1130 lines
45 KiB
Python
1130 lines
45 KiB
Python
from .md import *
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import json
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CATEGORY_TYPE = "🎈LAOGOU/Utils"
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def execute_command_with_realtime_output(cmd, cwd, message_path, clear_first=True, start_message=""):
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"""通用的实时命令执行方法"""
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import subprocess
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import sys
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from server import PromptServer
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import comfy.model_management as model_management
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try:
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# 发送开始消息
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if start_message:
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PromptServer.instance.send_sync(message_path, {
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"text": start_message,
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"clear": clear_first
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})
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# 执行命令，实时输出
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process = subprocess.Popen(
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cmd,
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cwd=cwd,
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stdout=subprocess.PIPE,
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stderr=subprocess.STDOUT,
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text=True,
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bufsize=1,
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universal_newlines=True
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)
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# 实时读取输出
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while True:
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# 检查是否被中断
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if model_management.processing_interrupted():
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process.terminate()
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PromptServer.instance.send_sync(message_path, {
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"text": "🛑 用户中断了操作",
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"clear": False
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})
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return False, "🛑 用户中断了操作"
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output = process.stdout.readline()
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if output == '' and process.poll() is not None:
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break
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if output:
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line = output.strip()
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if line:
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PromptServer.instance.send_sync(message_path, {
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"text": line,
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"clear": False
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})
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# 检查返回码
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return_code = process.poll()
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return return_code == 0, return_code
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except Exception as e:
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error_msg = f"💥 执行异常: {str(e)}"
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PromptServer.instance.send_sync(message_path, {
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"text": error_msg,
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"clear": False
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})
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return False, str(e)
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# 缓存相关的全局变量
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preview_bridge_cache = {}
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preview_bridge_image_hashes = {}
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class CachePreviewBridge:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"image": ("STRING", {"default": ""}),
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},
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"optional": {
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"images": ("IMAGE",),
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},
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"hidden": {"unique_id": "UNIQUE_ID", "extra_pnginfo": "EXTRA_PNGINFO"},
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}
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RETURN_TYPES = ("IMAGE", "MASK")
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FUNCTION = "doit"
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OUTPUT_NODE = True
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CATEGORY = CATEGORY_TYPE
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def __init__(self):
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super().__init__()
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self.output_dir = folder_paths.get_temp_directory()
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self.type = "temp"
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self.prev_hash = None
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@staticmethod
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def calculate_image_hash(images):
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"""计算图片的哈希值用于检测是否改变"""
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try:
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if images is None:
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return None
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np_images = images.cpu().numpy()
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image_bytes = np_images.tobytes()
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return hashlib.md5(image_bytes).hexdigest()
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except:
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return None
