feat: 新增 GPT-1.5 生图节点
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o1key
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@@ -19,5 +19,6 @@ from .multi_res_preview import MultiResPreview
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from .batch_images_o1key import BatchImagesO1key
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from .seedance_video import Seedance, SeedanceMultiModal
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from .doubao_image import DoubaoImage
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from .o1key_gpt_image import O1keyGPTImage
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__all__ = ['NanoBananaPro', 'BatchNanoBananaPro', 'GoogleGemini', 'LoadFile', 'ImageStitchPro', 'SaveCleanImage', 'BatchCleanMetadata', 'VideoPreview', 'KlingVideo', 'KlingFirstLastFrame', 'KlingMotionControlTest', 'AspectRatioPreset', 'GoogleVeo', 'FluxImageEdit', 'UniversalLLMChat', 'MultiResPreview', 'BatchImagesO1key', 'Seedance', 'SeedanceMultiModal', 'StreamPreview', 'DoubaoImage']
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__all__ = ['NanoBananaPro', 'BatchNanoBananaPro', 'GoogleGemini', 'LoadFile', 'ImageStitchPro', 'SaveCleanImage', 'BatchCleanMetadata', 'VideoPreview', 'KlingVideo', 'KlingFirstLastFrame', 'KlingMotionControlTest', 'AspectRatioPreset', 'GoogleVeo', 'FluxImageEdit', 'UniversalLLMChat', 'MultiResPreview', 'BatchImagesO1key', 'Seedance', 'SeedanceMultiModal', 'StreamPreview', 'DoubaoImage', 'O1keyGPTImage']
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@@ -0,0 +1,158 @@
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"""
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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 time
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import torch
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from ..clients.gpt_image_client import GptImageClient
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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 + image（无 mask）
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- 图像编辑：提供 prompt + image + mask（白色区域将被替换）
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参数：
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- prompt : 文本提示词（多行）
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- seed : 随机种子（0 表示不指定）
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- quality : 图像质量 low / medium / high
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- background : 背景模式 auto / opaque / transparent
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- size : 图像尺寸（auto 让 API 自动决定）
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- n : 生成数量 1-8
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- image : 可选参考图（用于图生图或编辑）
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- mask : 可选蒙版（白色区域将被替换）
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- model : 模型选择 gpt-image-1 / gpt-image-1.5
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"""
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@classmethod
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def INPUT_TYPES(cls):
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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",
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}),
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},
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"optional": {
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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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"tooltip": "Random seed (0 = not specified)",
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}),
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"quality": (["low", "medium", "high"], {
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"default": "low",
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"tooltip": "Image quality, affects cost and generation time.",
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}),
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"background": (["auto", "opaque", "transparent"], {
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"default": "auto",
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"tooltip": "Return image with or without background",
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}),
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"size": (["auto", "1024x1024", "1024x1536", "1536x1024"], {
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"default": "auto",
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"tooltip": "Image size (auto = API decides)",
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}),
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"n": ("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",
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}),
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"image": ("IMAGE", {
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"tooltip": "Optional reference image for image editing.",
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}),
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"mask": ("MASK", {
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"tooltip": "Optional mask for inpainting (white areas will be replaced)",
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}),
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"model": (["gpt-image-1", "gpt-image-1.5"], {
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"default": "gpt-image-1.5",
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}),
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},
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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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seed: int = 0,
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quality: str = "low",
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background: str = "auto",
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size: str = "auto",
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n: int = 1,
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image=None,
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mask=None,
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model: str = "gpt-image-1.5",
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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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路由逻辑：
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- 无 image → generations 接口（文生图）
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- 有 image，无 mask → generations 接口（图生图）
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- 有 image，有 mask → edits 接口（图像编辑 + 蒙版）
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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 mask is not None and image is None:
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raise ValueError("提供了蒙版但未提供图像，请同时提供 image 和 mask")
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# ── 2. 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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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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# ── 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=model,
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quality=quality,
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background=background,
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size=size,
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n=n,
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seed=seed,
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image_tensor=image,
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mask_tensor=mask,
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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. PIL → tensor ───────────────────────────────────────────────────
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output_tensor = GptImageClient._pil_list_to_tensor(pil_images)
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# ── 5. 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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