"""
AnyText: Multilingual Visual Text Generation And Editing
Paper: https://arxiv.org/abs/2311.03054
Code: https://github.com/tyxsspa/AnyText
Copyright (c) Alibaba, Inc. and its affiliates.
"""
import os
from pathlib import Path
from iopaint.model.utils import set_seed
from safetensors.torch import load_file
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
import torch
import re
import numpy as np
import cv2
import einops
from PIL import ImageFont
from iopaint.model.anytext.cldm.model import create_model, load_state_dict
from iopaint.model.anytext.cldm.ddim_hacked import DDIMSampler
from iopaint.model.anytext.utils import (
check_channels,
draw_glyph,
draw_glyph2,
)
BBOX_MAX_NUM = 8
PLACE_HOLDER = "*"
max_chars = 20
ANYTEXT_CFG = os.path.join(
os.path.dirname(os.path.abspath(__file__)), "anytext_sd15.yaml"
)
def check_limits(tensor):
float16_min = torch.finfo(torch.float16).min
float16_max = torch.finfo(torch.float16).max
# 检查张量中是否有值小于float16的最小值或大于float16的最大值
is_below_min = (tensor < float16_min).any()
is_above_max = (tensor > float16_max).any()
return is_below_min or is_above_max
class AnyTextPipeline:
def __init__(self, ckpt_path, font_path, device, use_fp16=True):
self.cfg_path = ANYTEXT_CFG
self.font_path = font_path
self.use_fp16 = use_fp16
self.device = device
self.font = ImageFont.truetype(font_path, size=60)
self.model = create_model(
self.cfg_path,
device=self.device,
use_fp16=self.use_fp16,
)
if self.use_fp16:
self.model = self.model.half()
if Path(ckpt_path).suffix == ".safetensors":
state_dict = load_file(ckpt_path, device="cpu")
else:
state_dict = load_state_dict(ckpt_path, location="cpu")
self.model.load_state_dict(state_dict, strict=False)
self.model = self.model.eval().to(self.device)
self.ddim_sampler = DDIMSampler(self.model, device=self.device)
def __call__(
self,
prompt: str,
negative_prompt: str,
image: np.ndarray,
masked_image: np.ndarray,
num_inference_steps: int,
strength: float,
guidance_scale: float,
height: int,
width: int,
seed: int,
sort_priority: str = "y",
callback=None,
):
"""
Args:
prompt:
negative_prompt:
image:
masked_image:
num_inference_steps:
strength:
guidance_scale:
height:
width:
seed:
sort_priority: x: left-right, y: top-down
Returns:
result: list of images in numpy.ndarray format
rst_code: 0: normal -1: error 1:warning
rst_info: string of error or warning
"""
set_seed(seed)
str_warning = ""
mode = "text-editing"
revise_pos = False
img_count = 1
ddim_steps = num_inference_steps
w = width
h = height
strength = strength
cfg_scale = guidance_scale
eta = 0.0
prompt, texts = self.modify_prompt(prompt)
if prompt is None and texts is None:
return (
None,
-1,
"You have input Chinese prompt but the translator is not loaded!",
"",
)
n_lines = len(texts)
if mode in ["text-generation", "gen"]:
edit_image = np.ones((h, w, 3)) * 127.5 # empty mask image
elif mode in ["text-editing", "edit"]:
if masked_image is None or image is None:
return (
None,
-1,
"Reference image and position image are needed for text editing!",
"",
)
if isinstance(image, str):
image = cv2.imread(image)[..., ::-1]
assert image is not None, f"Can't read ori_image image from{image}!"
elif isinstance(image, torch.Tensor):
image = image.cpu().numpy()
else:
assert isinstance(
image, np.ndarray
), f"Unknown format of ori_image: {type(image)}"
edit_image = image.clip(1, 255) # for mask reason
edit_image = check_channels(edit_image)
# edit_image = resize_image(
# edit_image, max_length=768
# ) # make w h multiple of 64, resize if w or h > max_length
h, w = edit_image.shape[:2] # change h, w by input ref_img
# preprocess pos_imgs(if numpy, make sure it's white pos in black bg)
if masked_image is None:
pos_imgs = np.zeros((w, h, 1))
if isinstance(masked_image, str):
masked_image = cv2.imread(masked_image)[..., ::-1]
assert (
masked_image is not None
), f"Can't read draw_pos image from{masked_image}!"