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@staticmethod
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def load_image_from_fileinfo(file_info_json):
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"""从 JSON 文件信息加载图片"""
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# 初始化默认值（64*64 遮罩表示无效遮罩）
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image = torch.zeros((1, 512, 512, 3), dtype=torch.float32, device="cpu")
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mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
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ui_item = {
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"filename": 'empty.png',
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"subfolder": '',
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"type": 'temp'
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}
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if not file_info_json:
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final_mask = mask.unsqueeze(0) if len(mask.shape) == 2 else mask
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return image, final_mask, ui_item
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try:
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# 只支持 JSON 格式
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file_info = json.loads(file_info_json)
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filename = file_info.get('filename')
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subfolder = file_info.get('subfolder', '')
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file_type = file_info.get('type', 'input')
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if not filename:
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final_mask = mask.unsqueeze(0) if len(mask.shape) == 2 else mask
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return image, final_mask, ui_item
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# 构建文件路径
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if file_type == 'input':
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base_dir = folder_paths.get_input_directory()
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elif file_type == 'output':
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base_dir = folder_paths.get_output_directory()
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elif file_type == 'temp':
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base_dir = folder_paths.get_temp_directory()
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else:
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base_dir = folder_paths.get_input_directory()
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if subfolder:
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file_path = os.path.join(base_dir, subfolder, filename)
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else:
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file_path = os.path.join(base_dir, filename)
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# 更新 ui_item
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ui_item = {
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"filename": filename,
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"subfolder": subfolder,
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"type": file_type
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}
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# 加载图片
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if os.path.exists(file_path):
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i = Image.open(file_path)
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i = ImageOps.exif_transpose(i)
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image = i.convert("RGB")
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image = np.array(image).astype(np.float32) / 255.0
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image = torch.from_numpy(image)[None,]
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if 'A' in i.getbands():
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mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
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mask = 1. - torch.from_numpy(mask)
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else:
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mask = torch.zeros((image.shape[1], image.shape[2]), dtype=torch.float32, device="cpu")
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except Exception as e:
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print(f"[CachePreviewBridge] 加载图片失败: {e}")
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final_mask = mask.unsqueeze(0) if len(mask.shape) == 2 else mask
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return image, final_mask, ui_item
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def doit(self, image, unique_id, images=None, extra_pnginfo=None):
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"""
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简化逻辑：
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1. 如果有 images 输入，保存并返回
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2. 如果没有 images 输入但有 image (文件信息)，从文件加载
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3. 否则返回空图像
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"""
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# 情况1: 有图像输入 - 从 clipspace 加载遮罩，或使用输入的图像
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if images is not None:
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# 更新 hash
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current_hash = self.calculate_image_hash(images)
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preview_bridge_image_hashes[unique_id] = current_hash
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# 默认遮罩（64*64 表示无效遮罩）
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mask = torch.zeros((1, 64, 64), dtype=torch.float32, device="cpu")
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ui_item = None
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# 如果有文件信息，尝试从文件加载遮罩
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if image and image.strip():
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_, loaded_mask, path_item = CachePreviewBridge.load_image_from_fileinfo(image)
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# 如果成功加载了遮罩，使用它
