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mz_train_tools_utils.py
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import json
import os
import shutil
import subprocess
import sys
import threading
import time
import traceback
from typing import Tuple
import warnings
import numpy as np
import folder_paths
import base64
from PIL import Image, ImageFilter
import io
import torch
import re
import hashlib
import cv2
# sys.path.append(os.path.join(os.path.dirname(__file__)))
temp_directory = folder_paths.get_temp_directory()
from tqdm import tqdm
import requests
import comfy.utils
CACHE_POOL = {}
class Utils:
def Md5(str):
return hashlib.md5(str.encode('utf-8')).hexdigest()
def check_frames_path(frames_path):
if frames_path == "" or frames_path.startswith(".") or frames_path.startswith("/") or frames_path.endswith("/") or frames_path.endswith("\\"):
return "frames_path不能为空"
frames_path = os.path.join(
folder_paths.get_output_directory(), frames_path)
if frames_path == folder_paths.get_output_directory():
return "frames_path不能为output目录"
return ""
def base64_to_pil_image(base64_str):
if base64_str is None:
return None
if len(base64_str) == 0:
return None
if type(base64_str) not in [str, bytes]:
return None
if base64_str.startswith("data:image/png;base64,"):
base64_str = base64_str.split(",")[-1]
base64_str = base64_str.encode("utf-8")
base64_str = base64.b64decode(base64_str)
return Image.open(io.BytesIO(base64_str))
def pil_image_to_base64(pil_image):
buffered = io.BytesIO()
pil_image.save(buffered, format="PNG")
img_str = base64.b64encode(buffered.getvalue())
img_str = str(img_str, encoding="utf-8")
return f"data:image/png;base64,{img_str}"
def listdir_png(path):
try:
files = Utils.listdir(path)
new_files = []
for file in files:
if file.lower().endswith(".png"):
new_files.append(file)
files = new_files
files.sort(key=lambda x: int(os.path.basename(x).split(".")[0]))
return files
except Exception as e:
return []
def listdir(path):
try:
files = os.listdir(path)
# 排除.开头的文件
files = [file for file in files if not file.startswith(".")]
return files
except Exception as e:
return []
def tensor2pil(image):
return Image.fromarray(np.clip(255.0 * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
def tensors2pil_list(images):
return [Utils.tensor2pil(image) for image in images]
# Convert PIL to Tensor
def pil2tensor(image):
return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)[0]
def pil2cv(image):
return cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
def cv2pil(image):
return Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
def list_tensor2tensor(data):
result_tensor = torch.stack(data)
return result_tensor
def loadImage(path):
img = Image.open(path)
img = img.convert("RGB")
return img
def vae_encode_crop_pixels(pixels):
x = (pixels.shape[1] // 8) * 8
y = (pixels.shape[2] // 8) * 8
if pixels.shape[1] != x or pixels.shape[2] != y:
x_offset = (pixels.shape[1] % 8) // 2
y_offset = (pixels.shape[2] % 8) // 2
pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :]
return pixels
def native_vae_encode(vae, image):
pixels = Utils.vae_encode_crop_pixels(image)
t = vae.encode(pixels[:, :, :, :3])
return {"samples": t}
def native_vae_encode_for_inpaint(vae, pixels, mask):
x = (pixels.shape[1] // 8) * 8
y = (pixels.shape[2] // 8) * 8
mask = torch.nn.functional.interpolate(mask.reshape(
(-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")
pixels = pixels.clone()
if pixels.shape[1] != x or pixels.shape[2] != y:
x_offset = (pixels.shape[1] % 8) // 2
y_offset = (pixels.shape[2] % 8) // 2
pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :]
mask = mask[:, :, x_offset:x + x_offset, y_offset:y + y_offset]
# grow mask by a few pixels to keep things seamless in latent space
mask_erosion = mask
m = (1.0 - mask.round()).squeeze(1)
for i in range(3):
pixels[:, :, :, i] -= 0.5
pixels[:, :, :, i] *= m
pixels[:, :, :, i] += 0.5
t = vae.encode(pixels)
return {"samples": t, "noise_mask": (mask_erosion[:, :, :x, :y].round())}
def native_vae_decode(vae, samples):
return vae.decode(samples["samples"])
def native_clip_text_encode(clip, text):
tokens = clip.tokenize(text)
cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
return [[cond, {"pooled_output": pooled}]]
def cache_get(key):
return CACHE_POOL.get(key, None)
def cache_set(key, value):
global CACHE_POOL
CACHE_POOL[key] = value
return True
def model_cache_clean(model_type):
global CACHE_POOL
for key in list(CACHE_POOL.keys()):
if key.startswith(f"model_cache_{model_type}"):
del CACHE_POOL[key]
torch.cuda.empty_cache()
return True
def model_cache_get(model_type, model_path):
resp = Utils.cache_get(f"model_cache_{model_type}")
if resp is None:
return None
cache_model_path = resp.get("model_path")
if cache_model_path != model_path:
Utils.model_cache_clean(f"model_cache_{model_type}")
return None
return resp.get("model")
def model_cache_set(model_type, model_path, model):
key = f"model_cache_{model_type}"
if Utils.cache_get(key) is not None:
Utils.model_cache_clean(key)
Utils.cache_set(key, {"model_path": model_path, "model": model})
return True
def get_minus_zone_models_path():
models_path = os.path.join(
folder_paths.models_dir, "minus_zone_models")
os.makedirs(models_path, exist_ok=True)
return models_path
def get_comfyui_models_path():
return folder_paths.models_dir
def translate_text(text, from_code, to_code):
try:
import argostranslate
from argostranslate import translate
except ImportError:
subprocess.run([
sys.executable, "-m",
"pip", "install", "argostranslate"], check=True)
import argostranslate
from argostranslate import translate
try:
translation = translate.get_translation_from_codes(
from_code, to_code)
if translation is None:
raise Exception("Translation not found")
except Exception as e:
print(e)
argostranslate.package.update_package_index()
available_packages = argostranslate.package.get_available_packages()
package_to_install = next(
filter(
lambda x: (x.from_code == from_code and x.to_code ==
to_code), available_packages,
)
)
download_path = package_to_install.download()
print("package_to_install.download():", download_path)
argostranslate.package.install_from_path(download_path)
translation = translate.get_translation_from_codes(
from_code, to_code)
if translation is None:
return text
# Translate
translatedText = translation.translate(
text)
return translatedText
def zh2en(text):
return Utils.translate_text(text, "zh", "en")
def en2zh(text):
return Utils.translate_text(text, "en", "zh")
def prompt_zh_to_en(prompt):
prompt = prompt.replace("，", ",")
prompt = prompt.replace("。", ",")
prompt = prompt.replace("\n", ",")
tags = prompt.split(",")
# 判断是否有中文
for i, tag in enumerate(tags):
if re.search(u'[\u4e00-\u9fff]', tag):
tags[i] = Utils.zh2en(tag)
# 如果第一个字母是大写,转为小写
if tags[i][0].isupper():
tags[i] = tags[i].lower().replace(".", "")
return ",".join(tags)
def mask_resize(mask, width, height):
mask = mask.unsqueeze(0).unsqueeze(0)
mask = torch.nn.functional.interpolate(
mask, size=(height, width), mode="bilinear")
mask = mask.squeeze(0).squeeze(0)
return mask
def mask_threshold(interested_mask):
mask_image = Utils.tensor2pil(interested_mask)
