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import numpy as np |
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import scipy.ndimage |
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import torch |
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import fcbh.utils |
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from nodes import MAX_RESOLUTION |
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def composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False): |
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if resize_source: |
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source = torch.nn.functional.interpolate(source, size=(destination.shape[2], destination.shape[3]), mode="bilinear") |
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source = fcbh.utils.repeat_to_batch_size(source, destination.shape[0]) |
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x = max(-source.shape[3] * multiplier, min(x, destination.shape[3] * multiplier)) |
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y = max(-source.shape[2] * multiplier, min(y, destination.shape[2] * multiplier)) |
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left, top = (x // multiplier, y // multiplier) |
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right, bottom = (left + source.shape[3], top + source.shape[2],) |
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if mask is None: |
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mask = torch.ones_like(source) |
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else: |
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mask = mask.clone() |
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mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(source.shape[2], source.shape[3]), mode="bilinear") |
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mask = fcbh.utils.repeat_to_batch_size(mask, source.shape[0]) |
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visible_width, visible_height = (destination.shape[3] - left + min(0, x), destination.shape[2] - top + min(0, y),) |
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mask = mask[:, :, :visible_height, :visible_width] |
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inverse_mask = torch.ones_like(mask) - mask |
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source_portion = mask * source[:, :, :visible_height, :visible_width] |
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destination_portion = inverse_mask * destination[:, :, top:bottom, left:right] |
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destination[:, :, top:bottom, left:right] = source_portion + destination_portion |
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return destination |
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class LatentCompositeMasked: |
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@classmethod |
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def INPUT_TYPES(s): |
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return { |
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"required": { |
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"destination": ("LATENT",), |
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"source": ("LATENT",), |
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"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), |
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"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), |
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"resize_source": ("BOOLEAN", {"default": False}), |
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}, |
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"optional": { |
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"mask": ("MASK",), |
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} |
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} |
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RETURN_TYPES = ("LATENT",) |
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FUNCTION = "composite" |
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CATEGORY = "latent" |
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def composite(self, destination, source, x, y, resize_source, mask = None): |
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output = destination.copy() |
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destination = destination["samples"].clone() |
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source = source["samples"] |
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output["samples"] = composite(destination, source, x, y, mask, 8, resize_source) |
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return (output,) |
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class ImageCompositeMasked: |
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@classmethod |
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def INPUT_TYPES(s): |
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return { |
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"required": { |
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"destination": ("IMAGE",), |
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"source": ("IMAGE",), |
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"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), |
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"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), |
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"resize_source": ("BOOLEAN", {"default": False}), |
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}, |
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"optional": { |
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"mask": ("MASK",), |
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} |
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} |
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RETURN_TYPES = ("IMAGE",) |
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FUNCTION = "composite" |
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CATEGORY = "image" |
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def composite(self, destination, source, x, y, resize_source, mask = None): |
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destination = destination.clone().movedim(-1, 1) |
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output = composite(destination, source.movedim(-1, 1), x, y, mask, 1, resize_source).movedim(1, -1) |
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return (output,) |
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class MaskToImage: |
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@classmethod |
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def INPUT_TYPES(s): |
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return { |
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"required": { |
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"mask": ("MASK",), |
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} |
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} |
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CATEGORY = "mask" |
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RETURN_TYPES = ("IMAGE",) |
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FUNCTION = "mask_to_image" |
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def mask_to_image(self, mask): |
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result = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3) |
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return (result,) |
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class ImageToMask: |
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@classmethod |
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def INPUT_TYPES(s): |
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return { |
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"required": { |
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"image": ("IMAGE",), |
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"channel": (["red", "green", "blue", "alpha"],), |
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} |
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} |
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CATEGORY = "mask" |
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RETURN_TYPES = ("MASK",) |
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FUNCTION = "image_to_mask" |
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def image_to_mask(self, image, channel): |
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channels = ["red", "green", "blue", "alpha"] |
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mask = image[:, :, :, channels.index(channel)] |
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return (mask,) |
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class ImageColorToMask: |
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@classmethod |
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def INPUT_TYPES(s): |
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return { |
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"required": { |
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"image": ("IMAGE",), |
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"color": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFF, "step": 1, "display": "color"}), |
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} |
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} |
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CATEGORY = "mask" |
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RETURN_TYPES = ("MASK",) |
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FUNCTION = "image_to_mask" |
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def image_to_mask(self, image, color): |
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temp = (torch.clamp(image, 0, 1.0) * 255.0).round().to(torch.int) |
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temp = torch.bitwise_left_shift(temp[:,:,:,0], 16) + torch.bitwise_left_shift(temp[:,:,:,1], 8) + temp[:,:,:,2] |
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mask = torch.where(temp == color, 255, 0).float() |
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return (mask,) |
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class SolidMask: |
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@classmethod |
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def INPUT_TYPES(cls): |
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return { |
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"required": { |
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"value": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), |
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"width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), |
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"height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), |
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} |
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} |
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CATEGORY = "mask" |
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RETURN_TYPES = ("MASK",) |
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FUNCTION = "solid" |
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def solid(self, value, width, height): |
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out = torch.full((1, height, width), value, dtype=torch.float32, device="cpu") |
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return (out,) |
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class InvertMask: |
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@classmethod |
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def INPUT_TYPES(cls): |
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return { |
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"required": { |
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"mask": ("MASK",), |
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} |
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} |
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CATEGORY = "mask" |
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RETURN_TYPES = ("MASK",) |
