""" o1key GPT Image 节点 支持 GPT Image 2 / 2.5 系列的文生图、图生图和带蒙版图像编辑 """ import os import time from typing import List, Optional, Tuple from PIL import Image from comfy_api.latest import io from ..clients.gpt_image_client import ( GPT_IMAGE_MODEL_OPTIONS, GPT_IMAGE_ROUTE_OPTIONS, GptImageClient, resolve_gpt_image_model, ) from ..utils.image_utils import parse_batch_prompts, pil_to_tensor, tensor_to_pil from ..utils.config import get_base_url_by_route from ..utils.o1key_image_catalog import ( GPT_IMAGE_BACKGROUND_OPTIONS, GPT_IMAGE_EXACT_SIZE_OPTIONS, GPT_IMAGE_OUTPUT_FORMAT_OPTIONS, GPT_IMAGE_25_QUALITY_OPTIONS, resolve_gpt_image_quality, resolve_gpt_image_size, ) from ..utils.file_utils import ( ImageInfo, generate_timestamp_filename, load_images_from_folder, pair_images_by_name, pair_images_cartesian, save_image, ) try: from comfy.model_management import processing_interrupted, InterruptProcessingException _INTERRUPT_AVAILABLE = True except ImportError: _INTERRUPT_AVAILABLE = False processing_interrupted = lambda: False InterruptProcessingException = RuntimeError try: from comfy.utils import ProgressBar _PROGRESS_BAR_AVAILABLE = True except ImportError: _PROGRESS_BAR_AVAILABLE = False try: import folder_paths _FOLDER_PATHS_AVAILABLE = True except ImportError: _FOLDER_PATHS_AVAILABLE = False def _make_node_progress_callback(progress_bar, task_index: int, total_tasks: int): if progress_bar is None: return None total_units = max(1, total_tasks) * 100 base_units = max(0, task_index - 1) * 100 last_pct = {"value": -1} def _callback(pct: int): try: pct_value = int(round(float(pct))) except (TypeError, ValueError): return pct_value = max(0, min(100, pct_value)) if pct_value < last_pct["value"]: return last_pct["value"] = pct_value progress_bar.update_absolute( min(total_units, base_units + pct_value), total_units, ) return _callback def _resolve_async_size(value: str) -> str: return resolve_gpt_image_size(value) MAX_GPT_IMAGE_REFERENCES = 9 def _collect_autogrow_inputs(value) -> list: """收集已连接的 Autogrow 输入，并兼容单个旧值。""" if value is None: return [] if isinstance(value, dict): return [item for item in value.values() if item is not None] return [value] class O1keyGPTImage(io.ComfyNode): """ o1key GPT Image 节点 功能： - 文生图：仅提供 prompt - 图生图：提供 prompt + 图片（无遮罩） - 图像编辑：提供 prompt + 图片 + 遮罩（白色区域将被替换） - 批量模式：prompt 中用单独一行 --- 分隔多条提示词 参数： - prompt : 文本提示词（多行；用 --- 独占一行分隔批量提示词） - 模型 : GPT Image 主模型 - 模型线路 : 畅速、直连或专线 - 分辨率 : 图像尺寸（auto 让 API 自动决定） - 生图数量 : 每条提示词生成数量 1-8 - 质量 : 生成质量 - seed : 随机种子（0 表示不指定） - 图片 : 可选参考图（用于图生图或编辑） - 遮罩 : 可选蒙版（白色区域将被替换） """ @classmethod def define_schema(cls): reference_images = io.Autogrow.Input( "参考图组", template=io.Autogrow.TemplateNames( input=io.Image.Input("参考图"), names=[f"参考图{i}" for i in range(1, MAX_GPT_IMAGE_REFERENCES + 1)], min=0, ), tooltip=f"连接后自动增加输入端口，合计最多 {MAX_GPT_IMAGE_REFERENCES} 张参考图。", ) return io.Schema( node_id="O1keyGPTImage", display_name="gpt image", category="o1key/image", inputs=[ io.String.Input( "prompt", default="", multiline=True, tooltip="Text prompt for GPT Image. Use --- on its own line to separate batch prompts.", ), io.Combo.Input( "模型", options=GPT_IMAGE_MODEL_OPTIONS, default="gpt-image-2.5-sunburst", ), io.Combo.Input( "模型线路", options=GPT_IMAGE_ROUTE_OPTIONS, default="畅速", ), io.Combo.Input( "分辨率", options=GPT_IMAGE_EXACT_SIZE_OPTIONS, default="智能", tooltip="Image size (智能 = API decides)", ), io.Int.Input( "生图数量", default=1, min=1, max=8, step=1, display_mode=io.NumberDisplay.number, tooltip="How many images to generate per