365 lines
18 KiB
Python
365 lines
18 KiB
Python
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import os
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
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import torch
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import random
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import re
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import numpy as np
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import cv2
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import einops
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import time
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from PIL import ImageFont
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from .cldm.model import create_model, load_state_dict
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from .cldm.ddim_hacked import DDIMSampler
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from .AnyText_t3_dataset import draw_glyph, draw_glyph2
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from .AnyText_pipeline_util import check_channels, resize_image
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from pytorch_lightning import seed_everything
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from .AnyText_bert_tokenizer import BasicTokenizer
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import folder_paths
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from huggingface_hub import hf_hub_download
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from ..utils import is_module_imported, t5_translate_en_ru_zh
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checker = BasicTokenizer()
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BBOX_MAX_NUM = 8
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PLACE_HOLDER = '*'
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max_chars = 20
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comfyui_models_dir = folder_paths.models_dir
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class AnyText_Pipeline():
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def __init__(self, ckpt_path, clip_path, translator_path, cfg_path, use_translator, device, use_fp16, all_to_device, loaded_model_tensor):
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self.device = device
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self.use_fp16 = use_fp16
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self.translator_path = translator_path
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self.cfg_path = cfg_path
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if ckpt_path != 'None':
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ckpt_path = ckpt_path
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else:
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if os.access(os.path.join(comfyui_models_dir, "checkpoints", "15", "anytext_v1.1.safetensors"), os.F_OK):
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ckpt_path = os.path.join(comfyui_models_dir, "checkpoints", "15", "anytext_v1.1.safetensors")
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else:
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hf_hub_download(repo_id="Sanster/AnyText", filename="pytorch_model.fp16.safetensors",local_dir=os.path.join(comfyui_models_dir, "checkpoints", "15"))
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old_file = os.path.join(comfyui_models_dir, "checkpoints", "15", "pytorch_model.fp16.safetensors")
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new_file = os.path.join(comfyui_models_dir, "checkpoints", "15", "anytext_v1.1.safetensors")
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os.rename(old_file, new_file)
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ckpt_path = new_file
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if "Auto_DownLoad" not in clip_path:
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clip_path = clip_path
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else:
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clip_path = "openai/clip-vit-large-patch14"
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self.clip_path = clip_path
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self.ckpt_path = ckpt_path
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if loaded_model_tensor == None:
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self.model = create_model(self.cfg_path, cond_stage_path=self.clip_path, use_fp16=self.use_fp16)
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if self.use_fp16:
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self.model = self.model.half().to(self.device)
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if all_to_device == True:
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self.model.load_state_dict(load_state_dict(self.ckpt_path, location=device), strict=False)
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else:
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self.model.load_state_dict(load_state_dict(self.ckpt_path, location='cpu'), strict=False)
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else:
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self.model = loaded_model_tensor
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self.model.to(device)
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self.model.eval()
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self.ddim_sampler = DDIMSampler(self.model, device=self.device)
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if use_translator == True:
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#加载中译英模型，模型地址https://modelscope.cn/models/iic/nlp_csanmt_translation_zh2en
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if "utrobinmv/t5_translate_en_ru_zh_small_1024" in translator_path:
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self.trans_pipe = "utrobinmv/t5_translate_en_ru_zh_small_1024"
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else:
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self.zh2en_path = os.path.join(folder_paths.models_dir, "prompt_generator", "nlp_csanmt_translation_zh2en")
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if not os.access(os.path.join(self.zh2en_path, "tf_ckpts", "ckpt-0.data-00000-of-00001"), os.F_OK):
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self.zh2en_path = "damo/nlp_csanmt_translation_zh2en"
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if not is_module_imported('pipeline'):
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from modelscope.pipelines import pipeline
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if not is_module_imported('Tasks'):
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from modelscope.utils.constant import Tasks
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self.trans_pipe = pipeline(task=Tasks.translation, model=self.zh2en_path, device=self.device)
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else:
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self.trans_pipe = None
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def __call__(self, input_tensor, font_path, cpu_offload, **forward_params):
