first commit
This commit is contained in:
yolain
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||||
nsp_pantry.json
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||||
__pycache__
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||||
@@ -0,0 +1,47 @@
|
||||
{
|
||||
"easy setNode": {
|
||||
"title": "设置点"
|
||||
},
|
||||
"easy getNode": {
|
||||
"title": "获取点"
|
||||
},
|
||||
"easy a1111Loader": {
|
||||
"title": "简易加载器（A1111）"
|
||||
},
|
||||
"easy comfyLoader": {
|
||||
"title": "简易加载器（comfy）"
|
||||
},
|
||||
"easy controlnetLoader": {
|
||||
"title": "简易Controlnet"
|
||||
},
|
||||
"easy globalSeed": {
|
||||
"title": "全局Seed"
|
||||
},
|
||||
"easy preSampling": {
|
||||
"title": "预采样参数（基础）"
|
||||
},
|
||||
"easy preSamplingSdTurbo": {
|
||||
"title": "预采样参数（SdTurbo）"
|
||||
},
|
||||
"easy preSamplingDynamicCFG": {
|
||||
"title": "预采样参数（动态CFG）"
|
||||
},
|
||||
"easy kSampler": {
|
||||
"title": "简易K采样器"
|
||||
},
|
||||
"easy kSamplerTiled": {
|
||||
"title": "简易采样器（分块解码）"
|
||||
},
|
||||
"easy kSamplerSDTurbo": {
|
||||
"title": "简易采样器（SDTurbo）"
|
||||
},
|
||||
"easy imageInsetCrop": {
|
||||
"title": "图像裁切"
|
||||
},
|
||||
"easy imageSize": {
|
||||
"title": "图像尺寸"
|
||||
},
|
||||
"easy imageSizeByLongerSide": {
|
||||
"title": "图像尺寸（长边）"
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,674 @@
|
||||
GNU GENERAL PUBLIC LICENSE
|
||||
Version 3, 29 June 2007
|
||||
|
||||
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
|
||||
Everyone is permitted to copy and distribute verbatim copies
|
||||
of this license document, but changing it is not allowed.
|
||||
|
||||
Preamble
|
||||
|
||||
The GNU General Public License is a free, copyleft license for
|
||||
software and other kinds of works.
|
||||
|
||||
The licenses for most software and other practical works are designed
|
||||
to take away your freedom to share and change the works. By contrast,
|
||||
the GNU General Public License is intended to guarantee your freedom to
|
||||
share and change all versions of a program--to make sure it remains free
|
||||
software for all its users. We, the Free Software Foundation, use the
|
||||
GNU General Public License for most of our software; it applies also to
|
||||
any other work released this way by its authors. You can apply it to
|
||||
your programs, too.
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||||
|
||||
When we speak of free software, we are referring to freedom, not
|
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price. Our General Public Licenses are designed to make sure that you
|
||||
have the freedom to distribute copies of free software (and charge for
|
||||
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|
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want it, that you can change the software or use pieces of it in new
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To protect your rights, we need to prevent others from denying you
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For example, if you distribute copies of such a program, whether
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Developers that use the GNU GPL protect your rights with two steps:
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For the developers' and authors' protection, the GPL clearly explains
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Some devices are designed to deny users access to install or run
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Finally, every program is threatened constantly by software patents.
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The precise terms and conditions for copying, distribution and
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||||
|
||||
TERMS AND CONDITIONS
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||||
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||||
0. Definitions.
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||||
"This License" refers to version 3 of the GNU General Public License.
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"Copyright" also means copyright-like laws that apply to other kinds of
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To "convey" a work means any kind of propagation that enables other
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An interactive user interface displays "Appropriate Legal Notices"
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The "source code" for a work means the preferred form of the work
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A "Standard Interface" means an interface that either is an official
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The "System Libraries" of an executable work include anything, other
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"Major Component", in this context, means a major essential component
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The "Corresponding Source" for a work in object code form means all
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The Corresponding Source need not include anything that users
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The Corresponding Source for a work in source code form is that
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||||
2. Basic Permissions.
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||||
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||||
All rights granted under this License are granted for the term of
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||||
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You may make, run and propagate covered works that you do not
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Conveying under any other circumstances is permitted solely under
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No covered work shall be deemed part of an effective technological
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||||
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||||
When you convey a covered work, you waive any legal power to forbid
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
4. Conveying Verbatim Copies.
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||||
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||||
You may convey verbatim copies of the Program's source code as you
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||||
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||||
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||||
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||||
keep intact all notices of the absence of any warranty; and give all
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||||
You may charge any price or no price for each copy that you convey,
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||||
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||||
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||||
You may convey a work based on the Program, or the modifications to
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||||
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||||
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||||
a) The work must carry prominent notices stating that you modified
|
||||
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||||
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||||
b) The work must carry prominent notices stating that it is
|
||||
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7. This requirement modifies the requirement in section 4 to
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||||
"keep intact all notices".
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||||
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||||
c) You must license the entire work, as a whole, under this
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||||
License to anyone who comes into possession of a copy. This
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||||
License will therefore apply, along with any applicable section 7
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||||
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||||
regardless of how they are packaged. This License gives no
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||||
permission to license the work in any other way, but it does not
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||||
invalidate such permission if you have separately received it.
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||||
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||||
d) If the work has interactive user interfaces, each must display
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A compilation of a covered work with other separate and independent
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in or on a volume of a storage or distribution medium, is called an
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||||
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||||
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||||
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||||
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||||
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||||
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||||
6. Conveying Non-Source Forms.
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||||
|
||||
You may convey a covered work in object code form under the terms
|
||||
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||||
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|
||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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|
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||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
|
||||
d) Convey the object code by offering access from a designated
|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
|
||||
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|
||||
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|
||||
Source of the work are being offered to the general public at no
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||||
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||||
|
||||
A separable portion of the object code, whose source code is excluded
|
||||
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|
||||
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||||
|
||||
A "User Product" is either (1) a "consumer product", which means any
|
||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
|
||||
"Installation Information" for a User Product means any methods,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
If you convey an object code work under this section in, or with, or
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
Corresponding Source conveyed, and Installation Information provided,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
7. Additional Terms.
|
||||
|
||||
"Additional permissions" are terms that supplement the terms of this
|
||||
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|
||||
Additional permissions that are applicable to the entire Program shall
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
When you convey a copy of a covered work, you may at your option
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
Notwithstanding any other provision of this License, for material you
|
||||
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|
||||
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|
||||
|
||||
a) Disclaiming warranty or limiting liability differently from the
|
||||
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|
||||
|
||||
b) Requiring preservation of specified reasonable legal notices or
|
||||
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|
||||
Notices displayed by works containing it; or
|
||||
|
||||
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|
||||
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|
||||
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|
||||
|
||||
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|
||||
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|
||||
|
||||
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|
||||
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||||
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
All other non-permissive additional terms are considered "further
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
a further restriction but permits relicensing or conveying under this
|
||||
License, you may add to a covered work material governed by the terms
|
||||
of that license document, provided that the further restriction does
|
||||
not survive such relicensing or conveying.
|
||||
|
||||
If you add terms to a covered work in accord with this section, you
|
||||
must place, in the relevant source files, a statement of the
|
||||
additional terms that apply to those files, or a notice indicating
|
||||
where to find the applicable terms.
|
||||
|
||||
Additional terms, permissive or non-permissive, may be stated in the
|
||||
form of a separately written license, or stated as exceptions;
|
||||
the above requirements apply either way.
|
||||
|
||||
8. Termination.
|
||||
|
||||
You may not propagate or modify a covered work except as expressly
|
||||
provided under this License. Any attempt otherwise to propagate or
|
||||
modify it is void, and will automatically terminate your rights under
|
||||
this License (including any patent licenses granted under the third
|
||||
paragraph of section 11).
|
||||
|
||||
However, if you cease all violation of this License, then your
|
||||
license from a particular copyright holder is reinstated (a)
|
||||
provisionally, unless and until the copyright holder explicitly and
|
||||
finally terminates your license, and (b) permanently, if the copyright
|
||||
holder fails to notify you of the violation by some reasonable means
|
||||
prior to 60 days after the cessation.
|
||||
|
||||
Moreover, your license from a particular copyright holder is
|
||||
reinstated permanently if the copyright holder notifies you of the
|
||||
violation by some reasonable means, this is the first time you have
|
||||
received notice of violation of this License (for any work) from that
|
||||
copyright holder, and you cure the violation prior to 30 days after
|
||||
your receipt of the notice.
|
||||
|
||||
Termination of your rights under this section does not terminate the
|
||||
licenses of parties who have received copies or rights from you under
|
||||
this License. If your rights have been terminated and not permanently
|
||||
reinstated, you do not qualify to receive new licenses for the same
|
||||
material under section 10.
|
||||
|
||||
9. Acceptance Not Required for Having Copies.
|
||||
|
||||
You are not required to accept this License in order to receive or
|
||||
run a copy of the Program. Ancillary propagation of a covered work
|
||||
occurring solely as a consequence of using peer-to-peer transmission
|
||||
to receive a copy likewise does not require acceptance. However,
|
||||
nothing other than this License grants you permission to propagate or
|
||||
modify any covered work. These actions infringe copyright if you do
|
||||
not accept this License. Therefore, by modifying or propagating a
|
||||
covered work, you indicate your acceptance of this License to do so.
|
||||
|
||||
10. Automatic Licensing of Downstream Recipients.
|
||||
|
||||
Each time you convey a covered work, the recipient automatically
|
||||
receives a license from the original licensors, to run, modify and
|
||||
propagate that work, subject to this License. You are not responsible
|
||||
for enforcing compliance by third parties with this License.
|
||||
|
||||
An "entity transaction" is a transaction transferring control of an
|
||||
organization, or substantially all assets of one, or subdividing an
|
||||
organization, or merging organizations. If propagation of a covered
|
||||
work results from an entity transaction, each party to that
|
||||
transaction who receives a copy of the work also receives whatever
|
||||
licenses to the work the party's predecessor in interest had or could
|
||||
give under the previous paragraph, plus a right to possession of the
|
||||
Corresponding Source of the work from the predecessor in interest, if
|
||||
the predecessor has it or can get it with reasonable efforts.
|
||||
|
||||
You may not impose any further restrictions on the exercise of the
|
||||
rights granted or affirmed under this License. For example, you may
|
||||
not impose a license fee, royalty, or other charge for exercise of
|
||||
rights granted under this License, and you may not initiate litigation
|
||||
(including a cross-claim or counterclaim in a lawsuit) alleging that
|
||||
any patent claim is infringed by making, using, selling, offering for
|
||||
sale, or importing the Program or any portion of it.
|
||||
|
||||
11. Patents.
|
||||
|
||||
A "contributor" is a copyright holder who authorizes use under this
|
||||
License of the Program or a work on which the Program is based. The
|
||||
work thus licensed is called the contributor's "contributor version".
|
||||
|
||||
A contributor's "essential patent claims" are all patent claims
|
||||
owned or controlled by the contributor, whether already acquired or
|
||||
hereafter acquired, that would be infringed by some manner, permitted
|
||||
by this License, of making, using, or selling its contributor version,
|
||||
but do not include claims that would be infringed only as a
|
||||
consequence of further modification of the contributor version. For
|
||||
purposes of this definition, "control" includes the right to grant
|
||||
patent sublicenses in a manner consistent with the requirements of
|
||||
this License.
|
||||
|
||||
Each contributor grants you a non-exclusive, worldwide, royalty-free
|
||||
patent license under the contributor's essential patent claims, to
|
||||
make, use, sell, offer for sale, import and otherwise run, modify and
|
||||
propagate the contents of its contributor version.
|
||||
|
||||
In the following three paragraphs, a "patent license" is any express
|
||||
agreement or commitment, however denominated, not to enforce a patent
|
||||
(such as an express permission to practice a patent or covenant not to
|
||||
sue for patent infringement). To "grant" such a patent license to a
|
||||
party means to make such an agreement or commitment not to enforce a
|
||||
patent against the party.
|
||||
|
||||
If you convey a covered work, knowingly relying on a patent license,
|
||||
and the Corresponding Source of the work is not available for anyone
|
||||
to copy, free of charge and under the terms of this License, through a
|
||||
publicly available network server or other readily accessible means,
|
||||
then you must either (1) cause the Corresponding Source to be so
|
||||
available, or (2) arrange to deprive yourself of the benefit of the
|
||||
patent license for this particular work, or (3) arrange, in a manner
|
||||
consistent with the requirements of this License, to extend the patent
|
||||
license to downstream recipients. "Knowingly relying" means you have
|
||||
actual knowledge that, but for the patent license, your conveying the
|
||||
covered work in a country, or your recipient's use of the covered work
|
||||
in a country, would infringe one or more identifiable patents in that
|
||||
country that you have reason to believe are valid.
|
||||
|
||||
If, pursuant to or in connection with a single transaction or
|
||||
arrangement, you convey, or propagate by procuring conveyance of, a
|
||||
covered work, and grant a patent license to some of the parties
|
||||
receiving the covered work authorizing them to use, propagate, modify
|
||||
or convey a specific copy of the covered work, then the patent license
|
||||
you grant is automatically extended to all recipients of the covered
|
||||
work and works based on it.
|
||||
|
||||
A patent license is "discriminatory" if it does not include within
|
||||
the scope of its coverage, prohibits the exercise of, or is
|
||||
conditioned on the non-exercise of one or more of the rights that are
|
||||
specifically granted under this License. You may not convey a covered
|
||||
work if you are a party to an arrangement with a third party that is
|
||||
in the business of distributing software, under which you make payment
|
||||
to the third party based on the extent of your activity of conveying
|
||||
the work, and under which the third party grants, to any of the
|
||||
parties who would receive the covered work from you, a discriminatory
|
||||
patent license (a) in connection with copies of the covered work
|
||||
conveyed by you (or copies made from those copies), or (b) primarily
|
||||
for and in connection with specific products or compilations that
|
||||
contain the covered work, unless you entered into that arrangement,
|
||||
or that patent license was granted, prior to 28 March 2007.
