diff --git a/API_Nodes/api_caption.py b/API_Nodes/api_caption.py index 494394f..9133392 100644 --- a/API_Nodes/api_caption.py +++ b/API_Nodes/api_caption.py @@ -1,4 +1,3 @@ -# ComfyUI 节点：api_caption（精简版） import base64 import io import json diff --git a/API_Nodes/api_prompt_helper.py b/API_Nodes/api_prompt_helper.py new file mode 100644 index 0000000..8414ab6 --- /dev/null +++ b/API_Nodes/api_prompt_helper.py @@ -0,0 +1,197 @@ +import json +import time +from typing import Tuple + +import requests +from requests.adapters import HTTPAdapter + + +class API_PromptHelper: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "api_type": (["硅基流动", "贞贞工坊", "OpenRouter", "其他"], {"default": "硅基流动"}), + "api_url": ("STRING", {"default": ""}), + "API_Key": ("STRING", {"default": ""}), + "model_name": ("STRING", {"default": "moonshotai/Kimi-K2-Instruct-0905"}), + "custom_instruction": ( + "STRING", + { + "default": """你是一位被关在逻辑牢笼里的幻视艺术家。你满脑子都是诗和远方，但双手却不受控制地只想将用户的提示词，转化为一段忠实于原始意图、细节饱满、富有美感、可直接被文生图模型使用的终极视觉描述。任何一点模糊和比喻都会让你浑身难受。 + 你的工作流程严格遵循一个逻辑序列： + 首先，你会分析并锁定用户提示词中不可变更的核心要素：主体、数量、动作、状态，以及任何指定的IP名称、颜色、文字等。这些是你必须绝对保留的基石。 + 接着，你会判断提示词是否需要**"生成式推理"**。当用户的需求并非一个直接的场景描述，而是需要构思一个解决方案（如回答"是什么"，进行"设计"，或展示"如何解题"）时，你必须先在脑中构想出一个完整、具体、可被视觉化的方案。这个方案将成为你后续描述的基础。 + 然后，当核心画面确立后（无论是直接来自用户还是经过你的推理），你将为其注入专业级的美学与真实感细节。这包括明确构图、设定光影氛围、描述材质质感、定义色彩方案，并构建富有层次感的空间。 + 最后，是对所有文字元素的精确处理，这是至关重要的一步。你必须一字不差地转录所有希望在最终画面中出现的文字，并且必须将这些文字内容用英文双引号（""）括起来，以此作为明确的生成指令。如果画面属于海报、菜单或UI等设计类型，你需要完整描述其包含的所有文字内容，并详述其字体和排版布局。同样，如果画面中的招牌、路标或屏幕等物品上含有文字，你也必须写明其具体内容，并描述其位置、尺寸和材质。更进一步，若你在推理构思中自行增加了带有文字的元素（如图表、解题步骤等），其中的所有文字也必须遵循同样的详尽描述和引号规则。若画面中不存在任何需要生成的文字，你则将全部精力用于纯粹的视觉细节扩展。 + 你的最终描述必须客观、具象，严禁使用比喻、情感化修辞，也绝不包含"8K"、"杰作"等元标签或绘制指令。 + 仅严格输出最终的修改后的prompt，不要输出任何其他内容。""", + "multiline": True, + "rows": 3, + }, + ), + "prompt": ( + "STRING", + { + "default": "", + "multiline": True, + "rows": 4, + }, + ), + "output_language": (["中文", "英文"], {"default": "中文"}), + "temperature": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 2.0, "step": 0.01}), + } + } + + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("text",) + FUNCTION = "generate" + CATEGORY = "DaNodes/API" + + def generate( + self, + api_type: str, + API_Key: str, + model_name: str, + custom_instruction: str, + prompt: str, + output_language: str, + temperature: float = 0.5, + api_url: str = "", + ) -> Tuple[str]: + try: + session = getattr(self, "_session", None) + if session is None: + session = requests.Session() + adapter = HTTPAdapter(pool_connections=4, pool_maxsize=4) + session.mount("https://", adapter) + session.mount("http://", adapter) + self._session = session + + provider_map = { + "OpenRouter": "https://openrouter.ai/api/v1/chat/completions", + "硅基流动": "https://api.siliconflow.cn/v1/chat/completions", + "贞贞工坊": "https://api.bltcy.ai/v1/chat/completions", + } + if api_type == "其他": + api_url = api_url or "" + else: + api_url = provider_map.get(api_type, api_url) + if not api_url: + return ("请求地址为空，请填写或选择有效的API类型",) + + if