750 lines
30 KiB
Python
750 lines
30 KiB
Python
"""
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o1key GPT Image 节点
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支持 gpt-image-1 / gpt-image-1.5 模型的文生图、图生图、图像编辑（带蒙版）
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"""
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import os
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import time
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from typing import List, Optional, Tuple
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from PIL import Image
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from ..clients.gpt_image_client import GptImageClient
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from ..utils.image_utils import parse_batch_prompts, pil_to_tensor, tensor_to_pil
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from ..utils.config import NETWORK_ROUTE_OPTIONS, get_base_url_by_route
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from ..utils.file_utils import (
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ImageInfo,
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generate_timestamp_filename,
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load_images_from_folder,
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pair_images_by_name,
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pair_images_cartesian,
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save_image,
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)
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try:
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from comfy.model_management import processing_interrupted, InterruptProcessingException
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_INTERRUPT_AVAILABLE = True
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except ImportError:
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_INTERRUPT_AVAILABLE = False
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processing_interrupted = lambda: False
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InterruptProcessingException = RuntimeError
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try:
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from comfy.utils import ProgressBar
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_PROGRESS_BAR_AVAILABLE = True
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except ImportError:
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_PROGRESS_BAR_AVAILABLE = False
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try:
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import folder_paths
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_FOLDER_PATHS_AVAILABLE = True
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except ImportError:
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_FOLDER_PATHS_AVAILABLE = False
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class O1keyGPTImage:
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"""
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o1key GPT Image 节点
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功能：
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- 文生图：仅提供 prompt
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- 图生图：提供 prompt + 图片（无遮罩）
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- 图像编辑：提供 prompt + 图片 + 遮罩（白色区域将被替换）
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- 批量模式：prompt 中用单独一行 --- 分隔多条提示词
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参数：
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- prompt : 文本提示词（多行；用 --- 独占一行分隔批量提示词）
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- 模型 : 模型选择
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- 分辨率 : 图像尺寸（auto 让 API 自动决定）
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- 生图数量 : 每条提示词生成数量 1-8
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- 质量 : 生成质量
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- seed : 随机种子（0 表示不指定）
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- 图片 : 可选参考图（用于图生图或编辑）
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- 遮罩 : 可选蒙版（白色区域将被替换）
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"""
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@classmethod
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def INPUT_TYPES(cls):
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# 创建9个独立的参考图输入
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optional_inputs = {}
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for i in range(1, 10):
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optional_inputs[f"参考图{i}"] = ("IMAGE", {
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"tooltip": f"Optional reference image {i} for image editing.",
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})
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optional_inputs["模型"] = ([
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"gpt-image-2-按量",
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"gpt-image-2-次卡",
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], {
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"default": "gpt-image-2-次卡",
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})
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optional_inputs["网络"] = (NETWORK_ROUTE_OPTIONS, {
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"default": "全球加速",
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})
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optional_inputs["分辨率"] = ([
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"智能",
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# ── 1K ──
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"1024x1024（1K 正方形 1:1）",
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"1536x1024（1K 横版 3:2）",
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"1024x1536（1K 竖版 2:3）",
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"1360x1024（1K 横版 4:3）",
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"1024x1360（1K 竖版 3:4）",
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"1824x1024（1K 横版 16:9）",
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"1024x1824（1K 竖版 9:16）",
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# ── 2K ──
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"2048x2048（2K 正方形 1:1）",
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"3072x2048（2K 横版 3:2）",
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"2048x3072（2K 竖版 2:3）",
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"2736x2048（2K 横版 4:3）",
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"2048x2736（2K 竖版 3:4）",
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"3648x2048（2K 横版 16:9）",
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"2048x3648（2K 竖版 9:16）",
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# ── 4K ──
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"2880x2880（4K 正方形 1:1）",
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"3504x2336（4K 横版 3:2）",
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"2336x3504（4K 竖版 2:3）",
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"3264x2448（4K 横版 4:3）",
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"2448x3264（4K 竖版 3:4）",
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"3840x2160（4K 横版 16:9）",
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"2160x3840（4K 竖版 9:16）",
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], {
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"default": "智能",
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"tooltip": "Image size (智能 = API decides)",
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})
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optional_inputs["生图数量"] = ("INT", {
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"default": 1,
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"min": 1,
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"max": 8,
