.5
        padding_im = torch.zeros((imgC, imgH, imgW), dtype=torch.float32).to(img.device)
        padding_im[:, :, 0:resized_w] = resized_image[0]
        return padding_im

    # img_list: list of tensors with shape chw 0-255
    def pred_imglist(self, img_list, show_debug=False):
        img_num = len(img_list)
        assert img_num > 0
        # Calculate the aspect ratio of all text bars
        width_list = []
        for img in img_list:
            width_list.append(img.shape[2] / float(img.shape[1]))
        # Sorting can speed up the recognition process
        indices = torch.from_numpy(np.argsort(np.array(width_list)))
        batch_num = self.rec_batch_num
        preds_all = [None] * img_num
        preds_neck_all = [None] * img_num
        for beg_img_no in range(0, img_num, batch_num):
            end_img_no = min(img_num, beg_img_no + batch_num)
            norm_img_batch = []

            imgC, imgH, imgW = self.rec_image_shape[:3]
            max_wh_ratio = imgW / imgH
            for ino in range(beg_img_no, end_img_no):
                h, w = img_list[indices[ino]].shape[1:]
                if h > w * 1.2:
                    img = img_list[indices[ino]]
                    img = torch.transpose(img, 1, 2).flip(dims=[1])
                    img_list[indices[ino]] = img
                    h, w = img.shape[1:]
                # wh_ratio = w * 1.0 / h
                # max_wh_ratio = max(max_wh_ratio, wh_ratio)  # comment to not use different ratio
            for ino in range(beg_img_no, end_img_no):
                norm_img = self.resize_norm_img(img_list[indices[ino]], max_wh_ratio)
                if self.use_fp16:
                    norm_img = norm_img.half()
                norm_img = norm_img.unsqueeze(0)
                norm_img_batch.append(norm_img)
            norm_img_batch = torch.cat(norm_img_batch, dim=0)
            if show_debug:
                for i in range(len(norm_img_batch)):
                    _img = norm_img_batch[i].permute(1, 2, 0).detach().cpu().numpy()
                    _img = (_img + 0.5) * 255
                    _img = _img[:, :, ::-1]
                    file_name = f"{indices[beg_img_no + i]}"
                    if os.path.exists(file_name + ".jpg"):
                        file_name += "_2"  # ori image
                    cv2.imwrite(file_name + ".jpg", _img)
            if self.is_onnx:
                input_dict = {}
                input_dict[self.predictor.get_inputs()[0].name] = norm_img_batch.detach().cpu().numpy()
                outputs = self.predictor.run(None, input_dict)
                preds = {}
                preds["ctc"] = torch.from_numpy(outputs[0])
                preds["ctc_neck"] = [torch.zeros(1)] * img_num
            else:
                preds = self.predictor(norm_img_batch)
            for rno in range(preds["ctc"].shape[0]):
                preds_all[indices[beg_img_no + rno]] = preds["ctc"][rno]
                preds_neck_all[indices[beg_img_no + rno]] = preds["ctc_neck"][rno]

        return torch.stack(preds_all, dim=0), torch.stack(preds_neck_all, dim=0)

    def get_char_dict(self, character_dict_path):
        character_str = []
        with open(character_dict_path, "rb") as fin:
            lines = fin.readlines()
            for line in lines:
                line = line.decode("utf-8").strip("\n").strip("\r\n")
                character_str.append(line)
        dict_character = list(character_str)
        dict_character = ["sos"] + dict_character + [" "]  # eos is space
        return dict_character

    def get_text(self, order):
        char_list = [self.chars[text_id] for text_id in order]
        return "".join(char_list)

    def decode(self, mat):
        text_index = mat.detach().cpu().numpy().argmax(axis=1)
        ignored_tokens = [0]
        selection = np.ones(len(text_index), dtype=bool)
        selection[1:] = text_index[1:] != text_index[:-1]
        for ignored_token in ignored_tokens:
            selection &= text_index != ignored_token
        return text_index[selection], np.where(selection)[0]

    def get_ctcloss(self, preds, gt_text, weight):
        if not isinstance(weight, torch.Tensor):
            weight = torch.tensor(weight).to(preds.device)
        ctc_loss = torch.nn.CTCLoss(reduction="none")
        log_probs = preds.log_softmax(dim=2).permute(1, 0, 2)  # NTC-->TNC
        targets = []
        target_lengths = []
        for t in gt_text:
            targets += [self.char2id.get(i, len(self.chars) - 1) for i in t]
            target_lengths += [len(t)]
        targets = torch.tensor(targets).to(preds.device)
        target_lengths = torch.tensor(target_lengths).to(preds.device)
        input_lengths = torch.tensor([log_probs.shape[0]] * (log_probs.shape[1])).to(preds.device)
        loss = ctc_loss(log_probs, targets, input_lengths, target_lengths)
        loss = loss / input_lengths * weight
        return loss


class TextEmbeddingModule(ModelMixin, ConfigMixin):
    @register_to_config
    def __init__(self, font_path, use_fp16=False, device="cpu"):
        super().__init__()
        # TODO: Learn if the recommended font file is free to use
        self.font = ImageFont.truetype(font_path, 60)
        self.frozen_CLIP_embedder_t3 = FrozenCLIPEmbedderT3(device=device, use_fp16=use_fp16)
        self.embedding_manager = EmbeddingManager(self.frozen_CLIP_embedder_t3, use_fp16=use_fp16)
        rec_model_dir = "./OCR/ppv3_rec.pth"
        self.text_predictor = create_predictor(rec_model_dir, device=device, use_fp16=use_fp16).eval()
        args = {}
        args["rec_image_shape"] = "3, 48, 320"
        args["rec_batch_num"] = 6
        args["rec_char_dict_path"] = "OCR/ppocr_keys_v1.txt"
        args["use_fp16"] = self.use_fp16
        self.embedding_manager.recog = TextRecognizer(args, self.text_predictor)