mask_image_cv2 = Utils.pil2cv(mask_image)
ret, thresh1 = cv2.threshold(
mask_image_cv2, 127, 255, cv2.THRESH_BINARY)
thresh1 = Utils.cv2pil(thresh1)
thresh1 = np.array(thresh1)
thresh1 = thresh1[:, :, 0]
return Utils.pil2tensor(thresh1)
def mask_erode(interested_mask, value):
value = int(value)
mask_image = Utils.tensor2pil(interested_mask)
mask_image_cv2 = Utils.pil2cv(mask_image)
kernel = np.ones((5, 5), np.uint8)
erosion = cv2.erode(mask_image_cv2, kernel, iterations=value)
erosion = Utils.cv2pil(erosion)
erosion = np.array(erosion)
erosion = erosion[:, :, 0]
return Utils.pil2tensor(erosion)
def mask_dilate(interested_mask, value):
value = int(value)
mask_image = Utils.tensor2pil(interested_mask)
mask_image_cv2 = Utils.pil2cv(mask_image)
kernel = np.ones((5, 5), np.uint8)
dilation = cv2.dilate(mask_image_cv2, kernel, iterations=value)
dilation = Utils.cv2pil(dilation)
dilation = np.array(dilation)
dilation = dilation[:, :, 0]
return Utils.pil2tensor(dilation)
def mask_edge_opt(interested_mask, edge_feathering):
mask_image = Utils.tensor2pil(interested_mask)
mask_image_cv2 = Utils.pil2cv(mask_image)
# 高斯模糊
dilation2 = Utils.cv2pil(mask_image_cv2)
dilation2 = mask_image.filter(
ImageFilter.GaussianBlur(edge_feathering))
# mask_image dilation2 图片蒙版叠加
dilation2 = Utils.pil2cv(dilation2)
# dilation2[mask_image_cv2 < 127] = 0
dilation2 = Utils.cv2pil(dilation2)
# to RGB
dilation2 = np.array(dilation2)
dilation2 = dilation2[:, :, 0]
return Utils.pil2tensor(dilation2)
def mask_composite(destination, source, x, y, mask=None, multiplier=8, resize_source=False):
source = source.to(destination.device)
if resize_source:
source = torch.nn.functional.interpolate(source, size=(
destination.shape[2], destination.shape[3]), mode="bilinear")
source = comfy.utils.repeat_to_batch_size(source, destination.shape[0])
x = max(-source.shape[3] * multiplier,
min(x, destination.shape[3] * multiplier))
y = max(-source.shape[2] * multiplier,
min(y, destination.shape[2] * multiplier))
left, top = (x // multiplier, y // multiplier)
right, bottom = (left + source.shape[3], top + source.shape[2],)
if mask is None:
mask = torch.ones_like(source)
else:
mask = mask.to(destination.device, copy=True)
mask = torch.nn.functional.interpolate(mask.reshape(
(-1, 1, mask.shape[-2], mask.shape[-1])), size=(source.shape[2], source.shape[3]), mode="bilinear")
mask = comfy.utils.repeat_to_batch_size(mask, source.shape[0])
# calculate the bounds of the source that will be overlapping the destination
# this prevents the source trying to overwrite latent pixels that are out of bounds
# of the destination
visible_width, visible_height = (
destination.shape[3] - left + min(0, x), destination.shape[2] - top + min(0, y),)
mask = mask[:, :, :visible_height, :visible_width]
inverse_mask = torch.ones_like(mask) - mask
source_portion = mask * source[:, :, :visible_height, :visible_width]
destination_portion = inverse_mask * \
destination[:, :, top:bottom, left:right]
destination[:, :, top:bottom,
left:right] = source_portion + destination_portion
return destination
def latent_upscale_by(samples, scale_by):
s = samples.copy()
width = round(samples["samples"].shape[3] * scale_by)
height = round(samples["samples"].shape[2] * scale_by)
s["samples"] = comfy.utils.common_upscale(
samples["samples"], width, height, "nearest-exact", "disabled")
return s
def resize_by(image, percent):
# 判断类型是否为PIL
if not isinstance(image, Image.Image):
image = Image.fromarray(image)
width, height = image.size
new_width = int(width * percent)
new_height = int(height * percent)
return image.resize((new_width, new_height), Image.LANCZOS)