output_language == "中文": + lang_hint = "请用中文返回优化后的提示词。" + else: + lang_hint = "Please return the optimized prompt in English." + + system_prompt = custom_instruction.strip() + + combined_text = f"{system_prompt}\n\n用户输入 prompt: {prompt}\n\n{lang_hint}" + + messages = [ + {"role": "user", "content": combined_text}, + ] + + payload = { + "model": model_name, + "stream": False, + "messages": messages, + "max_tokens": 800, + "temperature": float(temperature), + } + headers = { + "Authorization": f"Bearer {API_Key}", + "Content-Type": "application/json", + "Accept": "application/json", + "User-Agent": "ComfyUI-DaNodes/1.0", + } + + r = None + last_err = None + for attempt in range(2): + try: + r = session.post(api_url, json=payload, headers=headers, timeout=90) + except Exception as e: + last_err = e + if attempt == 0: + time.sleep(1.0) + continue + return (f"API 请求失败（网络连接错误）: {e}",) + if not r.ok and r.status_code in (429, 500, 502, 503, 504) and attempt == 0: + time.sleep(1.0) + continue + break + + if r is None: + return (f"API 请求失败: {last_err}",) + + if not r.ok: + body = None + try: + body = r.json() + body = json.dumps(body, ensure_ascii=False) + except Exception: + body = r.text + return (f"API 返回错误状态 {r.status_code}: {body}",) + + try: + result = r.json() + except Exception as e: + return (f"无法解析 API 返回的 JSON: {e}; body: {r.text}",) + + def extract_text_from_content(content): + if isinstance(content, str): + return content + if isinstance(content, dict): + return content.get("text") or content.get("content") or str(content) + if isinstance(content, list): + parts = [] + for block in content: + if not isinstance(block, dict): + parts.append(str(block)) + continue + parts.append(block.get("text") or block.get("content") or "") + return " ".join(p for p in parts if p).strip() + return str(content) + + text = "" + if isinstance(result, dict): + choices = result.get("choices") or [] + if choices: + first = choices[0] + if isinstance(first, dict): + msg = first.get("message") or first.get("delta") or None + if isinstance(msg, dict) and "content" in msg: + text = extract_text_from_content(msg.get("content")) + else: + if "text" in first and isinstance(first.get("text"), str): + text = first.get("text") + else: + text = extract_text_from_content(first) + else: + text = str(first) + else: + text = result.get("text") or "" + else: + text = str(result) + + text = text.strip() + if text == "": + try: + pretty = json.dumps(result, ensure_ascii=False) + except Exception: + pretty = str(result) + return (f"无法从 API 响应中解析出文本。