prompt", ), io.Combo.Input( "质量", options=GPT_IMAGE_25_QUALITY_OPTIONS, default="自动", tooltip="GPT Image 2 支持高/中/低/自动；GPT Image 2.5 另支持超高=xhigh、最高=max。", ), io.Combo.Input( "输出格式", options=["png", "jpeg", "webp"], default="png", tooltip="Generated image output format", ), io.Combo.Input( "背景", options=list(GPT_IMAGE_BACKGROUND_OPTIONS), default="auto", tooltip="透明背景仅支持 PNG 或 WebP 输出格式。", ), io.Mask.Input( "遮罩", optional=True, tooltip="Optional mask for inpainting (white areas will be replaced)", ), reference_images, io.Combo.Input( "缩放图片", options=["不缩放", "智能缩放"], default="智能缩放", tooltip="请求体超过 18 MiB 时，智能缩放会等比缩小占用最大的参考图。", ), io.Int.Input( "seed", default=0, min=0, max=2**31 - 1, step=1, display_mode=io.NumberDisplay.number, control_after_generate=io.ControlAfterGenerate.randomize, tooltip="Random seed (0 = not specified)", ), ], outputs=[io.Image.Output(display_name="IMAGE")], # 兼容 Autogrow 改造前保存的参考图1～参考图9端口。 accept_all_inputs=True, ) @classmethod def execute( cls, prompt: str, 模型: str = "gpt-image-2.5-sunburst", 模型线路: str = "畅速", 分辨率: str = "智能", 质量: str = "自动", 输出格式: str = "png", 生图数量: int = 1, seed: int = 0, 遮罩=None, 缩放图片: str = "智能缩放", 背景: str = "auto", **kwargs, ) -> io.NodeOutput: result = cls.generate( prompt=prompt, 模型=模型, 模型线路=模型线路, 分辨率=分辨率, 质量=质量, 输出格式=输出格式, 生图数量=生图数量, seed=seed, 遮罩=遮罩, 缩放图片=缩放图片, 背景=背景, **kwargs, ) return io.NodeOutput(*result) @classmethod def generate( cls, prompt: str, 模型: str = "gpt-image-2.5-sunburst", 模型线路: str = "畅速", 分辨率: str = "智能", 质量: str = "自动", 输出格式: str = "png", 生图数量: int = 1, seed: int = 0, 遮罩=None, 缩放图片: str = "智能缩放", 背景: str = "auto", **kwargs, ): """ 生成图像（文生图 / 图生图 / 图像编辑 / 批量提示词） 路由逻辑： - 无图片 → generations 接口（文生图） - 有图片，无遮罩 → edits 接口（图生图） - 有图片，有遮罩 → edits 接口（图像编辑 + 蒙版） - prompt 含 --- → 批量模式，逐条调用上述接口 """ start_time = time.time() # ── 0. 收集多参考图输入 ──────────────────────────────────────────────── reference_tensors = _collect_autogrow_inputs(kwargs.get("参考图组")) if not reference_tensors: # 兼容 Autogrow 改造前保存的固定参考图端口。 reference_tensors = [ kwargs[f"参考图{i}"] for i in range(1, MAX_GPT_IMAGE_REFERENCES + 1) if kwargs.get(f"参考图{i}") is not None ] 图片 = reference_tensors if reference_tensors else None # ── 1. 参数校验 ─────────────────────────────────────────────────────── if 遮罩 is not None and 图片 is None: raise ValueError("提供了遮罩但未提供图片，请同时提供图片和遮罩") if 缩放图片 not in {"不缩放", "智能缩放"}: raise ValueError("缩放图片参数无效") if 输出格式 not in GPT_IMAGE_OUTPUT_FORMAT_OPTIONS: raise ValueError("GPT Image 输出格式无效") if 背景 not in GPT_IMAGE_BACKGROUND_OPTIONS: raise ValueError("GPT Image 背景参数无效") if 背景 == "transparent" and 输出格式 == "jpeg": raise ValueError("GPT Image 透明背景仅支持 PNG 或 WebP 输出格式") # ── 2. 解析分辨率显示值 → API 参数值 ────────────────────────────────── size = _resolve_async_size(分辨率) # ── 2b. 主模型与线路共同解析为 API 模型名 ─────────────────────────── model = resolve_gpt_image_model(模型, 模型线路) # ── 2c. 解析质量显示值 → API 参数值 ─────────────────────────────────── quality = resolve_gpt_image_quality(模型, 质量) # ── 3. 创建客户端 ───────────────────────────────────────────────────── try: client = GptImageClient() client.base_url = get_base_url_by_route() client.response_log_enabled = False client.poll_log_enabled = False except ValueError as e: if str(e) == "未授权！": print("[o1key GPT Image] 请联系作者授权后方可使用！") raise ValueError("未授权！") from None raise try: # ── 4. 解析批量提示词 ───────────────────────────────────────────── batch_prompts = parse_batch_prompts(prompt) # ── 5. 