VAEEncodeForInpaint.py

# Provides demonstration of PR https://github.com/comfyanonymous/ComfyUI/pull/1574/commits/297d1cff422198806cda40e4b6d71a6e6aa05453

import torch

class WAS_VAEEncodeForInpaint:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", ), "mask": ("MASK", ), "mask_offset": ("INT", {"default": 6, "min": -128, "max": 128, "step": 1}),}}
    RETURN_TYPES = ("LATENT",)
    FUNCTION = "encode"

    CATEGORY = "latent/inpaint"

    def encode(self, vae, pixels, mask, mask_offset=6):
        x = (pixels.shape[1] // 8) * 8
        y = (pixels.shape[2] // 8) * 8
        mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")

        pixels = pixels.clone()
        if pixels.shape[1] != x or pixels.shape[2] != y:
            x_offset = (pixels.shape[1] % 8) // 2
            y_offset = (pixels.shape[2] % 8) // 2
            pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:]
            mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset]

        mask_erosion = self.modify_mask(mask, mask_offset)

        m = (1.0 - mask_erosion.round()).squeeze(1)
        for i in range(3):
            pixels[:,:,:,i] -= 0.5
            pixels[:,:,:,i] *= m
            pixels[:,:,:,i] += 0.5
        t = vae.encode(pixels)

        return ({"samples":t, "noise_mask": (mask_erosion[:,:,:x,:y].round())}, )
        
    def modify_mask(self, mask, modify_by):
        if modify_by == 0:
            return mask
        if modify_by > 0:
            kernel_size = 2 * modify_by + 1
            kernel_tensor = torch.ones((1, 1, kernel_size, kernel_size))
            padding = modify_by
            modified_mask = torch.clamp(torch.nn.functional.conv2d(mask.round(), kernel_tensor, padding=padding), 0, 1)
        else:
            kernel_size = 2 * abs(modify_by) + 1
            kernel_tensor = torch.ones((1, 1, kernel_size, kernel_size))
            padding = abs(modify_by)
            eroded_mask = torch.nn.functional.conv2d(1 - mask.round(), kernel_tensor, padding=padding)
            modified_mask = torch.clamp(1 - eroded_mask, 0, 1)
        return modified_mask
        


NODE_CLASS_MAPPINGS = {
    "VAEEncodeForInpaint (WAS)": WAS_VAEEncodeForInpaint,
}

NODE_DISPLAY_NAME_MAPPINGS = {
    "VAEEncodeForInpaint (WAS)": "Inpainting VAE Encode (WAS)",
}