You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
363 lines
12 KiB
363 lines
12 KiB
import numpy as np |
|
import scipy.ndimage |
|
import torch |
|
import comfy.utils |
|
|
|
from nodes import MAX_RESOLUTION |
|
|
|
def composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False): |
|
source = source.to(destination.device) |
|
if resize_source: |
|
source = torch.nn.functional.interpolate(source, size=(destination.shape[2], destination.shape[3]), mode="bilinear") |
|
|
|
source = comfy.utils.repeat_to_batch_size(source, destination.shape[0]) |
|
|
|
x = max(-source.shape[3] * multiplier, min(x, destination.shape[3] * multiplier)) |
|
y = max(-source.shape[2] * multiplier, min(y, destination.shape[2] * multiplier)) |
|
|
|
left, top = (x // multiplier, y // multiplier) |
|
right, bottom = (left + source.shape[3], top + source.shape[2],) |
|
|
|
if mask is None: |
|
mask = torch.ones_like(source) |
|
else: |
|
mask = mask.to(destination.device, copy=True) |
|
mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(source.shape[2], source.shape[3]), mode="bilinear") |
|
mask = comfy.utils.repeat_to_batch_size(mask, source.shape[0]) |
|
|
|
# calculate the bounds of the source that will be overlapping the destination |
|
# this prevents the source trying to overwrite latent pixels that are out of bounds |
|
# of the destination |
|
visible_width, visible_height = (destination.shape[3] - left + min(0, x), destination.shape[2] - top + min(0, y),) |
|
|
|
mask = mask[:, :, :visible_height, :visible_width] |
|
inverse_mask = torch.ones_like(mask) - mask |
|
|
|
source_portion = mask * source[:, :, :visible_height, :visible_width] |
|
destination_portion = inverse_mask * destination[:, :, top:bottom, left:right] |
|
|
|
destination[:, :, top:bottom, left:right] = source_portion + destination_portion |
|
return destination |
|
|
|
class LatentCompositeMasked: |
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return { |
|
"required": { |
|
"destination": ("LATENT",), |
|
"source": ("LATENT",), |
|
"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), |
|
"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), |
|
"resize_source": ("BOOLEAN", {"default": False}), |
|
}, |
|
"optional": { |
|
"mask": ("MASK",), |
|
} |
|
} |
|
RETURN_TYPES = ("LATENT",) |
|
FUNCTION = "composite" |
|
|
|
CATEGORY = "latent" |
|
|
|
def composite(self, destination, source, x, y, resize_source, mask = None): |
|
output = destination.copy() |
|
destination = destination["samples"].clone() |
|
source = source["samples"] |
|
output["samples"] = composite(destination, source, x, y, mask, 8, resize_source) |
|
return (output,) |
|
|
|
class ImageCompositeMasked: |
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return { |
|
"required": { |
|
"destination": ("IMAGE",), |
|
"source": ("IMAGE",), |
|
"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), |
|
"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), |
|
"resize_source": ("BOOLEAN", {"default": False}), |
|
}, |
|
"optional": { |
|
"mask": ("MASK",), |
|
} |
|
} |
|
RETURN_TYPES = ("IMAGE",) |
|
FUNCTION = "composite" |
|
|
|
CATEGORY = "image" |
|
|
|
def composite(self, destination, source, x, y, resize_source, mask = None): |
|
destination = destination.clone().movedim(-1, 1) |
|
output = composite(destination, source.movedim(-1, 1), x, y, mask, 1, resize_source).movedim(1, -1) |
|
return (output,) |
|
|
|
class MaskToImage: |
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return { |
|
"required": { |
|
"mask": ("MASK",), |
|
} |
|
} |
|
|
|
CATEGORY = "mask" |
|
|
|
RETURN_TYPES = ("IMAGE",) |
|
FUNCTION = "mask_to_image" |
|
|
|
def mask_to_image(self, mask): |
