Recraft Image to Image - ComfyUI 原生节点文档

class RecraftImageToImageNode:
    """
    Modify image based on prompt and strength.
    """

    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.",
                    },
                ),
                "strength": (
                    IO.FLOAT,
                    {
                        "default": 0.5,
                        "min": 0.0,
                        "max": 1.0,
                        "step": 0.01,
                        "tooltip": "Defines the difference with the original image, should lie in [0, 1], where 0 means almost identical, and 1 means miserable similarity."
                    }
                ),
                "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.",
                    },
                ),
                "recraft_controls": (
                    RecraftIO.CONTROLS,
                    {
                        "tooltip": "Optional additional controls over the generation via the Recraft Controls node."
                    },
                ),
            },
            "hidden": {
                "auth_token": "AUTH_TOKEN_COMFY_ORG",
            },
        }

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

        controls_api = None
        if recraft_controls:
            controls_api = recraft_controls.create_api_model()

        if not negative_prompt:
            negative_prompt = None

        request = RecraftImageGenerationRequest(
            prompt=prompt,
            negative_prompt=negative_prompt,
            model=RecraftModel.recraftv3,
            n=n,
            strength=round(strength, 2),
            style=recraft_style.style,
            substyle=recraft_style.substyle,
            style_id=recraft_style.style_id,
            controls=controls_api,
            random_seed=seed,
        )

        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/imageToImage",
                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, )