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if "Auto_DownLoad" not in font_path:
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font_path = font_path
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else:
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if os.access(os.path.join(comfyui_models_dir, "fonts", "SourceHanSansSC-Medium.otf"), os.F_OK):
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font_path = os.path.join(comfyui_models_dir, "fonts", "SourceHanSansSC-Medium.otf")
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else:
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hf_hub_download(repo_id="Sanster/AnyText", filename="SourceHanSansSC-Medium.otf",local_dir=os.path.join(comfyui_models_dir, "fonts"))
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font_path = os.path.join(comfyui_models_dir, "fonts", "SourceHanSansSC-Medium.otf")
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self.font = ImageFont.truetype(font_path, size=60, encoding='utf-8')
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tic = time.time()
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str_warning = ''
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# get inputs
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seed = input_tensor.get('seed', -1)
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if seed == -1:
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seed = random.randint(0, 99999999)
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seed_everything(seed)
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prompt = input_tensor.get('prompt')
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draw_pos = input_tensor.get('draw_pos')
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ori_image = input_tensor.get('ori_image')
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mode = forward_params.get('mode')
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use_fp16 = forward_params.get('use_fp16')
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Random_Gen = forward_params.get('Random_Gen')
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sort_priority = forward_params.get('sort_priority', '↕')
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show_debug = forward_params.get('show_debug', False)
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revise_pos = forward_params.get('revise_pos', False)
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img_count = forward_params.get('image_count', 1)
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ddim_steps = forward_params.get('ddim_steps', 20)
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w = forward_params.get('image_width', 512)
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h = forward_params.get('image_height', 512)
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strength = forward_params.get('strength', 1.0)
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cfg_scale = forward_params.get('cfg_scale', 9.0)
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eta = forward_params.get('eta', 0.0)
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a_prompt = forward_params.get('a_prompt', 'best quality, extremely detailed,4k, HD, supper legible text, clear text edges, clear strokes, neat writing, no watermarks')
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n_prompt = forward_params.get('n_prompt', 'low-res, bad anatomy, extra digit, fewer digits, cropped, worst quality, low quality, watermark, unreadable text, messy words, distorted text, disorganized writing, advertising picture')
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prompt, texts = self.modify_prompt(prompt)
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if prompt is None and texts is None:
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return None, -1, "You have input Chinese prompt but the translator is not loaded!", ""
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n_lines = len(texts)
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if mode in ['text-generation', 'gen']:
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if Random_Gen == True:
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edit_image = np.ones((h, w, 3)) * 127.5 # empty mask image
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edit_image = resize_image(edit_image, max_length=768)
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h, w = edit_image.shape[:2]
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else:
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edit_image = cv2.imread(draw_pos)[..., ::-1]
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edit_image = resize_image(edit_image, max_length=768)
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h, w = edit_image.shape[:2]
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edit_image = np.ones((h, w, 3)) * 127.5 # empty mask image
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elif mode in ['text-editing', 'edit']:
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if draw_pos is None or ori_image is None:
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return None, -1, "Reference image and position image are needed for text editing!", ""
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if isinstance(ori_image, str):
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ori_image = cv2.imread(ori_image)[..., ::-1]
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assert ori_image is not None, f"Can't read ori_image image from{ori_image}!"
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elif isinstance(ori_image, torch.Tensor):
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ori_image = ori_image.cpu().numpy()
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else:
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assert isinstance(ori_image, np.ndarray), f'Unknown format of ori_image: {type(ori_image)}'
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edit_image = ori_image.clip(1, 255) # for mask reason
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edit_image = check_channels(edit_image)
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edit_image = resize_image(edit_image, max_length=768) # make w h multiple of 64, resize if w or h > max_length
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h, w = edit_image.shape[:2] # change h, w by input ref_img
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# preprocess pos_imgs(if numpy, make sure it's white pos in black bg)
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if draw_pos is None:
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pos_imgs = np.zeros((w, h, 1))
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if isinstance(draw_pos, str):
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draw_pos = cv2.imread(draw_pos)[..., ::-1]
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draw_pos = resize_image(draw_pos, max_length=768)
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draw_pos = cv2.resize(draw_pos, (w, h))
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assert draw_pos is not None, f"Can't read draw_pos image from{draw_pos}!"
