storytold/storyteller-ml
2
0
Fork 0
mirror of https://github.com/storytold/storyteller-ml.git synced 2026-10-21 08:06:47 +00:00
Code Issues Packages Projects Releases Wiki Activity

remove old comfyUI

Browse Source
This commit is contained in:
salt
2024-02-29 14:11:16 -08:00
parent d44ea6619d
commit 9aad2f3d72
2932 changed files with 0 additions and 932769 deletions
Download Patch File Download Diff File Expand all files Collapse all files
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/.github/FUNDING.yml
Vendored
-13
View File
@@ -1,13 +0,0 @@
# These are supported funding model platforms
github: [jags111]
patreon: # jags111
open_collective: # Replace with a single Open Collective username
ko_fi: # B0B7IHIO1
tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
liberapay: # Replace with a single Liberapay username
issuehunt: # Replace with a single IssueHunt username
otechie: # Replace with a single Otechie username
lfx_crowdfunding: # Replace with a single LFX Crowdfunding project-name e.g., cloud-foundry
custom: ['https://www.buymeacoffee.com/jagsai','https://www. paypal.me/revsmart", orchidsasia.com' ]
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/LICENSE
-674
View File
@@ -1,674 +0,0 @@
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.
When we speak of free software, we are referring to freedom, not
price. Our General Public Licenses are designed to make sure that you
have the freedom to distribute copies of free software (and charge for
them if you wish), that you receive source code or can get it if you
want it, that you can change the software or use pieces of it in new
free programs, and that you know you can do these things.
To protect your rights, we need to prevent others from denying you
these rights or asking you to surrender the rights. Therefore, you have
certain responsibilities if you distribute copies of the software, or if
you modify it: responsibilities to respect the freedom of others.
For example, if you distribute copies of such a program, whether
gratis or for a fee, you must pass on to the recipients the same
freedoms that you received. You must make sure that they, too, receive
or can get the source code. And you must show them these terms so they
know their rights.
Developers that use the GNU GPL protect your rights with two steps:
(1) assert copyright on the software, and (2) offer you this License
giving you legal permission to copy, distribute and/or modify it.
For the developers' and authors' protection, the GPL clearly explains
that there is no warranty for this free software. For both users' and
authors' sake, the GPL requires that modified versions be marked as
changed, so that their problems will not be attributed erroneously to
authors of previous versions.
Some devices are designed to deny users access to install or run
modified versions of the software inside them, although the manufacturer
can do so. This is fundamentally incompatible with the aim of
protecting users' freedom to change the software. The systematic
pattern of such abuse occurs in the area of products for individuals to
use, which is precisely where it is most unacceptable. Therefore, we
have designed this version of the GPL to prohibit the practice for those
products. If such problems arise substantially in other domains, we
stand ready to extend this provision to those domains in future versions
of the GPL, as needed to protect the freedom of users.
Finally, every program is threatened constantly by software patents.
States should not allow patents to restrict development and use of
software on general-purpose computers, but in those that do, we wish to
avoid the special danger that patents applied to a free program could
make it effectively proprietary. To prevent this, the GPL assures that
patents cannot be used to render the program non-free.
The precise terms and conditions for copying, distribution and
modification follow.
TERMS AND CONDITIONS
0. Definitions.
"This License" refers to version 3 of the GNU General Public License.
"Copyright" also means copyright-like laws that apply to other kinds of
works, such as semiconductor masks.
"The Program" refers to any copyrightable work licensed under this
License. Each licensee is addressed as "you". "Licensees" and
"recipients" may be individuals or organizations.
To "modify" a work means to copy from or adapt all or part of the work
in a fashion requiring copyright permission, other than the making of an
exact copy. The resulting work is called a "modified version" of the
earlier work or a work "based on" the earlier work.
A "covered work" means either the unmodified Program or a work based
on the Program.
To "propagate" a work means to do anything with it that, without
permission, would make you directly or secondarily liable for
infringement under applicable copyright law, except executing it on a
computer or modifying a private copy. Propagation includes copying,
distribution (with or without modification), making available to the
public, and in some countries other activities as well.
To "convey" a work means any kind of propagation that enables other
parties to make or receive copies. Mere interaction with a user through
a computer network, with no transfer of a copy, is not conveying.
An interactive user interface displays "Appropriate Legal Notices"
to the extent that it includes a convenient and prominently visible
feature that (1) displays an appropriate copyright notice, and (2)
tells the user that there is no warranty for the work (except to the
extent that warranties are provided), that licensees may convey the
work under this License, and how to view a copy of this License. If
the interface presents a list of user commands or options, such as a
menu, a prominent item in the list meets this criterion.
1. Source Code.
The "source code" for a work means the preferred form of the work
for making modifications to it. "Object code" means any non-source
form of a work.
A "Standard Interface" means an interface that either is an official
standard defined by a recognized standards body, or, in the case of
interfaces specified for a particular programming language, one that
is widely used among developers working in that language.
The "System Libraries" of an executable work include anything, other
than the work as a whole, that (a) is included in the normal form of
packaging a Major Component, but which is not part of that Major
Component, and (b) serves only to enable use of the work with that
Major Component, or to implement a Standard Interface for which an
implementation is available to the public in source code form. A
"Major Component", in this context, means a major essential component
(kernel, window system, and so on) of the specific operating system
(if any) on which the executable work runs, or a compiler used to
produce the work, or an object code interpreter used to run it.
The "Corresponding Source" for a work in object code form means all
the source code needed to generate, install, and (for an executable
work) run the object code and to modify the work, including scripts to
control those activities. However, it does not include the work's
System Libraries, or general-purpose tools or generally available free
programs which are used unmodified in performing those activities but
which are not part of the work. For example, Corresponding Source
includes interface definition files associated with source files for
the work, and the source code for shared libraries and dynamically
linked subprograms that the work is specifically designed to require,
such as by intimate data communication or control flow between those
subprograms and other parts of the work.
The Corresponding Source need not include anything that users
can regenerate automatically from other parts of the Corresponding
Source.
The Corresponding Source for a work in source code form is that
same work.
2. Basic Permissions.
All rights granted under this License are granted for the term of
copyright on the Program, and are irrevocable provided the stated
conditions are met. This License explicitly affirms your unlimited
permission to run the unmodified Program. The output from running a
covered work is covered by this License only if the output, given its
content, constitutes a covered work. This License acknowledges your
rights of fair use or other equivalent, as provided by copyright law.
You may make, run and propagate covered works that you do not
convey, without conditions so long as your license otherwise remains
in force. You may convey covered works to others for the sole purpose
of having them make modifications exclusively for you, or provide you
with facilities for running those works, provided that you comply with
the terms of this License in conveying all material for which you do
not control copyright. Those thus making or running the covered works
for you must do so exclusively on your behalf, under your direction
and control, on terms that prohibit them from making any copies of
your copyrighted material outside their relationship with you.
Conveying under any other circumstances is permitted solely under
the conditions stated below. Sublicensing is not allowed; section 10
makes it unnecessary.
3. Protecting Users' Legal Rights From Anti-Circumvention Law.
No covered work shall be deemed part of an effective technological
measure under any applicable law fulfilling obligations under article
11 of the WIPO copyright treaty adopted on 20 December 1996, or
similar laws prohibiting or restricting circumvention of such
measures.
When you convey a covered work, you waive any legal power to forbid
circumvention of technological measures to the extent such circumvention
is effected by exercising rights under this License with respect to
the covered work, and you disclaim any intention to limit operation or
modification of the work as a means of enforcing, against the work's
users, your or third parties' legal rights to forbid circumvention of
technological measures.
4. Conveying Verbatim Copies.
You may convey verbatim copies of the Program's source code as you
receive it, in any medium, provided that you conspicuously and
appropriately publish on each copy an appropriate copyright notice;
keep intact all notices stating that this License and any
non-permissive terms added in accord with section 7 apply to the code;
keep intact all notices of the absence of any warranty; and give all
recipients a copy of this License along with the Program.
You may charge any price or no price for each copy that you convey,
and you may offer support or warranty protection for a fee.
5. Conveying Modified Source Versions.
You may convey a work based on the Program, or the modifications to
produce it from the Program, in the form of source code under the
terms of section 4, provided that you also meet all of these conditions:
a) The work must carry prominent notices stating that you modified
it, and giving a relevant date.
b) The work must carry prominent notices stating that it is
released under this License and any conditions added under section
7. This requirement modifies the requirement in section 4 to
"keep intact all notices".
c) You must license the entire work, as a whole, under this
License to anyone who comes into possession of a copy. This
License will therefore apply, along with any applicable section 7
additional terms, to the whole of the work, and all its parts,
regardless of how they are packaged. This License gives no
permission to license the work in any other way, but it does not
invalidate such permission if you have separately received it.
d) If the work has interactive user interfaces, each must display
Appropriate Legal Notices; however, if the Program has interactive
interfaces that do not display Appropriate Legal Notices, your
work need not make them do so.
A compilation of a covered work with other separate and independent
works, which are not by their nature extensions of the covered work,
and which are not combined with it such as to form a larger program,
in or on a volume of a storage or distribution medium, is called an
"aggregate" if the compilation and its resulting copyright are not
used to limit the access or legal rights of the compilation's users
beyond what the individual works permit. Inclusion of a covered work
in an aggregate does not cause this License to apply to the other
parts of the aggregate.
6. Conveying Non-Source Forms.
You may convey a covered work in object code form under the terms
of sections 4 and 5, provided that you also convey the
machine-readable Corresponding Source under the terms of this License,
in one of these ways:
a) Convey the object code in, or embodied in, a physical product
(including a physical distribution medium), accompanied by the
Corresponding Source fixed on a durable physical medium
customarily used for software interchange.
b) Convey the object code in, or embodied in, a physical product
(including a physical distribution medium), accompanied by a
written offer, valid for at least three years and valid for as
long as you offer spare parts or customer support for that product
model, to give anyone who possesses the object code either (1) a
copy of the Corresponding Source for all the software in the
product that is covered by this License, on a durable physical
medium customarily used for software interchange, for a price no
more than your reasonable cost of physically performing this
conveying of source, or (2) access to copy the
Corresponding Source from a network server at no charge.
c) Convey individual copies of the object code with a copy of the
written offer to provide the Corresponding Source. This
alternative is allowed only occasionally and noncommercially, and
only if you received the object code with such an offer, in accord
with subsection 6b.
d) Convey the object code by offering access from a designated
place (gratis or for a charge), and offer equivalent access to the
Corresponding Source in the same way through the same place at no
further charge. You need not require recipients to copy the
Corresponding Source along with the object code. If the place to
copy the object code is a network server, the Corresponding Source
may be on a different server (operated by you or a third party)
that supports equivalent copying facilities, provided you maintain
clear directions next to the object code saying where to find the
Corresponding Source. Regardless of what server hosts the
Corresponding Source, you remain obligated to ensure that it is
available for as long as needed to satisfy these requirements.
e) Convey the object code using peer-to-peer transmission, provided
you inform other peers where the object code and Corresponding
Source of the work are being offered to the general public at no
charge under subsection 6d.
A separable portion of the object code, whose source code is excluded
from the Corresponding Source as a System Library, need not be
included in conveying the object code work.
A "User Product" is either (1) a "consumer product", which means any
tangible personal property which is normally used for personal, family,
or household purposes, or (2) anything designed or sold for incorporation
into a dwelling. In determining whether a product is a consumer product,
doubtful cases shall be resolved in favor of coverage. For a particular
product received by a particular user, "normally used" refers to a
typical or common use of that class of product, regardless of the status
of the particular user or of the way in which the particular user
actually uses, or expects or is expected to use, the product. A product
is a consumer product regardless of whether the product has substantial
commercial, industrial or non-consumer uses, unless such uses represent
the only significant mode of use of the product.
"Installation Information" for a User Product means any methods,
procedures, authorization keys, or other information required to install
and execute modified versions of a covered work in that User Product from
a modified version of its Corresponding Source. The information must
suffice to ensure that the continued functioning of the modified object
code is in no case prevented or interfered with solely because
modification has been made.
If you convey an object code work under this section in, or with, or
specifically for use in, a User Product, and the conveying occurs as
part of a transaction in which the right of possession and use of the
User Product is transferred to the recipient in perpetuity or for a
fixed term (regardless of how the transaction is characterized), the
Corresponding Source conveyed under this section must be accompanied
by the Installation Information. But this requirement does not apply
if neither you nor any third party retains the ability to install
modified object code on the User Product (for example, the work has
been installed in ROM).
The requirement to provide Installation Information does not include a
requirement to continue to provide support service, warranty, or updates
for a work that has been modified or installed by the recipient, or for
the User Product in which it has been modified or installed. Access to a
network may be denied when the modification itself materially and
adversely affects the operation of the network or violates the rules and
protocols for communication across the network.
Corresponding Source conveyed, and Installation Information provided,
in accord with this section must be in a format that is publicly
documented (and with an implementation available to the public in
source code form), and must require no special password or key for
unpacking, reading or copying.
7. Additional Terms.
"Additional permissions" are terms that supplement the terms of this
License by making exceptions from one or more of its conditions.
Additional permissions that are applicable to the entire Program shall
be treated as though they were included in this License, to the extent
that they are valid under applicable law. If additional permissions
apply only to part of the Program, that part may be used separately
under those permissions, but the entire Program remains governed by
this License without regard to the additional permissions.
When you convey a copy of a covered work, you may at your option
remove any additional permissions from that copy, or from any part of
it. (Additional permissions may be written to require their own
removal in certain cases when you modify the work.) You may place
additional permissions on material, added by you to a covered work,
for which you have or can give appropriate copyright permission.
Notwithstanding any other provision of this License, for material you
add to a covered work, you may (if authorized by the copyright holders of
that material) supplement the terms of this License with terms:
a) Disclaiming warranty or limiting liability differently from the
terms of sections 15 and 16 of this License; or
b) Requiring preservation of specified reasonable legal notices or
author attributions in that material or in the Appropriate Legal
Notices displayed by works containing it; or
c) Prohibiting misrepresentation of the origin of that material, or
requiring that modified versions of such material be marked in
reasonable ways as different from the original version; or
d) Limiting the use for publicity purposes of names of licensors or
authors of the material; or
e) Declining to grant rights under trademark law for use of some
trade names, trademarks, or service marks; or
f) Requiring indemnification of licensors and authors of that
material by anyone who conveys the material (or modified versions of
it) with contractual assumptions of liability to the recipient, for
any liability that these contractual assumptions directly impose on
those licensors and authors.
All other non-permissive additional terms are considered "further
restrictions" within the meaning of section 10. If the Program as you
received it, or any part of it, contains a notice stating that it is
governed by this License along with a term that is a further
restriction, you may remove that term. If a license document contains
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>.
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/README.md
-251
View File
@@ -1,251 +0,0 @@
✨🍬Planning to help this branch stay alive and any issues will try to solve or fix .. But will be slow as I run many github repos . before raising any issues, please update comfyUI to the latest and esnure all the required packages are updated ass well. Share your workflow in issues to retest same at our end and update the patch.🍬
<b> Efficiency Nodes for ComfyUI Version 2.0+
=======
### A collection of <a href="https://github.com/comfyanonymous/ComfyUI" >ComfyUI</a> custom nodes to help streamline workflows and reduce total node count.
## Releases
Please check out our WIKI for any use cases and new developments including workflow and settings.<br>
[Efficiency Nodes Wiki](https://github.com/jags111/efficiency-nodes-comfyui/wiki)<br>
### Nodes:
<!-------------------------------------------------------------------------------------------------------------------------------------------------------->
<details>
<summary><b>Efficient Loader</b> & <b>Eff. Loader SDXL</b></summary>
<ul>
<li>Nodes that can load & cache Checkpoint, VAE, & LoRA type models. <i>(cache settings found in config file 'node_settings.json')</i></li>
<li>Able to apply LoRA & Control Net stacks via their <code>lora_stack</code> and <code>cnet_stack</code> inputs.</li>
<li>Come with positive and negative prompt text boxes. You can also set the way you want the prompt to be <a href="https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb">encoded</a> via the <code>token_normalization</code> and <code>weight_interpretation</code> widgets.</li>
<li>These node's also feature a variety of custom menu options as shown below.
<p></p><img src="https://github.com/LucianoCirino/efficiency-nodes-media/blob/main/images/nodes//NodeMenu%20-%20Efficient%20Loaders.png" width="240" style="display: inline-block;"></p>
<p><i>note: "🔍 View model info..." requires <a href="https://github.com/pythongosssss/ComfyUI-Custom-Scripts">ComfyUI-Custom-Scripts</a> to be installed to function.</i></p></li>
<li>These loaders are used by the <b>XY Plot</b> node for many of its plot type dependencies.</li>
</ul>
<p align="center">
<img src="https://github.com/LucianoCirino/efficiency-nodes-media/blob/main/images/nodes/NODE%20-%20Efficient%20Loader.png" width="240" style="display: inline-block;">
<img src="https://github.com/LucianoCirino/efficiency-nodes-media/blob/main/images/nodes/NODE%20-%20Eff.%20Loader%20SDXL.png" width="240" style="display: inline-block;">
</p>
</details>
<!-------------------------------------------------------------------------------------------------------------------------------------------------------->
<details>
<summary><b>KSampler (Efficient)</b>, <b>KSampler Adv. (Efficient)</b>, <b>KSampler SDXL (Eff.)</b></summary>
- Modded KSamplers with the ability to live preview generations and/or vae decode images.
- Feature a special seed box that allows for a clearer management of seeds. <i>(-1 seed to apply the selected seed behavior)</i>
- Can execute a variety of scripts, such as the <b>XY Plot</b> script. To activate the <code>script</code>, simply connect the input connection.
<p align="center">
<img src="https://github.com/LucianoCirino/efficiency-nodes-media/blob/main/images/nodes/NODE%20-%20KSampler%20(Efficient).png" width="240">
&nbsp; &nbsp; &nbsp;
<img src="https://github.com/LucianoCirino/efficiency-nodes-media/blob/main/images/nodes/NODE%20-%20KSampler%20Adv.%20(Efficient).png" width="240">
&nbsp; &nbsp; &nbsp;
<img src="https://github.com/LucianoCirino/efficiency-nodes-media/blob/main/images/nodes/NODE%20-%20KSampler%20SDXL%20(Eff.).png" width="240">
</p>
</details>
<!-------------------------------------------------------------------------------------------------------------------------------------------------------->
<details>
<summary><b>Script Nodes</b></summary>
- A group of node's that are used in conjuction with the Efficient KSamplers to execute a variety of 'pre-wired' set of actions.
- Script nodes can be chained if their input/outputs allow it. Multiple instances of the same Script Node in a chain does nothing.
<p align="center">
<img src="https://github.com/LucianoCirino/efficiency-nodes-media/blob/main/images/nodes/ScriptChain.png" width="1080">
</p>
<!-------------------------------------------------------------------------------------------------------------------------------------------------------->
<details>
<summary><b>XY Plot</b></summary>
<ul>
<li>Node that allows users to specify parameters for the Efficiency KSamplers to plot on a grid.</li>
</ul>
<p align="center">
<img src="https://github.com/LucianoCirino/efficiency-nodes-media/blob/main/images/nodes/XY%20Plot%20-%20Node%20Example.png" width="1080">
</p>
</details>
<!-------------------------------------------------------------------------------------------------------------------------------------------------------->
<details>
<summary><b>HighRes-Fix</b></summary>
<ul>
<li>Node that the gives user the ability to upscale KSampler results through variety of different methods.</li>
<li>Comes out of the box with popular Neural Network Latent Upscalers such as Ttl's <a href="https://github.com/Ttl/ComfyUi_NNLatentUpscale">ComfyUi_NNLatentUpscale</a> and City96's <a href="https://github.com/city96/SD-Latent-Upscaler">SD-Latent-Upscaler</a>.</li>
<li>Supports ControlNet guided latent upscaling. <i> (You must have Fannovel's <a href="https://github.com/Fannovel16/comfyui_controlnet_aux">comfyui_controlnet_aux</a> installed to unlock this feature)</i></li>
<li> Local models---The node pulls the required files from huggingface hub by default. You can create a models folder and place the modules there if you have a flaky connection or prefer to use it completely offline, it will load them locally instead. The path should be: ComfyUI/custom_nodes/efficiency-nodes-comfyui/models; Alternatively, just clone the entire HF repo to it: (git clone https://huggingface.co/city96/SD-Latent-Upscaler) to ComfyUI/custom_nodes/efficiency-nodes-comfyui/models</li>
</ul>
<p align="center">
<img src="https://github.com/LucianoCirino/efficiency-nodes-media/blob/main/images/nodes/HighResFix%20-%20Node%20Example.gif" width="1080">
</p>
</details>
<!-------------------------------------------------------------------------------------------------------------------------------------------------------->
<details>
<summary><b>Noise Control</b></summary>
<ul>
<li>This node gives the user the ability to manipulate noise sources in a variety of ways, such as the sampling's RNG source.</li>
<li>The <a href="https://github.com/shiimizu/ComfyUI_smZNodes">CFG Denoiser</a> noise hijack was developed by smZ, it allows you to get closer recreating Automatic1111 results.</li>
<p></p><i>Note: The CFG Denoiser does not work with a variety of conditioning types such as ControlNet & GLIGEN</i></p>
<li>This node also allows you to add noise <a href="https://github.com/chrisgoringe/cg-noise">Seed Variations</a> to your generations.</li>
<li>For trying to replicate Automatic1111 images, this node will help you achieve it. Encode your prompt using "length+mean" <code>token_normalization</code> with "A1111" <code>weight_interpretation</code>, set the Noise Control Script node's <code>rng_source</code> to "gpu", and turn the <code>cfg_denoiser</code> to true.</li>
</ul>
<p align="center">
<img src="https://github.com/LucianoCirino/efficiency-nodes-media/blob/main/images/nodes/NODE%20-%20Noise%20Control%20Script.png" width="320">
</p>
</details>
<!-------------------------------------------------------------------------------------------------------------------------------------------------------->
<details>
<summary><b>Tiled Upscaler</b></summary>
<ul>
<li>The Tiled Upscaler script attempts to encompas BlenderNeko's <a href="https://github.com/BlenderNeko/ComfyUI_TiledKSampler">ComfyUI_TiledKSampler</a> workflow into 1 node.</li>
<li>Script supports Tiled ControlNet help via the options.</li>
<li>Strongly recommend the <code>preview_method</code> be "vae_decoded_only" when running the script.</li>
</ul>
<p align="center">
<img src="https://github.com/LucianoCirino/efficiency-nodes-media/blob/main/images/nodes/Tiled%20Upscaler%20-%20Node%20Example.gif" width="1080">
</p>
</details>
<!-------------------------------------------------------------------------------------------------------------------------------------------------------->
<details>
<summary><b>AnimateDiff</b></summary>
<ul>
<li>To unlock the AnimateDiff script it is required you have installed Kosinkadink's <a href="https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved">ComfyUI-AnimateDiff-Evolved</a>.</li>
<li>The latent <code>batch_size</code> when running this script becomes your frame count.</li>
</ul>
<p align="center">
<img src="https://github.com/LucianoCirino/efficiency-nodes-media/blob/main/images/nodes/AnimateDiff%20-%20Node%20Example.gif" width="1080">
</p>
</details>
</details>
<!-------------------------------------------------------------------------------------------------------------------------------------------------------->
<details>
<summary><b>Image Overlay</b></summary>
<ul>
<li>Node that allows for flexible image overlaying. Works also with image batches.</li>
</ul>
<p align="center">
<img src="https://github.com/LucianoCirino/efficiency-nodes-media/blob/main/images/nodes/Image%20Overlay%20-%20Node%20Example.png" width="1080">
</p>
</details>
<!-------------------------------------------------------------------------------------------------------------------------------------------------------->
<details>
<summary><b>SimpleEval Nodes</b></summary>
<ul>
<li>A collection of nodes that allows users to write simple Python expressions for a variety of data types using the <i><a href="https://github.com/danthedeckie/simpleeval" >simpleeval</a></i> library.</li>
<li>To activate you must have installed the simpleeval library in your Python workspace.</li>
<pre>pip install simpleeval</pre>
</ul>
<p align="center">
<img src="https://github.com/LucianoCirino/efficiency-nodes-media/blob/main/images/nodes/NODE%20-%20Evaluate%20Integers.png" width="320">
&nbsp; &nbsp;
<img src="https://github.com/LucianoCirino/efficiency-nodes-media/blob/main/images/nodes/NODE%20-%20Evaluate%20Floats.png" width="320">
&nbsp; &nbsp;
<img src="https://github.com/LucianoCirino/efficiency-nodes-media/blob/main/images/nodes/NODE%20-%20Evaluate%20Strings.png" width="320">
</p>
</details>
<!-------------------------------------------------------------------------------------------------------------------------------------------------------->
<details>
<summary><b>Latent Upscale nodes</b></summary>
<ul>
<li>Forked from NN latent this node provides some remarkable neural enhancement to the latents making scaling a cool task</li>
<li>Both NN latent upscale and Latent upscaler does the Latent improvemnet in remarkable ways. If you face any issue regarding same please install the nodes from this link([SD-Latent-Upscaler](https://github.com/city96/SD-Latent-Upscaler) and the NN latent upscale from [ComfyUI_NNlatentUpscale](https://github.com/Ttl/ComfyUi_NNLatentUpscale) </li>
</ul>
<p align="center">
<img src="images/2023-12-08_19-53-37.png" width="320">
&nbsp; &nbsp;
<img src="images/2023-12-08_19-54-11.png" width="320">
&nbsp; &nbsp;
</p>
</details>
## Workflow Examples:
Kindly load all PNG files in same name in the (workflow driectory) to comfyUI to get all this workflows. The PNG files have the json embedded into them and are easy to drag and drop !<br>
1. HiRes-Fixing<br>
[<img src="https://github.com/jags111/efficiency-nodes-comfyui/blob/main/workflows/HiResfix_workflow.png" width="800">](https://github.com/jags111/efficiency-nodes-comfyui/blob/main/workflows/HiResfix_workflow.png)<br>
2. SDXL Refining & **Noise Control Script**<br>
[<img src="https://github.com/jags111/efficiency-nodes-comfyui/blob/main/workflows/SDXL_base_refine_noise_workflow.png" width="800">](https://github.com/jags111/efficiency-nodes-comfyui/blob/main/workflows/SDXL_base_refine_noise_workflow.png)<br>
3. **XY Plot**: LoRA <code>model_strength</code> vs <code>clip_strength</code><br>
[<img src="https://github.com/jags111/efficiency-nodes-comfyui/blob/main/workflows/Eff_XYPlot%20-%20LoRA%20Model%20vs%20Clip%20Strengths01.png" width="800">](https://github.com/jags111/efficiency-nodes-comfyui/blob/main/workflows/Eff_XYPlot%20-%20LoRA%20Model%20vs%20Clip%20Strengths01.png)<br>
4. Stacking Scripts: **XY Plot** + **Noise Control** + **HiRes-Fix**<br>
[<img src="https://github.com/LucianoCirino/efficiency-nodes-comfyui/blob/v2.0/workflows/XYPlot%20-%20Seeds%20vs%20Checkpoints%20%26%20Stacked%20Scripts.png" width="800">](https://github.com/LucianoCirino/efficiency-nodes-comfyui/blob/v2.0/workflows/XYPlot%20-%20Seeds%20vs%20Checkpoints%20%26%20Stacked%20Scripts.png)<br>
5. Stacking Scripts: **HiRes-Fix** (with ControlNet)<br>
[<img src="https://github.com/jags111/efficiency-nodes-comfyui/blob/main/workflows/eff_animatescriptWF001.gif" width="800">](https://github.com/jags111/efficiency-nodes-comfyui/blob/main/workflows/eff_animatescriptWF001.gif)<br>
6. SVD workflow: **Stable Video Diffusion** + *Kohya Hires** (with latent control)<br>
<br>
### Dependencies
The python library <i><a href="https://github.com/danthedeckie/simpleeval" >simpleeval</a></i> is required to be installed if you wish to use the **Simpleeval Nodes**.