    @torch.no_grad()
    def forward(
        self,
        prompt,
        texts,
        negative_prompt,
        num_images_per_prompt,
        mode,
        draw_pos,
        sort_priority="↕",
        max_chars=77,
        revise_pos=False,
        h=512,
        w=512,
    ):
        if prompt is None and texts is None:
            raise ValueError("Prompt or texts must be provided!")
        # preprocess pos_imgs(if numpy, make sure it's white pos in black bg)
        if draw_pos is None:
            pos_imgs = np.zeros((w, h, 1))
        if isinstance(draw_pos, str):
            draw_pos = cv2.imread(draw_pos)[..., ::-1]
            if draw_pos is None:
                raise ValueError(f"Can't read draw_pos image from {draw_pos}!")
            pos_imgs = 255 - draw_pos
        elif isinstance(draw_pos, torch.Tensor):
            pos_imgs = draw_pos.cpu().numpy()
        else:
            if not isinstance(draw_pos, np.ndarray):
                raise ValueError(f"Unknown format of draw_pos: {type(draw_pos)}")
        if mode == "edit":
            pos_imgs = cv2.resize(pos_imgs, (w, h))
        pos_imgs = pos_imgs[..., 0:1]
        pos_imgs = cv2.convertScaleAbs(pos_imgs)
        _, pos_imgs = cv2.threshold(pos_imgs, 254, 255, cv2.THRESH_BINARY)
        # separate pos_imgs
        pos_imgs = self.separate_pos_imgs(pos_imgs, sort_priority)
        if len(pos_imgs) == 0:
            pos_imgs = [np.zeros((h, w, 1))]
        n_lines = len(texts)
        if len(pos_imgs) < n_lines:
            if n_lines == 1 and texts[0] == " ":
                pass  # text-to-image without text
            else:
                raise ValueError(
                    f"Found {len(pos_imgs)} positions that < needed {n_lines} from prompt, check and try again!"
                )
        elif len(pos_imgs) > n_lines:
            str_warning = f"Warning: found {len(pos_imgs)} positions that > needed {n_lines} from prompt."
            logger.warning(str_warning)
        # get pre_pos, poly_list, hint that needed for anytext
        pre_pos = []
        poly_list = []
        for input_pos in pos_imgs:
            if input_pos.mean() != 0:
                input_pos = input_pos[..., np.newaxis] if len(input_pos.shape) == 2 else input_pos
                poly, pos_img = self.find_polygon(input_pos)
                pre_pos += [pos_img / 255.0]
                poly_list += [poly]
            else:
                pre_pos += [np.zeros((h, w, 1))]
                poly_list += [None]
        np_hint = np.sum(pre_pos, axis=0).clip(0, 1)
        # prepare info dict
        text_info = {}
        text_info["glyphs"] = []
        text_info["gly_line"] = []
        text_info["positions"] = []
        text_info["n_lines"] = [len(texts)] * num_images_per_prompt
        for i in range(len(texts)):
            text = texts[i]
            if len(text) > max_chars:
                str_warning = f'"{text}" length > max_chars: {max_chars}, will be cut off...'
                logger.warning(str_warning)
                text = text[:max_chars]
            gly_scale = 2
            if pre_pos[i].mean() != 0:
                gly_line = self.draw_glyph(self.font, text)
                glyphs = self.draw_glyph2(
                    self.font, text, poly_list[i], scale=gly_scale, width=w, height=h, add_space=False
                )
                if revise_pos:
                    resize_gly = cv2.resize(glyphs, (pre_pos[i].shape[1], pre_pos[i].shape[0]))
                    new_pos = cv2.morphologyEx(
                        (resize_gly * 255).astype(np.uint8),
                        cv2.MORPH_CLOSE,
                        kernel=np.ones((resize_gly.shape[0] // 10, resize_gly.shape[1] // 10), dtype=np.uint8),
                        iterations=1,
                    )
                    new_pos = new_pos[..., np.newaxis] if len(new_pos.shape) == 2 else new_pos
                    contours, _ = cv2.findContours(new_pos, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
                    if len(contours) != 1:
                        str_warning = f"Fail to revise position {i} to bounding rect, remain position unchanged..."
                        logger.warning(str_warning)
                    else:
                        rect = cv2.minAreaRect(contours[0])
                        poly = np.int0(cv2.boxPoints(rect))
                        pre_pos[i] = cv2.drawContours(new_pos, [poly], -1, 255, -1) / 255.0
            else:
                glyphs = np.zeros((h * gly_scale, w * gly_scale, 1))
                gly_line = np.zeros((80, 512, 1))
            pos = pre_pos[i]
            text_info["glyphs"] += [self.arr2tensor(glyphs, num_images_per_prompt)]
            text_info["gly_line"] += [self.arr2tensor(gly_line, num_images_per_prompt)]
            text_info["positions"] += [self.arr2tensor(pos, num_images_per_prompt)]