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FUNCTION = "invert" |
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def invert(self, mask): |
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out = 1.0 - mask |
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return (out,) |
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class CropMask: |
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@classmethod |
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def INPUT_TYPES(cls): |
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return { |
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"required": { |
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"mask": ("MASK",), |
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"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), |
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"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), |
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"width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), |
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"height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), |
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} |
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} |
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CATEGORY = "mask" |
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RETURN_TYPES = ("MASK",) |
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FUNCTION = "crop" |
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def crop(self, mask, x, y, width, height): |
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mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1])) |
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out = mask[:, y:y + height, x:x + width] |
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return (out,) |
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class MaskComposite: |
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@classmethod |
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def INPUT_TYPES(cls): |
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return { |
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"required": { |
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"destination": ("MASK",), |
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"source": ("MASK",), |
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"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), |
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"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), |
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"operation": (["multiply", "add", "subtract", "and", "or", "xor"],), |
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} |
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} |
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CATEGORY = "mask" |
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RETURN_TYPES = ("MASK",) |
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FUNCTION = "combine" |
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def combine(self, destination, source, x, y, operation): |
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output = destination.reshape((-1, destination.shape[-2], destination.shape[-1])).clone() |
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source = source.reshape((-1, source.shape[-2], source.shape[-1])) |
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left, top = (x, y,) |
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right, bottom = (min(left + source.shape[-1], destination.shape[-1]), min(top + source.shape[-2], destination.shape[-2])) |
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visible_width, visible_height = (right - left, bottom - top,) |
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source_portion = source[:, :visible_height, :visible_width] |
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destination_portion = destination[:, top:bottom, left:right] |
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if operation == "multiply": |
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output[:, top:bottom, left:right] = destination_portion * source_portion |
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elif operation == "add": |
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output[:, top:bottom, left:right] = destination_portion + source_portion |
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elif operation == "subtract": |
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output[:, top:bottom, left:right] = destination_portion - source_portion |
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elif operation == "and": |
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output[:, top:bottom, left:right] = torch.bitwise_and(destination_portion.round().bool(), source_portion.round().bool()).float() |
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elif operation == "or": |
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output[:, top:bottom, left:right] = torch.bitwise_or(destination_portion.round().bool(), source_portion.round().bool()).float() |
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elif operation == "xor": |
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output[:, top:bottom, left:right] = torch.bitwise_xor(destination_portion.round().bool(), source_portion.round().bool()).float() |
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output = torch.clamp(output, 0.0, 1.0) |
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return (output,) |
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class FeatherMask: |
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@classmethod |
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def INPUT_TYPES(cls): |
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return { |
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"required": { |
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"mask": ("MASK",), |
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"left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), |
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"top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), |
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"right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), |
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"bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), |
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} |
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} |
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CATEGORY = "mask" |
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RETURN_TYPES = ("MASK",) |
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FUNCTION = "feather" |
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def feather(self, mask, left, top, right, bottom): |
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output = mask.reshape((-1, mask.shape[-2], mask.shape[-1])).clone() |
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left = min(left, output.shape[-1]) |
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right = min(right, output.shape[-1]) |
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top = min(top, output.shape[-2]) |
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bottom = min(bottom, output.shape[-2]) |
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for x in range(left): |
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feather_rate = (x + 1.0) / left |
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output[:, :, x] *= feather_rate |
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for x in range(right): |
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feather_rate = (x + 1) / right |
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output[:, :, -x] *= feather_rate |
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for y in range(top): |
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feather_rate = (y + 1) / top |
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output[:, y, :] *= feather_rate |
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for y in range(bottom): |
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feather_rate = (y + 1) / bottom |
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output[:, -y, :] *= feather_rate |
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return (output,) |
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class GrowMask: |
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@classmethod |
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def INPUT_TYPES(cls): |
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return { |
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"required": { |
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"mask": ("MASK",), |
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"expand": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION, "step": 1}), |
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"tapered_corners": ("BOOLEAN", {"default": True}), |
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}, |
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} |
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CATEGORY = "mask" |
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RETURN_TYPES = ("MASK",) |
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FUNCTION = "expand_mask" |
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def expand_mask(self, mask, expand, tapered_corners): |
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c = 0 if tapered_corners else 1 |
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kernel = np.array([[c, 1, c], |
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[1, 1, 1], |
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[c, 1, c]]) |
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mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1])) |
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out = [] |
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for m in mask: |
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output = m.numpy() |
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for _ in range(abs(expand)): |
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if expand < 0: |
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output = scipy.ndimage.grey_erosion(output, footprint=kernel) |
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else: |
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output = scipy.ndimage.grey_dilation(output, footprint=kernel) |
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output = torch.from_numpy(output) |
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out.append(output) |
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return (torch.stack(out, dim=0),) |
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NODE_CLASS_MAPPINGS = { |
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"LatentCompositeMasked": LatentCompositeMasked, |
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"ImageCompositeMasked": ImageCompositeMasked, |
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"MaskToImage": MaskToImage, |
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"ImageToMask": ImageToMask, |
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"ImageColorToMask": ImageColorToMask, |
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"SolidMask": SolidMask, |
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"InvertMask": InvertMask, |
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"CropMask": CropMask, |
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"MaskComposite": MaskComposite, |
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"FeatherMask": FeatherMask, |
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"GrowMask": GrowMask, |
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} |
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NODE_DISPLAY_NAME_MAPPINGS = { |
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"ImageToMask": "Convert Image to Mask", |
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"MaskToImage": "Convert Mask to Image", |
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} |
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