pos_imgs = 255 - masked_image
elif isinstance(masked_image, torch.Tensor):
pos_imgs = masked_image.cpu().numpy()
else:
assert isinstance(
masked_image, np.ndarray
), f"Unknown format of draw_pos: {type(masked_image)}"
pos_imgs = 255 - masked_image
pos_imgs = pos_imgs[..., 0:1]
pos_imgs = cv2.convertScaleAbs(pos_imgs)
_, pos_imgs = cv2.threshold(pos_imgs, 254, 255, cv2.THRESH_BINARY)
# seprate pos_imgs
pos_imgs = self.separate_pos_imgs(pos_imgs, sort_priority)
if len(pos_imgs) == 0:
pos_imgs = [np.zeros((h, w, 1))]
if len(pos_imgs) < n_lines:
if n_lines == 1 and texts[0] == " ":
pass # text-to-image without text
else:
raise RuntimeError(
f"{n_lines} text line to draw from prompt, not enough mask area({len(pos_imgs)}) on images"
)
elif len(pos_imgs) > n_lines:
str_warning = f"Warning: found {len(pos_imgs)} positions that > needed {n_lines} from prompt."
# 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
info = {}
info["glyphs"] = []
info["gly_line"] = []
info["positions"] = []
info["n_lines"] = [len(texts)] * img_count
gly_pos_imgs = []
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...'
)
text = text[:max_chars]
gly_scale = 2
if pre_pos[i].mean() != 0:
gly_line = draw_glyph(self.font, text)
glyphs = draw_glyph2(
self.font,
text,
poly_list[i],
scale=gly_scale,
width=w,
height=h,
add_space=False,
)
gly_pos_img = cv2.drawContours(
glyphs * 255, [poly_list[i] * gly_scale], 0, (255, 255, 255), 1
)
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..."
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
)
gly_pos_img = cv2.drawContours(
glyphs * 255, [poly * gly_scale], 0, (255, 255, 255), 1
)
gly_pos_imgs += [gly_pos_img] # for show
else:
glyphs = np.zeros((h * gly_scale, w * gly_scale, 1))
gly_line = np.zeros((80, 512, 1))
gly_pos_imgs += [
np.zeros((h * gly_scale, w * gly_scale, 1))
] # for show
pos = pre_pos[i]
info["glyphs"] += [self.arr2tensor(glyphs, img_count)]
info["gly_line"] += [self.arr2tensor(gly_line, img_count)]
info["positions"] += [self.arr2tensor(pos, img_count)]
# get masked_x
masked_img = ((edit_image.astype(np.float32) / 127.5) - 1.0) * (1 - np_hint)
masked_img = np.transpose(masked_img, (2, 0, 1))
masked_img = torch.from_numpy(masked_img.copy()).float().to(self.device)
if self.use_fp16:
masked_img = masked_img.half()
encoder_posterior = self.model.encode_first_stage(masked_img[None, ...])
masked_x = self.model.get_first_stage_encoding(encoder_posterior).detach()
if self.use_fp16:
masked_x = masked_x.half()
info["masked_x"] = torch.cat([masked_x for _ in range(img_count)], dim=0)
hint = self.arr2tensor(np_hint, img_count)
cond = self.model.get_learned_conditioning(
dict(
c_concat=[hint],
c_crossattn=[[prompt] * img_count],
text_info=info,
)
)
un_cond = self.model.get_learned_conditioning(
dict(
c_concat=[hint],
c_crossattn=[[negative_prompt] * img_count],
text_info=info,
)
)
shape = (4, h // 8, w // 8)
self.model.control_scales = [strength] * 13
samples, intermediates = self.ddim_sampler.sample(
ddim_steps,
img_count,
shape,
cond,
verbose=False,
eta=eta,
unconditional_guidance_scale=cfg_scale,
unconditional_conditioning=un_cond,
callback=callback
)
if self.use_fp16:
samples = samples.half()
x_samples = self.model.decode_first_stage(samples)
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)
)
results = [x_samples[i] for i in range(img_count)]
# if (
# mode == "edit" and False
# ): # replace backgound in text editing but not ideal yet
# results = [r * np_hint + edit_image * (1 - np_hint) for r in results]
# results = [r.clip(0, 255).astype(np.uint8) for r in results]
# if len(gly_pos_imgs) > 0 and show_debug:
# glyph_bs = np.stack(gly_pos_imgs, axis=2)
# glyph_img = np.sum(glyph_bs, axis=2) * 255
# glyph_img = glyph_img.clip(0, 255).astype(np.uint8)
# results += [np.repeat(glyph_img, 3, axis=2)]
rst_code = 1 if str_warning else 0
return results, rst_code, str_warning
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
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 == "y":
fir, sec = 1, 0 # top-down first
elif sort_priority == "x":
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):
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