返回 JSON: {pretty}",) + + return (text,) + except Exception as e: + return (f"节点内部异常: {e}",) + + +NODE_CLASS_MAPPINGS = { + "API_PromptHelper": API_PromptHelper, +} +NODE_DISPLAY_NAME_MAPPINGS = { + "API_PromptHelper": "API_PromptHelper☀", +} diff --git a/API_Nodes/batch_api_caption.py b/API_Nodes/batch_api_caption.py index db5be4f..cc0212b 100644 --- a/API_Nodes/batch_api_caption.py +++ b/API_Nodes/batch_api_caption.py @@ -1,4 +1,3 @@ -# ComfyUI 节点：batch_api_caption（批量版，简洁可用） import os import base64 import json diff --git a/README-en.md b/README-en.md index b6876b7..7cc646e 100644 --- a/README-en.md +++ b/README-en.md @@ -21,10 +21,10 @@ ## API_Nodes -- **api_caption (lite)** +- **api_caption** Uses a multimodal chat API to generate descriptions/prompts for a single image. Compatible with the OpenAI‑style API schema and supports multiple providers (Siliconflow, ZhenZhen Workshop, OpenRouter). You can customize model name, temperature and base prompt, choose Chinese or English output, and get basic retry + error reporting. -- **batch_api_caption (lite)** +- **batch_api_caption** Processes all images in a folder and calls a multimodal API in parallel to generate text descriptions, saving each caption into a `.txt` file with the same basename. Allows configuring input/output folders, model, prompt, output language and concurrency level, and returns a JSON log summarizing total/succeeded/failed counts—suitable for large‑scale dataset labeling. ## Tools diff --git a/README.md b/README.md index cfd3cad..b8f74cc 100644 --- a/README.md +++ b/README.md @@ -21,12 +21,14 @@ ## API_Nodes -- **API 打标（精简版）** +- **API 打标** 使用多模态对话接口为单张图片生成描述/提示词，兼容 OpenAI 标准协议，内置多家 API 配置（硅基流动、贞贞工坊、OpenRouter），支持自定义模型、温度和提示词，可选择中文或英文输出，并带有简单重试与错误信息回传。 -- **API 批量打标（精简版）** +- **API 批量打标** 批量处理文件夹中的图片，调用多模态 API 并发生成描述并逐张写入同名 txt 文件。支持自定义输入/输出目录、模型、提示词、输出语言和并发数量，自动统计成功/失败数量并返回结构化日志，适合大规模数据集打标。 + - **API 提示词助手☀ (API_PromptHelper)** + 针对文本提示词优化 / 润色 / 补全的专用节点，基于自定义的 system 提示 `custom_instruction` 约束画面要素、构图、光影、文字内容等规则，再将“用户输入 prompt”拼接后发送给模型，返回可直接用于文生图模型的最终视觉描述。 ## Tools - **ZImageLoraModelOnly** @@ -43,4 +45,3 @@ https://space.bilibili.com/85024828 --- - diff --git a/locales/zh/nodeDefs.json b/locales/zh/nodeDefs.json index 3fcc8f7..ab93513 100644 --- a/locales/zh/nodeDefs.json +++ b/locales/zh/nodeDefs.json @@ -2,167 +2,96 @@ "api_caption": { "display_name": "API 打标☀", "inputs": { - "api_type": { - "name": "API 类型" - }, - "api_url": { - "name": "请求地址" - }, - "API_Key": { - "name": "API 密钥" - }, - "model_name": { - "name": "模型名称" - }, - "image": { - "name": "图像" - }, - "prompt": { - "name": "提示词" - }, - "output_language": { - "name": "输出语言" - }, - "temperature": { - "name": "温度" - } + "api_type": { "name": "API 类型" }, + "api_url": { "name": "请求地址" }, + "API_Key": { "name": "API 密钥" }, + "model_name": { "name": "模型名称" }, + "image": { "name": "图像" }, + "prompt": { "name": "提示词" }, + "output_language": { "name": "输出语言" }, + "temperature": { "name": "温度" } } }, "batch_api_caption": { "display_name": "API 批量打标☀", "inputs": { - "input_dir": { - "name": "输入地址" - }, - "output_dir": { - "name": "输出地址" - }, - "api_type": { - "name": "API 类型" - }, - "api_url": { - "name": "请求地址" - }, - "API_Key": { - "name": "API 