from .md import * CATEGORY_TYPE = "🎈LAOGOU/Utils" def execute_command_with_realtime_output(cmd, cwd, message_path, clear_first=True, start_message=""): """通用的实时命令执行方法""" import subprocess import sys from server import PromptServer import comfy.model_management as model_management try: # 发送开始消息 if start_message: PromptServer.instance.send_sync(message_path, { "text": start_message, "clear": clear_first }) # 执行命令，实时输出 process = subprocess.Popen( cmd, cwd=cwd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True, bufsize=1, universal_newlines=True ) # 实时读取输出 while True: # 检查是否被中断 if model_management.processing_interrupted(): process.terminate() PromptServer.instance.send_sync(message_path, { "text": "🛑 用户中断了操作", "clear": False }) return False, "🛑 用户中断了操作" output = process.stdout.readline() if output == '' and process.poll() is not None: break if output: line = output.strip() if line: PromptServer.instance.send_sync(message_path, { "text": line, "clear": False }) # 检查返回码 return_code = process.poll() return return_code == 0, return_code except Exception as e: error_msg = f"💥 执行异常: {str(e)}" PromptServer.instance.send_sync(message_path, { "text": error_msg, "clear": False }) return False, str(e) pb_id_cnt = time.time() preview_bridge_image_id_map = {} preview_bridge_image_name_map = {} preview_bridge_cache = {} preview_bridge_last_mask_cache = {} def set_previewbridge_image(node_id, file, item): global pb_id_cnt if file in preview_bridge_image_name_map: pb_id = preview_bridge_image_name_map[node_id, file] if pb_id.startswith(f"${node_id}"): return pb_id pb_id = f"${node_id}-{pb_id_cnt}" preview_bridge_image_id_map[pb_id] = (file, item) preview_bridge_image_name_map[node_id, file] = (pb_id, item) pb_id_cnt += 1 return pb_id class CachePreviewBridge: @classmethod def INPUT_TYPES(s): return {"required": { "image": ("STRING", {"default": ""}), "use_cache": ("BOOLEAN", {"default": True, "label_on": "Cache", "label_off": "Input"}), }, "optional": { "images": ("IMAGE",), }, "hidden": {"unique_id": "UNIQUE_ID", "extra_pnginfo": "EXTRA_PNGINFO"}, } RETURN_TYPES = ("IMAGE", "MASK") FUNCTION = "doit" OUTPUT_NODE = True CATEGORY = CATEGORY_TYPE def __init__(self): super().__init__() self.output_dir = folder_paths.get_temp_directory() self.type = "temp" self.prev_hash = None @staticmethod def load_image(pb_id): clipspace_dir = os.path.join(folder_paths.get_input_directory(), "clipspace") files = [f for f in os.listdir(clipspace_dir) if f.lower().startswith('clipspace') and f.lower().endswith('.png')] # 初始化默认值 image = torch.zeros((1, 512, 512, 3), dtype=torch.float32, device="cpu") mask = torch.zeros((512, 512), dtype=torch.float32, device="cpu") ui_item = { "filename": 'empty.png', "subfolder": '', "type": 'temp' } if files: latest_file = max(files, key=lambda f: os.path.getmtime(os.path.join(clipspace_dir, f))) latest_path = os.path.join(clipspace_dir, latest_file) latest_mtime = os.path.getmtime(latest_path) current_path = None if pb_id in preview_bridge_image_id_map: current_path, ui_item = preview_bridge_image_id_map[pb_id] current_mtime = os.path.getmtime(current_path) if os.path.exists(current_path) else 0 if current_path is None or latest_mtime > current_mtime: preview_dir = os.path.join(folder_paths.get_temp_directory(), 'PreviewBridge') os.makedirs(preview_dir, exist_ok=True) new_filename = f"PB-{os.path.splitext(latest_file)[0]}.png" new_path = os.path.join(preview_dir, new_filename) # 检查是否存在同名文件 if os.path.exists(new_path): if pb_id in preview_bridge_image_id_map: current_path, ui_item = preview_bridge_image_id_map[pb_id] if os.path.exists(current_path): new_path = current_path else: import shutil shutil.copy2(latest_path, new_path) ui_item = { "filename": new_filename, "subfolder": 'PreviewBridge', "type": 'temp' } preview_bridge_image_id_map[pb_id] = (new_path, ui_item) else: import shutil shutil.copy2(latest_path, new_path) ui_item = { "filename": new_filename, "subfolder": 'PreviewBridge', "type": 