        # hint = self.arr2tensor(np_hint, len(prompt))

        self.embedding_manager.encode_text(text_info)
        prompt_embeds = self.frozen_CLIP_embedder_t3.encode([prompt], embedding_manager=self.embedding_manager)

        self.embedding_manager.encode_text(text_info)
        negative_prompt_embeds = self.frozen_CLIP_embedder_t3.encode(
            [negative_prompt], embedding_manager=self.embedding_manager
        )

        return prompt_embeds, negative_prompt_embeds, text_info, np_hint

    def arr2tensor(self, arr, bs):
        arr = np.transpose(arr, (2, 0, 1))
        _arr = torch.from_numpy(arr.copy()).float().cpu()
        if self.use_fp16:
            _arr = _arr.half()
        _arr = torch.stack([_arr for _ in range(bs)], dim=0)
        return _arr

    def separate_pos_imgs(self, img, sort_priority, gap=102):
        num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(img)
        components = []
        for label in range(1, num_labels):
            component = np.zeros_like(img)
            component[labels == label] = 255
            components.append((component, centroids[label]))
        if sort_priority == "↕":
            fir, sec = 1, 0  # top-down first
        elif sort_priority == "↔":
            fir, sec = 0, 1  # left-right first
        else:
            raise ValueError(f"Unknown sort_priority: {sort_priority}")
        components.sort(key=lambda c: (c[1][fir] // gap, c[1][sec] // gap))
        sorted_components = [c[0] for c in components]
        return sorted_components

    def find_polygon(self, image, min_rect=False):
        contours, hierarchy = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
        max_contour = max(contours, key=cv2.contourArea)  # get contour with max area
        if min_rect:
            # get minimum enclosing rectangle
            rect = cv2.minAreaRect(max_contour)
            poly = np.int0(cv2.boxPoints(rect))
        else:
            # get approximate polygon
            epsilon = 0.01 * cv2.arcLength(max_contour, True)
            poly = cv2.approxPolyDP(max_contour, epsilon, True)
            n, _, xy = poly.shape
            poly = poly.reshape(n, xy)
        cv2.drawContours(image, [poly], -1, 255, -1)
        return poly, image

    def draw_glyph(self, font, text):
        g_size = 50
        W, H = (512, 80)
        new_font = font.font_variant(size=g_size)
        img = Image.new(mode="1", size=(W, H), color=0)
        draw = ImageDraw.Draw(img)
        left, top, right, bottom = new_font.getbbox(text)
        text_width = max(right - left, 5)
        text_height = max(bottom - top, 5)
        ratio = min(W * 0.9 / text_width, H * 0.9 / text_height)
        new_font = font.font_variant(size=int(g_size * ratio))

        text_width, text_height = new_font.getsize(text)
        offset_x, offset_y = new_font.getoffset(text)
        x = (img.width - text_width) // 2
        y = (img.height - text_height) // 2 - offset_y // 2
        draw.text((x, y), text, font=new_font, fill="white")
        img = np.expand_dims(np.array(img), axis=2).astype(np.float64)
        return img

    def draw_glyph2(self, font, text, polygon, vertAng=10, scale=1, width=512, height=512, add_space=True):
        enlarge_polygon = polygon * scale
        rect = cv2.minAreaRect(enlarge_polygon)
        box = cv2.boxPoints(rect)
        box = np.int0(box)
        w, h = rect[1]
        angle = rect[2]
        if angle < -45:
            angle += 90
        angle = -angle
        if w < h:
            angle += 90

        vert = False
        if abs(angle) % 90 < vertAng or abs(90 - abs(angle) % 90) % 90 < vertAng:
            _w = max(box[:, 0]) - min(box[:, 0])
            _h = max(box[:, 1]) - min(box[:, 1])
            if _h >= _w:
                vert = True
                angle = 0

        img = np.zeros((height * scale, width * scale, 3), np.uint8)
        img = Image.fromarray(img)

        # infer font size
        image4ratio = Image.new("RGB", img.size, "white")
        draw = ImageDraw.Draw(image4ratio)
        _, _, _tw, _th = draw.textbbox(xy=(0, 0), text=text, font=font)
        text_w = min(w, h) * (_tw / _th)
        if text_w <= max(w, h):
            # add space
            if len(text) > 1 and not vert and add_space:
                for i in range(1, 100):
                    text_space = self.insert_spaces(text, i)
                    _, _, _tw2, _th2 = draw.textbbox(xy=(0, 0), text=text_space, font=font)
                    if min(w, h) * (_tw2 / _th2) > max(w, h):
                        break
                text = self.insert_spaces(text, i - 1)
            font_size = min(w, h) * 0.80
        else:
            shrink = 0.75 if vert else 0.85
            font_size = min(w, h) / (text_w / max(w, h)) * shrink
        new_font = font.font_variant(size=int(font_size))

        left, top, right, bottom = new_font.getbbox(text)
        text_width = right - left
        text_height = bottom - top

        layer = Image.new("RGBA", img.size, (0, 0, 0, 0))
        draw = ImageDraw.Draw(layer)
        if not vert:
            draw.text(
                (rect[0][0] - text_width // 2, rect[0][1] - text_height // 2 - top),
                text,
                font=new_font,
                fill=(255, 255, 255, 255),
            )
        else:
            x_s = min(box[:, 0]) + _w // 2 - text_height // 2
            y_s = min(box[:, 1])
            for c in text:
                draw.text((x_s, y_s), c, font=new_font, fill=(255, 255, 255, 255))
                _, _t, _, _b = new_font.getbbox(c)
                y_s += _b

        rotated_layer = layer.rotate(angle, expand=1, center=(rect[0][0], rect[0][1]))

        x_offset = int((img.width - rotated_layer.width) / 2)
        y_offset = int((img.height - rotated_layer.height) / 2)
        img.paste(rotated_layer, (x_offset, y_offset), rotated_layer)
        img = np.expand_dims(np.array(img.convert("1")), axis=2).astype(np.float64)
        return img

    def insert_spaces(self, string, nSpace):
        if nSpace == 0:
            return string
        new_string = ""
        for char in string:
            new_string += char + " " * nSpace
        return new_string[:-nSpace]

    def to(self, *args, **kwargs):
        self.frozen_CLIP_embedder_t3 = self.frozen_CLIP_embedder_t3.to(*args, **kwargs)
        self.embedding_manager = self.embedding_manager.to(*args, **kwargs)
        self.text_predictor = self.text_predictor.to(*args, **kwargs)
        self.device = self.frozen_CLIP_embedder_t3.device
        return self