<pre>pip install simpleeval</pre>
Also can be installed with a simple pip command <br>
'pip install simpleeval'
A single file library for easily adding evaluatable expressions into python projects. Say you want to allow a user to set an alarm volume, which could depend on the time of day, alarm level, how many previous alarms had gone off, and if there is music playing at the time.
check Notes for more information.
## **Install:**
To install, drop the "_**efficiency-nodes-comfyui**_" folder into the "_**...\ComfyUI\ComfyUI\custom_nodes**_" directory and restart UI.
## Todo
[ ] Add guidance to notebook
# Comfy Resources
**Efficiency Linked Repos**
- [BlenderNeko ComfyUI_ADV_CLIP_emb](https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb) by@BlenderNeko
- [Chrisgoringe cg-noise](https://github.com/chrisgoringe/cg-noise) by@Chrisgoringe
- [pythongosssss ComfyUI-Custom-Scripts](https://github.com/pythongosssss/ComfyUI-Custom-Scripts) by@pythongosssss
- [shiimizu ComfyUI_smZNodes](https://github.com/shiimizu/ComfyUI_smZNodes) by@shiimizu
- [LEv145_images-grid-comfyUI-plugin](https://github.com/LEv145/images-grid-comfy-plugin)) by@LEv145
- [ltdrdata-ComfyUI-Inspire-Pack](https://github.com/ltdrdata/ComfyUI-Inspire-Pack) by@ltdrdata
- [pythongosssss-ComfyUI-custom-Scripts](https://github.com/pythongosssss/ComfyUI-Custom-Scripts) by@pythongosssss
- [RockOfFire-ComfyUI_Comfyroll_CustomNodes](https://github.com/RockOfFire/ComfyUI_Comfyroll_CustomNodes) by@RockOfFire
**Guides**:
- [Official Examples (eng)](https://comfyanonymous.github.io/ComfyUI_examples/)-
- [ComfyUI Community Manual (eng)](https://blenderneko.github.io/ComfyUI-docs/) by @BlenderNeko
- **Extensions and Custom Nodes**:
- [Plugins for Comfy List (eng)](https://github.com/WASasquatch/comfyui-plugins) by @WASasquatch
- [ComfyUI tag on CivitAI (eng)](https://civitai.com/tag/comfyui)-
- [Tomoaki's personal Wiki (jap)](https://comfyui.creamlab.net/guides/) by @tjhayasaka
## Support
If you create a cool image with our nodes, please show your result and message us on twitter at @jags111 or @NeuralismAI .
You can join the <a href="https://discord.gg/vNVqT82W" alt="Neuralism Discord"> NEURALISM AI DISCORD </a> or <a href="https://discord.gg/UmSd4qyh" alt =Jags AI Discord > JAGS AI DISCORD </a>
Share your work created with this model. Exchange experiences and parameters. And see more interesting custom workflows.
Support us in Patreon for more future models and new versions of AI notebooks.
- tip me on <a href="https://www.patreon.com/jags111"> [patreon]</a>
My buymeacoffee.com pages and links are here and if you feel you are happy with my work just buy me a coffee !
<a href="https://www.buymeacoffee.com/jagsAI"> coffee for JAGS AI</a>
Thank you for being awesome!
<img src = "images/ComfyUI_temp_vpose_00005_.png" width = "50%">
<!-- end support-pitch -->
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/__init__.py
-18
View File
@@ -1,18 +0,0 @@
import os
import subprocess
import importlib.util
import folder_paths
import shutil
import sys
import traceback
from .efficiency_nodes import NODE_CLASS_MAPPINGS
#from .py.ttl_nn_latent_upscaler import NODE_CLASS_MAPPINGS
#from .py.city96_latent_upscaler import NODE_CLASS_MAPPINGS
WEB_DIRECTORY = "js"
CC_VERSION = 2.0
__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS', 'CC_VERSION']
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/__pycache__/__init__.cpython-310.pyc
BIN
View File
Binary file not shown.
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/__pycache__/efficiency_nodes.cpython-310.pyc
BIN
View File
Binary file not shown.
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/__pycache__/tsc_utils.cpython-310.pyc
BIN
View File
Binary file not shown.
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/arial.ttf
BIN
View File
Binary file not shown.
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/efficiency_nodes.py
-4373
View File
File diff suppressed because it is too large Load Diff
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/funding.yml
-3
View File
@@ -1,3 +0,0 @@
github: ["https://www.buymeacoffee.com/jagsai", jags111]
patreon: jags111
custom: ["https://www.paypal.me/revsmart", orchidsasia.com]
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/2023-10-31_22-43-17.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 1.2 MiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/2023-11-01_14-25-01.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 586 KiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/2023-11-01_19-54-59.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 613 KiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/2023-11-04_22-32-57.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 1.6 MiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/2023-11-04_22-33-57.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 279 KiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/2023-11-04_22-46-20.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 1.9 MiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/2023-11-04_22-47-09.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 1.3 MiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/2023-11-04_22-57-28.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 994 KiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/2023-11-04_23-10-03.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 672 KiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/2023-11-05_00-02-07.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 230 KiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/2023-11-05_00-02-34.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 1.1 MiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/2023-12-08_19-53-37.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 58 KiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/2023-12-08_19-54-11.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 474 KiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/2024-01-19_22-39-03.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 314 KiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/2024-01-19_22-39-26.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 624 KiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/2024-01-19_22-39-50.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 503 KiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/ComfyUI_temp_vpose_00005_.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 10 MiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/README.md
-42
View File
@@ -1,42 +0,0 @@
Images in this group<br>
<table><tr>
<tr>
<td valign="bottom">
<img width="200" alt="2023-10-31_22-43-17" src="https://github.com/jags111/efficiency-nodes-comfyui/assets/5968619/f8f217fd-b36f-4b51-9c11-676020c07da8">
</td><br>
<td valign="bottom">
<img width="200" alt="2023-11-04_22-32-57" src="https://github.com/jags111/efficiency-nodes-comfyui/assets/5968619/f7dae80f-f499-4b07-a3aa-8dcbd9013f86">
</td><br>
<td valign="bottom">
<img width="200" alt="2023-11-01_19-54-59" src="https://github.com/jags111/efficiency-nodes-comfyui/assets/5968619/781e5c00-4b62-43f9-a7a3-fd9172c612db">
</td><br>
<td valign="bottom">
<img width="200" alt="2023-11-01_14-25-01" src="https://github.com/jags111/efficiency-nodes-comfyui/assets/5968619/65a390f8-19aa-4838-b3bb-8dfff35782ed">
</td><br>
</tr>
<tr>
<td valign="bottom">
<img width="200" alt="2023-11-05_00-02-34" src="https://github.com/jags111/efficiency-nodes-comfyui/assets/5968619/664c0280-e091-4200-a47f-987d66cfaecf">
</td><br>
<td valign="bottom">
<img width="200" alt="2023-11-05_00-02-07" src="https://github.com/jags111/efficiency-nodes-comfyui/assets/5968619/52df496f-5440-4f3e-bc22-23b7185e6c0e">
</td><br>
<td valign="bottom">
<img width="200" alt="2023-11-04_23-10-03" src="https://github.com/jags111/efficiency-nodes-comfyui/assets/5968619/64e865cf-52c2-4714-9e8b-41ef68ee77dd">
</td><br>
<td valign="bottom">
<img width="200" alt="2023-11-04_22-57-28" src="https://github.com/jags111/efficiency-nodes-comfyui/assets/5968619/21be4183-43b6-4ebf-ac07-b9ba3012bb57">
</td>
</tr>
<tr>
<td valign="bottom">
<img width="200" alt="2023-11-04_22-47-09" src="https://github.com/jags111/efficiency-nodes-comfyui/assets/5968619/35a007a9-6ee1-4f2f-9d53-5f52074482b5">
</td>
<td valign="bottom">
<img width="200" alt="2023-11-04_22-46-20" src="https://github.com/jags111/efficiency-nodes-comfyui/assets/5968619/5881ced2-fd87-4a24-8341-225748558afd">
</td>
<td valign="bottom">
<img width="200" alt="2023-11-04_22-33-57" src="https://github.com/jags111/efficiency-nodes-comfyui/assets/5968619/44154b1f-67c0-45f8-b09b-517258ec3332">
</td>
</tr>
</table>
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/nodes/AnimateDiff & HiResFix Scripts.gif
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 6.3 MiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/nodes/AnimateDiff - Node Example.gif
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 4.1 MiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/nodes/HighResFix - Node Example.gif
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 16 MiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/nodes/Image Overlay - Node Example.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 1.3 MiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/nodes/NODE - AnimateDiff Script.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 31 KiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/nodes/NODE - Eff. Loader SDXL.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 45 KiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/nodes/NODE - Efficient Loader.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 51 KiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/nodes/NODE - Evaluate Floats.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 20 KiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/nodes/NODE - Evaluate Integers.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 14 KiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/nodes/NODE - Evaluate Strings.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 17 KiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/nodes/NODE - HighRes-Fix Script.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 36 KiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/nodes/NODE - Image Overlay.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 23 KiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/nodes/NODE - KSampler (Efficient).png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 47 KiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/nodes/NODE - KSampler Adv. (Efficient).png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 58 KiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/nodes/NODE - KSampler SDXL (Eff.).png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 45 KiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/nodes/NODE - Noise Control Script.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 17 KiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/nodes/NODE - Tiled Upscaler Script.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 32 KiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/nodes/NODE - XY Plot Script.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 24 KiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/nodes/NODE - XY Plot.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 16 KiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/nodes/NodeMenu - Efficient Loaders.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 5.5 KiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/nodes/ScriptChain.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 323 KiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/nodes/Tiled Upscaler - Node Example.gif
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 14 MiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/nodes/XY Plot - Node Example.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 1.0 MiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/images/nodes/readme.md
-1
View File
@@ -1 +0,0 @@
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/appearance.js
-99
View File
@@ -1,99 +0,0 @@
import { app } from "../../scripts/app.js";
const COLOR_THEMES = {
red: { nodeColor: "#332222", nodeBgColor: "#553333" },
green: { nodeColor: "#223322", nodeBgColor: "#335533" },
blue: { nodeColor: "#222233", nodeBgColor: "#333355" },
pale_blue: { nodeColor: "#2a363b", nodeBgColor: "#3f5159" },
cyan: { nodeColor: "#223333", nodeBgColor: "#335555" },
purple: { nodeColor: "#332233", nodeBgColor: "#553355" },
yellow: { nodeColor: "#443322", nodeBgColor: "#665533" },
none: { nodeColor: null, nodeBgColor: null } // no color
};
const NODE_COLORS = {
"KSampler (Efficient)": "random",
"KSampler Adv. (Efficient)": "random",
"KSampler SDXL (Eff.)": "random",
"Efficient Loader": "random",
"Eff. Loader SDXL": "random",
"LoRA Stacker": "blue",
"Control Net Stacker": "green",
"Apply ControlNet Stack": "none",
"XY Plot": "purple",
"Unpack SDXL Tuple": "none",
"Pack SDXL Tuple": "none",
"XY Input: Seeds++ Batch": "cyan",
"XY Input: Add/Return Noise": "cyan",
"XY Input: Steps": "cyan",
"XY Input: CFG Scale": "cyan",
"XY Input: Sampler/Scheduler": "cyan",
"XY Input: Denoise": "cyan",
"XY Input: VAE": "cyan",
"XY Input: Prompt S/R": "cyan",
"XY Input: Aesthetic Score": "cyan",
"XY Input: Refiner On/Off": "cyan",
"XY Input: Checkpoint": "cyan",
"XY Input: Clip Skip": "cyan",
"XY Input: LoRA": "cyan",
"XY Input: LoRA Plot": "cyan",
"XY Input: LoRA Stacks": "cyan",
"XY Input: Control Net": "cyan",
"XY Input: Control Net Plot": "cyan",
"XY Input: Manual XY Entry": "cyan",
"Manual XY Entry Info": "cyan",
"Join XY Inputs of Same Type": "cyan",
"Image Overlay": "random",
"Noise Control Script": "none",
"HighRes-Fix Script": "yellow",
"Tiled Upscaler Script": "red",
"AnimateDiff Script": "random",
"Evaluate Integers": "pale_blue",
"Evaluate Floats": "pale_blue",
"Evaluate Strings": "pale_blue",
"Simple Eval Examples": "pale_blue",
};
function shuffleArray(array) {
for (let i = array.length - 1; i > 0; i--) {
const j = Math.floor(Math.random() * (i + 1));
[array[i], array[j]] = [array[j], array[i]]; // Swap elements
}
}
let colorKeys = Object.keys(COLOR_THEMES).filter(key => key !== "none");
shuffleArray(colorKeys); // Shuffle the color themes initially
function setNodeColors(node, theme) {
if (!theme) {return;}
node.shape = "box";
if(theme.nodeColor && theme.nodeBgColor) {
node.color = theme.nodeColor;
node.bgcolor = theme.nodeBgColor;
}
}
const ext = {
name: "efficiency.appearance",
nodeCreated(node) {
const nclass = node.comfyClass;
if (NODE_COLORS.hasOwnProperty(nclass)) {
let colorKey = NODE_COLORS[nclass];
if (colorKey === "random") {
// Check for a valid color key before popping
if (colorKeys.length === 0 || !COLOR_THEMES[colorKeys[colorKeys.length - 1]]) {
colorKeys = Object.keys(COLOR_THEMES).filter(key => key !== "none");
shuffleArray(colorKeys);
}
colorKey = colorKeys.pop();
}
const theme = COLOR_THEMES[colorKey];
setNodeColors(node, theme);
}
}
};
app.registerExtension(ext);
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/gif_preview.js
-144
View File
@@ -1,144 +0,0 @@
import { app } from '../../scripts/app.js'
import { api } from '../../scripts/api.js'
function offsetDOMWidget(
widget,
ctx,
node,
widgetWidth,
widgetY,
height
) {
const margin = 10
const elRect = ctx.canvas.getBoundingClientRect()
const transform = new DOMMatrix()
.scaleSelf(
elRect.width / ctx.canvas.width,
elRect.height / ctx.canvas.height
)
.multiplySelf(ctx.getTransform())
.translateSelf(0, widgetY + margin)
const scale = new DOMMatrix().scaleSelf(transform.a, transform.d)
Object.assign(widget.inputEl.style, {
transformOrigin: '0 0',
transform: scale,
left: `${transform.e}px`,
top: `${transform.d + transform.f}px`,
width: `${widgetWidth}px`,
height: `${(height || widget.parent?.inputHeight || 32) - margin}px`,
position: 'absolute',
background: !node.color ? '' : node.color,
color: !node.color ? '' : 'white',
zIndex: 5, //app.graph._nodes.indexOf(node),
})
}
export const hasWidgets = (node) => {
if (!node.widgets || !node.widgets?.[Symbol.iterator]) {
return false
}
return true
}
export const cleanupNode = (node) => {
if (!hasWidgets(node)) {
return
}
for (const w of node.widgets) {
if (w.canvas) {
w.canvas.remove()
}
if (w.inputEl) {
w.inputEl.remove()
}
// calls the widget remove callback
w.onRemoved?.()
}
}
const CreatePreviewElement = (name, val, format) => {
const [type] = format.split('/')
const w = {
name,
type,
value: val,
draw: function (ctx, node, widgetWidth, widgetY, height) {
const [cw, ch] = this.computeSize(widgetWidth)
offsetDOMWidget(this, ctx, node, widgetWidth, widgetY, ch)
},
computeSize: function (_) {
const ratio = this.inputRatio || 1
const width = Math.max(220, this.parent.size[0])
return [width, (width / ratio + 10)]
},
onRemoved: function () {
if (this.inputEl) {
this.inputEl.remove()
}
},
}
w.inputEl = document.createElement(type === 'video' ? 'video' : 'img')
w.inputEl.src = w.value
if (type === 'video') {
w.inputEl.setAttribute('type', 'video/webm');
w.inputEl.autoplay = true
w.inputEl.loop = true
w.inputEl.controls = false;
}
w.inputEl.onload = function () {
w.inputRatio = w.inputEl.naturalWidth / w.inputEl.naturalHeight
}
document.body.appendChild(w.inputEl)
return w
}
const gif_preview = {
name: 'efficiency.gif_preview',
async beforeRegisterNodeDef(nodeType, nodeData, app) {
switch (nodeData.name) {
case 'KSampler (Efficient)':{
const onExecuted = nodeType.prototype.onExecuted
nodeType.prototype.onExecuted = function (message) {
const prefix = 'ad_gif_preview_'
const r = onExecuted ? onExecuted.apply(this, message) : undefined
if (this.widgets) {
const pos = this.widgets.findIndex((w) => w.name === `${prefix}_0`)
if (pos !== -1) {
for (let i = pos; i < this.widgets.length; i++) {
this.widgets[i].onRemoved?.()
}
this.widgets.length = pos
}
if (message?.gifs) {
message.gifs.forEach((params, i) => {
const previewUrl = api.apiURL(
'/view?' + new URLSearchParams(params).toString()
)
const w = this.addCustomWidget(
CreatePreviewElement(`${prefix}_${i}`, previewUrl, params.format || 'image/gif')
)
w.parent = this
})
}
const onRemoved = this.onRemoved
this.onRemoved = () => {
cleanupNode(this)
return onRemoved?.()
}
}
if (message?.gifs && message.gifs.length > 0) {
this.setSize([this.size[0], this.computeSize([this.size[0], this.size[1]])[1]]);
}
return r
}
break
}
}
}
}
app.registerExtension(gif_preview)
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/addLinks.js
-180
View File
@@ -1,180 +0,0 @@
import { app } from "../../../scripts/app.js";
import { addMenuHandler } from "./common/utils.js";
import { addNode } from "./common/utils.js";
function createKSamplerEntry(node, samplerType, subNodeType = null, isSDXL = false) {
const samplerLabelMap = {
"Eff": "KSampler (Efficient)",
"Adv": "KSampler Adv. (Efficient)",
"SDXL": "KSampler SDXL (Eff.)"
};
const subNodeLabelMap = {
"XYPlot": "XY Plot",
"NoiseControl": "Noise Control Script",
"HiResFix": "HighRes-Fix Script",
"TiledUpscale": "Tiled Upscaler Script",
"AnimateDiff": "AnimateDiff Script"
};
const nicknameMap = {
"KSampler (Efficient)": "KSampler",
"KSampler Adv. (Efficient)": "KSampler(Adv)",
"KSampler SDXL (Eff.)": "KSampler",
"XY Plot": "XY Plot",
"Noise Control Script": "NoiseControl",
"HighRes-Fix Script": "HiResFix",
"Tiled Upscaler Script": "TiledUpscale",
"AnimateDiff Script": "AnimateDiff"
};
const kSamplerLabel = samplerLabelMap[samplerType];
const subNodeLabel = subNodeLabelMap[subNodeType];
const kSamplerNickname = nicknameMap[kSamplerLabel];
const subNodeNickname = nicknameMap[subNodeLabel];
const contentLabel = subNodeNickname ? `${kSamplerNickname} + ${subNodeNickname}` : kSamplerNickname;
return {
content: contentLabel,
callback: function() {
const kSamplerNode = addNode(kSamplerLabel, node, { shiftX: node.size[0] + 50 });
// Standard connections for all samplers
node.connect(0, kSamplerNode, 0); // MODEL
node.connect(1, kSamplerNode, 1); // CONDITIONING+
node.connect(2, kSamplerNode, 2); // CONDITIONING-
// Additional connections for non-SDXL
if (!isSDXL) {
node.connect(3, kSamplerNode, 3); // LATENT
node.connect(4, kSamplerNode, 4); // VAE
}
if (subNodeLabel) {
const subNode = addNode(subNodeLabel, node, { shiftX: 50, shiftY: node.size[1] + 50 });
const dependencyIndex = isSDXL ? 3 : 5;
node.connect(dependencyIndex, subNode, 0);
subNode.connect(0, kSamplerNode, dependencyIndex);
}
},
};
}
function createStackerNode(node, type) {
const stackerLabelMap = {
"LoRA": "LoRA Stacker",
"ControlNet": "Control Net Stacker"
};
const contentLabel = stackerLabelMap[type];
return {
content: contentLabel,
callback: function() {
const stackerNode = addNode(contentLabel, node);
// Calculate the left shift based on the width of the new node
const shiftX = -(stackerNode.size[0] + 25);
stackerNode.pos[0] += shiftX; // Adjust the x position of the new node
// Introduce a Y offset of 200 for ControlNet Stacker node
if (type === "ControlNet") {
stackerNode.pos[1] += 300;
}
// Connect outputs to the Efficient Loader based on type
if (type === "LoRA") {
stackerNode.connect(0, node, 0);
} else if (type === "ControlNet") {
stackerNode.connect(0, node, 1);
}
},
};
}
function createXYPlotNode(node, type) {
const contentLabel = "XY Plot";
return {
content: contentLabel,
callback: function() {
const xyPlotNode = addNode(contentLabel, node);
// Center the X coordinate of the XY Plot node
const centerXShift = (node.size[0] - xyPlotNode.size[0]) / 2;
xyPlotNode.pos[0] += centerXShift;
// Adjust the Y position to place it below the loader node
xyPlotNode.pos[1] += node.size[1] + 60;
// Depending on the node type, connect the appropriate output to the XY Plot node
if (type === "Efficient") {
node.connect(6, xyPlotNode, 0);
} else if (type === "SDXL") {
node.connect(3, xyPlotNode, 0);
}
},
};
}
function getMenuValues(type, node) {
const subNodeTypes = [null, "XYPlot", "NoiseControl", "HiResFix", "TiledUpscale", "AnimateDiff"];
const excludedSubNodeTypes = ["NoiseControl", "HiResFix", "TiledUpscale", "AnimateDiff"]; // Nodes to exclude from the menu
const menuValues = [];
// Add the new node types to the menu first for the correct order
menuValues.push(createStackerNode(node, "LoRA"));
menuValues.push(createStackerNode(node, "ControlNet"));
for (const subNodeType of subNodeTypes) {
// Skip adding submenu items that are in the excludedSubNodeTypes array
if (!excludedSubNodeTypes.includes(subNodeType)) {
const menuEntry = createKSamplerEntry(node, type === "Efficient" ? "Eff" : "SDXL", subNodeType, type === "SDXL");
menuValues.push(menuEntry);
}
}
// Insert the standalone XY Plot option after the KSampler without any subNodeTypes and before any other KSamplers with subNodeTypes
menuValues.splice(3, 0, createXYPlotNode(node, type));
return menuValues;
}
function showAddLinkMenuCommon(value, options, e, menu, node, type) {
const values = getMenuValues(type, node);
new LiteGraph.ContextMenu(values, {
event: e,
callback: null,
parentMenu: menu,
node: node
});
return false;
}
// Extension Definition
app.registerExtension({
name: "efficiency.addLinks",
async beforeRegisterNodeDef(nodeType, nodeData, app) {
const linkTypes = {
"Efficient Loader": "Efficient",
"Eff. Loader SDXL": "SDXL"
};
const linkType = linkTypes[nodeData.name];
if (linkType) {
addMenuHandler(nodeType, function(insertOption) {
insertOption({
content: "⛓ Add link...",
has_submenu: true,
callback: (value, options, e, menu, node) => showAddLinkMenuCommon(value, options, e, menu, node, linkType)
});
});
}
},
});
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/addScripts.js
-152
View File
@@ -1,152 +0,0 @@
import { app } from "../../../scripts/app.js";
import { addMenuHandler } from "./common/utils.js";
import { addNode } from "./common/utils.js";
const connectionMap = {
"KSampler (Efficient)": ["input", 5],
"KSampler Adv. (Efficient)": ["input", 5],
"KSampler SDXL (Eff.)": ["input", 3],
"XY Plot": ["output", 0],
"Noise Control Script": ["input & output", 0],
"HighRes-Fix Script": ["input & output", 0],
"Tiled Upscaler Script": ["input & output", 0],
"AnimateDiff Script": ["output", 0]
};
/**
* connect this node output to the input of another node
* @method connect
* @param {number_or_string} slot (could be the number of the slot or the string with the name of the slot)
* @param {LGraphNode} node the target node
* @param {number_or_string} target_slot the input slot of the target node (could be the number of the slot or the string with the name of the slot, or -1 to connect a trigger)
* @return {Object} the link_info is created, otherwise null
LGraphNode.prototype.connect = function(output_slot, target_node, input_slot)
**/
function addAndConnectScriptNode(scriptType, selectedNode) {
const selectedNodeType = connectionMap[selectedNode.type];
const newNodeType = connectionMap[scriptType];
// 1. Create the new node without position adjustments
const newNode = addNode(scriptType, selectedNode, { shiftX: 0, shiftY: 0 });
// 2. Adjust position of the new node based on conditions
if (newNodeType[0].includes("input") && selectedNodeType[0].includes("output")) {
newNode.pos[0] += selectedNode.size[0] + 50;
} else if (newNodeType[0].includes("output") && selectedNodeType[0].includes("input")) {
newNode.pos[0] -= (newNode.size[0] + 50);
}
// 3. Logic for connecting the nodes
switch (selectedNodeType[0]) {
case "output":
if (newNodeType[0] === "input & output") {
// For every node that was previously connected to the selectedNode's output
const connectedNodes = selectedNode.getOutputNodes(selectedNodeType[1]);
if (connectedNodes && connectedNodes.length) {
for (let connectedNode of connectedNodes) {
// Disconnect the node from selectedNode's output
selectedNode.disconnectOutput(selectedNodeType[1]);
// Connect the newNode's output to the previously connected node,
// using the appropriate slot based on the type of the connectedNode
const targetSlot = (connectedNode.type in connectionMap) ? connectionMap[connectedNode.type][1] : 0;
newNode.connect(0, connectedNode, targetSlot);
}
}
// Connect selectedNode's output to newNode's input
selectedNode.connect(selectedNodeType[1], newNode, newNodeType[1]);
}
break;
case "input":
if (newNodeType[0] === "output") {
newNode.connect(0, selectedNode, selectedNodeType[1]);
} else if (newNodeType[0] === "input & output") {
const ogInputNode = selectedNode.getInputNode(selectedNodeType[1]);
if (ogInputNode) {
ogInputNode.connect(0, newNode, 0);
}
newNode.connect(0, selectedNode, selectedNodeType[1]);
}
break;
case "input & output":
if (newNodeType[0] === "output") {
newNode.connect(0, selectedNode, 0);
} else if (newNodeType[0] === "input & output") {
const connectedNodes = selectedNode.getOutputNodes(0);
if (connectedNodes && connectedNodes.length) {
for (let connectedNode of connectedNodes) {
selectedNode.disconnectOutput(0);
newNode.connect(0, connectedNode, connectedNode.type in connectionMap ? connectionMap[connectedNode.type][1] : 0);
}
}
// Connect selectedNode's output to newNode's input
selectedNode.connect(selectedNodeType[1], newNode, newNodeType[1]);
}
break;
}
return newNode;
}
function createScriptEntry(node, scriptType) {
return {
content: scriptType,
callback: function() {
addAndConnectScriptNode(scriptType, node);
},
};
}
function getScriptOptions(nodeType, node) {
const allScriptTypes = [
"XY Plot",
"Noise Control Script",
"HighRes-Fix Script",
"Tiled Upscaler Script",
"AnimateDiff Script"
];
// Filter script types based on node type
const scriptTypes = allScriptTypes.filter(scriptType => {
const scriptBehavior = connectionMap[scriptType][0];
if (connectionMap[nodeType][0] === "output") {
return scriptBehavior.includes("input"); // Includes nodes that are "input" or "input & output"
} else {
return true;
}
});
return scriptTypes.map(script => createScriptEntry(node, script));
}
function showAddScriptMenu(_, options, e, menu, node) {
const scriptOptions = getScriptOptions(node.type, node);
new LiteGraph.ContextMenu(scriptOptions, {
event: e,
callback: null,
parentMenu: menu,
node: node
});
return false;
}
// Extension Definition
app.registerExtension({
name: "efficiency.addScripts",
async beforeRegisterNodeDef(nodeType, nodeData, app) {
if (connectionMap[nodeData.name]) {
addMenuHandler(nodeType, function(insertOption) {
insertOption({
content: "📜 Add script...",
has_submenu: true,
callback: showAddScriptMenu
});
});
}
},
});
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/addXYinputs.js
-89
View File
@@ -1,89 +0,0 @@
import { app } from "../../../scripts/app.js";
import { addMenuHandler, addNode } from "./common/utils.js";
const nodePxOffsets = 80;
function getXYInputNodes() {
return [
"XY Input: Seeds++ Batch",
"XY Input: Add/Return Noise",
"XY Input: Steps",
"XY Input: CFG Scale",
"XY Input: Sampler/Scheduler",
"XY Input: Denoise",
"XY Input: VAE",
"XY Input: Prompt S/R",
"XY Input: Aesthetic Score",
"XY Input: Refiner On/Off",
"XY Input: Checkpoint",
"XY Input: Clip Skip",
"XY Input: LoRA",
"XY Input: LoRA Plot",
"XY Input: LoRA Stacks",
"XY Input: Control Net",
"XY Input: Control Net Plot",
"XY Input: Manual XY Entry"
];
}
function showAddXYInputMenu(type, e, menu, node) {
const specialNodes = [
"XY Input: LoRA Plot",
"XY Input: Control Net Plot",
"XY Input: Manual XY Entry"
];
const values = getXYInputNodes().map(nodeType => {
return {
content: nodeType,
callback: function() {
const newNode = addNode(nodeType, node);
// Calculate the left shift based on the width of the new node
const shiftX = -(newNode.size[0] + 35);
newNode.pos[0] += shiftX;
if (specialNodes.includes(nodeType)) {
newNode.pos[1] += 20;
// Connect both outputs to the XY Plot's 2nd and 3rd input.