|
||||
|
||||
Nothing in this License shall be construed as excluding or limiting
|
||||
any implied license or other defenses to infringement that may
|
||||
otherwise be available to you under applicable patent law.
|
||||
|
||||
12. No Surrender of Others' Freedom.
|
||||
|
||||
If conditions are imposed on you (whether by court order, agreement or
|
||||
otherwise) that contradict the conditions of this License, they do not
|
||||
excuse you from the conditions of this License. If you cannot convey a
|
||||
covered work so as to satisfy simultaneously your obligations under this
|
||||
License and any other pertinent obligations, then as a consequence you may
|
||||
not convey it at all. For example, if you agree to terms that obligate you
|
||||
to collect a royalty for further conveying from those to whom you convey
|
||||
the Program, the only way you could satisfy both those terms and this
|
||||
License would be to refrain entirely from conveying the Program.
|
||||
|
||||
13. Use with the GNU Affero General Public License.
|
||||
|
||||
Notwithstanding any other provision of this License, you have
|
||||
permission to link or combine any covered work with a work licensed
|
||||
under version 3 of the GNU Affero General Public License into a single
|
||||
combined work, and to convey the resulting work. The terms of this
|
||||
License will continue to apply to the part which is the covered work,
|
||||
but the special requirements of the GNU Affero General Public License,
|
||||
section 13, concerning interaction through a network will apply to the
|
||||
combination as such.
|
||||
|
||||
14. Revised Versions of this License.
|
||||
|
||||
The Free Software Foundation may publish revised and/or new versions of
|
||||
the GNU General Public License from time to time. Such new versions will
|
||||
be similar in spirit to the present version, but may differ in detail to
|
||||
address new problems or concerns.
|
||||
|
||||
Each version is given a distinguishing version number. If the
|
||||
Program specifies that a certain numbered version of the GNU General
|
||||
Public License "or any later version" applies to it, you have the
|
||||
option of following the terms and conditions either of that numbered
|
||||
version or of any later version published by the Free Software
|
||||
Foundation. If the Program does not specify a version number of the
|
||||
GNU General Public License, you may choose any version ever published
|
||||
by the Free Software Foundation.
|
||||
|
||||
If the Program specifies that a proxy can decide which future
|
||||
versions of the GNU General Public License can be used, that proxy's
|
||||
public statement of acceptance of a version permanently authorizes you
|
||||
to choose that version for the Program.
|
||||
|
||||
Later license versions may give you additional or different
|
||||
permissions. However, no additional obligations are imposed on any
|
||||
author or copyright holder as a result of your choosing to follow a
|
||||
later version.
|
||||
|
||||
15. Disclaimer of Warranty.
|
||||
|
||||
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
|
||||
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
|
||||
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
|
||||
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
|
||||
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
|
||||
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
|
||||
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
|
||||
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
|
||||
|
||||
16. Limitation of Liability.
|
||||
|
||||
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
|
||||
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
|
||||
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
|
||||
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
|
||||
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
|
||||
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
|
||||
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
|
||||
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
|
||||
SUCH DAMAGES.
|
||||
|
||||
17. Interpretation of Sections 15 and 16.
|
||||
|
||||
If the disclaimer of warranty and limitation of liability provided
|
||||
above cannot be given local legal effect according to their terms,
|
||||
reviewing courts shall apply local law that most closely approximates
|
||||
an absolute waiver of all civil liability in connection with the
|
||||
Program, unless a warranty or assumption of liability accompanies a
|
||||
copy of the Program in return for a fee.
|
||||
|
||||
END OF TERMS AND CONDITIONS
|
||||
|
||||
How to Apply These Terms to Your New Programs
|
||||
|
||||
If you develop a new program, and you want it to be of the greatest
|
||||
possible use to the public, the best way to achieve this is to make it
|
||||
free software which everyone can redistribute and change under these terms.
|
||||
|
||||
To do so, attach the following notices to the program. It is safest
|
||||
to attach them to the start of each source file to most effectively
|
||||
state the exclusion of warranty; and each file should have at least
|
||||
the "copyright" line and a pointer to where the full notice is found.
|
||||
|
||||
<one line to give the program's name and a brief idea of what it does.>
|
||||
Copyright (C) <year> <name of author>
|
||||
|
||||
This program is free software: you can redistribute it and/or modify
|
||||
it under the terms of the GNU General Public License as published by
|
||||
the Free Software Foundation, either version 3 of the License, or
|
||||
(at your option) any later version.
|
||||
|
||||
This program is distributed in the hope that it will be useful,
|
||||
but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
GNU General Public License for more details.
|
||||
|
||||
You should have received a copy of the GNU General Public License
|
||||
along with this program. If not, see <https://www.gnu.org/licenses/>.
|
||||
|
||||
Also add information on how to contact you by electronic and paper mail.
|
||||
|
||||
If the program does terminal interaction, make it output a short
|
||||
notice like this when it starts in an interactive mode:
|
||||
|
||||
<program> Copyright (C) <year> <name of author>
|
||||
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
|
||||
This is free software, and you are welcome to redistribute it
|
||||
under certain conditions; type `show c' for details.
|
||||
|
||||
The hypothetical commands `show w' and `show c' should show the appropriate
|
||||
parts of the General Public License. Of course, your program's commands
|
||||
might be different; for a GUI interface, you would use an "about box".
|
||||
|
||||
You should also get your employer (if you work as a programmer) or school,
|
||||
if any, to sign a "copyright disclaimer" for the program, if necessary.
|
||||
For more information on this, and how to apply and follow the GNU GPL, see
|
||||
<https://www.gnu.org/licenses/>.
|
||||
|
||||
The GNU General Public License does not permit incorporating your program
|
||||
into proprietary programs. If your program is a subroutine library, you
|
||||
may consider it more useful to permit linking proprietary applications with
|
||||
the library. If this is what you want to do, use the GNU Lesser General
|
||||
Public License instead of this License. But first, please read
|
||||
<https://www.gnu.org/licenses/why-not-lgpl.html>.
|
||||
@@ -0,0 +1,37 @@
|
||||
<p align="right">
|
||||
<a href="./README.md">中文</a> | <strong>English</strong>
|
||||
</p>
|
||||
|
||||
<div align="center">
|
||||
|
||||
# ComfyUI Easy Use
|
||||
|
||||
In order to make it easier to use the ComfyUI, I have made some optimizations and integrations to some commonly used nodes.
|
||||
|
||||
[//]: # ([](https://space.bilibili.com/1840885116))
|
||||
</div>
|
||||
|
||||
## Workflow comparison
|
||||
|
||||
<img src="./docs/workflow_node_compare.png">
|
||||
|
||||
EasyUse is simplified on the basis of [tinyterraNodes](https://github.com/TinyTerra/ComfyUI_tinyterraNodes), and it is recommended to use it with the original [tinyterraNodes](https://github.com/TinyTerra/ComfyUI_tinyterraNodes) package.
|
||||
|
||||
### Major optimizations
|
||||
|
||||
- The **preSampling** node has been added to separate the sampling parameter configuration from the real-time preview image at the time of sampling。
|
||||
- Adjust the default generation timing of the seed, change **Control After Generate** to **Control Before Generate**.
|
||||
|
||||
## Workflow Examples
|
||||
|
||||
### Text to image
|
||||
|
||||
<img src="./docs/text_to_image.png">
|
||||
|
||||
### Image to image + controlnet
|
||||
|
||||
<img src="./docs/image_to_image_controlnet.png">
|
||||
|
||||
### SDTurbo + HiresFix + SVD
|
||||
|
||||
<img src="./docs/sdturbo_hiresfix_svd.png">
|
||||
@@ -0,0 +1,38 @@
|
||||
<p align="right">
|
||||
<strong>中文</strong> | <a href="./README.en.md">English</a>
|
||||
</p>
|
||||
|
||||
<div align="center">
|
||||
|
||||
# ComfyUI Easy Use
|
||||
|
||||
为了更加方便简单地使用ComfyUI，我对一部分常用的节点做了一些优化与整合。
|
||||
|
||||
[//]: # ([](https://space.bilibili.com/1840885116))
|
||||
</div>
|
||||
|
||||
## 流程对比
|
||||
|
||||
|
||||
<img src="./docs/workflow_node_compare.png">
|
||||
|
||||
EasyUse在[tinyterraNodes](https://github.com/TinyTerra/ComfyUI_tinyterraNodes)的基础上做了简化，建议搭配原版的[tinyterraNodes](https://github.com/TinyTerra/ComfyUI_tinyterraNodes)节点包进行使用。
|
||||
|
||||
### 主要的优化
|
||||
|
||||
- 增加了 **preSampling** 预采样参数节点，目的是为了把采样参数配置与采样时的实时预览图分离。
|
||||
- 调整种子默认的生成时序，从**control_after_generate**修改为**control_before_generate**。
|
||||
|
||||
## 示例
|
||||
|
||||
### 文生图
|
||||
|
||||
<img src="./docs/text_to_image.png">
|
||||
|
||||
### 图生图+controlnet
|
||||
|
||||
<img src="./docs/image_to_image_controlnet.png">
|
||||
|
||||
### SDTurbo+高清修复+SVD
|
||||
|
||||
<img src="./docs/sdturbo_hiresfix_svd.png">
|
||||
+56
@@ -0,0 +1,56 @@
|
||||
import os
|
||||
import json
|
||||
import folder_paths
|
||||
import importlib
|
||||
import shutil
|
||||
|
||||
node_list = [
|
||||
"server",
|
||||
"easyNodes",
|
||||
"image",
|
||||
]
|
||||
|
||||
NODE_CLASS_MAPPINGS = {}
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {}
|
||||
|
||||
for module_name in node_list:
|
||||
imported_module = importlib.import_module(".py.{}".format(module_name), __name__)
|
||||
NODE_CLASS_MAPPINGS = {**NODE_CLASS_MAPPINGS, **imported_module.NODE_CLASS_MAPPINGS}
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {**NODE_DISPLAY_NAME_MAPPINGS, **imported_module.NODE_DISPLAY_NAME_MAPPINGS}
|
||||
|
||||
# 复制翻译文本到多语言节点
|
||||
# cwd_path = os.path.dirname(os.path.realpath(__file__))
|
||||
# comfy_path = folder_paths.base_path
|
||||
# translate_path = os.path.join(comfy_path, "custom_nodes", "AIGODLIKE-COMFYUI-TRANSLATION", "zh-CN")
|
||||
# translate_path_old = os.path.join(comfy_path, "custom_nodes", "AIGODLIKE-COMFYUI-TRANSLATION-main", "zh-CN")
|
||||
# translate_file = os.path.join(cwd_path, "ComfyUI-Easy-Use.json")
|
||||
# def copy_file_to_nodes(path):
|
||||
# nodes_path = os.path.join(path, "Nodes")
|
||||
# shutil.copy(translate_file, nodes_path)
|
||||
# write_sth_to_category(path)
|
||||
# def write_sth_to_category(path):
|
||||
# with open(path+"/NodeCategory.json", encoding="utf-8") as f:
|
||||
# try:
|
||||
# content = json.load(f)
|
||||
# if content:
|
||||
# if "EasyUse" not in content:
|
||||
# content['EasyUse'] = "乱乱呀优化节点"
|
||||
# if "PreSampling" not in content:
|
||||
# content['PreSampling'] = "预采样参数"
|
||||
# if "Loader" not in content:
|
||||
# content['Loader'] = "加载器"
|
||||
# with open(path + "/NodeCategory.json", 'w', encoding="utf-8") as f:
|
||||
# f.write(json.dumps(content, indent=4, ensure_ascii=False))
|
||||
# except:
|
||||
# print("\033[31mWrite to category Error\033[0m")
|
||||
# if os.path.exists(translate_path):
|
||||
# copy_file_to_nodes(translate_path)
|
||||
# elif os.path.exists(translate_path_old):
|
||||
# copy_file_to_nodes(translate_path_old)
|
||||
|
||||
|
||||
WEB_DIRECTORY = "./web"
|
||||
__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS', "WEB_DIRECTORY"]
|
||||
|
||||
|
||||
print('\033[34mComfy-Easy-Use: \033[92mLoaded\033[0m')
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 549 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 1.2 MiB |
Binary file not shown.
