Delete workflow/lib directory

2025-03-12 17:04:44 +08:00
parent 64da7155d9
commit c2af538cdf
5 changed files with 0 additions and 256 deletions
@@ -1,70 +0,0 @@
-[
-    {
-        "name": "replace_character_names",
-        "prompt": "If there is a person/character in the image you must refer to them as {name}."
-    },
-    {
-        "name": "exclude_unchangeable_attributes",
-        "prompt": "Do NOT include information about people/characters that cannot be changed (like ethnicity, gender, etc), but do still include changeable attributes (like hair style)."
-    },
-    {
-        "name": "include_lighting_details",
-        "prompt": "Include information about lighting."
-    },
-    {
-        "name": "include_camera_angle",
-        "prompt": "Include information about camera angle."
-    },
-    {
-        "name": "mention_watermark_presence",
-        "prompt": "Include information about whether there is a watermark or not."
-    },
-    {
-        "name": "note_jpeg_artifacts",
-        "prompt": "Include information about whether there are JPEG artifacts or not."
-    },
-    {
-        "name": "include_exif_data",
-        "prompt": "If it is a photo you MUST include information about what camera was likely used and details such as aperture, shutter speed, ISO, etc."
-    },
-    {
-        "name": "exclude_sexual_content",
-        "prompt": "Do NOT include anything sexual; keep it PG."
-    },
-    {
-        "name": "exclude_image_resolution",
-        "prompt": "Do NOT mention the image's resolution."
-    },
-    {
-        "name": "describe_aesthetic_quality",
-        "prompt": "You MUST include information about the subjective aesthetic quality of the image from low to very high."
-    },
-    {
-        "name": "include_composition_style",
-        "prompt": "Include information on the image's composition style, such as leading lines, rule of thirds, or symmetry."
-    },
-    {
-        "name": "exclude_text_elements",
-        "prompt": "Do NOT mention any text that is in the image."
-    },
-    {
-        "name": "specify_depth_of_field",
-        "prompt": "Specify the depth of field and whether the background is in focus or blurred."
-    },
-    {
-        "name": "specify_lighting_sources",
-        "prompt": "If applicable, mention the likely use of artificial or natural lighting sources."
-    },
-    {
-        "name": "avoid_ambiguous_language",
-        "prompt": "Do NOT use any ambiguous language."
-    },
-    {
-        "name": "classify_image_as_sfw_nsfw",
-        "prompt": "Include whether the image is sfw, suggestive, or nsfw."
-    },
-    {
-        "name": "describe_key_elements_only",
-        "prompt": "ONLY describe the most important elements of the image."
-    }
-]
@@ -1,34 +0,0 @@
-import folder_paths
-import os
-import base64
-import numpy as np
-from PIL import Image,ImageOps, ImageFilter
-
-import io
-
-comfy_path = os.path.dirname(folder_paths.__file__)
-custom_nodes_path = os.path.join(comfy_path, "custom_nodes")
-
-# D:\comfyui\ComfyUI_windows_portable\ComfyUI\custom_nodes\Comfyui_CXH_ALY
-# current_folder = os.path.dirname(os.path.abspath(__file__))
-
-# 节点路径
-def node_path(node_name):
-    return os.path.join(custom_nodes_path,node_name)
-
-# 创建文件夹
-def mkdir(path):
-	folder = os.path.exists(path)
-	if not folder:                   #判断是否存在文件夹如果不存在则创建为文件夹
-		os.makedirs(path)            #makedirs 创建文件时如果路径不存在会创建这个路径
-
-# 获取所有图片文件路径
-def get_all_image_paths(directory):
-    image_paths = []
-    for root, dirs, files in os.walk(directory):
-        for file in files:
-            if file.lower().endswith(('.png', '.jpg', '.jpeg')):
-                image_paths.append(os.path.join(root, file))
-    return image_paths
-
-
@@ -1,129 +0,0 @@
-# -*- encoding: utf-8 -*-
-'''
-@File         :ximg.py
-@Description  :图片转换工具
-'''
-
-import os
-import torch
-from PIL import Image, ImageOps, ImageSequence, ImageFile,UnidentifiedImageError
-import numpy as np
-import cv2 as cv
-import io
-import base64
-import requests
-from io import BytesIO