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"step": 1,
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"display": "number",
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"tooltip": "How many images to generate per prompt",
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})
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optional_inputs["质量"] = (["高", "中", "低", "自动"], {
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"default": "自动",
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"tooltip": "Image quality: 高=high, 中=medium, 低=low, 自动=auto",
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})
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optional_inputs["seed"] = ("INT", {
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"default": 0,
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"min": 0,
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"max": 2**31 - 1,
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"step": 1,
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"display": "number",
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"control_after_generate": True,
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"tooltip": "Random seed (0 = not specified)",
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})
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optional_inputs["遮罩"] = ("MASK", {
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"tooltip": "Optional mask for inpainting (white areas will be replaced)",
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})
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return {
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"required": {
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"prompt": ("STRING", {
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"default": "",
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"multiline": True,
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"tooltip": "Text prompt for GPT Image. Use --- on its own line to separate batch prompts.",
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}),
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},
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"optional": optional_inputs,
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("IMAGE",)
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FUNCTION = "generate"
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CATEGORY = "o1key/image"
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OUTPUT_NODE = False
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def generate(
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self,
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prompt: str,
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模型: str = "gpt-image-2-次卡",
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网络: str = "全球加速",
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分辨率: str = "auto",
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质量: str = "自动",
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生图数量: int = 1,
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seed: int = 0,
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遮罩=None,
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**kwargs,
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):
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"""
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生成图像（文生图 / 图生图 / 图像编辑 / 批量提示词）
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路由逻辑：
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- 无图片 → generations 接口（文生图）
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- 有图片，无遮罩 → edits 接口（图生图）
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- 有图片，有遮罩 → edits 接口（图像编辑 + 蒙版）
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- prompt 含 --- → 批量模式，逐条调用上述接口
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"""
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start_time = time.time()
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# ── 0. 收集多参考图输入 ────────────────────────────────────────────────
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reference_tensors = []
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for i in range(1, 10):
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key = f"参考图{i}"
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if key in kwargs and kwargs[key] is not None:
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reference_tensors.append(kwargs[key])
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图片 = reference_tensors if reference_tensors else None
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# ── 1. 参数校验 ───────────────────────────────────────────────────────
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if 遮罩 is not None and 图片 is None:
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raise ValueError("提供了遮罩但未提供图片，请同时提供图片和遮罩")
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# ── 2. 解析分辨率显示值 → API 参数值 ──────────────────────────────────
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size = "auto" if 分辨率 == "智能" else 分辨率.split("（")[0].strip()
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# ── 2b. 解析模型显示值 → API 参数值 ───────────────────────────────────
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_model_map = {"gpt-image-2-次卡": "gpt-image-2-c", "gpt-image-2-按量": "gpt-image-2"}
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model = _model_map.get(模型, 模型)
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# ── 2c. 解析质量显示值 → API 参数值 ───────────────────────────────────
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_quality_map = {"高": "high", "中": "medium", "低": "low", "自动": "auto"}
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quality = _quality_map.get(质量, "auto")
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# ── 3. 创建客户端 ─────────────────────────────────────────────────────
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try:
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client = GptImageClient()
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client.base_url = get_base_url_by_route(网络)
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except ValueError as e:
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if str(e) == "未授权！":
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print("[o1key GPT Image] 请联系作者授权后方可使用！")
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raise ValueError("未授权！") from None
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raise
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try:
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# ── 4. 解析批量提示词 ─────────────────────────────────────────────
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batch_prompts = parse_batch_prompts(prompt)
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# ── 5. 调用 API ───────────────────────────────────────────────────
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all_pil_images = []
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if batch_prompts:
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# 批量模式：逐条提示词调用
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total = len(batch_prompts)
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print(f"[o1key GPT Image] 批量模式 | {total} 条提示词 | 每条生成 {生图数量} 张")
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for idx, p in enumerate(batch_prompts, 1):
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if _INTERRUPT_AVAILABLE and processing_interrupted():
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print("[o1key GPT Image] 用户取消，已中断批量生成")
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raise InterruptProcessingException()
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try:
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pil_images = client.run_sync(
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prompt=p,
