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nodes.py
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import os
import re
import torch
import hashlib
import folder_paths
import numpy as np
from PIL import Image
from datetime import datetime
from torchvision.transforms.v2 import ToTensor, ToPILImage
##################################
# Global Variables and Functions #
##################################
class AnyType(str):
def __eq__(self, _) -> bool:
return True
def __ne__(self, __value: object) -> bool:
return False
any = AnyType("*")
to_tensor = ToTensor()
to_image = ToPILImage()
###########################
# ACE Nodes of Primitives #
###########################
class ACE_Integer:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"int": ("INT", {"default": 0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff, "step": 1}),
}
}
RETURN_TYPES = ("INT",)
FUNCTION = "execute"
CATEGORY = "Ace Nodes"
def execute(self, int):
return (int,)
class ACE_Float:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"float": ("FLOAT", {"default": 0.0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff, "step": 0.001}),
}
}
RETURN_TYPES = ("FLOAT",)
FUNCTION = "execute"
CATEGORY = "Ace Nodes"
def execute(self, float):
return (float,)
class ACE_Text:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"text": ("STRING", {"default": '', "multiline": True}),
}
}
RETURN_TYPES = ("STRING",)
FUNCTION = "execute"
CATEGORY = "Ace Nodes"
def execute(self, text):
return (text,)
class ACE_Seed:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
}
}
RETURN_TYPES = ("INT",)
FUNCTION = "execute"
CATEGORY = "Ace Nodes"
def execute(self, seed):
return (seed,)
#####################
# ACE Nodes of Text #
#####################
class ACE_TextConcatenate:
@ classmethod
def INPUT_TYPES(cls):
return {
"required": {
"text1": ("STRING", {"multiline": True, "forceInput": True}),
"text2": ("STRING", {"multiline": True, "forceInput": True}),
"separator": ("STRING", {"default": ", ", "multiline": False}),
}
}
RETURN_TYPES = ("STRING",)
FUNCTION = "execute"
CATEGORY = "Ace Nodes"
def execute(self, text1, text2, separator):
return (text1 + separator + text2,)
class ACE_TextInputSwitch2Way:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"input": ("INT", {"default": 1, "min": 1, "max": 2}),
},
"optional": {
"text1": ("STRING", {"default": '', "multiline": True, "forceInput": True}),
"text2": ("STRING", {"default": '', "multiline": True, "forceInput": True}),
}
}
RETURN_TYPES = ("STRING",)
FUNCTION = "execute"
CATEGORY = "Ace Nodes"
def execute(self, input, text1='', text2=''):
if input <= 1:
return (text1,)
else:
return (text2,)
class ACE_TextInputSwitch4Way:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"input": ("INT", {"default": 1, "min": 1, "max": 4}),
},
"optional": {
"text1": ("STRING", {"default": '', "multiline": True, "forceInput": True}),
"text2": ("STRING", {"default": '', "multiline": True, "forceInput": True}),
"text3": ("STRING", {"default": '', "multiline": True, "forceInput": True}),
"text4": ("STRING", {"default": '', "multiline": True, "forceInput": True}),
}
}
RETURN_TYPES = ("STRING",)
FUNCTION = "execute"
CATEGORY = "Ace Nodes"
def execute(self, input, text1='', text2='', text3='', text4=''):
if input <= 1:
return (text1,)
elif input == 2:
return (text2,)
elif input == 3:
return (text3,)
else:
return (text4,)
class ACE_TextInputSwitch8Way:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"input": ("INT", {"default": 1, "min": 1, "max": 8}),
},
"optional": {
"text1": ("STRING", {"default": '', "multiline": True, "forceInput": True}),
"text2": ("STRING", {"default": '', "multiline": True, "forceInput": True}),