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if path_item["filename"] != 'empty.png':
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# 调整遮罩尺寸以匹配图像
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if loaded_mask.shape[-2:] == (images.shape[1], images.shape[2]):
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mask = loaded_mask
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ui_item = path_item # 保存 clipspace 文件信息用于 UI 预览
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# 如果有 clipspace 文件，直接返回 clipspace 的预览
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if ui_item:
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return {
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"ui": {"images": [ui_item]},
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"result": (images, mask),
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}
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# 否则保存新预览图并返回默认的 64*64 遮罩
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res = PreviewImage().save_images(
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images,
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filename_prefix=f"PreviewBridge/PB-{unique_id}-",
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extra_pnginfo=extra_pnginfo
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)
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# 缓存结果
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preview_bridge_cache[unique_id] = (images, res['ui']['images'])
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return {
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"ui": {"images": res['ui']['images']},
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"result": (images, mask),
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}
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# 情况2: 没有图像输入，但有文件信息 - 从文件加载
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if image and image.strip():
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pixels, mask, path_item = CachePreviewBridge.load_image_from_fileinfo(image)
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# 检查是否成功加载
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if path_item["filename"] != 'empty.png':
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return {
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"ui": {"images": [path_item]},
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"result": (pixels, mask),
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}
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# 情况3: 检查是否有缓存
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if unique_id in preview_bridge_cache:
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cached_images, cached_ui = preview_bridge_cache[unique_id]
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# 返回 64*64 遮罩表示无效遮罩
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mask = torch.zeros((1, 64, 64), dtype=torch.float32, device="cpu")
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return {
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"ui": {"images": cached_ui},
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"result": (cached_images, mask),
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}
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# 情况4: 没有任何数据 - 返回空图像和 64*64 遮罩
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empty_image = torch.zeros((1, 512, 512, 3), dtype=torch.float32, device="cpu")
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empty_mask = torch.zeros((1, 64, 64), dtype=torch.float32, device="cpu")
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return {
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"ui": {"images": []},
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"result": (empty_image, empty_mask),
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}
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class LG_Noise:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"type": (["fade", "dissolve", "gaussian"], ),
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"opacity": ("FLOAT", { "default": 1.0, "min": 0, "max": 1, "step": 0.01 }),
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"strength": ("INT", { "default": 1, "min": 1, "max": 32, "step": 1 }),
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"density": ("FLOAT", { "default": 1.0, "min": 0, "max": 1, "step": 0.05 }),
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"sharpen": ("INT", { "default": 0, "min": -32, "max": 32, "step": 1 }),
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"brightness": ("FLOAT", { "default": 1.0, "min": 0, "max": 3, "step": 0.05 }),
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"random_color": ("BOOLEAN", {"default": True}),
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"color": ("COLOR", {"default": "#808080"}),
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"seed": ("INT", {"default": -1, "min": -1, "max": 0x7fffffff}),
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},
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"optional": {
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"image_optional": ("IMAGE",),
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"mask_optional": ("MASK",),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "make_noise"
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CATEGORY = CATEGORY_TYPE
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def hex_to_rgb(self, hex_color):
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hex_color = hex_color.lstrip('#')
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r = int(hex_color[0:2], 16) / 255.0
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g = int(hex_color[2:4], 16) / 255.0
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b = int(hex_color[4:6], 16) / 255.0
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return torch.tensor([r, g, b])
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def make_noise(self, type, opacity, density, strength, sharpen, random_color, color, brightness, seed,
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image_optional=None, mask_optional=None):
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if image_optional is None:
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image = torch.zeros([1, 1, 1, 3])
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else:
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image = image_optional
