diff --git a/nodes/tools/lora_info.py b/nodes/tools/lora_info.py index 0ae3416..6abdd29 100644 --- a/nodes/tools/lora_info.py +++ b/nodes/tools/lora_info.py @@ -41,13 +41,27 @@ def save_dict_to_json(data_dict, file_path): def get_model_version_info(hash_value): api_url = f"https://civitai.com/api/v1/model-versions/by-hash/{hash_value}" - response = requests.get(api_url) - - if response.status_code == 200: - return response.json() - else: + try: + response = requests.get(api_url, timeout=10) # 设置10秒超时 + + if response.status_code == 200: + return response.json() + else: + print(f"[LoraInfo_UTK] CivitAI API返回错误状态码: {response.status_code}") + return {} + except requests.exceptions.ConnectionError: + print("[LoraInfo_UTK] 无法连接到CivitAI服务器，请检查网络连接") return {} - + except requests.exceptions.Timeout: + print("[LoraInfo_UTK] 连接CivitAI服务器超时，请稍后重试") + return {} + except requests.exceptions.RequestException as e: + print(f"[LoraInfo_UTK] 请求CivitAI API时发生错误: {e}") + return {} + except Exception as e: + print(f"[LoraInfo_UTK] 获取模型信息时发生未知错误: {e}") + return {} + def calculate_sha256(file_path): sha256_hash = hashlib.sha256() with open(file_path, "rb") as f: @@ -98,90 +112,135 @@ def sort_tags_by_frequency(meta_tags): return [] def get_lora_info(lora_name): - db = load_json_from_file(db_path) - output = None - examplePrompt = None - trainedWords = None - baseModel = None - metaInfo = None - - loraInfo = db.get(lora_name, {}) - - if isinstance(loraInfo, str): - loraInfo = {} - - output = loraInfo.get('output', None) - examplePrompt = loraInfo.get('examplePrompt', None) - trainedWords = loraInfo.get('trainedWords', None) - baseModel = loraInfo.get('baseModel', None) - metaInfo = loraInfo.get('metaInfo', None) - - if output is None or baseModel is None: - output = "" - lora_path = folder_paths.get_full_path("loras", lora_name) - LORAsha256 = calculate_sha256(lora_path) - model_info = get_model_version_info(LORAsha256) - if model_info.get("trainedWords", None) is None: - trainedWords = "" - else: - trainedWords = ",".join(model_info.get("trainedWords")) - baseModel = model_info.get("baseModel", "") - images = model_info.get('images') + try: + db = load_json_from_file(db_path) + output = None examplePrompt = None - modelID = model_info.get("modelId") - - if modelID: - output += f"URL: https://civitai.com/models/{modelID}\n" - if trainedWords: - output += "Triggers: " + trainedWords - output += "\n" - - if baseModel: - output += f"Base Model: {baseModel}\n" - if images: - output += "\nExamples:\n" - for image in images: - output += f"\nOutput: {image.get('url')}\n" - meta = image.get("meta") - if meta: - for key, value in meta.items(): - if examplePrompt is None and key == "prompt": - examplePrompt = value - output += f"{key}: {value}\n" - output += '\n' + trainedWords = None + baseModel = None + metaInfo = None - # 获取元数据信息 - metadata = get_metadata(lora_name) - if metadata: - metaInfo = json.dumps(metadata, indent=2, ensure_ascii=False) - else: - metaInfo = "" + loraInfo = db.get(lora_name, {}) - db[lora_name] = { - "output": output, - "trainedWords": trainedWords, - "examplePrompt": examplePrompt, - "baseModel": baseModel, - "metaInfo": metaInfo - } - save_dict_to_json(db, db_path) - - return (output, trainedWords, examplePrompt, baseModel, metaInfo) + if isinstance(loraInfo, str): + loraInfo = {} + + output = loraInfo.get('output', None) + examplePrompt = loraInfo.get('examplePrompt', None) + trainedWords = loraInfo.get('trainedWords', None) + baseModel = loraInfo.get('baseModel', None) + metaInfo = loraInfo.get('metaInfo', None) + + if output is None or baseModel is None: + output = "" + try: + lora_path = folder_paths.get_full_path("loras", lora_name) + if not lora_path: + print(f"[LoraInfo_UTK] 无法找到LoRA文件: {lora_name}") + return ("", "", "", "", "") + + LORAsha256 = calculate_sha256(lora_path) + model_info = get_model_version_info(LORAsha256) + + if model_info.get("trainedWords", None) is None: + trainedWords = "" + else: + trainedWords = ",".join(model_info.get("trainedWords")) + baseModel = model_info.get("baseModel", "") + images = model_info.get('images') + examplePrompt = None + modelID = model_info.get("modelId") + + if modelID: + output += f"URL: https://civitai.com/models/{modelID}\n" + if trainedWords: + output += "Triggers: " + trainedWords + output += "\n" + + if baseModel: + output += f"Base Model: {baseModel}\n" + if images: + output += "\nExamples:\n" + for image in images: + output += f"\nOutput: {image.get('url')}\n" + meta = image.get("meta") + if meta: + for key, value in meta.items(): + if examplePrompt is None and key == "prompt": + examplePrompt = value + output += f"{key}: {value}\n" + output += '\n' + + # 获取元数据信息 + try: + metadata = get_metadata(lora_name) + if metadata: + metaInfo = json.dumps(metadata, indent=2, ensure_ascii=False) + else: + metaInfo = "" + except Exception as e: + print(f"[LoraInfo_UTK] 