"text3": ("STRING", {"default": '', "multiline": True, "forceInput": True}),
"text4": ("STRING", {"default": '', "multiline": True, "forceInput": True}),
"text5": ("STRING", {"default": '', "multiline": True, "forceInput": True}),
"text6": ("STRING", {"default": '', "multiline": True, "forceInput": True}),
"text7": ("STRING", {"default": '', "multiline": True, "forceInput": True}),
"text8": ("STRING", {"default": '', "multiline": True, "forceInput": True}),
}
}
RETURN_TYPES = ("STRING",)
FUNCTION = "execute"
CATEGORY = "Ace Nodes"
def execute(self, input, text1='', text2='', text3='', text4='', text5='', text6='', text7='', text8=''):
if input <= 1:
return (text1,)
elif input == 2:
return (text2,)
elif input == 3:
return (text3,)
elif input == 4:
return (text4,)
elif input == 5:
return (text5,)
elif input == 6:
return (text6,)
elif input == 7:
return (text7,)
else:
return (text8,)
class ACE_TextList:
@classmethod
def INPUT_TYPES(cls):
return {
"required":{
"list_text": ("STRING", {"default": '', "multiline": True}),
}
}
RETURN_TYPES = ("STRING",)
OUTPUT_IS_LIST = (True,)
FUNCTION = "execute"
CATEGORY = "Ace Nodes"
def execute(self, list_text):
lines = list_text.split('\n')
list_out = [x.strip() for x in lines if x.strip()]
return (list_out,)
class ACE_TextPreview:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"text": ("STRING", {"forceInput": True}),
}
}
INPUT_IS_LIST = True
RETURN_TYPES = ("STRING",)
OUTPUT_NODE = True
OUTPUT_IS_LIST = (True,)
FUNCTION = "execute"
CATEGORY = "Ace Nodes"
def execute(self, text):
return {"ui": {"text": text}, "result": (text,)}
class ACE_TextSelector:
@classmethod
def INPUT_TYPES(cls):
return {
"required":{
"list_text": ("STRING", {"default": '', "multiline": True}),
"select": ("INT", {"default": 0, "min": 0}),
}
}
RETURN_TYPES = ("STRING",)
FUNCTION = "execute"
CATEGORY = "Ace Nodes"
def execute(self, list_text, select):
lines = list_text.split('\n')
list_out = [x.strip() for x in lines if x.strip()]
select = max(min(select, len(list_out)-1), 0)
return (list_out[select],)
class ACE_TextToResolution:
@classmethod
def INPUT_TYPES(cls):
return {
"required":{
"text": ("STRING", {"default": '512x512', "forceInput": True}),
}
}
RETURN_TYPES = ("INT","INT",)
RETURN_NAMES = ("WIDTH","HEIGHT",)
FUNCTION = "execute"
CATEGORY = "Ace Nodes"
def execute(self, text):
width, height = text.strip().split(" ")[0].split("x")
width, height = int(width), int(height)
return (width,height,)
class ACE_TextTranslate:
def __init__(self):
self.model_checkpoint = None
self.tokenizer = None
self.model = None
self.device = (
torch.device("mps")
if torch.backends.mps.is_available()
else torch.device("cuda")
if torch.cuda.is_available()
else torch.device("cpu")
)
@classmethod
def INPUT_TYPES(cls):
supported_lang = [
'en', # 英语
'zh', # 中文
'es', # 西班牙语
'hi', # 印度语
'ar', # 阿拉伯语
'pt', # 葡萄牙语
'ru', # 俄语
'ja', # 日语
'de', # 德语
'fr', # 法语
'ko', # 韩语
'it', # 意大利语
'nl', # 荷兰语
'tr', # 土耳其语
'sv', # 瑞典语
'pl', # 波兰语
'th', # 泰语
'vi', # 越南语
'id', # 印尼语
'el', # 希腊语
'cs', # 捷克语
'da', # 丹麦语
'fi', # 芬兰语
'hu', # 匈牙利语
'no', # 挪威语
'ro', # 罗马尼亚语
'sk', # 斯洛伐克语
'uk', # 乌克兰语
]
return {
"required":{
"text": ("STRING", {"default": '', "multiline": True}),
"from_lang": (supported_lang, {"default": 'en'}),
"to_lang": (supported_lang, {"default": 'en'}),
}
}
RETURN_TYPES = ("STRING",)
FUNCTION = "execute"
CATEGORY = "Ace Nodes"
def execute(self, text, from_lang, to_lang):
if from_lang == to_lang:
return (text,)
model_id = f'Helsinki-NLP/opus-mt-{from_lang}-{to_lang}'
model_checkpoint = os.path.join(folder_paths.models_dir, 'prompt_generator', os.path.basename(model_id))
if not os.path.exists(model_checkpoint):
from huggingface_hub import snapshot_download