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h, w = image.shape[1:3]
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if h == 1 and w == 1:
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image = image.repeat(1, 512, 512, 1)
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h, w = 512, 512
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if mask_optional is not None:
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mask = mask_optional.unsqueeze(-1).repeat(1, 1, 1, 3)
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else:
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mask = torch.ones((1, h, w, 1), device=image.device).repeat(1, 1, 1, 3)
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if seed == -1:
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seed = torch.randint(0, 0x7fffffff, (1,)).item()
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torch.manual_seed(seed)
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print(f"[LG_Noise] Using seed: {seed}")
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color_rgb = self.hex_to_rgb(color).to(image.device)
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def generate_noise(size_h, size_w, is_gaussian=False):
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"""统一的噪声生成函数"""
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if random_color:
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if is_gaussian:
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noise = torch.randn(1, size_h, size_w, 3, device=image.device)
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return torch.clamp(noise * 0.5 + 0.5, 0, 1)
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else:
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return torch.rand(1, size_h, size_w, 3, device=image.device)
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else:
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return torch.ones(1, size_h, size_w, 3, device=image.device)
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def generate_density_mask(size_h, size_w):
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"""统一的密度遮罩生成函数"""
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return (torch.rand(1, size_h, size_w, 1, device=image.device) < density).float()
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if strength > 1:
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small_h, small_w = h // strength, w // strength
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density_mask = generate_density_mask(small_h, small_w)
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noise = generate_noise(small_h, small_w) * density_mask
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noise = torch.nn.functional.interpolate(
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noise.permute(0, 3, 1, 2),
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size=(h, w),
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mode='nearest'
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).permute(0, 2, 3, 1)
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if type != "fade":
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density_mask = torch.nn.functional.interpolate(
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density_mask.permute(0, 3, 1, 2),
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size=(h, w),
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mode='nearest'
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).permute(0, 2, 3, 1)
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else:
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density_mask = generate_density_mask(h, w)
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noise = generate_noise(h, w) * density_mask
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colored_noise = noise * color_rgb.view(1, 1, 1, 3) * brightness
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if type == "fade":
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result = image * (1 - opacity) + colored_noise * opacity
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elif type == "dissolve":
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dissolve_mask = (torch.rand(1, h//strength if strength > 1 else h,
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w//strength if strength > 1 else w, 1,
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device=image.device) < opacity).float()
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density_mask = (torch.rand(1, h//strength if strength > 1 else h,
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w//strength if strength > 1 else w, 1,
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device=image.device) < density).float()
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dissolve_mask = dissolve_mask * density_mask
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if strength > 1:
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dissolve_mask = torch.nn.functional.interpolate(
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dissolve_mask.permute(0, 3, 1, 2),
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size=(h, w),
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mode='nearest'
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).permute(0, 2, 3, 1)
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dissolve_mask = dissolve_mask.repeat(1, 1, 1, 3)
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if random_color:
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noise = torch.rand(1, h, w, 3, device=image.device)
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else:
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noise = torch.ones(1, h, w, 3, device=image.device)
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noise = noise * dissolve_mask
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colored_noise = noise * color_rgb.view(1, 1, 1, 3) * brightness
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result = image * (1-dissolve_mask) + colored_noise * dissolve_mask
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elif type == "gaussian":
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noise = generate_noise(h//strength if strength > 1 else h,
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w//strength if strength > 1 else w,
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is_gaussian=True)
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if strength > 1:
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noise = torch.nn.functional.interpolate(
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noise.permute(0, 3, 1, 2),
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size=(h, w),
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mode='nearest'
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).permute(0, 2, 3, 1)
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noise = (noise - 0.5) * opacity * 2
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noise = noise * density_mask
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colored_noise = noise * color_rgb.view(1, 1, 1, 3) * brightness
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result = torch.clamp(image + colored_noise, 0, 1)
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result = torch.clamp(result, 0, 1)
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if sharpen != 0:
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kernel_size = abs(sharpen) * 2 + 1
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noise_part = result * mask - image * mask
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if sharpen < 0:
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blurred_noise = T.functional.gaussian_blur(
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noise_part.permute([0,3,1,2]),
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kernel_size
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).permute([0,2,3,1])
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result = image + blurred_noise
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else:
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blurred = T.functional.gaussian_blur(
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noise_part.permute([0,3,1,2]),
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kernel_size
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).permute([0,2,3,1])
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sharpened_noise = noise_part + (noise_part - blurred) * (sharpen / 8)
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result = image + torch.clamp(sharpened_noise, 0, 1)
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result = torch.clamp(result, 0, 1)
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result = image * (1 - mask) + result * mask
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return (result,)
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WEIGHT_TYPES = ["linear", "ease in", "ease out", 'ease in-out', 'reverse in-out', 'weak input', 'weak output', 'weak middle', 'strong middle', 'style transfer', 'composition', 'strong style transfer', 'style and composition', 'style transfer precise', 'composition precise']
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class IPAdapterWeightTypes:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"weight_type": (WEIGHT_TYPES, ),
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}}
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RETURN_TYPES = (AlwaysEqualProxy('*'),)
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RETURN_NAMES = ("weight_type",)
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FUNCTION = "get_weight_types"
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CATEGORY = CATEGORY_TYPE
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def get_weight_types(self, weight_type):
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return (weight_type,)
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class LG_LoadImage(LoadImage):
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@classmethod
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def INPUT_TYPES(s):
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input_dir = folder_paths.get_input_directory()
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files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
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files = folder_paths.filter_files_content_types(files, ["image"])
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return {"required":
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{"image": (sorted(files), {"image_upload": True}),
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},
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}
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RETURN_TYPES = ("IMAGE", "MASK", "STRING")
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RETURN_NAMES = ("image", "mask", "filename")
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DESCRIPTION = "从temp或output文件夹加载最新图片并复制到input文件夹。点击刷新按钮时，节点将更新图片列表并自动选择第一张图片，方便快速迭代。"
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CATEGORY = CATEGORY_TYPE
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FUNCTION = "load_image"
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@classmethod
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def IS_CHANGED(s, image):
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# 调用父类的IS_CHANGED方法
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return LoadImage.IS_CHANGED(image)
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def load_image(self, image):
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# 调用父类方法获取完整的图像和遮罩
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image_tensor, mask_tensor = super().load_image(image)
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# 返回图像、遮罩和文件名
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return (image_tensor, mask_tensor, image)
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# API路由：删除文件
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@PromptServer.instance.routes.delete("/lg/delete_image")
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async def delete_image(request):
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try:
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json_data = await request.json()
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filename = json_data.get("filename")
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|
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if not filename:
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return web.json_response({"error": "未指定文件名"}, status=400)
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# 删除input目录中的文件
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input_dir = folder_paths.get_input_directory()