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


class AuxiliaryLatentModule(nn.Module):
    def __init__(
        self,
        font_path,
        vae=None,
        device="cpu",
        use_fp16=False,
    ):
        super().__init__()
        self.font = ImageFont.truetype(font_path, 60)
        self.use_fp16 = use_fp16
        self.device = device

        self.vae = vae.eval() if vae is not None else None

    @torch.no_grad()
    def forward(
        self,
        text_info,
        mode,
        draw_pos,
        ori_image,
        num_images_per_prompt,
        np_hint,
        h=512,
        w=512,
    ):
        if mode == "generate":
            edit_image = np.ones((h, w, 3)) * 127.5  # empty mask image
        elif mode == "edit":
            if draw_pos is None or ori_image is None:
                raise ValueError("Reference image and position image are needed for text editing!")
            if isinstance(ori_image, str):
                ori_image = cv2.imread(ori_image)[..., ::-1]
                if ori_image is None:
                    raise ValueError(f"Can't read ori_image image from {ori_image}!")
            elif isinstance(ori_image, torch.Tensor):
                ori_image = ori_image.cpu().numpy()
            else:
                if not isinstance(ori_image, np.ndarray):
                    raise ValueError(f"Unknown format of ori_image: {type(ori_image)}")
            edit_image = ori_image.clip(1, 255)  # for mask reason
            edit_image = self.check_channels(edit_image)
            edit_image = self.resize_image(
                edit_image, max_length=768
            )  # make w h multiple of 64, resize if w or h > max_length

        # get masked_x
        masked_img = ((edit_image.astype(np.float32) / 127.5) - 1.0) * (1 - np_hint)
        masked_img = np.transpose(masked_img, (2, 0, 1))
        masked_img = torch.from_numpy(masked_img.copy()).float().to(self.device)
        if self.use_fp16:
            masked_img = masked_img.half()
        masked_x = (retrieve_latents(self.vae.encode(masked_img[None, ...])) * self.vae.config.scaling_factor).detach()
        if self.use_fp16:
            masked_x = masked_x.half()
        text_info["masked_x"] = torch.cat([masked_x for _ in range(num_images_per_prompt)], dim=0)

        glyphs = torch.cat(text_info["glyphs"], dim=1).sum(dim=1, keepdim=True)
        positions = torch.cat(text_info["positions"], dim=1).sum(dim=1, keepdim=True)

        return glyphs, positions, text_info

    def check_channels(self, image):
        channels = image.shape[2] if len(image.shape) == 3 else 1
        if channels == 1:
            image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
        elif channels > 3:
            image = image[:, :, :3]
        return image

    def resize_image(self, img, max_length=768):
        height, width = img.shape[:2]
        max_dimension = max(height, width)

        if max_dimension > max_length:
            scale_factor = max_length / max_dimension
            new_width = int(round(width * scale_factor))
            new_height = int(round(height * scale_factor))
            new_size = (new_width, new_height)
            img = cv2.resize(img, new_size)
        height, width = img.shape[:2]
        img = cv2.resize(img, (width - (width % 64), height - (height % 64)))
        return img

    def insert_spaces(self, string, nSpace):
        if nSpace == 0:
            return string
        new_string = ""
        for char in string:
            new_string += char + " " * nSpace
        return new_string[:-nSpace]

    def to(self, device):
        self.device = device
        self.vae = self.vae.to(device)
        return self


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

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

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


class AnyTextPipeline(
    DiffusionPipeline,
    StableDiffusionMixin,
    TextualInversionLoaderMixin,
    StableDiffusionLoraLoaderMixin,
    IPAdapterMixin,
    FromSingleFileMixin,
):
    r"""
    Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance.

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

    The pipeline also inherits the following loading methods:
        - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
        - [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
        - [`~loaders.StableDiffusionLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
        - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
        - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters

    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.
        unet ([`UNet2DConditionModel`]):
            A `UNet2DConditionModel` to denoise the encoded image latents.
        controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):
            Provides additional conditioning to the `unet` during the denoising process. If you set multiple
            ControlNets as a list, the outputs from each ControlNet are added together to create one combined
            additional conditioning.
        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`.
    """

    model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
    _optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
    _exclude_from_cpu_offload = ["safety_checker"]
    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]

    def __init__(
        self,
        font_path: str,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        tokenizer: CLIPTokenizer,
        unet: UNet2DConditionModel,
        controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],
        scheduler: KarrasDiffusionSchedulers,
        safety_checker: StableDiffusionSafetyChecker,
        feature_extractor: CLIPImageProcessor,
        image_encoder: CLIPVisionModelWithProjection = None,
        requires_safety_checker: bool = True,
    ):
        super().__init__()
        self.text_embedding_module = TextEmbeddingModule(
            use_fp16=unet.dtype == torch.float16, device=unet.device, font_path=font_path
        )
        self.auxiliary_latent_module = AuxiliaryLatentModule(
            vae=vae, use_fp16=unet.dtype == torch.float16, device=unet.device, font_path=font_path
        )

        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."
            )

        if isinstance(controlnet, (list, tuple)):
            controlnet = MultiControlNetModel(controlnet)