读取元数据时发生错误: {e}") + metaInfo = "" + + db[lora_name] = { + "output": output, + "trainedWords": trainedWords, + "examplePrompt": examplePrompt, + "baseModel": baseModel, + "metaInfo": metaInfo + } + save_dict_to_json(db, db_path) + + except Exception as e: + print(f"[LoraInfo_UTK] 处理LoRA文件时发生错误: {e}") + output = f"处理LoRA文件时发生错误: {e}" + trainedWords = "" + examplePrompt = "" + baseModel = "" + metaInfo = "" + + return (output, trainedWords, examplePrompt, baseModel, metaInfo) + + except Exception as e: + print(f"[LoraInfo_UTK] 获取LoRA信息时发生严重错误: {e}") + return ("", "", "", "", "") @server.PromptServer.instance.routes.post('/lora_info_utk') async def fetch_lora_info(request): - post = await request.post() - lora_name = post.get("lora_name") - (output, triggerWords, examplePrompt, baseModel, metaInfo) = get_lora_info(lora_name) + try: + post = await request.post() + lora_name = post.get("lora_name") + + if not lora_name: + return web.json_response({ + "error": "未提供LoRA名称", + "output": "", + "triggerWords": "", + "examplePrompt": "", + "baseModel": "", + "metaInfo": "" + }) + + (output, triggerWords, examplePrompt, baseModel, metaInfo) = get_lora_info(lora_name) - return web.json_response({ - "output": output, - "triggerWords": triggerWords, - "examplePrompt": examplePrompt, - "baseModel": baseModel, - "metaInfo": metaInfo - }) + return web.json_response({ + "output": output, + "triggerWords": triggerWords, + "examplePrompt": examplePrompt, + "baseModel": baseModel, + "metaInfo": metaInfo + }) + except Exception as e: + print(f"[LoraInfo_UTK] Web API调用时发生错误: {e}") + return web.json_response({ + "error": f"处理请求时发生错误: {e}", + "output": "", + "triggerWords": "", + "examplePrompt": "", + "baseModel": "", + "metaInfo": "" + }) class LoraInfo_UTK: """ @@ -212,26 +271,37 @@ class LoraInfo_UTK: CATEGORY = "UniversalToolkit/Tools" def lora_info(self, lora_name): - (output, triggerWords, examplePrompt, baseModel, metaInfo) = get_lora_info(lora_name) - - # 构建信息文本 - info_text = f"LoRA: {lora_name}\n" - if baseModel: - info_text += f"Base Model: {baseModel}\n" - if triggerWords: - info_text += f"Trigger Words: {triggerWords}\n" - if examplePrompt: - info_text += f"Example Prompt: {examplePrompt}\n" - if output: - info_text += f"\n详细信息:\n{output}" - - return { - "ui": { - "text": (info_text,), - "model": (baseModel,) - }, - "result": (lora_name, triggerWords or "", examplePrompt or "", info_text, metaInfo or "") - } + try: + (output, triggerWords, examplePrompt, baseModel, metaInfo) = get_lora_info(lora_name) + + # 构建信息文本 + info_text = f"LoRA: {lora_name}\n" + if baseModel: + info_text += f"Base Model: {baseModel}\n" + if triggerWords: + info_text += f"Trigger Words: {triggerWords}\n" + if examplePrompt: + info_text += f"Example Prompt: {examplePrompt}\n" + if output: + info_text += f"\n详细信息:\n{output}" + + return { + "ui": { + "text": (info_text,), + "model": (baseModel,) + }, + "result": (lora_name, triggerWords or "", examplePrompt or "", info_text, metaInfo or "") + } + except Exception as e: + print(f"[LoraInfo_UTK] 节点执行时发生错误: {e}") + error_text = f"LoRA: {lora_name}\n获取信息时发生错误: {e}" + return { + "ui": { + "text": (error_text,), + "model": ("",) + }, + "result": (lora_name, "", "", error_text, "") + } # Node mappings diff --git a/web/lora_info.js b/web/lora_info.js index 3484b66..6377ddd 100644 --- a/web/lora_info.js +++ b/web/lora_info.js @@ -36,9 +36,24 @@ app.registerExtension({ .fetchApi("/lora_info_utk", { method: "POST", body }) .then((response) => response.json()) .then((resp) => { - baseModelWidget.value = resp.baseModel; - outputWidget.value = resp.output; - metaInfoWidget.value = resp.metaInfo; + if (resp.error) { + // 显示错误信息 + baseModelWidget.value = "错误"; + outputWidget.value = `获取信息失败: ${resp.error}`; + metaInfoWidget.value = "无法获取元数据"; + } else { + // 正常显示信息 + baseModelWidget.value = resp.baseModel; + outputWidget.value = resp.output; + metaInfoWidget.value = resp.metaInfo; + } + }) + .catch((error) => { + // 处理网络错误 + console.error("[LoraInfo_UTK] API调用失败:", error); + baseModelWidget.value = "网络错误"; + outputWidget.value = "无法连接到服务器，请检查网络连接"; + metaInfoWidget.value = "无法获取元数据"; }); }; } @@ -46,9 +61,16 @@ app.registerExtension({ const onExecuted = nodeType.prototype.onExecuted; nodeType.prototype.onExecuted = function (message) { onExecuted?.apply(this, [message]); - this.showValueWidget.value = message.text[0]; - this.baseModelWidget.value = message.model[0]; - this.metaInfoWidget.value = message.metaInfo ? message.metaInfo[0] : ""; + try { + this.showValueWidget.value = message.text[0]; + this.baseModelWidget.value = message.model[0]; + this.metaInfoWidget.value = message.metaInfo ? message.metaInfo[0] : ""; + } catch (error) { + console.error("[LoraInfo_UTK] 更新界面时发生错误:", error); + this.showValueWidget.value = "界面更新失败"; + this.baseModelWidget.value = ""; + this.metaInfoWidget.value = ""; + } } } },