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file_path = os.path.join(input_dir, filename)
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|
|
|
if not os.path.exists(file_path):
|
|
return web.json_response({"error": f"文件不存在: {filename}"}, status=404)
|
|
|
|
# 删除文件
|
|
os.remove(file_path)
|
|
|
|
return web.json_response({
|
|
"success": True,
|
|
"message": f"文件 {filename} 已删除"
|
|
})
|
|
except Exception as e:
|
|
print(f"[LG_LoadImage] 删除文件失败: {str(e)}")
|
|
traceback.print_exc()
|
|
return web.json_response({"error": str(e)}, status=500)
|
|
|
|
class LG_LatentBatchToList:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"latent": ("LATENT",),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("LATENT",)
|
|
OUTPUT_IS_LIST = (True,) # 表示输出是列表
|
|
FUNCTION = "batch_to_list"
|
|
CATEGORY = CATEGORY_TYPE
|
|
|
|
def batch_to_list(self, latent):
|
|
"""将latent batch转换为latent列表"""
|
|
samples = latent["samples"]
|
|
batch_size = samples.shape[0]
|
|
|
|
# 将batch分离为单独的latent
|
|
latent_list = []
|
|
for i in range(batch_size):
|
|
single_latent = {"samples": samples[i:i+1]} # 保持4维，但batch_size=1
|
|
latent_list.append(single_latent)
|
|
|
|
return (latent_list,)
|
|
|
|
class LG_PipManager:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"package_name": ("STRING", {
|
|
"default": "",
|
|
"multiline": True,
|
|
"placeholder": "支持多种格式:\nnumpy==1.21.0 # 指定版本\ntorch>=1.9.0 # 版本范围\nrequests[security] # 额外依赖\nnumpy torch pandas # 多个包",
|
|
"tooltip": "包名支持版本号、额外依赖、多包安装等pip标准格式"
|
|
}),
|
|
"operation": (["install", "uninstall", "upgrade", "list"], {
|
|
"default": "install",
|
|
"tooltip": "选择操作类型：安装、卸载、升级或列出已安装包"
|
|
}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ()
|
|
FUNCTION = "manage_package"
|
|
OUTPUT_NODE = True
|
|
CATEGORY = CATEGORY_TYPE
|
|
|
|
def manage_package(self, package_name, operation):
|
|
import sys
|
|
import re
|
|
|
|
# 如果是 list 操作，忽略输入直接列出已安装包
|
|
if operation == "list":
|
|
cmd = [sys.executable, "-m", "pip", "list"]
|
|
emoji = "📋"
|
|
action = "列出已安装包"
|
|
start_message = f"{emoji} 正在{action}..."
|
|
|
|
# 使用通用方法执行命令
|
|
success, result = execute_command_with_realtime_output(
|
|
cmd, None, "/lg/pip_manager", True, start_message
|
|
)
|
|
|
|
if isinstance(result, str): # 中断或异常
|
|
return {"ui": {"text": (result,)}}
|
|
|
|
if success:
|
|
success_msg = "✅ 已列出所有已安装的包"
|
|
PromptServer.instance.send_sync("/lg/pip_manager", {
|
|
"text": success_msg,
|
|
"clear": False
|
|
})
|
|
return {"ui": {"text": (success_msg,)}}
|
|
else:
|
|
error_msg = f"❌ 列出包失败 (退出码: {result})"
|
|
PromptServer.instance.send_sync("/lg/pip_manager", {
|
|
"text": error_msg,
|
|
"clear": False
|
|
})
|
|
return {"ui": {"text": (error_msg,)}}
|
|
|
|
# 其他操作需要检查包名输入
|
|
if not package_name.strip():
|
|
return {"ui": {"text": ("❌ 请输入包名",)}}
|
|
|
|
# 处理多行输入，过滤注释和空行
|
|
lines = []
|
|
for line in package_name.strip().split('\n'):
|
|
line = line.split('#')[0].strip() # 移除注释
|
|
if line: # 忽略空行
|
|
lines.append(line)
|
|
|
|
if not lines:
|
|
return {"ui": {"text": ("❌ 请输入有效的包名",)}}
|
|
|
|
# 构建pip命令
|
|
if operation == "install":
|
|
cmd = [sys.executable, "-m", "pip", "install"] + lines
|
|
emoji = "📦"
|
|
action = "安装"
|
|
packages_text = " ".join(lines)
|
|
elif operation == "uninstall":
|
|
# 对于卸载，需要提取基础包名（去掉版本号和额外依赖）
|
|
base_packages = []
|
|
for line in lines:
|
|
for pkg in line.split():
|
|
# 提取基础包名：移除版本号、额外依赖等
|
|
base_name = re.split(r'[><=!~\[]', pkg)[0].strip()
|
|
if base_name:
|
|
base_packages.append(base_name)
|
|
|
|
if not base_packages:
|
|
return {"ui": {"text": ("❌ 无法提取有效的包名",)}}
|
|
|
|
cmd = [sys.executable, "-m", "pip", "uninstall", "-y"] + base_packages
|
|
emoji = "🗑️"
|
|
action = "卸载"
|
|
packages_text = " ".join(base_packages)
|
|
else: # upgrade
|
|
cmd = [sys.executable, "-m", "pip", "install", "--upgrade"] + lines
|
|
emoji = "⬆️"
|
|
action = "升级"
|
|
packages_text = " ".join(lines)
|
|
|
|
# 添加卸载警告
|
|
warning_msg = ""
|
|
if operation == "uninstall":
|
|
warning_msg = "\n⚠️ 注意：如果包正在使用中，卸载可能失败。建议重启ComfyUI后再尝试。"
|
|
|
|
start_message = f"{emoji} 开始{action} {packages_text}...{warning_msg}\n📝 执行命令: {' '.join(cmd)}"
|
|
|
|
# 使用通用方法执行命令
|
|
success, result = execute_command_with_realtime_output(
|
|
cmd, None, "/lg/pip_manager", True, start_message
|
|
)
|
|
|
|
if isinstance(result, str): # 中断或异常
|
|
return {"ui": {"text": (result,)}}
|
|
|
|
if success:
|
|
success_msg = f"✅ 成功{action} {packages_text}"
|
|
PromptServer.instance.send_sync("/lg/pip_manager", {
|
|
"text": success_msg,
|
|
"clear": False
|
|
})
|
|
return {"ui": {"text": (success_msg,)}}
|
|
else:
|
|
error_msg = f"❌ {action}失败 (退出码: {result})"
|
|
PromptServer.instance.send_sync("/lg/pip_manager", {
|
|
"text": error_msg,
|
|
"clear": False
|
|
})
|
|
return {"ui": {"text": (error_msg,)}}
|
|
|
|
class LG_SaveImage:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"images": ("IMAGE",),
|
|
"filename_prefix": ("STRING", {
|
|
"default": "ComfyUI_{timestamp}",
|
|
"multiline": False,
|
|
"tooltip": "文件名前缀，支持表达式：{timestamp}时间戳、{date}日期、{time}时间、{datetime}日期时间、{batch}批次号、{counter}计数器"
|
|
}),
|
|
"path": ("STRING", {
|
|
"default": "",
|
|
"multiline": False,
|
|
"placeholder": "留空使用默认输出目录",
|
|
"tooltip": "保存路径，支持绝对路径和相对路径，不存在时自动创建"
|
|
}),
|
|
"format": (["png", "jpg", "webp"], {
|
|
"default": "png",
|
|
"tooltip": "图像保存格式：PNG无损、JPG/WebP有损压缩"
|
|
}),
|
|
"quality": ("INT", {
|
|
"default": 95,
|
|
"min": 1,
|
|
"max": 100,
|
|
"step": 1,
|
|
"tooltip": "图像质量(1-100)，仅对JPG和WebP格式有效，PNG格式忽略此参数"
|
|
}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ()
|
|
FUNCTION = "save_images"
|
|
OUTPUT_NODE = True
|
|
CATEGORY = CATEGORY_TYPE
|
|
|
|
def save_images(self, images, filename_prefix="ComfyUI_{timestamp}", path="", format="png", quality=95):
|
|
# 确定保存路径
|
|
if path:
|
|
# 支持相对路径和绝对路径
|
|
if os.path.isabs(path):
|
|
save_dir = path
|
|
else:
|
|
# 相对路径基于ComfyUI根目录
|
|
save_dir = os.path.join(os.getcwd(), path)
|
|
else:
|
|
# 使用默认输出目录
|
|
save_dir = folder_paths.get_output_directory()
|
|
|
|
# 创建目录（如果不存在）
|
|
os.makedirs(save_dir, exist_ok=True)
|
|
|
|
# 一次性获取时间信息（避免重复计算）
|
|
import datetime
|
|
now = datetime.datetime.now()
|
|
timestamp = str(int(now.timestamp()))
|
|
date_str = now.strftime("%Y%m%d")
|
|
time_str = now.strftime("%H%M%S")
|
|
datetime_str = now.strftime("%Y%m%d_%H%M%S")
|
|
|
|
# 预处理文件名前缀，只替换非批次相关的变量
|
|
base_prefix = filename_prefix.replace("{timestamp}", timestamp)
|
|
base_prefix = base_prefix.replace("{date}", date_str)
|
|
base_prefix = base_prefix.replace("{time}", time_str)
|
|