        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            unet=unet,
            controlnet=controlnet,
            scheduler=scheduler,
            safety_checker=safety_checker,
            feature_extractor=feature_extractor,
            image_encoder=image_encoder,
            text_embedding_module=self.text_embedding_module,
            auxiliary_latent_module=self.auxiliary_latent_module,
        )
        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.control_image_processor = VaeImageProcessor(
            vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False
        )
        self.register_to_config(requires_safety_checker=requires_safety_checker, font_path=font_path)

    def modify_prompt(self, prompt):
        prompt = prompt.replace("“", '"')
        prompt = prompt.replace("”", '"')
        p = '"(.*?)"'
        strs = re.findall(p, prompt)
        if len(strs) == 0:
            strs = [" "]
        else:
            for s in strs:
                prompt = prompt.replace(f'"{s}"', f" {PLACE_HOLDER} ", 1)
        if self.is_chinese(prompt):
            if self.trans_pipe is None:
                return None, None
            old_prompt = prompt
            prompt = self.trans_pipe(input=prompt + " .")["translation"][:-1]
            print(f"Translate: {old_prompt} --> {prompt}")
        return prompt, strs

    def is_chinese(self, text):
        text = checker._clean_text(text)
        for char in text:
            cp = ord(char)
            if checker._is_chinese_char(cp):
                return True
        return False

    # 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.Tensor] = None,
        negative_prompt_embeds: Optional[torch.Tensor] = None,
        lora_scale: Optional[float] = None,
        **kwargs,
    ):
        deprecation_message = "`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple."
        deprecate("_encode_prompt()", "1.0.0", deprecation_message, standard_warn=False)

        prompt_embeds_tuple = self.encode_prompt(
            prompt=prompt,
            device=device,
            num_images_per_prompt=num_images_per_prompt,
            do_classifier_free_guidance=do_classifier_free_guidance,
            negative_prompt=negative_prompt,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            lora_scale=lora_scale,
            **kwargs,
        )

        # concatenate for backwards comp
        prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])

        return prompt_embeds

    # 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.Tensor] = None,
        negative_prompt_embeds: Optional[torch.Tensor] = None,
        lora_scale: Optional[float] = None,
        clip_skip: Optional[int] = None,
    ):
        r"""
        Encodes the prompt into text encoder hidden states.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                prompt to be encoded
            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.Tensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                provided, text embeddings will be generated from `prompt` input argument.
            negative_prompt_embeds (`torch.Tensor`, *optional*):
                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
                argument.
            lora_scale (`float`, *optional*):
                A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
            clip_skip (`int`, *optional*):
                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
                the output of the pre-final layer will be used for computing the prompt embeddings.
        """
        # 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, StableDiffusionLoraLoaderMixin):
            self._lora_scale = lora_scale

            # dynamically adjust the LoRA scale
            if not USE_PEFT_BACKEND:
                adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
            else:
                scale_lora_layers(self.text_encoder, lora_scale)

        if 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: process 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

            if clip_skip is None:
                prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
                prompt_embeds = prompt_embeds[0]
            else:
                prompt_embeds = self.text_encoder(
                    text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True
                )
                # Access the `hidden_states` first, that contains a tuple of
                # all the hidden states from the encoder layers. Then index into
                # the tuple to access the hidden states from the desired layer.
                prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
                # We also need to apply the final LayerNorm here to not mess with the
                # representations. The `last_hidden_states` that we typically use for
                # obtaining the final prompt representations passes through the LayerNorm
                # layer.
                prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)

        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: process 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)

        if self.text_encoder is not None:
            if isinstance(self, StableDiffusionLoraLoaderMixin) and USE_PEFT_BACKEND:
                # Retrieve the original scale by scaling back the LoRA layers
                unscale_lora_layers(self.text_encoder, lora_scale)

        return prompt_embeds, negative_prompt_embeds

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
    def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
        dtype = next(self.image_encoder.parameters()).dtype

        if not isinstance(image, torch.Tensor):
            image = self.feature_extractor(image, return_tensors="pt").pixel_values

        image = image.to(device=device, dtype=dtype)
        if output_hidden_states:
            image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
            image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
            uncond_image_enc_hidden_states = self.image_encoder(
                torch.zeros_like(image), output_hidden_states=True
            ).hidden_states[-2]
            uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(
                num_images_per_prompt, dim=0
            )
            return image_enc_hidden_states, uncond_image_enc_hidden_states
        else:
            image_embeds = self.image_encoder(image).image_embeds
            image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
            uncond_image_embeds = torch.zeros_like(image_embeds)

            return image_embeds, uncond_image_embeds

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
    def prepare_ip_adapter_image_embeds(
        self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance
    ):
        image_embeds = []
        if do_classifier_free_guidance:
            negative_image_embeds = []
        if ip_adapter_image_embeds is None:
            if not isinstance(ip_adapter_image, list):
                ip_adapter_image = [ip_adapter_image]

            if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):
                raise ValueError(
                    f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters."
                )

            for single_ip_adapter_image, image_proj_layer in zip(
                ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
            ):
                output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
                single_image_embeds, single_negative_image_embeds = self.encode_image(
                    single_ip_adapter_image, device, 1, output_hidden_state
                )

                image_embeds.append(single_image_embeds[None, :])
                if do_classifier_free_guidance:
                    negative_image_embeds.append(single_negative_image_embeds[None, :])
        else:
            for single_image_embeds in ip_adapter_image_embeds:
                if do_classifier_free_guidance:
                    single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)
                    negative_image_embeds.append(single_negative_image_embeds)
                image_embeds.append(single_image_embeds)

        ip_adapter_image_embeds = []
        for i, single_image_embeds in enumerate(image_embeds):
            single_image_embeds = torch.cat([single_image_embeds] * num_images_per_prompt, dim=0)
            if do_classifier_free_guidance:
                single_negative_image_embeds = torch.cat([negative_image_embeds[i]] * num_images_per_prompt, dim=0)
                single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds], dim=0)

            single_image_embeds = single_image_embeds.to(device=device)
            ip_adapter_image_embeds.append(single_image_embeds)

        return ip_adapter_image_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.decode_latents
    def decode_latents(self, latents):
        deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead"
        deprecate("decode_latents", "1.0.0", deprecation_message, standard_warn=False)

        latents = 1 / self.vae.config.scaling_factor * latents
        image = self.vae.decode(latents, return_dict=False)[0]
        image = (image / 2 + 0.5).clamp(0, 1)
        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
        return image

