118 lines
4.0 KiB
Markdown
118 lines
4.0 KiB
Markdown
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# Documentation
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- Class name: SaltImageBlendingModes
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- Category: SALT/Image/Composite
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- Output node: False
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该节点专为将两组图像混合在一起而设计，可使用各种混合模式。它允许调整混合百分比并应用蒙版来控制混合过程，为创建合成图像提供了灵活的方法。
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# Input types
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## Required
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- images_a
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- 要混合的第一组图像。这些图像在混合过程中作为基础层。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- images_b
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- 要与第一组图像混合的第二组图像。这些图像作为叠加层。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- mode
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- 指定要使用的混合模式。每种模式应用不同的算法来组合图像，影响混合的视觉效果。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: List[str]
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- blend_percentage
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- 确定两组图像之间的混合强度。较高的百分比会导致叠加图像的存在感更强。
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- Comfy dtype: FLOAT
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- Python dtype: float
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## Optional
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- masks
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- 可选的蒙版，可应用于图像以控制混合发生的位置。对于创建更精确或复杂的合成图像很有用。
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- Comfy dtype: MASK
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- Python dtype: Optional[torch.Tensor]
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# Output types
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- images
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- 混合后的图像集。
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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 SaltImageBlendingModes:
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def __init__(self):
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pass
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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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"images_a": ("IMAGE",),
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"images_b": ("IMAGE",),
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"mode": ([
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"normal",
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"color",
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"color_burn",
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"color_dodge",
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"darken",
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"difference",
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"exclusion",
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"hard_light",
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"hue",
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"lighten",
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"multiply",
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"overlay",
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"screen",
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"soft_light",
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],),
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"blend_percentage": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
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},
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"optional": {
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"masks": ("MASK",),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("images",)
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FUNCTION = "blend"
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CATEGORY = "SALT/Image/Composite"
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def blend(self, images_a, images_b, mode, blend_percentage, masks=None):
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blended_images = []
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if not isinstance(blend_percentage, list):
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blend_percentage = [blend_percentage]
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if isinstance(masks, torch.Tensor):
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masks = masks2pils(masks)
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for i in range(len(images_a)):
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img_a = tensor2pil(images_a[i].unsqueeze(0))
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img_b = tensor2pil(images_b[i if i < len(images_b) else -1].unsqueeze(0))
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img_b_resized = img_b.resize(img_a.size, Image.Resampling.BILINEAR).convert(img_a.mode)
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out_image = getattr(pilgram.css.blending, mode)(img_a, img_b_resized)
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if masks:
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mask_resized = masks[i if i < len(masks) else -1].resize(img_a.size, Image.Resampling.BILINEAR).convert('L')
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black_image = Image.new("L", img_a.size, 0) # Ensure this black image matches the size
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blend_mask = Image.blend(black_image, mask_resized, blend_percentage[i if i < len(blend_percentage) else -1])
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final_image = Image.composite(out_image, img_a, blend_mask)
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else:
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blend_intensity = int(255 * blend_percentage[i if i < len(blend_percentage) else -1])
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blend_mask = Image.new("L", img_a.size, blend_intensity)
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final_image = Image.composite(out_image, img_a, blend_mask)
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blended_images.append(pil2tensor(final_image))
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blended_images_batch = torch.cat(blended_images, dim=0)
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return (blended_images_batch,)
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```
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