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remove GPL-3 licensed package easing-functions #7337

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Nov 18, 2024
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remove GPL-3 licensed package easing-functions #7337

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1 change: 0 additions & 1 deletion docs/nodes/defaultNodes.md
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Original file line number Diff line number Diff line change
Expand Up @@ -99,7 +99,6 @@ their descriptions.
| Scale Latents | Scales latents by a given factor. |
| Segment Anything Processor | Applies segment anything processing to image |
| Show Image | Displays a provided image, and passes it forward in the pipeline. |
| Step Param Easing | Experimental per-step parameter easing for denoising steps |
| String Primitive Collection | A collection of string primitive values |
| String Primitive | A string primitive value |
| Subtract Integers | Subtracts two numbers |
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227 changes: 0 additions & 227 deletions invokeai/app/invocations/param_easing.py
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@@ -1,43 +1,4 @@
import io
from typing import Literal, Optional

import matplotlib.pyplot as plt
import numpy as np
import PIL.Image
from easing_functions import (
BackEaseIn,
BackEaseInOut,
BackEaseOut,
BounceEaseIn,
BounceEaseInOut,
BounceEaseOut,
CircularEaseIn,
CircularEaseInOut,
CircularEaseOut,
CubicEaseIn,
CubicEaseInOut,
CubicEaseOut,
ElasticEaseIn,
ElasticEaseInOut,
ElasticEaseOut,
ExponentialEaseIn,
ExponentialEaseInOut,
ExponentialEaseOut,
LinearInOut,
QuadEaseIn,
QuadEaseInOut,
QuadEaseOut,
QuarticEaseIn,
QuarticEaseInOut,
QuarticEaseOut,
QuinticEaseIn,
QuinticEaseInOut,
QuinticEaseOut,
SineEaseIn,
SineEaseInOut,
SineEaseOut,
)
from matplotlib.ticker import MaxNLocator

from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
from invokeai.app.invocations.fields import InputField
Expand Down Expand Up @@ -65,191 +26,3 @@ class FloatLinearRangeInvocation(BaseInvocation):
def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
param_list = list(np.linspace(self.start, self.stop, self.steps))
return FloatCollectionOutput(collection=param_list)


EASING_FUNCTIONS_MAP = {
"Linear": LinearInOut,
"QuadIn": QuadEaseIn,
"QuadOut": QuadEaseOut,
"QuadInOut": QuadEaseInOut,
"CubicIn": CubicEaseIn,
"CubicOut": CubicEaseOut,
"CubicInOut": CubicEaseInOut,
"QuarticIn": QuarticEaseIn,
"QuarticOut": QuarticEaseOut,
"QuarticInOut": QuarticEaseInOut,
"QuinticIn": QuinticEaseIn,
"QuinticOut": QuinticEaseOut,
"QuinticInOut": QuinticEaseInOut,
"SineIn": SineEaseIn,
"SineOut": SineEaseOut,
"SineInOut": SineEaseInOut,
"CircularIn": CircularEaseIn,
"CircularOut": CircularEaseOut,
"CircularInOut": CircularEaseInOut,
"ExponentialIn": ExponentialEaseIn,
"ExponentialOut": ExponentialEaseOut,
"ExponentialInOut": ExponentialEaseInOut,
"ElasticIn": ElasticEaseIn,
"ElasticOut": ElasticEaseOut,
"ElasticInOut": ElasticEaseInOut,
"BackIn": BackEaseIn,
"BackOut": BackEaseOut,
"BackInOut": BackEaseInOut,
"BounceIn": BounceEaseIn,
"BounceOut": BounceEaseOut,
"BounceInOut": BounceEaseInOut,
}

EASING_FUNCTION_KEYS = Literal[tuple(EASING_FUNCTIONS_MAP.keys())]


# actually I think for now could just use CollectionOutput (which is list[Any]
@invocation(
"step_param_easing",
title="Step Param Easing",
tags=["step", "easing"],
category="step",
version="1.0.2",
)
class StepParamEasingInvocation(BaseInvocation):
"""Experimental per-step parameter easing for denoising steps"""

easing: EASING_FUNCTION_KEYS = InputField(default="Linear", description="The easing function to use")
num_steps: int = InputField(default=20, description="number of denoising steps")
start_value: float = InputField(default=0.0, description="easing starting value")
end_value: float = InputField(default=1.0, description="easing ending value")
start_step_percent: float = InputField(default=0.0, description="fraction of steps at which to start easing")
end_step_percent: float = InputField(default=1.0, description="fraction of steps after which to end easing")
# if None, then start_value is used prior to easing start
pre_start_value: Optional[float] = InputField(default=None, description="value before easing start")
# if None, then end value is used prior to easing end
post_end_value: Optional[float] = InputField(default=None, description="value after easing end")
mirror: bool = InputField(default=False, description="include mirror of easing function")
# FIXME: add alt_mirror option (alternative to default or mirror), or remove entirely
# alt_mirror: bool = InputField(default=False, description="alternative mirroring by dual easing")
show_easing_plot: bool = InputField(default=False, description="show easing plot")

def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
log_diagnostics = False
# convert from start_step_percent to nearest step <= (steps * start_step_percent)
# start_step = int(np.floor(self.num_steps * self.start_step_percent))
start_step = int(np.round(self.num_steps * self.start_step_percent))
# convert from end_step_percent to nearest step >= (steps * end_step_percent)
# end_step = int(np.ceil((self.num_steps - 1) * self.end_step_percent))
end_step = int(np.round((self.num_steps - 1) * self.end_step_percent))