newNode.connect(0, node, 1);
newNode.connect(1, node, 2);
} else if (type === 'X') {
newNode.pos[1] += 20;
newNode.connect(0, node, 1); // Connect to 2nd input
} else {
newNode.pos[1] += node.size[1] + 45;
newNode.connect(0, node, 2); // Connect to 3rd input
}
}
};
});
new LiteGraph.ContextMenu(values, {
event: e,
callback: null,
parentMenu: menu,
node: node
});
return false;
}
app.registerExtension({
name: "efficiency.addXYinputs",
async beforeRegisterNodeDef(nodeType, nodeData, app) {
if (nodeData.name === "XY Plot") {
addMenuHandler(nodeType, function(insertOption) {
insertOption({
content: "✏️ Add 𝚇 input...",
has_submenu: true,
callback: (value, options, e, menu, node) => showAddXYInputMenu('X', e, menu, node)
});
insertOption({
content: "✏️ Add 𝚈 input...",
has_submenu: true,
callback: (value, options, e, menu, node) => showAddXYInputMenu('Y', e, menu, node)
});
});
}
},
});
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/common/modelInfoDialog.css
-104
View File
@@ -1,104 +0,0 @@
.pysssss-model-info {
color: white;
font-family: sans-serif;
max-width: 90vw;
}
.pysssss-model-content {
display: flex;
flex-direction: column;
overflow: hidden;
}
.pysssss-model-info h2 {
text-align: center;
margin: 0 0 10px 0;
}
.pysssss-model-info p {
margin: 5px 0;
}
.pysssss-model-info a {
color: dodgerblue;
}
.pysssss-model-info a:hover {
text-decoration: underline;
}
.pysssss-model-tags-list {
display: flex;
flex-wrap: wrap;
list-style: none;
gap: 10px;
max-height: 200px;
overflow: auto;
margin: 10px 0;
padding: 0;
}
.pysssss-model-tag {
background-color: rgb(128, 213, 247);
color: #000;
display: flex;
align-items: center;
gap: 5px;
border-radius: 5px;
padding: 2px 5px;
cursor: pointer;
}
.pysssss-model-tag--selected span::before {
content: "✅";
position: absolute;
background-color: dodgerblue;
left: 0;
top: 0;
right: 0;
bottom: 0;
text-align: center;
}
.pysssss-model-tag:hover {
outline: 2px solid dodgerblue;
}
.pysssss-model-tag p {
margin: 0;
}
.pysssss-model-tag span {
text-align: center;
border-radius: 5px;
background-color: dodgerblue;
color: #fff;
padding: 2px;
position: relative;
min-width: 20px;
overflow: hidden;
}
.pysssss-model-metadata .comfy-modal-content {
max-width: 100%;
}
.pysssss-model-metadata label {
margin-right: 1ch;
color: #ccc;
}
.pysssss-model-metadata span {
color: dodgerblue;
}
.pysssss-preview {
max-width: 50%;
margin-left: 10px;
position: relative;
}
.pysssss-preview img {
max-height: 300px;
}
.pysssss-preview button {
position: absolute;
font-size: 12px;
bottom: 10px;
right: 10px;
}
.pysssss-model-notes {
background-color: rgba(0, 0, 0, 0.25);
padding: 5px;
margin-top: 5px;
}
.pysssss-model-notes:empty {
display: none;
}
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/common/modelInfoDialog.js
-303
View File
@@ -1,303 +0,0 @@
import { $el, ComfyDialog } from "../../../../scripts/ui.js";
import { api } from "../../../../scripts/api.js";
import { addStylesheet } from "./utils.js";
addStylesheet(import.meta.url);
class MetadataDialog extends ComfyDialog {
constructor() {
super();
this.element.classList.add("pysssss-model-metadata");
}
show(metadata) {
super.show(
$el(
"div",
Object.keys(metadata).map((k) =>
$el("div", [$el("label", { textContent: k }), $el("span", { textContent: metadata[k] })])
)
)
);
}
}
export class ModelInfoDialog extends ComfyDialog {
constructor(name) {
super();
this.name = name;
this.element.classList.add("pysssss-model-info");
}
get customNotes() {
return this.metadata["pysssss.notes"];
}
set customNotes(v) {
this.metadata["pysssss.notes"] = v;
}
get hash() {
return this.metadata["pysssss.sha256"];
}
async show(type, value) {
this.type = type;
const req = api.fetchApi("/pysssss/metadata/" + encodeURIComponent(`${type}/${value}`));
this.info = $el("div", { style: { flex: "auto" } });
this.img = $el("img", { style: { display: "none" } });
this.imgWrapper = $el("div.pysssss-preview", [this.img]);
this.main = $el("main", { style: { display: "flex" } }, [this.info, this.imgWrapper]);
this.content = $el("div.pysssss-model-content", [$el("h2", { textContent: this.name }), this.main]);
const loading = $el("div", { textContent: "ℹ️ Loading...", parent: this.content });
super.show(this.content);
this.metadata = await (await req).json();
this.viewMetadata.style.cursor = this.viewMetadata.style.opacity = "";
this.viewMetadata.removeAttribute("disabled");
loading.remove();
this.addInfo();
}
createButtons() {
const btns = super.createButtons();
this.viewMetadata = $el("button", {
type: "button",
textContent: "View raw metadata",
disabled: "disabled",
style: {
opacity: 0.5,
cursor: "not-allowed",
},
onclick: (e) => {
if (this.metadata) {
new MetadataDialog().show(this.metadata);
}
},
});
btns.unshift(this.viewMetadata);
return btns;
}
getNoteInfo() {
function parseNote() {
if (!this.customNotes) return [];
let notes = [];
// Extract links from notes
const r = new RegExp("(\\bhttps?:\\/\\/[^\\s]+)", "g");
let end = 0;
let m;
do {
m = r.exec(this.customNotes);
let pos;
let fin = 0;
if (m) {
pos = m.index;
fin = m.index + m[0].length;
} else {
pos = this.customNotes.length;
}
let pre = this.customNotes.substring(end, pos);
if (pre) {
pre = pre.replaceAll("\n", "<br>");
notes.push(
$el("span", {
innerHTML: pre,
})
);
}
if (m) {
notes.push(
$el("a", {
href: m[0],
textContent: m[0],
target: "_blank",
})
);
}
end = fin;
} while (m);
return notes;
}
let textarea;
let notesContainer;
const editText = "✏️ Edit";
const edit = $el("a", {
textContent: editText,
href: "#",
style: {
float: "right",
color: "greenyellow",
textDecoration: "none",
},
onclick: async (e) => {
e.preventDefault();
if (textarea) {
this.customNotes = textarea.value;
const resp = await api.fetchApi(
"/pysssss/metadata/notes/" + encodeURIComponent(`${this.type}/${this.name}`),
{
method: "POST",
body: this.customNotes,
}
);
if (resp.status !== 200) {
console.error(resp);
alert(`Error saving notes (${req.status}) ${req.statusText}`);
return;
}
e.target.textContent = editText;
textarea.remove();
textarea = null;
notesContainer.replaceChildren(...parseNote.call(this));
} else {
e.target.textContent = "💾 Save";
textarea = $el("textarea", {
style: {
width: "100%",
minWidth: "200px",
minHeight: "50px",
},
textContent: this.customNotes,
});
e.target.after(textarea);
notesContainer.replaceChildren();
textarea.style.height = Math.min(textarea.scrollHeight, 300) + "px";
}
},
});
notesContainer = $el("div.pysssss-model-notes", parseNote.call(this));
return $el(
"div",
{
style: { display: "contents" },
},
[edit, notesContainer]
);
}
addInfo() {
this.addInfoEntry("Notes", this.getNoteInfo());
}
addInfoEntry(name, value) {
return $el(
"p",
{
parent: this.info,
},
[
typeof name === "string" ? $el("label", { textContent: name + ": " }) : name,
typeof value === "string" ? $el("span", { textContent: value }) : value,
]
);
}
async getCivitaiDetails() {
const req = await fetch("https://civitai.com/api/v1/model-versions/by-hash/" + this.hash);
if (req.status === 200) {
return await req.json();
} else if (req.status === 404) {
throw new Error("Model not found");
} else {
throw new Error(`Error loading info (${req.status}) ${req.statusText}`);
}
}
addCivitaiInfo() {
const promise = this.getCivitaiDetails();
const content = $el("span", { textContent: "ℹ️ Loading..." });
this.addInfoEntry(
$el("label", [
$el("img", {
style: {
width: "18px",
position: "relative",
top: "3px",
margin: "0 5px 0 0",
},
src: "https://civitai.com/favicon.ico",
}),
$el("span", { textContent: "Civitai: " }),
]),
content
);
return promise
.then((info) => {
content.replaceChildren(
$el("a", {
href: "https://civitai.com/models/" + info.modelId,
textContent: "View " + info.model.name,
target: "_blank",
})
);
if (info.images?.length) {
this.img.src = info.images[0].url;
this.img.style.display = "";
this.imgSave = $el("button", {
textContent: "Use as preview",
parent: this.imgWrapper,
onclick: async () => {
// Convert the preview to a blob
const blob = await (await fetch(this.img.src)).blob();
// Store it in temp
const name = "temp_preview." + new URL(this.img.src).pathname.split(".")[1];
const body = new FormData();
body.append("image", new File([blob], name));
body.append("overwrite", "true");
body.append("type", "temp");
const resp = await api.fetchApi("/upload/image", {
method: "POST",
body,
});
if (resp.status !== 200) {
console.error(resp);
alert(`Error saving preview (${req.status}) ${req.statusText}`);
return;
}
// Use as preview
await api.fetchApi("/pysssss/save/" + encodeURIComponent(`${this.type}/${this.name}`), {
method: "POST",
body: JSON.stringify({
filename: name,
type: "temp",
}),
headers: {
"content-type": "application/json",
},
});
app.refreshComboInNodes();
},
});
}
return info;
})
.catch((err) => {
content.textContent = "⚠️ " + err.message;
});
}
}
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/common/utils.js
-94
View File
@@ -1,94 +0,0 @@
import { app } from '../../../../scripts/app.js'
import { $el } from "../../../../scripts/ui.js";
export function addStylesheet(url) {
if (url.endsWith(".js")) {
url = url.substr(0, url.length - 2) + "css";
}
$el("link", {
parent: document.head,
rel: "stylesheet",
type: "text/css",
href: url.startsWith("http") ? url : getUrl(url),
});
}
export function getUrl(path, baseUrl) {
if (baseUrl) {
return new URL(path, baseUrl).toString();
} else {
return new URL("../" + path, import.meta.url).toString();
}
}
export async function loadImage(url) {
return new Promise((res, rej) => {
const img = new Image();
img.onload = res;
img.onerror = rej;
img.src = url;
});
}
export function addMenuHandler(nodeType, cb) {
const GROUPED_MENU_ORDER = {
"🔄 Swap with...": 0,
"⛓ Add link...": 1,
"📜 Add script...": 2,
"🔍 View model info...": 3,
"🌱 Seed behavior...": 4,
"📐 Set Resolution...": 5,
"✏️ Add 𝚇 input...": 6,
"✏️ Add 𝚈 input...": 7
};
const originalGetOpts = nodeType.prototype.getExtraMenuOptions;
nodeType.prototype.getExtraMenuOptions = function () {
let r = originalGetOpts ? originalGetOpts.apply(this, arguments) || [] : [];
const insertOption = (option) => {
if (GROUPED_MENU_ORDER.hasOwnProperty(option.content)) {
// Find the right position for the option
let targetPos = r.length; // default to the end
for (let i = 0; i < r.length; i++) {
if (GROUPED_MENU_ORDER.hasOwnProperty(r[i].content) &&
GROUPED_MENU_ORDER[option.content] < GROUPED_MENU_ORDER[r[i].content]) {
targetPos = i;
break;
}
}
// Insert the option at the determined position
r.splice(targetPos, 0, option);
} else {
// If the option is not in the GROUPED_MENU_ORDER, simply add it to the end
r.push(option);
}
};
cb.call(this, insertOption);
return r;
};
}
export function findWidgetByName(node, widgetName) {
return node.widgets.find(widget => widget.name === widgetName);
}
// Utility functions
export function addNode(name, nextTo, options) {
options = { select: true, shiftX: 0, shiftY: 0, before: false, ...(options || {}) };
const node = LiteGraph.createNode(name);
app.graph.add(node);
node.pos = [
nextTo.pos[0] + options.shiftX,
nextTo.pos[1] + options.shiftY,
];
if (options.select) {
app.canvas.selectNode(node, false);
}
return node;
}
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/modelInfo.js
-336
View File
@@ -1,336 +0,0 @@
import { app } from "../../../scripts/app.js";
import { $el } from "../../../scripts/ui.js";
import { ModelInfoDialog } from "./common/modelInfoDialog.js";
import { addMenuHandler } from "./common/utils.js";
const MAX_TAGS = 500;
class LoraInfoDialog extends ModelInfoDialog {
getTagFrequency() {
if (!this.metadata.ss_tag_frequency) return [];
const datasets = JSON.parse(this.metadata.ss_tag_frequency);
const tags = {};
for (const setName in datasets) {
const set = datasets[setName];
for (const t in set) {
if (t in tags) {
tags[t] += set[t];
} else {
tags[t] = set[t];
}
}
}
return Object.entries(tags).sort((a, b) => b[1] - a[1]);
}
getResolutions() {
let res = [];
if (this.metadata.ss_bucket_info) {
const parsed = JSON.parse(this.metadata.ss_bucket_info);
if (parsed?.buckets) {
for (const { resolution, count } of Object.values(parsed.buckets)) {
res.push([count, `${resolution.join("x")} * ${count}`]);
}
}
}
res = res.sort((a, b) => b[0] - a[0]).map((a) => a[1]);
let r = this.metadata.ss_resolution;
if (r) {
const s = r.split(",");
const w = s[0].replace("(", "");
const h = s[1].replace(")", "");
res.push(`${w.trim()}x${h.trim()} (Base res)`);
} else if ((r = this.metadata["modelspec.resolution"])) {
res.push(r + " (Base res");
}
if (!res.length) {
res.push("⚠️ Unknown");
}
return res;
}
getTagList(tags) {
return tags.map((t) =>
$el(
"li.pysssss-model-tag",
{
dataset: {
tag: t[0],
},
$: (el) => {
el.onclick = () => {
el.classList.toggle("pysssss-model-tag--selected");
};
},
},
[
$el("p", {
textContent: t[0],
}),
$el("span", {
textContent: t[1],
}),
]
)
);
}
addTags() {
let tags = this.getTagFrequency();
let hasMore;
if (tags?.length) {
const c = tags.length;
let list;
if (c > MAX_TAGS) {
tags = tags.slice(0, MAX_TAGS);
hasMore = $el("p", [
$el("span", { textContent: `⚠️ Only showing first ${MAX_TAGS} tags ` }),
$el("a", {
href: "#",
textContent: `Show all ${c}`,
onclick: () => {
list.replaceChildren(...this.getTagList(this.getTagFrequency()));
hasMore.remove();
},
}),
]);
}
list = $el("ol.pysssss-model-tags-list", this.getTagList(tags));
this.tags = $el("div", [list]);
} else {
this.tags = $el("p", { textContent: "⚠️ No tag frequency metadata found" });
}
this.content.append(this.tags);
if (hasMore) {
this.content.append(hasMore);
}
}
async addInfo() {
this.addInfoEntry("Name", this.metadata.ss_output_name || "⚠️ Unknown");
this.addInfoEntry("Base Model", this.metadata.ss_sd_model_name || "⚠️ Unknown");
this.addInfoEntry("Clip Skip", this.metadata.ss_clip_skip || "⚠️ Unknown");
this.addInfoEntry(
"Resolution",
$el(
"select",
this.getResolutions().map((r) => $el("option", { textContent: r }))
)
);
super.addInfo();
const p = this.addCivitaiInfo();
this.addTags();
const info = await p;
if (info) {
$el(
"p",
{
parent: this.content,
textContent: "Trained Words: ",
},
[
$el("pre", {
textContent: info.trainedWords.join(", "),
style: {
whiteSpace: "pre-wrap",
margin: "10px 0",
background: "#222",
padding: "5px",
borderRadius: "5px",
maxHeight: "250px",
overflow: "auto",
},
}),
]
);
$el("div", {
parent: this.content,
innerHTML: info.description,
style: {
maxHeight: "250px",
overflow: "auto",
},
});
}
}
createButtons() {
const btns = super.createButtons();
function copyTags(e, tags) {
const textarea = $el("textarea", {
parent: document.body,
style: {
position: "fixed",
},
textContent: tags.map((el) => el.dataset.tag).join(", "),
});
textarea.select();
try {
document.execCommand("copy");
if (!e.target.dataset.text) {
e.target.dataset.text = e.target.textContent;
}
e.target.textContent = "Copied " + tags.length + " tags";
setTimeout(() => {
e.target.textContent = e.target.dataset.text;
}, 1000);
} catch (ex) {
prompt("Copy to clipboard: Ctrl+C, Enter", text);
} finally {
document.body.removeChild(textarea);
}
}
btns.unshift(
$el("button", {
type: "button",
textContent: "Copy Selected",
onclick: (e) => {
copyTags(e, [...this.tags.querySelectorAll(".pysssss-model-tag--selected")]);
},
}),
$el("button", {
type: "button",
textContent: "Copy All",
onclick: (e) => {
copyTags(e, [...this.tags.querySelectorAll(".pysssss-model-tag")]);
},
})
);
return btns;
}
}
class CheckpointInfoDialog extends ModelInfoDialog {
async addInfo() {
super.addInfo();
const info = await this.addCivitaiInfo();
if (info) {
this.addInfoEntry("Base Model", info.baseModel || "⚠️ Unknown");
$el("div", {
parent: this.content,
innerHTML: info.description,
style: {
maxHeight: "250px",
overflow: "auto",
},
});
}
}
}
const generateNames = (prefix, start, end) => {
const result = [];
if (start < end) {
for (let i = start; i <= end; i++) {
result.push(`${prefix}${i}`);
}
} else {
for (let i = start; i >= end; i--) {
result.push(`${prefix}${i}`);
}
}
return result
}
// NOTE: Orders reversed so they appear in ascending order
const infoHandler = {
"Efficient Loader": {
"loras": ["lora_name"],
"checkpoints": ["ckpt_name"]
},
"Eff. Loader SDXL": {
"checkpoints": ["refiner_ckpt_name", "base_ckpt_name"]
},
"LoRA Stacker": {
"loras": generateNames("lora_name_", 50, 1)
},
"XY Input: LoRA": {
"loras": generateNames("lora_name_", 50, 1)
},
"HighRes-Fix Script": {
"checkpoints": ["hires_ckpt_name"]
}
};
// Utility functions and other parts of your code remain unchanged
app.registerExtension({
name: "efficiency.ModelInfo",
beforeRegisterNodeDef(nodeType) {
const types = infoHandler[nodeType.comfyClass];
if (types) {
addMenuHandler(nodeType, function (insertOption) { // Here, we are calling addMenuHandler
let submenuItems = []; // to store submenu items
const addSubMenuOption = (type, widgetNames) => {
widgetNames.forEach(widgetName => {
const widgetValue = this.widgets.find(w => w.name === widgetName)?.value;
// Check if widgetValue is "None"
if (!widgetValue || widgetValue === "None") {
return;
}
let value = widgetValue;
if (value.content) {
value = value.content;
}
const cls = type === "loras" ? LoraInfoDialog : CheckpointInfoDialog;
const label = widgetName;
// Push to submenuItems
submenuItems.push({
content: label,
callback: async () => {
new cls(value).show(type, value);
},
});
});
};
if (typeof types === 'object') {
Object.keys(types).forEach(type => {
addSubMenuOption(type, types[type]);
});
}
// If we have submenu items, use insertOption
if (submenuItems.length) {
insertOption({ // Using insertOption here
content: "🔍 View model info...",
has_submenu: true,
callback: (value, options, e, menu, node) => {
new LiteGraph.ContextMenu(submenuItems, {
event: e,
callback: null,
parentMenu: menu,
node: node
});
return false; // This ensures the original context menu doesn't proceed
}
});
}
});
}
},
});