|
After Width: | Height: | Size: 324 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 37 KiB |
@@ -0,0 +1,316 @@
|
||||
import torch
|
||||
import numpy as np
|
||||
import itertools
|
||||
from math import gcd
|
||||
|
||||
from comfy import model_management
|
||||
from comfy.sdxl_clip import SDXLClipModel, SDXLRefinerClipModel, SDXLClipG
|
||||
|
||||
|
||||
def _grouper(n, iterable):
|
||||
it = iter(iterable)
|
||||
while True:
|
||||
chunk = list(itertools.islice(it, n))
|
||||
if not chunk:
|
||||
return
|
||||
yield chunk
|
||||
|
||||
|
||||
def _norm_mag(w, n):
|
||||
d = w - 1
|
||||
return 1 + np.sign(d) * np.sqrt(np.abs(d) ** 2 / n)
|
||||
# return np.sign(w) * np.sqrt(np.abs(w)**2 / n)
|
||||
|
||||
|
||||
def divide_length(word_ids, weights):
|
||||
sums = dict(zip(*np.unique(word_ids, return_counts=True)))
|
||||
sums[0] = 1
|
||||
weights = [[_norm_mag(w, sums[id]) if id != 0 else 1.0
|
||||
for w, id in zip(x, y)] for x, y in zip(weights, word_ids)]
|
||||
return weights
|
||||
|
||||
|
||||
def shift_mean_weight(word_ids, weights):
|
||||
delta = 1 - np.mean([w for x, y in zip(weights, word_ids) for w, id in zip(x, y) if id != 0])
|
||||
weights = [[w if id == 0 else w + delta
|
||||
for w, id in zip(x, y)] for x, y in zip(weights, word_ids)]
|
||||
return weights
|
||||
|
||||
|
||||
def scale_to_norm(weights, word_ids, w_max):
|
||||
top = np.max(weights)
|
||||
w_max = min(top, w_max)
|
||||
weights = [[w_max if id == 0 else (w / top) * w_max
|
||||
for w, id in zip(x, y)] for x, y in zip(weights, word_ids)]
|
||||
return weights
|
||||
|
||||
|
||||
def from_zero(weights, base_emb):
|
||||
weight_tensor = torch.tensor(weights, dtype=base_emb.dtype, device=base_emb.device)
|
||||
weight_tensor = weight_tensor.reshape(1, -1, 1).expand(base_emb.shape)
|
||||
return base_emb * weight_tensor
|
||||
|
||||
|
||||
def mask_word_id(tokens, word_ids, target_id, mask_token):
|
||||
new_tokens = [[mask_token if wid == target_id else t
|
||||
for t, wid in zip(x, y)] for x, y in zip(tokens, word_ids)]
|
||||
mask = np.array(word_ids) == target_id
|
||||
return (new_tokens, mask)
|
||||
|
||||
|
||||
def batched_clip_encode(tokens, length, encode_func, num_chunks):
|
||||
embs = []
|
||||
for e in _grouper(32, tokens):
|
||||
enc, pooled = encode_func(e)
|
||||
enc = enc.reshape((len(e), length, -1))
|
||||
embs.append(enc)
|
||||
embs = torch.cat(embs)
|
||||
embs = embs.reshape((len(tokens) // num_chunks, length * num_chunks, -1))
|
||||
return embs
|
||||
|
||||
|
||||
def from_masked(tokens, weights, word_ids, base_emb, length, encode_func, m_token=266):
|
||||
pooled_base = base_emb[0, length - 1:length, :]
|
||||
wids, inds = np.unique(np.array(word_ids).reshape(-1), return_index=True)
|
||||
weight_dict = dict((id, w)
|
||||
for id, w in zip(wids, np.array(weights).reshape(-1)[inds])
|
||||
if w != 1.0)
|
||||
|
||||
if len(weight_dict) == 0:
|
||||
return torch.zeros_like(base_emb), base_emb[0, length - 1:length, :]
|
||||
|
||||
weight_tensor = torch.tensor(weights, dtype=base_emb.dtype, device=base_emb.device)
|
||||
weight_tensor = weight_tensor.reshape(1, -1, 1).expand(base_emb.shape)
|
||||
|
||||
# m_token = (clip.tokenizer.end_token, 1.0) if clip.tokenizer.pad_with_end else (0,1.0)
|
||||
# TODO: find most suitable masking token here
|
||||
m_token = (m_token, 1.0)
|
||||
|
||||
ws = []
|
||||
masked_tokens = []
|
||||
masks = []
|
||||
|
||||
# create prompts
|
||||
for id, w in weight_dict.items():
|
||||
masked, m = mask_word_id(tokens, word_ids, id, m_token)
|
||||
masked_tokens.extend(masked)
|
||||
|
||||
m = torch.tensor(m, dtype=base_emb.dtype, device=base_emb.device)
|
||||
m = m.reshape(1, -1, 1).expand(base_emb.shape)
|
||||
masks.append(m)
|
||||
|
||||
ws.append(w)
|
||||
|
||||
# batch process prompts
|
||||
embs = batched_clip_encode(masked_tokens, length, encode_func, len(tokens))
|
||||
masks = torch.cat(masks)
|
||||
|
||||
embs = (base_emb.expand(embs.shape) - embs)
|
||||
pooled = embs[0, length - 1:length, :]
|
||||
|
||||
embs *= masks
|
||||
embs = embs.sum(axis=0, keepdim=True)
|
||||
|
||||
pooled_start = pooled_base.expand(len(ws), -1)
|
||||
ws = torch.tensor(ws).reshape(-1, 1).expand(pooled_start.shape)
|
||||
pooled = (pooled - pooled_start) * (ws - 1)
|
||||
pooled = pooled.mean(axis=0, keepdim=True)
|
||||
|
||||
return ((weight_tensor - 1) * embs), pooled_base + pooled
|
||||
|
||||
|
||||
def mask_inds(tokens, inds, mask_token):
|
||||
clip_len = len(tokens[0])
|
||||
inds_set = set(inds)
|
||||
new_tokens = [[mask_token if i * clip_len + j in inds_set else t
|
||||
for j, t in enumerate(x)] for i, x in enumerate(tokens)]
|
||||
return new_tokens
|
||||
|
||||
|
||||
def down_weight(tokens, weights, word_ids, base_emb, length, encode_func, m_token=266):
|
||||
w, w_inv = np.unique(weights, return_inverse=True)
|
||||
|
||||
if np.sum(w < 1) == 0:
|
||||
return base_emb, tokens, base_emb[0, length - 1:length, :]
|
||||
# m_token = (clip.tokenizer.end_token, 1.0) if clip.tokenizer.pad_with_end else (0,1.0)
|
||||
# using the comma token as a masking token seems to work better than aos tokens for SD 1.x
|
||||
m_token = (m_token, 1.0)
|
||||
|
||||
masked_tokens = []
|
||||
|
||||
masked_current = tokens
|
||||
for i in range(len(w)):
|
||||
if w[i] >= 1:
|
||||
continue
|
||||
masked_current = mask_inds(masked_current, np.where(w_inv == i)[0], m_token)
|
||||
masked_tokens.extend(masked_current)
|
||||
|
||||
embs = batched_clip_encode(masked_tokens, length, encode_func, len(tokens))
|
||||
embs = torch.cat([base_emb, embs])
|
||||
w = w[w <= 1.0]
|
||||
w_mix = np.diff([0] + w.tolist())
|
||||
w_mix = torch.tensor(w_mix, dtype=embs.dtype, device=embs.device).reshape((-1, 1, 1))
|
||||
|
||||
weighted_emb = (w_mix * embs).sum(axis=0, keepdim=True)
|
||||
return weighted_emb, masked_current, weighted_emb[0, length - 1:length, :]
|
||||
|
||||
|
||||
def scale_emb_to_mag(base_emb, weighted_emb):
|
||||
norm_base = torch.linalg.norm(base_emb)
|
||||
norm_weighted = torch.linalg.norm(weighted_emb)
|
||||
embeddings_final = (norm_base / norm_weighted) * weighted_emb
|
||||
return embeddings_final
|
||||
|
||||
|
||||
def recover_dist(base_emb, weighted_emb):
|
||||
fixed_std = (base_emb.std() / weighted_emb.std()) * (weighted_emb - weighted_emb.mean())
|
||||
embeddings_final = fixed_std + (base_emb.mean() - fixed_std.mean())
|
||||
return embeddings_final
|
||||
|
||||
|
||||
def A1111_renorm(base_emb, weighted_emb):
|
||||
embeddings_final = (base_emb.mean() / weighted_emb.mean()) * weighted_emb
|
||||
return embeddings_final
|
||||
|
||||
|
||||
def advanced_encode_from_tokens(tokenized, token_normalization, weight_interpretation, encode_func, m_token=266,
|
||||
length=77, w_max=1.0, return_pooled=False, apply_to_pooled=False):
|
||||
tokens = [[t for t, _, _ in x] for x in tokenized]
|
||||
weights = [[w for _, w, _ in x] for x in tokenized]
|
||||
word_ids = [[wid for _, _, wid in x] for x in tokenized]
|
||||
|
||||
# weight normalization
|
||||
# ====================
|
||||
|
||||
# distribute down/up weights over word lengths
|
||||
if token_normalization.startswith("length"):
|
||||
weights = divide_length(word_ids, weights)
|
||||
|
||||
# make mean of word tokens 1
|
||||
if token_normalization.endswith("mean"):
|
||||
weights = shift_mean_weight(word_ids, weights)
|
||||
|
||||
# weight interpretation
|
||||
# =====================
|
||||
pooled = None
|
||||
|
||||
if weight_interpretation == "comfy":
|
||||
weighted_tokens = [[(t, w) for t, w in zip(x, y)] for x, y in zip(tokens, weights)]
|
||||
weighted_emb, pooled_base = encode_func(weighted_tokens)
|
||||
pooled = pooled_base
|
||||
else:
|
||||
unweighted_tokens = [[(t, 1.0) for t, _, _ in x] for x in tokenized]
|
||||
base_emb, pooled_base = encode_func(unweighted_tokens)
|
||||
|
||||
if weight_interpretation == "A1111":
|
||||
weighted_emb = from_zero(weights, base_emb)
|
||||
weighted_emb = A1111_renorm(base_emb, weighted_emb)
|
||||
pooled = pooled_base
|
||||
|
||||
if weight_interpretation == "compel":
|
||||
pos_tokens = [[(t, w) if w >= 1.0 else (t, 1.0) for t, w in zip(x, y)] for x, y in zip(tokens, weights)]
|
||||
weighted_emb, _ = encode_func(pos_tokens)
|
||||
weighted_emb, _, pooled = down_weight(pos_tokens, weights, word_ids, weighted_emb, length, encode_func)
|
||||
|
||||
if weight_interpretation == "comfy++":
|
||||
weighted_emb, tokens_down, _ = down_weight(unweighted_tokens, weights, word_ids, base_emb, length, encode_func)
|
||||
weights = [[w if w > 1.0 else 1.0 for w in x] for x in weights]
|
||||
# unweighted_tokens = [[(t,1.0) for t, _,_ in x] for x in tokens_down]
|
||||
embs, pooled = from_masked(unweighted_tokens, weights, word_ids, base_emb, length, encode_func)
|
||||
weighted_emb += embs
|
||||
|
||||
if weight_interpretation == "down_weight":
|
||||
weights = scale_to_norm(weights, word_ids, w_max)
|
||||
weighted_emb, _, pooled = down_weight(unweighted_tokens, weights, word_ids, base_emb, length, encode_func)
|
||||
|
||||
if return_pooled:
|
||||
if apply_to_pooled:
|
||||
return weighted_emb, pooled
|
||||
else:
|
||||
return weighted_emb, pooled_base
|
||||
return weighted_emb, None
|
||||
|
||||
|
||||
def encode_token_weights_g(model, token_weight_pairs):
|
||||
return model.clip_g.encode_token_weights(token_weight_pairs)
|
||||
|
||||
|
||||
def encode_token_weights_l(model, token_weight_pairs):
|
||||
l_out, _ = model.clip_l.encode_token_weights(token_weight_pairs)
|
||||
return l_out, None
|
||||
|
||||
|
||||
def encode_token_weights(model, token_weight_pairs, encode_func):
|
||||
if model.layer_idx is not None:
|
||||
model.cond_stage_model.clip_layer(model.layer_idx)
|
||||
|
||||
model_management.load_model_gpu(model.patcher)
|
||||
return encode_func(model.cond_stage_model, token_weight_pairs)
|
||||
|
||||
|
||||
def prepareXL(embs_l, embs_g, pooled, clip_balance):
|
||||
l_w = 1 - max(0, clip_balance - .5) * 2
|
||||
g_w = 1 - max(0, .5 - clip_balance) * 2
|
||||
if embs_l is not None:
|
||||
return torch.cat([embs_l * l_w, embs_g * g_w], dim=-1), pooled
|
||||
else:
|
||||
return embs_g, pooled
|
||||
|
||||
|
||||
def advanced_encode(clip, text, token_normalization, weight_interpretation, w_max=1.0, clip_balance=.5,
|
||||
apply_to_pooled=True):
|
||||
tokenized = clip.tokenize(text, return_word_ids=True)
|
||||
if isinstance(clip.cond_stage_model, (SDXLClipModel, SDXLRefinerClipModel, SDXLClipG)):
|
||||
embs_l = None
|
||||
embs_g = None
|
||||
pooled = None
|
||||
if 'l' in tokenized and isinstance(clip.cond_stage_model, SDXLClipModel):
|
||||
embs_l, _ = advanced_encode_from_tokens(tokenized['l'],
|
||||
token_normalization,
|
||||
weight_interpretation,
|
||||
lambda x: encode_token_weights(clip, x, encode_token_weights_l),
|
||||
w_max=w_max,
|
||||
return_pooled=False)
|
||||
if 'g' in tokenized:
|
||||
embs_g, pooled = advanced_encode_from_tokens(tokenized['g'],
|
||||
token_normalization,
|
||||
weight_interpretation,
|
||||
lambda x: encode_token_weights(clip, x,
|
||||
encode_token_weights_g),
|
||||
w_max=w_max,
|
||||
return_pooled=True,
|
||||
apply_to_pooled=apply_to_pooled)
|
||||
return prepareXL(embs_l, embs_g, pooled, clip_balance)
|
||||
else:
|
||||
return advanced_encode_from_tokens(tokenized['l'],
|
||||
token_normalization,
|
||||
weight_interpretation,
|
||||
lambda x: (clip.encode_from_tokens({'l': x}), None),
|
||||
w_max=w_max)
|
||||
|
||||
|
||||
def advanced_encode_XL(clip, text1, text2, token_normalization, weight_interpretation, w_max=1.0, clip_balance=.5,
|
||||
apply_to_pooled=True):
|
||||
tokenized1 = clip.tokenize(text1, return_word_ids=True)
