7.5 KiB
7.5 KiB
Documentation
- Class name: PasteByMask
- Category: Masquerade Nodes
- Output node: False
- Repo Ref: https://github.com/BadCafeCode/masquerade-nodes-comfyui
PasteByMask节点旨在使用遮罩确定位置，将一个图像粘贴到另一个图像上。它智能地调整要粘贴的图像的大小以适应遮罩的尺寸，有选项可以保持原始纵横比或完全填充遮罩区域。这个节点在图像处理任务中扮演着关键角色，当需要无缝集成多个图像时，提供了放置和调整图像元素的灵活性和精确性。
Input types
Required
- image_base
- 将要在其上粘贴另一个图像的基础图像。它作为操作的画布，其属性影响最终的组合。
- Comfy dtype: IMAGE
- Python dtype: torch.Tensor
- image_to_paste
- 将要粘贴到基础图像上的图像。其视觉内容和大小在确定粘贴操作的结果中起着关键作用。
- Comfy dtype: IMAGE
- Python dtype: torch.Tensor
- mask
- 决定image_to_paste将放置在image_base上的位置的遮罩。它充当模板，控制粘贴图像的可见性和位置。
- Comfy dtype: IMAGE
- Python dtype: torch.Tensor
- resize_behavior
- 确定如何调整image_to_paste的大小以适应遮罩内。这是一个关键参数，影响粘贴图像的缩放和纵横比。
- Comfy dtype: COMBO['resize', 'keep_ratio_fill', 'keep_ratio_fit', 'source_size', 'source_size_unmasked']
- Python dtype: str
Optional
- mask_mapping_optional
- 一个可选参数，如果提供，它指定了特定的image_to_paste应该粘贴到哪个基础图像上。它为涉及多个图像的复杂操作增加了一个额外的控制层。
- Comfy dtype: MASK_MAPPING
- Python dtype: torch.Tensor
Output types
- result
- 最终的输出图像，根据遮罩和resize_behavior参数，将image_to_paste应用到image_base上。
- Comfy dtype: IMAGE
- Python dtype: torch.Tensor
Usage tips
- Infra type: CPU
Source code
class PasteByMask:
"""
Pastes `image_to_paste` onto `image_base` using `mask` to determine the location. The `resize_behavior` parameter determines how the image to paste is resized to fit the mask. If `mask_mapping_optional` obtained from a 'Separate Mask Components' node is used, it will control which image gets pasted onto which base image.
"""
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {'required': {'image_base': ('IMAGE',), 'image_to_paste': ('IMAGE',), 'mask': ('IMAGE',), 'resize_behavior': (['resize', 'keep_ratio_fill', 'keep_ratio_fit', 'source_size', 'source_size_unmasked'],)}, 'optional': {'mask_mapping_optional': ('MASK_MAPPING',)}}
RETURN_TYPES = ('IMAGE',)
FUNCTION = 'paste'
CATEGORY = 'Masquerade Nodes'
def paste(self, image_base, image_to_paste, mask, resize_behavior, mask_mapping_optional=None):
image_base = tensor2rgba(image_base)
image_to_paste = tensor2rgba(image_to_paste)
mask = tensor2mask(mask)
(B, H, W, C) = image_base.shape
MB = mask.shape[0]
PB = image_to_paste.shape[0]
if mask_mapping_optional is None:
if B < PB:
assert PB % B == 0
image_base = image_base.repeat(PB // B, 1, 1, 1)
(B, H, W, C) = image_base.shape
if MB < B:
assert B % MB == 0
mask = mask.repeat(B // MB, 1, 1)
elif B < MB:
assert MB % B == 0
image_base = image_base.repeat(MB // B, 1, 1, 1)
if PB < B:
assert B % PB == 0
image_to_paste = image_to_paste.repeat(B // PB, 1, 1, 1)
mask = torch.nn.functional.interpolate(mask.unsqueeze(1), size=(H, W), mode='nearest')[:, 0, :, :]
(MB, MH, MW) = mask.shape
is_empty = ~torch.gt(torch.max(torch.reshape(mask, [MB, MH * MW]), dim=1).values, 0.0)
mask[is_empty, 0, 0] = 1.0
boxes = masks_to_boxes(mask)
mask[is_empty, 0, 0] = 0.0
min_x = boxes[:, 0]
min_y = boxes[:, 1]
max_x = boxes[:, 2]
max_y = boxes[:, 3]
mid_x = (min_x + max_x) / 2
mid_y = (min_y + max_y) / 2
target_width = max_x - min_x + 1
target_height = max_y - min_y + 1
result = image_base.detach().clone()
for i in range(0, MB):
if is_empty[i]:
continue
else:
image_index = i
if mask_mapping_optional is not None:
image_index = mask_mapping_optional[i].item()
source_size = image_to_paste.size()
(SB, SH, SW, _) = image_to_paste.shape
width = int(target_width[i].item())
height = int(target_height[i].item())
if resize_behavior == 'keep_ratio_fill':
target_ratio = width / height
actual_ratio = SW / SH
if actual_ratio > target_ratio:
width = int(height * actual_ratio)
elif actual_ratio < target_ratio:
height = int(width / actual_ratio)
elif resize_behavior == 'keep_ratio_fit':
target_ratio = width / height
actual_ratio = SW / SH
if actual_ratio > target_ratio:
height = int(width / actual_ratio)
elif actual_ratio < target_ratio:
width = int(height * actual_ratio)
elif resize_behavior == 'source_size' or resize_behavior == 'source_size_unmasked':
width = SW
height = SH
resized_image = image_to_paste[i].unsqueeze(0)
if SH != height or SW != width:
resized_image = torch.nn.functional.interpolate(resized_image.permute(0, 3, 1, 2), size=(height, width), mode='bicubic').permute(0, 2, 3, 1)
pasting = torch.ones([H, W, C])
ymid = float(mid_y[i].item())
ymin = int(math.floor(ymid - height / 2)) + 1
ymax = int(math.floor(ymid + height / 2)) + 1
xmid = float(mid_x[i].item())
xmin = int(math.floor(xmid - width / 2)) + 1
xmax = int(math.floor(xmid + width / 2)) + 1
(_, source_ymax, source_xmax, _) = resized_image.shape
(source_ymin, source_xmin) = (0, 0)
if xmin < 0:
source_xmin = abs(xmin)
xmin = 0
if ymin < 0:
source_ymin = abs(ymin)
ymin = 0
if xmax > W:
source_xmax -= xmax - W
xmax = W
if ymax > H:
source_ymax -= ymax - H
ymax = H
pasting[ymin:ymax, xmin:xmax, :] = resized_image[0, source_ymin:source_ymax, source_xmin:source_xmax, :]
pasting[:, :, 3] = 1.0
pasting_alpha = torch.zeros([H, W])
pasting_alpha[ymin:ymax, xmin:xmax] = resized_image[0, source_ymin:source_ymax, source_xmin:source_xmax, 3]
if resize_behavior == 'keep_ratio_fill' or resize_behavior == 'source_size_unmasked':
paste_mask = pasting_alpha.unsqueeze(2).repeat(1, 1, 4)
else:
paste_mask = torch.min(pasting_alpha, mask[i]).unsqueeze(2).repeat(1, 1, 4)
result[image_index] = pasting * paste_mask + result[image_index] * (1.0 - paste_mask)
return (result,)