|
result = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3) |
|
return (result,) |
|
|
|
class ImageToMask: |
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return { |
|
"required": { |
|
"image": ("IMAGE",), |
|
"channel": (["red", "green", "blue", "alpha"],), |
|
} |
|
} |
|
|
|
CATEGORY = "mask" |
|
|
|
RETURN_TYPES = ("MASK",) |
|
FUNCTION = "image_to_mask" |
|
|
|
def image_to_mask(self, image, channel): |
|
channels = ["red", "green", "blue", "alpha"] |
|
mask = image[:, :, :, channels.index(channel)] |
|
return (mask,) |
|
|
|
class ImageColorToMask: |
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return { |
|
"required": { |
|
"image": ("IMAGE",), |
|
"color": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFF, "step": 1, "display": "color"}), |
|
} |
|
} |
|
|
|
CATEGORY = "mask" |
|
|
|
RETURN_TYPES = ("MASK",) |
|
FUNCTION = "image_to_mask" |
|
|
|
def image_to_mask(self, image, color): |
|
temp = (torch.clamp(image, 0, 1.0) * 255.0).round().to(torch.int) |
|
temp = torch.bitwise_left_shift(temp[:,:,:,0], 16) + torch.bitwise_left_shift(temp[:,:,:,1], 8) + temp[:,:,:,2] |
|
mask = torch.where(temp == color, 255, 0).float() |
|
return (mask,) |
|
|
|
class SolidMask: |
|
@classmethod |
|
def INPUT_TYPES(cls): |
|
return { |
|
"required": { |
|
"value": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), |
|
"width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), |
|
"height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), |
|
} |
|
} |
|
|
|
CATEGORY = "mask" |
|
|
|
RETURN_TYPES = ("MASK",) |
|
|
|
FUNCTION = "solid" |
|
|
|
def solid(self, value, width, height): |
|
out = torch.full((1, height, width), value, dtype=torch.float32, device="cpu") |
|
return (out,) |
|
|
|
class InvertMask: |
|
@classmethod |
|
def INPUT_TYPES(cls): |
|
return { |
|
"required": { |
|
"mask": ("MASK",), |
|
} |
|
} |
|
|
|
CATEGORY = "mask" |
|
|
|
RETURN_TYPES = ("MASK",) |
|
|
|
FUNCTION = "invert" |
|
|
|
def invert(self, mask): |
|
out = 1.0 - mask |
|
return (out,) |
|
|
|
class CropMask: |
|
@classmethod |
|
def INPUT_TYPES(cls): |
|
return { |
|
"required": { |
|
"mask": ("MASK",), |
|
"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), |
|
"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), |
|
"width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), |
|
"height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), |
|
} |
|
} |
|
|
|
CATEGORY = "mask" |
|
|
|
RETURN_TYPES = ("MASK",) |
|
|
|
FUNCTION = "crop" |
|
|
|
def crop(self, mask, x, y, width, height): |
|
mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1])) |
|
out = mask[:, y:y + height, x:x + width] |
|
return (out,) |
|
|
|
class MaskComposite: |
|
@classmethod |
|
def INPUT_TYPES(cls): |
|
return { |
|
"required": { |
|
"destination": ("MASK",), |
|
"source": ("MASK",), |
|
"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), |
|
"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), |
|
"operation": (["multiply", "add", "subtract", "and", "or", "xor"],), |
|
} |
|
} |
|
|
|
CATEGORY = "mask" |
|
|
|
RETURN_TYPES = ("MASK",) |
|
|
|
FUNCTION = "combine" |
|
|
|
def combine(self, destination, source, x, y, operation): |
|
output = destination.reshape((-1, destination.shape[-2], destination.shape[-1])).clone() |
|
source = source.reshape((-1, source.shape[-2], source.shape[-1])) |
|
|
|
left, top = (x, y,) |
|
right, bottom = (min(left + source.shape[-1], destination.shape[-1]), min(top + source.shape[-2], destination.shape[-2])) |
|
visible_width, visible_height = (right - left, bottom - top,) |
|
|
|
source_portion = source[:, :visible_height, :visible_width] |
|