|
||||
tokenized2 = clip.tokenize(text2, return_word_ids=True)
|
||||
|
||||
embs_l, _ = advanced_encode_from_tokens(tokenized1['l'],
|
||||
token_normalization,
|
||||
weight_interpretation,
|
||||
lambda x: encode_token_weights(clip, x, encode_token_weights_l),
|
||||
w_max=w_max,
|
||||
return_pooled=False)
|
||||
|
||||
embs_g, pooled = advanced_encode_from_tokens(tokenized2['g'],
|
||||
token_normalization,
|
||||
weight_interpretation,
|
||||
lambda x: encode_token_weights(clip, x, encode_token_weights_g),
|
||||
w_max=w_max,
|
||||
return_pooled=True,
|
||||
apply_to_pooled=apply_to_pooled)
|
||||
|
||||
gcd_num = gcd(embs_l.shape[1], embs_g.shape[1])
|
||||
repeat_l = int((embs_g.shape[1] / gcd_num) * embs_l.shape[1])
|
||||
repeat_g = int((embs_l.shape[1] / gcd_num) * embs_g.shape[1])
|
||||
|
||||
return prepareXL(embs_l.expand((-1, repeat_l, -1)), embs_g.expand((-1, repeat_g, -1)), pooled, clip_balance)
|
||||
@@ -0,0 +1,21 @@
|
||||
BASE_RESOLUTIONS = [
|
||||
(1024, 1024),
|
||||
(576, 1024),
|
||||
(768, 1024),
|
||||
(768, 1280),
|
||||
(768, 1344),
|
||||
(768, 1536),
|
||||
(832, 1152),
|
||||
(896, 1152),
|
||||
(896, 1088),
|
||||
(1024, 576),
|
||||
(1024, 768),
|
||||
(1088, 896),
|
||||
(1152, 832),
|
||||
(1152, 896),
|
||||
(1280, 768),
|
||||
(1344, 768),
|
||||
(1536, 640),
|
||||
(1536, 768),
|
||||
("自定义", "自定义")
|
||||
]
|
||||
@@ -0,0 +1,167 @@
|
||||
import torch, math
|
||||
|
||||
######################### DynThresh Core #########################
|
||||
|
||||
class DynThresh:
|
||||
|
||||
Modes = ["Constant", "Linear Down", "Cosine Down", "Half Cosine Down", "Linear Up", "Cosine Up", "Half Cosine Up", "Power Up", "Power Down", "Linear Repeating", "Cosine Repeating", "Sawtooth"]
|
||||
Startpoints = ["MEAN", "ZERO"]
|
||||
Variabilities = ["AD", "STD"]
|
||||
|
||||
def __init__(self, mimic_scale, threshold_percentile, mimic_mode, mimic_scale_min, cfg_mode, cfg_scale_min, sched_val, experiment_mode, max_steps, separate_feature_channels, scaling_startpoint, variability_measure, interpolate_phi):
|
||||
self.mimic_scale = mimic_scale
|
||||
self.threshold_percentile = threshold_percentile
|
||||
self.mimic_mode = mimic_mode
|
||||
self.cfg_mode = cfg_mode
|
||||
self.max_steps = max_steps
|
||||
self.cfg_scale_min = cfg_scale_min
|
||||
self.mimic_scale_min = mimic_scale_min
|
||||
self.experiment_mode = experiment_mode
|
||||
self.sched_val = sched_val
|
||||
self.sep_feat_channels = separate_feature_channels
|
||||
self.scaling_startpoint = scaling_startpoint
|
||||
self.variability_measure = variability_measure
|
||||
self.interpolate_phi = interpolate_phi
|
||||
|
||||
def interpret_scale(self, scale, mode, min):
|
||||
scale -= min
|
||||
max = self.max_steps - 1
|
||||
frac = self.step / max
|
||||
if mode == "Constant":
|
||||
pass
|
||||
elif mode == "Linear Down":
|
||||
scale *= 1.0 - frac
|
||||
elif mode == "Half Cosine Down":
|
||||
scale *= math.cos(frac)
|
||||
elif mode == "Cosine Down":
|
||||
scale *= math.cos(frac * 1.5707)
|
||||
elif mode == "Linear Up":
|
||||
scale *= frac
|
||||
elif mode == "Half Cosine Up":
|
||||
scale *= 1.0 - math.cos(frac)
|
||||
elif mode == "Cosine Up":
|
||||
scale *= 1.0 - math.cos(frac * 1.5707)
|
||||
elif mode == "Power Up":
|
||||
scale *= math.pow(frac, self.sched_val)
|
||||
elif mode == "Power Down":
|
||||
scale *= 1.0 - math.pow(frac, self.sched_val)
|
||||
elif mode == "Linear Repeating":
|
||||
portion = (frac * self.sched_val) % 1.0
|
||||
scale *= (0.5 - portion) * 2 if portion < 0.5 else (portion - 0.5) * 2
|
||||
elif mode == "Cosine Repeating":
|
||||
scale *= math.cos(frac * 6.28318 * self.sched_val) * 0.5 + 0.5
|
||||
elif mode == "Sawtooth":
|
||||
scale *= (frac * self.sched_val) % 1.0
|
||||
scale += min
|
||||
return scale
|
||||
|
||||
def dynthresh(self, cond, uncond, cfg_scale, weights):
|
||||
mimic_scale = self.interpret_scale(self.mimic_scale, self.mimic_mode, self.mimic_scale_min)
|
||||
cfg_scale = self.interpret_scale(cfg_scale, self.cfg_mode, self.cfg_scale_min)
|
||||
# uncond shape is (batch, 4, height, width)
|
||||
conds_per_batch = cond.shape[0] / uncond.shape[0]
|
||||
assert conds_per_batch == int(conds_per_batch), "Expected # of conds per batch to be constant across batches"
|
||||
cond_stacked = cond.reshape((-1, int(conds_per_batch)) + uncond.shape[1:])
|
||||
|
||||
### Normal first part of the CFG Scale logic, basically
|
||||
diff = cond_stacked - uncond.unsqueeze(1)
|
||||
if weights is not None:
|
||||
diff = diff * weights
|
||||
relative = diff.sum(1)
|
||||
|
||||
### Get the normal result for both mimic and normal scale
|
||||
mim_target = uncond + relative * mimic_scale
|
||||
cfg_target = uncond + relative * cfg_scale
|
||||
### If we weren't doing mimic scale, we'd just return cfg_target here
|
||||
|
||||
### Now recenter the values relative to their average rather than absolute, to allow scaling from average
|
||||
mim_flattened = mim_target.flatten(2)
|
||||
cfg_flattened = cfg_target.flatten(2)
|
||||
mim_means = mim_flattened.mean(dim=2).unsqueeze(2)
|
||||
cfg_means = cfg_flattened.mean(dim=2).unsqueeze(2)
|
||||
mim_centered = mim_flattened - mim_means
|
||||
cfg_centered = cfg_flattened - cfg_means
|
||||
|
||||
if self.sep_feat_channels:
|
||||
if self.variability_measure == 'STD':
|
||||
mim_scaleref = mim_centered.std(dim=2).unsqueeze(2)
|
||||
cfg_scaleref = cfg_centered.std(dim=2).unsqueeze(2)
|
||||
else: # 'AD'
|
||||
mim_scaleref = mim_centered.abs().max(dim=2).values.unsqueeze(2)
|
||||
cfg_scaleref = torch.quantile(cfg_centered.abs(), self.threshold_percentile, dim=2).unsqueeze(2)
|
||||
|
||||
else:
|
||||
if self.variability_measure == 'STD':
|
||||
mim_scaleref = mim_centered.std()
|
||||
cfg_scaleref = cfg_centered.std()
|
||||
else: # 'AD'
|
||||
mim_scaleref = mim_centered.abs().max()
|
||||
cfg_scaleref = torch.quantile(cfg_centered.abs(), self.threshold_percentile)
|
||||
|
||||
if self.scaling_startpoint == 'ZERO':
|
||||
scaling_factor = mim_scaleref / cfg_scaleref
|
||||
result = cfg_flattened * scaling_factor
|
||||
|
||||
else: # 'MEAN'
|
||||
if self.variability_measure == 'STD':
|
||||
cfg_renormalized = (cfg_centered / cfg_scaleref) * mim_scaleref
|
||||
else: # 'AD'
|
||||
### Get the maximum value of all datapoints (with an optional threshold percentile on the uncond)
|
||||
max_scaleref = torch.maximum(mim_scaleref, cfg_scaleref)
|
||||
### Clamp to the max
|
||||
cfg_clamped = cfg_centered.clamp(-max_scaleref, max_scaleref)
|
||||
### Now shrink from the max to normalize and grow to the mimic scale (instead of the CFG scale)
|
||||
cfg_renormalized = (cfg_clamped / max_scaleref) * mim_scaleref
|
||||
|
||||
### Now add it back onto the averages to get into real scale again and return
|
||||
result = cfg_renormalized + cfg_means
|
||||
|
||||
actual_res = result.unflatten(2, mim_target.shape[2:])
|
||||
|
||||
if self.interpolate_phi != 1.0:
|
||||
actual_res = actual_res * self.interpolate_phi + cfg_target * (1.0 - self.interpolate_phi)
|
||||
|
||||
if self.experiment_mode == 1:
|
||||
num = actual_res.cpu().numpy()
|
||||
for y in range(0, 64):
|
||||
for x in range (0, 64):
|
||||
if num[0][0][y][x] > 1.0:
|
||||
num[0][1][y][x] *= 0.5
|
||||
if num[0][1][y][x] > 1.0:
|
||||
num[0][1][y][x] *= 0.5
|
||||
if num[0][2][y][x] > 1.5:
|
||||
num[0][2][y][x] *= 0.5
|
||||
actual_res = torch.from_numpy(num).to(device=uncond.device)
|
||||
elif self.experiment_mode == 2:
|
||||
num = actual_res.cpu().numpy()
|
||||
for y in range(0, 64):
|
||||
for x in range (0, 64):
|
||||
over_scale = False
|
||||
for z in range(0, 4):
|
||||
if abs(num[0][z][y][x]) > 1.5:
|
||||
over_scale = True
|
||||
if over_scale:
|
||||
for z in range(0, 4):
|
||||
num[0][z][y][x] *= 0.7
|
||||
actual_res = torch.from_numpy(num).to(device=uncond.device)
|
||||
elif self.experiment_mode == 3:
|
||||
coefs = torch.tensor([
|
||||
# R G B W
|
||||
[0.298, 0.207, 0.208, 0.0], # L1
|
||||
[0.187, 0.286, 0.173, 0.0], # L2
|
||||
[-0.158, 0.189, 0.264, 0.0], # L3
|
||||
[-0.184, -0.271, -0.473, 1.0], # L4
|
||||
], device=uncond.device)
|
||||
res_rgb = torch.einsum("laxy,ab -> lbxy", actual_res, coefs)
|
||||
max_r, max_g, max_b, max_w = res_rgb[0][0].max(), res_rgb[0][1].max(), res_rgb[0][2].max(), res_rgb[0][3].max()
|
||||
max_rgb = max(max_r, max_g, max_b)
|
||||
print(f"test max = r={max_r}, g={max_g}, b={max_b}, w={max_w}, rgb={max_rgb}")
|
||||
if self.step / (self.max_steps - 1) > 0.2:
|
||||
if max_rgb < 2.0 and max_w < 3.0:
|
||||
res_rgb /= max_rgb / 2.4
|
||||
else:
|
||||
if max_rgb > 2.4 and max_w > 3.0:
|
||||
res_rgb /= max_rgb / 2.4
|
||||
actual_res = torch.einsum("laxy,ab -> lbxy", res_rgb, coefs.inverse())
|
||||
|
||||
return actual_res
|
||||
+1792
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,273 @@
|
||||
import torch
|
||||
from torchvision.transforms.functional import gaussian_blur
|
||||
from comfy.k_diffusion.sampling import default_noise_sampler, get_ancestral_step, to_d, BrownianTreeNoiseSampler
|
||||
from tqdm.auto import trange
|
||||
|
||||
@torch.no_grad()
|
||||
def sample_euler_ancestral(
|
||||
model,
|
||||
x,
|
||||
sigmas,
|
||||
extra_args=None,
|
||||
callback=None,
|
||||
disable=None,
|
||||
eta=1.0,
|
||||
s_noise=1.0,
|
||||
noise_sampler=None,
|
||||
upscale_ratio=2.0,
|
||||
start_step=5,
|
||||
end_step=15,
|
||||
upscale_n_step=3,
|
||||
unsharp_kernel_size=3,
|
||||
unsharp_sigma=0.5,
|
||||
unsharp_strength=0.0,
|
||||
):
|
||||
"""Ancestral sampling with Euler method steps."""
|
||||
extra_args = {} if extra_args is None else extra_args
|
||||
noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
|
||||
s_in = x.new_ones([x.shape[0]])
|
||||
|
||||
# make upscale info
|
||||
upscale_steps = []
|
||||
step = start_step - 1
|
||||
while step < end_step - 1:
|
||||
upscale_steps.append(step)
|
||||
step += upscale_n_step
|
||||
height, width = x.shape[2:]
|
||||
upscale_shapes = [
|
||||
(int(height * (((upscale_ratio - 1) / i) + 1)), int(width * (((upscale_ratio - 1) / i) + 1)))
|
||||
for i in reversed(range(1, len(upscale_steps) + 1))
|
||||
]
|
||||
upscale_info = {k: v for k, v in zip(upscale_steps, upscale_shapes)}
|
||||
|
||||
for i in trange(len(sigmas) - 1, disable=disable):
|
||||
denoised = model(x, sigmas[i] * s_in, **extra_args)
|
||||
sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
|
||||
if callback is not None:
|
||||
callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised})
|
||||
d = to_d(x, sigmas[i], denoised)
|
||||
# Euler method
|
||||
dt = sigma_down - sigmas[i]
|
||||
x = x + d * dt
|
||||
if sigmas[i + 1] > 0:
|
||||
# Resize
|
||||
if i in upscale_info:
|
||||
x = torch.nn.functional.interpolate(x, size=upscale_info[i], mode="bicubic", align_corners=False)
|
||||
if unsharp_strength > 0:
|
||||
blurred = gaussian_blur(x, kernel_size=unsharp_kernel_size, sigma=unsharp_sigma)
|
||||
x = x + unsharp_strength * (x - blurred)
|
||||
|
||||
noise_sampler = default_noise_sampler(x)
|
||||
noise = noise_sampler(sigmas[i], sigmas[i + 1])
|
||||
x = x + noise * sigma_up * s_noise
|
||||
return x
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def sample_dpmpp_2s_ancestral(
|
||||
model,
|
||||
x,
|
||||
sigmas,
|
||||
extra_args=None,
|
||||
callback=None,
|
||||
disable=None,
|
||||
eta=1.0,
|
||||
s_noise=1.0,
|
||||
noise_sampler=None,
|
||||
upscale_ratio=2.0,
|
||||
start_step=5,
|
||||
end_step=15,
|
||||
upscale_n_step=3,
|
||||
unsharp_kernel_size=3,
|
||||
unsharp_sigma=0.5,
|
||||
unsharp_strength=0.0,
|
||||
):
|
||||
"""Ancestral sampling with DPM-Solver++(2S) second-order steps."""