'temp' } preview_bridge_image_id_map[pb_id] = (new_path, ui_item) current_path = new_path try: i = Image.open(current_path) i = ImageOps.exif_transpose(i) 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((image.shape[1], image.shape[2]), dtype=torch.float32, device="cpu") except Exception as e: print(f"Error loading image: {e}") # 出错时使用默认值 final_mask = mask.unsqueeze(0) if len(mask.shape) == 2 else mask return image, final_mask, ui_item def doit(self, image, use_cache, unique_id, images=None, extra_pnginfo=None): # 检查是否有新的剪贴板图片需要更新缓存 if images is None and image and image not in preview_bridge_image_id_map: print("Component value changed, updating cache") # 生成新的pb_id并更新缓存 pb_id = f"${unique_id}-{time.time()}" pixels, mask, path_item = CachePreviewBridge.load_image(pb_id) if path_item["type"] != "temp" or path_item["filename"] != "empty.png": return { "ui": {"images": [path_item]}, "result": (pixels, mask), } # 原有的判断逻辑 if images is None and not image: empty_image = torch.zeros((1, 512, 512, 3), dtype=torch.float32, device="cpu") empty_mask = torch.zeros((1, 512, 512), dtype=torch.float32, device="cpu") return { "ui": {"images": []}, "result": (empty_image, empty_mask), } if use_cache: if not image: empty_image = torch.zeros((1, 512, 512, 3), dtype=torch.float32, device="cpu") empty_mask = torch.zeros((1, 512, 512), dtype=torch.float32, device="cpu") return { "ui": {"images": []}, "result": (empty_image, empty_mask), } if image.startswith('$'): node_id = image.split('-')[0][1:] related_pb_ids = [pb_id for pb_id in preview_bridge_image_id_map.keys() if pb_id.startswith(f"${node_id}-")] if related_pb_ids: latest_pb_id = max(related_pb_ids, key=lambda x: float(x.split('-')[1])) image = latest_pb_id pixels, mask, path_item = CachePreviewBridge.load_image(image) return { "ui": {"images": [path_item]}, "result": (pixels, mask), } # 非缓存模式或需要创建新缓存 if images is not None: mask = torch.zeros((1, images.shape[1], images.shape[2]), dtype=torch.float32, device="cpu") res = PreviewImage().save_images( images, filename_prefix=f"PreviewBridge/PB-{unique_id}-", extra_pnginfo=extra_pnginfo ) image2 = res['ui']['images'] pixels = images path = os.path.join(folder_paths.get_temp_directory(), 'PreviewBridge', image2[0]['filename']) pb_id = set_previewbridge_image(unique_id, path, image2[0]) preview_bridge_cache[unique_id] = (images, image2) return { "ui": {"images": image2}, "result": (pixels, mask), } # 如果走到这里,说明既没有有效的缓存也没有新的图像输入 empty_image = torch.zeros((1, 512, 512, 3), dtype=torch.float32, device="cpu") empty_mask = torch.zeros((1, 512, 512), dtype=torch.float32, device="cpu") return { "ui": {"images": []}, "result": (empty_image, empty_mask), } class LG_Noise: @classmethod def INPUT_TYPES(s): return { "required": { "type": (["fade", "dissolve", "gaussian"], ), "opacity": ("FLOAT", { "default": 1.0, "min": 0, "max": 1, "step": 0.01 }), "strength": ("INT", { "default": 1, "min": 1, "max": 32, "step": 1 }), "density": ("FLOAT", { "default": 1.0, "min": 0, "max": 1, "step": 0.05 }), "sharpen": ("INT", { "default": 0, "min": -32, "max": 32, "step": 1 }), "brightness": ("FLOAT", { "default": 1.0, "min": 0, "max": 3, "step": 0.05 }), "random_color": ("BOOLEAN", {"default": True}), "color": ("COLOR", {"default": "#808080"}), "seed": ("INT", {"default": -1, "min": -1, "max": 0x7fffffff}), }, "optional": { "image_optional": ("IMAGE",), "mask_optional": ("MASK",), } } RETURN_TYPES = ("IMAGE",) FUNCTION = "make_noise" CATEGORY = CATEGORY_TYPE def hex_to_rgb(self, hex_color): hex_color = hex_color.lstrip('#') r = int(hex_color[0:2], 16) / 255.0 g = int(hex_color[2:4], 16) / 255.0 b = int(hex_color[4:6], 16) / 255.0 return torch.tensor([r, g, b]) def make_noise(self, type, opacity, density, strength, sharpen, random_color, color, brightness, seed, image_optional=None, mask_optional=None): if image_optional is None: image = torch.zeros([1, 1, 1, 3]) else: image = image_optional h, w = image.shape[1:3] if