> ## 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 Crisp Upscale - ComfyUI 原生节点文档

> 使用AI技术增加图像清晰度和分辨率的 Recraft 合作伙伴节点

<img src="https://mintcdn.com/dripart/5003JSxULDwNImme/images/built-in-nodes/api_nodes/recraft/recraft-crisp-upscale-image.jpg?fit=max&auto=format&n=5003JSxULDwNImme&q=85&s=69f9f1c716832688e95c562c1f9be1c2" alt="ComfyUI 原生Recraft Crisp Upscale节点" width="1506" height="557" data-path="images/built-in-nodes/api_nodes/recraft/recraft-crisp-upscale-image.jpg" />

Recraft Crisp Upscale 节点利用 Recraft 的 API 增强图像分辨率和清晰度。

## 参数说明

### 基本参数

| 参数    | 类型 | 默认值 | 说明        |
| ----- | -- | --- | --------- |
| image | 图像 | -   | 需要放大的输入图像 |

### 输出

| 输出    | 类型 | 说明        |
| ----- | -- | --------- |
| IMAGE | 图像 | 放大和增强后的图像 |

## 源码参考

\[节点源码 (更新于2025-05-03)]

```python theme={null}
class RecraftCrispUpscaleNode:
    """
    Upscale image synchronously.
    Enhances a given raster image using ‘crisp upscale’ tool, increasing image resolution, making the image sharper and cleaner.
    """

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

    RECRAFT_PATH = "/proxy/recraft/images/crispUpscale"

    @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=self.RECRAFT_PATH,
                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)
        return (images_tensor,)
```