62 lines
2.0 KiB
Markdown
62 lines
2.0 KiB
Markdown
---
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tags:
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- AlphaChannel
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- Image
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---
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# Join Image with Alpha
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## Documentation
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- Class name: `JoinImageWithAlpha`
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- Category: `mask/compositing`
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- Output node: `False`
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This node is designed for compositing operations, specifically to join an image with its corresponding alpha mask to produce a single output image. It effectively combines visual content with transparency information, enabling the creation of images where certain areas are transparent or semi-transparent.
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## Input types
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### Required
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- **`image`**
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- The main visual content to be combined with an alpha mask. It represents the image without transparency information.
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- Comfy dtype: `IMAGE`
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- Python dtype: `torch.Tensor`
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- **`alpha`**
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- The alpha mask that defines the transparency of the corresponding image. It is used to determine which parts of the image should be transparent or semi-transparent.
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- Comfy dtype: `MASK`
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- Python dtype: `torch.Tensor`
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## Output types
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- **`image`**
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- Comfy dtype: `IMAGE`
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- The output is a single image that combines the input image with the alpha mask, incorporating transparency information into the visual content.
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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 JoinImageWithAlpha:
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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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"alpha": ("MASK",),
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}
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}
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CATEGORY = "mask/compositing"
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "join_image_with_alpha"
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def join_image_with_alpha(self, image: torch.Tensor, alpha: torch.Tensor):
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batch_size = min(len(image), len(alpha))
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out_images = []
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alpha = 1.0 - resize_mask(alpha, image.shape[1:])
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for i in range(batch_size):
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out_images.append(torch.cat((image[i][:,:,:3], alpha[i].unsqueeze(2)), dim=2))
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result = (torch.stack(out_images),)
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return result
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```
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