|
||||
extra_args = {} if extra_args is None else extra_args
|
||||
s_in = x.new_ones([x.shape[0]])
|
||||
sigma_fn = lambda t: t.neg().exp()
|
||||
t_fn = lambda sigma: sigma.log().neg()
|
||||
|
||||
# make upscale info
|
||||
upscale_steps = []
|
||||
step = start_step - 1
|
||||
while step < end_step - 1:
|
||||
upscale_steps.append(step)
|
||||
step += upscale_n_step
|
||||
height, width = x.shape[2:]
|
||||
upscale_shapes = [
|
||||
(int(height * (((upscale_ratio - 1) / i) + 1)), int(width * (((upscale_ratio - 1) / i) + 1)))
|
||||
for i in reversed(range(1, len(upscale_steps) + 1))
|
||||
]
|
||||
upscale_info = {k: v for k, v in zip(upscale_steps, upscale_shapes)}
|
||||
|
||||
for i in trange(len(sigmas) - 1, disable=disable):
|
||||
denoised = model(x, sigmas[i] * s_in, **extra_args)
|
||||
sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
|
||||
if callback is not None:
|
||||
callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised})
|
||||
if sigma_down == 0:
|
||||
# Euler method
|
||||
d = to_d(x, sigmas[i], denoised)
|
||||
dt = sigma_down - sigmas[i]
|
||||
x = x + d * dt
|
||||
else:
|
||||
# DPM-Solver++(2S)
|
||||
t, t_next = t_fn(sigmas[i]), t_fn(sigma_down)
|
||||
r = 1 / 2
|
||||
h = t_next - t
|
||||
s = t + r * h
|
||||
x_2 = (sigma_fn(s) / sigma_fn(t)) * x - (-h * r).expm1() * denoised
|
||||
denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args)
|
||||
x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_2
|
||||
# Noise addition
|
||||
if sigmas[i + 1] > 0:
|
||||
# Resize
|
||||
if i in upscale_info:
|
||||
x = torch.nn.functional.interpolate(x, size=upscale_info[i], mode="bicubic", align_corners=False)
|
||||
if unsharp_strength > 0:
|
||||
blurred = gaussian_blur(x, kernel_size=unsharp_kernel_size, sigma=unsharp_sigma)
|
||||
x = x + unsharp_strength * (x - blurred)
|
||||
noise_sampler = default_noise_sampler(x)
|
||||
noise = noise_sampler(sigmas[i], sigmas[i + 1])
|
||||
x = x + noise * sigma_up * s_noise
|
||||
return x
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def sample_dpmpp_2m_sde(
|
||||
model,
|
||||
x,
|
||||
sigmas,
|
||||
extra_args=None,
|
||||
callback=None,
|
||||
disable=None,
|
||||
eta=1.0,
|
||||
s_noise=1.0,
|
||||
noise_sampler=None,
|
||||
solver_type="midpoint",
|
||||
upscale_ratio=2.0,
|
||||
start_step=5,
|
||||
end_step=15,
|
||||
upscale_n_step=3,
|
||||
unsharp_kernel_size=3,
|
||||
unsharp_sigma=0.5,
|
||||
unsharp_strength=0.0,
|
||||
):
|
||||
"""DPM-Solver++(2M) SDE."""
|
||||
|
||||
if solver_type not in {"heun", "midpoint"}:
|
||||
raise ValueError("solver_type must be 'heun' or 'midpoint'")
|
||||
|
||||
seed = extra_args.get("seed", None)
|
||||
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
|
||||
extra_args = {} if extra_args is None else extra_args
|
||||
s_in = x.new_ones([x.shape[0]])
|
||||
|
||||
old_denoised = None
|
||||
h_last = None
|
||||
h = None
|
||||
|
||||
# make upscale info
|
||||
upscale_steps = []
|
||||
step = start_step - 1
|
||||
while step < end_step - 1:
|
||||
upscale_steps.append(step)
|
||||
step += upscale_n_step
|
||||
height, width = x.shape[2:]
|
||||
upscale_shapes = [
|
||||
(int(height * (((upscale_ratio - 1) / i) + 1)), int(width * (((upscale_ratio - 1) / i) + 1)))
|
||||
for i in reversed(range(1, len(upscale_steps) + 1))
|
||||
]
|
||||
upscale_info = {k: v for k, v in zip(upscale_steps, upscale_shapes)}
|
||||
|
||||
for i in trange(len(sigmas) - 1, disable=disable):
|
||||
denoised = model(x, sigmas[i] * s_in, **extra_args)
|
||||
if callback is not None:
|
||||
callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised})
|
||||
if sigmas[i + 1] == 0:
|
||||
# Denoising step
|
||||
x = denoised
|
||||
else:
|
||||
# DPM-Solver++(2M) SDE
|
||||
t, s = -sigmas[i].log(), -sigmas[i + 1].log()
|
||||
h = s - t
|
||||
eta_h = eta * h
|
||||
|
||||
x = sigmas[i + 1] / sigmas[i] * (-eta_h).exp() * x + (-h - eta_h).expm1().neg() * denoised
|
||||
|
||||
if old_denoised is not None:
|
||||
r = h_last / h
|
||||
if solver_type == "heun":
|
||||
x = x + ((-h - eta_h).expm1().neg() / (-h - eta_h) + 1) * (1 / r) * (denoised - old_denoised)
|
||||
elif solver_type == "midpoint":
|
||||
x = x + 0.5 * (-h - eta_h).expm1().neg() * (1 / r) * (denoised - old_denoised)
|
||||
|
||||
if eta:
|
||||
# Resize
|
||||
if i in upscale_info:
|
||||
x = torch.nn.functional.interpolate(x, size=upscale_info[i], mode="bicubic", align_corners=False)
|
||||
if unsharp_strength > 0:
|
||||
blurred = gaussian_blur(x, kernel_size=unsharp_kernel_size, sigma=unsharp_sigma)
|
||||
x = x + unsharp_strength * (x - blurred)
|
||||
denoised = None # 次ステップとサイズがあわないのでとりあえずNoneにしておく。
|
||||
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True)
|
||||
x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * eta_h).expm1().neg().sqrt() * s_noise
|
||||
|
||||
old_denoised = denoised
|
||||
h_last = h
|
||||
return x
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def sample_lcm(
|
||||
model,
|
||||
x,
|
||||
sigmas,
|
||||
extra_args=None,
|
||||
callback=None,
|
||||
disable=None,
|
||||
noise_sampler=None,
|
||||
eta=None,
|
||||
s_noise=None,
|
||||
upscale_ratio=2.0,
|
||||
start_step=5,
|
||||
end_step=15,
|
||||
upscale_n_step=3,
|
||||
unsharp_kernel_size=3,
|
||||
unsharp_sigma=0.5,
|
||||
unsharp_strength=0.0,
|
||||
):
|
||||
extra_args = {} if extra_args is None else extra_args
|
||||
s_in = x.new_ones([x.shape[0]])
|
||||
|
||||
# make upscale info
|
||||
upscale_steps = []
|
||||
step = start_step - 1
|
||||
while step < end_step - 1:
|
||||
upscale_steps.append(step)
|
||||
step += upscale_n_step
|
||||
height, width = x.shape[2:]
|
||||
upscale_shapes = [
|
||||
(int(height * (((upscale_ratio - 1) / i) + 1)), int(width * (((upscale_ratio - 1) / i) + 1)))
|
||||
for i in reversed(range(1, len(upscale_steps) + 1))
|
||||
]
|
||||
upscale_info = {k: v for k, v in zip(upscale_steps, upscale_shapes)}
|
||||
|
||||
for i in trange(len(sigmas) - 1, disable=disable):
|
||||
denoised = model(x, sigmas[i] * s_in, **extra_args)
|
||||
if callback is not None:
|
||||
callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised})
|
||||
|
||||
x = denoised
|
||||
if sigmas[i + 1] > 0:
|
||||
# Resize
|
||||
if i in upscale_info:
|
||||
x = torch.nn.functional.interpolate(x, size=upscale_info[i], mode="bicubic", align_corners=False)
|
||||
if unsharp_strength > 0:
|
||||
blurred = gaussian_blur(x, kernel_size=unsharp_kernel_size, sigma=unsharp_sigma)
|
||||
x = x + unsharp_strength * (x - blurred)
|
||||
noise_sampler = default_noise_sampler(x)
|
||||
x += sigmas[i + 1] * noise_sampler(sigmas[i], sigmas[i + 1])
|
||||
|
||||
return x
|
||||
+158
@@ -0,0 +1,158 @@
|
||||
from PIL import Image
|
||||
import numpy as np
|
||||
from nodes import MAX_RESOLUTION
|
||||
from .log import log_node_info
|
||||
|
||||
def get_new_bounds(width, height, left, right, top, bottom):
|
||||
"""Returns the new bounds for an image with inset crop data."""
|
||||
left = 0 + left
|
||||
right = width - right
|
||||
top = 0 + top
|
||||
bottom = height - bottom
|
||||
return (left, right, top, bottom)
|
||||
|
||||
# Tensor to PIL
|
||||
def tensor2pil(image):
|
||||
return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
|
||||
|
||||
# 图像裁切
|
||||
class imageInsetCrop:
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls): # pylint: disable = invalid-name, missing-function-docstring
|
||||
return {
|
||||
"required": {
|
||||
"image": ("IMAGE",),
|
||||
"measurement": (['Pixels', 'Percentage'],),
|
||||
"left": ("INT", {
|
||||
"default": 0,
|
||||
"min": 0,
|
||||
"max": MAX_RESOLUTION,
|
||||
"step": 8
|
||||
}),
|
||||
"right": ("INT", {
|
||||
"default": 0,
|
||||
"min": 0,
|
||||
"max": MAX_RESOLUTION,
|
||||
"step": 8
|
||||
}),
|
||||
"top": ("INT", {
|
||||
"default": 0,
|
||||
"min": 0,
|
||||
"max": MAX_RESOLUTION,
|
||||
"step": 8
|
||||
}),
|
||||
"bottom": ("INT", {
|
||||
"default": 0,
|
||||
"min": 0,
|
||||
"max": MAX_RESOLUTION,
|
||||
"step": 8
|
||||
}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
FUNCTION = "crop"
|
||||
|
||||
CATEGORY = "EasyUse/Image"
|
||||
|
||||
# pylint: disable = too-many-arguments
|
||||
def crop(self, measurement, left, right, top, bottom, image=None):
|
||||
"""Does the crop."""
|
||||
|
||||
_, height, width, _ = image.shape
|
||||
|
||||
if measurement == 'Percentage':
|
||||
left = int(width - (width * (100 - left) / 100))
|
||||
right = int(width - (width * (100 - right) / 100))
|
||||
top = int(height - (height * (100 - top) / 100))
|
||||
bottom = int(height - (height * (100 - bottom) / 100))
|
||||
|
||||
# Snap to 8 pixels
|
||||
left = left // 8 * 8
|
||||
right = right // 8 * 8
|
||||
top = top // 8 * 8
|
||||
bottom = bottom // 8 * 8
|
||||
|
||||
if left == 0 and right == 0 and bottom == 0 and top == 0:
|
||||
return (image,)
|
||||
|
||||
inset_left, inset_right, inset_top, inset_bottom = get_new_bounds(width, height, left, right,
|
||||
top, bottom)
|
||||
if inset_top > inset_bottom:
|
||||
raise ValueError(
|
||||
f"Invalid cropping dimensions top ({inset_top}) exceeds bottom ({inset_bottom})")
|
||||
if inset_left > inset_right:
|
||||
raise ValueError(
|
||||
f"Invalid cropping dimensions left ({inset_left}) exceeds right ({inset_right})")
|
||||
|
||||
log_node_info("Image Inset Crop", f'Cropping image {width}x{height} width inset by {inset_left},{inset_right}, ' +
|
||||
f'and height inset by {inset_top}, {inset_bottom}')
|
||||
image = image[:, inset_top:inset_bottom, inset_left:inset_right, :]
|
||||
|
||||
return (image,)
|
||||
|
||||
# 图像尺寸
|
||||
class imageSize:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"image": ("IMAGE",),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("INT", "INT")
|
||||
RETURN_NAMES = ("width_int", "height_int")
|
||||
FUNCTION = "image_width_height"
|
||||
|
||||
CATEGORY = "EasyUse/Image"
|
||||
|
||||
def image_width_height(self, image):
|
||||
image = tensor2pil(image)
|
||||
if image.size:
|
||||
return (image.size[0], image.size[1])
|
||||
return (0, 0)
|
||||
|
||||
# 图像尺寸
|
||||
class imageSizeByLongerSide:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"image": ("IMAGE",),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("INT",)
|
||||
RETURN_NAMES = ("resolution",)
|
||||
FUNCTION = "image_longer_side"
|
||||
|
||||
CATEGORY = "EasyUse/Image"
|
||||
|
||||
def image_longer_side(self, image):
|
||||
image = tensor2pil(image)
|
||||
if image.size:
|
||||
if image.size[0] > image.size[1]:
|
||||
return (image.size[0],)
|
||||
else:
|
||||
return (image.size[1],)
|
||||
return (0,)
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"easy imageInsetCrop": imageInsetCrop,
|
||||
"easy imageSize": imageSize,
|
||||
"easy imageSizeByLongerSide": imageSizeByLongerSide
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"easy imageInsetCrop": "ImageInsetCrop",
|
||||
"easy imageSize": "ImageSize",
|
||||
"easy imageSizeByLongerSide": "ImageSize (LongerSide)"
|
||||
}
|
||||
@@ -0,0 +1,86 @@
|
||||
COLORS_FG = {
|
||||
'BLACK': '\33[30m',
|
||||
'RED': '\33[31m',
|
||||
'GREEN': '\33[32m',
|
||||
'YELLOW': '\33[33m',
|
||||
'BLUE': '\33[34m',
|
||||
'MAGENTA': '\33[35m',
|
||||
'CYAN': '\33[36m',
|
||||
'WHITE': '\33[37m',
|
||||
'GREY': '\33[90m',
|
||||
'BRIGHT_RED': '\33[91m',
|
||||
'BRIGHT_GREEN': '\33[92m',
|
||||
'BRIGHT_YELLOW': '\33[93m',
|
||||
'BRIGHT_BLUE': '\33[94m',
|
||||
'BRIGHT_MAGENTA': '\33[95m',
|
||||
'BRIGHT_CYAN': '\33[96m',
|
||||
'BRIGHT_WHITE': '\33[97m',
|
||||
}
|
||||
COLORS_STYLE = {
|
||||
'RESET': '\33[0m',
|
||||
'BOLD': '\33[1m',
|
||||
'NORMAL': '\33[22m',
|
||||
'ITALIC': '\33[3m',
|
||||
'UNDERLINE': '\33[4m',
|
||||
'BLINK': '\33[5m',
|
||||
'BLINK2': '\33[6m',
|
||||
'SELECTED': '\33[7m',
|
||||
}
|
||||
COLORS_BG = {
|
||||
'BLACK': '\33[40m',
|
||||
'RED': '\33[41m',
|
||||
'GREEN': '\33[42m',
|
||||
'YELLOW': '\33[43m',
|
||||
'BLUE': '\33[44m',
|
||||
'MAGENTA': '\33[45m',
|
||||
'CYAN': '\33[46m',
|
||||
'WHITE': '\33[47m',
|
||||
'GREY': '\33[100m',
|
||||
'BRIGHT_RED': '\33[101m',
|
||||
'BRIGHT_GREEN': '\33[102m',
|
||||
'BRIGHT_YELLOW': '\33[103m',
|
||||
'BRIGHT_BLUE': '\33[104m',
|
||||
'BRIGHT_MAGENTA': '\33[105m',
|
||||
'BRIGHT_CYAN': '\33[106m',
|
||||
'BRIGHT_WHITE': '\33[107m',
|
||||
}
|
||||
|
||||
|
||||
def log_welcome(num_nodes=None):
|
||||
"""Logs the welcome message."""