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/setResolution.js
-88
View File
@@ -1,88 +0,0 @@
// Additional functions and imports
import { app } from "../../../scripts/app.js";
import { addMenuHandler, findWidgetByName } from "./common/utils.js";
// A mapping for resolutions based on the type of the loader
const RESOLUTIONS = {
"Efficient Loader": [
{width: 512, height: 512},
{width: 512, height: 768},
{width: 512, height: 640},
{width: 640, height: 512},
{width: 640, height: 768},
{width: 640, height: 640},
{width: 768, height: 512},
{width: 768, height: 768},
{width: 768, height: 640},
],
"Eff. Loader SDXL": [
{width: 1024, height: 1024},
{width: 1152, height: 896},
{width: 896, height: 1152},
{width: 1216, height: 832},
{width: 832, height: 1216},
{width: 1344, height: 768},
{width: 768, height: 1344},
{width: 1536, height: 640},
{width: 640, height: 1536}
]
};
// Function to set the resolution of a node
function setNodeResolution(node, width, height) {
let widthWidget = findWidgetByName(node, "empty_latent_width");
let heightWidget = findWidgetByName(node, "empty_latent_height");
if (widthWidget) {
widthWidget.value = width;
}
if (heightWidget) {
heightWidget.value = height;
}
}
// The callback for the resolution submenu
function resolutionMenuCallback(node, width, height) {
return function() {
setNodeResolution(node, width, height);
};
}
// Show the set resolution submenu
function showResolutionMenu(value, options, e, menu, node) {
const resolutions = RESOLUTIONS[node.type];
if (!resolutions) {
return false;
}
const resolutionOptions = resolutions.map(res => ({
content: `${res.width} x ${res.height}`,
callback: resolutionMenuCallback(node, res.width, res.height)
}));
new LiteGraph.ContextMenu(resolutionOptions, {
event: e,
callback: null,
parentMenu: menu,
node: node
});
return false; // This ensures the original context menu doesn't proceed
}
// Extension Definition
app.registerExtension({
name: "efficiency.SetResolution",
async beforeRegisterNodeDef(nodeType, nodeData, app) {
if (["Efficient Loader", "Eff. Loader SDXL"].includes(nodeData.name)) {
addMenuHandler(nodeType, function (insertOption) {
insertOption({
content: "📐 Set Resolution...",
has_submenu: true,
callback: showResolutionMenu
});
});
}
},
});
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/swapLoaders.js
-135
View File
@@ -1,135 +0,0 @@
import { app } from "../../../scripts/app.js";
import { addMenuHandler } from "./common/utils.js";
import { findWidgetByName } from "./common/utils.js";
function replaceNode(oldNode, newNodeName) {
const newNode = LiteGraph.createNode(newNodeName);
if (!newNode) {
return;
}
app.graph.add(newNode);
newNode.pos = oldNode.pos.slice();
newNode.size = oldNode.size.slice();
// Transfer widget values
const widgetMapping = {
"ckpt_name": "base_ckpt_name",
"vae_name": "vae_name",
"clip_skip": "base_clip_skip",
"positive": "positive",
"negative": "negative",
"prompt_style": "prompt_style",
"empty_latent_width": "empty_latent_width",
"empty_latent_height": "empty_latent_height",
"batch_size": "batch_size"
};
let effectiveWidgetMapping = widgetMapping;
// Invert the mapping when going from "Eff. Loader SDXL" to "Efficient Loader"
if (oldNode.type === "Eff. Loader SDXL" && newNodeName === "Efficient Loader") {
effectiveWidgetMapping = {};
for (const [key, value] of Object.entries(widgetMapping)) {
effectiveWidgetMapping[value] = key;
}
}
oldNode.widgets.forEach(widget => {
const newName = effectiveWidgetMapping[widget.name];
if (newName) {
const newWidget = findWidgetByName(newNode, newName);
if (newWidget) {
newWidget.value = widget.value;
}
}
});
// Hardcoded transfer for specific outputs based on the output names from the nodes in the image
const outputMapping = {
"MODEL": null, // Not present in "Eff. Loader SDXL"
"CONDITIONING+": null, // Not present in "Eff. Loader SDXL"
"CONDITIONING-": null, // Not present in "Eff. Loader SDXL"
"LATENT": "LATENT",
"VAE": "VAE",
"CLIP": null, // Not present in "Eff. Loader SDXL"
"DEPENDENCIES": "DEPENDENCIES"
};
// Transfer connections from old node outputs to new node outputs based on the outputMapping
oldNode.outputs.forEach((output, index) => {
if (output && output.links && outputMapping[output.name]) {
const newOutputName = outputMapping[output.name];
// If the new node does not have this output, skip
if (newOutputName === null) {
return;
}
const newOutputIndex = newNode.findOutputSlot(newOutputName);
if (newOutputIndex !== -1) {
output.links.forEach(link => {
const targetLinkInfo = oldNode.graph.links[link];
if (targetLinkInfo) {
const targetNode = oldNode.graph.getNodeById(targetLinkInfo.target_id);
if (targetNode) {
newNode.connect(newOutputIndex, targetNode, targetLinkInfo.target_slot);
}
}
});
}
}
});
// Remove old node
app.graph.remove(oldNode);
}
function replaceNodeMenuCallback(currentNode, targetNodeName) {
return function() {
replaceNode(currentNode, targetNodeName);
};
}
function showSwapMenu(value, options, e, menu, node) {
const swapOptions = [];
if (node.type !== "Efficient Loader") {
swapOptions.push({
content: "Efficient Loader",
callback: replaceNodeMenuCallback(node, "Efficient Loader")
});
}
if (node.type !== "Eff. Loader SDXL") {
swapOptions.push({
content: "Eff. Loader SDXL",
callback: replaceNodeMenuCallback(node, "Eff. Loader SDXL")
});
}
new LiteGraph.ContextMenu(swapOptions, {
event: e,
callback: null,
parentMenu: menu,
node: node
});
return false; // This ensures the original context menu doesn't proceed
}
// Extension Definition
app.registerExtension({
name: "efficiency.SwapLoaders",
async beforeRegisterNodeDef(nodeType, nodeData, app) {
if (["Efficient Loader", "Eff. Loader SDXL"].includes(nodeData.name)) {
addMenuHandler(nodeType, function (insertOption) {
insertOption({
content: "🔄 Swap with...",
has_submenu: true,
callback: showSwapMenu
});
});
}
},
});
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/swapSamplers.js
-191
View File
@@ -1,191 +0,0 @@
import { app } from "../../../scripts/app.js";
import { addMenuHandler } from "./common/utils.js";
import { findWidgetByName } from "./common/utils.js";
function replaceNode(oldNode, newNodeName) {
// Create new node
const newNode = LiteGraph.createNode(newNodeName);
if (!newNode) {
return;
}
app.graph.add(newNode);
// Position new node at the same position as the old node
newNode.pos = oldNode.pos.slice();
// Define widget mappings
const mappings = {
"KSampler (Efficient) <-> KSampler Adv. (Efficient)": {
seed: "noise_seed",
cfg: "cfg",
sampler_name: "sampler_name",
scheduler: "scheduler",
preview_method: "preview_method",
vae_decode: "vae_decode"
},
"KSampler (Efficient) <-> KSampler SDXL (Eff.)": {
seed: "noise_seed",
cfg: "cfg",
sampler_name: "sampler_name",
scheduler: "scheduler",
preview_method: "preview_method",
vae_decode: "vae_decode"
},
"KSampler Adv. (Efficient) <-> KSampler SDXL (Eff.)": {
noise_seed: "noise_seed",
steps: "steps",
cfg: "cfg",
sampler_name: "sampler_name",
scheduler: "scheduler",
start_at_step: "start_at_step",
preview_method: "preview_method",
vae_decode: "vae_decode"}
};
const swapKey = `${oldNode.type} <-> ${newNodeName}`;
let widgetMapping = {};
// Check if a reverse mapping is needed
if (!mappings[swapKey]) {
const reverseKey = `${newNodeName} <-> ${oldNode.type}`;
const reverseMapping = mappings[reverseKey];
if (reverseMapping) {
widgetMapping = Object.entries(reverseMapping).reduce((acc, [key, value]) => {
acc[value] = key;
return acc;
}, {});
}
} else {
widgetMapping = mappings[swapKey];
}
if (oldNode.type === "KSampler (Efficient)" && (newNodeName === "KSampler Adv. (Efficient)" || newNodeName === "KSampler SDXL (Eff.)")) {
const denoise = Math.min(Math.max(findWidgetByName(oldNode, "denoise").value, 0), 1); // Ensure denoise is between 0 and 1
const steps = Math.min(Math.max(findWidgetByName(oldNode, "steps").value, 0), 10000); // Ensure steps is between 0 and 10000
const total_steps = Math.floor(steps / denoise);
const start_at_step = total_steps - steps;
findWidgetByName(newNode, "steps").value = Math.min(Math.max(total_steps, 0), 10000); // Ensure total_steps is between 0 and 10000
findWidgetByName(newNode, "start_at_step").value = Math.min(Math.max(start_at_step, 0), 10000); // Ensure start_at_step is between 0 and 10000
}
else if ((oldNode.type === "KSampler Adv. (Efficient)" || oldNode.type === "KSampler SDXL (Eff.)") && newNodeName === "KSampler (Efficient)") {
const stepsAdv = Math.min(Math.max(findWidgetByName(oldNode, "steps").value, 0), 10000); // Ensure stepsAdv is between 0 and 10000
const start_at_step = Math.min(Math.max(findWidgetByName(oldNode, "start_at_step").value, 0), 10000); // Ensure start_at_step is between 0 and 10000
const denoise = Math.min(Math.max((stepsAdv - start_at_step) / stepsAdv, 0), 1); // Ensure denoise is between 0 and 1
const stepsTotal = stepsAdv - start_at_step;
findWidgetByName(newNode, "denoise").value = denoise;
findWidgetByName(newNode, "steps").value = Math.min(Math.max(stepsTotal, 0), 10000); // Ensure stepsTotal is between 0 and 10000
}
// Transfer widget values from old node to new node
oldNode.widgets.forEach(widget => {
const newName = widgetMapping[widget.name];
if (newName) {
const newWidget = findWidgetByName(newNode, newName);
if (newWidget) {
newWidget.value = widget.value;
}
}
});
// Determine the starting indices based on the node types
let oldNodeInputStartIndex = 0;
let newNodeInputStartIndex = 0;
let oldNodeOutputStartIndex = 0;
let newNodeOutputStartIndex = 0;
if (oldNode.type === "KSampler SDXL (Eff.)" || newNodeName === "KSampler SDXL (Eff.)") {
oldNodeInputStartIndex = (oldNode.type === "KSampler SDXL (Eff.)") ? 1 : 3;
newNodeInputStartIndex = (newNodeName === "KSampler SDXL (Eff.)") ? 1 : 3;
oldNodeOutputStartIndex = (oldNode.type === "KSampler SDXL (Eff.)") ? 1 : 3;
newNodeOutputStartIndex = (newNodeName === "KSampler SDXL (Eff.)") ? 1 : 3;
}
// Transfer connections from old node to new node
oldNode.inputs.slice(oldNodeInputStartIndex).forEach((input, index) => {
if (input && input.link !== null) {
const originLinkInfo = oldNode.graph.links[input.link];
if (originLinkInfo) {
const originNode = oldNode.graph.getNodeById(originLinkInfo.origin_id);
if (originNode) {
originNode.connect(originLinkInfo.origin_slot, newNode, index + newNodeInputStartIndex);
}
}
}
});
oldNode.outputs.slice(oldNodeOutputStartIndex).forEach((output, index) => {
if (output && output.links) {
output.links.forEach(link => {
const targetLinkInfo = oldNode.graph.links[link];
if (targetLinkInfo) {
const targetNode = oldNode.graph.getNodeById(targetLinkInfo.target_id);
if (targetNode) {
newNode.connect(index + newNodeOutputStartIndex, targetNode, targetLinkInfo.target_slot);
}
}
});
}
});
// Remove old node
app.graph.remove(oldNode);
}
function replaceNodeMenuCallback(currentNode, targetNodeName) {
return function() {
replaceNode(currentNode, targetNodeName);
};
}
function showSwapMenu(value, options, e, menu, node) {
const swapOptions = [];
if (node.type !== "KSampler (Efficient)") {
swapOptions.push({
content: "KSampler (Efficient)",
callback: replaceNodeMenuCallback(node, "KSampler (Efficient)")
});
}
if (node.type !== "KSampler Adv. (Efficient)") {
swapOptions.push({
content: "KSampler Adv. (Efficient)",
callback: replaceNodeMenuCallback(node, "KSampler Adv. (Efficient)")
});
}
if (node.type !== "KSampler SDXL (Eff.)") {
swapOptions.push({
content: "KSampler SDXL (Eff.)",
callback: replaceNodeMenuCallback(node, "KSampler SDXL (Eff.)")
});
}
new LiteGraph.ContextMenu(swapOptions, {
event: e,
callback: null,
parentMenu: menu,
node: node
});
return false; // This ensures the original context menu doesn't proceed
}
// Extension Definition
app.registerExtension({
name: "efficiency.SwapSamplers",
async beforeRegisterNodeDef(nodeType, nodeData, app) {
if (["KSampler (Efficient)", "KSampler Adv. (Efficient)", "KSampler SDXL (Eff.)"].includes(nodeData.name)) {
addMenuHandler(nodeType, function (insertOption) {
insertOption({
content: "🔄 Swap with...",
has_submenu: true,
callback: showSwapMenu
});
});
}
},
});
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/swapScripts.js
-100
View File
@@ -1,100 +0,0 @@
import { app } from "../../../scripts/app.js";
import { addMenuHandler } from "./common/utils.js";
function replaceNode(oldNode, newNodeName) {
const newNode = LiteGraph.createNode(newNodeName);
if (!newNode) {
return;
}
app.graph.add(newNode);
newNode.pos = oldNode.pos.slice();
// Transfer connections from old node to new node
// XY Plot and AnimateDiff have only one output
if(["XY Plot", "AnimateDiff Script"].includes(oldNode.type)) {
if (oldNode.outputs[0] && oldNode.outputs[0].links) {
oldNode.outputs[0].links.forEach(link => {
const targetLinkInfo = oldNode.graph.links[link];
if (targetLinkInfo) {
const targetNode = oldNode.graph.getNodeById(targetLinkInfo.target_id);
if (targetNode) {
newNode.connect(0, targetNode, targetLinkInfo.target_slot);
}
}
});
}
} else {
// Noise Control Script, HighRes-Fix Script, and Tiled Upscaler Script have 1 input and 1 output at index 0
if (oldNode.inputs[0] && oldNode.inputs[0].link !== null) {
const originLinkInfo = oldNode.graph.links[oldNode.inputs[0].link];
if (originLinkInfo) {
const originNode = oldNode.graph.getNodeById(originLinkInfo.origin_id);
if (originNode) {
originNode.connect(originLinkInfo.origin_slot, newNode, 0);
}
}
}
if (oldNode.outputs[0] && oldNode.outputs[0].links) {
oldNode.outputs[0].links.forEach(link => {
const targetLinkInfo = oldNode.graph.links[link];
if (targetLinkInfo) {
const targetNode = oldNode.graph.getNodeById(targetLinkInfo.target_id);
if (targetNode) {
newNode.connect(0, targetNode, targetLinkInfo.target_slot);
}
}
});
}
}
// Remove old node
app.graph.remove(oldNode);
}
function replaceNodeMenuCallback(currentNode, targetNodeName) {
return function() {
replaceNode(currentNode, targetNodeName);
};
}
function showSwapMenu(value, options, e, menu, node) {
const scriptNodes = [
"XY Plot",
"Noise Control Script",
"HighRes-Fix Script",
"Tiled Upscaler Script",
"AnimateDiff Script"
];
const swapOptions = scriptNodes.filter(n => n !== node.type).map(n => ({
content: n,
callback: replaceNodeMenuCallback(node, n)
}));
new LiteGraph.ContextMenu(swapOptions, {
event: e,
callback: null,
parentMenu: menu,
node: node
});
return false;
}
// Extension Definition
app.registerExtension({
name: "efficiency.SwapScripts",
async beforeRegisterNodeDef(nodeType, nodeData, app) {
if (["XY Plot", "Noise Control Script", "HighRes-Fix Script", "Tiled Upscaler Script", "AnimateDiff Script"].includes(nodeData.name)) {
addMenuHandler(nodeType, function (insertOption) {
insertOption({
content: "🔄 Swap with...",
has_submenu: true,
callback: showSwapMenu
});
});
}
},
});
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/node_options/swapXYinputs.js
-98
View File
@@ -1,98 +0,0 @@
import { app } from "../../../scripts/app.js";
import { addMenuHandler } from "./common/utils.js";
function replaceNode(oldNode, newNodeName) {
const newNode = LiteGraph.createNode(newNodeName);
if (!newNode) {
return;
}
app.graph.add(newNode);
newNode.pos = oldNode.pos.slice();
// Handle the special nodes with two outputs
const nodesWithTwoOutputs = ["XY Input: LoRA Plot", "XY Input: Control Net Plot", "XY Input: Manual XY Entry"];
let outputCount = nodesWithTwoOutputs.includes(oldNode.type) ? 2 : 1;
// Transfer output connections from old node to new node
oldNode.outputs.slice(0, outputCount).forEach((output, index) => {
if (output && output.links) {
output.links.forEach(link => {
const targetLinkInfo = oldNode.graph.links[link];
if (targetLinkInfo) {
const targetNode = oldNode.graph.getNodeById(targetLinkInfo.target_id);
if (targetNode) {
newNode.connect(index, targetNode, targetLinkInfo.target_slot);
}
}
});
}
});
// Remove old node
app.graph.remove(oldNode);
}
function replaceNodeMenuCallback(currentNode, targetNodeName) {
return function() {
replaceNode(currentNode, targetNodeName);
};
}
function showSwapMenu(value, options, e, menu, node) {
const swapOptions = [];
const xyInputNodes = [
"XY Input: Seeds++ Batch",
"XY Input: Add/Return Noise",
"XY Input: Steps",
"XY Input: CFG Scale",
"XY Input: Sampler/Scheduler",
"XY Input: Denoise",
"XY Input: VAE",
"XY Input: Prompt S/R",
"XY Input: Aesthetic Score",
"XY Input: Refiner On/Off",
"XY Input: Checkpoint",
"XY Input: Clip Skip",
"XY Input: LoRA",
"XY Input: LoRA Plot",
"XY Input: LoRA Stacks",
"XY Input: Control Net",
"XY Input: Control Net Plot",
"XY Input: Manual XY Entry"
];
for (const nodeType of xyInputNodes) {
if (node.type !== nodeType) {
swapOptions.push({
content: nodeType,
callback: replaceNodeMenuCallback(node, nodeType)
});
}
}
new LiteGraph.ContextMenu(swapOptions, {
event: e,
callback: null,
parentMenu: menu,
node: node
});
return false; // This ensures the original context menu doesn't proceed
}
// Extension Definition
app.registerExtension({
name: "efficiency.swapXYinputs",
async beforeRegisterNodeDef(nodeType, nodeData, app) {
if (nodeData.name.startsWith("XY Input:")) {
addMenuHandler(nodeType, function (insertOption) {
insertOption({
content: "🔄 Swap with...",
has_submenu: true,
callback: showSwapMenu
});
});
}
},
});
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/previewfix.js
-67
View File
@@ -1,67 +0,0 @@
import { app } from "../../scripts/app.js";
import { api } from "../../scripts/api.js";
app.registerExtension({
name: "efficiency.previewfix",
lastExecutedNodeId: null,
blobsToRevoke: [], // Array to accumulate blob URLs for revocation
debug: false,
log(...args) {
if (this.debug) console.log(...args);
},
error(...args) {
if (this.debug) console.error(...args);
},
shouldRevokeBlobForNode(nodeId) {
const node = app.graph.getNodeById(nodeId);
const validTitles = [
"KSampler (Efficient)",
"KSampler Adv. (Efficient)",
"KSampler SDXL (Eff.)"