调用 API ─────────────────────────────────────────────────── all_pil_images = [] def _submitted(task_id, status, elapsed): print(f"[o1key GPT Image] 已提交 | task_id={task_id} | 状态={status} | 耗时={elapsed:.1f}s") def _completed(task_id, image_count, elapsed, urls): del urls print(f"[o1key GPT Image] 完成 ✓ | task_id={task_id} | 生成={image_count} 张 | 耗时={elapsed:.1f}s") progress_total = len(batch_prompts) if batch_prompts else 1 progress_bar = ProgressBar(progress_total * 100) if _PROGRESS_BAR_AVAILABLE else None if batch_prompts: # 批量模式：逐条提示词调用 total = len(batch_prompts) print(f"[o1key GPT Image] 批量模式 | {total} 条提示词 | 每条生成 {生图数量} 张") for idx, p in enumerate(batch_prompts, 1): if _INTERRUPT_AVAILABLE and processing_interrupted(): print("[o1key GPT Image] 用户取消，已中断批量生成") raise InterruptProcessingException() try: pil_images = client.generate_image_async_sync( prompt=p, model=model, quality=quality, size=size, n=生图数量, seed=seed, image_tensor=图片, mask_tensor=遮罩, output_format=输出格式, background=背景, progress_callback=_make_node_progress_callback(progress_bar, idx, total), special_price_parallel=True, task_submitted_callback=_submitted, task_completed_callback=_completed, log_request_start=False, log_downloads=True, log_prefix=f"[o1key GPT Image] [{idx}/{total}]", resize_mode=缩放图片, ) all_pil_images.extend(pil_images) except InterruptProcessingException: raise except Exception as e: error_msg = str(e).split('\n')[0] print(f"[o1key GPT Image] [{idx}/{total}] ❌ {error_msg}") if progress_bar is not None: progress_bar.update_absolute(idx * 100, total * 100) else: # 单提示词模式 if not prompt or not prompt.strip(): raise ValueError("提示词不能为空") try: pil_images = client.generate_image_async_sync( prompt=prompt, model=model, quality=quality, size=size, n=生图数量, seed=seed, image_tensor=图片, mask_tensor=遮罩, output_format=输出格式, background=背景, progress_callback=_make_node_progress_callback(progress_bar, 1, 1), special_price_parallel=True, task_submitted_callback=_submitted, task_completed_callback=_completed, log_request_start=False, log_downloads=True, resize_mode=缩放图片, ) all_pil_images.extend(pil_images) except InterruptProcessingException: raise except Exception as e: error_msg = str(e).split('\n')[0] print(f"[o1key GPT Image] ❌ {error_msg}") raise RuntimeError(error_msg) from None # ── 6. 检查是否有可用图像 ───────────────────────────────────────── if not all_pil_images: raise RuntimeError("所有提示词均生成失败，无可用图像输出") # ── 7. PIL → tensor ─────────────────────────────────────────────── output_tensor = GptImageClient._pil_list_to_tensor(all_pil_images) # ── 8. 完成日志 ─────────────────────────────────────────────────── elapsed = time.time() - start_time print( f"[o1key GPT Image] 完成！耗时 {elapsed:.1f}s，" f"输出 {output_tensor.shape[0]} 张 " f"{output_tensor.shape[2]}×{output_tensor.shape[1]}" ) return (output_tensor,) finally: cls._print_balance(client) @staticmethod def _print_balance(client): try: balance_data = client.query_balance_sync() balance_info = client.format_balance_info(balance_data) print(f"[o1key GPT Image] {balance_info}") except Exception: pass class _LegacyO1keyGPTImageBatch: """ o1key GPT Image 批量节点 复用 BatchNanoBananaPro 的批量思路： - 从文件夹批量加载图片 - 按文件名同名 / 1*N / 不配对 三种模式创建任务 - 可追加节点手动输入参考图 - prompt 支持用独占一行 --- 展开为多提示词任务 - 每个任务调用 GPT Image 客户端并保存到磁盘 """ PAIRING_MODES = ["按相同图片命名", "1*N", "不配对"] IMAGE_FORMATS = ["原始", "JPEG", "PNG", "WebP"] MODEL_OPTIONS = GPT_IMAGE_ROUTE_OPTIONS QUALITY_OPTIONS = ["高", "中", "低", "自动"] RESOLUTION_OPTIONS = [ "智能", "1024x1024（1K 正方形 1:1）", "1536x1024（1K 横版 3:2）", "1024x1536（1K 竖版 2:3）", "1360x1024（1K 横版 4:3）", "1024x1360（1K 竖版 3:4）", "1824x1024（1K 横版 16:9）", "1024x1824（1K 竖版 9:16）", "2048x2048（2K 正方形 1:1）", "3072x2048（2K 