feat: GPT Image 节点新增质量参数、批量提示词及模型调整
- 新增「质量」下拉参数（高/中/低/自动），默认 auto，两个接口均传递 quality 字段 - 支持批量提示词模式：prompt 中用单独一行 --- 分隔多条提示词，逐条调用 API 并合并输出 - 模型列表更新：移除 gpt-image-1 / gpt-image-1-特价，新增 gpt-image-2，默认改为 gpt-image-2 - size 字段始终传递（auto 也上传），移除 seed 参数传递 Co-Authored-By: Claude Sonnet 4.5 <[email protected]>
This commit is contained in:
o1key
co-authored by
Claude Sonnet 4.5
Claude Sonnet 4.5
parent
afa732b93a
commit
949f7bb180
@@ -33,7 +33,6 @@ _ENDPOINT_EDITS = "/v1/images/edits/"
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# ── 模型名映射（UI 显示名 → API 实际参数名）─────────────────────────────────
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_MODEL_NAME_MAP = {
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"gpt-image-1-特价": "gpt-image-1-special",
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"gpt-image-1.5-特价": "gpt-image-1.5-special",
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"gpt-image-2-特价": "gpt-image-2-special",
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}
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@@ -215,13 +214,7 @@ class GptImageClient:
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"moderation": "low",
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}
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# size = "auto" 时不传该字段，让 API 自行决定
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if size and size != "auto":
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body["size"] = size
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# seed > 0 时才传递（0 视为不指定）
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if seed > 0:
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body["seed"] = seed
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body["size"] = size if size else "auto"
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# 图生图：将 tensor 转成 data URI 内联
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if image_tensor is not None:
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@@ -298,15 +291,15 @@ class GptImageClient:
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image_tensor = image_tensor.unsqueeze(0) # [H,W,C] → [1,H,W,C]
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num_images = image_tensor.shape[0]
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# o1key 中转服务的 edits 接口暂不支持 quality / background / moderation / seed，
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# o1key 中转服务的 edits 接口暂不支持 background / moderation / seed，
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# 待服务方更新后可重新加入。
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form = aiohttp.FormData()
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form.add_field("model", api_model)
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form.add_field("prompt", prompt)
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form.add_field("n", str(n))
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form.add_field("model", api_model)
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form.add_field("prompt", prompt)
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form.add_field("n", str(n))
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form.add_field("quality", quality)
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if size and size != "auto":
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form.add_field("size", size)
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form.add_field("size", size if size else "auto")
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# 多图：用 image[] 数组字段逐张附加，支持 gpt-image-1.5 最多 16 张
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for i in range(num_images):
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+80
-33
@@ -7,6 +7,7 @@ import time
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import torch
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from ..clients.gpt_image_client import GptImageClient
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from ..utils.image_utils import parse_batch_prompts
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class O1keyGPTImage:
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@@ -17,13 +18,15 @@ class O1keyGPTImage:
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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）
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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 : 生成数量 1-8
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- 生图数量 : 每条提示词生成数量 1-8
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- seed : 随机种子（0 表示不指定）
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- 质量 : 生成质量
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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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@@ -35,18 +38,17 @@ class O1keyGPTImage:
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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",
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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": {
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"模型": ([
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"gpt-image-1",
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"gpt-image-2",
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"gpt-image-1.5",
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"gpt-image-2-特价",
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"gpt-image-1-特价",
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"gpt-image-1.5-特价",
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], {
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"default": "gpt-image-1.5",
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"default": "gpt-image-2",
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}),
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"分辨率": ([
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"auto（默认）",
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@@ -67,7 +69,7 @@ class O1keyGPTImage:
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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",
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"tooltip": "How many images to generate per prompt",
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}),
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"seed": ("INT", {
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"default": 0,
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@@ -78,6 +80,10 @@ class O1keyGPTImage:
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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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"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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"图片": ("IMAGE", {
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"tooltip": "Optional reference image for image editing.",
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}),
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@@ -96,34 +102,37 @@ class O1keyGPTImage:
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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-1.5",
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模型: str = "gpt-image-2",
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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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图片=None,
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遮罩=None,
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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）
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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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# ── 1. 参数校验 ───────────────────────────────────────────────────────
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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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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 = 分辨率.split("（")[0].strip()
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# ── 2. 创建客户端 ─────────────────────────────────────────────────────
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# ── 2b. 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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except ValueError as e:
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@@ -132,28 +141,66 @@ class O1keyGPTImage:
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raise ValueError("未授权！") from None
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raise
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# ── 3. 调用 API ───────────────────────────────────────────────────────
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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=模型,
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quality="low",
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background="auto",
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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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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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# ── 4. 解析批量提示词 ─────────────────────────────────────────────────
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batch_prompts = parse_batch_prompts(prompt)
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# ── 4. PIL → tensor ───────────────────────────────────────────────────
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output_tensor = GptImageClient._pil_list_to_tensor(pil_images)
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# ── 5. 调用 API ───────────────────────────────────────────────────────
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all_pil_images = []
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# ── 5. 完成日志 ───────────────────────────────────────────────────────
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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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try:
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pil_images = client.run_sync(
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prompt=p,
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model=模型,
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quality=quality,
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background="auto",
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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 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=模型,
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quality=quality,
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background="auto",
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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 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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