|
||||
msg = f"{COLORS_FG['GREEN']}{COLORS_STYLE['BOLD']}[rgthree] Loaded"
|
||||
print()
|
||||
if num_nodes:
|
||||
print(f"{msg} {num_nodes} exciting nodes.{COLORS_STYLE['RESET']}")
|
||||
else:
|
||||
print(f"{msg}.{COLORS_STYLE['RESET']}")
|
||||
|
||||
|
||||
def log_node_success(node_name, message):
|
||||
"""Logs a success message."""
|
||||
_log_node(COLORS_FG["GREEN"], node_name, message)
|
||||
|
||||
|
||||
def log_node_info(node_name, message):
|
||||
"""Logs an info message."""
|
||||
_log_node(COLORS_FG["CYAN"], node_name, message)
|
||||
|
||||
|
||||
def log_node_warn(node_name, message):
|
||||
"""Logs an warn message."""
|
||||
_log_node(COLORS_FG["YELLOW"], node_name, message)
|
||||
|
||||
|
||||
def log_node(node_name, message):
|
||||
"""Logs a message."""
|
||||
_log_node(COLORS_FG["CYAN"], node_name, message)
|
||||
|
||||
|
||||
def _log_node(color, node_name, message, prefix=''):
|
||||
print(_get_log_msg(color, node_name, message, prefix=prefix))
|
||||
|
||||
|
||||
def _get_log_msg(color, node_name, message, prefix=''):
|
||||
msg = f'{COLORS_STYLE["BOLD"]}{color}{prefix}[rgthree] {node_name.replace(" (rgthree)", "")}'
|
||||
msg += f':{COLORS_STYLE["RESET"]} {message}'
|
||||
return msg
|
||||
+155
@@ -0,0 +1,155 @@
|
||||
import random
|
||||
import server
|
||||
from enum import Enum
|
||||
|
||||
class SGmode(Enum):
|
||||
FIX = 1
|
||||
INCR = 2
|
||||
DECR = 3
|
||||
RAND = 4
|
||||
|
||||
class SeedGenerator:
|
||||
def __init__(self, base_value, action):
|
||||
self.base_value = base_value
|
||||
|
||||
if action == "fixed" or action == "increment" or action == "decrement" or action == "randomize":
|
||||
self.action = SGmode.FIX
|
||||
elif action == 'increment for each node':
|
||||
self.action = SGmode.INCR
|
||||
elif action == 'decrement for each node':
|
||||
self.action = SGmode.DECR
|
||||
elif action == 'randomize for each node':
|
||||
self.action = SGmode.RAND
|
||||
|
||||
def next(self):
|
||||
seed = self.base_value
|
||||
|
||||
if self.action == SGmode.INCR:
|
||||
self.base_value += 1
|
||||
if self.base_value > 1125899906842624:
|
||||
self.base_value = 0
|
||||
elif self.action == SGmode.DECR:
|
||||
self.base_value -= 1
|
||||
if self.base_value < 0:
|
||||
self.base_value = 1125899906842624
|
||||
elif self.action == SGmode.RAND:
|
||||
self.base_value = random.randint(0, 1125899906842624)
|
||||
|
||||
return seed
|
||||
|
||||
|
||||
def control_seed(v):
|
||||
action = v['inputs']['action']
|
||||
value = v['inputs']['value']
|
||||
|
||||
if action == 'increment' or action == 'increment for each node':
|
||||
value += 1
|
||||
if value > 1125899906842624:
|
||||
value = 0
|
||||
elif action == 'decrement' or action == 'decrement for each node':
|
||||
value -= 1
|
||||
if value < 0:
|
||||
value = 1125899906842624
|
||||
elif action == 'randomize' or action == 'randomize for each node':
|
||||
value = random.randint(0, 1125899906842624)
|
||||
|
||||
v['inputs']['value'] = value
|
||||
|
||||
return value
|
||||
|
||||
|
||||
def prompt_seed_update(json_data):
|
||||
try:
|
||||
seed_widget_map = json_data['extra_data']['extra_pnginfo']['workflow']['seed_widgets']
|
||||
except:
|
||||
return None
|
||||
|
||||
seed_widget_map = json_data['extra_data']['extra_pnginfo']['workflow']['seed_widgets']
|
||||
value = None
|
||||
mode = None
|
||||
node = None
|
||||
action = None
|
||||
|
||||
for k, v in json_data['prompt'].items():
|
||||
if 'class_type' not in v:
|
||||
continue
|
||||
|
||||
cls = v['class_type']
|
||||
if cls == 'globalSeed':
|
||||
mode = v['inputs']['mode']
|
||||
action = v['inputs']['action']
|
||||
value = v['inputs']['value']
|
||||
node = k, v
|
||||
|
||||
# control before generated
|
||||
if mode is not None and mode:
|
||||
value = control_seed(node[1])
|
||||
|
||||
if value is not None:
|
||||
seed_generator = SeedGenerator(value, action)
|
||||
|
||||
for k, v in json_data['prompt'].items():
|
||||
for k2, v2 in v['inputs'].items():
|
||||
if isinstance(v2, str) and '$GlobalSeed.value$' in v2:
|
||||
v['inputs'][k2] = v2.replace('$GlobalSeed.value$', str(value))
|
||||
|
||||
if k not in seed_widget_map:
|
||||
continue
|
||||
|
||||
if 'seed' in v['inputs']:
|
||||
if isinstance(v['inputs']['seed'], int):
|
||||
v['inputs']['seed'] = seed_generator.next()
|
||||
|
||||
if 'noise_seed' in v['inputs']:
|
||||
if isinstance(v['inputs']['noise_seed'], int):
|
||||
v['inputs']['noise_seed'] = seed_generator.next()
|
||||
|
||||
for k2, v2 in v['inputs'].items():
|
||||
if isinstance(v2, str) and '$GlobalSeed.value$' in v2:
|
||||
v['inputs'][k2] = v2.replace('$GlobalSeed.value$', str(value))
|
||||
|
||||
# control after generated
|
||||
if mode is not None and not mode:
|
||||
control_seed(node[1])
|
||||
|
||||
return value is not None
|
||||
|
||||
|
||||
def workflow_seed_update(json_data):
|
||||
nodes = json_data['extra_data']['extra_pnginfo']['workflow']['nodes']
|
||||
seed_widget_map = json_data['extra_data']['extra_pnginfo']['workflow']['seed_widgets']
|
||||
prompt = json_data['prompt']
|
||||
|
||||
updated_seed_map = {}
|
||||
value = None
|
||||
for node in nodes:
|
||||
node_id = str(node['id'])
|
||||
if node_id in prompt:
|
||||
if node['type'] == 'globalSeed':
|
||||
value = prompt[node_id]['inputs']['value']
|
||||
node['widgets_values'][0] = value
|
||||
elif node_id in seed_widget_map:
|
||||
widget_idx = seed_widget_map[node_id]
|
||||
|
||||
if 'noise_seed' in prompt[node_id]['inputs']:
|
||||
seed = prompt[node_id]['inputs']['noise_seed']
|
||||
else:
|
||||
seed = prompt[node_id]['inputs']['seed']
|
||||
|
||||
node['widgets_values'][widget_idx] = seed
|
||||
updated_seed_map[node_id] = seed
|
||||
|
||||
server.PromptServer.instance.send_sync("easyuse-global-seed", {"id": node_id, "value": value, "seed_map": updated_seed_map})
|
||||
|
||||
def onprompt(json_data):
|
||||
is_changed = prompt_seed_update(json_data)
|
||||
if is_changed:
|
||||
workflow_seed_update(json_data)
|
||||
|
||||
return json_data
|
||||
|
||||
server.PromptServer.instance.add_on_prompt_handler(onprompt)
|
||||
|
||||
|
||||
NODE_CLASS_MAPPINGS = {}
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {}
|
||||
@@ -0,0 +1,386 @@
|
||||
import { app } from "../../../scripts/app.js";
|
||||
import { ComfyWidgets } from "../../../scripts/widgets.js";
|
||||
|
||||
let origProps = {};
|
||||
|
||||
const findWidgetByName = (node, name) => node.widgets.find((w) => w.name === name);
|
||||
|
||||
const doesInputWithNameExist = (node, name) => node.inputs ? node.inputs.some((input) => input.name === name) : false;
|
||||
|
||||
function updateNodeHeight(node) {
|
||||
node.setSize([node.size[0], node.computeSize()[1]]);
|
||||
}
|
||||
|
||||
function toggleWidget(node, widget, show = false, suffix = "") {
|
||||
if (!widget || doesInputWithNameExist(node, widget.name)) return;
|
||||
if (!origProps[widget.name]) {
|
||||
origProps[widget.name] = { origType: widget.type, origComputeSize: widget.computeSize };
|
||||
}
|
||||
const origSize = node.size;
|
||||
|
||||
widget.type = show ? origProps[widget.name].origType : "esayHidden" + suffix;
|
||||
widget.computeSize = show ? origProps[widget.name].origComputeSize : () => [0, -4];
|
||||
|
||||
widget.linkedWidgets?.forEach(w => toggleWidget(node, w, ":" + widget.name, show));
|
||||
|
||||
const height = show ? Math.max(node.computeSize()[1], origSize[1]) : node.size[1];
|
||||
node.setSize([node.size[0], height]);
|
||||
|
||||
}
|
||||
|
||||
function widgetLogic(node, widget) {
|
||||
if (widget.name === 'lora_name') {
|
||||
if (widget.value === "None") {
|
||||
toggleWidget(node, findWidgetByName(node, 'lora_model_strength'))
|
||||
toggleWidget(node, findWidgetByName(node, 'lora_clip_strength'))
|
||||
} else {
|
||||
toggleWidget(node, findWidgetByName(node, 'lora_model_strength'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 'lora_clip_strength'), true)
|
||||
}
|
||||
}
|
||||
if (widget.name === 'lora1_name') {
|
||||
if (widget.value === "None") {
|
||||
toggleWidget(node, findWidgetByName(node, 'lora1_model_strength'))
|
||||
toggleWidget(node, findWidgetByName(node, 'lora1_clip_strength'))
|
||||
} else {
|
||||
toggleWidget(node, findWidgetByName(node, 'lora1_model_strength'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 'lora1_clip_strength'), true)
|
||||
}
|
||||
}
|
||||
if (widget.name === 'lora2_name') {
|
||||
if (widget.value === "None") {
|
||||
toggleWidget(node, findWidgetByName(node, 'lora2_model_strength'))
|
||||
toggleWidget(node, findWidgetByName(node, 'lora2_clip_strength'))
|
||||
} else {
|
||||
toggleWidget(node, findWidgetByName(node, 'lora2_model_strength'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 'lora2_clip_strength'), true)
|
||||
}
|
||||
}
|
||||
if (widget.name === 'lora3_name') {
|
||||
if (widget.value === "None") {
|
||||
toggleWidget(node, findWidgetByName(node, 'lora3_model_strength'))
|
||||
toggleWidget(node, findWidgetByName(node, 'lora3_clip_strength'))
|
||||
} else {
|
||||
toggleWidget(node, findWidgetByName(node, 'lora3_model_strength'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 'lora3_clip_strength'), true)
|
||||
}
|
||||
}
|
||||
if (widget.name === 'refiner_ckpt_name') {
|
||||
let refiner_lora1 = findWidgetByName(node, 'refiner_lora1_name').value
|
||||
let refiner_lora2 = findWidgetByName(node, 'refiner_lora2_name').value
|
||||
if (widget.value === "None") {
|
||||
toggleWidget(node, findWidgetByName(node, 'refiner_vae_name'))
|
||||
toggleWidget(node, findWidgetByName(node, 'refiner_lora1_name'))
|
||||
toggleWidget(node, findWidgetByName(node, 'refiner_lora1_model_strength'))
|
||||
toggleWidget(node, findWidgetByName(node, 'refiner_lora1_clip_strength'))
|
||||
toggleWidget(node, findWidgetByName(node, 'refiner_lora2_name'))
|
||||
toggleWidget(node, findWidgetByName(node, 'refiner_lora2_model_strength'))
|
||||
toggleWidget(node, findWidgetByName(node, 'refiner_lora2_clip_strength'))
|
||||
} else {
|
||||
toggleWidget(node, findWidgetByName(node, 'refiner_vae_name'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 'refiner_lora1_name'), true)
|
||||
if (refiner_lora1 !== "None") {
|
||||
toggleWidget(node, findWidgetByName(node, 'refiner_lora1_model_strength'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 'refiner_lora1_clip_strength'), true)
|
||||
}
|
||||
toggleWidget(node, findWidgetByName(node, 'refiner_lora2_name'), true)
|
||||
if (refiner_lora2 !== "None") {
|
||||
toggleWidget(node, findWidgetByName(node, 'refiner_lora2_model_strength'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 'refiner_lora2_clip_strength'), true)
|
||||
}
|
||||
}
|
||||
}
|
||||
if (widget.name === 'refiner_lora1_name') {
|
||||
let refiner_ckpt = findWidgetByName(node, 'refiner_ckpt_name').value
|
||||
|
||||
if (widget.value === "None" || refiner_ckpt === "None") {
|
||||
toggleWidget(node, findWidgetByName(node, 'refiner_lora1_model_strength'))
|
||||
toggleWidget(node, findWidgetByName(node, 'refiner_lora1_clip_strength'))
|
||||
} else {
|
||||
toggleWidget(node, findWidgetByName(node, 'refiner_lora1_model_strength'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 'refiner_lora1_clip_strength'), true)
|
||||
}
|
||||
}
|
||||
if (widget.name === 'refiner_lora2_name') {
|
||||
let refiner_ckpt = findWidgetByName(node, 'refiner_ckpt_name').value
|
||||
|
||||
if (widget.value === "None" || refiner_ckpt === "None") {
|
||||
toggleWidget(node, findWidgetByName(node, 'refiner_lora2_model_strength'))
|
||||
toggleWidget(node, findWidgetByName(node, 'refiner_lora2_clip_strength'))
|
||||
} else {
|
||||
toggleWidget(node, findWidgetByName(node, 'refiner_lora2_model_strength'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 'refiner_lora2_clip_strength'), true)
