Tile¶
Documentation¶
- Class name:
TilePreprocessor
- Category:
ControlNet Preprocessors/others
- Output node:
False
The TilePreprocessor node is designed to enhance image quality by applying a tiling effect. It preprocesses images for further processing or analysis, focusing on improving the visual aspects or extracting specific features through tiling.
Input types¶
Required¶
image
- The input image to be processed. It serves as the primary data for the tiling effect application.
- Comfy dtype:
IMAGE
- Python dtype:
numpy.ndarray
Optional¶
pyrUp_iters
- Specifies the number of iterations for the pyramid upscaling process, which affects the intensity of the tiling effect applied to the image.
- Comfy dtype:
INT
- Python dtype:
int
resolution
- The target resolution for the output image. This parameter influences the final size and quality of the processed image.
- Comfy dtype:
INT
- Python dtype:
int
Output types¶
image
- Comfy dtype:
IMAGE
- Outputs an image that has undergone the tiling preprocessing, potentially enhancing certain features or aspects for further analysis.
- Python dtype:
numpy.ndarray
- Comfy dtype:
Usage tips¶
- Infra type:
CPU
- Common nodes:
Source code¶
class Tile_Preprocessor:
@classmethod
def INPUT_TYPES(s):
return create_node_input_types(
pyrUp_iters = ("INT", {"default": 3, "min": 1, "max": 10, "step": 1})
)
RETURN_TYPES = ("IMAGE",)
FUNCTION = "execute"
CATEGORY = "ControlNet Preprocessors/others"
def execute(self, image, pyrUp_iters, resolution=512, **kwargs):
from controlnet_aux.tile import TileDetector
return (common_annotator_call(TileDetector(), image, pyrUp_iters=pyrUp_iters, resolution=resolution),)