];
if (!node || !validTitles.includes(node.title)) {
return false;
}
const getValue = name => ((node.widgets || []).find(w => w.name === name) || {}).value;
return getValue("preview_method") !== "none" && getValue("vae_decode").includes("true");
},
setup() {
// Intercepting blob creation to store and immediately revoke the last blob URL
const originalCreateObjectURL = URL.createObjectURL;
URL.createObjectURL = (object) => {
const blobURL = originalCreateObjectURL(object);
if (blobURL.startsWith('blob:')) {
this.log("[BlobURLLogger] Blob URL created:", blobURL);
// If the current node meets the criteria, add the blob URL to the revocation list
if (this.shouldRevokeBlobForNode(this.lastExecutedNodeId)) {
this.blobsToRevoke.push(blobURL);
}
}
return blobURL;
};
// Listen to the start of the node execution to revoke all accumulated blob URLs
api.addEventListener("executing", ({ detail }) => {
if (this.lastExecutedNodeId !== detail || detail === null) {
this.blobsToRevoke.forEach(blob => {
this.log("[BlobURLLogger] Revoking Blob URL:", blob);
URL.revokeObjectURL(blob);
});
this.blobsToRevoke = []; // Clear the list after revoking all blobs
}
// Update the last executed node ID
this.lastExecutedNodeId = detail;
});
this.log("[BlobURLLogger] Hook attached.");
},
});
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/seedcontrol.js
-251
View File
@@ -1,251 +0,0 @@
import { app } from "../../scripts/app.js";
import { addMenuHandler } from "./node_options/common/utils.js";
const LAST_SEED_BUTTON_LABEL = '🎲 Randomize / ♻️ Last Queued Seed';
const SEED_BEHAVIOR_RANDOMIZE = 'Randomize';
const SEED_BEHAVIOR_INCREMENT = 'Increment';
const SEED_BEHAVIOR_DECREMENT = 'Decrement';
const NODE_WIDGET_MAP = {
"KSampler (Efficient)": "seed",
"KSampler Adv. (Efficient)": "noise_seed",
"KSampler SDXL (Eff.)": "noise_seed",
"Noise Control Script": "seed",
"HighRes-Fix Script": "seed",
"Tiled Upscaler Script": "seed"
};
const SPECIFIC_WIDTH = 325; // Set to desired width
function setNodeWidthForMappedTitles(node) {
if (NODE_WIDGET_MAP[node.comfyClass]) {
node.setSize([SPECIFIC_WIDTH, node.size[1]]);
}
}
class SeedControl {
constructor(node, seedName) {
this.lastSeed = -1;
this.serializedCtx = {};
this.node = node;
this.seedBehavior = 'randomize'; // Default behavior
let controlAfterGenerateIndex;
for (const [i, w] of this.node.widgets.entries()) {
if (w.name === seedName) {
this.seedWidget = w;
} else if (w.name === 'control_after_generate') {
controlAfterGenerateIndex = i;
this.node.widgets.splice(i, 1);
}
}
if (!this.seedWidget) {
throw new Error('Something\'s wrong; expected seed widget');
}
this.lastSeedButton = this.node.addWidget("button", LAST_SEED_BUTTON_LABEL, null, () => {
const isValidValue = Number.isInteger(this.seedWidget.value) && this.seedWidget.value >= min && this.seedWidget.value <= max;
// Special case: if the current label is the default and seed value is -1
if (this.lastSeedButton.name === LAST_SEED_BUTTON_LABEL && this.seedWidget.value == -1) {
return; // Do nothing and return early
}
if (isValidValue && this.seedWidget.value != -1) {
this.lastSeed = this.seedWidget.value;
this.seedWidget.value = -1;
} else if (this.lastSeed !== -1) {
this.seedWidget.value = this.lastSeed;
} else {
this.seedWidget.value = -1; // Set to -1 if the label didn't update due to a seed value issue
}
if (isValidValue) {
this.updateButtonLabel(); // Update the button label to reflect the change
}
}, { width: 50, serialize: false });
setNodeWidthForMappedTitles(node);
if (controlAfterGenerateIndex !== undefined) {
const addedWidget = this.node.widgets.pop();
this.node.widgets.splice(controlAfterGenerateIndex, 0, addedWidget);
setNodeWidthForMappedTitles(node);
}
const max = Math.min(1125899906842624, this.seedWidget.options.max);
const min = Math.max(-1125899906842624, this.seedWidget.options.min);
const range = (max - min) / (this.seedWidget.options.step / 10);
this.seedWidget.serializeValue = async (node, index) => {
// Check if the button is disabled
if (this.lastSeedButton.disabled) {
return this.seedWidget.value;
}
const currentSeed = this.seedWidget.value;
this.serializedCtx = {
wasSpecial: currentSeed == -1,
};
if (this.serializedCtx.wasSpecial) {
switch (this.seedBehavior) {
case 'increment':
this.serializedCtx.seedUsed = this.lastSeed + 1;
break;
case 'decrement':
this.serializedCtx.seedUsed = this.lastSeed - 1;
break;
default:
this.serializedCtx.seedUsed = Math.floor(Math.random() * range) * (this.seedWidget.options.step / 10) + min;
break;
}
// Ensure the seed value is an integer and remains within the accepted range
this.serializedCtx.seedUsed = Number.isInteger(this.serializedCtx.seedUsed) ? Math.min(Math.max(this.serializedCtx.seedUsed, min), max) : this.seedWidget.value;
} else {
this.serializedCtx.seedUsed = this.seedWidget.value;
}
if (node && node.widgets_values) {
node.widgets_values[index] = this.serializedCtx.seedUsed;
} else {
// Update the last seed value and the button's label to show the current seed value
this.lastSeed = this.serializedCtx.seedUsed;
this.updateButtonLabel();
}
this.seedWidget.value = this.serializedCtx.seedUsed;
if (this.serializedCtx.wasSpecial) {
this.lastSeed = this.serializedCtx.seedUsed;
this.updateButtonLabel();
}
return this.serializedCtx.seedUsed;
};
this.seedWidget.afterQueued = () => {
// Check if the button is disabled
if (this.lastSeedButton.disabled) {
return; // Exit the function immediately
}
if (this.serializedCtx.wasSpecial) {
this.seedWidget.value = -1;
}
// Check if seed has changed to a non -1 value, and if so, update lastSeed
if (this.seedWidget.value !== -1) {
this.lastSeed = this.seedWidget.value;
}
this.updateButtonLabel();
this.serializedCtx = {};
};
}
setBehavior(behavior) {
this.seedBehavior = behavior;
// Capture the current seed value as lastSeed and then set the seed widget value to -1
if (this.seedWidget.value != -1) {
this.lastSeed = this.seedWidget.value;
this.seedWidget.value = -1;
}
this.updateButtonLabel();
}
updateButtonLabel() {
switch (this.seedBehavior) {
case 'increment':
this.lastSeedButton.name = `➕ Increment / ♻️ ${this.lastSeed === -1 ? "Last Queued Seed" : this.lastSeed}`;
break;
case 'decrement':
this.lastSeedButton.name = `➖ Decrement / ♻️ ${this.lastSeed === -1 ? "Last Queued Seed" : this.lastSeed}`;
break;
default:
this.lastSeedButton.name = `🎲 Randomize / ♻️ ${this.lastSeed === -1 ? "Last Queued Seed" : this.lastSeed}`;
break;
}
}
}
function showSeedBehaviorMenu(value, options, e, menu, node) {
const behaviorOptions = [
{
content: "🎲 Randomize",
callback: () => {
node.seedControl.setBehavior('randomize');
}
},
{
content: "➕ Increment",
callback: () => {
node.seedControl.setBehavior('increment');
}
},
{
content: "➖ Decrement",
callback: () => {
node.seedControl.setBehavior('decrement');
}
}
];
new LiteGraph.ContextMenu(behaviorOptions, {
event: e,
callback: null,
parentMenu: menu,
node: node
});
return false; // This ensures the original context menu doesn't proceed
}
// Extension Definition
app.registerExtension({
name: "efficiency.seedcontrol",
async beforeRegisterNodeDef(nodeType, nodeData, _app) {
if (NODE_WIDGET_MAP[nodeData.name]) {
addMenuHandler(nodeType, function (insertOption) {
// Check conditions before showing the seed behavior option
let showSeedOption = true;
if (nodeData.name === "Noise Control Script") {
// Check for 'add_seed_noise' widget being false
const addSeedNoiseWidget = this.widgets.find(w => w.name === 'add_seed_noise');
if (addSeedNoiseWidget && !addSeedNoiseWidget.value) {
showSeedOption = false;
}
} else if (nodeData.name === "HighRes-Fix Script") {
// Check for 'use_same_seed' widget being true
const useSameSeedWidget = this.widgets.find(w => w.name === 'use_same_seed');
if (useSameSeedWidget && useSameSeedWidget.value) {
showSeedOption = false;
}
}
if (showSeedOption) {
insertOption({
content: "🌱 Seed behavior...",
has_submenu: true,
callback: showSeedBehaviorMenu
});
}
});
const onNodeCreated = nodeType.prototype.onNodeCreated;
nodeType.prototype.onNodeCreated = function () {
onNodeCreated ? onNodeCreated.apply(this, []) : undefined;
this.seedControl = new SeedControl(this, NODE_WIDGET_MAP[nodeData.name]);
this.seedControl.seedWidget.value = -1;
};
}
},
});
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/widgethider.js
-596
View File
@@ -1,596 +0,0 @@
import { app } from "../../scripts/app.js";
let origProps = {};
let initialized = false;
const findWidgetByName = (node, name) => {
return node.widgets ? node.widgets.find((w) => w.name === name) : null;
};
const doesInputWithNameExist = (node, name) => {
return node.inputs ? node.inputs.some((input) => input.name === name) : false;
};
const HIDDEN_TAG = "tschide";
// Toggle Widget + change size
function toggleWidget(node, widget, show = false, suffix = "") {
if (!widget || doesInputWithNameExist(node, widget.name)) return;
// Store the original properties of the widget if not already stored
if (!origProps[widget.name]) {
origProps[widget.name] = { origType: widget.type, origComputeSize: widget.computeSize };
}
const origSize = node.size;
// Set the widget type and computeSize based on the show flag
widget.type = show ? origProps[widget.name].origType : HIDDEN_TAG + suffix;
widget.computeSize = show ? origProps[widget.name].origComputeSize : () => [0, -4];
// Recursively handle linked widgets if they exist
widget.linkedWidgets?.forEach(w => toggleWidget(node, w, ":" + widget.name, show));
// Calculate the new height for the node based on its computeSize method
const newHeight = node.computeSize()[1];
node.setSize([node.size[0], newHeight]);
}
const WIDGET_HEIGHT = 24;
// Use for Multiline Widget Nodes (aka Efficient Loaders)
function toggleWidget_2(node, widget, show = false, suffix = "") {
if (!widget || doesInputWithNameExist(node, widget.name)) return;
const isCurrentlyVisible = widget.type !== HIDDEN_TAG + suffix;
if (isCurrentlyVisible === show) return; // Early exit if widget is already in the desired state
if (!origProps[widget.name]) {
origProps[widget.name] = { origType: widget.type, origComputeSize: widget.computeSize };
}
widget.type = show ? origProps[widget.name].origType : HIDDEN_TAG + suffix;
widget.computeSize = show ? origProps[widget.name].origComputeSize : () => [0, -4];
if (initialized){
const adjustment = show ? WIDGET_HEIGHT : -WIDGET_HEIGHT;
node.setSize([node.size[0], node.size[1] + adjustment]);
}
}
// New function to handle widget visibility based on input_mode
function handleInputModeWidgetsVisibility(node, inputModeValue) {
// Utility function to generate widget names up to a certain count
function generateWidgetNames(baseName, count) {
return Array.from({ length: count }, (_, i) => `${baseName}_${i + 1}`);
}
// Common widget groups
const batchWidgets = ["batch_path", "subdirectories", "batch_sort", "batch_max"];
const xbatchWidgets = ["X_batch_path", "X_subdirectories", "X_batch_sort", "X_batch_max"];
const ckptWidgets = [...generateWidgetNames("ckpt_name", 50)];
const clipSkipWidgets = [...generateWidgetNames("clip_skip", 50)];
const vaeNameWidgets = [...generateWidgetNames("vae_name", 50)];
const loraNameWidgets = [...generateWidgetNames("lora_name", 50)];
const loraWtWidgets = [...generateWidgetNames("lora_wt", 50)];
const modelStrWidgets = [...generateWidgetNames("model_str", 50)];
const clipStrWidgets = [...generateWidgetNames("clip_str", 50)];
const xWidgets = ["X_batch_count", "X_first_value", "X_last_value"]
const yWidgets = ["Y_batch_count", "Y_first_value", "Y_last_value"]
const nodeVisibilityMap = {
"LoRA Stacker": {
"simple": [...modelStrWidgets, ...clipStrWidgets],
"advanced": [...loraWtWidgets]
},
"XY Input: Steps": {
"steps": ["first_start_step", "last_start_step", "first_end_step", "last_end_step", "first_refine_step", "last_refine_step"],
"start_at_step": ["first_step", "last_step", "first_end_step", "last_end_step", "first_refine_step", "last_refine_step"],
"end_at_step": ["first_step", "last_step", "first_start_step", "last_start_step", "first_refine_step", "last_refine_step"],
"refine_at_step": ["first_step", "last_step", "first_start_step", "last_start_step", "first_end_step", "last_end_step"]
},
"XY Input: VAE": {
"VAE Names": [...batchWidgets],
"VAE Batch": [...vaeNameWidgets, "vae_count"]
},
"XY Input: Checkpoint": {
"Ckpt Names": [...clipSkipWidgets, ...vaeNameWidgets, ...batchWidgets],
"Ckpt Names+ClipSkip": [...vaeNameWidgets, ...batchWidgets],
"Ckpt Names+ClipSkip+VAE": [...batchWidgets],
"Checkpoint Batch": [...ckptWidgets, ...clipSkipWidgets, ...vaeNameWidgets, "ckpt_count"]
},
"XY Input: LoRA": {
"LoRA Names": [...modelStrWidgets, ...clipStrWidgets, ...batchWidgets],
"LoRA Names+Weights": [...batchWidgets, "model_strength", "clip_strength"],
"LoRA Batch": [...loraNameWidgets, ...modelStrWidgets, ...clipStrWidgets, "lora_count"]
},
"XY Input: LoRA Plot": {
"X: LoRA Batch, Y: LoRA Weight": ["lora_name", "model_strength", "clip_strength", "X_first_value", "X_last_value"],
"X: LoRA Batch, Y: Model Strength": ["lora_name", "model_strength", "model_strength", "X_first_value", "X_last_value"],
"X: LoRA Batch, Y: Clip Strength": ["lora_name", "clip_strength", "X_first_value", "X_last_value"],
"X: Model Strength, Y: Clip Strength": [...xbatchWidgets, "model_strength", "clip_strength"],
},
"XY Input: Control Net": {
"strength": ["first_start_percent", "last_start_percent", "first_end_percent", "last_end_percent", "strength"],
"start_percent": ["first_strength", "last_strength", "first_end_percent", "last_end_percent", "start_percent"],
"end_percent": ["first_strength", "last_strength", "first_start_percent", "last_start_percent", "end_percent"]
},
"XY Input: Control Net Plot": {
"X: Strength, Y: Start%": ["strength", "start_percent"],
"X: Strength, Y: End%": ["strength","end_percent"],
"X: Start%, Y: Strength": ["start_percent", "strength"],
"X: Start%, Y: End%": ["start_percent", "end_percent"],
"X: End%, Y: Strength": ["end_percent", "strength"],
"X: End%, Y: Start%": ["end_percent", "start_percent"],
}
};
const inputModeVisibilityMap = nodeVisibilityMap[node.comfyClass];
if (!inputModeVisibilityMap || !inputModeVisibilityMap[inputModeValue]) return;
// Reset all widgets to visible
for (const key in inputModeVisibilityMap) {
for (const widgetName of inputModeVisibilityMap[key]) {
const widget = findWidgetByName(node, widgetName);
toggleWidget(node, widget, true);
}
}
// Hide the specific widgets for the current input_mode value
for (const widgetName of inputModeVisibilityMap[inputModeValue]) {
const widget = findWidgetByName(node, widgetName);
toggleWidget(node, widget, false);
}
}
// Handle multi-widget visibilities
function handleVisibility(node, countValue, node_type) {
const inputModeValue = findWidgetByName(node, "input_mode").value;
const baseNamesMap = {
"LoRA": ["lora_name", "model_str", "clip_str"],
"Checkpoint": ["ckpt_name", "clip_skip", "vae_name"],
"LoRA Stacker": ["lora_name", "model_str", "clip_str", "lora_wt"]
};
const baseNames = baseNamesMap[node_type];
const isBatchMode = inputModeValue.includes("Batch");
if (isBatchMode) {countValue = 0;}
for (let i = 1; i <= 50; i++) {
const nameWidget = findWidgetByName(node, `${baseNames[0]}_${i}`);
const firstWidget = findWidgetByName(node, `${baseNames[1]}_${i}`);
const secondWidget = findWidgetByName(node, `${baseNames[2]}_${i}`);
const thirdWidget = node_type === "LoRA Stacker" ? findWidgetByName(node, `${baseNames[3]}_${i}`) : null;
if (i <= countValue) {
toggleWidget(node, nameWidget, true);
if (node_type === "LoRA Stacker") {
if (inputModeValue === "simple") {
toggleWidget(node, firstWidget, false); // model_str
toggleWidget(node, secondWidget, false); // clip_str
toggleWidget(node, thirdWidget, true); // lora_wt
} else if (inputModeValue === "advanced") {
toggleWidget(node, firstWidget, true); // model_str
toggleWidget(node, secondWidget, true); // clip_str
toggleWidget(node, thirdWidget, false); // lora_wt
}
} else if (node_type === "Checkpoint") {
if (inputModeValue.includes("ClipSkip")){toggleWidget(node, firstWidget, true);}
if (inputModeValue.includes("VAE")){toggleWidget(node, secondWidget, true);}
} else if (node_type === "LoRA") {
if (inputModeValue.includes("Weights")){
toggleWidget(node, firstWidget, true);
toggleWidget(node, secondWidget, true);
}
}
}
else {
toggleWidget(node, nameWidget, false);
toggleWidget(node, firstWidget, false);
toggleWidget(node, secondWidget, false);
if (thirdWidget) {toggleWidget(node, thirdWidget, false);}
}
}
}
// Sampler & Scheduler XY input visibility logic
function handleSamplerSchedulerVisibility(node, countValue, targetParameter) {
for (let i = 1; i <= 50; i++) {
const samplerWidget = findWidgetByName(node, `sampler_${i}`);
const schedulerWidget = findWidgetByName(node, `scheduler_${i}`);
if (i <= countValue) {
if (targetParameter === "sampler") {
toggleWidget(node, samplerWidget, true);
toggleWidget(node, schedulerWidget, false);
} else if (targetParameter === "scheduler") {
toggleWidget(node, samplerWidget, false);
toggleWidget(node, schedulerWidget, true);
} else { // targetParameter is "sampler & scheduler"
toggleWidget(node, samplerWidget, true);
toggleWidget(node, schedulerWidget, true);
}
} else {
toggleWidget(node, samplerWidget, false);
toggleWidget(node, schedulerWidget, false);
}
}
}
// Handle simple widget visibility based on a count
function handleWidgetVisibility(node, thresholdValue, widgetNamePrefix, maxCount) {
for (let i = 1; i <= maxCount; i++) {
const widget = findWidgetByName(node, `${widgetNamePrefix}${i}`);
if (widget) {
toggleWidget(node, widget, i <= thresholdValue);
}
}
}
// Disable the 'Ckpt Name+ClipSkip+VAE' option if 'target_ckpt' is "Refiner"
let last_ckpt_input_mode;
let last_target_ckpt;
function xyCkptRefinerOptionsRemove(widget, node) {
let target_ckpt = findWidgetByName(node, "target_ckpt").value
let input_mode = widget.value
if ((input_mode === "Ckpt Names+ClipSkip+VAE") && (target_ckpt === "Refiner")) {
if (last_ckpt_input_mode === "Ckpt Names+ClipSkip") {
if (last_target_ckpt === "Refiner"){
widget.value = "Checkpoint Batch";
} else {widget.value = "Ckpt Names+ClipSkip";}
} else if (last_ckpt_input_mode === "Checkpoint Batch") {
if (last_target_ckpt === "Refiner"){
widget.value = "Ckpt Names+ClipSkip";
} else {widget.value = "Checkpoint Batch";}
} else if (last_ckpt_input_mode !== 'undefined') {
widget.value = last_ckpt_input_mode;
} else {
widget.value = "Ckpt Names";
}
} else if (input_mode !== "Ckpt Names+ClipSkip+VAE"){
last_ckpt_input_mode = input_mode;
}
last_target_ckpt = target_ckpt
}
// Create a map of node titles to their respective widget handlers
const nodeWidgetHandlers = {
"Efficient Loader": {
'lora_name': handleEfficientLoaderLoraName
},
"Eff. Loader SDXL": {
'refiner_ckpt_name': handleEffLoaderSDXLRefinerCkptName
},
"LoRA Stacker": {
'input_mode': handleLoRAStackerInputMode,
'lora_count': handleLoRAStackerLoraCount
},
"XY Input: Steps": {
'target_parameter': handleXYInputStepsTargetParameter
},
"XY Input: Sampler/Scheduler": {
'target_parameter': handleXYInputSamplerSchedulerTargetParameter,
'input_count': handleXYInputSamplerSchedulerInputCount
},
"XY Input: VAE": {
'input_mode': handleXYInputVAEInputMode,
'vae_count': handleXYInputVAEVaeCount
},
"XY Input: Prompt S/R": {
'replace_count': handleXYInputPromptSRReplaceCount
},
"XY Input: Checkpoint": {
'input_mode': handleXYInputCheckpointInputMode,
'ckpt_count': handleXYInputCheckpointCkptCount,
'target_ckpt': handleXYInputCheckpointTargetCkpt
},
"XY Input: LoRA": {
'input_mode': handleXYInputLoRAInputMode,
'lora_count': handleXYInputLoRALoraCount
},
"XY Input: LoRA Plot": {
'input_mode': handleXYInputLoRAPlotInputMode
},
"XY Input: LoRA Stacks": {
'node_state': handleXYInputLoRAStacksNodeState
},
"XY Input: Control Net": {
'target_parameter': handleXYInputControlNetTargetParameter
},
"XY Input: Control Net Plot": {
'plot_type': handleXYInputControlNetPlotPlotType
},
"Noise Control Script": {
'add_seed_noise': handleNoiseControlScript
},
"HighRes-Fix Script": {
'upscale_type': handleHiResFixScript,
'use_same_seed': handleHiResFixScript,
'use_controlnet':handleHiResFixScript
},
"Tiled Upscaler Script": {
'use_controlnet':handleTiledUpscalerScript
},
};
// In the main function where widgetLogic is called
function widgetLogic(node, widget) {
// Retrieve the handler for the current node title and widget name
const handler = nodeWidgetHandlers[node.comfyClass]?.[widget.name];
if (handler) {
handler(node, widget);
}
}
// Efficient Loader Handlers
function handleEfficientLoaderLoraName(node, widget) {
if (widget.value === 'None') {
toggleWidget_2(node, findWidgetByName(node, 'lora_model_strength'));
toggleWidget_2(node, findWidgetByName(node, 'lora_clip_strength'));
} else {
toggleWidget_2(node, findWidgetByName(node, 'lora_model_strength'), true);
toggleWidget_2(node, findWidgetByName(node, 'lora_clip_strength'), true);
}
}
// Eff. Loader SDXL Handlers
function handleEffLoaderSDXLRefinerCkptName(node, widget) {
if (widget.value === 'None') {
toggleWidget_2(node, findWidgetByName(node, 'refiner_clip_skip'));
toggleWidget_2(node, findWidgetByName(node, 'positive_ascore'));
toggleWidget_2(node, findWidgetByName(node, 'negative_ascore'));
} else {
toggleWidget_2(node, findWidgetByName(node, 'refiner_clip_skip'), true);
toggleWidget_2(node, findWidgetByName(node, 'positive_ascore'), true);
toggleWidget_2(node, findWidgetByName(node, 'negative_ascore'), true);
}
}
// Noise Control Script Seed Handler
function handleNoiseControlScript(node, widget) {
function ensureSeedControlExists(callback) {
if (node.seedControl && node.seedControl.lastSeedButton) {
callback();
} else {
setTimeout(() => ensureSeedControlExists(callback), 0);
}
}
ensureSeedControlExists(() => {
if (widget.value === false) {
toggleWidget(node, findWidgetByName(node, 'seed'));
toggleWidget(node, findWidgetByName(node, 'weight'));
toggleWidget(node, node.seedControl.lastSeedButton);
node.seedControl.lastSeedButton.disabled = true; // Disable the button
} else {
toggleWidget(node, findWidgetByName(node, 'seed'), true);
toggleWidget(node, findWidgetByName(node, 'weight'), true);
node.seedControl.lastSeedButton.disabled = false; // Enable the button
toggleWidget(node, node.seedControl.lastSeedButton, true);
}
});
}
/// HighRes-Fix Script Handlers
function handleHiResFixScript(node, widget) {
function ensureSeedControlExists(callback) {
if (node.seedControl && node.seedControl.lastSeedButton) {
callback();
} else {
setTimeout(() => ensureSeedControlExists(callback), 0);
}
}
if (findWidgetByName(node, 'upscale_type').value === "latent") {
toggleWidget(node, findWidgetByName(node, 'pixel_upscaler'));
toggleWidget(node, findWidgetByName(node, 'hires_ckpt_name'), true);
toggleWidget(node, findWidgetByName(node, 'latent_upscaler'), true);
toggleWidget(node, findWidgetByName(node, 'use_same_seed'), true);
toggleWidget(node, findWidgetByName(node, 'hires_steps'), true);
toggleWidget(node, findWidgetByName(node, 'denoise'), true);
toggleWidget(node, findWidgetByName(node, 'iterations'), true);
ensureSeedControlExists(() => {
if (findWidgetByName(node, 'use_same_seed').value == true) {
toggleWidget(node, findWidgetByName(node, 'seed'));
toggleWidget(node, node.seedControl.lastSeedButton);
node.seedControl.lastSeedButton.disabled = true; // Disable the button
} else {
toggleWidget(node, findWidgetByName(node, 'seed'), true);
node.seedControl.lastSeedButton.disabled = false; // Enable the button
toggleWidget(node, node.seedControl.lastSeedButton, true);
}
});
if (findWidgetByName(node, 'use_controlnet').value == '_'){
toggleWidget(node, findWidgetByName(node, 'use_controlnet'));
toggleWidget(node, findWidgetByName(node, 'control_net_name'));
toggleWidget(node, findWidgetByName(node, 'strength'));
toggleWidget(node, findWidgetByName(node, 'preprocessor'));
toggleWidget(node, findWidgetByName(node, 'preprocessor_imgs'));
}
else{
toggleWidget(node, findWidgetByName(node, 'use_controlnet'), true);
if (findWidgetByName(node, 'use_controlnet').value == true){
toggleWidget(node, findWidgetByName(node, 'control_net_name'), true);
toggleWidget(node, findWidgetByName(node, 'strength'), true);
toggleWidget(node, findWidgetByName(node, 'preprocessor'), true);
toggleWidget(node, findWidgetByName(node, 'preprocessor_imgs'), true);
}
else{
toggleWidget(node, findWidgetByName(node, 'control_net_name'));
toggleWidget(node, findWidgetByName(node, 'strength'));
toggleWidget(node, findWidgetByName(node, 'preprocessor'));
toggleWidget(node, findWidgetByName(node, 'preprocessor_imgs'));
}
}
} else if (findWidgetByName(node, 'upscale_type').value === "pixel") {
toggleWidget(node, findWidgetByName(node, 'hires_ckpt_name'));
toggleWidget(node, findWidgetByName(node, 'latent_upscaler'));
toggleWidget(node, findWidgetByName(node, 'use_same_seed'));
toggleWidget(node, findWidgetByName(node, 'hires_steps'));
toggleWidget(node, findWidgetByName(node, 'denoise'));
toggleWidget(node, findWidgetByName(node, 'iterations'));
toggleWidget(node, findWidgetByName(node, 'seed'));
ensureSeedControlExists(() => {
toggleWidget(node, node.seedControl.lastSeedButton);
node.seedControl.lastSeedButton.disabled = true; // Disable the button
});
toggleWidget(node, findWidgetByName(node, 'use_controlnet'));
toggleWidget(node, findWidgetByName(node, 'control_net_name'));
toggleWidget(node, findWidgetByName(node, 'strength'));
toggleWidget(node, findWidgetByName(node, 'preprocessor'));
toggleWidget(node, findWidgetByName(node, 'preprocessor_imgs'));
toggleWidget(node, findWidgetByName(node, 'pixel_upscaler'), true);
}
}
/// Tiled Upscaler Script Handler
function handleTiledUpscalerScript(node, widget) {
if (findWidgetByName(node, 'use_controlnet').value == true){
toggleWidget(node, findWidgetByName(node, 'tile_controlnet'), true);
toggleWidget(node, findWidgetByName(node, 'strength'), true);
}
else{
toggleWidget(node, findWidgetByName(node, 'tile_controlnet'));
toggleWidget(node, findWidgetByName(node, 'strength'));
}
}
// LoRA Stacker Handlers
function handleLoRAStackerInputMode(node, widget) {
handleInputModeWidgetsVisibility(node, widget.value);
handleVisibility(node, findWidgetByName(node, "lora_count").value, "LoRA Stacker");
}
function handleLoRAStackerLoraCount(node, widget) {
handleVisibility(node, widget.value, "LoRA Stacker");
}
// XY Input: Steps Handlers
function handleXYInputStepsTargetParameter(node, widget) {
handleInputModeWidgetsVisibility(node, widget.value);
}
// XY Input: Sampler/Scheduler Handlers
function handleXYInputSamplerSchedulerTargetParameter(node, widget) {
handleSamplerSchedulerVisibility(node, findWidgetByName(node, 'input_count').value, widget.value);
}
function handleXYInputSamplerSchedulerInputCount(node, widget) {
handleSamplerSchedulerVisibility(node, widget.value, findWidgetByName(node, 'target_parameter').value);
}
// XY Input: VAE Handlers
function handleXYInputVAEInputMode(node, widget) {
handleInputModeWidgetsVisibility(node, widget.value);
if (widget.value === "VAE Names") {
handleWidgetVisibility(node, findWidgetByName(node, "vae_count").value, "vae_name_", 50);
} else {
handleWidgetVisibility(node, 0, "vae_name_", 50);
}
}
function handleXYInputVAEVaeCount(node, widget) {
if (findWidgetByName(node, "input_mode").value === "VAE Names") {
handleWidgetVisibility(node, widget.value, "vae_name_", 50);
}
}
// XY Input: Prompt S/R Handlers
function handleXYInputPromptSRReplaceCount(node, widget) {
handleWidgetVisibility(node, widget.value, "replace_", 49);
}
// XY Input: Checkpoint Handlers
function handleXYInputCheckpointInputMode(node, widget) {
xyCkptRefinerOptionsRemove(widget, node);
handleInputModeWidgetsVisibility(node, widget.value);
handleVisibility(node, findWidgetByName(node, "ckpt_count").value, "Checkpoint");
}
function handleXYInputCheckpointCkptCount(node, widget) {
handleVisibility(node, widget.value, "Checkpoint");
}
function handleXYInputCheckpointTargetCkpt(node, widget) {
xyCkptRefinerOptionsRemove(findWidgetByName(node, "input_mode"), node);
}
// XY Input: LoRA Handlers
function handleXYInputLoRAInputMode(node, widget) {
handleInputModeWidgetsVisibility(node, widget.value);
handleVisibility(node, findWidgetByName(node, "lora_count").value, "LoRA");
}
function handleXYInputLoRALoraCount(node, widget) {
handleVisibility(node, widget.value, "LoRA");
}
// XY Input: LoRA Plot Handlers
function handleXYInputLoRAPlotInputMode(node, widget) {
handleInputModeWidgetsVisibility(node, widget.value);
}
// XY Input: LoRA Stacks Handlers
function handleXYInputLoRAStacksNodeState(node, widget) {
toggleWidget(node, findWidgetByName(node, "node_state"), false);
}
// XY Input: Control Net Handlers
function handleXYInputControlNetTargetParameter(node, widget) {
handleInputModeWidgetsVisibility(node, widget.value);
}
// XY Input: Control Net Plot Handlers
function handleXYInputControlNetPlotPlotType(node, widget) {
handleInputModeWidgetsVisibility(node, widget.value);
}
app.registerExtension({
name: "efficiency.widgethider",
nodeCreated(node) {
for (const w of node.widgets || []) {
let widgetValue = w.value;
// Store the original descriptor if it exists
let originalDescriptor = Object.getOwnPropertyDescriptor(w, 'value');
widgetLogic(node, w);
Object.defineProperty(w, 'value', {
get() {
// If there's an original getter, use it. Otherwise, return widgetValue.
let valueToReturn = originalDescriptor && originalDescriptor.get
? originalDescriptor.get.call(w)
: widgetValue;
return valueToReturn;
},
set(newVal) {
// If there's an original setter, use it. Otherwise, set widgetValue.
if (originalDescriptor && originalDescriptor.set) {
originalDescriptor.set.call(w, newVal);
} else {
widgetValue = newVal;
}
widgetLogic(node, w);
}
});
}
setTimeout(() => {initialized = true;}, 500);
}
});
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/js/workflowfix.js
-142
View File
@@ -1,142 +0,0 @@
// Detect and update Efficiency Nodes from v1.92 to v2.00 changes (Final update?)
import { app } from '../../scripts/app.js'
import { addNode } from "./node_options/common/utils.js";
const ext = {
name: "efficiency.WorkflowFix",
};
function reloadHiResFixNode(originalNode) {
// Safeguard against missing 'pos' property
const position = originalNode.pos && originalNode.pos.length === 2 ? { x: originalNode.pos[0], y: originalNode.pos[1] } : { x: 0, y: 0 };
// Recreate the node
const newNode = addNode("HighRes-Fix Script", originalNode, position);
// Transfer input connections from old node to new node
originalNode.inputs.forEach((input, index) => {
if (input && input.link !== null) {
const originLinkInfo = originalNode.graph.links[input.link];
if (originLinkInfo) {
const originNode = originalNode.graph.getNodeById(originLinkInfo.origin_id);
if (originNode) {
originNode.connect(originLinkInfo.origin_slot, newNode, index);
}
}
}
});
// Transfer output connections from old node to new node
originalNode.outputs.forEach((output, index) => {
if (output && output.links) {
output.links.forEach(link => {
const targetLinkInfo = originalNode.graph.links[link];
if (targetLinkInfo) {
const targetNode = originalNode.graph.getNodeById(targetLinkInfo.target_id);
if (targetNode) {
newNode.connect(index, targetNode, targetLinkInfo.target_slot);
}
}
});
}
});
// Remove the original node after all connections are transferred
originalNode.graph.remove(originalNode);
return newNode;
}
ext.loadedGraphNode = function(node, app) {
const originalNode = node; // This line ensures that originalNode refers to the provided node
const kSamplerTypes = [
"KSampler (Efficient)",
"KSampler Adv. (Efficient)",
"KSampler SDXL (Eff.)"