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

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

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

    def check_inputs(
        self,
        prompt,
        # image,
        callback_steps,
        negative_prompt=None,
        prompt_embeds=None,
        negative_prompt_embeds=None,
        ip_adapter_image=None,
        ip_adapter_image_embeds=None,
        controlnet_conditioning_scale=1.0,
        control_guidance_start=0.0,
        control_guidance_end=1.0,
        callback_on_step_end_tensor_inputs=None,
    ):
        if 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 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 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}."
                )

        # Check `image`
        is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance(
            self.controlnet, torch._dynamo.eval_frame.OptimizedModule
        )

        # Check `controlnet_conditioning_scale`
        if (
            isinstance(self.controlnet, ControlNetModel)
            or is_compiled
            and isinstance(self.controlnet._orig_mod, ControlNetModel)
        ):
            if not isinstance(controlnet_conditioning_scale, float):
                print(controlnet_conditioning_scale)
                raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.")
        elif (
            isinstance(self.controlnet, MultiControlNetModel)
            or is_compiled
            and isinstance(self.controlnet._orig_mod, MultiControlNetModel)
        ):
            if isinstance(controlnet_conditioning_scale, list):
                if any(isinstance(i, list) for i in controlnet_conditioning_scale):
                    raise ValueError(
                        "A single batch of varying conditioning scale settings (e.g. [[1.0, 0.5], [0.2, 0.8]]) is not supported at the moment. "
                        "The conditioning scale must be fixed across the batch."
                    )
            elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(
                self.controlnet.nets
            ):
                raise ValueError(
                    "For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have"
                    " the same length as the number of controlnets"
                )
        else:
            assert False

        if not isinstance(control_guidance_start, (tuple, list)):
            control_guidance_start = [control_guidance_start]

        if not isinstance(control_guidance_end, (tuple, list)):
            control_guidance_end = [control_guidance_end]

        if len(control_guidance_start) != len(control_guidance_end):
            raise ValueError(
                f"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list."
            )

        if isinstance(self.controlnet, MultiControlNetModel):
            if len(control_guidance_start) != len(self.controlnet.nets):
                raise ValueError(
                    f"`control_guidance_start`: {control_guidance_start} has {len(control_guidance_start)} elements but there are {len(self.controlnet.nets)} controlnets available. Make sure to provide {len(self.controlnet.nets)}."
                )

        for start, end in zip(control_guidance_start, control_guidance_end):
            if start >= end:
                raise ValueError(
                    f"control guidance start: {start} cannot be larger or equal to control guidance end: {end}."
                )
            if start < 0.0:
                raise ValueError(f"control guidance start: {start} can't be smaller than 0.")
            if end > 1.0:
                raise ValueError(f"control guidance end: {end} can't be larger than 1.0.")

        if ip_adapter_image is not None and ip_adapter_image_embeds is not None:
            raise ValueError(
                "Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined."
            )

        if ip_adapter_image_embeds is not None:
            if not isinstance(ip_adapter_image_embeds, list):
                raise ValueError(
                    f"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}"
                )
            elif ip_adapter_image_embeds[0].ndim not in [3, 4]:
                raise ValueError(
                    f"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D"
                )

    def check_image(self, image, prompt, prompt_embeds):
        image_is_pil = isinstance(image, PIL.Image.Image)
        image_is_tensor = isinstance(image, torch.Tensor)
        image_is_np = isinstance(image, np.ndarray)
        image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)
        image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)
        image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray)

        if (
            not image_is_pil
            and not image_is_tensor
            and not image_is_np
            and not image_is_pil_list
            and not image_is_tensor_list
            and not image_is_np_list
        ):
            raise TypeError(
                f"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}"
            )

        if image_is_pil:
            image_batch_size = 1
        else:
            image_batch_size = len(image)

        if prompt is not None and isinstance(prompt, str):
            prompt_batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            prompt_batch_size = len(prompt)
        elif prompt_embeds is not None:
            prompt_batch_size = prompt_embeds.shape[0]

        if image_batch_size != 1 and image_batch_size != prompt_batch_size:
            raise ValueError(
                f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}"
            )

    def prepare_image(
        self,
        image,
        width,
        height,
        batch_size,
        num_images_per_prompt,
        device,
        dtype,
        do_classifier_free_guidance=False,
        guess_mode=False,
    ):
        image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)
        image_batch_size = image.shape[0]

        if image_batch_size == 1:
            repeat_by = batch_size
        else:
            # image batch size is the same as prompt batch size
            repeat_by = num_images_per_prompt

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

        image = image.to(device=device, dtype=dtype)

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

        return image

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
        shape = (
            batch_size,
            num_channels_latents,
            int(height) // self.vae_scale_factor,
            int(width) // self.vae_scale_factor,
        )
        if isinstance(generator, list) and len(generator) != batch_size:
            raise ValueError(
                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
            )

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

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

    # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
    def get_guidance_scale_embedding(
        self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
    ) -> torch.Tensor:
        """
        See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298

        Args:
            w (`torch.Tensor`):
                Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
            embedding_dim (`int`, *optional*, defaults to 512):
                Dimension of the embeddings to generate.
            dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):
                Data type of the generated embeddings.

