Claude
efde7038b6
- Changed io.Input to io.Combo.Input for constraint_mode and crop_position - Added options= parameter for combo box inputs - Changed all Output definitions to use display_name= parameter instead of positional argument This resolves the error: "Input.__init__() got an unexpected keyword argument 'default'"
397 lines
16 KiB
Python
397 lines
16 KiB
Python
import torch
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import torch.nn.functional as F
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from typing import Tuple
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from comfy_api.latest import ComfyExtension, io
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from enum import StrEnum
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class ConstraintMode(StrEnum):
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"""Constraint mode for handling extreme aspect ratios"""
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MIN_RES = "Prioritize Min Resolution"
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MAX_RES_STRICT = "Prioritize Max Resolution (Strict)"
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class CropPosition(StrEnum):
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"""Position for cropping when aspect ratios don't match"""
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CENTER = "center"
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TOP = "top"
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BOTTOM = "bottom"
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LEFT = "left"
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RIGHT = "right"
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class ConstrainResolution(io.ComfyNode):
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"""
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A ComfyUI node that analyzes and resizes images to optimal dimensions
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while preserving or constraining aspect ratio based on resolution limits.
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"""
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@classmethod
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def define_schema(cls) -> io.Schema:
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"""Define the node schema with inputs and outputs"""
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return io.Schema(
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node_id="ConstrainResolution",
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display_name="Constrain Resolution",
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category="image/resolution",
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description=(
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"Intelligently resizes images to fit within resolution constraints while preserving aspect ratio. "
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"Perfect for preparing images for AI models with specific dimension requirements. "
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"\n\n"
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"💡 USAGE TIPS:\n"
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"• Use 'Prioritize Min Resolution' to ensure images are never too small (may exceed max on one dimension)\n"
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"• Use 'Prioritize Max Resolution (Strict)' for hard VRAM limits (may go below min on one dimension)\n"
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"• Set 'Multiple Of' to 8 for SDXL, 16 for some models, or 64 for optimal performance\n"
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"• 'Crop as Required' is enabled by default for immediate compatibility with strict dimension requirements\n"
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"• The node outputs both the resized image and the original for workflow flexibility"
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),
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inputs=[
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# Image input
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io.Image.Input(
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"image",
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tooltip="Input image to analyze and resize"
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),
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# Resolution constraints
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io.Int.Input(
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"min_res",
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default=704,
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min=1,
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max=65536,
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tooltip="Minimum resolution in pixels for width and height. Images smaller than this will be upscaled."
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),
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io.Int.Input(
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"max_res",
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default=1280,
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min=1,
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max=65536,
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tooltip="Maximum resolution in pixels for width and height. Images larger than this will be downscaled."
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),
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io.Int.Input(
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"multiple_of",
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default=8,
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min=1,
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max=256,
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tooltip=(
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"Ensures output dimensions are multiples of this number. "
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"Common values: 8 (SDXL), 16 (some models), 32 or 64 (optimal performance). "
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"Set to 1 to disable rounding."
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)
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),
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# Constraint behavior
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io.Combo.Input(
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"constraint_mode",
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options=[e.value for e in ConstraintMode],
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default=ConstraintMode.MIN_RES.value,
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tooltip=(
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"How to handle conflicts when extreme aspect ratios make it impossible to satisfy both min and max.\n"
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"• Prioritize Min Resolution: Ensures neither dimension falls below min_res (may exceed max_res)\n"
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"• Prioritize Max Resolution (Strict): Strictly enforces max_res limit (may go below min_res)"
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)
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),
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# Crop options
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io.Boolean.Input(
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"crop_as_required",
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default=True,
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tooltip=(
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"Enable cropping to achieve exact target dimensions when rounding causes aspect ratio changes. "
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"Disable if preserving the entire image is more important than exact dimensions."
