63 lines
2.0 KiB
Markdown
63 lines
2.0 KiB
Markdown
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# Documentation
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- Class name: TilePreprocessor
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- Category: ControlNet Preprocessors/others
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- Output node: False
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TilePreprocessor节点旨在通过应用平铺效果来提升图像质量。它通过平铺处理来预处理图像，为后续的处理或分析做准备，主要关注于改善视觉效果或提取特定特征。
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# Input types
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## Required
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- image
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- 需要处理的输入图像。它是应用平铺效果的主要数据源。
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- Comfy dtype: IMAGE
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- Python dtype: numpy.ndarray
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## Optional
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- pyrUp_iters
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- 指定金字塔上采样过程的迭代次数，这会影响应用于图像的平铺效果的强度。
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- Comfy dtype: INT
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- Python dtype: int
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- resolution
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- 输出图像的目标分辨率。这个参数会影响处理后图像的最终大小和质量。
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- Comfy dtype: INT
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- Python dtype: int
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# Output types
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- image
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- 输出经过平铺预处理的图像，可能会增强某些特征或方面以便进行进一步分析。
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- Comfy dtype: IMAGE
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- Python dtype: numpy.ndarray
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## Usage tips
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- Infra type: `CPU`
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- Common nodes:
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- [ControlNetApplyAdvanced](../../Comfy/Nodes/ControlNetApplyAdvanced.md)
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- [CR Thumbnail Preview](../../ComfyUI_Comfyroll_CustomNodes/Nodes/CR Thumbnail Preview.md)
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- [PreviewImage](../../Comfy/Nodes/PreviewImage.md)
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- [ACN_AdvancedControlNetApply](../../ComfyUI-Advanced-ControlNet/Nodes/ACN_AdvancedControlNetApply.md)
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## Source code
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```python
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class Tile_Preprocessor:
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@classmethod
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def INPUT_TYPES(s):
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return create_node_input_types(
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pyrUp_iters = ("INT", {"default": 3, "min": 1, "max": 10, "step": 1})
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)
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "execute"
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CATEGORY = "ControlNet Preprocessors/others"
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def execute(self, image, pyrUp_iters, resolution=512, **kwargs):
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from controlnet_aux.tile import TileDetector
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return (common_annotator_call(TileDetector(), image, pyrUp_iters=pyrUp_iters, resolution=resolution),)
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```
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