];
// EFFICIENT LOADER & EFF. LOADER SDXL
/* Changes:
Added "token_normalization" & "weight_interpretation" widget below prompt text boxes,
below code fixes the widget values for empty_latent_width, empty_latent_height, and batch_size
by shifting down by 2 widget values starting from the "token_normalization" widget.
Logic triggers when "token_normalization" is a number instead of a string.
*/
if (node.comfyClass === "Efficient Loader" || node.comfyClass === "Eff. Loader SDXL") {
const tokenWidget = node.widgets.find(w => w.name === "token_normalization");
const weightWidget = node.widgets.find(w => w.name === "weight_interpretation");
if (typeof tokenWidget.value === 'number') {
console.log("[EfficiencyUpdate]", `Fixing '${node.comfyClass}' token and weight widgets:`, node);
const index = node.widgets.indexOf(tokenWidget);
if (index !== -1) {
for (let i = node.widgets.length - 1; i > index + 1; i--) {
node.widgets[i].value = node.widgets[i - 2].value;
}
}
tokenWidget.value = "none";
weightWidget.value = "comfy";
}
}
// KSAMPLER (EFFICIENT), KSAMPLER ADV. (EFFICIENT), & KSAMPLER SDXL (EFF.)
/* Changes:
Removed the "sampler_state" widget which cause all widget values to shift down by a factor of 1.
Fix involves moving all widget values by -1. "vae_decode" value is lost in this process, so in
below fix I manually set it to its default value of "true".
*/
else if (kSamplerTypes.includes(node.comfyClass)) {
const seedWidgetName = (node.comfyClass === "KSampler (Efficient)") ? "seed" : "noise_seed";
const stepsWidgetName = (node.comfyClass === "KSampler (Efficient)") ? "steps" : "start_at_step";
const seedWidget = node.widgets.find(w => w.name === seedWidgetName);
const stepsWidget = node.widgets.find(w => w.name === stepsWidgetName);
if (isNaN(seedWidget.value) && isNaN(stepsWidget.value)) {
console.log("[EfficiencyUpdate]", `Fixing '${node.comfyClass}' node widgets:`, node);
for (let i = 0; i < node.widgets.length - 1; i++) {
node.widgets[i].value = node.widgets[i + 1].value;
}
node.widgets[node.widgets.length - 1].value = "true";
}
}
// HIGHRES-FIX SCRIPT
/* Changes:
Many new changes where added, so in order to properly update, aquired the values of the original
widgets, reload a new node, transffer the known original values, and transffer connection.
This fix is triggered when the upscale_type widget is neither "latent" or "pixel".
*/
// Check if the current node is "HighRes-Fix Script" and if any of the above fixes were applied
else if (node.comfyClass === "HighRes-Fix Script") {
const upscaleTypeWidget = node.widgets.find(w => w.name === "upscale_type");
if (upscaleTypeWidget && upscaleTypeWidget.value !== "latent" && upscaleTypeWidget.value !== "pixel") {
console.log("[EfficiencyUpdate]", "Reloading 'HighRes-Fix Script' node:", node);
// Reload the node and get the new node instance
const newNode = reloadHiResFixNode(node);
// Update the widgets of the new node
const targetWidgetNames = ["latent_upscaler", "upscale_by", "hires_steps", "denoise", "iterations"];
// Extract the first five values of the original node
const originalValues = originalNode.widgets.slice(0, 5).map(w => w.value);
targetWidgetNames.forEach((name, index) => {
const widget = newNode.widgets.find(w => w.name === name);
if (widget && originalValues[index] !== undefined) {
if (name === "latent_upscaler" && typeof originalValues[index] === 'string') {
widget.value = originalValues[index].replace("SD-Latent-Upscaler", "city96");
} else {
widget.value = originalValues[index];
}
}
});
}
}
}
app.registerExtension(ext);
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/models/readme.md
-1
View File
@@ -1 +0,0 @@
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/node_settings.json
-19
View File
@@ -1,19 +0,0 @@
{
"Efficient Loader": {
"model_cache": {
"vae": 1,
"ckpt": 1,
"lora": 1,
"refn": 1
}
},
"XY Plot": {
"model_cache": {
"vae": 5,
"ckpt": 5,
"lora": 5,
"refn": 1
}
}
}
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/__init__.py
-1
View File
@@ -1 +0,0 @@
#
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/__pycache__/__init__.cpython-310.pyc
BIN
View File
Binary file not shown.
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/__pycache__/bnk_adv_encode.cpython-310.pyc
BIN
View File
Binary file not shown.
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/__pycache__/bnk_tiled_samplers.cpython-310.pyc
BIN
View File
Binary file not shown.
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/__pycache__/bnk_tiling.cpython-310.pyc
BIN
View File
Binary file not shown.
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/__pycache__/cg_mixed_seed_noise.cpython-310.pyc
BIN
View File
Binary file not shown.
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/__pycache__/city96_latent_upscaler.cpython-310.pyc
BIN
View File
Binary file not shown.
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/__pycache__/smZ_cfg_denoiser.cpython-310.pyc
BIN
View File
Binary file not shown.
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/__pycache__/smZ_rng_source.cpython-310.pyc
BIN
View File
Binary file not shown.
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/__pycache__/ttl_nn_latent_upscaler.cpython-310.pyc
BIN
View File
Binary file not shown.
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/bnk_adv_encode.py
-341
View File
@@ -1,341 +0,0 @@
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)
########################################################################################################################
from nodes import MAX_RESOLUTION
class AdvancedCLIPTextEncode:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"text": ("STRING", {"multiline": True}),
"clip": ("CLIP",),
"token_normalization": (["none", "mean", "length", "length+mean"],),
"weight_interpretation": (["comfy", "A1111", "compel", "comfy++", "down_weight"],),
# "affect_pooled": (["disable", "enable"],),
}}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "encode"
CATEGORY = "conditioning/advanced"
def encode(self, clip, text, token_normalization, weight_interpretation, affect_pooled='disable'):
embeddings_final, pooled = advanced_encode(clip, text, token_normalization, weight_interpretation, w_max=1.0,
apply_to_pooled=affect_pooled == 'enable')
return ([[embeddings_final, {"pooled_output": pooled}]],)
class AddCLIPSDXLRParams:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"conditioning": ("CONDITIONING",),
"width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
"height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
"ascore": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 1000.0, "step": 0.01}),
}}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "encode"
CATEGORY = "conditioning/advanced"
def encode(self, conditioning, width, height, ascore):
c = []
for t in conditioning:
n = [t[0], t[1].copy()]
n[1]['width'] = width
n[1]['height'] = height
n[1]['aesthetic_score'] = ascore
c.append(n)
return (c,)
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/bnk_tiled_samplers.py
-345
View File
@@ -1,345 +0,0 @@
# https://github.com/BlenderNeko/ComfyUI_TiledKSampler
import sys
import os
import itertools
import numpy as np
from tqdm.auto import tqdm
import torch
#sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
import comfy.sd
import comfy.controlnet
import comfy.model_management
import comfy.sample
from . import bnk_tiling as tiling
import latent_preview
#import torch
#import itertools
#import numpy as np
MAX_RESOLUTION=8192
#######################
def recursion_to_list(obj, attr):
current = obj
yield current
while True:
current = getattr(current, attr, None)
if current is not None:
yield current
else:
return
def copy_cond(cond):
return [[c1,c2.copy()] for c1,c2 in cond]
def slice_cond(tile_h, tile_h_len, tile_w, tile_w_len, cond, area, device):
tile_h_end = tile_h + tile_h_len
tile_w_end = tile_w + tile_w_len
coords = area[0] #h_len, w_len, h, w,
mask = area[1]
if coords is not None:
h_len, w_len, h, w = coords
h_end = h + h_len
w_end = w + w_len
if h < tile_h_end and h_end > tile_h and w < tile_w_end and w_end > tile_w:
new_h = max(0, h - tile_h)
new_w = max(0, w - tile_w)
new_h_end = min(tile_h_end, h_end - tile_h)
new_w_end = min(tile_w_end, w_end - tile_w)
cond[1]['area'] = (new_h_end - new_h, new_w_end - new_w, new_h, new_w)
else:
return (cond, True)
if mask is not None:
new_mask = tiling.get_slice(mask, tile_h,tile_h_len,tile_w,tile_w_len)
if new_mask.sum().to(device) == 0.0 and 'mask' in cond[1]:
return (cond, True)
else:
cond[1]['mask'] = new_mask
return (cond, False)
def slice_gligen(tile_h, tile_h_len, tile_w, tile_w_len, cond, gligen):
tile_h_end = tile_h + tile_h_len
tile_w_end = tile_w + tile_w_len
if gligen is None:
return
gligen_type = gligen[0]
gligen_model = gligen[1]
gligen_areas = gligen[2]
gligen_areas_new = []
for emb, h_len, w_len, h, w in gligen_areas:
h_end = h + h_len
w_end = w + w_len
if h < tile_h_end and h_end > tile_h and w < tile_w_end and w_end > tile_w:
new_h = max(0, h - tile_h)
new_w = max(0, w - tile_w)
new_h_end = min(tile_h_end, h_end - tile_h)
new_w_end = min(tile_w_end, w_end - tile_w)
gligen_areas_new.append((emb, new_h_end - new_h, new_w_end - new_w, new_h, new_w))
if len(gligen_areas_new) == 0:
del cond['gligen']
else:
cond['gligen'] = (gligen_type, gligen_model, gligen_areas_new)
def slice_cnet(h, h_len, w, w_len, model:comfy.controlnet.ControlBase, img):
if img is None:
img = model.cond_hint_original
model.cond_hint = tiling.get_slice(img, h*8, h_len*8, w*8, w_len*8).to(model.control_model.dtype).to(model.device)
def slices_T2I(h, h_len, w, w_len, model:comfy.controlnet.ControlBase, img):
model.control_input = None
if img is None:
img = model.cond_hint_original
model.cond_hint = tiling.get_slice(img, h*8, h_len*8, w*8, w_len*8).float().to(model.device)
# TODO: refactor some of the mess
from PIL import Image
def sample_common(model, add_noise, noise_seed, tile_width, tile_height, tiling_strategy, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0, preview=False):
end_at_step = min(end_at_step, steps)
device = comfy.model_management.get_torch_device()
samples = latent_image["samples"]
noise_mask = latent_image["noise_mask"] if "noise_mask" in latent_image else None
force_full_denoise = return_with_leftover_noise == "enable"
if add_noise == "disable":
noise = torch.zeros(samples.size(), dtype=samples.dtype, layout=samples.layout, device="cpu")
else:
skip = latent_image["batch_index"] if "batch_index" in latent_image else None
noise = comfy.sample.prepare_noise(samples, noise_seed, skip)
if noise_mask is not None:
noise_mask = comfy.sample.prepare_mask(noise_mask, noise.shape, device='cpu')
shape = samples.shape
samples = samples.clone()
tile_width = min(shape[-1] * 8, tile_width)
tile_height = min(shape[2] * 8, tile_height)
real_model = None
positive_copy = comfy.sample.convert_cond(positive)
negative_copy = comfy.sample.convert_cond(negative)
modelPatches, inference_memory = comfy.sample.get_additional_models(positive_copy, negative_copy, model.model_dtype())
comfy.model_management.load_models_gpu([model] + modelPatches, model.memory_required(noise.shape) + inference_memory)
real_model = model.model
sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
if tiling_strategy != 'padded':
if noise_mask is not None:
samples += sampler.sigmas[start_at_step].cpu() * noise_mask * model.model.process_latent_out(noise)
else:
samples += sampler.sigmas[start_at_step].cpu() * model.model.process_latent_out(noise)
#cnets
cnets = [c['control'] for (_, c) in positive + negative if 'control' in c and isinstance(c['control'], comfy.controlnet.ControlNet)]
cnets = list(set([x for m in cnets for x in recursion_to_list(m, "previous_controlnet")]))
cnet_imgs = [
torch.nn.functional.interpolate(m.cond_hint_original, (shape[-2] * 8, shape[-1] * 8), mode='nearest-exact').to('cpu')
if m.cond_hint_original.shape[-2] != shape[-2] * 8 or m.cond_hint_original.shape[-1] != shape[-1] * 8 else None
for m in cnets]
#T2I
T2Is = [c['control'] for (_, c) in positive + negative if 'control' in c and isinstance(c['control'], comfy.controlnet.T2IAdapter)]
T2Is = [x for m in T2Is for x in recursion_to_list(m, "previous_controlnet")]
T2I_imgs = [
torch.nn.functional.interpolate(m.cond_hint_original, (shape[-2] * 8, shape[-1] * 8), mode='nearest-exact').to('cpu')
if m.cond_hint_original.shape[-2] != shape[-2] * 8 or m.cond_hint_original.shape[-1] != shape[-1] * 8 or (m.channels_in == 1 and m.cond_hint_original.shape[1] != 1) else None
for m in T2Is
]
T2I_imgs = [
torch.mean(img, 1, keepdim=True) if img is not None and m.channels_in == 1 and m.cond_hint_original.shape[1] else img
for m, img in zip(T2Is, T2I_imgs)
]
#cond area and mask
spatial_conds_pos = [
(c[1]['area'] if 'area' in c[1] else None,
comfy.sample.prepare_mask(c[1]['mask'], shape, device) if 'mask' in c[1] else None)
for c in positive
]
spatial_conds_neg = [
(c[1]['area'] if 'area' in c[1] else None,
comfy.sample.prepare_mask(c[1]['mask'], shape, device) if 'mask' in c[1] else None)
for c in negative
]
#gligen
gligen_pos = [
c[1]['gligen'] if 'gligen' in c[1] else None
for c in positive
]
gligen_neg = [
c[1]['gligen'] if 'gligen' in c[1] else None
for c in negative
]
gen = torch.manual_seed(noise_seed)
if tiling_strategy == 'random' or tiling_strategy == 'random strict':
tiles = tiling.get_tiles_and_masks_rgrid(end_at_step - start_at_step, samples.shape, tile_height, tile_width, gen)
elif tiling_strategy == 'padded':
tiles = tiling.get_tiles_and_masks_padded(end_at_step - start_at_step, samples.shape, tile_height, tile_width)
else:
tiles = tiling.get_tiles_and_masks_simple(end_at_step - start_at_step, samples.shape, tile_height, tile_width)
total_steps = sum([num_steps for img_pass in tiles for steps_list in img_pass for _,_,_,_,num_steps,_ in steps_list])
current_step = [0]
preview_format = "JPEG"
if preview_format not in ["JPEG", "PNG"]:
preview_format = "JPEG"
previewer = None
if preview:
previewer = latent_preview.get_previewer(device, model.model.latent_format)
with tqdm(total=total_steps) as pbar_tqdm:
pbar = comfy.utils.ProgressBar(total_steps)
def callback(step, x0, x, total_steps):
current_step[0] += 1
preview_bytes = None
if previewer:
preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0)
pbar.update_absolute(current_step[0], preview=preview_bytes)
pbar_tqdm.update(1)
if tiling_strategy == "random strict":
samples_next = samples.clone()
for img_pass in tiles:
for i in range(len(img_pass)):
for tile_h, tile_h_len, tile_w, tile_w_len, tile_steps, tile_mask in img_pass[i]:
tiled_mask = None
if noise_mask is not None:
tiled_mask = tiling.get_slice(noise_mask, tile_h, tile_h_len, tile_w, tile_w_len).to(device)
if tile_mask is not None:
if tiled_mask is not None:
tiled_mask *= tile_mask.to(device)
else:
tiled_mask = tile_mask.to(device)
if tiling_strategy == 'padded' or tiling_strategy == 'random strict':
tile_h, tile_h_len, tile_w, tile_w_len, tiled_mask = tiling.mask_at_boundary( tile_h, tile_h_len, tile_w, tile_w_len,
tile_height, tile_width, samples.shape[-2], samples.shape[-1],
tiled_mask, device)
if tiled_mask is not None and tiled_mask.sum().cpu() == 0.0:
continue
tiled_latent = tiling.get_slice(samples, tile_h, tile_h_len, tile_w, tile_w_len).to(device)
if tiling_strategy == 'padded':
tiled_noise = tiling.get_slice(noise, tile_h, tile_h_len, tile_w, tile_w_len).to(device)
else:
if tiled_mask is None or noise_mask is None:
tiled_noise = torch.zeros_like(tiled_latent)
else:
tiled_noise = tiling.get_slice(noise, tile_h, tile_h_len, tile_w, tile_w_len).to(device) * (1 - tiled_mask)
#TODO: all other condition based stuff like area sets and GLIGEN should also happen here
#cnets
for m, img in zip(cnets, cnet_imgs):
slice_cnet(tile_h, tile_h_len, tile_w, tile_w_len, m, img)
#T2I
for m, img in zip(T2Is, T2I_imgs):
slices_T2I(tile_h, tile_h_len, tile_w, tile_w_len, m, img)
pos = [c.copy() for c in positive_copy]#copy_cond(positive_copy)
neg = [c.copy() for c in negative_copy]#copy_cond(negative_copy)
#cond areas
pos = [slice_cond(tile_h, tile_h_len, tile_w, tile_w_len, c, area, device) for c, area in zip(pos, spatial_conds_pos)]
pos = [c for c, ignore in pos if not ignore]
neg = [slice_cond(tile_h, tile_h_len, tile_w, tile_w_len, c, area, device) for c, area in zip(neg, spatial_conds_neg)]
neg = [c for c, ignore in neg if not ignore]
#gligen
for cond, gligen in zip(pos, gligen_pos):
slice_gligen(tile_h, tile_h_len, tile_w, tile_w_len, cond, gligen)
for cond, gligen in zip(neg, gligen_neg):
slice_gligen(tile_h, tile_h_len, tile_w, tile_w_len, cond, gligen)
tile_result = sampler.sample(tiled_noise, pos, neg, cfg=cfg, latent_image=tiled_latent, start_step=start_at_step + i * tile_steps, last_step=start_at_step + i*tile_steps + tile_steps, force_full_denoise=force_full_denoise and i+1 == end_at_step - start_at_step, denoise_mask=tiled_mask, callback=callback, disable_pbar=True, seed=noise_seed)
tile_result = tile_result.cpu()
if tiled_mask is not None:
tiled_mask = tiled_mask.cpu()
if tiling_strategy == "random strict":
tiling.set_slice(samples_next, tile_result, tile_h, tile_h_len, tile_w, tile_w_len, tiled_mask)
else:
tiling.set_slice(samples, tile_result, tile_h, tile_h_len, tile_w, tile_w_len, tiled_mask)
if tiling_strategy == "random strict":
samples = samples_next.clone()
comfy.sample.cleanup_additional_models(modelPatches)
out = latent_image.copy()
out["samples"] = samples.cpu()
return (out, )
class TiledKSampler:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"model": ("MODEL",),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"tile_width": ("INT", {"default": 512, "min": 256, "max": MAX_RESOLUTION, "step": 64}),
"tile_height": ("INT", {"default": 512, "min": 256, "max": MAX_RESOLUTION, "step": 64}),
"tiling_strategy": (["random", "random strict", "padded", 'simple'], ),
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"latent_image": ("LATENT", ),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "sample"
CATEGORY = "sampling"
def sample(self, model, seed, tile_width, tile_height, tiling_strategy, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise):
steps_total = int(steps / denoise)
return sample_common(model, 'enable', seed, tile_width, tile_height, tiling_strategy, steps_total, cfg, sampler_name, scheduler, positive, negative, latent_image, steps_total-steps, steps_total, 'disable', denoise=1.0, preview=True)
class TiledKSamplerAdvanced:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"model": ("MODEL",),
"add_noise": (["enable", "disable"], ),
"noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"tile_width": ("INT", {"default": 512, "min": 256, "max": MAX_RESOLUTION, "step": 64}),
"tile_height": ("INT", {"default": 512, "min": 256, "max": MAX_RESOLUTION, "step": 64}),
"tiling_strategy": (["random", "random strict", "padded", 'simple'], ),
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"latent_image": ("LATENT", ),
"start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
"end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
"return_with_leftover_noise": (["disable", "enable"], ),
"preview": (["disable", "enable"], ),
}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "sample"
CATEGORY = "sampling"
def sample(self, model, add_noise, noise_seed, tile_width, tile_height, tiling_strategy, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, preview, denoise=1.0):
return sample_common(model, add_noise, noise_seed, tile_width, tile_height, tiling_strategy, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0, preview= preview == 'enable')
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/bnk_tiling.py
-175
View File
@@ -1,175 +0,0 @@
import torch
import itertools
import numpy as np
def grouper(n, iterable):
it = iter(iterable)
while True:
chunk = list(itertools.islice(it, n))
if not chunk:
return
yield chunk
def create_batches(n, iterable):
groups = itertools.groupby(iterable, key= lambda x: (x[1], x[3]))
for _, x in groups:
for y in grouper(n, x):
yield y
def get_slice(tensor, h, h_len, w, w_len):
t = tensor.narrow(-2, h, h_len)
t = t.narrow(-1, w, w_len)
return t
def set_slice(tensor1,tensor2, h, h_len, w, w_len, mask=None):
if mask is not None:
tensor1[:,:,h:h+h_len,w:w+w_len] = tensor1[:,:,h:h+h_len,w:w+w_len] * (1 - mask) + tensor2 * mask
else:
tensor1[:,:,h:h+h_len,w:w+w_len] = tensor2
def get_tiles_and_masks_simple(steps, latent_shape, tile_height, tile_width):
latent_size_h = latent_shape[-2]
latent_size_w = latent_shape[-1]
tile_size_h = int(tile_height // 8)
tile_size_w = int(tile_width // 8)
h = np.arange(0,latent_size_h, tile_size_h)
w = np.arange(0,latent_size_w, tile_size_w)
def create_tile(hs, ws, i, j):
h = int(hs[i])
w = int(ws[j])
h_len = min(tile_size_h, latent_size_h - h)
w_len = min(tile_size_w, latent_size_w - w)
return (h, h_len, w, w_len, steps, None)
passes = [
[[create_tile(h, w, i, j) for i in range(len(h)) for j in range(len(w))]],
]
return passes
def get_tiles_and_masks_padded(steps, latent_shape, tile_height, tile_width):
batch_size = latent_shape[0]
latent_size_h = latent_shape[-2]
latent_size_w = latent_shape[-1]
tile_size_h = int(tile_height // 8)
tile_size_h = int((tile_size_h // 4) * 4)
tile_size_w = int(tile_width // 8)
tile_size_w = int((tile_size_w // 4) * 4)
#masks
mask_h = [0,tile_size_h // 4, tile_size_h - tile_size_h // 4, tile_size_h]
mask_w = [0,tile_size_w // 4, tile_size_w - tile_size_w // 4, tile_size_w]
masks = [[] for _ in range(3)]
for i in range(3):
for j in range(3):
mask = torch.zeros((batch_size,1,tile_size_h, tile_size_w), dtype=torch.float32, device='cpu')
mask[:,:,mask_h[i]:mask_h[i+1],mask_w[j]:mask_w[j+1]] = 1.0
masks[i].append(mask)
def create_mask(h_ind, w_ind, h_ind_max, w_ind_max, mask_h, mask_w, h_len, w_len):
mask = masks[1][1]
if not (h_ind == 0 or h_ind == h_ind_max or w_ind == 0 or w_ind == w_ind_max):
return get_slice(mask, 0, h_len, 0, w_len)
mask = mask.clone()
if h_ind == 0 and mask_h:
mask += masks[0][1]
if h_ind == h_ind_max and mask_h:
mask += masks[2][1]
if w_ind == 0 and mask_w:
mask += masks[1][0]
if w_ind == w_ind_max and mask_w:
mask += masks[1][2]
if h_ind == 0 and w_ind == 0 and mask_h and mask_w:
mask += masks[0][0]
if h_ind == 0 and w_ind == w_ind_max and mask_h and mask_w:
mask += masks[0][2]
if h_ind == h_ind_max and w_ind == 0 and mask_h and mask_w:
mask += masks[2][0]
if h_ind == h_ind_max and w_ind == w_ind_max and mask_h and mask_w:
mask += masks[2][2]
return get_slice(mask, 0, h_len, 0, w_len)
h = np.arange(0,latent_size_h, tile_size_h)
h_shift = np.arange(tile_size_h // 2, latent_size_h - tile_size_h // 2, tile_size_h)
w = np.arange(0,latent_size_w, tile_size_w)
w_shift = np.arange(tile_size_w // 2, latent_size_w - tile_size_h // 2, tile_size_w)
def create_tile(hs, ws, mask_h, mask_w, i, j):
h = int(hs[i])
w = int(ws[j])
h_len = min(tile_size_h, latent_size_h - h)
w_len = min(tile_size_w, latent_size_w - w)
mask = create_mask(i,j,len(hs)-1, len(ws)-1, mask_h, mask_w, h_len, w_len)
return (h, h_len, w, w_len, steps, mask)
passes = [
[[create_tile(h, w, True, True, i, j) for i in range(len(h)) for j in range(len(w))]],
[[create_tile(h_shift, w, False, True, i, j) for i in range(len(h_shift)) for j in range(len(w))]],
[[create_tile(h, w_shift, True, False, i, j) for i in range(len(h)) for j in range(len(w_shift))]],
[[create_tile(h_shift, w_shift, False, False, i,j) for i in range(len(h_shift)) for j in range(len(w_shift))]],
]
return passes
def mask_at_boundary(h, h_len, w, w_len, tile_size_h, tile_size_w, latent_size_h, latent_size_w, mask, device='cpu'):
tile_size_h = int(tile_size_h // 8)
tile_size_w = int(tile_size_w // 8)
if (h_len == tile_size_h or h_len == latent_size_h) and (w_len == tile_size_w or w_len == latent_size_w):
return h, h_len, w, w_len, mask
h_offset = min(0, latent_size_h - (h + tile_size_h))
w_offset = min(0, latent_size_w - (w + tile_size_w))
new_mask = torch.zeros((1,1,tile_size_h, tile_size_w), dtype=torch.float32, device=device)
new_mask[:,:,-h_offset:h_len if h_offset == 0 else tile_size_h, -w_offset:w_len if w_offset == 0 else tile_size_w] = 1.0 if mask is None else mask
return h + h_offset, tile_size_h, w + w_offset, tile_size_w, new_mask
def get_tiles_and_masks_rgrid(steps, latent_shape, tile_height, tile_width, generator):
def calc_coords(latent_size, tile_size, jitter):
tile_coords = int((latent_size + jitter - 1) // tile_size + 1)
tile_coords = [np.clip(tile_size * c - jitter, 0, latent_size) for c in range(tile_coords + 1)]
tile_coords = [(c1, c2-c1) for c1, c2 in zip(tile_coords, tile_coords[1:])]
return tile_coords
#calc stuff
batch_size = latent_shape[0]
latent_size_h = latent_shape[-2]
latent_size_w = latent_shape[-1]
tile_size_h = int(tile_height // 8)
tile_size_w = int(tile_width // 8)
tiles_all = []
for s in range(steps):
rands = torch.rand((2,), dtype=torch.float32, generator=generator, device='cpu').numpy()
jitter_w1 = int(rands[0] * tile_size_w)
jitter_w2 = int(((rands[0] + .5) % 1.0) * tile_size_w)
jitter_h1 = int(rands[1] * tile_size_h)
jitter_h2 = int(((rands[1] + .5) % 1.0) * tile_size_h)
#calc number of tiles
tiles_h = [
calc_coords(latent_size_h, tile_size_h, jitter_h1),
calc_coords(latent_size_h, tile_size_h, jitter_h2)
]
tiles_w = [
calc_coords(latent_size_w, tile_size_w, jitter_w1),
calc_coords(latent_size_w, tile_size_w, jitter_w2)
]
tiles = []
if s % 2 == 0:
for i, h in enumerate(tiles_h[0]):
for w in tiles_w[i%2]:
tiles.append((int(h[0]), int(h[1]), int(w[0]), int(w[1]), 1, None))
else:
for i, w in enumerate(tiles_w[0]):
for h in tiles_h[i%2]:
tiles.append((int(h[0]), int(h[1]), int(w[0]), int(w[1]), 1, None))
tiles_all.append(tiles)
return [tiles_all]
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/cg_mixed_seed_noise.py
-16
View File
@@ -1,16 +0,0 @@
# https://github.com/chrisgoringe/cg-noise
import torch
def get_mixed_noise_function(original_noise_function, variation_seed, variation_weight):
def prepare_mixed_noise(latent_image:torch.Tensor, seed, batch_inds):
single_image_latent = latent_image[0].unsqueeze_(0)
different_noise = original_noise_function(single_image_latent, variation_seed, batch_inds)
original_noise = original_noise_function(single_image_latent, seed, batch_inds)
if latent_image.shape[0]==1:
mixed_noise = original_noise * (1.0-variation_weight) + different_noise * (variation_weight)
else:
mixed_noise = torch.empty_like(latent_image)
for i in range(latent_image.shape[0]):
mixed_noise[i] = original_noise * (1.0-variation_weight*i) + different_noise * (variation_weight*i)
return mixed_noise
return prepare_mixed_noise
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/city96_latent_upscaler.py
-94
View File
@@ -1,94 +0,0 @@
# https://github.com/city96/SD-Latent-Upscaler
# check if any issues on weights and updates and local install for same
import torch
import torch.nn as nn
from safetensors.torch import load_file
from huggingface_hub import hf_hub_download
class Upscaler(nn.Module):
"""
Basic NN layout, ported from:
https://github.com/city96/SD-Latent-Upscaler/blob/main/upscaler.py
"""
version = 2.1 # network revision
def head(self):
return [
nn.Conv2d(self.chan, self.size, kernel_size=self.krn, padding=self.pad),
nn.ReLU(),
nn.Upsample(scale_factor=self.fac, mode="nearest"),
nn.ReLU(),
]
def core(self):
layers = []
for _ in range(self.depth):
layers += [
nn.Conv2d(self.size, self.size, kernel_size=self.krn, padding=self.pad),
nn.ReLU(),
]
return layers
def tail(self):
return [
nn.Conv2d(self.size, self.chan, kernel_size=self.krn, padding=self.pad),
]
def __init__(self, fac, depth=16):
super().__init__()
self.size = 64 # Conv2d size
self.chan = 4 # in/out channels
self.depth = depth # no. of layers
self.fac = fac # scale factor
self.krn = 3 # kernel size
self.pad = 1 # padding
self.sequential = nn.Sequential(
*self.head(),
*self.core(),
*self.tail(),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.sequential(x)
class LatentUpscaler:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"samples": ("LATENT", ),
"latent_ver": (["v1", "xl"],),
"scale_factor": (["1.25", "1.5", "2.0"],),
}
}
RETURN_TYPES = ("LATENT",)
FUNCTION = "upscale"
CATEGORY = "latent"
def upscale(self, samples, latent_ver, scale_factor):
model = Upscaler(scale_factor)
weights = str(hf_hub_download(
repo_id="city96/SD-Latent-Upscaler",
filename=f"latent-upscaler-v{model.version}_SD{latent_ver}-x{scale_factor}.safetensors")
)
# weights = f"./latent-upscaler-v{model.version}_SD{latent_ver}-x{scale_factor}.safetensors"
model.load_state_dict(load_file(weights))
lt = samples["samples"]
lt = model(lt)
del model
if "noise_mask" in samples.keys():
# expand the noise mask to the same shape as the latent
mask = torch.nn.functional.interpolate(samples['noise_mask'], scale_factor=float(scale_factor), mode='bicubic')
return ({"samples": lt, "noise_mask": mask},)
return ({"samples": lt},)
NODE_CLASS_MAPPINGS = {
"LatentUpscaler": LatentUpscaler,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"LatentUpscaler": "EFF_C Latent Upscaler"
}
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/sd15_resizer.pt
BIN
View File
Binary file not shown.