        Returns:
            `torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.
        """
        assert len(w.shape) == 1
        w = w * 1000.0

        half_dim = embedding_dim // 2
        emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
        emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
        emb = w.to(dtype)[:, None] * emb[None, :]
        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
        if embedding_dim % 2 == 1:  # zero pad
            emb = torch.nn.functional.pad(emb, (0, 1))
        assert emb.shape == (w.shape[0], embedding_dim)
        return emb

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

    @property
    def clip_skip(self):
        return self._clip_skip

    # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
    # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
    # corresponds to doing no classifier free guidance.
    @property
    def do_classifier_free_guidance(self):
        return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None

    @property
    def cross_attention_kwargs(self):
        return self._cross_attention_kwargs

    @property
    def num_timesteps(self):
        return self._num_timesteps

    @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,
        mode: Optional[str] = "generate",
        draw_pos: Optional[Union[str, torch.Tensor]] = None,
        ori_image: Optional[Union[str, torch.Tensor]] = None,
        timesteps: List[int] = None,
        sigmas: List[float] = None,
        guidance_scale: float = 7.5,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        eta: float = 0.0,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        latents: Optional[torch.Tensor] = None,
        prompt_embeds: Optional[torch.Tensor] = None,
        negative_prompt_embeds: Optional[torch.Tensor] = None,
        ip_adapter_image: Optional[PipelineImageInput] = None,
        ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
        guess_mode: bool = False,
        control_guidance_start: Union[float, List[float]] = 0.0,
        control_guidance_end: Union[float, List[float]] = 1.0,
        clip_skip: Optional[int] = None,
        callback_on_step_end: Optional[
            Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
        ] = None,
        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
        **kwargs,
    ):
        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`.
            image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
                    `List[List[torch.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):
                The ControlNet input condition to provide guidance to the `unet` for generation. If the type is
                specified as `torch.Tensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be accepted
                as an image. The dimensions of the output image defaults to `image`'s dimensions. If height and/or
                width are passed, `image` is resized accordingly. If multiple ControlNets are specified in `init`,
                images must be passed as a list such that each element of the list can be correctly batched for input
                to a single ControlNet. When `prompt` is a list, and if a list of images is passed for a single
                ControlNet, each will be paired with each prompt in the `prompt` list. This also applies to multiple
                ControlNets, where a list of image lists can be passed to batch for each prompt and each ControlNet.
            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.
            timesteps (`List[int]`, *optional*):
                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
                passed will be used. Must be in descending order.
            sigmas (`List[float]`, *optional*):
                Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
                their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
                will be used.
            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`.
            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.Tensor`, *optional*):
                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
                tensor is generated by sampling using the supplied random `generator`.
            prompt_embeds (`torch.Tensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
                provided, text embeddings are generated from the `prompt` input argument.
            negative_prompt_embeds (`torch.Tensor`, *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.
            ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
            ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):
                Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
                IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should
                contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not
                provided, embeddings are computed from the `ip_adapter_image` input argument.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the 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.Tensor)`.
            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).
            controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
                The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added
                to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set
                the corresponding scale as a list.
            guess_mode (`bool`, *optional*, defaults to `False`):
                The ControlNet encoder tries to recognize the content of the input image even if you remove all
                prompts. A `guidance_scale` value between 3.0 and 5.0 is recommended.
            control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):
                The percentage of total steps at which the ControlNet starts applying.
            control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):
                The percentage of total steps at which the ControlNet stops applying.
            clip_skip (`int`, *optional*):
                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
                the output of the pre-final layer will be used for computing the prompt embeddings.
            callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):
                A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
                each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
                DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
                list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
            callback_on_step_end_tensor_inputs (`List`, *optional*):
                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
                `._callback_tensor_inputs` attribute of your pipeline class.

        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.
        """

        callback = kwargs.pop("callback", None)
        callback_steps = kwargs.pop("callback_steps", None)

        if callback is not None:
            deprecate(
                "callback",
                "1.0.0",
                "Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
            )
        if callback_steps is not None:
            deprecate(
                "callback_steps",
                "1.0.0",
                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
            )

        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs

        controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet

        # align format for control guidance
        if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
            control_guidance_start = len(control_guidance_end) * [control_guidance_start]
        elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
            control_guidance_end = len(control_guidance_start) * [control_guidance_end]
        elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
            mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1
            control_guidance_start, control_guidance_end = (
                mult * [control_guidance_start],
                mult * [control_guidance_end],
            )

        # 1. Check inputs. Raise error if not correct
        self.check_inputs(
            prompt,
            # image,
            callback_steps,
            negative_prompt,
            prompt_embeds,
            negative_prompt_embeds,
            ip_adapter_image,
            ip_adapter_image_embeds,
            controlnet_conditioning_scale,
            control_guidance_start,
            control_guidance_end,
            callback_on_step_end_tensor_inputs,
        )

        self._guidance_scale = guidance_scale
        self._clip_skip = clip_skip
        self._cross_attention_kwargs = cross_attention_kwargs

        # 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

        if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
            controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)

        global_pool_conditions = (
            controlnet.config.global_pool_conditions
            if isinstance(controlnet, ControlNetModel)
            else controlnet.nets[0].config.global_pool_conditions
        )
        guess_mode = guess_mode or global_pool_conditions

        prompt, texts = self.modify_prompt(prompt)

        # 3. Encode input prompt
        text_encoder_lora_scale = (
            self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
        )
        prompt_embeds, negative_prompt_embeds, text_info, np_hint = self.text_embedding_module(
            prompt,
            texts,
            negative_prompt,
            num_images_per_prompt,
            mode,
            draw_pos,
        )