def resize_max(im, dst_w, dst_h):
src_w, src_h = im.size
if src_h > src_w:
newWidth = dst_w
newHeight = dst_w * src_h // src_w
else:
newWidth = dst_h * src_w // src_h
newHeight = dst_h
newHeight = newHeight // 8 * 8
newWidth = newWidth // 8 * 8
return im.resize((newWidth, newHeight), Image.Resampling.LANCZOS)
def resize_min(im, dst_w, dst_h):
src_w, src_h = im.size
if src_h < src_w:
newWidth = dst_w
newHeight = dst_w * src_h // src_w
else:
newWidth = dst_h * src_w // src_h
newHeight = dst_h
newHeight = newHeight // 8 * 8
newWidth = newWidth // 8 * 8
return im.resize((newWidth, newHeight), Image.Resampling.LANCZOS)
def add_watermark(image, watermark):
if watermark == "":
return image
try:
import PIL
from PIL import ImageDraw, ImageFont
except ImportError:
subprocess.run([
sys.executable, "-m",
"pip", "install", "Pillow"], check=True)
import PIL
from PIL import ImageDraw, ImageFont
# 获取PIL版本号
pil_version = PIL.__version__
if pil_version >= "10.0.0":
def textsize(self, text, font):
left, top, right, bottom = self.textbbox((0, 0), text, font)
return right - left, bottom - top
ImageDraw.ImageDraw.textsize = textsize
font_fullpath = Utils.download_model(
{
"url": "https://www.modelscope.cn/api/v1/models/wailovet/MinusZoneAIModels/repo?Revision=master&FilePath=font%2FAlibabaPuHuiTi-2-75-SemiBold.ttf",
"output": "font/AlibabaPuHuiTi-2-75-SemiBold.ttf",
}
)
watermarks = watermark.split("\n")
width, height = image.size
short_edge = min(width, height)
font_size = short_edge // 12
font = ImageFont.truetype(font_fullpath, font_size)
# print("pil_version:", pil_version)
draw = ImageDraw.Draw(image)
text = watermarks[0]
textwidth, textheight = draw.textsize(text, font)
x = (width - textwidth) // 2
bottom = 10
y = height - textheight - (textheight * 0.4 + bottom + 8)
draw.text((x, y), text, font=font)
if len(watermarks) > 1:
y1 = y + textheight
text = watermarks[1]
font_size = int(font_size * 0.4)
font = ImageFont.truetype(font_fullpath, font_size)
textwidth, textheight = draw.textsize(text, font)
x = (width - textwidth) // 2
y = y1 - bottom + 4
draw.text((x, y), text, font=font)
return image
def get_device():
return comfy.model_management.get_torch_device()
def download_file(url, filepath, threads=8, retries=6):
get_size_tmp = requests.get(url, stream=True)
total_size = int(get_size_tmp.headers.get("content-length", 0))
print(f"Downloading {url} to {filepath} with size {total_size} bytes")
base_filename = os.path.basename(filepath)
cache_dir = os.path.join(os.path.dirname(
filepath), f"{base_filename}.t_{threads}_cache")
os.makedirs(cache_dir, exist_ok=True)
def get_total_existing_size():
fs = os.listdir(cache_dir)
existing_size = 0
for f in fs:
if f.startswith("block_"):
existing_size += os.path.getsize(
os.path.join(cache_dir, f))
return existing_size
total_existing_size = get_total_existing_size()
if total_size != 0 and total_existing_size != total_size:
with tqdm(total=total_size, initial=total_existing_size, unit="B", unit_scale=True) as progress_bar:
all_threads = []
for i in range(threads):
cache_filepath = os.path.join(cache_dir, f"block_{i}")
start = total_size // threads * i
end = total_size // threads * (i + 1) - 1
if i == threads - 1:
end = total_size
# Check if the file already exists
if os.path.exists(cache_filepath):
# Get the size of the existing file
existing_size = os.path.getsize(cache_filepath)
else:
existing_size = 0
headers = {"Range": f"bytes={start + existing_size}-{end}"}
if end == total_size:
headers = {"Range": f"bytes={start + existing_size}-"}
if start + existing_size >= end:
