nodes_upscale: v3
This commit is contained in:
Lex Darlog (DRL)
+50
-63
@@ -6,60 +6,26 @@ Advanced versions of nodes - with upscale.
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import typing as _t
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from inspect import cleandoc as _cleandoc
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from itertools import chain as _chain
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from frozendict import deepfreeze as _deepfreeze, frozendict as _frozendict
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from comfy.comfy_types.node_typing import IO as _IO
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from comfy_api.latest import io as _io
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from . import _io_enum as _enums
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from . import _io_simple as _i
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from ._io_simple import _io_override
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from . import _meta
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from . import _tooltips as _tt
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from .docstring_formatter import format_docstring as _format_docstring
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from .funcs import (
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number_to_int as _number_to_int,
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float_width_height_from_area as _float_width_height_from_area,
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upscale_result_from_approx_wh as _upscale_result_from_approx_wh
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)
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from . import _meta
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from . import _tooltips as _tt
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from .docstring_formatter import format_docstring as _format_docstring
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from .enums import *
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from .nodes_simple import _input_types_area
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from .nodes_prims import _res_priority_in_type, _res_priority_verify
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from .slot_types import (
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type_dict_step_upscale1 as _type_dict_step_upscale1,
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upscale_in_type as _upscale_in_type
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)
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from .nodes_simple import BestResolutionFromArea as _BestResolutionFromArea
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# ----------------------------------------------------------
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_return_types_upscale = (_IO.FLOAT, _IO.INT, _IO.INT, _IO.INT, _IO.INT)
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_return_ttips_upscale = _frozendict({
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'upscale': _tt.upscale,
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'init_width': _tt.init_width,
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'init_height': _tt.init_height,
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'HD_width': _tt.HD_width,
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'HD_height': _tt.HD_height,
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})
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__extra_inputs_for_upscale_only = {
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'priority': _res_priority_in_type,
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'upscale': _upscale_in_type,
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'HD_step': (_IO.INT, dict(_type_dict_step_upscale1, **{
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'tooltip': "Same as the main `step`, but for the upscaled resolution.\n144 = 8 * 2 * 3 * 3",
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})),
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}
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_input_types_area_upscale = _deepfreeze({
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'required': dict(
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(k_v for k_v in _input_types_area['required'].items() if k_v[0] != 'show'),
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**__extra_inputs_for_upscale_only,
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# show=_input_types_area['required']['show'],
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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 BestResolutionFromAreaUpscale:
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class BestResolutionFromAreaUpscale(_io.ComfyNode):
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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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@@ -76,32 +42,53 @@ class BestResolutionFromAreaUpscale:
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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 = 'BestResolutionFromAreaUpscale'
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CATEGORY = _meta.category
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DESCRIPTION = _format_docstring(_cleandoc(__doc__))
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_schema = _io.Schema(
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'BestResolutionFromAreaUpscale',
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display_name="Best-Res (area+scale)",
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category=_meta.category,
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description=_format_docstring(_cleandoc(__doc__)),
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OUTPUT_NODE = True
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inputs=list(_chain(
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(
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x for x in _BestResolutionFromArea._schema.inputs
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if x.id != 'show'
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),
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[
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_enums.res_priority_in,
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_i.upscale,
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_io_override(_i.step_upscale1, 'HD_step')
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]
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)),
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FUNCTION = 'main'
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RETURN_TYPES = _return_types_upscale
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RETURN_NAMES = tuple(_return_ttips_upscale.keys())
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OUTPUT_TOOLTIPS = tuple(_return_ttips_upscale.values())
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hidden=[_io.Hidden.unique_id],
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outputs=[
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_io.Float.Output('UPSCALE', display_name='upscale', tooltip=_tt.upscale),
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_io.Int.Output('init_width', display_name='init_width', tooltip=_tt.init_width),
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_io.Int.Output('init_height', display_name='init_height', tooltip=_tt.init_height),
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_io.Int.Output('HD_width', display_name='HD_width', tooltip=_tt.HD_width),
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_io.Int.Output('HD_height', display_name='HD_height', tooltip=_tt.HD_height),
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],
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is_output_node=True,
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)
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@classmethod
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def INPUT_TYPES(cls):
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return _input_types_area_upscale
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def define_schema(cls) -> _io.Schema:
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return cls._schema
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def main(
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self, square_size: int, step: int, landscape: bool, aspect_a: float, aspect_b: float,
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priority: _t.Union[RoundingPriority, str], upscale: float, HD_step:int,
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@classmethod
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def execute(
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cls,
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square_size: int, step: int, landscape: bool, aspect_a: float, aspect_b: float,
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priority: _t.Union[_enums.RoundingPriority, str], upscale: float, HD_step:int,
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# show: bool,
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unique_id: str = None
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):
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) -> _io.NodeOutput:
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square_size: int = _number_to_int(square_size)
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width_f, height_f = _float_width_height_from_area(square_size, landscape, aspect_a, aspect_b)
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return _upscale_result_from_approx_wh(
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return _io.NodeOutput(*_upscale_result_from_approx_wh(
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width_f, height_f, step,
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_res_priority_verify(priority), upscale, HD_step,
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_enums.RoundingPriority.validate(priority), upscale, HD_step,
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# show,
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unique_id=unique_id, target_square_size=square_size
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)
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unique_id=cls.hidden.unique_id, target_square_size=square_size
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))
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