216 lines
7.4 KiB
Python
216 lines
7.4 KiB
Python
# encoding: utf-8
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"""
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The actual nodes defined by the package.
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"""
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import typing as _t
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from inspect import cleandoc as _cleandoc
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from math import sqrt as _sqrt
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import sys as _sys
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from frozendict import deepfreeze
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from comfy.comfy_types.node_typing import IO as _IO
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from .node_types import *
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from ._funcs import (
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aspect_ratios_sorted as _aspect_ratios_sorted,
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number_to_int as _number_to_int,
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simple_result_from_approx_wh as _simple_result_from_approx_wh
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)
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# Tiny optimization by reusing the same immutable dict:
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_input_types_simple = deepfreeze({
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"required": {
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"width": (_IO.INT, dict(type_dict_res, **{"tooltip": "Approximate width"})),
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"height": (_IO.INT, dict(type_dict_res, **{"tooltip": "Approximate height"})),
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"step": (_IO.INT, dict(type_dict_step_init, **{"tooltip": (
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"Both width and height will be divisible by this value - by rounding them "
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"to the closest appropriate resolution.\n\n"
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"The default 48 is (8 * 3 * 2), so it's a safe choice because:\n"
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"- it's compatible with SD1.5/XL downsampling factor (divisible by 8),\n"
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"- it can be upscaled by x1.5 or x1.333 at the first iteration, which is optimal for latent-upscale,\n"
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"- after x1.5 upscale, if you only do x2 later (it's OK for already high resolutions) - it will be divisible "
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"by 3 AND 9, which might become handy at that point, where you'll probably use UltimateSDUpscale.\n\n"
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"Other values worth trying first: 64, 96, 128."
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)})),
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},
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"hidden": {
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"unique_id": "UNIQUE_ID", # used for text display at the bottom of the node
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},
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# "optional": {},
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})
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# _return_types_simple = (_IO.INT, _IO.INT, _IO.STRING)
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# _return_names_simple = ("width", "height", "report")
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_return_types_simple = (_IO.INT, _IO.INT)
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_return_names_simple = ("width", "height")
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class BestResolutionSimple:
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"""
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The simplest node for easier selection of an optimal resolution:
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only rounding performed, desired image size specified directly.
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"""
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NODE_NAME = "BestResolutionSimple"
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CATEGORY = "utils/resolution"
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DESCRIPTION = _cleandoc(__doc__)
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OUTPUT_NODE = True
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FUNCTION = "main"
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RETURN_TYPES = _return_types_simple
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# RETURN_TYPES_TOOLTIPS = {}
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RETURN_NAMES = _return_names_simple
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@classmethod
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def INPUT_TYPES(cls):
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return _input_types_simple
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def main(self, width: int, height: int, step: int, unique_id: str = None):
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return _simple_result_from_approx_wh(float(width), height, step, unique_id, _sqrt(width * height))
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_tooltip_aspect = (
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"Two aspects together define an aspect ratio (16:9, 4:3, etc).\n"
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"Order doesn't matter: image orientation is defined by the 'landscape' toggle.\n\n"
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"The specified aspect ratio is APPROXIMATE: step parameter has priority over the exact image proportions."
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)
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_input_types_orient = deepfreeze({
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"required": {
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"size": (_IO.INT, dict(type_dict_res, **{"tooltip": (
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"Approximate size of one of the image sides.\nWhich one - see the 'size_is_big' and 'landscape' toggles."
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)})),
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"step": _input_types_simple["required"]["step"],
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"size_is_big": (_IO.BOOLEAN, {
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"default": True, "label_on": "bigger", "label_off": "smaller",
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"tooltip": "When ON, size parameter represents the bigger image side.\nWhen OFF, it specifies the smaller one."
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}),
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"landscape": (_IO.BOOLEAN, {
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"default": True, "label_on": "landscape", "label_off": "portrait",
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"tooltip": (
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"Specifies image orientation:\n\n"
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"When ON, width is bigger (image is horizontal).\n"
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"When OFF, height is bigger (image is vertical)."