snapshot_download(repo_id=model_id, local_dir=model_checkpoint, local_dir_use_symlinks=False)
if self.model_checkpoint != model_checkpoint:
self.model_checkpoint = model_checkpoint
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
self.tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
self.model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint).to(self.device).eval()
with torch.no_grad():
texts = [x.strip() for x in text.split("\n") if x.strip()]
encoded = self.tokenizer(texts, padding=True, truncation=True, return_tensors="pt").to(self.device)
sequences = self.model.generate(**encoded)
translation = self.tokenizer.batch_decode(sequences, skip_special_tokens=True)
translation_text = "\n".join([x.rstrip('.') for x in translation])
return (translation_text,)
class ACE_TextGoogleTranslate:
@classmethod
def INPUT_TYPES(cls):
supported_lang = [
'en', # 英语
'zh', # 中文
'es', # 西班牙语
'hi', # 印度语
'ar', # 阿拉伯语
'pt', # 葡萄牙语
'ru', # 俄语
'ja', # 日语
'de', # 德语
'fr', # 法语
'ko', # 韩语
'it', # 意大利语
'nl', # 荷兰语
'tr', # 土耳其语
'sv', # 瑞典语
'pl', # 波兰语
'th', # 泰语
'vi', # 越南语
'id', # 印尼语
'el', # 希腊语
'cs', # 捷克语
'da', # 丹麦语
'fi', # 芬兰语
'hu', # 匈牙利语
'no', # 挪威语
'ro', # 罗马尼亚语
'sk', # 斯洛伐克语
'uk', # 乌克兰语
]
return {
"required":{
"text": ("STRING", {"default": '', "multiline": True}),
"from_lang": (supported_lang, {"default": 'en'}),
"to_lang": (supported_lang, {"default": 'en'}),
}
}
RETURN_TYPES = ("STRING",)
FUNCTION = "execute"
CATEGORY = "Ace Nodes"
def execute(self, text, from_lang, to_lang):
if from_lang == to_lang:
return (text,)
import requests
response = requests.get(f'https://translate.google.com/m?sl={from_lang}&tl={to_lang}&q={text}')
if response.status_code == 200:
from bs4 import BeautifulSoup
soup = BeautifulSoup(response.text, "html.parser")
element = soup.find('div', {"class": "result-container"})
if element:
translation_text = element.get_text(strip=True)
return (translation_text,)
return (text,)
######################
# ACE Nodes of Image #
######################
class ACE_ImageConstrain:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"images": ("IMAGE",),
"max_width": ("INT", {"default": 1024, "min": 0}),
"max_height": ("INT", {"default": 1024, "min": 0}),
"min_width": ("INT", {"default": 0, "min": 0}),
"min_height": ("INT", {"default": 0, "min": 0}),
"crop_if_required": ("BOOLEAN", {"default": False, "label_on": "yes", "label_off": "no"}),
},
"optional": {
"crop_position": (["center", "top", "bottom", "left", "right"], {"default": "center"}),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "execute"
CATEGORY = "Ace Nodes"
def execute(self, images, max_width, max_height, min_width, min_height, crop_if_required, crop_position="center"):
images = images.permute([0,3,1,2])
output = []
for image in images:
image = to_image(image)
current_width, current_height = image.size
aspect_ratio = current_width / current_height
target_width = min(max(current_width, min_width), max_width)
target_height = min(max(current_height, min_height), max_height)
if crop_if_required:
if target_width / target_height < aspect_ratio:
resize_width, resize_height = int(target_height * aspect_ratio), int(target_height)
else:
resize_width, resize_height = int(target_width), int(target_width / aspect_ratio)
image = image.resize((resize_width, resize_height), resample=Image.Resampling.LANCZOS)
x0, y0 = max((resize_width-target_width)/2, 0), max((resize_height-target_height)/2, 0)
if crop_position == "top":
y0 = 0
elif crop_position == "bottom":
y0 = resize_height - target_height
elif crop_position == "left":
x0 = 0
elif crop_position == "right":
x0 = resize_width - target_width
image = image.crop((x0, y0, x0+target_width, y0+target_height))
else:
if target_width / target_height > aspect_ratio:
target_width, target_height = int(target_height * aspect_ratio), int(target_height)
else:
target_width, target_height = int(target_width), int(target_width / aspect_ratio)
resize_width, resize_height = max(target_width, min_width), max(target_height, min_height)
image = image.resize((resize_width, resize_height), resample=Image.Resampling.LANCZOS)
output.append(to_tensor(image))
output = torch.stack(output, dim=0)
output = output.permute([0,2,3,1])
return (output[:, :, :, :3],)
class ACE_ImageRemoveBackground:
def __init__(self):
self.model_dir = os.path.join(folder_paths.models_dir, "rembg")
os.environ["U2NET_HOME"] = self.model_dir
self.model_checkpoint = None
self.model = None
self.device = (
torch.device("mps")
if torch.backends.mps.is_available()
else torch.device("cuda")
if torch.cuda.is_available()
else torch.device("cpu")
)
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"images": ("IMAGE",),
"model": (["briarmbg", "u2net", "u2netp", "u2net_human_seg", "u2net_cloth_seg", "silueta", "isnet-general-use", "isnet-anime", "sam"],),
},
}
RETURN_TYPES = ("IMAGE","MASK",)
FUNCTION = "execute"
CATEGORY = "Ace Nodes"
def execute(self, images, model):
if model == "briarmbg":
model_checkpoint = os.path.join(self.model_dir, 'briarmbg.pth')
from .core.image.briarmbg import BriaRMBG
if self.model_checkpoint != model_checkpoint:
if not os.path.exists(model_checkpoint):
from huggingface_hub import hf_hub_download
hf_hub_download(repo_id='briaai/RMBG-1.4', filename='model.pth', local_dir=self.model_dir)
os.rename(os.path.join(self.model_dir, 'model.pth'), model_checkpoint)
self.model_checkpoint = model_checkpoint
self.model = BriaRMBG()
self.model.load_state_dict(torch.load(self.model_checkpoint, map_location=self.device))
self.model.to(self.device)
self.model.eval()
images = images.permute([0,3,1,2])
processed_images = []
processed_masks = []
import torch.nn.functional as F
from torchvision.transforms.v2.functional import normalize
for image in images:
orig_image = to_image(image)
w,h = orig_image.size
image = orig_image.convert('RGB')
image = image.resize((1024, 1024), Image.Resampling.LANCZOS)
im_np = np.array(image)
im_tensor = torch.tensor(im_np, dtype=torch.float32).permute(2,0,1)
im_tensor = torch.unsqueeze(im_tensor,0)
im_tensor = torch.divide(im_tensor,255.0)
im_tensor = normalize(im_tensor,[0.5,0.5,0.5],[1.0,1.0,1.0])
im_tensor = im_tensor.to(self.device)
result = self.model(im_tensor)
result = torch.squeeze(F.interpolate(result[0][0], size=(h,w), mode='bilinear') ,0)
ma = torch.max(result)
mi = torch.min(result)
result = (result-mi)/(ma-mi)
im_array = (result*255).cpu().data.numpy().astype(np.uint8)
pil_im = Image.fromarray(np.squeeze(im_array))
new_im = Image.new("RGB", pil_im.size, (0,0,0))
new_im.paste(orig_image, mask=pil_im)
processed_images.append(to_tensor(new_im))
processed_masks.append(to_tensor(pil_im))
new_ims = torch.stack(processed_images, dim=0)
new_masks = torch.stack(processed_masks, dim=0)
new_ims = new_ims.permute([0,2,3,1])
return (new_ims, new_masks,)
else:
from rembg import new_session, remove
if self.model_checkpoint != model:
self.model_checkpoint = model
self.model = new_session(self.model_checkpoint, providers=['CPUExecutionProvider'])
images = images.permute([0,3,1,2])
output = []
for image in images:
image = to_image(image)
image = remove(image, session=self.model)
output.append(to_tensor(image))
output = torch.stack(output, dim=0)
output = output.permute([0,2,3,1])
mask = output[:, :, :, 3] if output.shape[3] == 4 else torch.ones_like(output[:, :, :, 0])
return (output[:, :, :, :3], mask,)
class ACE_ImageColorFix:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"images": ("IMAGE",),
"color_map_images": ("IMAGE",),
"color_fix": (