destination_portion = destination[:, top:bottom, left:right] |
|
|
|
if operation == "multiply": |
|
output[:, top:bottom, left:right] = destination_portion * source_portion |
|
elif operation == "add": |
|
output[:, top:bottom, left:right] = destination_portion + source_portion |
|
elif operation == "subtract": |
|
output[:, top:bottom, left:right] = destination_portion - source_portion |
|
elif operation == "and": |
|
output[:, top:bottom, left:right] = torch.bitwise_and(destination_portion.round().bool(), source_portion.round().bool()).float() |
|
elif operation == "or": |
|
output[:, top:bottom, left:right] = torch.bitwise_or(destination_portion.round().bool(), source_portion.round().bool()).float() |
|
elif operation == "xor": |
|
output[:, top:bottom, left:right] = torch.bitwise_xor(destination_portion.round().bool(), source_portion.round().bool()).float() |
|
|
|
output = torch.clamp(output, 0.0, 1.0) |
|
|
|
return (output,) |
|
|
|
class FeatherMask: |
|
@classmethod |
|
def INPUT_TYPES(cls): |
|
return { |
|
"required": { |
|
"mask": ("MASK",), |
|
"left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), |
|
"top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), |
|
"right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), |
|
"bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), |
|
} |
|
} |
|
|
|
CATEGORY = "mask" |
|
|
|
RETURN_TYPES = ("MASK",) |
|
|
|
FUNCTION = "feather" |
|
|
|
def feather(self, mask, left, top, right, bottom): |
|
output = mask.reshape((-1, mask.shape[-2], mask.shape[-1])).clone() |
|
|
|
left = min(left, output.shape[-1]) |
|
right = min(right, output.shape[-1]) |
|
top = min(top, output.shape[-2]) |
|
bottom = min(bottom, output.shape[-2]) |
|
|
|
for x in range(left): |
|
feather_rate = (x + 1.0) / left |
|
output[:, :, x] *= feather_rate |
|
|
|
for x in range(right): |
|
feather_rate = (x + 1) / right |
|
output[:, :, -x] *= feather_rate |
|
|
|
for y in range(top): |
|
feather_rate = (y + 1) / top |
|
output[:, y, :] *= feather_rate |
|
|
|
for y in range(bottom): |
|
feather_rate = (y + 1) / bottom |
|
output[:, -y, :] *= feather_rate |
|
|
|
return (output,) |
|
|
|
class GrowMask: |
|
@classmethod |
|
def INPUT_TYPES(cls): |
|
return { |
|
"required": { |
|
"mask": ("MASK",), |
|
"expand": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION, "step": 1}), |
|
"tapered_corners": ("BOOLEAN", {"default": True}), |
|
}, |
|
} |
|
|
|
CATEGORY = "mask" |
|
|
|
RETURN_TYPES = ("MASK",) |
|
|
|
FUNCTION = "expand_mask" |
|
|
|
def expand_mask(self, mask, expand, tapered_corners): |
|
c = 0 if tapered_corners else 1 |
|
kernel = np.array([[c, 1, c], |
|
[1, 1, 1], |
|
[c, 1, c]]) |
|
mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1])) |
|
out = [] |
|
for m in mask: |
|
output = m.numpy() |
|
for _ in range(abs(expand)): |
|
if expand < 0: |
|
output = scipy.ndimage.grey_erosion(output, footprint=kernel) |
|
else: |
|
output = scipy.ndimage.grey_dilation(output, footprint=kernel) |
|
output = torch.from_numpy(output) |
|
out.append(output) |
|
return (torch.stack(out, dim=0),) |
|
|
|
|
|
|
|
NODE_CLASS_MAPPINGS = { |
|
"LatentCompositeMasked": LatentCompositeMasked, |
|
"ImageCompositeMasked": ImageCompositeMasked, |
|
"MaskToImage": MaskToImage, |
|
"ImageToMask": ImageToMask, |
|
"ImageColorToMask": ImageColorToMask, |
|
"SolidMask": SolidMask, |
|
"InvertMask": InvertMask, |
|
"CropMask": CropMask, |
|
"MaskComposite": MaskComposite, |
|
"FeatherMask": FeatherMask, |
|
"GrowMask": GrowMask, |
|
} |
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = { |
|
"ImageToMask": "Convert Image to Mask", |
|
"MaskToImage": "Convert Mask to Image", |
|
}
|
|
|