base_prefix = base_prefix.replace("{datetime}", datetime_str)
|
|
|
|
file_extension = f".{format}"
|
|
|
|
# 使用类似系统的计数器逻辑
|
|
full_output_folder, filename, counter, subfolder, _ = folder_paths.get_save_image_path(base_prefix, save_dir, images[0].shape[1], images[0].shape[0])
|
|
|
|
for batch_number, image in enumerate(images):
|
|
# 转换tensor为PIL图像
|
|
i = 255. * image.cpu().numpy()
|
|
img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
|
|
|
|
# 处理批次和计数器变量
|
|
processed_prefix = base_prefix
|
|
if "{batch}" in filename_prefix:
|
|
processed_prefix = processed_prefix.replace("{batch}", f"{batch_number:05d}")
|
|
if "{counter}" in filename_prefix:
|
|
processed_prefix = processed_prefix.replace("{counter}", f"{counter:05d}")
|
|
|
|
# 生成文件名
|
|
final_filename = f"{processed_prefix}{file_extension}"
|
|
|
|
# 如果没有使用批次或计数器变量，且有多张图片，需要避免重名
|
|
if len(images) > 1 and "{batch}" not in filename_prefix and "{counter}" not in filename_prefix:
|
|
name_without_ext = os.path.splitext(final_filename)[0]
|
|
final_filename = f"{name_without_ext}_{batch_number:05d}{file_extension}"
|
|
|
|
file_path = os.path.join(full_output_folder, final_filename)
|
|
counter += 1
|
|
|
|
# 根据格式保存图像，移除optimize减少处理时间
|
|
if format == "png":
|
|
# 使用与系统相同的compress_level
|
|
img.save(file_path, format='PNG', compress_level=4)
|
|
|
|
elif format == "jpg":
|
|
# 确保RGB模式（JPEG不支持透明度）
|
|
if img.mode in ('RGBA', 'LA', 'P'):
|
|
img = img.convert('RGB')
|
|
img.save(file_path, format='JPEG', quality=quality)
|
|
|
|
elif format == "webp":
|
|
img.save(file_path, format='WebP', quality=quality)
|
|
|
|
|
|
return {}
|
|
|
|
class LG_InstallDependencies:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
# 获取custom_nodes目录
|
|
custom_nodes_paths = folder_paths.get_folder_paths("custom_nodes")
|
|
custom_nodes_dir = custom_nodes_paths[0] if custom_nodes_paths else None
|
|
|
|
# 获取所有包含requirements.txt的插件文件夹
|
|
plugin_folders = []
|
|
if custom_nodes_dir and os.path.exists(custom_nodes_dir):
|
|
for item in os.listdir(custom_nodes_dir):
|
|
item_path = os.path.join(custom_nodes_dir, item)
|
|
if os.path.isdir(item_path):
|
|
requirements_path = os.path.join(item_path, "requirements.txt")
|
|
if os.path.exists(requirements_path):
|
|
plugin_folders.append(item)
|
|
|
|
# 如果没有找到包含requirements.txt的插件，添加一个提示项
|
|
if not plugin_folders:
|
|
plugin_folders = ["未找到包含requirements.txt的插件"]
|
|
|
|
return {
|
|
"required": {
|
|
"plugin_folder": (sorted(plugin_folders), {"tooltip": "选择要安装依赖的插件文件夹"}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ()
|
|
FUNCTION = "install_dependencies"
|
|
OUTPUT_NODE = True
|
|
CATEGORY = CATEGORY_TYPE
|
|
|
|
def install_dependencies(self, plugin_folder):
|
|
if plugin_folder == "未找到包含requirements.txt的插件":
|
|
return {"ui": {"text": ("错误: 没有找到包含requirements.txt的插件文件夹",)}}
|
|
|
|
# 获取custom_nodes目录和插件路径
|
|
custom_nodes_paths = folder_paths.get_folder_paths("custom_nodes")
|
|
custom_nodes_dir = custom_nodes_paths[0] if custom_nodes_paths else None
|
|
|
|
if not custom_nodes_dir:
|
|
return {"ui": {"text": ("❌ 无法获取custom_nodes目录路径",)}}
|
|
|
|
plugin_path = os.path.join(custom_nodes_dir, plugin_folder)
|
|
requirements_path = os.path.join(plugin_path, "requirements.txt")
|
|
|
|
if not os.path.exists(plugin_path):
|
|
return {"ui": {"text": (f"❌ 插件目录不存在",)}}
|
|
|
|
if not os.path.exists(requirements_path):
|
|
return {"ui": {"text": (f"❌ requirements.txt不存在",)}}
|
|
|
|
import sys
|
|
|
|
# 构建pip命令
|
|
cmd = [sys.executable, "-m", "pip", "install", "-r", requirements_path]
|
|
start_message = f"🚀 开始安装 {plugin_folder} 的依赖包...\n📝 执行命令: {' '.join(cmd)}"
|
|
|
|
# 使用通用方法执行命令
|
|
success, result = execute_command_with_realtime_output(
|
|
cmd, plugin_path, "/lg/install_dependencies", True, start_message
|
|
)
|
|
|
|
if isinstance(result, str): # 中断或异常
|
|
return {"ui": {"text": (result,)}}
|
|
|
|
if success:
|
|
success_msg = f"✅ 成功安装 {plugin_folder} 的依赖包"
|
|
PromptServer.instance.send_sync("/lg/install_dependencies", {
|
|
"text": success_msg,
|
|
"clear": False
|
|
})
|
|
return {"ui": {"text": (success_msg,)}}
|
|
else:
|
|
error_msg = f"❌ 安装失败 (退出码: {result})"
|
|
PromptServer.instance.send_sync("/lg/install_dependencies", {
|
|
"text": error_msg,
|
|
"clear": False
|
|
})
|
|
return {"ui": {"text": (error_msg,)}}
|
|
|
|
class LG_FloatRange:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"value": ("FLOAT", {
|
|
"default": 0.00,
|
|
"min": 0.00,
|
|
"max": 1.00,
|
|
"step": 0.01,
|
|
"round": 0.01,
|
|
"tooltip": "0-1范围的浮点数，精度控制在小数点后2位"
|
|
}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("FLOAT",)
|
|
RETURN_NAMES = ("float",)
|
|
FUNCTION = "get_float_value"
|
|
CATEGORY = CATEGORY_TYPE
|
|
|
|
def get_float_value(self, value):
|
|
rounded_value = round(value, 2)
|
|
return (rounded_value,)
|
|
|
|
|
|
|
|
# 计数器节点状态存储
|
|
counter_states = {}
|
|
|
|
class LG_Counter:
|
|
"""计数器节点"""
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"total": ("INT", {
|
|
"default": 10,
|
|
"min": 1,
|
|
"max": 10000,
|
|
"step": 1
|
|
}),
|
|
"mode": (["increase", "decrease", "None"], {
|
|
"default": "increase"
|
|
}),
|
|
},
|
|
"hidden": {
|
|
"unique_id": "UNIQUE_ID"
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("INT",)
|
|
RETURN_NAMES = ("count",)
|
|
FUNCTION = "count"
|
|
CATEGORY = CATEGORY_TYPE
|
|
|
|
@classmethod
|
|
def IS_CHANGED(s, total, mode, unique_id):
|
|
# None模式不触发自动更新，只在参数改变时更新
|
|
if mode == "None":
|
|
return f"{total}_{mode}"
|
|
# 其他模式每次都返回不同的浮点数，确保每次都触发执行
|
|
import time
|
|
return float(time.time())
|
|
|
|
def count(self, total, mode, unique_id):
|
|
try:
|
|
# None模式：直接返回total的值，不进行计数
|
|
if mode == "None":
|
|
print(f"[Counter] 节点 {unique_id} None模式，返回total值: {total}")
|
|
return (total,)
|
|
|
|
# 初始化或获取当前节点的计数状态
|
|
if unique_id not in counter_states:
|
|
counter_states[unique_id] = {
|
|
"current": 0 if mode == "increase" else total,
|
|
"total": total,
|
|
"mode": mode,
|
|
"first_run": True
|
|
}
|
|
print(f"[Counter] 初始化节点 {unique_id}, 模式: {mode}, 总数: {total}")
|
|
|
|
state = counter_states[unique_id]
|
|
|
|
# 如果total或mode改变了，重置计数器
|
|
if state["total"] != total or state["mode"] != mode:
|
|
state["total"] = total
|
|
state["mode"] = mode
|
|
state["current"] = 0 if mode == "increase" else total
|
|
state["first_run"] = True
|
|
print(f"[Counter] 参数改变，重置节点 {unique_id}, 新模式: {mode}, 新总数: {total}")
|
|
|
|
current_count = state["current"]
|
|
|
|
# 如果不是第一次运行，则先更新计数器再返回
|
|
if not state["first_run"]:
|
|
if mode == "increase":
|
|
# 递增模式：0 -> total-1, 然后循环回0
|
|