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model=model,
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quality=quality,
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size=size,
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n=生图数量,
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seed=seed,
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image_tensor=图片,
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mask_tensor=遮罩,
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)
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all_pil_images.extend(pil_images)
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snippet = p[:30] + ("..." if len(p) >= 30 else "")
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print(f"[o1key GPT Image] [{idx}/{total}] ✓ {snippet}")
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except InterruptProcessingException:
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raise
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except Exception as e:
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error_msg = str(e).split('\n')[0]
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snippet = p[:30] + ("..." if len(p) >= 30 else "")
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print(f"[o1key GPT Image] [{idx}/{total}] ❌ {snippet} → {error_msg}")
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else:
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# 单提示词模式
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if not prompt or not prompt.strip():
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raise ValueError("提示词不能为空")
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try:
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pil_images = client.run_sync(
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prompt=prompt,
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model=model,
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quality=quality,
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size=size,
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n=生图数量,
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seed=seed,
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image_tensor=图片,
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mask_tensor=遮罩,
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)
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all_pil_images.extend(pil_images)
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except InterruptProcessingException:
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raise
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except Exception as e:
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error_msg = str(e).split('\n')[0]
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print(f"[o1key GPT Image] ❌ {error_msg}")
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raise RuntimeError(error_msg) from None
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# ── 6. 检查是否有可用图像 ─────────────────────────────────────────
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if not all_pil_images:
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raise RuntimeError("所有提示词均生成失败，无可用图像输出")
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# ── 7. PIL → tensor ───────────────────────────────────────────────
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output_tensor = GptImageClient._pil_list_to_tensor(all_pil_images)
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# ── 8. 完成日志 ───────────────────────────────────────────────────
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elapsed = time.time() - start_time
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print(
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f"[o1key GPT Image] 完成！耗时 {elapsed:.1f}s，"
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f"输出 {output_tensor.shape[0]} 张 "
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f"{output_tensor.shape[2]}×{output_tensor.shape[1]}"
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)
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return (output_tensor,)
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finally:
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self._print_balance(client)
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def _print_balance(self, client):
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try:
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balance_data = client.query_balance_sync()
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balance_info = client.format_balance_info(balance_data)
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print(f"[o1key GPT Image] {balance_info}")
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except Exception:
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pass
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class O1keyGPTImageBatch:
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"""
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o1key GPT Image 批量节点
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复用 BatchNanoBananaPro 的批量思路：
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- 从文件夹批量加载图片
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- 按文件名同名 / 1*N / 不配对 三种模式创建任务
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- 可追加节点手动输入参考图
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- prompt 支持用独占一行 --- 展开为多提示词任务
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- 每个任务调用 GPT Image 客户端并保存到磁盘
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"""
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PAIRING_MODES = ["按相同图片命名", "1*N", "不配对"]
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IMAGE_FORMATS = ["原始", "JPEG", "PNG", "WebP"]
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MODEL_OPTIONS = ["gpt-image-2-按量", "gpt-image-2-次卡"]
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QUALITY_OPTIONS = ["高", "中", "低", "自动"]
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RESOLUTION_OPTIONS = [
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"智能",
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"1024x1024（1K 正方形 1:1）",
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"1536x1024（1K 横版 3:2）",
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"1024x1536（1K 竖版 2:3）",
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"1360x1024（1K 横版 4:3）",
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"1024x1360（1K 竖版 3:4）",
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"1824x1024（1K 横版 16:9）",
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"1024x1824（1K 竖版 9:16）",
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"2048x2048（2K 正方形 1:1）",
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"3072x2048（2K 横版 3:2）",
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"2048x3072（2K 竖版 2:3）",
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"2736x2048（2K 横版 4:3）",
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"2048x2736（2K 竖版 3:4）",
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"3648x2048（2K 横版 16:9）",
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"2048x3648（2K 竖版 9:16）",
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"2880x2880（4K 正方形 1:1）",
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"3504x2336（4K 横版 3:2）",
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"2336x3504（4K 竖版 2:3）",
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"3264x2448（4K 横版 4:3）",
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"2448x3264（4K 竖版 3:4）",
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"3840x2160（4K 横版 16:9）",
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"2160x3840（4K 竖版 9:16）",
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]
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@classmethod
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def INPUT_TYPES(cls):
