diff --git "a/text_embedding_module/anytext.py" "b/text_embedding_module/anytext.py" deleted file mode 100644--- "a/text_embedding_module/anytext.py" +++ /dev/null @@ -1,2215 +0,0 @@ -# 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 -import unicodedata -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 easydict import EasyDict as edict -from frozen_clip_embedder_t3 import FrozenCLIPEmbedderT3 -from huggingface_hub import hf_hub_download -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.constants import HF_MODULES_CACHE -from diffusers.utils.torch_utils import is_compiled_module, is_torch_version, randn_tensor - - -class Checker: - def __init__(self): - pass - - def _is_chinese_char(self, cp): - """Checks whether CP is the codepoint of a CJK character.""" - # This defines a "chinese character" as anything in the CJK Unicode block: - # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) - # - # Note that the CJK Unicode block is NOT all Japanese and Korean characters, - # despite its name. The modern Korean Hangul alphabet is a different block, - # as is Japanese Hiragana and Katakana. Those alphabets are used to write - # space-separated words, so they are not treated specially and handled - # like the all of the other languages. - if ( - (cp >= 0x4E00 and cp <= 0x9FFF) - or (cp >= 0x3400 and cp <= 0x4DBF) - or (cp >= 0x20000 and cp <= 0x2A6DF) - or (cp >= 0x2A700 and cp <= 0x2B73F) - or (cp >= 0x2B740 and cp <= 0x2B81F) - or (cp >= 0x2B820 and cp <= 0x2CEAF) - or (cp >= 0xF900 and cp <= 0xFAFF) - or (cp >= 0x2F800 and cp <= 0x2FA1F) - ): - return True - - return False - - def _clean_text(self, text): - """Performs invalid character removal and whitespace cleanup on text.""" - output = [] - for char in text: - cp = ord(char) - if cp == 0 or cp == 0xFFFD or self._is_control(char): - continue - if self._is_whitespace(char): - output.append(" ") - else: - output.append(char) - return "".join(output) - - def _is_control(self, char): - """Checks whether `chars` is a control character.""" - # These are technically control characters but we count them as whitespace - # characters. - if char == "\t" or char == "\n" or char == "\r": - return False - cat = unicodedata.category(char) - if cat in ("Cc", "Cf"): - return True - return False - - def _is_whitespace(self, char): - """Checks whether `chars` is a whitespace character.""" - # \t, \n, and \r are technically control characters but we treat them - # as whitespace since they are generally considered as such. - if char == " " or char == "\t" or char == "\n" or char == "\r": - return True - cat = unicodedata.category(char) - if cat == "Zs": - return True - return False - - -checker = Checker() - - -PLACE_HOLDER = "*" -logger = logging.get_logger(__name__) # pylint: disable=invalid-name - - -EXAMPLE_DOC_STRING = """ - Examples: - ```py - >>> import torch - >>> from diffusers import DiffusionPipeline - >>> from anytext_controlnet import AnyTextControlNetModel - >>> from diffusers import DDIMScheduler - >>> from diffusers.utils import load_image - - >>> # I chose a font file shared by an HF staff: - >>> !wget https://huggingface.co/spaces/ysharma/TranslateQuotesInImageForwards/resolve/main/arial-unicode-ms.ttf - - >>> # load control net and stable diffusion v1-5 - >>> anytext_controlnet = AnyTextControlNetModel.from_pretrained("tolgacangoz/anytext-controlnet", torch_dtype=torch.float16, - ... variant="fp16",) - >>> pipe = DiffusionPipeline.from_pretrained("tolgacangoz/anytext", font_path="arial-unicode-ms.ttf", - ... controlnet=anytext_controlnet, torch_dtype=torch.float16, - ... trust_remote_code=True, - ... ).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 - >>> prompt = 'photo of caramel macchiato coffee on the table, top-down perspective, with "Any" "Text" written on it using cream' - >>> draw_pos = load_image("https://raw.githubusercontent.com/tyxsspa/AnyText/refs/heads/main/example_images/gen9.png") - >>> image = pipe(prompt, num_inference_steps=20, 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) - proj_dir = hf_hub_download( - repo_id="tolgacangoz/anytext", - filename="text_embedding_module/proj.safetensors", - cache_dir=HF_MODULES_CACHE, - ) - self.proj.load_state_dict(load_file(proj_dir, 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): - if model_dir is None or not os.path.exists(model_dir): - model_dir = hf_hub_download( - repo_id="tolgacangoz/anytext", - filename="text_embedding_module/OCR/ppv3_rec.pth", - cache_dir=HF_MODULES_CACHE, - ) - if not os.path.exists(model_dir): - raise ValueError("not find model file path {}".format(model_dir)) - - 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) - state_dict = torch.load(model_dir, map_location=device) - rec_model.load_state_dict(state_dict) - 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.to(next(self.predictor.parameters()).device)) - 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.use_fp16 = use_fp16 - self.device = device - 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["rec_char_dict_path"] = hf_hub_download( - repo_id="tolgacangoz/anytext", - filename="text_embedding_module/OCR/ppocr_keys_v1.txt", - cache_dir=HF_MODULES_CACHE, - ) - args["use_fp16"] = 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, PIL.Image.Image): - pos_imgs = np.array(draw_pos)[..., ::-1] - pos_imgs = 255 - pos_imgs - elif 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 or ""], 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)) - device = next(self.vae.parameters()).device - masked_img = torch.from_numpy(masked_img.copy()).float().to(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, *args, **kwargs): - self.vae = self.vae.to(*args, **kwargs) - self.device = self.vae.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, - trust_remote_code: bool = False, - text_embedding_module: TextEmbeddingModule = None, - auxiliary_latent_module: AuxiliaryLatentModule = None, - 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 - ) - draw_pos = draw_pos.to(device=device) if isinstance(draw_pos, torch.Tensor) else draw_pos - 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, - controlnet_cond=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) - - def to(self, *args, **kwargs): - super().to(*args, **kwargs) - self.text_embedding_module.to(*args, **kwargs) - self.auxiliary_latent_module.to(*args, **kwargs) - return self