feat: 发布 gpt-image-1.5 生图模型
- 新增 gpt-image-1.5 / gpt-image-1.5-特价 模型选项 - 图生图统一走 edits 接口，支持多图批次输入（image[]） - 节点参数中文化：模型、分辨率、生图数量、图片、遮罩 - 移除 quality / background 前端参数（服务端暂不支持） - 请求超时上限调整为 900s
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Jony
+46
-31
@@ -31,8 +31,14 @@ from ..utils.image_utils import tensor_to_pil, encode_image_to_base64
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_ENDPOINT_GENERATIONS = "/v1/images/generations/"
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_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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}
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# ── 超时 ──────────────────────────────────────────────────────────────────────
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_REQUEST_TIMEOUT = 300 # 秒
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_REQUEST_TIMEOUT = 900 # 秒
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class GptImageClient:
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@@ -196,8 +202,11 @@ class GptImageClient:
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调用 /v1/images/generations/ 接口。
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当传入 image_tensor 时，以 data URI 格式内联图像（图生图）。
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"""
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# 模型名映射：UI 显示名 → API 参数名
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api_model = _MODEL_NAME_MAP.get(model, model)
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body: dict = {
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"model": model,
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"model": api_model,
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"prompt": prompt,
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"quality": quality,
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"background": background,
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@@ -259,7 +268,9 @@ class GptImageClient:
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print(f"[o1key GPT Image] API 响应耗时 {elapsed:.1f}s")
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return await self._parse_response(resp_json, session)
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# ── 图像编辑（edits 接口，必须带蒙版）───────────────────────────────────
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# ── 图像编辑（edits 接口，multipart/form-data）──────────────────────────
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# 注意：o1key 中转服务的 edits 接口暂不支持 quality / background / moderation 参数，
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# 这些字段暂时不传递，待服务方更新后可恢复。
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async def _edit_async(
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self,
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@@ -275,37 +286,40 @@ class GptImageClient:
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) -> List[Image.Image]:
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"""
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调用 /v1/images/edits/ 接口（multipart/form-data）。
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image_tensor 取第一帧（edits 接口仅支持单张输入图）。
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当前仅传递 model / prompt / n / size / image / mask，
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quality / background / moderation 暂不支持（o1key 服务端限制）。
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"""
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# 取第一张图
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single = image_tensor[:1] if image_tensor.dim() == 4 else image_tensor.unsqueeze(0)
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image_png = self._tensor_to_png_bytes(single)
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ih, iw = single.shape[1], single.shape[2]
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# 模型名映射：UI 显示名 → API 参数名
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api_model = _MODEL_NAME_MAP.get(model, model)
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# 构建 multipart 表单
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# 将 batch tensor 拆成逐帧列表
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if image_tensor.dim() == 3:
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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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# 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", model)
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form.add_field("prompt", prompt)
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form.add_field("quality", quality)
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form.add_field("background", background)
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form.add_field("n", str(n))
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form.add_field("moderation", "low")
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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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if size and size != "auto":
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form.add_field("size", size)
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if seed > 0:
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form.add_field("seed", str(seed))
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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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frame = image_tensor[i:i+1] # [1,H,W,C]
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img_bytes = self._tensor_to_png_bytes(frame)
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form.add_field(
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"image[]",
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img_bytes,
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filename=f"image_{i}.png",
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content_type="image/png",
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)
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# 主图（PNG）
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form.add_field(
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"image",
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image_png,
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filename="image.png",
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content_type="image/png",
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)
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ih, iw = image_tensor.shape[1], image_tensor.shape[2]
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# 蒙版（PNG，RGBA 格式，透明区域为待编辑区）
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if mask_tensor is not None:
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mask_png = self._mask_tensor_to_rgba_png_bytes(mask_tensor, (ih, iw))
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form.add_field(
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@@ -319,7 +333,7 @@ class GptImageClient:
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mode = "图像编辑（无蒙版）"
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url = f"{self.base_url}{_ENDPOINT_EDITS}"
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print(f"[o1key GPT Image] {mode} | 模型={model} | quality={quality} | "
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print(f"[o1key GPT Image] {mode} | 模型={model} | 参考图={num_images}张 | quality={quality} | "
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f"background={background} | size={size} | n={n}")
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connector = aiohttp.TCPConnector(ssl=False, force_close=True)
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@@ -330,7 +344,7 @@ class GptImageClient:
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async with session.post(
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url,
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data=form,
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headers=self._auth_headers(), # Content-Type 由 FormData 自动设置
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headers=self._auth_headers(),
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) as resp:
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elapsed = time.time() - t0
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text = await resp.text()
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@@ -374,11 +388,12 @@ class GptImageClient:
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同步入口，在独立线程中运行事件循环，避免与 ComfyUI 主循环冲突。
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路由逻辑：
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- 无 image_tensor → generations 接口（文生图）
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- 有 image_tensor，无 mask → generations 接口（图生图，data URI）
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- 有 image_tensor，有 mask → edits 接口（图像编辑 + 蒙版）
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- 无 image_tensor → generations 接口（文生图，JSON body）
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- 有 image_tensor → edits 接口（图生图/编辑，multipart/form-data）
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所有模型统一走 multipart，quality/background 通过表单字段传递，
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new-api 开启"透传请求体"后原样转发给上游。
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"""
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use_edits = (image_tensor is not None and mask_tensor is not None)
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use_edits = (image_tensor is not None)
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if use_edits:
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coro = self._edit_async(
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