state["current"] += 1
|
|
if state["current"] >= total:
|
|
state["current"] = 0
|
|
else:
|
|
# 递减模式：total -> 0, 然后循环回total
|
|
state["current"] -= 1
|
|
if state["current"] < 0:
|
|
state["current"] = total
|
|
|
|
current_count = state["current"]
|
|
else:
|
|
# 第一次运行，标记为非首次
|
|
state["first_run"] = False
|
|
|
|
print(f"[Counter] 节点 {unique_id} 执行, 返回: {current_count}, 下次: {state['current']}")
|
|
|
|
return (current_count,)
|
|
|
|
except Exception as e:
|
|
print(f"[Counter] 计数器执行错误: {str(e)}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
return (0,)
|
|
|
|
# API端点：重置计数器
|
|
@routes.post('/counter/reset')
|
|
async def reset_counter(request):
|
|
try:
|
|
data = await request.json()
|
|
node_id = data.get('node_id')
|
|
|
|
if not node_id:
|
|
return web.json_response({"status": "error", "message": "节点ID不能为空"}, status=400)
|
|
|
|
print(f"[Counter] 重置请求 - node_id: {node_id}, 类型: {type(node_id)}")
|
|
print(f"[Counter] 当前存储的keys: {list(counter_states.keys())}")
|
|
|
|
# 重置计数器状态 - 检查字符串和整数格式
|
|
found = False
|
|
target_key = None
|
|
|
|
# 先直接查找
|
|
if node_id in counter_states:
|
|
target_key = node_id
|
|
found = True
|
|
# 尝试整数格式
|
|
elif str(node_id) in counter_states:
|
|
target_key = str(node_id)
|
|
found = True
|
|
# 尝试字符串转整数
|
|
else:
|
|
try:
|
|
int_id = int(node_id)
|
|
if int_id in counter_states:
|
|
target_key = int_id
|
|
found = True
|
|
except (ValueError, TypeError):
|
|
pass
|
|
|
|
if found:
|
|
state = counter_states[target_key]
|
|
if state["mode"] == "increase":
|
|
state["current"] = 0
|
|
else:
|
|
state["current"] = state["total"]
|
|
|
|
state["first_run"] = True
|
|
|
|
print(f"[Counter] 重置成功 - 当前值: {state['current']}")
|
|
|
|
return web.json_response({
|
|
"status": "success",
|
|
"current": state["current"],
|
|
"message": "计数器已重置"
|
|
})
|
|
else:
|
|
print(f"[Counter] 未找到计数器状态")
|
|
return web.json_response({
|
|
"status": "success",
|
|
"message": "计数器状态不存在，将在下次执行时初始化"
|
|
})
|
|
|
|
except Exception as e:
|
|
print(f"[Counter] 重置计数器失败: {str(e)}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
return web.json_response({"status": "error", "message": str(e)}, status=500)
|
|
|
|
# 存储每个节点的基准图片信息
|
|
loadimage_baseline = {}
|
|
|
|
class LG_LoadImage_V2(LoadImage):
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
input_dir = folder_paths.get_input_directory()
|
|
files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
|
|
files = folder_paths.filter_files_content_types(files, ["image"])
|
|
return {"required":
|
|
{"image": (sorted(files), {"image_upload": True}),
|
|
"auto_refresh": ("BOOLEAN", {"default": True}),
|
|
},
|
|
"hidden": {"unique_id": "UNIQUE_ID"},
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE", "MASK", "STRING")
|
|
RETURN_NAMES = ("image", "mask", "filename")
|
|
DESCRIPTION = "加载图片节点。auto_refresh开启后，自动加载输入文件夹的最新图片。"
|
|
CATEGORY = CATEGORY_TYPE
|
|
FUNCTION = "load_image"
|
|
|
|
@classmethod
|
|
def IS_CHANGED(s, image, auto_refresh, unique_id):
|
|
if auto_refresh:
|
|
# 在auto_refresh模式下，返回浮点数确保每次更新
|
|
import time
|
|
return float(time.time())
|
|
# 否则调用父类的IS_CHANGED方法
|
|
return LoadImage.IS_CHANGED(image)
|
|
|
|
def load_image(self, image, auto_refresh, unique_id):
|
|
input_dir = folder_paths.get_input_directory()
|
|
|
|
# 如果auto_refresh开启，执行智能加载逻辑
|
|
if auto_refresh:
|
|
# 获取当前传入的图片路径和时间戳
|
|
current_image_path = os.path.join(input_dir, image)
|
|
|
|
if os.path.exists(current_image_path):
|
|
current_timestamp = os.path.getmtime(current_image_path)
|
|
|
|
# 初始化：第一次运行
|
|
if unique_id not in loadimage_baseline:
|
|
loadimage_baseline[unique_id] = {
|
|
"image": image,
|
|
"timestamp": current_timestamp,
|
|
"last_input_image": image # 记录上次前端传入的image参数
|
|
}
|
|
print(f"[LG_LoadImage] 节点 {unique_id} 初始化基准图片: {image}, 时间戳: {current_timestamp}")
|
|
else:
|
|
# 检查用户是否在前端手动选择了新图片
|
|
# 比较传入的image和上次传入的image参数（来自前端）
|
|
last_input_image = loadimage_baseline[unique_id].get("last_input_image", loadimage_baseline[unique_id]["image"])
|
|
|
|
if image != last_input_image:
|
|
# 前端的image参数变了，说明用户手动选择了新图片，更新基准
|
|
loadimage_baseline[unique_id] = {
|
|
"image": image,
|
|
"timestamp": current_timestamp,
|
|
"last_input_image": image
|
|
}
|
|
print(f"[LG_LoadImage] 节点 {unique_id} 用户手动选择新图片: {image}, 时间戳: {current_timestamp}")
|
|
else:
|
|
# 前端的image参数没变，执行自动加载逻辑
|
|
baseline_timestamp = loadimage_baseline[unique_id]["timestamp"]
|
|
|
|
# 获取所有图片文件
|
|
files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
|
|
files = folder_paths.filter_files_content_types(files, ["image"])
|
|
|
|
if files:
|
|
# 找到所有比基准图片更新的图片
|
|
newer_files = [f for f in files if os.path.getmtime(os.path.join(input_dir, f)) > baseline_timestamp]
|
|
|
|
if newer_files:
|
|
# 从更新的图片中选择最新的
|
|
latest_file = max(newer_files, key=lambda f: os.path.getmtime(os.path.join(input_dir, f)))
|
|
latest_timestamp = os.path.getmtime(os.path.join(input_dir, latest_file))
|
|
|
|
# 更新基准为新加载的图片（但保持last_input_image不变）
|
|
loadimage_baseline[unique_id]["image"] = latest_file
|
|
loadimage_baseline[unique_id]["timestamp"] = latest_timestamp
|
|
# last_input_image 保持不变，因为前端传入的image参数没变
|
|
|
|
image = latest_file
|
|
print(f"[LG_LoadImage] 节点 {unique_id} 自动加载更新的图片: {image}, 时间戳: {latest_timestamp}")
|
|
else:
|
|
print(f"[LG_LoadImage] 节点 {unique_id} 没有比基准更新的图片，继续使用基准: {loadimage_baseline[unique_id]['image']}")
|
|
# 使用基准图片
|
|
image = loadimage_baseline[unique_id]["image"]
|
|
|
|
# 调用父类方法获取完整的图像和遮罩
|
|
image_tensor, mask_tensor = super().load_image(image)
|
|
|
|
# 返回图像、遮罩和文件名
|
|
return (image_tensor, mask_tensor, image)
|
|
NODE_CLASS_MAPPINGS = {
|
|
"CachePreviewBridge": CachePreviewBridge,
|
|
"LG_Noise": LG_Noise,
|
|
"IPAdapterWeightTypes": IPAdapterWeightTypes,
|
|
"LG_LoadImage": LG_LoadImage,
|
|
"LG_LatentBatchToList": LG_LatentBatchToList,
|
|
"LG_SaveImage": LG_SaveImage,
|
|
"LG_LoadImage_V2": LG_LoadImage_V2,
|
|
"LG_InstallDependencies": LG_InstallDependencies,
|
|
"LG_PipManager": LG_PipManager,
|
|
"LG_FloatRange": LG_FloatRange,
|
|
"LG_Counter": LG_Counter,
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"CachePreviewBridge": "🎈LG_PreviewBridge",
|
|
"LG_Noise": "🎈LG_Noise",
|
|
"IPAdapterWeightTypes": "🎈IPAdapter权重类型",
|
|
"LG_LoadImage": "🎈LG_LoadImage",
|
|
"LG_LatentBatchToList": "🎈LG_Latent批次转列表",
|
|
"LG_SaveImage": "🎈LG_SaveImage",
|
|
"LG_LoadImage_V2": "🎈LG_LoadImage_V2",
|
|
"LG_InstallDependencies": "🎈LG_安装依赖",
|
|
"LG_PipManager": "🎈LG_Pip管理器",
|
|
"LG_FloatRange": "🎈LG_浮点数[0-1]",
|
|
"LG_Counter": "🎈LG_计数器",
|
|
}
|
|
|
|
|
|
|