h == 1 and w == 1: image = image.repeat(1, 512, 512, 1) h, w = 512, 512 if mask_optional is not None: mask = mask_optional.unsqueeze(-1).repeat(1, 1, 1, 3) else: mask = torch.ones((1, h, w, 1), device=image.device).repeat(1, 1, 1, 3) if seed == -1: seed = torch.randint(0, 0x7fffffff, (1,)).item() torch.manual_seed(seed) print(f"[LG_Noise] Using seed: {seed}") color_rgb = self.hex_to_rgb(color).to(image.device) def generate_noise(size_h, size_w, is_gaussian=False): """统一的噪声生成函数""" if random_color: if is_gaussian: noise = torch.randn(1, size_h, size_w, 3, device=image.device) return torch.clamp(noise * 0.5 + 0.5, 0, 1) else: return torch.rand(1, size_h, size_w, 3, device=image.device) else: return torch.ones(1, size_h, size_w, 3, device=image.device) def generate_density_mask(size_h, size_w): """统一的密度遮罩生成函数""" return (torch.rand(1, size_h, size_w, 1, device=image.device) < density).float() if strength > 1: small_h, small_w = h // strength, w // strength density_mask = generate_density_mask(small_h, small_w) noise = generate_noise(small_h, small_w) * density_mask noise = torch.nn.functional.interpolate( noise.permute(0, 3, 1, 2), size=(h, w), mode='nearest' ).permute(0, 2, 3, 1) if type != "fade": density_mask = torch.nn.functional.interpolate( density_mask.permute(0, 3, 1, 2), size=(h, w), mode='nearest' ).permute(0, 2, 3, 1) else: density_mask = generate_density_mask(h, w) noise = generate_noise(h, w) * density_mask colored_noise = noise * color_rgb.view(1, 1, 1, 3) * brightness if type == "fade": result = image * (1 - opacity) + colored_noise * opacity elif type == "dissolve": dissolve_mask = (torch.rand(1, h//strength if strength > 1 else h, w//strength if strength > 1 else w, 1, device=image.device) < opacity).float() density_mask = (torch.rand(1, h//strength if strength > 1 else h, w//strength if strength > 1 else w, 1, device=image.device) < density).float() dissolve_mask = dissolve_mask * density_mask if strength > 1: dissolve_mask = torch.nn.functional.interpolate( dissolve_mask.permute(0, 3, 1, 2), size=(h, w), mode='nearest' ).permute(0, 2, 3, 1) dissolve_mask = dissolve_mask.repeat(1, 1, 1, 3) if random_color: noise = torch.rand(1, h, w, 3, device=image.device) else: noise = torch.ones(1, h, w, 3, device=image.device) noise = noise * dissolve_mask colored_noise = noise * color_rgb.view(1, 1, 1, 3) * brightness result = image * (1-dissolve_mask) + colored_noise * dissolve_mask elif type == "gaussian": noise = generate_noise(h//strength if strength > 1 else h, w//strength if strength > 1 else w, is_gaussian=True) if strength > 1: noise = torch.nn.functional.interpolate( noise.permute(0, 3, 1, 2), size=(h, w), mode='nearest' ).permute(0, 2, 3, 1) noise = (noise - 0.5) * opacity * 2 noise = noise * density_mask colored_noise = noise * color_rgb.view(1, 1, 1, 3) * brightness result = torch.clamp(image + colored_noise, 0, 1) result = torch.clamp(result, 0, 1) if sharpen != 0: kernel_size = abs(sharpen) * 2 + 1 noise_part = result * mask - image * mask if sharpen < 0: blurred_noise = T.functional.gaussian_blur( noise_part.permute([0,3,1,2]), kernel_size ).permute([0,2,3,1]) result = image + blurred_noise else: blurred = T.functional.gaussian_blur( noise_part.permute([0,3,1,2]), kernel_size ).permute([0,2,3,1]) sharpened_noise = noise_part + (noise_part - blurred) * (sharpen / 8) result = image + torch.clamp(sharpened_noise, 0, 1) result = torch.clamp(result, 0, 1) result = image * (1 - mask) + result * mask return (result,) 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'] class IPAdapterWeightTypes: @classmethod def INPUT_TYPES(s): return {"required": { "weight_type": (WEIGHT_TYPES, ), }} RETURN_TYPES = (AlwaysEqualProxy('*'),) RETURN_NAMES = ("weight_type",) FUNCTION = "get_weight_types" CATEGORY = CATEGORY_TYPE def get_weight_types(self, weight_type): return (weight_type,) class LG_LoadImage(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}), "keep_alpha": ("BOOLEAN", {"default": False, "label_on": "RGBA", "label_off": "RGB"}), }, } RETURN_TYPES = ("IMAGE", "MASK", "STRING") RETURN_NAMES = ("image", "mask", "filename") 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。" CATEGORY = CATEGORY_TYPE FUNCTION = "load_image" @classmethod def IS_CHANGED(s, image, keep_alpha=False): # 调用父类的IS_CHANGED方法，只传递image参数 return LoadImage.IS_CHANGED(image) def load_image(self, image, keep_alpha=False): # 先调用父类方法获取完整的图像和遮罩 image_tensor, mask_tensor = super().load_image(image) if keep_alpha: # 如果需要保持alpha，重新加载为RGBA格式 image_path = folder_paths.get_annotated_filepath(image) i = Image.open(image_path) i = ImageOps.exif_transpose(i) if 'A' in i.getbands(): image = i.convert("RGBA") image = np.array(image).astype(np.float32) / 255.0 image_tensor = torch.from_numpy(image)[None,] # 遮罩直接使用父类提取的结果，不受keep_alpha影响 return (image_tensor, mask_tensor, image) @PromptServer.instance.routes.get("/lg/get/latest_image") async def get_latest_image(request): try: folder_type = request.query.get("type", "temp") if folder_type == "temp": target_dir = folder_paths.get_temp_directory() elif folder_type == "output": target_dir = folder_paths.get_output_directory() else: return web.json_response({"error": f"未知的文件夹类型: {folder_type}"}, status=400) files = [f for f in os.listdir(target_dir) if os.path.isfile(os.path.join(target_dir, f)) and f.lower().endswith(('.png', '.jpg', '.jpeg', '.webp'))] if not files: return web.json_response({"error": f"在{folder_type}目录中未找到图像"}, status=404) latest_file = max(files, key=lambda f: os.path.getmtime(os.path.join(target_dir, f))) return web.json_response({ "filename": latest_file, "subfolder": "", "type": folder_type }) except Exception as e: print(f"[PreviewBridge] 获取最新图像出错: {str(e)}") traceback.print_exc() return web.json_response({"error": str(e)}, status=500) # 添加新的API路由，用于从temp/output复制图片到input @PromptServer.instance.routes.post("/lg/copy_to_input") async def copy_to_input(request): try: json_data = await request.json() folder_type = json_data.get("type", "temp") filename = json_data.get("filename") if not filename: return web.json_response({"error": "未指定文件名"}, status=400) # 确定源目录 if folder_type == "temp": source_dir = folder_paths.get_temp_directory() elif folder_type == "output": source_dir = folder_paths.get_output_directory() else: return web.json_response({"error": f"未知的文件夹类型: {folder_type}"}, status=400) # 源文件完整路径 source_path = os.path.join(source_dir, filename) if not os.path.exists(source_path): return web.json_response({"error": f"文件不存在: {source_path}"}, status=404) # 目标目录为input target_dir = folder_paths.get_input_directory() # 使用原始文件名 target_filename = filename target_path = os.path.join(target_dir, target_filename) # 复制文件 import shutil shutil.copy2(source_path, target_path) return web.json_response({ "success": True, "filename": target_filename, "subfolder": "", "type": "input" }) except Exception as e: print(f"[LG_LoadImage] 复制图片到input目录失败: {str(e)}") traceback.print_exc() return web.json_response({"error": str(e)}, status=500) # 在现有的API路由后添加删除文件的路由 @PromptServer.instance.routes.delete("/lg/delete_image") async def delete_image(request): try: json_data = await request.json() filename = json_data.get("filename") if not filename: return web.json_response({"error": "未指定文件名"}, status=400) # 删除input目录中的文件 input_dir = folder_paths.get_input_directory() file_path = os.path.join(input_dir, filename) 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,) NODE_CLASS_MAPPINGS = { "CachePreviewBridge": CachePreviewBridge, "LG_Noise": LG_Noise, "IPAdapterWeightTypes": IPAdapterWeightTypes, "LG_LoadImage": LG_LoadImage, "LG_LatentBatchToList": LG_LatentBatchToList, "LG_SaveImage": LG_SaveImage, "LG_InstallDependencies": LG_InstallDependencies, "LG_PipManager": LG_PipManager, "LG_FloatRange": LG_FloatRange, } 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_InstallDependencies": "🎈LG_安装依赖", "LG_PipManager": "🎈LG_Pip管理器", "LG_FloatRange": "🎈LG_浮点数[0-1]", }