Recraft Replace Background - ComfyUI Native Node Documentation

A Recraft Partner node that automatically detects foreground subjects and replaces backgrounds

The Recraft Replace Background node uses Recraft’s API to intelligently detect subjects in images and generate new backgrounds based on text prompts.

Parameters

Basic Parameters

Parameter	Type	Default	Description
image	image	-	Input image with subject to preserve
prompt	string	""	Text prompt for background generation
n	integer	1	Number of images to generate (1-6)
seed	integer	0	Random seed value for node re-runs

Optional Parameters

Parameter	Type	Description
recraft_style	Recraft Style	Style settings for background generation
negative_prompt	string	Text describing elements to avoid

Output

Output	Type	Description
IMAGE	image	Final image with replaced background

Source Code

[Node source code (Updated on 2025-05-03)]

class RecraftReplaceBackgroundNode:
    """
    Replace background on image, based on provided prompt.
    """

    RETURN_TYPES = (IO.IMAGE,)
    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, ),
                "prompt": (
                    IO.STRING,
                    {
                        "multiline": True,
                        "default": "",
                        "tooltip": "Prompt for the image generation.",
                    },
                ),
                "n": (
                    IO.INT,
                    {
                        "default": 1,
                        "min": 1,
                        "max": 6,
                        "tooltip": "The number of images to generate.",
                    },
                ),
                "seed": (
                    IO.INT,
                    {
                        "default": 0,
                        "min": 0,
                        "max": 0xFFFFFFFFFFFFFFFF,
                        "control_after_generate": True,
                        "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
                    },
                ),
            },
            "optional": {
                "recraft_style": (RecraftIO.STYLEV3,),
                "negative_prompt": (
                    IO.STRING,
                    {
                        "default": "",
                        "forceInput": True,
                        "tooltip": "An optional text description of undesired elements on an image.",
                    },
                ),
            },
            "hidden": {
                "auth_token": "AUTH_TOKEN_COMFY_ORG",
            },
        }

    def api_call(
        self,
        image: torch.Tensor,
        prompt: str,
        n: int,
        seed,
        auth_token=None,
        recraft_style: RecraftStyle = None,
        negative_prompt: str = None,
        **kwargs,
    ):
        default_style = RecraftStyle(RecraftStyleV3.realistic_image)
        if recraft_style is None:
            recraft_style = default_style

        if not negative_prompt:
            negative_prompt = None

        request = RecraftImageGenerationRequest(
            prompt=prompt,
            negative_prompt=negative_prompt,
            model=RecraftModel.recraftv3,
            n=n,
            style=recraft_style.style,
            substyle=recraft_style.substyle,
            style_id=recraft_style.style_id,
        )

        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/replaceBackground",
                request=request,
                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, )