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pos_imgs = 255-draw_pos
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elif isinstance(draw_pos, torch.Tensor):
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pos_imgs = draw_pos.cpu().numpy()
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else:
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assert isinstance(draw_pos, np.ndarray), f'Unknown format of draw_pos: {type(draw_pos)}'
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pos_imgs = pos_imgs[..., 0:1]
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pos_imgs = cv2.convertScaleAbs(pos_imgs)
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_, pos_imgs = cv2.threshold(pos_imgs, 254, 255, cv2.THRESH_BINARY)
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# seprate pos_imgs
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pos_imgs = self.separate_pos_imgs(pos_imgs, sort_priority)
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if len(pos_imgs) == 0:
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pos_imgs = [np.zeros((h, w, 1))]
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if len(pos_imgs) < n_lines:
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if n_lines == 1 and texts[0] == ' ':
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pass # text-to-image without text
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else:
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return None, -1, f'Found {len(pos_imgs)} positions that < needed {n_lines} from prompt, check and try again!', ''
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elif len(pos_imgs) > n_lines:
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str_warning = f'Warning: found {len(pos_imgs)} positions that > needed {n_lines} from prompt.'
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# get pre_pos, poly_list, hint that needed for anytext
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pre_pos = []
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poly_list = []
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for input_pos in pos_imgs:
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if input_pos.mean() != 0:
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input_pos = input_pos[..., np.newaxis] if len(input_pos.shape) == 2 else input_pos
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poly, pos_img = self.find_polygon(input_pos)
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pre_pos += [pos_img/255.]
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poly_list += [poly]
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else:
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pre_pos += [np.zeros((h, w, 1))]
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poly_list += [None]
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np_hint = np.sum(pre_pos, axis=0).clip(0, 1)
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# prepare info dict
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info = {}
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info['glyphs'] = []
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info['gly_line'] = []
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info['positions'] = []
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info['n_lines'] = [len(texts)]*img_count
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gly_pos_imgs = []
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for i in range(len(texts)):
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text = texts[i]
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if len(text) > max_chars:
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str_warning = f'"{text}" length > max_chars: {max_chars}, will be cut off...'
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text = text[:max_chars]
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gly_scale = 2
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if pre_pos[i].mean() != 0:
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gly_line = draw_glyph(self.font, text)
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glyphs = draw_glyph2(self.font, text, poly_list[i], scale=gly_scale, width=w, height=h, add_space=False)
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gly_pos_img = cv2.drawContours(glyphs*255, [poly_list[i]*gly_scale], 0, (255, 255, 255), 1)
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if revise_pos:
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resize_gly = cv2.resize(glyphs, (pre_pos[i].shape[1], pre_pos[i].shape[0]))
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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)
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new_pos = new_pos[..., np.newaxis] if len(new_pos.shape) == 2 else new_pos
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contours, _ = cv2.findContours(new_pos, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
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if len(contours) != 1:
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str_warning = f'Fail to revise position {i} to bounding rect, remain position unchanged...'
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else:
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rect = cv2.minAreaRect(contours[0])
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poly = np.int0(cv2.boxPoints(rect))
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pre_pos[i] = cv2.drawContours(new_pos, [poly], -1, 255, -1) / 255.
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gly_pos_img = cv2.drawContours(glyphs*255, [poly*gly_scale], 0, (255, 255, 255), 1)
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gly_pos_imgs += [gly_pos_img] # for show
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else:
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glyphs = np.zeros((h*gly_scale, w*gly_scale, 1))
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gly_line = np.zeros((80, 512, 1))
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gly_pos_imgs += [np.zeros((h*gly_scale, w*gly_scale, 1))] # for show
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pos = pre_pos[i]
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info['glyphs'] += [self.arr2tensor(glyphs, img_count, use_fp16)]
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info['gly_line'] += [self.arr2tensor(gly_line, img_count, use_fp16)]
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info['positions'] += [self.arr2tensor(pos, img_count, use_fp16)]
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# get masked_x
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masked_img = ((edit_image.astype(np.float32) / 127.5) - 1.0)*(1-np_hint)
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masked_img = np.transpose(masked_img, (2, 0, 1))
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masked_img = torch.from_numpy(masked_img.copy()).float().to(self.device)
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# 确保模型在正确的设备上
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self.model = self.model.to(self.device)
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# 将masked_img移动到正确的设备并设置正确的数据类型
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masked_img = masked_img.to(self.device)
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if self.use_fp16:
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masked_img = masked_img.half()
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else:
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masked_img = masked_img.float()
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encoder_posterior = self.model.encode_first_stage(masked_img[None, ...])