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)
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),
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io.Combo.Input(
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"crop_position",
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options=[e.value for e in CropPosition],
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default=CropPosition.CENTER.value,
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tooltip=(
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"Where to crop from when 'Crop as Required' is enabled.\n"
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"• center: Crop equally from all sides\n"
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"• top: Keep top portion, crop from bottom\n"
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"• bottom: Keep bottom portion, crop from top\n"
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"• left: Keep left portion, crop from right\n"
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"• right: Keep right portion, crop from left"
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)
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),
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],
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outputs=[
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io.Image.Output(
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display_name="resized_image",
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tooltip="Image resized to the constrained dimensions"
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),
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io.Image.Output(
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display_name="original_image",
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tooltip="Original image passed through unchanged for workflow flexibility"
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),
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io.Int.Output(
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display_name="width",
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tooltip="Final width after constraints and rounding"
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),
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io.Int.Output(
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display_name="height",
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tooltip="Final height after constraints and rounding"
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),
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io.Float.Output(
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display_name="final_aspect_ratio",
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tooltip="Aspect ratio of the output image (width/height)"
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),
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io.Float.Output(
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display_name="original_aspect_ratio",
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tooltip="Aspect ratio of the input image for comparison"
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),
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],
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is_output_node=False,
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is_deprecated=False,
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is_experimental=False
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)
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@classmethod
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def validate_inputs(cls, min_res, max_res, multiple_of, **kwargs):
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"""Validate input parameters"""
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if max_res < min_res:
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return f"max_res ({max_res}) must be greater than or equal to min_res ({min_res})"
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if multiple_of < 1:
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return f"multiple_of must be at least 1, got {multiple_of}"
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if min_res < 1:
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return f"min_res must be at least 1, got {min_res}"
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return True
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@staticmethod
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def calculate_aspect_ratio(width: int, height: int) -> float:
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"""Calculate aspect ratio from width and height"""
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if height == 0:
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return 0.0
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return round(width / height, 4)
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@staticmethod
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def round_to_multiple(value: int, multiple: int) -> int:
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"""Round a value to the nearest multiple of the specified number"""
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if multiple == 1:
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return value
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return max(multiple, multiple * round(value / multiple))
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@staticmethod
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def calculate_optimal_dimensions(
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width: int,
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height: int,
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min_res: int,
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max_res: int,
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multiple_of: int,
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constraint_mode: str
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) -> Tuple[int, int]:
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"""Calculate optimal dimensions based on constraints"""
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if height == 0 or width == 0:
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return 0, 0
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aspect_ratio = width / height
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# 1. Initial scaling to fit the longest side to max_res
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if aspect_ratio >= 1: # Landscape or square
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new_width = max_res
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new_height = new_width / aspect_ratio
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else: # Portrait
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new_height = max_res
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new_width = new_height * aspect_ratio
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# 2. Apply constraint logic based on user's choice
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if constraint_mode == ConstraintMode.MIN_RES.value:
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# If a dimension is below min_res, scale the entire image up to meet it
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scale_factor = 1.0
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if new_width < min_res:
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scale_factor = max(scale_factor, min_res / new_width)
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if new_height < min_res:
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scale_factor = max(scale_factor, min_res / new_height)
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new_width *= scale_factor
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new_height *= scale_factor
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# If mode is "Prioritize Max Resolution (Strict)", we do nothing here.
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# The initial scaling already guarantees we are within the max_res bounds,
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# and we accept that a dimension may fall below min_res.
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# 3. Round final dimensions to the nearest multiple
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final_width = ConstrainResolution.round_to_multiple(int(new_width), multiple_of)
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final_height = ConstrainResolution.round_to_multiple(int(new_height), multiple_of)
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return final_width, final_height
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@staticmethod
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def resize_image(image: torch.Tensor, target_width: int, target_height: int) -> torch.Tensor:
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"""
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Resize image tensor to target dimensions using bilinear interpolation.
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Args:
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image: Input tensor in format [batch, height, width, channels]
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target_width: Target width in pixels
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target_height: Target height in pixels
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Returns:
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Resized tensor in same format as input
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"""
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# ComfyUI images are [batch, height, width, channels]
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# torch needs [batch, channels, height, width] for interpolate
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batch, height, width, channels = image.shape
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# Permute to [batch, channels, height, width]
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image_permuted = image.permute(0, 3, 1, 2)
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# Resize using bilinear interpolation
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resized = F.interpolate(
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image_permuted,
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size=(target_height, target_width),
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mode='bilinear',
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align_corners=False
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)
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# Permute back to [batch, height, width, channels]
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resized = resized.permute(0, 2, 3, 1)
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return resized
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@staticmethod
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def crop_image(
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image: torch.Tensor,
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target_width: int,
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target_height: int,
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position: str
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) -> torch.Tensor:
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"""
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Crop image to exact target dimensions from specified position.