|
||||
}
|
||||
}
|
||||
if (widget.name === 'rescale_after_model') {
|
||||
if (widget.value === false) {
|
||||
toggleWidget(node, findWidgetByName(node, 'rescale_method'))
|
||||
toggleWidget(node, findWidgetByName(node, 'rescale'))
|
||||
toggleWidget(node, findWidgetByName(node, 'percent'))
|
||||
toggleWidget(node, findWidgetByName(node, 'width'))
|
||||
toggleWidget(node, findWidgetByName(node, 'height'))
|
||||
toggleWidget(node, findWidgetByName(node, 'longer_side'))
|
||||
toggleWidget(node, findWidgetByName(node, 'crop'))
|
||||
} else {
|
||||
toggleWidget(node, findWidgetByName(node, 'rescale_method'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 'rescale'), true)
|
||||
|
||||
let rescale_value = findWidgetByName(node, 'rescale').value
|
||||
|
||||
if (rescale_value === 'by percentage') {
|
||||
toggleWidget(node, findWidgetByName(node, 'percent'), true)
|
||||
} else if (rescale_value === 'to Width/Height') {
|
||||
toggleWidget(node, findWidgetByName(node, 'width'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 'height'), true)
|
||||
} else {
|
||||
toggleWidget(node, findWidgetByName(node, 'longer_side'), true)
|
||||
}
|
||||
toggleWidget(node, findWidgetByName(node, 'crop'), true)
|
||||
}
|
||||
}
|
||||
if (widget.name === 'rescale') {
|
||||
let rescale_after_model = findWidgetByName(node, 'rescale_after_model').value
|
||||
if (widget.value === 'by percentage' && rescale_after_model) {
|
||||
toggleWidget(node, findWidgetByName(node, 'width'))
|
||||
toggleWidget(node, findWidgetByName(node, 'height'))
|
||||
toggleWidget(node, findWidgetByName(node, 'longer_side'))
|
||||
toggleWidget(node, findWidgetByName(node, 'percent'), true)
|
||||
} else if (widget.value === 'to Width/Height' && rescale_after_model) {
|
||||
toggleWidget(node, findWidgetByName(node, 'width'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 'height'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 'percent'))
|
||||
toggleWidget(node, findWidgetByName(node, 'longer_side'))
|
||||
} else if (rescale_after_model) {
|
||||
toggleWidget(node, findWidgetByName(node, 'longer_side'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 'width'))
|
||||
toggleWidget(node, findWidgetByName(node, 'height'))
|
||||
toggleWidget(node, findWidgetByName(node, 'percent'))
|
||||
}
|
||||
}
|
||||
if (widget.name === 'upscale_method') {
|
||||
if (widget.value === "None") {
|
||||
toggleWidget(node, findWidgetByName(node, 'factor'))
|
||||
toggleWidget(node, findWidgetByName(node, 'crop'))
|
||||
} else {
|
||||
toggleWidget(node, findWidgetByName(node, 'factor'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 'crop'), true)
|
||||
}
|
||||
}
|
||||
if (widget.name === 'image_output') {
|
||||
if (widget.value === 'Sender' || widget.value === 'Sender/Save'){
|
||||
toggleWidget(node, findWidgetByName(node, 'link_id'), true)
|
||||
}else {
|
||||
toggleWidget(node, findWidgetByName(node, 'link_id'))
|
||||
}
|
||||
if (widget.value === 'Hide' || widget.value === 'Preview' || widget.value === 'Sender') {
|
||||
toggleWidget(node, findWidgetByName(node, 'save_prefix'))
|
||||
toggleWidget(node, findWidgetByName(node, 'output_path'))
|
||||
toggleWidget(node, findWidgetByName(node, 'embed_workflow'))
|
||||
toggleWidget(node, findWidgetByName(node, 'number_padding'))
|
||||
toggleWidget(node, findWidgetByName(node, 'overwrite_existing'))
|
||||
} else if (widget.value === 'Save' || widget.value === 'Hide/Save' || widget.value === 'Sender/Save') {
|
||||
toggleWidget(node, findWidgetByName(node, 'save_prefix'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 'output_path'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 'embed_workflow'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 'number_padding'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 'overwrite_existing'), true)
|
||||
}
|
||||
}
|
||||
if (widget.name === 'add_noise') {
|
||||
if (widget.value === "disable") {
|
||||
toggleWidget(node, findWidgetByName(node, 'seed_num'))
|
||||
toggleWidget(node, findWidgetByName(node, 'control_before_generate'))
|
||||
} else {
|
||||
toggleWidget(node, findWidgetByName(node, 'seed_num'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 'control_before_generate'), true)
|
||||
}
|
||||
}
|
||||
if (widget.name === 'ckpt_B_name') {
|
||||
if (widget.value === "None") {
|
||||
toggleWidget(node, findWidgetByName(node, 'config_B_name'))
|
||||
} else {
|
||||
toggleWidget(node, findWidgetByName(node, 'config_B_name'), true)
|
||||
}
|
||||
}
|
||||
if (widget.name === 'ckpt_C_name') {
|
||||
if (widget.value === "None") {
|
||||
toggleWidget(node, findWidgetByName(node, 'config_C_name'))
|
||||
} else {
|
||||
toggleWidget(node, findWidgetByName(node, 'config_C_name'), true)
|
||||
}
|
||||
}
|
||||
if (widget.name === 'save_model') {
|
||||
if (widget.value === "True") {
|
||||
toggleWidget(node, findWidgetByName(node, 'save_prefix'), true)
|
||||
|
||||
} else {
|
||||
toggleWidget(node, findWidgetByName(node, 'save_prefix'))
|
||||
}
|
||||
}
|
||||
if (widget.name === 'num_loras') {
|
||||
let number_to_show = widget.value + 1
|
||||
for (let i = 0; i < number_to_show; i++) {
|
||||
toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_name'), true)
|
||||
if (findWidgetByName(node, 'mode').value === "simple") {
|
||||
toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_strength'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_model_strength'))
|
||||
toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_clip_strength'))
|
||||
} else {
|
||||
toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_strength'))
|
||||
toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_model_strength'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_clip_strength'), true)
|
||||
}
|
||||
}
|
||||
for (let i = number_to_show; i < 21; i++) {
|
||||
toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_name'))
|
||||
toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_strength'))
|
||||
toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_model_strength'))
|
||||
toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_clip_strength'))
|
||||
}
|
||||
updateNodeHeight(node)
|
||||
}
|
||||
if (widget.name === 'mode') {
|
||||
let number_to_show = findWidgetByName(node, 'num_loras').value + 1
|
||||
for (let i = 0; i < number_to_show; i++) {
|
||||
if (widget.value === "simple") {
|
||||
toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_strength'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_model_strength'))
|
||||
toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_clip_strength'))
|
||||
} else {
|
||||
toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_strength'))
|
||||
toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_model_strength'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 'lora_'+i+'_clip_strength'), true)}
|
||||
}
|
||||
updateNodeHeight(node)
|
||||
}
|
||||
if (widget.name === 'resolution') {
|
||||
if (widget.value === "自定义 x 自定义") {
|
||||
toggleWidget(node, findWidgetByName(node, 'empty_latent_width'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 'empty_latent_height'), true)
|
||||
} else {
|
||||
toggleWidget(node, findWidgetByName(node, 'empty_latent_width'), false)
|
||||
toggleWidget(node, findWidgetByName(node, 'empty_latent_height'), false)
|
||||
}
|
||||
}
|
||||
|
||||
if (widget.name === 'toggle') {
|
||||
widget.type = 'toggle'
|
||||
widget.options = {on: 'Enabled', off: 'Disabled'}
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
function widgetLogic2(node, widget) {
|
||||
if (widget.name === 'sampler_name') {
|
||||
if (["euler_ancestral", "dpmpp_2s_ancestral", "dpmpp_2m_sde", "lcm"].includes(widget.value)) {
|
||||
toggleWidget(node, findWidgetByName(node, 'eta'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 's_noise'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 'upscale_ratio'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 'start_step'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 'end_step'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 'upscale_n_step'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 'unsharp_kernel_size'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 'unsharp_sigma'), true)
|
||||
toggleWidget(node, findWidgetByName(node, 'unsharp_strength'), true)
|
||||
} else {
|
||||
toggleWidget(node, findWidgetByName(node, 'eta'))
|
||||
toggleWidget(node, findWidgetByName(node, 's_noise'))
|
||||
toggleWidget(node, findWidgetByName(node, 'upscale_ratio'))
|
||||
toggleWidget(node, findWidgetByName(node, 'start_step'))
|
||||
toggleWidget(node, findWidgetByName(node, 'end_step'))
|
||||
toggleWidget(node, findWidgetByName(node, 'upscale_n_step'))
|
||||
toggleWidget(node, findWidgetByName(node, 'unsharp_kernel_size'))
|
||||
toggleWidget(node, findWidgetByName(node, 'unsharp_sigma'))
|
||||
toggleWidget(node, findWidgetByName(node, 'unsharp_strength'))
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
app.registerExtension({
|
||||
name: "comfy.easyUse.dynamicWidgets",
|
||||
|
||||
nodeCreated(node) {
|
||||
if (["easy a1111Loader","easy comfyLoader","easy preSamplingAdvanced","easy preSamplingSdTurbo", "easy kSampler","easy kSamplerSDTurbo","easy kSamplerTiled"].includes(node.comfyClass)) {
|
||||
getSetters(node)
|
||||
}
|
||||
},
|
||||
async beforeRegisterNodeDef(nodeType, nodeData, app) {
|
||||
if (["easy kSampler","easy kSamplerTiled","easy kSamplerSDTurbo"].includes(nodeData.name)) {
|
||||
function populate(arr_text) {
|
||||
var text = '';
|
||||
for (let i = 0; i < arr_text.length; i++){
|
||||
text += arr_text[i];
|
||||
}
|
||||
if (this.widgets) {
|
||||
const pos = this.widgets.findIndex((w) => w.name === "text");
|
||||
if (pos !== -1 && this.widgets[pos]) {
|
||||
const w = this.widgets[pos]
|
||||
w.value = text;
|
||||
}
|
||||
}
|
||||
requestAnimationFrame(() => {
|
||||
const sz = this.computeSize();
|
||||
if (sz[0] < this.size[0]) {
|
||||
sz[0] = this.size[0];
|
||||
}
|
||||
if (sz[1] < this.size[1]) {
|
||||
sz[1] = this.size[1];
|
||||
}
|
||||
this.onResize?.(sz);
|
||||
app.graph.setDirtyCanvas(true, false);
|
||||
});
|
||||
}
|
||||
|
||||
// When the node is executed we will be sent the input text, display this in the widget
|
||||
const onExecuted = nodeType.prototype.onExecuted;
|
||||
nodeType.prototype.onExecuted = function (message) {
|
||||
onExecuted?.apply(this, arguments);
|
||||
populate.call(this, message.text);
|
||||
};
|
||||
}
|
||||
|
||||
if (["easy preSampling", "easy preSamplingAdvanced", "easy preSamplingSdTurbo", "easy preSamplingDynamicCFG"].includes(nodeData.name)) {
|
||||
const onExecuted = nodeType.prototype.onExecuted;
|
||||
nodeType.prototype.onExecuted = function (message) {
|
||||
onExecuted?.apply(this, arguments);
|
||||
const seed_changed = message.value[0]
|
||||
const seed_index = this.widgets.findIndex((w) => w.name === "seed_num")
|
||||
const w = this.widgets[seed_index]
|
||||
w.value = seed_changed;
|
||||
};
|
||||
}
|
||||
},
|
||||
});
|
||||
|
||||
|
||||
const getSetWidgets = ['rescale_after_model', 'rescale', 'image_output',
|
||||
'lora_name', 'lora1_name', 'lora2_name', 'lora3_name',
|
||||
'refiner_lora1_name', 'refiner_lora2_name', 'upscale_method',
|
||||
'image_output', 'add_noise', 'info', 'sampler_name',
|
||||
'ckpt_B_name', 'ckpt_C_name', 'save_model', 'refiner_ckpt_name',
|
||||
'num_loras', 'mode', 'toggle', "resolution"]
|
||||
|
||||
function getSetters(node) {
|
||||
if (node.widgets)
|
||||
for (const w of node.widgets) {
|
||||
if (getSetWidgets.includes(w.name)) {
|
||||
widgetLogic(node, w);
|
||||