        # For classifier free guidance, we need to do two forward passes.
        # Here we concatenate the unconditional and text embeddings into a single batch
        # to avoid doing two forward passes
        if self.do_classifier_free_guidance:
            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])

        if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
            image_embeds = self.prepare_ip_adapter_image_embeds(
                ip_adapter_image,
                ip_adapter_image_embeds,
                device,
                batch_size * num_images_per_prompt,
                self.do_classifier_free_guidance,
            )

        # 3.5 Optionally get Guidance Scale Embedding
        timestep_cond = None
        if self.unet.config.time_cond_proj_dim is not None:
            guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
            timestep_cond = self.get_guidance_scale_embedding(
                guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
            ).to(device=device, dtype=latents.dtype)

        # 4. Prepare image
        if isinstance(controlnet, ControlNetModel):
            # image = self.prepare_image(
            #     image=image,
            #     width=width,
            #     height=height,
            #     batch_size=batch_size * num_images_per_prompt,
            #     num_images_per_prompt=num_images_per_prompt,
            #     device=device,
            #     dtype=controlnet.dtype,
            #     do_classifier_free_guidance=self.do_classifier_free_guidance,
            #     guess_mode=guess_mode,
            # )
            # height, width = image.shape[-2:]
            guided_hint = self.auxiliary_latent_module(
                text_info=text_info,
                mode=mode,
                draw_pos=draw_pos,
                ori_image=ori_image,
                num_images_per_prompt=num_images_per_prompt,
                np_hint=np_hint,
            )
            height, width = 512, 512
        # elif isinstance(controlnet, MultiControlNetModel):
        #     images = []

        #     # Nested lists as ControlNet condition
        #     if isinstance(image[0], list):
        #         # Transpose the nested image list
        #         image = [list(t) for t in zip(*image)]

        #     for image_ in image:
        #         image_ = self.prepare_image(
        #             image=image_,
        #             width=width,
        #             height=height,
        #             batch_size=batch_size * num_images_per_prompt,
        #             num_images_per_prompt=num_images_per_prompt,
        #             device=device,
        #             dtype=controlnet.dtype,
        #             do_classifier_free_guidance=self.do_classifier_free_guidance,
        #             guess_mode=guess_mode,
        #         )

        #         images.append(image_)

        #     image = images
        #     height, width = image[0].shape[-2:]
        else:
            assert False

        # 5. Prepare timesteps
        timesteps, num_inference_steps = retrieve_timesteps(
            self.scheduler, num_inference_steps, device, timesteps, sigmas
        )
        self._num_timesteps = len(timesteps)

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

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

        # 7.1 Add image embeds for IP-Adapter
        added_cond_kwargs = (
            {"image_embeds": image_embeds}
            if ip_adapter_image is not None or ip_adapter_image_embeds is not None
            else None
        )

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

        # 8. Denoising loop
        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
        is_unet_compiled = is_compiled_module(self.unet)
        is_controlnet_compiled = is_compiled_module(self.controlnet)
        is_torch_higher_equal_2_1 = is_torch_version(">=", "2.1")
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                # Relevant thread:
                # https://dev-discuss.pytorch.org/t/cudagraphs-in-pytorch-2-0/1428
                if (is_unet_compiled and is_controlnet_compiled) and is_torch_higher_equal_2_1:
                    torch._inductor.cudagraph_mark_step_begin()
                # expand the latents if we are doing classifier free guidance
                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

                # controlnet(s) inference
                if guess_mode and self.do_classifier_free_guidance:
                    # Infer ControlNet only for the conditional batch.
                    control_model_input = latents
                    control_model_input = self.scheduler.scale_model_input(control_model_input, t)
                    controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
                else:
                    control_model_input = latent_model_input
                    controlnet_prompt_embeds = prompt_embeds

                if isinstance(controlnet_keep[i], list):
                    cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
                else:
                    controlnet_cond_scale = controlnet_conditioning_scale
                    if isinstance(controlnet_cond_scale, list):
                        controlnet_cond_scale = controlnet_cond_scale[0]
                    cond_scale = controlnet_cond_scale * controlnet_keep[i]

                down_block_res_samples, mid_block_res_sample = self.controlnet(
                    control_model_input,
                    t,
                    encoder_hidden_states=controlnet_prompt_embeds,
                    guided_hint=guided_hint,
                    conditioning_scale=cond_scale,
                    guess_mode=guess_mode,
                    return_dict=False,
                )

                if guess_mode and self.do_classifier_free_guidance:
                    # Inferred ControlNet only for the conditional batch.
                    # To apply the output of ControlNet to both the unconditional and conditional batches,
                    # add 0 to the unconditional batch to keep it unchanged.
                    down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
                    mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])

                # predict the noise residual
                noise_pred = self.unet(
                    latent_model_input,
                    t,
                    encoder_hidden_states=prompt_embeds,
                    timestep_cond=timestep_cond,
                    cross_attention_kwargs=self.cross_attention_kwargs,
                    down_block_additional_residuals=down_block_res_samples,
                    mid_block_additional_residual=mid_block_res_sample,
                    added_cond_kwargs=added_cond_kwargs,
                    return_dict=False,
                )[0]

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

                # compute the previous noisy sample x_t -> x_t-1
                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]

                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)

                # 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:
                        step_idx = i // getattr(self.scheduler, "order", 1)
                        callback(step_idx, t, latents)

        # If we do sequential model offloading, let's offload unet and controlnet
        # manually for max memory savings
        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
            self.unet.to("cpu")
            self.controlnet.to("cpu")
            torch.cuda.empty_cache()

        if not output_type == "latent":
            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[
                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 all models
        self.maybe_free_model_hooks()

        if not return_dict:
            return (image, has_nsfw_concept)

        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)