-
-def tensor2pil(t_image: torch.Tensor)  -> Image:
-    return Image.fromarray(
-        np.clip(255.0 * t_image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
-
-def pil2tensor(image:Image) -> torch.Tensor:
-    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
-
-def tensor2cv2(image:torch.Tensor) -> np.array:
-    if image.dim() == 4:
-        image = image.squeeze()
-    npimage = image.numpy()
-    cv2image = np.uint8(npimage * 255 / npimage.max())
-    return cv.cvtColor(cv2image, cv.COLOR_RGB2BGR)
-
-def cv22pil(cv2_img:np.ndarray) -> Image:
-    cv2_img = cv.cvtColor(cv2_img, cv.COLOR_BGR2RGB)
-    return Image.fromarray(cv2_img)
-
-# pil转io
-def pil2iobyte(pil_image,format='PNG'):
-    byte_arr = io.BytesIO()
-    pil_image.save(byte_arr, format=format)  
-    byte_arr = byte_arr.getvalue() 
-    return byte_arr
-
-# pil转64
-def pilTobase64(pil_image,format='PNG'):
-    byte_arr = pil2iobyte(pil_image,format)
-    image_base64 = base64.b64encode(byte_arr).decode('utf-8') 
-    return image_base64
-
-def ioBytes2tensor(bytes):
-    image = Image.open(bytes)
-    return pil2tensor(image)
-
-def getImageSize(image):
-    if image.shape[0] > 0:
-        image = torch.unsqueeze(image[0], 0)
-    _image = tensor2pil(image)
-
-    return (_image.width, _image.height)
-
-# 转成mask
-def imageToMask(img):
-    i = img
-    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")
-    return tensor2pil(mask)
-
-#   ret_masks.append(image2mask(_mask))
-def image2mask(image:Image) -> torch.Tensor:
-    _image = image.convert('RGBA')
-    alpha = _image.split() [0]
-    bg = Image.new("L", _image.size)
-    _image = Image.merge('RGBA', (bg, bg, bg, alpha))
-    ret_mask = torch.tensor([pil2tensor(_image)[0, :, :, 3].tolist()])
-    return ret_mask
-
-# 图像回帖
-def croppImg(original_image,cropped_avatar,left_x,top_y):
-    # 获取原始图像的大小
-    original_width, original_height = original_image.size
-    return croppImageBySize(cropped_avatar,left_x,top_y,original_width,original_height)
-
-def croppImageBySize(cropped_avatar,left_x,top_y,original_w,original_h):
-    # 获取原始图像的大小
-    original_width, original_height = original_w,original_h
-    # 获取头像的大小
-    avatar_width, avatar_height = cropped_avatar.size
-    # 创建一个与原始图像相同大小的透明图像
-    extended_image = Image.new("RGBA", (original_width, original_height), (0, 0, 0, 0))
-    # 将裁剪后的头像粘贴到新图像
-    extended_image.paste(cropped_avatar, (left_x, top_y), cropped_avatar)
-   
-    return extended_image
-
-
-# 将图片转换为Base64编码
-def image_to_base64(image_path):
-    with open(image_path, 'rb') as image_file:
-        return base64.b64encode(image_file.read()).decode('utf-8')
-
-# 获取网络图片
-def img_from_url(url):
-    # 发送HTTP请求获取图片  
-    response = requests.get(url)  
-    response.raise_for_status()  # 如果请求失败，这会抛出异常
-    # 将响应内容作为BytesIO对象打开，以便PIL可以读取它  
-    image = Image.open(BytesIO(response.content))
-    return image
-
-def open_image(path):
-    prev_value = None
-
-    try:
-        img = Image.open(path)
-    except (UnidentifiedImageError, ValueError): #PIL issues #4472 and #2445
-        prev_value = ImageFile.LOAD_TRUNCATED_IMAGES
-        ImageFile.LOAD_TRUNCATED_IMAGES = True
-        img = Image.open(path)
-    finally:
-        if prev_value is not None:
-            ImageFile.LOAD_TRUNCATED_IMAGES = prev_value
-        return img
@@ -1,23 +0,0 @@
-import os
-import folder_paths
-import json
-from transformers import  AutoProcessor
-
-# 下载hg 模型到本地
-def download_hg_model(model_id:str,exDir:str=''):
-    # 下载本地
-    model_checkpoint = os.path.join(folder_paths.models_dir, exDir, os.path.basename(model_id))
-    print(model_checkpoint)
-    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)
-    return model_checkpoint
-
-# clip_model = AutoModelForCausalLM.from_pretrained(
-#                 CLIP_PATH,
-#                 device_map="cuda",
-#                 trust_remote_code=True, 
-#                 torch_dtype="auto"
-#             )
-            
-#         clip_processor = AutoProcessor.from_pretrained(CLIP_PATH, trust_remote_code=True)