235 lines
9.8 KiB
Python
235 lines
9.8 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 time
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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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"""
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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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- 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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@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",
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"gpt-image-1.5",
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"gpt-image-2-特价",
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"gpt-image-1.5-特价",
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], {
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"default": "gpt-image-2",
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})
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optional_inputs["分辨率"] = ([
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"auto（默认）",
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"1024x1024（正方形）",
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"1536x1024（景观）",
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"1024x1536（肖像）",
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"2048x2048（2K 平方）",
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"2048x1152（2K 横屏）",
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"3840x2160（4K 横屏）",
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"2160x3840（4K 竖屏）",
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], {
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"default": "auto（默认）",
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"tooltip": "Image size (auto = 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["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["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["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",
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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 = 分辨率.split("（")[0].strip()
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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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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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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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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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