[
"Wavelet",
"AdaIN",
],
),
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "execute"
CATEGORY = "Ace Nodes"
def execute(self, images, color_map_images, color_fix):
from .core.image.colorfix import adain_color_fix, wavelet_color_fix
color_fix_func = (
wavelet_color_fix if color_fix == "Wavelet" else adain_color_fix
)
images = images.permute([0,3,1,2])
color_map_images = color_map_images.permute([0,3,1,2])
output = []
last_element = images[-1] if len(images) < len(color_map_images) else color_map_images[-1]
from itertools import zip_longest
for image, color_map_image in zip_longest(images, color_map_images, fillvalue=last_element):
result_image = color_fix_func(to_image(image), to_image(color_map_image))
output.append(to_tensor(result_image))
output = torch.stack(output, dim=0)
output = output.permute([0,2,3,1])
return (output[:, :, :, :3],)
class ACE_ImageQA:
def __init__(self):
self.model_checkpoint = None
self.tokenizer = None
self.model = None
self.device = (
torch.device("mps")
if torch.backends.mps.is_available()
else torch.device("cuda")
if torch.cuda.is_available()
else torch.device("cpu")
)
self.bf16_support = (
torch.backends.mps.is_available() or
(torch.cuda.is_available() and torch.cuda.get_device_capability(self.device)[0] >= 8)
)
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
"text": ("STRING", {"default": '', "multiline": True}),
"model": (["moondream2", "MiniCPM-V-2"],),
},
}
RETURN_TYPES = ("STRING",)
FUNCTION = "execute"
CATEGORY = "Ace Nodes"
def execute(self, image, text, model):
if model == "moondream2":
model_id = "vikhyatk/moondream2"
elif model == "MiniCPM-V-2":
model_id = "openbmb/MiniCPM-V-2"
else:
raise Exception(f'Model "{model}" is not supported')
model_checkpoint = os.path.join(folder_paths.models_dir, 'prompt_generator', os.path.basename(model_id))
if not os.path.exists(model_checkpoint):
from huggingface_hub import snapshot_download
snapshot_download(repo_id=model_id, local_dir=model_checkpoint, local_dir_use_symlinks=False)
if self.model_checkpoint != model_checkpoint:
self.model_checkpoint = model_checkpoint
if model == "moondream2":
from transformers import AutoTokenizer, AutoModelForCausalLM
self.tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, trust_remote_code=True)
self.model = AutoModelForCausalLM.from_pretrained(model_checkpoint, trust_remote_code=True)
self.model = self.model.to(self.device).eval()
elif model == "MiniCPM-V-2":
from transformers import AutoTokenizer, AutoModel
self.tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, trust_remote_code=True)
self.model = AutoModel.from_pretrained(model_checkpoint, trust_remote_code=True, torch_dtype=torch.bfloat16)
self.model = self.model.to(self.device, dtype=torch.bfloat16 if self.bf16_support else torch.float16).eval()
with torch.no_grad():
image = to_image(image.permute([0,3,1,2])[0]).convert("RGB")
if model == "moondream2":
encoded_image = self.model.encode_image(image)
result = self.model.answer_question(encoded_image, text, self.tokenizer)
elif model == "MiniCPM-V-2":
result, context, _ = self.model.chat(
image=image,
msgs=[{'role': 'user', 'content': text}],
context=None,
tokenizer=self.tokenizer,
sampling=True
)
return (result,)
class ACE_ImageLoadFromCloud:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"filepath": ("STRING", {"default": ''}),
"bucket": ("STRING", {"default": ''}),
"region": ("STRING", {"default": ''}),
"cloud": (["aws-s3","aliyun-oss"],),
"access_key_id": ("STRING", {"default": ''}),
"access_key_secret": ("STRING", {"default": ''}),
},
}
RETURN_TYPES = ("IMAGE", "MASK",)
FUNCTION = "execute"
CATEGORY = "Ace Nodes"
def execute(self, filepath, bucket, region, cloud, access_key_id, access_key_secret):
