diff --git "a/anytext.py" "b/anytext.py"
new file mode 100644--- /dev/null
+++ "b/anytext.py"
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+# 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(nn.Module):
+    # @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 = "./text_embedding_module/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"] = "./text_embedding_module/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)