if(w.name == 'sampler_name' && node.comfyClass == 'easy preSamplingSdTurbo') widgetLogic2(node, w);
|
||||
let widgetValue = w.value;
|
||||
|
||||
// Define getters and setters for widget values
|
||||
Object.defineProperty(w, 'value', {
|
||||
get() {
|
||||
return widgetValue;
|
||||
},
|
||||
set(newVal) {
|
||||
if (newVal !== widgetValue) {
|
||||
widgetValue = newVal;
|
||||
widgetLogic(node, w);
|
||||
if(w.name == 'sampler_name' && node.comfyClass == 'easy preSamplingSdTurbo') widgetLogic2(node, w);
|
||||
}
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,345 @@
|
||||
import { app } from "/scripts/app.js";
|
||||
import { ComfyWidgets } from '/scripts/widgets.js'
|
||||
|
||||
// Node that allows you to tunnel connections for cleaner graphs
|
||||
|
||||
app.registerExtension({
|
||||
name: "easy setNode",
|
||||
registerCustomNodes() {
|
||||
class SetNode {
|
||||
defaultVisibility = true;
|
||||
serialize_widgets = true;
|
||||
constructor() {
|
||||
if (!this.properties) {
|
||||
this.properties = {
|
||||
"previousName": ""
|
||||
};
|
||||
}
|
||||
this.properties.showOutputText = SetNode.defaultVisibility;
|
||||
|
||||
const node = this;
|
||||
|
||||
|
||||
this.addWidget(
|
||||
"text",
|
||||
"Constant",
|
||||
'',
|
||||
(s, t, u, v, x) => {
|
||||
node.validateName(node.graph);
|
||||
this.update();
|
||||
this.properties.previousName = this.widgets[0].value;
|
||||
},
|
||||
{}
|
||||
)
|
||||
|
||||
this.addInput("*", "*");
|
||||
|
||||
|
||||
this.onConnectionsChange = function(
|
||||
slotType, //1 = input, 2 = output
|
||||
slot,
|
||||
isChangeConnect,
|
||||
link_info,
|
||||
output
|
||||
) {
|
||||
console.log("onConnectionsChange");
|
||||
//On Disconnect
|
||||
if (slotType == 1 && !isChangeConnect) {
|
||||
this.inputs[slot].type = '*';
|
||||
this.inputs[slot].name = '*';
|
||||
}
|
||||
|
||||
//On Connect
|
||||
if (link_info && node.graph && slotType == 1 && isChangeConnect) {
|
||||
const fromNode = node.graph._nodes.find((otherNode) => otherNode.id == link_info.origin_id);
|
||||
const type = fromNode.outputs[link_info.origin_slot].type;
|
||||
|
||||
this.inputs[0].type = type;
|
||||
this.inputs[0].name = type;
|
||||
}
|
||||
|
||||
//Update either way
|
||||
this.update();
|
||||
}
|
||||
|
||||
this.validateName = function(graph) {
|
||||
let widgetValue = node.widgets[0].value;
|
||||
|
||||
if (widgetValue != '') {
|
||||
let tries = 0;
|
||||
let collisions = [];
|
||||
|
||||
do {
|
||||
collisions = graph._nodes.filter((otherNode) => {
|
||||
if (otherNode == this) {
|
||||
return false;
|
||||
}
|
||||
if (otherNode.type == 'easy setNode' && otherNode.widgets[0].value === widgetValue) {
|
||||
return true;
|
||||
}
|
||||
return false;
|
||||
})
|
||||
if (collisions.length > 0) {
|
||||
widgetValue = node.widgets[0].value + "_" + tries;
|
||||
}
|
||||
tries++;
|
||||
} while (collisions.length > 0)
|
||||
node.widgets[0].value = widgetValue;
|
||||
this.update();
|
||||
}
|
||||
}
|
||||
|
||||
this.clone = function () {
|
||||
console.log("CLONE");
|
||||
const cloned = SetNode.prototype.clone.apply(this);
|
||||
//cloned.inputs = [];
|
||||
cloned.inputs[0].name = '*';
|
||||
cloned.inputs[0].type = '*';
|
||||
cloned.properties.previousName = '';
|
||||
cloned.size = cloned.computeSize();
|
||||
return cloned;
|
||||
};
|
||||
|
||||
this.onAdded = function(graph) {
|
||||
this.validateName(graph);
|
||||
}
|
||||
|
||||
|
||||
this.update = function() {
|
||||
console.log("SetNode.update()");
|
||||
console.log(this.widgets[0].value);
|
||||
if (node.graph) {
|
||||
this.findGetters(node.graph).forEach((getter) => {
|
||||
getter.setType(this.inputs[0].type);
|
||||
});
|
||||
if (this.widgets[0].value) {
|
||||
this.findGetters(node.graph, true).forEach((getter) => {
|
||||
getter.setName(this.widgets[0].value)
|
||||
});
|
||||
}
|
||||
|
||||
const allGetters = node.graph._nodes.filter((otherNode) => otherNode.type == "easy getNode");
|
||||
allGetters.forEach((otherNode) => {
|
||||
if (otherNode.setComboValues) {
|
||||
otherNode.setComboValues();
|
||||
}
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
this.findGetters = function(graph, checkForPreviousName) {
|
||||
const name = checkForPreviousName ? this.properties.previousName : this.widgets[0].value;
|
||||
return graph._nodes.filter((otherNode) => {
|
||||
//console.log("otherNode.type:");
|
||||
//console.log(otherNode.type)
|
||||
if (otherNode.type == 'easy getNode' && otherNode.widgets[0].value === name && name != '') {
|
||||
return true;
|
||||
}
|
||||
return false;
|
||||
})
|
||||
}
|
||||
|
||||
|
||||
// This node is purely frontend and does not impact the resulting prompt so should not be serialized
|
||||
this.isVirtualNode = true;
|
||||
}
|
||||
|
||||
onRemoved() {
|
||||
console.log("onRemove");
|
||||
console.log(this);
|
||||
console.log(this.flags);
|
||||
const allGetters = this.graph._nodes.filter((otherNode) => otherNode.type == "easy getNode");
|
||||
allGetters.forEach((otherNode) => {
|
||||
if (otherNode.setComboValues) {
|
||||
otherNode.setComboValues([this]);
|
||||
}
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
LiteGraph.registerNodeType(
|
||||
"easy setNode",
|
||||
Object.assign(SetNode, {
|
||||
title: "Set",
|
||||
})
|
||||
);
|
||||
|
||||
SetNode.category = "utils";
|
||||
},
|
||||
});
|
||||
|
||||
|
||||
app.registerExtension({
|
||||
name: "easy getNode",
|
||||
registerCustomNodes() {
|
||||
class GetNode {
|
||||
|
||||
defaultVisibility = true;
|
||||
serialize_widgets = true;
|
||||
|
||||
constructor() {
|
||||
if (!this.properties) {
|
||||
this.properties = {};
|
||||
}
|
||||
this.properties.showOutputText = GetNode.defaultVisibility;
|
||||
|
||||
const node = this;
|
||||
this.addWidget(
|
||||
"combo",
|
||||
"Constant",
|
||||
"",
|
||||
(e) => {
|
||||
this.onRename();
|
||||
},
|
||||
{
|
||||
values: () => {
|
||||
const setterNodes = graph._nodes.filter((otherNode) => otherNode.type == 'easy setNode');
|
||||
//console.log("setting combo values");
|
||||
/*setterNodes.forEach((otherNode) => {
|
||||
console.log(otherNode.widgets[0].value)
|
||||
})*/
|
||||
return setterNodes.map((otherNode) => otherNode.widgets[0].value).sort();
|
||||
}
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
this.addOutput("*", '*');
|
||||
|
||||
|
||||
this.onConnectionsChange = function(
|
||||
slotType, //0 = output, 1 = input
|
||||
slot, //self-explanatory
|
||||
isChangeConnect,
|
||||
link_info,
|
||||
output
|
||||
) {
|
||||
this.validateLinks();
|
||||
}
|
||||
|
||||
|
||||
this.setName = function(name) {
|
||||
console.log("renaming getter: ");
|
||||
console.log(node.widgets[0].value + " -> " + name);
|
||||
node.widgets[0].value = name;
|
||||
node.onRename();
|
||||
node.serialize();
|
||||
}
|
||||
|
||||
|
||||
this.onRename = function() {
|
||||
console.log("onRename");
|
||||
|
||||
const setter = this.findSetter(node.graph);
|
||||
if (setter) {
|
||||
this.setType(setter.inputs[0].type);
|
||||
} else {
|
||||
this.setType('*');
|
||||
}
|
||||
}
|
||||
|
||||
this.clone = function () {
|
||||
const cloned = GetNode.prototype.clone.apply(this);
|
||||
cloned.size = cloned.computeSize();
|
||||
//this.update();
|
||||
return cloned;
|
||||
};
|
||||
|
||||
this.validateLinks = function() {
|
||||
console.log("validating links");
|
||||
if (this.outputs[0].type != '*' && this.outputs[0].links) {
|
||||
console.log("in");
|
||||
this.outputs[0].links.forEach((linkId) => {
|
||||
const link = node.graph.links[linkId];
|
||||
if (link && link.type != this.outputs[0].type && link.type != '*') {
|
||||
console.log("removing link");
|
||||
node.graph.removeLink(linkId)
|
||||
}
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
this.setType = function(type) {
|
||||
this.outputs[0].name = type;
|
||||
this.outputs[0].type = type;
|
||||
this.validateLinks();
|
||||
}
|
||||
|
||||
this.findSetter = function(graph) {
|
||||
const name = this.widgets[0].value;
|
||||
return graph._nodes.find((otherNode) => {
|
||||
//console.log("findSetter");
|
||||
//console.log("otherNode.type");
|
||||
//console.log(otherNode.type);
|
||||
if (otherNode.type == 'easy setNode' && otherNode.widgets[0].value === name && name != '') {
|
||||
return true;
|
||||
}
|
||||
return false;
|
||||
})
|
||||
}
|
||||
|
||||
// This node is purely frontend and does not impact the resulting prompt so should not be serialized
|
||||
this.isVirtualNode = true;
|
||||
}
|
||||
|
||||
|
||||
getInputLink(slot) {
|
||||
console.log("get.getInputLink(): " + slot);
|
||||
const setter = this.findSetter(this.graph);
|
||||
console.log("setter:");
|
||||
console.log(setter);
|
||||
|
||||
|
||||
// const setters = app.graph._nodes.filter((otherNode) => {
|
||||
// const name = this.widgets[0].value
|
||||
// if (otherNode.type == 'TunnelIn' && otherNode.widgets[0].value === name && name != '') {
|
||||
// return true;
|
||||
// }
|
||||
// return false;
|
||||
// });
|
||||
|
||||
// if (setters.length > 1) {
|
||||
// throw new Error("Multiple setters found for " + this.widgets[0].value);
|
||||
// }
|
||||
|
||||
// if (setters.length == 0) {
|
||||
// throw new Error("No setter found for " + this.widgets[0].value);
|
||||
// }
|
||||
|
||||
|
||||
if (setter) {
|
||||
const slot_info = setter.inputs[slot];
|
||||
console.log("slot info");
|
||||
console.log(slot_info);
|
||||
console.log(this.graph.links);
|
||||
const link = this.graph.links[ slot_info.link ];
|
||||
console.log("link:");
|
||||
console.log(link);
|
||||
return link;
|
||||
} else {
|
||||
console.log(this.widgets[0]);
|
||||
console.log(this.widgets[0].value);
|
||||
throw new Error("No setter found for " + this.widgets[0].value + "(" + this.type + ")");
|
||||
}
|
||||
|
||||
}
|
||||
onAdded(graph) {
|
||||
//this.setComboValues();
|
||||
//this.validateName(graph);
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
|
||||
LiteGraph.registerNodeType(
|
||||
"easy getNode",
|
||||
Object.assign(GetNode, {
|
||||
title: "Get",
|
||||
})
|
||||
);
|
||||
|
||||
GetNode.category = "utils";
|
||||
},
|
||||
});
|
||||
@@ -0,0 +1,47 @@
|
||||
import { api } from "../../../scripts/api.js";
|
||||
|
||||
// 全局Seed
|
||||
function globalSeedHandler(event) {
|
||||
let nodes = app.graph._nodes_by_id;
|
||||
for(let i in nodes) {
|
||||
let node = nodes[i];
|
||||
if(node.type == 'globalSeed') {
|
||||
if(node.widgets) {
|
||||
const w = node.widgets.find((w) => w.name == 'value');
|
||||
const last_w = node.widgets.find((w) => w.name == 'last_seed');
|
||||
last_w.value = w.value;
|
||||
w.value = event.detail.value;
|
||||
}
|
||||
}
|
||||
else{
|
||||
if(node.widgets) {
|
||||
const w = node.widgets.find((w) => w.name == 'seed_num' || w.name == 'seed' || w.name == 'noise_seed');
|
||||
if(w && event.detail.seed_map[node.id] != undefined) {
|
||||
w.value = event.detail.seed_map[node.id];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
api.addEventListener("easyuse-global-seed", globalSeedHandler);
|
||||
|
||||
const original_queuePrompt = api.queuePrompt;
|
||||
async function queuePrompt_with_seed(number, { output, workflow }) {
|
||||
workflow.seed_widgets = {};
|
||||
|
||||
for(let i in app.graph._nodes_by_id) {
|
||||
let widgets = app.graph._nodes_by_id[i].widgets;
|
||||
if(widgets) {
|
||||
for(let j in widgets) {
|
||||
if((widgets[j].name == 'seed_num' || widgets[j].name == 'seed' || widgets[j].name == 'noise_seed') && widgets[j].type != 'converted-widget')
|
||||
workflow.seed_widgets[i] = parseInt(j);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return await original_queuePrompt.call(api, number, { output, workflow });
|
||||
}
|
||||
|
||||
api.queuePrompt = queuePrompt_with_seed;
|
||||
Reference in New Issue
Block a user