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/sdxl_resizer.pt
BIN
View File
Binary file not shown.
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/smZ_cfg_denoiser.py
-321
View File
@@ -1,321 +0,0 @@
# https://github.com/shiimizu/ComfyUI_smZNodes
import comfy
import torch
from typing import List
import comfy.sample
from comfy import model_base, model_management
from comfy.samplers import KSampler, KSamplerX0Inpaint, wrap_model
#from comfy.k_diffusion.external import CompVisDenoiser
import nodes
import inspect
import functools
import importlib
import os
import re
import itertools
import torch
from comfy import model_management
def catenate_conds(conds):
if not isinstance(conds[0], dict):
return torch.cat(conds)
return {key: torch.cat([x[key] for x in conds]) for key in conds[0].keys()}
def subscript_cond(cond, a, b):
if not isinstance(cond, dict):
return cond[a:b]
return {key: vec[a:b] for key, vec in cond.items()}
def pad_cond(tensor, repeats, empty):
if not isinstance(tensor, dict):
return torch.cat([tensor, empty.repeat((tensor.shape[0], repeats, 1)).to(device=tensor.device)], axis=1)
tensor['crossattn'] = pad_cond(tensor['crossattn'], repeats, empty)
return tensor
class CFGDenoiser(torch.nn.Module):
"""
Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet)
that can take a noisy picture and produce a noise-free picture using two guidances (prompts)
instead of one. Originally, the second prompt is just an empty string, but we use non-empty
negative prompt.
"""
def __init__(self, model):
super().__init__()
self.inner_model = model
self.model_wrap = None
self.mask = None
self.nmask = None
self.init_latent = None
self.steps = None
"""number of steps as specified by user in UI"""
self.total_steps = None
"""expected number of calls to denoiser calculated from self.steps and specifics of the selected sampler"""
self.step = 0
self.image_cfg_scale = None
self.padded_cond_uncond = False
self.sampler = None
self.model_wrap = None
self.p = None
self.mask_before_denoising = False
def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
denoised_uncond = x_out[-uncond.shape[0]:]
denoised = torch.clone(denoised_uncond)
for i, conds in enumerate(conds_list):
for cond_index, weight in conds:
denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
return denoised
def combine_denoised_for_edit_model(self, x_out, cond_scale):
out_cond, out_img_cond, out_uncond = x_out.chunk(3)
denoised = out_uncond + cond_scale * (out_cond - out_img_cond) + self.image_cfg_scale * (out_img_cond - out_uncond)
return denoised
def get_pred_x0(self, x_in, x_out, sigma):
return x_out
def update_inner_model(self):
self.model_wrap = None
c, uc = self.p.get_conds()
self.sampler.sampler_extra_args['cond'] = c
self.sampler.sampler_extra_args['uncond'] = uc
def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
model_management.throw_exception_if_processing_interrupted()
is_edit_model = False
conds_list, tensor = cond
assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
if self.mask_before_denoising and self.mask is not None:
x = self.init_latent * self.mask + self.nmask * x
batch_size = len(conds_list)
repeats = [len(conds_list[i]) for i in range(batch_size)]
if False:
image_uncond = torch.zeros_like(image_cond)
make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": c_crossattn, "c_adm": c_adm, 'transformer_options': {'from_smZ': True}} # pylint: disable=C3001
else:
image_uncond = image_cond
if isinstance(uncond, dict):
make_condition_dict = lambda c_crossattn, c_concat: {**c_crossattn, "c_concat": None, "c_adm": x.c_adm, 'transformer_options': {'from_smZ': True}}
else:
make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": c_crossattn, "c_concat": None, "c_adm": x.c_adm, 'transformer_options': {'from_smZ': True}}
if not is_edit_model:
x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond])
else:
x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)])
skip_uncond = False
# alternating uncond allows for higher thresholds without the quality loss normally expected from raising it
if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model:
skip_uncond = True
x_in = x_in[:-batch_size]
sigma_in = sigma_in[:-batch_size]
if tensor.shape[1] == uncond.shape[1] or skip_uncond:
if is_edit_model:
cond_in = catenate_conds([tensor, uncond, uncond])
elif skip_uncond:
cond_in = tensor
else:
cond_in = catenate_conds([tensor, uncond])
x_out = torch.zeros_like(x_in)
for batch_offset in range(0, x_out.shape[0], batch_size):
a = batch_offset
b = a + batch_size
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], **make_condition_dict(subscript_cond(cond_in, a, b), image_cond_in[a:b]))
else:
x_out = torch.zeros_like(x_in)
for batch_offset in range(0, tensor.shape[0], batch_size):
a = batch_offset
b = min(a + batch_size, tensor.shape[0])
if not is_edit_model:
c_crossattn = subscript_cond(tensor, a, b)
else:
c_crossattn = torch.cat([tensor[a:b]], uncond)
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], **make_condition_dict(c_crossattn, image_cond_in[a:b]))
if not skip_uncond:
x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], **make_condition_dict(uncond, image_cond_in[-uncond.shape[0]:]))
denoised_image_indexes = [x[0][0] for x in conds_list]
if skip_uncond:
fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
x_out = torch.cat([x_out, fake_uncond]) # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
if is_edit_model:
denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
elif skip_uncond:
denoised = self.combine_denoised(x_out, conds_list, uncond, 1.0)
else:
denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
if not self.mask_before_denoising and self.mask is not None:
denoised = self.init_latent * self.mask + self.nmask * denoised
self.step += 1
del x_out
return denoised
# ========================================================================
def expand(tensor1, tensor2):
def adjust_tensor_shape(tensor_small, tensor_big):
# Calculate replication factor
# -(-a // b) is ceiling of division without importing math.ceil
replication_factor = -(-tensor_big.size(1) // tensor_small.size(1))
# Use repeat to extend tensor_small
tensor_small_extended = tensor_small.repeat(1, replication_factor, 1)
# Take the rows of the extended tensor_small to match tensor_big
tensor_small_matched = tensor_small_extended[:, :tensor_big.size(1), :]
return tensor_small_matched
# Check if their second dimensions are different
if tensor1.size(1) != tensor2.size(1):
# Check which tensor has the smaller second dimension and adjust its shape
if tensor1.size(1) < tensor2.size(1):
tensor1 = adjust_tensor_shape(tensor1, tensor2)
else:
tensor2 = adjust_tensor_shape(tensor2, tensor1)
return (tensor1, tensor2)
def _find_outer_instance(target, target_type):
import inspect
frame = inspect.currentframe()
while frame:
if target in frame.f_locals:
found = frame.f_locals[target]
if isinstance(found, target_type) and found != 1: # steps == 1
return found
frame = frame.f_back
return None
# ========================================================================
def bounded_modulo(number, modulo_value):
return number if number < modulo_value else modulo_value
def calc_cond(c, current_step):
"""Group by smZ conds that may do prompt-editing / regular conds / comfy conds."""
_cond = []
# Group by conds from smZ
fn=lambda x : x[1].get("from_smZ", None) is not None
an_iterator = itertools.groupby(c, fn )
for key, group in an_iterator:
ls=list(group)
# Group by prompt-editing conds
fn2=lambda x : x[1].get("smZid", None)
an_iterator2 = itertools.groupby(ls, fn2)
for key2, group2 in an_iterator2:
ls2=list(group2)
if key2 is not None:
orig_len = ls2[0][1].get('orig_len', 1)
i = bounded_modulo(current_step, orig_len - 1)
_cond = _cond + [ls2[i]]
else:
_cond = _cond + ls2
return _cond
class CFGNoisePredictor(torch.nn.Module):
def __init__(self, model):
super().__init__()
self.ksampler = _find_outer_instance('self', comfy.samplers.KSampler)
self.step = 0
self.orig = comfy.samplers.CFGNoisePredictor(model) #CFGNoisePredictorOrig(model)
self.inner_model = model
self.inner_model2 = CFGDenoiser(model.apply_model)
self.inner_model2.num_timesteps = model.num_timesteps
self.inner_model2.device = self.ksampler.device if hasattr(self.ksampler, "device") else None
self.s_min_uncond = 0.0
self.alphas_cumprod = model.alphas_cumprod
self.c_adm = None
self.init_cond = None
self.init_uncond = None
self.is_prompt_editing_u = False
self.is_prompt_editing_c = False
def apply_model(self, x, timestep, cond, uncond, cond_scale, cond_concat=None, model_options={}, seed=None):
cc=calc_cond(cond, self.step)
uu=calc_cond(uncond, self.step)
self.step += 1
if (any([p[1].get('from_smZ', False) for p in cc]) or
any([p[1].get('from_smZ', False) for p in uu])):
if model_options.get('transformer_options',None) is None:
model_options['transformer_options'] = {}
model_options['transformer_options']['from_smZ'] = True
# Only supports one cond
for ix in range(len(cc)):
if cc[ix][1].get('from_smZ', False):
cc = [cc[ix]]
break
for ix in range(len(uu)):
if uu[ix][1].get('from_smZ', False):
uu = [uu[ix]]
break
c=cc[0][1]
u=uu[0][1]
_cc = cc[0][0]
_uu = uu[0][0]
if c.get("adm_encoded", None) is not None:
self.c_adm = torch.cat([c['adm_encoded'], u['adm_encoded']])
# SDXL. Need to pad with repeats
_cc, _uu = expand(_cc, _uu)
_uu, _cc = expand(_uu, _cc)
x.c_adm = self.c_adm
conds_list = c.get('conds_list', [[(0, 1.0)]])
image_cond = txt2img_image_conditioning(None, x)
out = self.inner_model2(x, timestep, cond=(conds_list, _cc), uncond=_uu, cond_scale=cond_scale, s_min_uncond=self.s_min_uncond, image_cond=image_cond)
return out
def txt2img_image_conditioning(sd_model, x, width=None, height=None):
return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
# =======================================================================================
def set_model_k(self: KSampler):
self.model_denoise = CFGNoisePredictor(self.model) # main change
if ((getattr(self.model, "parameterization", "") == "v") or
(getattr(self.model, "model_type", -1) == model_base.ModelType.V_PREDICTION)):
self.model_wrap = wrap_model(self.model_denoise, quantize=True)
self.model_wrap.parameterization = getattr(self.model, "parameterization", "v")
else:
self.model_wrap = wrap_model(self.model_denoise, quantize=True)
self.model_wrap.parameterization = getattr(self.model, "parameterization", "eps")
self.model_k = KSamplerX0Inpaint(self.model_wrap)
class SDKSampler(comfy.samplers.KSampler):
def __init__(self, *args, **kwargs):
super(SDKSampler, self).__init__(*args, **kwargs)
set_model_k(self)
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/smZ_rng_source.py
-140
View File
@@ -1,140 +0,0 @@
# https://github.com/shiimizu/ComfyUI_smZNodes
import numpy as np
philox_m = [0xD2511F53, 0xCD9E8D57]
philox_w = [0x9E3779B9, 0xBB67AE85]
two_pow32_inv = np.array([2.3283064e-10], dtype=np.float32)
two_pow32_inv_2pi = np.array([2.3283064e-10 * 6.2831855], dtype=np.float32)
def uint32(x):
"""Converts (N,) np.uint64 array into (2, N) np.unit32 array."""
return x.view(np.uint32).reshape(-1, 2).transpose(1, 0)
def philox4_round(counter, key):
"""A single round of the Philox 4x32 random number generator."""
v1 = uint32(counter[0].astype(np.uint64) * philox_m[0])
v2 = uint32(counter[2].astype(np.uint64) * philox_m[1])
counter[0] = v2[1] ^ counter[1] ^ key[0]
counter[1] = v2[0]
counter[2] = v1[1] ^ counter[3] ^ key[1]
counter[3] = v1[0]
def philox4_32(counter, key, rounds=10):
"""Generates 32-bit random numbers using the Philox 4x32 random number generator.
Parameters:
counter (numpy.ndarray): A 4xN array of 32-bit integers representing the counter values (offset into generation).
key (numpy.ndarray): A 2xN array of 32-bit integers representing the key values (seed).
rounds (int): The number of rounds to perform.
Returns:
numpy.ndarray: A 4xN array of 32-bit integers containing the generated random numbers.
"""
for _ in range(rounds - 1):
philox4_round(counter, key)
key[0] = key[0] + philox_w[0]
key[1] = key[1] + philox_w[1]
philox4_round(counter, key)
return counter
def box_muller(x, y):
"""Returns just the first out of two numbers generated by Box–Muller transform algorithm."""
u = x * two_pow32_inv + two_pow32_inv / 2
v = y * two_pow32_inv_2pi + two_pow32_inv_2pi / 2
s = np.sqrt(-2.0 * np.log(u))
r1 = s * np.sin(v)
return r1.astype(np.float32)
class Generator:
"""RNG that produces same outputs as torch.randn(..., device='cuda') on CPU"""
def __init__(self, seed):
self.seed = seed
self.offset = 0
def randn(self, shape):
"""Generate a sequence of n standard normal random variables using the Philox 4x32 random number generator and the Box-Muller transform."""
n = 1
for x in shape:
n *= x
counter = np.zeros((4, n), dtype=np.uint32)
counter[0] = self.offset
counter[2] = np.arange(n, dtype=np.uint32) # up to 2^32 numbers can be generated - if you want more you'd need to spill into counter[3]
self.offset += 1
key = np.empty(n, dtype=np.uint64)
key.fill(self.seed)
key = uint32(key)
g = philox4_32(counter, key)
return box_muller(g[0], g[1]).reshape(shape) # discard g[2] and g[3]
#=======================================================================================================================
# Monkey Patch "prepare_noise" function
# https://github.com/shiimizu/ComfyUI_smZNodes
import torch
import functools
from comfy.sample import np
import comfy.model_management
def rng_rand_source(rand_source='cpu'):
device = comfy.model_management.text_encoder_device()
def prepare_noise(latent_image, seed, noise_inds=None, device='cpu'):
"""
creates random noise given a latent image and a seed.
optional arg skip can be used to skip and discard x number of noise generations for a given seed
"""
generator = torch.Generator(device).manual_seed(seed)
if rand_source == 'nv':
rng = Generator(seed)
if noise_inds is None:
shape = latent_image.size()
if rand_source == 'nv':
return torch.asarray(rng.randn(shape), device=device)
else:
return torch.randn(shape, dtype=latent_image.dtype, layout=latent_image.layout, generator=generator,
device=device)
unique_inds, inverse = np.unique(noise_inds, return_inverse=True)
noises = []
for i in range(unique_inds[-1] + 1):
shape = [1] + list(latent_image.size())[1:]
if rand_source == 'nv':
noise = torch.asarray(rng.randn(shape), device=device)
else:
noise = torch.randn(shape, dtype=latent_image.dtype, layout=latent_image.layout, generator=generator,
device=device)
if i in unique_inds:
noises.append(noise)
noises = [noises[i] for i in inverse]
noises = torch.cat(noises, axis=0)
return noises
if rand_source == 'cpu':
if hasattr(comfy.sample, 'prepare_noise_orig'):
comfy.sample.prepare_noise = comfy.sample.prepare_noise_orig
else:
if not hasattr(comfy.sample, 'prepare_noise_orig'):
comfy.sample.prepare_noise_orig = comfy.sample.prepare_noise
_prepare_noise = functools.partial(prepare_noise, device=device)
comfy.sample.prepare_noise = _prepare_noise
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/py/ttl_nn_latent_upscaler.py
-321
View File
@@ -1,321 +0,0 @@
import torch
#from .latent_resizer import LatentResizer
from comfy import model_management
import os
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
def normalization(channels):
return nn.GroupNorm(32, channels)
def zero_module(module):
for p in module.parameters():
p.detach().zero_()
return module
class AttnBlock(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.in_channels = in_channels
self.norm = normalization(in_channels)
self.q = nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
self.k = nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
self.v = nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
self.proj_out = nn.Conv2d(
in_channels, in_channels, kernel_size=1, stride=1, padding=0
)
def attention(self, h_: torch.Tensor) -> torch.Tensor:
h_ = self.norm(h_)
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
b, c, h, w = q.shape
q, k, v = map(
lambda x: rearrange(x, "b c h w -> b 1 (h w) c").contiguous(), (q, k, v)
)
h_ = nn.functional.scaled_dot_product_attention(
q, k, v
) # scale is dim ** -0.5 per default
return rearrange(h_, "b 1 (h w) c -> b c h w", h=h, w=w, c=c, b=b)
def forward(self, x, **kwargs):
h_ = x
h_ = self.attention(h_)
h_ = self.proj_out(h_)
return x + h_
def make_attn(in_channels, attn_kwargs=None):
return AttnBlock(in_channels)
class ResBlockEmb(nn.Module):
def __init__(
self,
channels,
emb_channels,
dropout=0,
out_channels=None,
use_conv=False,
use_scale_shift_norm=False,
kernel_size=3,
exchange_temb_dims=False,
skip_t_emb=False,
):
super().__init__()
self.channels = channels
self.emb_channels = emb_channels
self.dropout = dropout
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.use_scale_shift_norm = use_scale_shift_norm
self.exchange_temb_dims = exchange_temb_dims
padding = kernel_size // 2
self.in_layers = nn.Sequential(
normalization(channels),
nn.SiLU(),
nn.Conv2d(channels, self.out_channels, kernel_size, padding=padding),
)
self.skip_t_emb = skip_t_emb
self.emb_out_channels = (
2 * self.out_channels if use_scale_shift_norm else self.out_channels
)
if self.skip_t_emb:
print(f"Skipping timestep embedding in {self.__class__.__name__}")
assert not self.use_scale_shift_norm
self.emb_layers = None
self.exchange_temb_dims = False
else:
self.emb_layers = nn.Sequential(
nn.SiLU(),
nn.Linear(
emb_channels,
self.emb_out_channels,
),
)
self.out_layers = nn.Sequential(
normalization(self.out_channels),
nn.SiLU(),
nn.Dropout(p=dropout),
zero_module(
nn.Conv2d(
self.out_channels,
self.out_channels,
kernel_size,
padding=padding,
)
),
)
if self.out_channels == channels:
self.skip_connection = nn.Identity()
elif use_conv:
self.skip_connection = nn.Conv2d(
channels, self.out_channels, kernel_size, padding=padding
)
else:
self.skip_connection = nn.Conv2d(channels, self.out_channels, 1)
def forward(self, x, emb):
h = self.in_layers(x)
if self.skip_t_emb:
emb_out = torch.zeros_like(h)
else:
emb_out = self.emb_layers(emb).type(h.dtype)
while len(emb_out.shape) < len(h.shape):
emb_out = emb_out[..., None]
if self.use_scale_shift_norm:
out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
scale, shift = torch.chunk(emb_out, 2, dim=1)
h = out_norm(h) * (1 + scale) + shift
h = out_rest(h)
else:
if self.exchange_temb_dims:
emb_out = rearrange(emb_out, "b t c ... -> b c t ...")