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Args:
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image: Input tensor in format [batch, height, width, channels]
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target_width: Target width in pixels
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target_height: Target height in pixels
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position: One of "center", "top", "bottom", "left", "right"
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Returns:
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Cropped tensor
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"""
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batch, height, width, channels = image.shape
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# Calculate crop amounts
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width_diff = width - target_width
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height_diff = height - target_height
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# No crop needed if dimensions match
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if width_diff == 0 and height_diff == 0:
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return image
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# Calculate crop coordinates based on position
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if position == CropPosition.CENTER.value:
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left = width_diff // 2
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top = height_diff // 2
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elif position == CropPosition.TOP.value:
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left = width_diff // 2
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top = 0
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elif position == CropPosition.BOTTOM.value:
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left = width_diff // 2
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top = height_diff
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elif position == CropPosition.LEFT.value:
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left = 0
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top = height_diff // 2
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elif position == CropPosition.RIGHT.value:
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left = width_diff
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top = height_diff // 2
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else:
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# Default to center
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left = width_diff // 2
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top = height_diff // 2
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# Ensure we don't go out of bounds
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left = max(0, min(left, width_diff))
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top = max(0, min(top, height_diff))
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# Crop the image
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right = left + target_width
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bottom = top + target_height
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cropped = image[:, top:bottom, left:right, :]
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return cropped
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@classmethod
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def execute(
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cls,
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image,
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min_res,
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max_res,
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multiple_of,
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constraint_mode,
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crop_as_required,
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crop_position
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) -> io.NodeOutput:
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"""Execute the node logic"""
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# Get original dimensions
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batch, height, width, channels = image.shape
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original_aspect_ratio = cls.calculate_aspect_ratio(width, height)
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# Calculate optimal dimensions
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target_width, target_height = cls.calculate_optimal_dimensions(
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width, height, min_res, max_res, multiple_of, constraint_mode
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)
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# Resize image to target dimensions
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resized_image = cls.resize_image(image, target_width, target_height)
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# Calculate aspect ratio after resize
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final_aspect_ratio = cls.calculate_aspect_ratio(target_width, target_height)
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# Check if cropping is needed and enabled
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if crop_as_required and original_aspect_ratio > 0:
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# Determine if we need to crop
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# After rounding to multiples, the aspect ratio might have changed slightly
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# We'll resize to maintain aspect ratio on the larger dimension, then crop
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aspect_ratio_deviation = abs((final_aspect_ratio - original_aspect_ratio) / original_aspect_ratio * 100)
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if aspect_ratio_deviation > 0.1: # If more than 0.1% deviation
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# Resize to the dimension that needs to be larger, then crop
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if width / height > target_width / target_height:
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# Original is wider, resize based on width and crop height
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intermediate_height = int(target_width / (width / height))
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resized_image = cls.resize_image(image, target_width, intermediate_height)
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else:
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# Original is taller, resize based on height and crop width
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intermediate_width = int(target_height * (width / height))
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resized_image = cls.resize_image(image, intermediate_width, target_height)
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# Crop to exact target dimensions
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resized_image = cls.crop_image(resized_image, target_width, target_height, crop_position)
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print(f"Image cropped from center to achieve exact dimensions {target_width}x{target_height}")
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# Log aspect ratio deviation warning if significant
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if original_aspect_ratio > 0 and not crop_as_required:
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aspect_ratio_deviation = abs((final_aspect_ratio - original_aspect_ratio) / original_aspect_ratio * 100)
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if aspect_ratio_deviation > 1: # 1% tolerance for rounding
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print(
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f"ℹ️ Aspect ratio changed by {aspect_ratio_deviation:.2f}% due to rounding. "
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f"Enable 'Crop as Required' to preserve exact aspect ratio."
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)
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return io.NodeOutput(
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resized_image, # resized image
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image, # original image passthrough
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target_width, # final width
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target_height, # final height
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final_aspect_ratio, # final aspect ratio
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original_aspect_ratio # original aspect ratio
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)
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class ConstrainResolutionExtension(ComfyExtension):
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"""Extension class for registering nodes"""
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async def get_node_list(self) -> list[type[io.ComfyNode]]:
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"""Return list of nodes provided by this extension"""
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return [ConstrainResolution]
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async def comfy_entrypoint() -> ComfyExtension:
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"""Entry point for ComfyUI v3"""
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return ConstrainResolutionExtension()
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