[Inference.Core] Inpaint Preprocessor

Documentation

• Class name: Inference_Core_InpaintPreprocessor
• Category: ControlNet Preprocessors/others
• Output node: False

The Inpaint Preprocessor node is designed for image preprocessing in inpainting tasks, where it prepares images by applying masks to indicate areas for inpainting, effectively setting masked pixels to a specific value for further processing.

Input types

Required

• image
  • The 'image' parameter represents the input image to be preprocessed for inpainting, serving as the primary data for mask application.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• mask
  • The 'mask' parameter specifies the areas of the input image to be inpainted, indicating which pixels should be considered for modification.
  • Comfy dtype: MASK
  • Python dtype: torch.Tensor

Output types

• image
  • Comfy dtype: IMAGE
  • The output is a preprocessed image with specified areas masked for inpainting, ready for further processing steps.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class InpaintPreprocessor:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "image": ("IMAGE",), "mask": ("MASK",)}}
    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "preprocess"

    CATEGORY = "ControlNet Preprocessors/others"

    def preprocess(self, image, mask):
        mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(image.shape[1], image.shape[2]), mode="bilinear")
        mask = mask.movedim(1,-1).expand((-1,-1,-1,3))
        image = image.clone()
        image[mask > 0.5] = -1.0  # set as masked pixel
        return (image,)