save_path = os.path.join(folder_paths.temp_directory, bucket, filepath)
os.makedirs(os.path.dirname(save_path), exist_ok=True)
if cloud == "aws-s3":
import boto3
s3 = boto3.client('s3',
aws_access_key_id=access_key_id,
aws_secret_access_key=access_key_secret,
region_name=region)
s3.download_file(bucket, filepath, save_path)
elif cloud == "aliyun-oss":
import oss2
oss_auth = oss2.Auth(access_key_id, access_key_secret)
oss_bucket = oss2.Bucket(oss_auth, region, bucket)
oss_bucket.get_object_to_file(filepath, save_path)
else:
raise Exception(f'Cloud "{cloud}" is not supported')
image = f"{bucket}/{filepath} [temp]"
image_path = folder_paths.get_annotated_filepath(image)
from PIL import ImageSequence, ImageOps
img = Image.open(image_path)
output_images = []
output_masks = []
for i in ImageSequence.Iterator(img):
i = ImageOps.exif_transpose(i)
if i.mode == 'I':
i = i.point(lambda i: i * (1 / 255))
image = i.convert("RGB")
image = np.array(image).astype(np.float32) / 255.0
image = torch.from_numpy(image)[None,]
if 'A' in i.getbands():
mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
mask = 1. - torch.from_numpy(mask)
else:
mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
output_images.append(image)
output_masks.append(mask.unsqueeze(0))
if len(output_images) > 1:
output_image = torch.cat(output_images, dim=0)
output_mask = torch.cat(output_masks, dim=0)
else:
output_image = output_images[0]
output_mask = output_masks[0]
return (output_image, output_mask)
class ACE_ImageSaveToCloud:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"images": ("IMAGE",),
"filepath": ("STRING", {"default": 'ComfyUI_{0:05d}_{1:%Y-%m-%d_%H:%M:%S}.png'}),
"bucket": ("STRING", {"default": ''}),
"region": ("STRING", {"default": ''}),
"cloud": (["aws-s3","aliyun-oss"],),
"access_key_id": ("STRING", {"default": ''}),
"access_key_secret": ("STRING", {"default": ''}),
},
}
RETURN_TYPES = ()
OUTPUT_NODE = True
FUNCTION = "execute"
CATEGORY = "Ace Nodes"
def execute(self, images, filepath, bucket, region, cloud, access_key_id, access_key_secret):
now = datetime.now()
results = []
files_to_upload = []
for (batch_number, image) in enumerate(images):
i = 255. * image.cpu().numpy()
img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
filepath_formated = filepath.format(batch_number, now)
save_path = os.path.join(folder_paths.temp_directory, bucket, filepath_formated)
os.makedirs(os.path.dirname(save_path), exist_ok=True)
img.save(save_path, compress_level=1)
file, subfolder = os.path.basename(filepath_formated), os.path.join(bucket, os.path.dirname(filepath_formated))
results.append({
"filename": file,
"subfolder": subfolder,
"type": "temp"
})
files_to_upload.append((save_path, filepath_formated))
if cloud == "aws-s3":
import boto3
s3 = boto3.client('s3',
aws_access_key_id=access_key_id,
aws_secret_access_key=access_key_secret,
region_name=region)
for filename, objectname in files_to_upload:
try:
s3.upload_file(filename, bucket, objectname)
except Exception as e:
print(f'An error occurred: {e}')
elif cloud == "aliyun-oss":
import oss2
oss_auth = oss2.Auth(access_key_id, access_key_secret)
oss_bucket = oss2.Bucket(oss_auth, region, bucket)
for filename, objectname in files_to_upload:
try:
oss_bucket.put_object_from_file(objectname, filename)
except Exception as e:
print(f'An error occurred: {e}')
else:
raise Exception(f'Cloud "{cloud}" is not supported')
return { "ui": { "images": results } }
class ACE_ImageGetSize:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
}
}
RETURN_TYPES = ("INT", "INT",)
RETURN_NAMES = ("WIDTH","HEIGHT",)
FUNCTION = "execute"
CATEGORY = "Ace Nodes"
def execute(self, image):
return (image.shape[2], image.shape[1],)
class ACE_ImageFaceCrop:
