> ## Documentation Index
> Fetch the complete documentation index at: https://docs.comfy.org/llms.txt
> Use this file to discover all available pages before exploring further.

# OpenAI DALL·E 2 - ComfyUI 原生节点文档

> 使用OpenAI的DALL·E 2模型生成图像的节点

<img src="https://mintcdn.com/dripart/5003JSxULDwNImme/images/built-in-nodes/api_nodes/openai/openai-dall-e-2.jpg?fit=max&auto=format&n=5003JSxULDwNImme&q=85&s=2a1ad9dab55bb9b02c4551dbf05078ff" alt="ComfyUI 原生 Stability Stable Image Ultra 节点" width="1576" height="954" data-path="images/built-in-nodes/api_nodes/openai/openai-dall-e-2.jpg" />

OpenAI DALL·E 2 节点让你能够使用OpenAI的DALL·E 2 API，通过文本描述生成创意图像。

## 参数说明

### 基本参数

| 参数     | 类型  | 默认值         | 说明                                    |
| ------ | --- | ----------- | ------------------------------------- |
| prompt | 字符串 | ""          | 用于DALL·E生成图像的文本提示，支持多行输入              |
| seed   | 整数  | 0           | 实际结果与种子并无关系，这个参数只是决定是否重新执行            |
| size   | 选择项 | "1024x1024" | 输出图像尺寸，选项：256x256, 512x512, 1024x1024 |
| n      | 整数  | 1           | 生成的图像数量，范围1-8                         |

### 可选参数

| 参数    | 类型 | 默认值  | 说明                    |
| ----- | -- | ---- | --------------------- |
| image | 图像 | None | 用于图像编辑的可选参考图像         |
| mask  | 蒙版 | None | 用于修复绘制的可选蒙版（白色区域将被替换） |

### 输出

| 输出    | 类型 | 说明      |
| ----- | -- | ------- |
| IMAGE | 图像 | 生成的图像结果 |

## 功能说明

* 基本功能：通过文本提示生成图像
* 图像编辑：当同时提供image和mask参数时，将执行图像编辑操作（白色蒙版区域会被替换）

## 源码参考

\[节点源码 (更新于2025-05-03)]

```python theme={null}

class OpenAIDalle2(ComfyNodeABC):
    """
    Generates images synchronously via OpenAI's DALL·E 2 endpoint.

    Uses the proxy at /proxy/openai/images/generations. Returned URLs are short‑lived,
    so download or cache results if you need to keep them.
    """

    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(cls) -> InputTypeDict:
        return {
            "required": {
                "prompt": (
                    IO.STRING,
                    {
                        "multiline": True,
                        "default": "",
                        "tooltip": "Text prompt for DALL·E",
                    },
                ),
            },
            "optional": {
                "seed": (
                    IO.INT,
                    {
                        "default": 0,
                        "min": 0,
                        "max": 2**31 - 1,
                        "step": 1,
                        "display": "number",
                        "control_after_generate": True,
                        "tooltip": "not implemented yet in backend",
                    },
                ),
                "size": (
                    IO.COMBO,
                    {
                        "options": ["256x256", "512x512", "1024x1024"],
                        "default": "1024x1024",
                        "tooltip": "Image size",
                    },
                ),
                "n": (
                    IO.INT,
                    {
                        "default": 1,
                        "min": 1,
                        "max": 8,
                        "step": 1,
                        "display": "number",
                        "tooltip": "How many images to generate",
                    },
                ),
                "image": (
                    IO.IMAGE,
                    {
                        "default": None,
                        "tooltip": "Optional reference image for image editing.",
                    },
                ),
                "mask": (
                    IO.MASK,
                    {
                        "default": None,
                        "tooltip": "Optional mask for inpainting (white areas will be replaced)",
                    },
                ),
            },
            "hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"},
        }

    RETURN_TYPES = (IO.IMAGE,)
    FUNCTION = "api_call"
    CATEGORY = "api node/image/openai"
    DESCRIPTION = cleandoc(__doc__ or "")
    API_NODE = True

    def api_call(
        self,
        prompt,
        seed=0,
        image=None,
        mask=None,
        n=1,
        size="1024x1024",
        auth_token=None,
    ):
        model = "dall-e-2"
        path = "/proxy/openai/images/generations"
        content_type = "application/json"
        request_class = OpenAIImageGenerationRequest
        img_binary = None

        if image is not None and mask is not None:
            path = "/proxy/openai/images/edits"
            content_type = "multipart/form-data"
            request_class = OpenAIImageEditRequest

            input_tensor = image.squeeze().cpu()
            height, width, channels = input_tensor.shape
            rgba_tensor = torch.ones(height, width, 4, device="cpu")
            rgba_tensor[:, :, :channels] = input_tensor

            if mask.shape[1:] != image.shape[1:-1]:
                raise Exception("Mask and Image must be the same size")
            rgba_tensor[:, :, 3] = 1 - mask.squeeze().cpu()

            rgba_tensor = downscale_image_tensor(rgba_tensor.unsqueeze(0)).squeeze()

            image_np = (rgba_tensor.numpy() * 255).astype(np.uint8)
            img = Image.fromarray(image_np)
            img_byte_arr = io.BytesIO()
            img.save(img_byte_arr, format="PNG")
            img_byte_arr.seek(0)
            img_binary = img_byte_arr  # .getvalue()
            img_binary.name = "image.png"
        elif image is not None or mask is not None:
            raise Exception("Dall-E 2 image editing requires an image AND a mask")

        # Build the operation
        operation = SynchronousOperation(
            endpoint=ApiEndpoint(
                path=path,
                method=HttpMethod.POST,
                request_model=request_class,
                response_model=OpenAIImageGenerationResponse,
            ),
            request=request_class(
                model=model,
                prompt=prompt,
                n=n,
                size=size,
                seed=seed,
            ),
            files=(
                {
                    "image": img_binary,
                }
                if img_binary
                else None
            ),
            content_type=content_type,
            auth_token=auth_token,
        )

        response = operation.execute()

        img_tensor = validate_and_cast_response(response)
        return (img_tensor,)
```