continue
# print(f"Downloading {cache_filepath} with headers bytes={start + existing_size}-{end}")
# Streaming, so we can iterate over the response.
response = requests.get(url, stream=True, headers=headers)
def download_file_thread(response, cache_filepath):
block_size = 1024
if end - (start + existing_size) < block_size:
block_size = end - (start + existing_size)
with open(cache_filepath, "ab") as file:
for data in response.iter_content(block_size):
file.write(data)
progress_bar.update(
len(data)
)
t = threading.Thread(
target=download_file_thread, args=(response, cache_filepath))
all_threads.append(t)
t.start()
for t in all_threads:
t.join()
if total_size != 0 and get_total_existing_size() > total_size:
# 文件下载失败
shutil.rmtree(cache_dir)
raise RuntimeError("Download failed, file is incomplete")
if total_size != 0 and total_size != get_total_existing_size():
if retries > 0:
retries -= 1
print(
f"Download failed: {total_size} != {get_total_existing_size()}, retrying... {retries} retries left")
return Utils.download_file(url, filepath, threads, retries)
# 文件损坏
raise RuntimeError(
f"Download failed: {total_size} != {get_total_existing_size()}")
if os.path.exists(filepath):
shutil.move(filepath, filepath + ".old." +
time.strftime("%Y%m%d%H%M%S"))
# merge the files
with open(filepath, "wb") as f:
for i in range(threads):
cache_filepath = os.path.join(cache_dir, f"block_{i}")
with open(cache_filepath, "rb") as cf:
f.write(cf.read())
shutil.rmtree(cache_dir)
return filepath
def hf_download_model(url, only_get_path=False):
if not url.startswith("https://"):
raise ValueError("URL must start with https://")
if url.startswith("https://huggingface.co/") or url.startswith("https://hf-mirror.com/"):
base_model_path = os.path.abspath(os.path.join(
Utils.get_models_path(), "transformers_models"))
# https://huggingface.co/FaradayDotDev/llama-3-8b-Instruct-GGUF/resolve/main/llama-3-8b-Instruct.Q2_K.gguf?download=true
texts = url.split("?")[0].split("/")
file_name = texts[-1]
zone_path = f"{texts[3]}/{texts[4]}"
save_path = os.path.join(base_model_path, zone_path, file_name)
if os.path.exists(save_path) is False:
if only_get_path:
return None
os.makedirs(os.path.join(
base_model_path, zone_path), exist_ok=True)
Utils.download_file(url, save_path)
Utils.print_log(
f"File {save_path} => {os.path.getsize(save_path)} ")
# 获取大小
if os.path.getsize(save_path) == 0:
if only_get_path:
return None
os.remove(save_path)
raise ValueError(f"Download failed: {url}")
return save_path
else:
texts = url.split("?")[0].split("/")
host = texts[2].replace(".", "_")
base_model_path = os.path.abspath(os.path.join(
Utils.get_models_path(), f"{host}_models"))
file_name = texts[-1]
file_name_no_ext = os.path.splitext(file_name)[0]
file_ext = os.path.splitext(file_name)[1]
md5_hash = Utils.Md5(url)
save_path = os.path.join(
base_model_path, f"{file_name_no_ext}.{md5_hash}{file_ext}")
if os.path.exists(save_path) is False:
if only_get_path:
return None
os.makedirs(base_model_path, exist_ok=True)
Utils.download_file(url, save_path)
return save_path
def print_log(*args):
if os.environ.get("MZ_DEV", None) is not None:
print(*args)
def download_model(model_info, only_get_path=False):
url = model_info["url"]
output = model_info["output"]
save_path = os.path.abspath(
os.path.join(Utils.get_comfyui_models_path(), output))
if not os.path.exists(save_path):
if only_get_path:
return None
save_path = Utils.download_file(url, save_path)
return save_path
def load_lora(model, lora_path, strength_model):
lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
model_lora, _ = comfy.sd.load_lora_for_models(
model, None, lora, strength_model, 0)
return model_lora
def load_checkpoint(ckpt_name):
cache_data = Utils.cache_get(ckpt_name)
if cache_data is not None:
print("load from cache: ", ckpt_name)
return cache_data
ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
out = comfy.sd.load_checkpoint_guess_config(
ckpt_path, output_vae=True, output_clip=True)
model, clip, vae = out[:3]
Utils.cache_set(ckpt_name, (model, clip, vae))
return model, clip, vae
def file_hash(file_path, hash_method):
if not os.path.isfile(file_path):
return ''
h = hash_method()
with open(file_path, 'rb') as f:
while b := f.read(8192):
h.update(b)
return h.hexdigest()
def file_sha256(file_path):
return Utils.file_hash(file_path, hashlib.sha256)
def get_auto_model_fullpath(model_name):
find_paths = []
target_sha256 = ""
file_path = ""
download_url = ""
for model in MODEL_ZOO:
if model["model"] == model_name:
find_paths = model["find_path"]
target_sha256 = model["SHA256"]
file_path = model["file_path"]
download_url = model["url"]
break
if target_sha256 == "":
raise ValueError(f"Model {model_name} not found in MODEL_ZOO")
if os.path.exists(file_path):
if Utils.file_sha256(file_path) != target_sha256:
print(f"Model {model_name} file hash not match...")
return file_path
for find_path in find_paths:
find_fullpath = os.path.join(
folder_paths.get_output_directory(), find_path)
if os.path.exists(find_fullpath):
for root, dirs, files in os.walk(find_fullpath):
# 排除隐藏文件夹
dirs[:] = [d for d in dirs if not d.startswith('.')]