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optional_inputs = {}
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for image_index in range(1, 10):
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optional_inputs[f"参考图{image_index}"] = ("IMAGE", {
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"tooltip": "追加到每个批量任务末尾的固定参考图。",
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})
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optional_inputs["遮罩"] = ("MASK", {
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"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。",
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})
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optional_inputs["图片配对模式"] = (cls.PAIRING_MODES, {
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"default": "不配对",
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"tooltip": "文件夹图片的组合方式；手动参考图只追加，不参与配对。",
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})
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return {
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"required": {
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"prompt": ("STRING", {
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"default": "",
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"multiline": True,
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"tooltip": "提示词；可用独占一行的 --- 分隔多条批量提示词。",
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}),
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"模型": (cls.MODEL_OPTIONS, {
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"default": "gpt-image-2-次卡",
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}),
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"网络": (NETWORK_ROUTE_OPTIONS, {
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"default": "全球加速",
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}),
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"分辨率": (cls.RESOLUTION_OPTIONS, {
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"default": "智能",
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}),
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"生图数量": ("INT", {
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"default": 1,
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"min": 1,
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"max": 8,
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"step": 1,
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"display": "number",
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}),
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"质量": (cls.QUALITY_OPTIONS, {
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"default": "自动",
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}),
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"seed": ("INT", {
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"default": 0,
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"min": 0,
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"max": 2**31 - 1,
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"step": 1,
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"display": "number",
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"control_after_generate": True,
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}),
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"图片格式": (cls.IMAGE_FORMATS, {
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"default": "原始",
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}),
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"文件夹1": ("STRING", {
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"default": "",
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"multiline": False,
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}),
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"文件夹2": ("STRING", {
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"default": "",
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"multiline": False,
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}),
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"文件夹3": ("STRING", {
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"default": "",
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"multiline": False,
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}),
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"文件夹4": ("STRING", {
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"default": "",
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"multiline": False,
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}),
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"文件夹5": ("STRING", {
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"default": "",
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"multiline": False,
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}),
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"保存路径": ("STRING", {
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"default": "",
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"multiline": False,
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"tooltip": "为空时优先使用 ComfyUI 默认 output 目录。",
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}),
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},
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"optional": optional_inputs,
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("IMAGE",)
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FUNCTION = "process_batch"
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CATEGORY = "o1key/image"
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OUTPUT_NODE = False
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def _load_folders(self, folders: List[str]) -> List[List[ImageInfo]]:
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image_lists = []
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for folder_index, folder in enumerate(folders, 1):
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if not folder or not folder.strip():
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continue
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try:
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loaded_images = load_images_from_folder(folder)
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if loaded_images:
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image_lists.append(loaded_images)
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except ValueError as error:
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print(f"[o1key GPT Image Batch] 文件夹{folder_index} 加载失败 - {error}")
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return image_lists
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def _create_pairs(
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self,
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image_lists: List[List[ImageInfo]],
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pairing_mode: str,
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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 "auto" if 分辨率 == "智能" else 分辨率.split("（")[0].strip()
|
||
|
||
@staticmethod
|
||
def _resolve_model(模型: str) -> str:
|
||
model_map = {
|
||
"gpt-image-2-次卡": "gpt-image-2-c",
|
||
"gpt-image-2-按量": "gpt-image-2",
|
||
}
|
||
return model_map.get(模型, 模型)
|
||
|
||
@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 _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: str,
|
||
网络: 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)
|
||
quality = self._resolve_quality(质量)
|
||
|
||
client = GptImageClient()
|
||
client.base_url = get_base_url_by_route(网络)
|
||
|
||
progress_bar = ProgressBar(total_tasks) 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.run_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,
|
||
)
|
||
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(1)
|
||
|
||
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
|