Vito Sansevero
5485aa8c19
BREAKING CHANGE: Node categories now use emoji-based organization Add 6 new xyz-helper nodes adapted from comfyui-essentials-nodes: - FluxSamplerParams: FLUX-optimized parameter generator with batch support - LoRAFolderBatch: Batch process multiple LoRAs from folders - PlotParameters: Visualize parameter effects with graphs - SamplerSelectHelper: Intelligent sampler selection with recommendations - SchedulerSelectHelper: Optimal scheduler selection for samplers - TextEncodeSamplerParams: Combined text encoding and parameter management Changes: - Port and enhance nodes from comfyui-essentials (now in maintenance mode) - Add comprehensive documentation with attribution to original author (cubiq) - Create example workflows for xyz-helpers tools - Update all node categories to use emoji-based organization - Fix all unit tests to pass with new category system - Update README with xyz-helpers section and attribution Attribution: xyz-helpers adapted from github.com/cubiq/ComfyUI_essentials All tests passing (318 pass, 2 skip)
88 lines
2.7 KiB
Python
88 lines
2.7 KiB
Python
"""ComfyUI node implementation for ImageScaleDownBy."""
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from typing import Dict, Any, Tuple
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from torch import Tensor
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from ...base import ComfyAssetsBaseNode
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from .logic import scale_down_image
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class ImageScaleDownByNode(ComfyAssetsBaseNode):
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"""
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Scales down images by a specified factor.
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Reduces image dimensions proportionally using bilinear interpolation
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with antialiasing for smooth downscaling.
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"""
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@classmethod
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def INPUT_TYPES(cls) -> Dict[str, Any]:
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return {
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"required": {
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"images": ("IMAGE",),
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"scale_by": (
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"FLOAT",
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{
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"default": 0.5,
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"min": 0.01,
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"max": 1.0,
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"step": 0.01,
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"display": "number",
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},
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),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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CATEGORY = "ComfyAssets/🖼️ Resolution"
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RETURN_NAMES = ("images",)
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FUNCTION = "scale_down"
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def scale_down(self, images: Tensor, scale_by: float) -> Tuple[Tensor]:
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"""
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Scale down images by the specified factor.
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Args:
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images: Input image tensor
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scale_by: Scale factor between 0.01 and 1.0
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Returns:
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Tuple containing scaled down image tensor
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"""
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try:
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self.validate_inputs(images=images, scale_by=scale_by)
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# Scale down the images
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scaled_images = scale_down_image(images, scale_by)
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_, new_height, new_width, _ = scaled_images.shape
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_, orig_height, orig_width, _ = images.shape
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self.log_info(
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f"Scaled down images from {orig_height}x{orig_width} "
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f"to {new_height}x{new_width} (scale factor: {scale_by})"
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)
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return (scaled_images,)
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except Exception as e:
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self.handle_error(f"Failed to scale down images: {str(e)}", e)
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def validate_inputs(self, **kwargs) -> None:
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"""Validate inputs for ImageScaleDownBy node."""
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images = kwargs.get("images")
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scale_by = kwargs.get("scale_by")
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if images is None:
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raise ValueError("Images input is required")
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if not isinstance(images, Tensor) or len(images.shape) != 4:
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raise ValueError(
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f"Expected image tensor with shape (batch, height, width, channels), "
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f"got shape {images.shape if isinstance(images, Tensor) else 'non-tensor'}"
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)
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if scale_by <= 0 or scale_by > 1.0:
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raise ValueError(f"scale_by must be between 0.01 and 1.0, got {scale_by}")
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