# end_step = int(np.ceil(self.num_steps * self.end_step_percent))
num_easing_steps = end_step - start_step + 1

# num_presteps = max(start_step - 1, 0)
num_presteps = start_step
num_poststeps = self.num_steps - (num_presteps + num_easing_steps)
prelist = list(num_presteps * [self.pre_start_value])
postlist = list(num_poststeps * [self.post_end_value])

if log_diagnostics:
context.logger.debug("start_step: " + str(start_step))
context.logger.debug("end_step: " + str(end_step))
context.logger.debug("num_easing_steps: " + str(num_easing_steps))
context.logger.debug("num_presteps: " + str(num_presteps))
context.logger.debug("num_poststeps: " + str(num_poststeps))
context.logger.debug("prelist size: " + str(len(prelist)))
context.logger.debug("postlist size: " + str(len(postlist)))
context.logger.debug("prelist: " + str(prelist))
context.logger.debug("postlist: " + str(postlist))

easing_class = EASING_FUNCTIONS_MAP[self.easing]
if log_diagnostics:
context.logger.debug("easing class: " + str(easing_class))
easing_list = []
if self.mirror: # "expected" mirroring
# if number of steps is even, squeeze duration down to (number_of_steps)/2
# and create reverse copy of list to append
# if number of steps is odd, squeeze duration down to ceil(number_of_steps/2)
# and create reverse copy of list[1:end-1]
# but if even then number_of_steps/2 === ceil(number_of_steps/2), so can just use ceil always

base_easing_duration = int(np.ceil(num_easing_steps / 2.0))
if log_diagnostics:
context.logger.debug("base easing duration: " + str(base_easing_duration))
even_num_steps = num_easing_steps % 2 == 0 # even number of steps
easing_function = easing_class(
start=self.start_value,
end=self.end_value,
duration=base_easing_duration - 1,
)
base_easing_vals = []
for step_index in range(base_easing_duration):
easing_val = easing_function.ease(step_index)
base_easing_vals.append(easing_val)
if log_diagnostics:
context.logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(easing_val))
if even_num_steps:
mirror_easing_vals = list(reversed(base_easing_vals))
else:
mirror_easing_vals = list(reversed(base_easing_vals[0:-1]))
if log_diagnostics:
context.logger.debug("base easing vals: " + str(base_easing_vals))
context.logger.debug("mirror easing vals: " + str(mirror_easing_vals))
easing_list = base_easing_vals + mirror_easing_vals

# FIXME: add alt_mirror option (alternative to default or mirror), or remove entirely
# elif self.alt_mirror: # function mirroring (unintuitive behavior (at least to me))
# # half_ease_duration = round(num_easing_steps - 1 / 2)
# half_ease_duration = round((num_easing_steps - 1) / 2)
# easing_function = easing_class(start=self.start_value,
# end=self.end_value,
# duration=half_ease_duration,
# )
#
# mirror_function = easing_class(start=self.end_value,
# end=self.start_value,
# duration=half_ease_duration,
# )
# for step_index in range(num_easing_steps):
# if step_index <= half_ease_duration:
# step_val = easing_function.ease(step_index)
# else:
# step_val = mirror_function.ease(step_index - half_ease_duration)
# easing_list.append(step_val)
# if log_diagnostics: logger.debug(step_index, step_val)
#

else: # no mirroring (default)
easing_function = easing_class(
start=self.start_value,
end=self.end_value,
duration=num_easing_steps - 1,
)
for step_index in range(num_easing_steps):
step_val = easing_function.ease(step_index)
easing_list.append(step_val)
if log_diagnostics:
context.logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(step_val))

if log_diagnostics:
context.logger.debug("prelist size: " + str(len(prelist)))
context.logger.debug("easing_list size: " + str(len(easing_list)))
context.logger.debug("postlist size: " + str(len(postlist)))

param_list = prelist + easing_list + postlist

if self.show_easing_plot:
plt.figure()
plt.xlabel("Step")
plt.ylabel("Param Value")
plt.title("Per-Step Values Based On Easing: " + self.easing)
plt.bar(range(len(param_list)), param_list)
# plt.plot(param_list)
ax = plt.gca()
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
buf = io.BytesIO()
plt.savefig(buf, format="png")
buf.seek(0)
im = PIL.Image.open(buf)
im.show()
buf.close()

# output array of size steps, each entry list[i] is param value for step i
return FloatCollectionOutput(collection=param_list)
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