h = h + emb_out
h = self.out_layers(h)
return self.skip_connection(x) + h
class LatentResizer(nn.Module):
def __init__(self, in_blocks=10, out_blocks=10, channels=128, dropout=0, attn=True):
super().__init__()
self.conv_in = nn.Conv2d(4, channels, 3, padding=1)
self.channels = channels
embed_dim = 32
self.embed = nn.Sequential(
nn.Linear(1, embed_dim),
nn.SiLU(),
nn.Linear(embed_dim, embed_dim),
)
self.in_blocks = nn.ModuleList([])
for b in range(in_blocks):
if (b == 1 or b == in_blocks - 1) and attn:
self.in_blocks.append(make_attn(channels))
self.in_blocks.append(ResBlockEmb(channels, embed_dim, dropout))
self.out_blocks = nn.ModuleList([])
for b in range(out_blocks):
if (b == 1 or b == out_blocks - 1) and attn:
self.out_blocks.append(make_attn(channels))
self.out_blocks.append(ResBlockEmb(channels, embed_dim, dropout))
self.norm_out = normalization(channels)
self.conv_out = nn.Conv2d(channels, 4, 3, padding=1)
@classmethod
def load_model(cls, filename, device="cpu", dtype=torch.float32, dropout=0):
if not 'weights_only' in torch.load.__code__.co_varnames:
weights = torch.load(filename, map_location=torch.device("cpu"))
else:
weights = torch.load(filename, map_location=torch.device("cpu"), weights_only=True)
in_blocks = 0
out_blocks = 0
in_tfs = 0
out_tfs = 0
channels = weights["conv_in.bias"].shape[0]
for k in weights.keys():
k = k.split(".")
if k[0] == "in_blocks":
in_blocks = max(in_blocks, int(k[1]))
if k[2] == "q" and k[3] == "weight":
in_tfs += 1
if k[0] == "out_blocks":
out_blocks = max(out_blocks, int(k[1]))
if k[2] == "q" and k[3] == "weight":
out_tfs += 1
in_blocks = in_blocks + 1 - in_tfs
out_blocks = out_blocks + 1 - out_tfs
resizer = cls(
in_blocks=in_blocks,
out_blocks=out_blocks,
channels=channels,
dropout=dropout,
attn=(out_tfs != 0),
)
resizer.load_state_dict(weights)
resizer.eval()
resizer.to(device, dtype=dtype)
return resizer
def forward(self, x, scale=None, size=None):
if scale is None and size is None:
raise ValueError("Either scale or size needs to be not None")
if scale is not None and size is not None:
raise ValueError("Both scale or size can't be not None")
if scale is not None:
size = (x.shape[-2] * scale, x.shape[-1] * scale)
size = tuple([int(round(i)) for i in size])
else:
scale = size[-1] / x.shape[-1]
# Output is the same size as input
if size == x.shape[-2:]:
return x
scale = torch.tensor([scale - 1], dtype=x.dtype).to(x.device).unsqueeze(0)
emb = self.embed(scale)
x = self.conv_in(x)
for b in self.in_blocks:
if isinstance(b, ResBlockEmb):
x = b(x, emb)
else:
x = b(x)
x = F.interpolate(x, size=size, mode="bilinear")
for b in self.out_blocks:
if isinstance(b, ResBlockEmb):
x = b(x, emb)
else:
x = b(x)
x = self.norm_out(x)
x = F.silu(x)
x = self.conv_out(x)
return x
########################################################
class NNLatentUpscale:
"""
Upscales SDXL latent using neural network
"""
def __init__(self):
self.local_dir = os.path.dirname(os.path.realpath(__file__))
self.scale_factor = 0.13025
self.dtype = torch.float32
if model_management.should_use_fp16():
self.dtype = torch.float16
self.weight_path = {
"SDXL": os.path.join(self.local_dir, "sdxl_resizer.pt"),
"SD 1.x": os.path.join(self.local_dir, "sd15_resizer.pt"),
}
self.version = "none"
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"latent": ("LATENT",),
"version": (["SDXL", "SD 1.x"],),
"upscale": (
"FLOAT",
{
"default": 1.5,
"min": 1.0,
"max": 2.0,
"step": 0.01,
"display": "number",
},
),
},
}
RETURN_TYPES = ("LATENT",)
FUNCTION = "upscale"
CATEGORY = "latent"
def upscale(self, latent, version, upscale):
device = model_management.get_torch_device()
samples = latent["samples"].to(device=device, dtype=self.dtype)
if version != self.version:
self.model = LatentResizer.load_model(self.weight_path[version], device, self.dtype)
self.version = version
self.model.to(device=device)
latent_out = (self.model(self.scale_factor * samples, scale=upscale) / self.scale_factor)
if self.dtype != torch.float32:
latent_out = latent_out.to(dtype=torch.float32)
latent_out = latent_out.to(device="cpu")
self.model.to(device=model_management.vae_offload_device())
return ({"samples": latent_out},)
NODE_CLASS_MAPPINGS = {
"NNLatentUpscale": NNLatentUpscale
}
NODE_DISPLAY_NAME_MAPPINGS = {
"NNlLatentUpscale": "EFF Latent Upscale"
}
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/requirements.txt
-2
View File
@@ -1,2 +0,0 @@
clip-interrogator
simpleeval
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/tsc_utils.py
-555
View File
@@ -1,555 +0,0 @@
# Efficiency Nodes Utility functions
from torch import Tensor
import torch
from PIL import Image
import numpy as np
import os
import sys
import io
from contextlib import contextmanager
import json
import folder_paths
# Get the absolute path of the parent directory of the current script
my_dir = os.path.dirname(os.path.abspath(__file__))
# Add the My directory path to the sys.path list
sys.path.append(my_dir)
# Construct the absolute path to the ComfyUI directory
comfy_dir = os.path.abspath(os.path.join(my_dir, '..', '..'))
# Add the ComfyUI directory path to the sys.path list
sys.path.append(comfy_dir)
# Import functions from ComfyUI
import comfy.sd
import comfy.utils
import latent_preview
from comfy.cli_args import args
# Cache for Efficiency Node models
loaded_objects = {
"ckpt": [], # (ckpt_name, ckpt_model, clip, bvae, [id])
"refn": [], # (ckpt_name, ckpt_model, clip, bvae, [id])
"vae": [], # (vae_name, vae, [id])
"lora": [] # ([(lora_name, strength_model, strength_clip)], ckpt_name, lora_model, clip_lora, [id])
}
# Cache for Efficient Ksamplers
last_helds = {
"latent": [], # (latent, [parameters], id) # Base sampling latent results
"image": [], # (image, id) # Base sampling image results
"cnet_img": [] # (cnet_img, [parameters], id) # HiRes-Fix control net preprocessor image results
}
def load_ksampler_results(key: str, my_unique_id, parameters_list=None):
global last_helds
for data in last_helds[key]:
id_ = data[-1] # ID is always the last element in the tuple
if id_ == my_unique_id:
if parameters_list is not None:
# Ensure tuple has at least 3 elements and match with parameters_list
if len(data) >= 3 and data[1] == parameters_list:
return data[0]
else:
return data[0]
return None
def store_ksampler_results(key: str, my_unique_id, value, parameters_list=None):
global last_helds
for i, data in enumerate(last_helds[key]):
id_ = data[-1] # ID will always be the last in the tuple
if id_ == my_unique_id:
# Check if parameters_list is provided or not
updated_data = (value, parameters_list, id_) if parameters_list is not None else (value, id_)
last_helds[key][i] = updated_data
return True
# If parameters_list is given
if parameters_list is not None:
last_helds[key].append((value, parameters_list, my_unique_id))
else:
last_helds[key].append((value, my_unique_id))
return True
# Tensor to PIL (grabbed from WAS Suite)
def tensor2pil(image: torch.Tensor) -> Image.Image:
return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
# Convert PIL to Tensor (grabbed from WAS Suite)
def pil2tensor(image: Image.Image) -> torch.Tensor:
return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
# Convert tensor to PIL, resize it, and convert back to tensor
def quick_resize(source_tensor: torch.Tensor, target_shape: tuple) -> torch.Tensor:
resized_images = []
for img in source_tensor:
resized_pil = tensor2pil(img.squeeze(0)).resize((target_shape[2], target_shape[1]), Image.ANTIALIAS)
resized_images.append(pil2tensor(resized_pil).squeeze(0))
return torch.stack(resized_images, dim=0)
# Create a function to compute the hash of a tensor
import hashlib
def tensor_to_hash(tensor):
byte_repr = tensor.cpu().numpy().tobytes() # Convert tensor to bytes
return hashlib.sha256(byte_repr).hexdigest() # Compute hash
# Color coded messages functions
MESSAGE_COLOR = "\033[36m" # Cyan
XYPLOT_COLOR = "\033[35m" # Purple
SUCCESS_COLOR = "\033[92m" # Green
WARNING_COLOR = "\033[93m" # Yellow
ERROR_COLOR = "\033[91m" # Red
INFO_COLOR = "\033[90m" # Gray
def format_message(text, color_code):
RESET_COLOR = "\033[0m"
return f"{color_code}{text}{RESET_COLOR}"
def message(text):
return format_message(text, MESSAGE_COLOR)
def warning(text):
return format_message(text, WARNING_COLOR)
def error(text):
return format_message(text, ERROR_COLOR)
def success(text):
return format_message(text, SUCCESS_COLOR)
def xyplot_message(text):
return format_message(text, XYPLOT_COLOR)
def info(text):
return format_message(text, INFO_COLOR)
def extract_node_info(prompt, id, indirect_key=None):
# Convert ID to string
id = str(id)
node_id = None
# If an indirect_key (like 'script') is provided, perform a two-step lookup
if indirect_key:
# Ensure the id exists in the prompt and has an 'inputs' entry with the indirect_key
if id in prompt and 'inputs' in prompt[id] and indirect_key in prompt[id]['inputs']:
# Extract the indirect_id
indirect_id = prompt[id]['inputs'][indirect_key][0]
# Ensure the indirect_id exists in the prompt
if indirect_id in prompt:
node_id = indirect_id
return prompt[indirect_id].get('class_type', None), node_id
# If indirect_key is not found within the prompt
return None, None
# If no indirect_key is provided, perform a direct lookup
return prompt.get(id, {}).get('class_type', None), node_id
def extract_node_value(prompt, id, key):
# If ID is in data, return its 'inputs' value for a given key. Otherwise, return None.
return prompt.get(str(id), {}).get('inputs', {}).get(key, None)
def print_loaded_objects_entries(id=None, prompt=None, show_id=False):
print("-" * 40) # Print an empty line followed by a separator line
if id is not None:
id = str(id) # Convert ID to string
if prompt is not None and id is not None:
node_name, _ = extract_node_info(prompt, id)
if show_id:
print(f"\033[36m{node_name} Models Cache: (node_id:{int(id)})\033[0m")
else:
print(f"\033[36m{node_name} Models Cache:\033[0m")
elif id is None:
print(f"\033[36mGlobal Models Cache:\033[0m")
else:
print(f"\033[36mModels Cache: \nnode_id:{int(id)}\033[0m")
entries_found = False
for key in ["ckpt", "refn", "vae", "lora"]:
entries_with_id = loaded_objects[key] if id is None else [entry for entry in loaded_objects[key] if id in entry[-1]]
if not entries_with_id: # If no entries with the chosen ID, print None and skip this key
continue
entries_found = True
print(f"{key.capitalize()}:")
for i, entry in enumerate(entries_with_id, 1): # Start numbering from 1
if key == "lora":
base_ckpt_name = os.path.splitext(os.path.basename(entry[1]))[0] # Split logic for base_ckpt
if id is None:
associated_ids = ', '.join(map(str, entry[-1])) # Gather all associated ids
print(f" [{i}] base_ckpt: {base_ckpt_name} (ids: {associated_ids})")
else:
print(f" [{i}] base_ckpt: {base_ckpt_name}")
for name, strength_model, strength_clip in entry[0]:
lora_model_info = f"{os.path.splitext(os.path.basename(name))[0]}({round(strength_model, 2)},{round(strength_clip, 2)})"
print(f" lora(mod,clip): {lora_model_info}")
else:
name_without_ext = os.path.splitext(os.path.basename(entry[0]))[0]
if id is None:
associated_ids = ', '.join(map(str, entry[-1])) # Gather all associated ids
print(f" [{i}] {name_without_ext} (ids: {associated_ids})")
else:
print(f" [{i}] {name_without_ext}")
if not entries_found:
print("-")
# This function cleans global variables associated with nodes that are no longer detected on UI
def globals_cleanup(prompt):
global loaded_objects
global last_helds
# Step 1: Clean up last_helds
for key in list(last_helds.keys()):
original_length = len(last_helds[key])
last_helds[key] = [
(*values, id_)
for *values, id_ in last_helds[key]
if str(id_) in prompt.keys()
]
# Step 2: Clean up loaded_objects
for key in list(loaded_objects.keys()):
for i, tup in enumerate(list(loaded_objects[key])):
# Remove ids from id array in each tuple that don't exist in prompt
id_array = [id for id in tup[-1] if str(id) in prompt.keys()]
if len(id_array) != len(tup[-1]):
if id_array:
loaded_objects[key][i] = tup[:-1] + (id_array,)
###print(f'Updated tuple at index {i} in {key} in loaded_objects: {loaded_objects[key][i]}')
else:
# If id array becomes empty, delete the corresponding tuple
loaded_objects[key].remove(tup)
###print(f'Deleted tuple at index {i} in {key} in loaded_objects because its id array became empty.')
def load_checkpoint(ckpt_name, id, output_vae=True, cache=None, cache_overwrite=True, ckpt_type="ckpt"):
global loaded_objects
# Create copies of the arguments right at the start
ckpt_name = ckpt_name.copy() if isinstance(ckpt_name, (list, dict, set)) else ckpt_name
# Check if the type is valid
if ckpt_type not in ["ckpt", "refn"]:
raise ValueError(f"Invalid checkpoint type: {ckpt_type}")
for entry in loaded_objects[ckpt_type]:
if entry[0] == ckpt_name:
_, model, clip, vae, ids = entry
cache_full = cache and len([entry for entry in loaded_objects[ckpt_type] if id in entry[-1]]) >= cache
if cache_full:
clear_cache(id, cache, ckpt_type)
elif id not in ids:
ids.append(id)
return model, clip, vae
if os.path.isabs(ckpt_name):
ckpt_path = ckpt_name
else:
ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
with suppress_output():
out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
model = out[0]
clip = out[1]
vae = out[2] if output_vae else None # Load VAE from the checkpoint path only if output_vae is True
if cache:
cache_list = [entry for entry in loaded_objects[ckpt_type] if id in entry[-1]]
if len(cache_list) < cache:
loaded_objects[ckpt_type].append((ckpt_name, model, clip, vae, [id]))
else:
clear_cache(id, cache, ckpt_type)
if cache_overwrite:
for e in loaded_objects[ckpt_type]:
if id in e[-1]:
e[-1].remove(id)
# If the id list becomes empty, remove the entry from the ckpt_type list
if not e[-1]:
loaded_objects[ckpt_type].remove(e)
break
loaded_objects[ckpt_type].append((ckpt_name, model, clip, vae, [id]))
return model, clip, vae
def get_bvae_by_ckpt_name(ckpt_name):
for ckpt in loaded_objects["ckpt"]:
if ckpt[0] == ckpt_name:
return ckpt[3] # return 'bvae' variable
return None # return None if no match is found
def load_vae(vae_name, id, cache=None, cache_overwrite=False):
global loaded_objects
# Create copies of the argument right at the start
vae_name = vae_name.copy() if isinstance(vae_name, (list, dict, set)) else vae_name
for i, entry in enumerate(loaded_objects["vae"]):
if entry[0] == vae_name:
vae, ids = entry[1], entry[2]
if id not in ids:
if cache and len([entry for entry in loaded_objects["vae"] if id in entry[-1]]) >= cache:
return vae
ids.append(id)
if cache:
clear_cache(id, cache, "vae")
return vae
if os.path.isabs(vae_name):
vae_path = vae_name
else:
vae_path = folder_paths.get_full_path("vae", vae_name)
sd = comfy.utils.load_torch_file(vae_path)
vae = comfy.sd.VAE(sd=sd)
if cache:
if len([entry for entry in loaded_objects["vae"] if id in entry[-1]]) < cache:
loaded_objects["vae"].append((vae_name, vae, [id]))
else:
clear_cache(id, cache, "vae")
if cache_overwrite:
# Find the first entry with the id, remove the id from the entry's id list
for e in loaded_objects["vae"]:
if id in e[-1]:
e[-1].remove(id)
# If the id list becomes empty, remove the entry from the "vae" list
if not e[-1]:
loaded_objects["vae"].remove(e)
break
loaded_objects["vae"].append((vae_name, vae, [id]))
return vae
def load_lora(lora_params, ckpt_name, id, cache=None, ckpt_cache=None, cache_overwrite=False):
global loaded_objects
# Create copies of the arguments right at the start
lora_params = lora_params.copy() if isinstance(lora_params, (list, dict, set)) else lora_params
ckpt_name = ckpt_name.copy() if isinstance(ckpt_name, (list, dict, set)) else ckpt_name
for entry in loaded_objects["lora"]:
# Convert to sets and compare
if set(entry[0]) == set(lora_params) and entry[1] == ckpt_name:
_, _, lora_model, lora_clip, ids = entry
cache_full = cache and len([entry for entry in loaded_objects["lora"] if id in entry[-1]]) >= cache
if cache_full:
clear_cache(id, cache, "lora")
elif id not in ids:
ids.append(id)
# Additional cache handling for 'ckpt' just like in 'load_checkpoint' function
for ckpt_entry in loaded_objects["ckpt"]:
if ckpt_entry[0] == ckpt_name:
_, _, _, _, ckpt_ids = ckpt_entry
ckpt_cache_full = ckpt_cache and len(
[ckpt_entry for ckpt_entry in loaded_objects["ckpt"] if id in ckpt_entry[-1]]) >= ckpt_cache
if ckpt_cache_full:
clear_cache(id, ckpt_cache, "ckpt")
elif id not in ckpt_ids:
ckpt_ids.append(id)
return lora_model, lora_clip
def recursive_load_lora(lora_params, ckpt, clip, id, ckpt_cache, cache_overwrite, folder_paths):
if len(lora_params) == 0:
return ckpt, clip
lora_name, strength_model, strength_clip = lora_params[0]
if os.path.isabs(lora_name):
lora_path = lora_name
else:
lora_path = folder_paths.get_full_path("loras", lora_name)
lora_model, lora_clip = comfy.sd.load_lora_for_models(ckpt, clip, comfy.utils.load_torch_file(lora_path), strength_model, strength_clip)
# Call the function again with the new lora_model and lora_clip and the remaining tuples
return recursive_load_lora(lora_params[1:], lora_model, lora_clip, id, ckpt_cache, cache_overwrite, folder_paths)
# Unpack lora parameters from the first element of the list for now
lora_name, strength_model, strength_clip = lora_params[0]
ckpt, clip, _ = load_checkpoint(ckpt_name, id, cache=ckpt_cache)
lora_model, lora_clip = recursive_load_lora(lora_params, ckpt, clip, id, ckpt_cache, cache_overwrite, folder_paths)
if cache:
if len([entry for entry in loaded_objects["lora"] if id in entry[-1]]) < cache:
loaded_objects["lora"].append((lora_params, ckpt_name, lora_model, lora_clip, [id]))
else:
clear_cache(id, cache, "lora")
if cache_overwrite:
# Find the first entry with the id, remove the id from the entry's id list
for e in loaded_objects["lora"]:
if id in e[-1]:
e[-1].remove(id)
# If the id list becomes empty, remove the entry from the "lora" list
if not e[-1]:
loaded_objects["lora"].remove(e)
break
loaded_objects["lora"].append((lora_params, ckpt_name, lora_model, lora_clip, [id]))
return lora_model, lora_clip
def clear_cache(id, cache, dict_name):
"""
Clear the cache for a specific id in a specific dictionary.
If the cache limit is reached for a specific id, deletes the id from the oldest entry.
If the id array of the entry becomes empty, deletes the entry.
"""
# Get all entries associated with the id_element
id_associated_entries = [entry for entry in loaded_objects[dict_name] if id in entry[-1]]
while len(id_associated_entries) > cache:
# Identify an older entry (but not necessarily the oldest) containing id
older_entry = id_associated_entries[0]
# Remove the id_element from the older entry
older_entry[-1].remove(id)
# If the id array of the older entry becomes empty after this, delete the entry
if not older_entry[-1]:
loaded_objects[dict_name].remove(older_entry)
# Update the id_associated_entries
id_associated_entries = [entry for entry in loaded_objects[dict_name] if id in entry[-1]]
def clear_cache_by_exception(node_id, vae_dict=None, ckpt_dict=None, lora_dict=None, refn_dict=None):
global loaded_objects
dict_mapping = {
"vae_dict": "vae",
"ckpt_dict": "ckpt",
"lora_dict": "lora",
"refn_dict": "refn"
}
for arg_name, arg_val in {"vae_dict": vae_dict, "ckpt_dict": ckpt_dict, "lora_dict": lora_dict, "refn_dict": refn_dict}.items():
if arg_val is None:
continue
dict_name = dict_mapping[arg_name]
for tuple_idx, tuple_item in enumerate(loaded_objects[dict_name].copy()):
if arg_name == "lora_dict":
# Iterate over the tuples (lora_params, ckpt_name) in arg_val
for lora_params, ckpt_name in arg_val:
# Compare lists of tuples considering order inside tuples, but not order of tuples
if set(lora_params) == set(tuple_item[0]) and ckpt_name == tuple_item[1]:
break
else: # If no match was found in lora_dict, remove the tuple from loaded_objects
if node_id in tuple_item[-1]:
tuple_item[-1].remove(node_id)
if not tuple_item[-1]:
loaded_objects[dict_name].remove(tuple_item)
continue
elif tuple_item[0] not in arg_val: # Only remove the tuple if it's not in arg_val
if node_id in tuple_item[-1]:
tuple_item[-1].remove(node_id)
if not tuple_item[-1]:
loaded_objects[dict_name].remove(tuple_item)
# Retrieve the cache number from 'node_settings' json file
def get_cache_numbers(node_name):
# Get the directory path of the current file
my_dir = os.path.dirname(os.path.abspath(__file__))
# Construct the file path for node_settings.json
settings_file = os.path.join(my_dir, 'node_settings.json')
# Load the settings from the JSON file
with open(settings_file, 'r') as file:
node_settings = json.load(file)
# Retrieve the cache numbers for the given node
model_cache_settings = node_settings.get(node_name, {}).get('model_cache', {})
vae_cache = int(model_cache_settings.get('vae', 1))
ckpt_cache = int(model_cache_settings.get('ckpt', 1))
lora_cache = int(model_cache_settings.get('lora', 1))
refn_cache = int(model_cache_settings.get('ckpt', 1))
return vae_cache, ckpt_cache, lora_cache, refn_cache,
def print_last_helds(id=None):
print("\n" + "-" * 40) # Print an empty line followed by a separator line
if id is not None:
id = str(id) # Convert ID to string
print(f"Node-specific Last Helds (node_id:{int(id)})")
else:
print(f"Global Last Helds:")
for key in ["preview_images", "latent", "output_images", "vae_decode"]:
entries_with_id = last_helds[key] if id is None else [entry for entry in last_helds[key] if id == entry[-1]]
if not entries_with_id: # If no entries with the chosen ID, print None and skip this key
continue
print(f"{key.capitalize()}:")
for i, entry in enumerate(entries_with_id, 1): # Start numbering from 1
if isinstance(entry[0], bool): # Special handling for boolean types
output = entry[0]
else:
output = len(entry[0])
if id is None:
print(f" [{i}] Output: {output} (id: {entry[-1]})")
else:
print(f" [{i}] Output: {output}")
print("-" * 40) # Print a separator line
print("\n") # Print an empty line
# For suppressing print outputs from functions
@contextmanager
def suppress_output():
original_stdout = sys.stdout
original_stderr = sys.stderr
sys.stdout = io.StringIO()
sys.stderr = io.StringIO()
try:
yield
finally:
sys.stdout = original_stdout
sys.stderr = original_stderr
# Set global preview_method
def set_preview_method(method):
if method == 'auto' or method == 'LatentPreviewMethod.Auto':
args.preview_method = latent_preview.LatentPreviewMethod.Auto
elif method == 'latent2rgb' or method == 'LatentPreviewMethod.Latent2RGB':
args.preview_method = latent_preview.LatentPreviewMethod.Latent2RGB
elif method == 'taesd' or method == 'LatentPreviewMethod.TAESD':
args.preview_method = latent_preview.LatentPreviewMethod.TAESD
else:
args.preview_method = latent_preview.LatentPreviewMethod.NoPreviews
# Extract global preview_method
def global_preview_method():
return args.preview_method
#-----------------------------------------------------------------------------------------------------------------------
# Delete efficiency nodes web extensions from 'ComfyUI\web\extensions'.
# Pull https://github.com/comfyanonymous/ComfyUI/pull/1273 now allows defining web extensions through a dir path in init
import shutil
# Destination directory
destination_dir = os.path.join(comfy_dir, 'web', 'extensions', 'efficiency-nodes-comfyui')
# Check if the directory exists and delete it
if os.path.exists(destination_dir):
shutil.rmtree(destination_dir)
#-----------------------------------------------------------------------------------------------------------------------
# Other
class XY_Capsule:
def pre_define_model(self, model, clip, vae):
return model, clip, vae
def set_result(self, image, latent):
pass
def get_result(self, model, clip, vae):
return None
def set_x_capsule(self, capsule):
return None
def getLabel(self):
return "Unknown"
workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/workflows/AnimateDiff & HiResFix Scripts.gif
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 6.3 MiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/workflows/EFF_TiledscriptWorkflow.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 529 KiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/workflows/Eff_XYPlot - LoRA Model vs Clip Strengths01.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 1.4 MiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/workflows/Eff_animatediff_script_wf001.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 306 KiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/workflows/Eff_multiKsampler_withScriptsSDXL.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 5.8 MiB

workflows/comfy/ComfyUI/custom_nodes/efficiency-nodes-comfyui/workflows/HiResFix Script.png
BIN
View File
Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 1022 KiB

Some files were not shown because too many files have changed in this diff Show More