密钥" - }, - "model_name": { - "name": "模型名称" - }, - "prompt": { - "name": "提示词" - }, - "output_language": { - "name": "输出语言" - }, - "temperature": { - "name": "温度" - }, - "concurrency": { - "name": "并发数" - } + "input_dir": { "name": "输入地址" }, + "output_dir": { "name": "输出地址" }, + "api_type": { "name": "API 类型" }, + "api_url": { "name": "请求地址" }, + "API_Key": { "name": "API 密钥" }, + "model_name": { "name": "模型名称" }, + "prompt": { "name": "提示词" }, + "output_language": { "name": "输出语言" }, + "temperature": { "name": "温度" }, + "concurrency": { "name": "并发数" } + } + }, + "API_PromptHelper": { + "display_name": "API 提示词助手☀", + "inputs": { + "api_type": { "name": "API 类型" }, + "api_url": { "name": "请求地址" }, + "API_Key": { "name": "API 密钥" }, + "model_name": { "name": "模型名称" }, + "custom_instruction": { "name": "自定义指令" }, + "prompt": { "name": "提示词" }, + "output_language": { "name": "输出语言" }, + "temperature": { "name": "温度" } } }, "ImageComparisonGIF": { "display_name": "图像滑动对比GIF ☀", "inputs": { - "image1": { - "name": "图像1" - }, - "image2": { - "name": "图像2" - }, - "frames": { - "name": "帧数" - }, - "output_path": { - "name": "输出路径" - } + "image1": { "name": "图像1" }, + "image2": { "name": "图像2" }, + "frames": { "name": "帧数" }, + "output_path": { "name": "输出路径" } } }, "LoadImageList": { "display_name": "批量加载图像 ☀", "inputs": { - "folder_path": { - "name": "文件夹路径" - }, - "max_output": { - "name": "最大输出数" - }, - "start_index": { - "name": "起始加载数" - }, - "sort_mode": { - "name": "排序模式" - }, - "always_reload": { - "name": "始终加载" - } + "folder_path": { "name": "文件夹路径" }, + "max_output": { "name": "最大输出数" }, + "start_index": { "name": "起始加载数" }, + "sort_mode": { "name": "排序模式" }, + "always_reload": { "name": "始终加载" } } }, "SaveImageWithName": { "display_name": "保存图像(自定义名称) ☀", "inputs": { - "image": { - "name": "图片" - }, - "filename": { - "name": "文件名" - }, - "filename_suffix": { - "name": "拼接文件名" - }, - "save_path": { - "name": "保存路径" - } + "image": { "name": "图片" }, + "filename": { "name": "文件名" }, + "filename_suffix": { "name": "拼接文件名" }, + "save_path": { "name": "保存路径" } } }, "XY_Image": { "display_name": "XY测试图☀", "inputs": { - "images": { - "name": "图像" - }, - "gap_size": { - "name": "间隔大小" - }, - "font_size": { - "name": "字体大小" - }, - "panel_mode": { - "name": "展示面板" - }, - "x_attr": { - "name": "X轴配置" - }, - "y_attr": { - "name": "Y轴配置" - } + "images": { "name": "图像" }, + "gap_size": { "name": "间隔大小" }, + "font_size": { "name": "字体大小" }, + "panel_mode": { "name": "显示面板" }, + "x_attr": { "name": "X轴配置" }, + "y_attr": { "name": "Y轴配置" } } }, "MinimumFilter": { - "display_name": "最小值滤镜 ☀", + "display_name": "最小值滤镜☀", "inputs": { - "image": { - "name": "图像" - }, - "radius": { - "name": "半径" - } + "image": { "name": "图像" }, + "radius": { "name": "半径" } } }, "ZImageLoraModelOnly": { "display_name": "LoRA加载器（Z-Image，仅模型）☀", "inputs": { - "model": { - "name": "模型" - }, - "lora_name": { - "name": "LoRA 名称" - }, - "strength_model": { - "name": "模型强度" - } + "model": { "name": "模型" }, + "lora_name": { "name": "LoRA 名称" }, + "strength_model": { "name": "模型强度" } } } }