> ## 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.

# Recraft Remove Background - ComfyUI 原生节点文档

> 自动移除图像背景并生成透明Alpha通道的 Recraft 合作伙伴节点

<img src="https://mintcdn.com/dripart/5003JSxULDwNImme/images/built-in-nodes/api_nodes/recraft/recraft-remove-background.jpg?fit=max&auto=format&n=5003JSxULDwNImme&q=85&s=1248f93a0a34044b97c8d43a384fcf42" alt="ComfyUI 原生Recraft Remove Background节点" width="1506" height="576" data-path="images/built-in-nodes/api_nodes/recraft/recraft-remove-background.jpg" />

Recraft Remove Background 节点通过 Recraft 的 API 能够智能识别并移除图像背景，生成带有透明背景的图像和对应的Alpha蒙版。

## 参数说明

### 基本参数

| 参数    | 类型 | 默认值 | 说明          |
| ----- | -- | --- | ----------- |
| image | 图像 | -   | 需要移除背景的输入图像 |

### 输出

| 输出    | 类型 | 说明                  |
| ----- | -- | ------------------- |
| IMAGE | 图像 | 移除背景后的图像(带Alpha通道)  |
| MASK  | 蒙版 | 主体对象的蒙版(白色区域为保留的主体) |

## 源码参考

\[节点源码 (更新于2025-05-03)]

```python theme={null}
class RecraftRemoveBackgroundNode:
    """
    Remove background from image, and return processed image and mask.
    """

    RETURN_TYPES = (IO.IMAGE, IO.MASK)
    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
    FUNCTION = "api_call"
    API_NODE = True
    CATEGORY = "api node/image/Recraft"

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "image": (IO.IMAGE, ),
            },
            "optional": {
            },
            "hidden": {
                "auth_token": "AUTH_TOKEN_COMFY_ORG",
            },
        }

    def api_call(
        self,
        image: torch.Tensor,
        auth_token=None,
        **kwargs,
    ):
        images = []
        total = image.shape[0]
        pbar = ProgressBar(total)
        for i in range(total):
            sub_bytes = handle_recraft_file_request(
                image=image[i],
                path="/proxy/recraft/images/removeBackground",
                auth_token=auth_token,
            )
            images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0))
            pbar.update(1)

        images_tensor = torch.cat(images, dim=0)
        # use alpha channel as masks, in B,H,W format
        masks_tensor = images_tensor[:,:,:,-1:].squeeze(-1)
        return (images_tensor, masks_tensor)

```