78 lines
3.5 KiB
Markdown
78 lines
3.5 KiB
Markdown
# Documentation
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- Class name: MergeLayers
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- Category: ♾️Mixlab/Layer
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- Output node: False
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- Repo Ref: https://github.com/shadowcz007/comfyui-mixlab-nodes.git
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MergeLayers节点旨在将多个层集成到基础图像上。它处理一系列图像和相应的层，将每个层应用到基础图像上，位置已指定，可进行可选缩放，并生成一个合成图像以及一个遮罩，该遮罩描绘了分层区域。
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# Input types
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## Required
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- layers
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- 'layers'参数是字典列表，每个字典代表要合并到基础图像上的一个层。每个字典包含一个层的图像、遮罩和位置属性，这些属性对于确定分层图像的最终组合至关重要。
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- Comfy dtype: COMBO[str]
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- Python dtype: List[Dict[str, Union[str, torch.Tensor]]]
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- images
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- 'images'参数是要合并层的基础图像列表。每个图像都应该是张量格式，表示将作为分层过程画布的原始像素数据。
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- Comfy dtype: COMBO[torch.Tensor]
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- Python dtype: List[torch.Tensor]
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# Output types
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- IMAGE
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- 'IMAGE'输出是一个张量，代表通过将输入层合并到基础图像上形成的最终合成图像。它包含了分层过程的视觉结果。
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- Comfy dtype: torch.Tensor
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- Python dtype: torch.Tensor
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- MASK
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- 'MASK'输出是一个张量，代表在分层过程中应用的遮罩。它用于识别图像的分层区域，清晰地区分不同的层。
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- Comfy dtype: torch.Tensor
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- Python dtype: torch.Tensor
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# Usage tips
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- Infra type: CPU
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# Source code
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```
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class MergeLayers:
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@classmethod
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def INPUT_TYPES(s):
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return {'required': {'layers': ('LAYER',), 'images': ('IMAGE',)}}
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RETURN_TYPES = ('IMAGE', 'MASK')
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RETURN_NAMES = ('IMAGE', 'MASK')
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FUNCTION = 'run'
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CATEGORY = '♾️Mixlab/Layer'
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INPUT_IS_LIST = True
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def run(self, layers, images):
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bg_images = []
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masks = []
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for img in images:
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for bg_image in img:
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bg_image = tensor2pil(bg_image)
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layers_new = sorted(layers, key=lambda x: x['z_index'])
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(width, height) = bg_image.size
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final_mask = Image.new('L', (width, height), 0)
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for layer in layers_new:
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image = layer['image']
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mask = layer['mask']
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if 'type' in layer and layer['type'] == 'base64' and (type(image) == str):
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im = base64_to_image(image)
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im = im.convert('RGB')
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image = pil2tensor(im)
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mask = base64_to_image(mask)
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mask = mask.convert('L')
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mask = pil2tensor(mask)
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layer_image = tensor2pil(image)
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layer_mask = tensor2pil(mask)
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bg_image = merge_images(bg_image, layer_image, layer_mask, layer['x'], layer['y'], layer['width'], layer['height'], layer['scale_option'])
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final_mask = merge_images(final_mask, layer_mask.convert('RGB'), layer_mask, layer['x'], layer['y'], layer['width'], layer['height'], layer['scale_option'])
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final_mask = final_mask.convert('L')
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final_mask = pil2tensor(final_mask)
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bg_image = bg_image.convert('RGB')
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bg_image = pil2tensor(bg_image)
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bg_images.append(bg_image)
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masks.append(final_mask)
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bg_images = torch.cat(bg_images, dim=0)
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masks = torch.cat(masks, dim=0)
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return (bg_images, masks)
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``` |