def __init__(self):
self.model_name = None
self.model = None
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
"model": (["retinaface", "insightface"],),
"crop_width": ("INT", {"default": 512, "min": 1, "max": 16384, "step": 1}),
"crop_height": ("INT", {"default": 512, "min": 1, "max": 16384, "step": 1}),
}
}
RETURN_TYPES = ("IMAGE","MASK","BOOLEAN",)
RETURN_NAMES = ("IMAGE","MASK","FACE_DETECTED",)
FUNCTION = "execute"
CATEGORY = "Ace Nodes"
def execute(self, image, model, crop_width, crop_height):
image = to_image(image.permute([0,3,1,2])[0]).convert("RGB")
im_np = np.array(image)
face_bboxes = []
face_images = []
face_masks = []
if model == 'retinaface':
from retinaface import RetinaFace
if self.model_name != model:
self.model_name = model
self.model = RetinaFace.build_model()
faces = RetinaFace.detect_faces(im_np, model=self.model)
if faces:
for face in faces.values():
face_bboxes.append(face['facial_area'])
elif model == 'insightface':
from insightface.app import FaceAnalysis
if self.model_name != model:
self.model_name = model
self.model = FaceAnalysis(providers=['CUDAExecutionProvider', 'CPUExecutionProvider'], root=os.path.join(folder_paths.models_dir, 'insightface'))
self.model.prepare(ctx_id=0)
faces = self.model.get(im_np)
if faces:
for face in faces:
face_bboxes.append(face.bbox.astype(int))
else:
raise Exception(f'Model "{model}" is not supported')
if face_bboxes:
face_bboxes = sorted(face_bboxes, key=lambda x: -(x[2]-x[0])*(x[3]-x[1])) # sort by area
for bbox in face_bboxes:
x1, y1, x2, y2 = bbox
width, height = x2-x1, y2-y1
if width / height > crop_width / crop_height:
new_height = int(width * crop_height / crop_width)
y_offset = int((new_height - height) / 2)
y1, y2 = y1-y_offset, y2+y_offset
else:
new_width = int(height * crop_width / crop_height)
x_offset = int((new_width - width) / 2)
x1, x2 = x1-x_offset, x2+x_offset
face_image = image.crop((x1, y1, x2, y2)).resize((crop_width, crop_height), Image.Resampling.LANCZOS)
face_images.append(to_tensor(face_image))
face_mask = torch.zeros(image.size)
face_mask[max(x1,0):min(x2,image.size[0]-1), max(y1,0):min(y2,image.size[0]-1)] = 1
face_masks.append(face_mask)
else:
face_image = torch.zeros((3, crop_width, crop_height))
face_images.append(to_tensor(face_image))
face_mask = torch.zeros(image.size)
face_masks.append(face_mask)
output_images = torch.stack(face_images, dim=0)
output_images = output_images.permute([0,2,3,1])
output_masks = torch.stack(face_masks, dim=0)
output_masks = output_masks.permute([0,2,1])
return (output_images, output_masks, len(face_bboxes)>0)
class ACE_ImagePixelate:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"images": ("IMAGE",),
"pixel_size": ("INT", {"default": 8, "min": 1, "max": 64, "step": 1}),
"palette_colors": ("STRING", {"default": '121212\n31342F\n555F50\n7E8C77\n9DA09B', "multiline": True}),
}
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "execute"
CATEGORY = "Ace Nodes"
def execute(self, images, pixel_size, palette_colors):
images = images.permute([0,3,1,2])
palette = palette_colors.split('\n')
output = []
from PIL import ImageDraw
def hex_to_rgb(hex_color):
hex_color = hex_color.lstrip('0x').strip()
return tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4))
def get_color_for_depth(depth, palette):
step = 256//len(palette)
threshold = list(range(0, 255, step))
for i in range(1, len(threshold)):
if depth < threshold[i]:
return hex_to_rgb(palette[i-1])
return hex_to_rgb(palette[-1])
for image in images:
image = to_image(image).convert('L')
width, height = image.size
small_image = image.resize((width // pixel_size, height // pixel_size), Image.LANCZOS)
pixelated_image = small_image.resize((width, height), Image.LANCZOS)