横版 3:2）", "2048x3072（2K 竖版 2:3）", "2736x2048（2K 横版 4:3）", "2048x2736（2K 竖版 3:4）", "3648x2048（2K 横版 16:9）", "2048x3648（2K 竖版 9:16）", "2880x2880（4K 正方形 1:1）", "3504x2336（4K 横版 3:2）", "2336x3504（4K 竖版 2:3）", "3264x2448（4K 横版 4:3）", "2448x3264（4K 竖版 3:4）", "3840x2160（4K 横版 16:9）", "2160x3840（4K 竖版 9:16）", ] @classmethod def INPUT_TYPES(cls): optional_inputs = {} for image_index in range(1, 10): optional_inputs[f"参考图{image_index}"] = ("IMAGE", { "tooltip": "追加到每个批量任务末尾的固定参考图。", }) optional_inputs["遮罩"] = ("MASK", { "tooltip": "可选蒙版，会应用到每个任务的第一张参考图；请确保尺寸一致。", }) optional_inputs["图片配对模式"] = (cls.PAIRING_MODES, { "default": "不配对", "tooltip": "文件夹图片的组合方式；手动参考图只追加，不参与配对。", }) return { "required": { "prompt": ("STRING", { "default": "", "multiline": True, "tooltip": "提示词；可用独占一行的 --- 分隔多条批量提示词。", }), "模型线路": (cls.MODEL_OPTIONS, { "default": "畅速", }), "分辨率": (cls.RESOLUTION_OPTIONS, { "default": "智能", }), "生图数量": ("INT", { "default": 1, "min": 1, "max": 8, "step": 1, "display": "number", }), "质量": (cls.QUALITY_OPTIONS, { "default": "自动", }), "seed": ("INT", { "default": 0, "min": 0, "max": 2**31 - 1, "step": 1, "display": "number", "control_after_generate": True, }), "图片格式": (cls.IMAGE_FORMATS, { "default": "原始", }), "文件夹1": ("STRING", { "default": "", "multiline": False, }), "文件夹2": ("STRING", { "default": "", "multiline": False, }), "文件夹3": ("STRING", { "default": "", "multiline": False, }), "文件夹4": ("STRING", { "default": "", "multiline": False, }), "文件夹5": ("STRING", { "default": "", "multiline": False, }), "保存路径": ("STRING", { "default": "", "multiline": False, "tooltip": "为空时优先使用 ComfyUI 默认 output 目录。", }), }, "optional": optional_inputs, } RETURN_TYPES = ("IMAGE",) RETURN_NAMES = ("IMAGE",) FUNCTION = "process_batch" CATEGORY = "o1key/image" OUTPUT_NODE = False def _load_folders(self, folders: List[str]) -> List[List[ImageInfo]]: image_lists = [] for folder_index, folder in enumerate(folders, 1): if not folder or not folder.strip(): continue try: loaded_images = load_images_from_folder(folder) if loaded_images: image_lists.append(loaded_images) except ValueError as error: print(f"[o1key GPT Image Batch] 文件夹{folder_index} 加载失败 - {error}") return image_lists def _create_pairs( self, image_lists: List[List[ImageInfo]], pairing_mode: str, manual_images: Optional[List[ImageInfo]] = None, ) -> List[Tuple[ImageInfo, ...]]: if pairing_mode == "不配对": if len(image_lists) > 1: raise ValueError("「不配对」模式只支持单个文件夹，请清空其他文件夹路径") if image_lists and manual_images: return [ (folder_image,) + tuple(manual_images) for folder_image in image_lists[0] ] if image_lists: return [(folder_image,) for folder_image in image_lists[0]] return [] if not image_lists: return [] if len(image_lists) == 1: base_pairs = [(folder_image,) for folder_image in image_lists[0]] elif pairing_mode == "按相同图片命名": base_pairs = list(pair_images_by_name(*image_lists)) else: base_pairs = list(pair_images_cartesian(*image_lists)) if manual_images: manual_tuple = tuple(manual_images) base_pairs = [pair + manual_tuple for pair in base_pairs] return base_pairs def _collect_manual_images(self, kwargs) -> List[ImageInfo]: manual_images = [] for image_index in range(1, 10): key = f"参考图{image_index}" if key not in kwargs or kwargs[key] is None: continue for tensor_index, image in enumerate(tensor_to_pil(kwargs[key])): manual_images.append(ImageInfo( image=image, filename=f"manual_{image_index}_{tensor_index}", extension=".png", source_path="", )) return manual_images @staticmethod def _pair_to_tensors(pair: Tuple[ImageInfo, ...]) -> List: return [pil_to_tensor([image_info.image]) for image_info in pair] @staticmethod def _resolve_size(分辨率: str) -> str: return _resolve_async_size(分辨率) @staticmethod def _resolve_model(模型线路: str) -> str: # 直接返回，客户端会映射到 API 值 return 模型线路 @staticmethod def _resolve_quality(质量: str) -> str: quality_map = {"高": "high", "中": "medium", "低": "low", "自动": "auto"} return quality_map.get(质量, "auto") @staticmethod def _resolve_output_format(图片格式: str) -> str: output_format_map = { "JPEG": "jpeg", "PNG": "png", "WebP": "webp", } return output_format_map.get(图片格式, "png") @staticmethod def _ensure_output_folder(保存路径: str) -> str: output_folder = (保存路径 or "").strip() if not output_folder and _FOLDER_PATHS_AVAILABLE: output_folder = folder_paths.get_output_directory() print(f"[o1key GPT Image Batch] 未设置保存路径，使用 ComfyUI 默认 output 目录: {output_folder}") if not output_folder: raise ValueError("未设置保存路径，且当前环境无法获取 ComfyUI 默认 output 目录") os.makedirs(output_folder, exist_ok=True) test_path = os.path.join(output_folder, ".write_test") with open(test_path, "w", encoding="utf-8") as test_file: test_file.write("test") os.remove(test_path) return output_folder @staticmethod def _save_images( images: List[Image.Image], output_folder: str, image_format: str, base_filename: Optional[str] = None, ) -> List[str]: format_ext_map = {"JPEG": ".jpg", "PNG": ".png", "WebP": ".webp"} save_ext = format_ext_map.get(image_format, ".png") saved_files = [] for image in images: if base_filename: counter = 0 while True: suffix = "" if counter == 0 else f"+{counter}" filename = f"{base_filename}{suffix}{save_ext}" output_path = os.path.join(output_folder, filename) if not os.path.exists(output_path): break counter += 1 else: output_path = generate_timestamp_filename( output_folder=output_folder, extension=save_ext, ) if image_format == "JPEG": if image.mode != "RGB": image = image.convert("RGB") image.save(output_path, quality=100) elif image_format == "WebP": image.save(output_path, lossless=True) else: save_image(image, output_path) saved_files.append(output_path) return saved_files def process_batch( self, prompt: str, 模型线路: str, 分辨率: str, 生图数量: int, 质量: str, seed: int, 图片格式: str, 文件夹1: str, 文件夹2: str, 文件夹3: str, 文件夹4: str, 文件夹5: str, 保存路径: str = "", 图片配对模式: str = "不配对", 遮罩=None, **kwargs, ): start_time = time.time() client = None try: if not prompt or not prompt.strip(): raise ValueError("提示词不能为空") folders = [文件夹1, 文件夹2, 文件夹3, 文件夹4, 文件夹5] if not any(folder and folder.strip() for folder in folders): raise ValueError("请至少填写一个文件夹路径，该节点专为批量文件夹处理设计") image_lists = self._load_folders(folders) total_folder_images = sum(len(image_list) for image_list in image_lists) if total_folder_images == 0: raise ValueError("文件夹中未找到任何图片，请检查文件夹路径是否正确") manual_images = self._collect_manual_images(kwargs) pairs = self._create_pairs( image_lists=image_lists, pairing_mode=图片配对模式, manual_images=manual_images if manual_images else None, ) if not pairs: raise ValueError("配对结果为空，请检查输入") batch_prompts = parse_batch_prompts(prompt) prompts_per_task = None if batch_prompts: expanded_pairs = [] expanded_prompts = [] for pair in pairs: for batch_prompt in batch_prompts: expanded_pairs.append(pair) expanded_prompts.append(batch_prompt) pairs = expanded_pairs prompts_per_task = expanded_prompts total_tasks = len(pairs) if batch_prompts: print( f"[o1key GPT Image Batch] 批量任务 | {图片配对模式} × " f"{len(batch_prompts)} 个提示词 | 共 {total_tasks} 任务" ) else: print(f"[o1key GPT Image Batch] 批量任务 | {图片配对模式} | 共 {total_tasks} 任务") output_folder = self._ensure_output_folder(保存路径) size = self._resolve_size(分辨率) model = self._resolve_model(模型线路) quality = self._resolve_quality(质量) output_format = self._resolve_output_format(图片格式) client = GptImageClient() client.base_url = get_base_url_by_route() progress_bar = ProgressBar(total_tasks * 100) if _PROGRESS_BAR_AVAILABLE else None results = [] all_saved_files = [] for task_index, pair in enumerate(pairs, 1): if _INTERRUPT_AVAILABLE and processing_interrupted(): print("[o1key GPT Image Batch] 用户取消，已中断批量生成") raise InterruptProcessingException() task_prompt = prompts_per_task[task_index - 1] if prompts_per_task else prompt base_filename = pair[0].filename if pair else None result = { "task_index": task_index, "success": False, "generated_count": 0, "saved_files": [], "error": None, } try: pil_images = client.generate_image_async_sync( prompt=task_prompt, model=model, quality=quality, size=size, n=生图数量, seed=seed, image_tensor=self._pair_to_tensors(pair), mask_tensor=遮罩, output_format=output_format, progress_callback=_make_node_progress_callback(progress_bar, task_index, total_tasks), ) saved_files = self._save_images( images=pil_images, output_folder=output_folder, image_format=图片格式, base_filename=base_filename, ) result["success"] = bool(pil_images) result["generated_count"] = len(pil_images) result["saved_files"] = saved_files all_saved_files.extend(saved_files) print(f"[o1key GPT Image Batch] [{task_index}/{total_tasks}] ✓ {base_filename or 'task'}") except InterruptProcessingException: raise except Exception as error: error_msg = str(error).split("\n")[0] result["error"] = error_msg print(f"[o1key GPT Image Batch] [{task_index}/{total_tasks}] ❌ {base_filename or 'task'} → {error_msg}") results.append(result) if progress_bar is not None: progress_bar.update_absolute(task_index * 100, total_tasks * 100) success_count = sum(1 for result in results if result.get("success", False)) total_generated = sum(result.get("generated_count", 0) for result in results) if success_count == 0: raise RuntimeError("所有批量任务均生成失败，无可用图像输出") output_images = [] for file_path in all_saved_files[-10:]: try: loaded_image = Image.open(file_path) loaded_image.load() output_images.append(loaded_image) except Exception as error: print(f"[o1key GPT Image Batch] 无法加载输出图片 {file_path} - {error}") if not output_images: output_images = [Image.new("RGBA", (512, 512), (128, 128, 128, 255))] output_tensor = GptImageClient._pil_list_to_tensor(output_images) elapsed = time.time() - start_time print("=" * 60) print( f"[o1key GPT Image Batch] 完成！耗时 {elapsed:.1f}s | " f"成功 {success_count}/{total_tasks} | 生成 {total_generated} 张" ) print(f"[o1key GPT Image Batch] 保存路径: {output_folder}") if all_saved_files: print(f"[o1key GPT Image Batch] 最新保存文件: {all_saved_files[-1]}") failed_results = [result for result in results if not result.get("success", False)] if failed_results: print(f"[o1key GPT Image Batch] 失败任务: {len(failed_results)} 个") for failed_result in failed_results[:3]: print( f" - #{failed_result.get('task_index')}: " f"{failed_result.get('error', '未知错误')}" ) return (output_tensor,) except ValueError as error: if str(error) == "未授权！": print("[o1key GPT Image Batch] 请联系作者授权后方可使用！") raise ValueError("未授权！") from None raise ValueError(str(error)) from None except RuntimeError as error: raise RuntimeError(str(error)) from None finally: if client is not None: try: balance_data = client.query_balance_sync() balance_info = client.format_balance_info(balance_data) print(f"[o1key GPT Image Batch] {balance_info}") except Exception: pass