82 lines
3.0 KiB
Markdown
82 lines
3.0 KiB
Markdown
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# Documentation
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- Class name: OverlayInpaintedImage
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- Category: Art Venture/Inpainting
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- Output node: False
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OverlayInpaintedImage节点旨在将一个修复后的图像叠加到另一个图像上，可能会在指定的裁剪区域内进行。它确保叠加操作尊重输入图像的尺寸和批量大小，将它们无缝地融合以生成一个复合输出。
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# Input types
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## Required
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- inpainted
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- 要叠加到基础图像上的修复图像张量。它在叠加过程中扮演着至关重要的角色，提供填充原始图像中缺失或改变部分的内容。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- overlay_image
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- 作为修复图像叠加基础的图像张量。这个图像充当修复内容的背景，将两者整合以创建一个连贯的视觉输出。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- crop_region
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- 指定基础图像中应该叠加修复图像的区域的张量。这允许精确控制修复内容在更大图像上下文中的放置和集成。
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- Comfy dtype: CROP_REGION
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- Python dtype: torch.Tensor
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# Output types
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- image
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- 在基础图像上叠加修复图像后的结果图像张量，如果适用的话，会在指定的裁剪区域内进行。这个图像以统一的方式结合了两个输入的视觉元素。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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## Usage tips
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- Infra type: `GPU`
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- Common nodes: unknown
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## Source code
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```python
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class OverlayInpaintedImage:
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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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"inpainted": ("IMAGE",),
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"overlay_image": ("IMAGE",),
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"crop_region": ("CROP_REGION",),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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CATEGORY = "Art Venture/Inpainting"
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FUNCTION = "overlay"
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def overlay(self, inpainted: torch.Tensor, overlay_image: torch.Tensor, crop_region: torch.Tensor):
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if inpainted.shape[0] != overlay_image.shape[0]:
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raise ValueError("inpainted and overlay_image must have same batch size")
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if inpainted.shape[0] != crop_region.shape[0]:
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raise ValueError("inpainted and crop_region must have same batch size")
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images = []
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for image, overlay, region in zip(inpainted, overlay_image, crop_region):
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image = tensor2pil(image.unsqueeze(0))
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overlay = tensor2pil(overlay.unsqueeze(0), mode="RGBA")
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x1, y1, x2, y2 = region.tolist()
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if (x1, y1, x2, y2) == (0, 0, 0, 0):
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pass
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else:
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base_image = Image.new("RGBA", (overlay.width, overlay.height))
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image = resize_image(image, x2 - x1, y2 - y1, ResizeMode.RESIZE_TO_FILL)
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base_image.paste(image, (x1, y1))
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image = base_image
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image = image.convert("RGBA")
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image.alpha_composite(overlay)
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image = image.convert("RGB")
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images.append(pil2tensor(image))
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return (torch.cat(images, dim=0),)
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```
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