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masked_x = self.model.get_first_stage_encoding(encoder_posterior).detach()
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if self.use_fp16:
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masked_x = masked_x.half()
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info['masked_x'] = torch.cat([masked_x for _ in range(img_count)], dim=0)
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hint = self.arr2tensor(np_hint, img_count, use_fp16)
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cond = self.model.get_learned_conditioning(dict(c_concat=[hint], c_crossattn=[[prompt + ' , ' + a_prompt] * img_count], text_info=info))
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un_cond = self.model.get_learned_conditioning(dict(c_concat=[hint], c_crossattn=[[n_prompt] * img_count], text_info=info))
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shape = (4, h // 8, w // 8)
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self.model.control_scales = ([strength] * 13)
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samples, intermediates = self.ddim_sampler.sample(ddim_steps, img_count,
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shape, cond, verbose=False, eta=eta,
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unconditional_guidance_scale=cfg_scale,
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unconditional_conditioning=un_cond)
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if self.use_fp16:
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samples = samples.half()
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x_samples = self.model.decode_first_stage(samples)
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x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(np.uint8)
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results = [x_samples[i] for i in range(img_count)]
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if len(gly_pos_imgs) > 0 and show_debug:
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glyph_bs = np.stack(gly_pos_imgs, axis=2)
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glyph_img = np.sum(glyph_bs, axis=2) * 255
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glyph_img = glyph_img.clip(0, 255).astype(np.uint8)
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results += [np.repeat(glyph_img, 3, axis=2)]
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input_prompt = prompt
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for t in texts:
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input_prompt = input_prompt.replace('*', f'"{t}"', 1)
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print(f'Prompt: {input_prompt}')
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|
|
# debug_info
|
|||
|
|
if not show_debug:
|
|||
|
|
debug_info = ''
|
|||
|
|
else:
|
|||
|
|
debug_info = f'\033[93mPrompt(提示词): {input_prompt}\n\033[0m \
|
|||
|
|
\033[93mSize(尺寸): {w}x{h}\n\033[0m \
|
|||
|
|
\033[93mImage Count(生成数量): {img_count}\n\033[0m \
|
|||
|
|
\033[93mSeed(种子): {seed}\n\033[0m \
|
|||
|
|
\033[93mUse FP16(使用FP16): {self.use_fp16}\n\033[0m \
|
|||
|
|
\033[93mUse Device(使用设备): {self.device}\n\033[0m \
|
|||
|
|
\033[93mCost Time(生成耗时): {(time.time()-tic):.2f}s\033[0m'
|
|||
|
|
rst_code = 1 if str_warning else 0
|
|||
|
|
|
|||
|
|
if cpu_offload == True:
|
|||
|
|
self.model.to('cpu')
|
|||
|
|
else:
|
|||
|
|
if self.model != None:
|
|||
|
|
del self.model
|
|||
|
|
import gc
|
|||
|
|
gc.collect()
|
|||
|
|
if torch.cuda.is_available():
|
|||
|
|
torch.cuda.empty_cache()
|
|||
|
|
self.model = None
|
|||
|
|
|
|||
|
|
return x_samples, results, rst_code, str_warning, debug_info, self.model
|
|||
|
|
|
|||
|
|
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
|
|||
|
|
if self.trans_pipe == "utrobinmv/t5_translate_en_ru_zh_small_1024":
|
|||
|
|
self.zh2en_path = os.path.join(folder_paths.models_dir, "prompt_generator", "models--utrobinmv--t5_translate_en_ru_zh_small_1024")
|
|||
|
|
if not os.access(os.path.join(self.zh2en_path, "model.safetensors"), os.F_OK):
|
|||
|
|
self.zh2en_path = "utrobinmv/t5_translate_en_ru_zh_small_1024"
|
|||
|
|
prompt = t5_translate_en_ru_zh('en', prompt + ' .', self.zh2en_path, self.device)[0]
|
|||
|
|
else:
|
|||
|
|
prompt = self.trans_pipe(input=prompt + ' .')['translation'][:-1]
|
|||
|
|
del self.trans_pipe
|
|||
|
|
if torch.cuda.is_available():
|
|||
|
|
torch.cuda.empty_cache()
|
|||
|
|
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
|
|||
|
|
|
|||
|
|
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
|
|||
|
|
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 arr2tensor(self, arr, bs, use_fp16):
|
|||
|
|
self.use_fp16 = use_fp16
|
|||
|
|
arr = np.transpose(arr, (2, 0, 1))
|
|||
|
|
_arr = torch.from_numpy(arr.copy()).float().to(self.device)
|
|||
|
|
if self.use_fp16:
|
|||
|
|
_arr = _arr.half()
|
|||
|
|
_arr = torch.stack([_arr for _ in range(bs)], dim=0)
|
|||
|
|
return _arr
|