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)
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}),
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"aspect_a": (_IO.FLOAT, {
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"default": 16.0, "min": 1.0, "max": _sys.float_info.max, "step": 1.0, "round": 0.001,
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"tooltip": _tooltip_aspect
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}),
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"aspect_b": (_IO.FLOAT, {
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"default": 9.0, "min": 1.0, "max": _sys.float_info.max, "step": 1.0, "round": 0.001,
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"tooltip": _tooltip_aspect
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}),
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},
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"hidden": {
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"unique_id": "UNIQUE_ID",
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},
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# "optional": {},
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})
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class BestResolutionFromAspectRatio:
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"""
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A more advanced node for easier selection of an optimal resolution:
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image size selected indirectly - by one of the sides + aspect ratio.
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"""
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NODE_NAME = "BestResolutionFromAspectRatio"
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CATEGORY = "utils/resolution"
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DESCRIPTION = _cleandoc(__doc__)
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OUTPUT_NODE = True
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FUNCTION = "main"
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RETURN_TYPES = _return_types_simple
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RETURN_NAMES = _return_names_simple
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@classmethod
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def INPUT_TYPES(cls):
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return _input_types_orient
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def main(self, size: int, step: int, size_is_big: bool, landscape: bool, aspect_a: float, aspect_b: float, unique_id: str = None):
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aspect_big, aspect_small = _aspect_ratios_sorted(aspect_a, aspect_b)
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side_main_f = float(size)
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side_other_f = side_main_f * (
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(aspect_small / aspect_big) if size_is_big else (aspect_big / aspect_small)
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)
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width_f, height_f = (side_other_f, side_main_f) if side_other_f > side_main_f else (side_main_f, side_other_f)
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if not landscape:
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# The opposite: height is bigger
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width_f, height_f = height_f, width_f
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return _simple_result_from_approx_wh(width_f, height_f, step, unique_id, _sqrt(width_f * height_f))
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_input_types_area = deepfreeze({
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"required": {
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"square_size": (_IO.INT, dict(type_dict_res, **{"tooltip": (
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"The total resolution of the image would be the same as of a square with this side.\n"
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"The width and height would be such to respect aspect ratio, but also be as close as possible to the "
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"total number of pixels as in this square image.\n\n"
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"- 512x512 square (SD 1.5): ~0.25 megapixels\n"
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"- 1024x1024 square (SDXL): ~1 megapixel"
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)})),
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"step": _input_types_simple["required"]["step"],
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"landscape": _input_types_orient["required"]["landscape"],
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"aspect_a": _input_types_orient["required"]["aspect_a"],
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"aspect_b": _input_types_orient["required"]["aspect_b"],
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},
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"hidden": {
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"unique_id": "UNIQUE_ID",
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},
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# "optional": {},
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})
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class BestResolutionFromArea:
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"""
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The most efficient way of selecting an optimal resolution:
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image size selected indirectly - by the total desired resolution (area) + aspect ratio.
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Desired resolution (aka image area/megapixels/pixel count) is specified with a side of a square image. This isn't
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accidental: most models disclose what image resolution they're trained on, and usually they're square:
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- SD 1.5 - 512x512 pixels
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- SDXL - 1024x1024 pixels
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By simply providing this single number and setting your aspect ratio/orientation, you get the width and height to
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produce the closest total resolution to the training set, while also respecting image proportions and step-rounding.
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"""
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NODE_NAME = "BestResolutionFromArea"
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CATEGORY = "utils/resolution"
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DESCRIPTION = _cleandoc(__doc__)
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OUTPUT_NODE = True
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FUNCTION = "main"
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RETURN_TYPES = _return_types_simple
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RETURN_NAMES = _return_names_simple
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@classmethod
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def INPUT_TYPES(cls):
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return _input_types_area
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def main(self, square_size: int, step: int, landscape: bool, aspect_a: float, aspect_b: float, unique_id: str = None):
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# square_size = 1024; step = 48; landscape = True; aspect_a = 9.0; aspect_b = 16.0
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aspect_big, aspect_small = _aspect_ratios_sorted(aspect_a, aspect_b)
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aspect_x, aspect_y = (aspect_big, aspect_small) if landscape else (aspect_small, aspect_big)
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aspect_area = aspect_x * aspect_y
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aspect_norm_scale = 1.0 / _sqrt(aspect_area)
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aspect_x *= aspect_norm_scale
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aspect_y *= aspect_norm_scale
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# Now the two aspects produce a normalized rectangle - i.e., it's area is 1
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square_size = _number_to_int(square_size)
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width_f = aspect_x * square_size
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height_f = aspect_y * square_size
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return _simple_result_from_approx_wh(width_f, height_f, step, unique_id, square_size)
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