for file in files:
if target_sha256 == Utils.file_sha256(os.path.join(root, file)):
return os.path.join(root, file)
return Utils.download_model({"url": download_url, "output": file_path})
def is_sd15_model(model):
type_str = str(type(model.model.model_config).__name__)
return "SD15" in type_str
def is_sdxl_model(model):
type_str = str(type(model.model.model_config).__name__)
return "SDXL" in type_str
def progress_bar(steps, taesd_type="sd1_5"):
class pb:
if taesd_type == "sd1_5":
taesd_decoder_name = "taesd_decoder"
elif taesd_type == "sdxl":
taesd_decoder_name = "taesdxl_decoder"
latent_rgb_factors = None
latent_channels = 4
def __init__(self, steps):
self.steps = steps
self.pbar = comfy.utils.ProgressBar(steps)
def get_previewer(self):
import latent_preview
previewer = latent_preview.get_previewer(
'cuda', self)
return previewer
def update(self, step, total_steps, pil_img=None):
try:
pil_img_info = ("JPEG", pil_img, 512)
if pil_img is None:
pil_img_info = None
if type(pil_img) == Tuple or type(pil_img) == list or type(pil_img) == tuple:
pil_img_info = pil_img
# print("pil_img_info:", type(pil_img), pil_img_info)
# print("step:", step, "total_steps:", total_steps)
self.pbar.update_absolute(
step, total_steps, pil_img_info)
except Exception as e:
print("progress_bar:", e)
raise e
return pb(steps)
def get_free_port():
import socket
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.bind(('localhost', 0))
port = s.getsockname()[1]
s.close()
return port
def Simple_Server(reader):
import threading
from http.server import HTTPServer, BaseHTTPRequestHandler
port = Utils.get_free_port()
is_running = {"value": True}
def stop_server():
is_running.update({"value": False})
httpd = None
class SimpleHTTPRequestHandler(BaseHTTPRequestHandler):
def do_POST(self):
self.send_response(200)
self.end_headers()
content_length = int(self.headers['Content-Length'])
post_data = self.rfile.read(content_length)
data = json.loads(post_data)
if reader(data) == False:
stop_server()
def log_message(self, format, *args):
pass
httpd = HTTPServer(('localhost', port), SimpleHTTPRequestHandler)
def serve_forever():
try:
print(
"===========================httpd.serve_forever() start=======================================")
httpd.serve_forever()
except Exception as e:
pass
print(
"===========================httpd.serve_forever() end=======================================")
threading.Thread(target=serve_forever).start()
def check_server_stop():
while is_running["value"]:
# print("is_running : ", is_running)
time.sleep(1)
httpd.shutdown()
httpd.server_close()
threading.Thread(target=check_server_stop).start()
return stop_server, port
def xy_image(pre_render_images, pre_render_texts_x, pre_render_texts_y):
# 去掉pre_render_texts_x中所有相同字符串前缀
def get_common_prefix(pre_render_texts_x):
if len(pre_render_texts_x) == 0:
return ""
common_prefix = ""
for i in range(len(pre_render_texts_x[0])):
c = pre_render_texts_x[0][i]
for j in range(len(pre_render_texts_x)):
if pre_render_texts_x[j][i] != c:
return common_prefix
else:
common_prefix += c
return common_prefix
common_prefix = get_common_prefix(pre_render_texts_x)
if common_prefix != "":
pre_render_texts_x = [
x.replace(common_prefix, "") for x in pre_render_texts_x]
x_enable_num = len(pre_render_images)
y_enable_num = len(pre_render_images[0])
max_width = 0
max_height = 0
for x in range(0, len(pre_render_images)):
for y in range(0, len(pre_render_images[x])):
if pre_render_images[x][y].width < 512:
org_width = pre_render_images[x][y].width
org_height = pre_render_images[x][y].height
pre_render_images[x][y] = pre_render_images[x][y].resize(
(512, int(org_height * 512 / org_width)))
if pre_render_images[x][y].width > max_width:
max_width = pre_render_images[x][y].width
if pre_render_images[x][y].height > max_height:
max_height = pre_render_images[x][y].height
image_xy_canvas = Image.new(
"RGB", (max_width * x_enable_num, max_height * y_enable_num))
for i in range(x_enable_num):
for j in range(y_enable_num):
image_xy_canvas.paste(
pre_render_images[i][j], (max_width * i, max_height * j))
# draw axis
from PIL import ImageDraw, ImageFont
import PIL
# 获取PIL版本号
pil_version = PIL.__version__
if pil_version >= "10.0.0":
def textsize(self, text, font):
left, top, right, bottom = self.textbbox((0, 0), text, font)
return right - left, bottom - top
ImageDraw.ImageDraw.textsize = textsize
full_padding = 260
full_canvas_width = max_width * x_enable_num + full_padding * 2
full_canvas_height = max_height * y_enable_num + full_padding * 2
full_canvas = Image.new(
"RGB", (full_canvas_width, full_canvas_height), "white")
full_canvas.paste(image_xy_canvas, (full_padding, full_padding))
draw = ImageDraw.Draw(full_canvas)
# top
draw.line((full_padding, full_padding, full_canvas_width - full_padding,
full_padding), fill="black", width=4)
# left
draw.line((full_padding, full_padding, full_padding,
full_canvas_height - full_padding), fill="black", width=4)
# bottom
draw.line((full_padding, full_canvas_height - full_padding, full_canvas_width - full_padding,
full_canvas_height - full_padding), fill="black", width=4)
# right
draw.line((full_canvas_width - full_padding, full_padding, full_canvas_width - full_padding,
full_canvas_height - full_padding), fill="black", width=4)
font_fullpath = Utils.download_model(
{
"url": "https://www.modelscope.cn/api/v1/models/wailovet/MinusZoneAIModels/repo?Revision=master&FilePath=font%2FAlibabaPuHuiTi-2-75-SemiBold.ttf",
"output": "font/AlibabaPuHuiTi-2-75-SemiBold.ttf",
}
)
if os.path.exists(font_fullpath):
font = ImageFont.truetype(font_fullpath, size=32,)
else:
font = ImageFont.load_default()
for i in range(x_enable_num):
textwidth, textheight = draw.textsize(
pre_render_texts_x[i], font)
offset_x = max_width * i + full_padding + \
((max_width - textwidth) // 2)
offset_y = full_padding - textheight - 24
draw.text((offset_x, offset_y),
pre_render_texts_x[i], font=font, fill="black")
for j in range(y_enable_num):
textwidth, textheight = draw.textsize(
pre_render_texts_y[j], font)
label_text = pre_render_texts_y[j]
def is_exceed_width(t):
textwidth, _ = draw.textsize(
t, font)
return textwidth > max_width - 24
if textwidth > full_padding - 24:
# 超过宽度就换行
label_text = ""
for c in pre_render_texts_y[j]:
if is_exceed_width(label_text + c):
break
label_text += c
offset_x = full_padding - textwidth - 24
offset_y = max_height * j + full_padding + \
((max_height - textheight) // 2)
draw.text((offset_x, offset_y),
label_text, font=font, fill="black")
return full_canvas
def get_models_by_folder(dir_path):
models = []
for root, dirs, files in os.walk(dir_path):
# 排除隐藏文件夹
dirs[:] = [d for d in dirs if not d.startswith('.')]
for file in files:
if file.endswith(".pth") or file.endswith(".pt") or file.endswith(".pkl") or file.endswith(".onnx") or file.endswith(".safetensors"):
models.append(os.path.join(root, file))
return models
def get_folders_by_folder(dir_path):
folders = []
for root, dirs, files in os.walk(dir_path):
# 排除隐藏文件夹
dirs[:] = [d for d in dirs if not d.startswith('.')]
for dir in dirs:
folders.append(os.path.join(root, dir))
return folders
from torch import nn
class CustomizeEmbedsModel(nn.Module):
dtype = torch.float16
shared = None
# x = torch.zeros(1, 1, 256, 2048)
x = None
def __init__(self, *args, **kwargs):
super().__init__()
def to(self, *args, **kwargs):
return self
def forward(self, *args, **kwargs):
# print("CustomizeEmbedsModel forward: args:", args)
# print("CustomizeEmbedsModel forward: kwargs:", kwargs)
input_ids = kwargs.get("input_ids", None)
# if self.x is None:
if True:
if input_ids is None:
batch_size = 1
else: