Upload 1941 files

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+custom_nodes/ComfyUI_TiledKSampler/examples/ComfyUI_02006_.png filter=lfs diff=lfs merge=lfs -text
+custom_nodes/ComfyUI_TiledKSampler/examples/ComfyUI_02010_.png filter=lfs diff=lfs merge=lfs -text
+custom_nodes/ComfyUI-sampler-lcm-alternative/SamplerLCMCycle-example.png filter=lfs diff=lfs merge=lfs -text

custom_nodes/ComfyUI-Advanced-ControlNet/.gitignore

@@ -0,0 +1,160 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

custom_nodes/ComfyUI-Advanced-ControlNet/LICENSE

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU General Public License is a free, copyleft license for
+software and other kinds of works.
+
+  The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works.  By contrast,
+the GNU General Public License is intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users.  We, the Free Software Foundation, use the
+GNU General Public License for most of our software; it applies also to
+any other work released this way by its authors.  You can apply it to
+your programs, too.
+
+  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>.

custom_nodes/ComfyUI-Advanced-ControlNet/README.md

@@ -0,0 +1,151 @@
+# ComfyUI-Advanced-ControlNet
+Nodes for scheduling ControlNet strength across timesteps and batched latents, as well as applying custom weights and attention masks. The ControlNet nodes here fully support sliding context sampling, like the one used in the  [ComfyUI-AnimateDiff-Evolved](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved) nodes. Currently supports ControlNets, T2IAdapters, and ControlLoRAs. Kohya Controllllite support coming soon.
+
+Custom weights allow replication of the "My prompt is more important" feature of Auto1111's sd-webui ControlNet extension.
+
+ControlNet preprocessors are available through [comfyui_controlnet_aux](https://github.com/Fannovel16/comfyui_controlnet_aux) nodes
+
+## Features
+- Timestep and latent strength scheduling
+- Attention masks
+- Soft weights to replicate "My prompt is more important" feature from sd-webui ControlNet extension, and also change the scaling.
+- ControlNet, T2IAdapter, and ControlLoRA support for sliding context windows.
+
+## Table of Contents:
+- [Scheduling Explanation](#scheduling-explanation)
+- [Nodes](#nodes)
+- [Usage](#usage) (will fill this out soon)
+
+
+# Scheduling Explanation
+
+The two core concepts for scheduling are ***Timestep Keyframes*** and ***Latent Keyframes***.
+
+***Timestep Keyframes*** hold the values that guide the settings for a controlnet, and begin to take effect based on their start_percent, which corresponds to the percentage of the sampling process. They can contain masks for the strengths of each latent, control_net_weights, and latent_keyframes (specific strengths for each latent), all optional.
+
+***Latent Keyframes*** determine the strength of the controlnet for specific latents - all they contain is the batch_index of the latent, and the strength the controlnet should apply for that latent. As a concept, latent keyframes achieve the same affect as a uniform mask with the chosen strength value.
+
+![advcn_image](https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet/assets/7365912/e6275264-6c3f-4246-a319-111ee48f4cd9)
+
+# Nodes
+
+The ControlNet nodes provided here are the ***Apply Advanced ControlNet*** and ***Load Advanced ControlNet Model*** (or diff) nodes. The vanilla ControlNet nodes are also compatible, and can be used almost interchangeably - the only difference is that **at least one of these nodes must be used** for Advanced versions of ControlNets to be used (important for sliding context sampling, like with AnimateDiff-Evolved).
+
+Key:
+- 🟩 - required inputs
+- 🟨 - optional inputs
+- 🟦 - start as widgets, can be converted to inputs
+- 🟥 - optional input/output, but not recommended to use unless needed
+- 🟪 - output
+
+## Apply Advanced ControlNet
+![image](https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet/assets/7365912/dc541d41-70df-4a71-b832-efa65af98f06)
+
+Same functionality as the vanilla Apply Advanced ControlNet (Advanced) node, except with Advanced ControlNet features added to it. Automatically converts any ControlNet from ControlNet loaders into Advanced versions.
+
+### Inputs
+- 🟩***positive***: conditioning (positive).
+- 🟩***negative***: conditioning (negative).
+- 🟩***control_net***: loaded controlnet; will be converted to Advanced version automatically by this node, if it's a supported type.
+- 🟩***image***: images to guide controlnets - if the loaded controlnet requires it, they must preprocessed images. If one image provided, will be used for all latents. If more images provided, will use each image separately for each latent. If not enough images to meet latent count, will repeat the images from the beginning to match vanilla ControlNet functionality.
+- 🟨***mask_optional***: attention masks to apply to controlnets; basically, decides what part of the image the controlnet to apply to (and the relative strength, if the mask is not binary). Same as image input, if you provide more than one mask, each can apply to a different latent.
+- 🟨***timestep_kf***: timestep keyframes to guide controlnet effect throughout sampling steps.
+- 🟨***latent_kf_override***: override for latent keyframes, useful if no other features from timestep keyframes is needed. *NOTE: this latent keyframe will be applied to ALL timesteps, regardless if there are other latent keyframes attached to connected timestep keyframes.*
+- 🟨***weights_override***: override for weights, useful if no other features from timestep keyframes is needed. *NOTE: this weight will be applied to ALL timesteps, regardless if there are other weights attached to connected timestep keyframes.*
+- 🟦***strength***: strength of controlnet; 1.0 is full strength, 0.0 is no effect at all.
+- 🟦***start_percent***: sampling step percentage at which controlnet should start to be applied - no matter what start_percent is set on timestep keyframes, they won't take effect until this start_percent is reached.
+- 🟦***stop_percent***: sampling step percentage at which controlnet should stop being applied - no matter what start_percent is set on timestep keyframes, they won't take effect once this end_percent is reached.
+
+### Outputs
+- 🟪***positive***: conditioning (positive) with applied controlnets
+- 🟪***negative***: conditioning (negative) with applied controlnets
+
+## Load Advanced ControlNet Model
+![image](https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet/assets/7365912/4a7f58a9-783d-4da4-bf82-bc9c167e4722)
+
+Loads a ControlNet model and converts it into an Advanced version that supports all the features in this repo. When used with **Apply Advanced ControlNet** node, there is no reason to use the timestep_keyframe input on this node - use timestep_kf on the Apply node instead.
+
+### Inputs
+- 🟥***timestep_keyframe***: optional and likely unnecessary input to have ControlNet use selected timestep_keyframes - should not be used unless you need to. Useful if this node is not attached to **Apply Advanced ControlNet** node, but still want to use Timestep Keyframe, or to use TK_SHORTCUT outputs from ControlWeights in the same scenario. Will be overriden by the timestep_kf input on **Apply Advanced ControlNet** node, if one is provided there.
+- 🟨***model***: model to plug into the diff version of the node. Some controlnets are designed for receive the model; if you don't know what this does, you probably don't want tot use the diff version of the node.
+
+### Outputs
+- 🟪***CONTROL_NET***: loaded Advanced ControlNet
+
+## Timestep Keyframe
+![image](https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet/assets/7365912/c6f2a86e-fc96-4f8b-b976-7c2062a6eba2)
+
+Scheduling node across timesteps (sampling steps) based on the set start_percent. Chaining Timestep Keyframes allows ControlNet scheduling across sampling steps (percentage-wise), through a timestep keyframe schedule.
+
+### Inputs
+- 🟨***prev_timestep_kf***: used to chain Timestep Keyframes together to create a schedule. The order does not matter - the Timestep Keyframes sort themselves automatically by their start_percent. *Any Timestep Keyframe contained in the prev_timestep_keyframe that contains the same start_percent as the Timestep Keyframe will be overwritten.*
+- 🟨***cn_weights***: weights to apply to controlnet while this Timestep Keyframe is in effect. Must be compatible with the loaded controlnet, or will throw an error explaining what weight types are compatible. If inherit_missing is True, if no control_net_weight is passed in, will attempt to reuse the last-used weights in the timestep keyframe schedule. *If Apply Advanced ControlNet node has a weight_override, the weight_override will be used during sampling instead of control_net_weight.*
+- 🟨***latent_keyframe***: latent keyframes to apply to controlnet while this Timestep Keyframe is in effect. If inherit_missing is True, if no latent_keyframe is passed in, will attempt to reuse the last-used weights in the timestep keyframe schedule. *If Apply Advanced ControlNet node has a latent_kf_override, the latent_lf_override will be used during sampling instead of latent_keyframe.*
+- 🟨***mask_optional***: attention masks to apply to controlnets; basically, decides what part of the image the controlnet to apply to (and the relative strength, if the mask is not binary). Same as mask_optional on the Apply Advanced ControlNet node, can apply either one maks to all latents, or individual masks for each latent. If inherit_missing is True, if no mask_optional is passed in, will attempt to reuse the last-used mask_optional in the timestep keyframe schedule. It is NOT overriden by mask_optional on the Apply  Advanced ControlNet node; will be used together.
+- 🟦***start_percent***: sampling step percentage at which this Timestep Keyframe qualifies to be used. Acts as the 'key' for the Timestep Keyframe in the timestep keyframe schedule.
+- 🟦***strength***: strength of the controlnet; multiplies the controlnet by this value, basically, applied alongside the strength on the Apply ControlNet node. If set to 0.0 will not have any effect during the duration of this Timestep Keyframe's effect, and will increase sampling speed by not doing any work.
+- 🟦***null_latent_kf_strength***: strength to assign to latents that are unaccounted for in the passed in latent_keyframes. Has no effect if no latent_keyframes are passed in, or no batch_indeces are unaccounted in the latent_keyframes for during sampling.
+- 🟦***inherit_missing***: determines if should reuse values from previous Timestep Keyframes for optional values (control_net_weights, latent_keyframe, and mask_option) that are not included on this TimestepKeyframe. To inherit only specific inputs, use default inputs.
+- 🟦***guarantee_usage***: when true, even if a Timestep Keyframe's start_percent ahead of this one in the schedule is closer to current sampling percentage, this Timestep Keyframe will still be used for one step before moving on to the next selected Timestep Keyframe in the following step. Whether the Timestep Keyframe is used or not, its inputs will still be accounted for inherit_missing purposes.  
+
+### Outputs
+- 🟪***TIMESTEP_KF***: the created Timestep Keyframe, that can either be linked to another or into a Timestep Keyframe input.
+
+## Latent Keyframe
+![image](https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet/assets/7365912/7eb2cc4c-255c-4f32-b09b-699f713fada3)
+
+A singular Latent Keyframe, selects the strength for a specific batch_index. If batch_index is not present during sampling, will simply have no effect. Can be chained with any other Latent Keyframe-type node to create a latent keyframe schedule.
+
+### Inputs
+- 🟨***prev_latent_kf***: used to chain Latent Keyframes together to create a schedule. *If a Latent Keyframe contained in prev_latent_keyframes have the same batch_index as this Latent Keyframe, they will take priority over this node's value.*
+- 🟦***batch_index***: index of latent in batch to apply controlnet strength to. Acts as the 'key' for the Latent Keyframe in the latent keyframe schedule.
+- 🟦***strength***: strength of controlnet to apply to the corresponding latent.
+
+### Outputs
+- 🟪***LATENT_KF***: the created Latent Keyframe, that can either be linked to another or into a Latent Keyframe input.
+
+## Latent Keyframe Group
+![image](https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet/assets/7365912/5ce3b795-f5fc-4dc3-ae30-a4c7f87e278c)
+
+Allows to create Latent Keyframes via individual indeces or python-style ranges.
+
+### Inputs
+- 🟨***prev_latent_kf***: used to chain Latent Keyframes together to create a schedule. *If any Latent Keyframes contained in prev_latent_keyframes have the same batch_index as a this Latent Keyframe, they will take priority over this node's version.* 
+- 🟨***latent_optional***: the latents expected to be passed in for sampling; only required if you wish to use negative indeces (will be automatically converted to real values).
+- 🟦***index_strengths***: string list of indeces or python-style ranges of indeces to assign strengths to. If latent_optional is passed in, can contain negative indeces or ranges that contain negative numbers, python-style. The different indeces must be comma separated. Individual latents can be specified by ```batch_index=strength```, like ```0=0.9```. Ranges can be specified by ```start_index_inclusive:end_index_exclusive=strength```, like ```0:8=strength```. Negative indeces are possible when latents_optional has an input, with a string such as ```0,-4=0.25```.
+- 🟦***print_keyframes***: if True, will print the Latent Keyframes generated by this node for debugging purposes.
+
+### Outputs
+- 🟪***LATENT_KF***: the created Latent Keyframe, that can either be linked to another or into a Latent Keyframe input.
+
+## Latent Keyframe Interpolation
+![image](https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet/assets/7365912/7986c737-83b9-46bc-aab0-ae4c368df446)
+
+Allows to create Latent Keyframes with interpolated values in a range.
+
+### Inputs
+- 🟨***prev_latent_kf***: used to chain Latent Keyframes together to create a schedule. *If any Latent Keyframes contained in prev_latent_keyframes have the same batch_index as a this Latent Keyframe, they will take priority over this node's version.*
+- 🟦***batch_index_from***: starting batch_index of range, included.
+- 🟦***batch_index_to***: end batch_index of range, excluded (python-style range).
+- 🟦***strength_from***: starting strength of interpolation.
+- 🟦***strength_to***: end strength of interpolation.
+- 🟦***interpolation***: the method of interpolation.
+- 🟦***print_keyframes***: if True, will print the Latent Keyframes generated by this node for debugging purposes.
+
+### Outputs
+- 🟪***LATENT_KF***: the created Latent Keyframe, that can either be linked to another or into a Latent Keyframe input.
+
+## Latent Keyframe Batched Group
+![image](https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet/assets/7365912/6cec701f-6183-4aeb-af5c-cac76f5591b7)
+
+Allows to create Latent Keyframes via a list of floats, such as with Batch Value Schedule from [ComfyUI_FizzNodes](https://github.com/FizzleDorf/ComfyUI_FizzNodes) nodes.
+
+### Inputs
+- 🟨***prev_latent_kf***: used to chain Latent Keyframes together to create a schedule. *If any Latent Keyframes contained in prev_latent_keyframes have the same batch_index as a this Latent Keyframe, they will take priority over this node's version.* 
+- 🟦***float_strengths***: a list of floats, that will correspond to the strength of each Latent Keyframe; the batch_index is the index of each float value in the list.
+- 🟦***print_keyframes***: if True, will print the Latent Keyframes generated by this node for debugging purposes.
+
+### Outputs
+- 🟪***LATENT_KF***: the created Latent Keyframe, that can either be linked to another or into a Latent Keyframe input.
+
+# There are more nodes to document and show usage - will add this soon! TODO

custom_nodes/ComfyUI-Advanced-ControlNet/__init__.py

@@ -0,0 +1,3 @@
+from .control.nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']

custom_nodes/ComfyUI-Advanced-ControlNet/control/control.py

@@ -0,0 +1,770 @@
+from typing import Union
+from torch import Tensor
+import torch
+
+import comfy.utils
+import comfy.controlnet as comfy_cn
+from comfy.controlnet import ControlBase, ControlNet, ControlLora, T2IAdapter, broadcast_image_to
+
+
+def get_properly_arranged_t2i_weights(initial_weights: list[float]):
+    new_weights = []
+    new_weights.extend([initial_weights[0]]*3)
+    new_weights.extend([initial_weights[1]]*3)
+    new_weights.extend([initial_weights[2]]*3)
+    new_weights.extend([initial_weights[3]]*3)
+    return new_weights
+
+
+class ControlWeightType:
+    DEFAULT = "default"
+    UNIVERSAL = "universal"
+    T2IADAPTER = "t2iadapter"
+    CONTROLNET = "controlnet"
+    CONTROLLORA = "controllora"
+    CONTROLLLLITE = "controllllite"
+
+
+class ControlWeights:
+    def __init__(self, weight_type: str, base_multiplier: float=1.0, flip_weights: bool=False, weights: list[float]=None, weight_mask: Tensor=None):
+        self.weight_type = weight_type
+        self.base_multiplier = base_multiplier
+        self.flip_weights = flip_weights
+        self.weights = weights
+        if self.weights is not None and self.flip_weights:
+            self.weights.reverse()
+        self.weight_mask = weight_mask
+
+    def get(self, idx: int) -> Union[float, Tensor]:
+        # if weights is not none, return index
+        if self.weights is not None:
+            return self.weights[idx]
+        return 1.0
+
+    @classmethod
+    def default(cls):
+        return cls(ControlWeightType.DEFAULT)
+
+    @classmethod
+    def universal(cls, base_multiplier: float, flip_weights: bool=False):
+        return cls(ControlWeightType.UNIVERSAL, base_multiplier=base_multiplier, flip_weights=flip_weights)
+    
+    @classmethod
+    def universal_mask(cls, weight_mask: Tensor):
+        return cls(ControlWeightType.UNIVERSAL, weight_mask=weight_mask)
+
+    @classmethod
+    def t2iadapter(cls, weights: list[float]=None, flip_weights: bool=False):
+        if weights is None:
+            weights = [1.0]*12
+        return cls(ControlWeightType.T2IADAPTER, weights=weights,flip_weights=flip_weights)
+
+    @classmethod
+    def controlnet(cls, weights: list[float]=None, flip_weights: bool=False):
+        if weights is None:
+            weights = [1.0]*13
+        return cls(ControlWeightType.CONTROLNET, weights=weights, flip_weights=flip_weights)
+    
+    @classmethod
+    def controllora(cls, weights: list[float]=None, flip_weights: bool=False):
+        if weights is None:
+            weights = [1.0]*10
+        return cls(ControlWeightType.CONTROLLORA, weights=weights, flip_weights=flip_weights)
+    
+    @classmethod
+    def controllllite(cls, weights: list[float]=None, flip_weights: bool=False):
+        if weights is None:
+            # TODO: make this have a real value
+            weights = [1.0]*200
+        return cls(ControlWeightType.CONTROLLLLITE, weights=weights, flip_weights=flip_weights)
+
+
+class StrengthInterpolation:
+    LINEAR = "linear"
+    EASE_IN = "ease-in"
+    EASE_OUT = "ease-out"
+    EASE_IN_OUT = "ease-in-out"
+    NONE = "none"
+
+
+class LatentKeyframe:
+    def __init__(self, batch_index: int, strength: float) -> None:
+        self.batch_index = batch_index
+        self.strength = strength
+
+
+# always maintain sorted state (by batch_index of LatentKeyframe)
+class LatentKeyframeGroup:
+    def __init__(self) -> None:
+        self.keyframes: list[LatentKeyframe] = []
+
+    def add(self, keyframe: LatentKeyframe) -> None:
+        added = False
+        # replace existing keyframe if same batch_index
+        for i in range(len(self.keyframes)):
+            if self.keyframes[i].batch_index == keyframe.batch_index:
+                self.keyframes[i] = keyframe
+                added = True
+                break
+        if not added:
+            self.keyframes.append(keyframe)
+        self.keyframes.sort(key=lambda k: k.batch_index)
+    
+    def get_index(self, index: int) -> Union[LatentKeyframe, None]:
+        try:
+            return self.keyframes[index]
+        except IndexError:
+            return None
+    
+    def __getitem__(self, index) -> LatentKeyframe:
+        return self.keyframes[index]
+    
+    def is_empty(self) -> bool:
+        return len(self.keyframes) == 0
+
+    def clone(self) -> 'LatentKeyframeGroup':
+        cloned = LatentKeyframeGroup()
+        for tk in self.keyframes:
+            cloned.add(tk)
+        return cloned
+
+
+class TimestepKeyframe:
+    def __init__(self,
+                 start_percent: float = 0.0,
+                 strength: float = 1.0,
+                 interpolation: str = StrengthInterpolation.NONE,
+                 control_weights: ControlWeights = None,
+                 latent_keyframes: LatentKeyframeGroup = None,
+                 null_latent_kf_strength: float = 0.0,
+                 inherit_missing: bool = True,
+                 guarantee_usage: bool = True,
+                 mask_hint_orig: Tensor = None) -> None:
+        self.start_percent = start_percent
+        self.start_t = 999999999.9
+        self.strength = strength
+        self.interpolation = interpolation
+        self.control_weights = control_weights
+        self.latent_keyframes = latent_keyframes
+        self.null_latent_kf_strength = null_latent_kf_strength
+        self.inherit_missing = inherit_missing
+        self.guarantee_usage = guarantee_usage
+        self.mask_hint_orig = mask_hint_orig
+
+    def has_control_weights(self):
+        return self.control_weights is not None
+    
+    def has_latent_keyframes(self):
+        return self.latent_keyframes is not None
+    
+    def has_mask_hint(self):
+        return self.mask_hint_orig is not None
+    
+    
+    @classmethod
+    def default(cls) -> 'TimestepKeyframe':
+        return cls(0.0)
+
+
+# always maintain sorted state (by start_percent of TimestepKeyFrame)
+class TimestepKeyframeGroup:
+    def __init__(self) -> None:
+        self.keyframes: list[TimestepKeyframe] = []
+        self.keyframes.append(TimestepKeyframe.default())
+
+    def add(self, keyframe: TimestepKeyframe) -> None:
+        added = False
+        # replace existing keyframe if same start_percent
+        for i in range(len(self.keyframes)):
+            if self.keyframes[i].start_percent == keyframe.start_percent:
+                self.keyframes[i] = keyframe
+                added = True
+                break
+        if not added:
+            self.keyframes.append(keyframe)
+        self.keyframes.sort(key=lambda k: k.start_percent)
+
+    def get_index(self, index: int) -> Union[TimestepKeyframe, None]:
+        try:
+            return self.keyframes[index]
+        except IndexError:
+            return None
+    
+    def has_index(self, index: int) -> int:
+        return index >=0 and index < len(self.keyframes)
+
+    def __getitem__(self, index) -> TimestepKeyframe:
+        return self.keyframes[index]
+    
+    def __len__(self) -> int:
+        return len(self.keyframes)
+
+    def is_empty(self) -> bool:
+        return len(self.keyframes) == 0
+    
+    def clone(self) -> 'TimestepKeyframeGroup':
+        cloned = TimestepKeyframeGroup()
+        for tk in self.keyframes:
+            cloned.add(tk)
+        return cloned
+    
+    @classmethod
+    def default(cls, keyframe: TimestepKeyframe) -> 'TimestepKeyframeGroup':
+        group = cls()
+        group.keyframes[0] = keyframe
+        return group
+
+
+# used to inject ControlNetAdvanced and T2IAdapterAdvanced control_merge function
+
+
+class AdvancedControlBase:
+    def __init__(self, base: ControlBase, timestep_keyframes: TimestepKeyframeGroup, weights_default: ControlWeights):
+        self.base = base
+        self.compatible_weights = [ControlWeightType.UNIVERSAL]
+        self.add_compatible_weight(weights_default.weight_type)
+        # mask for which parts of controlnet output to keep
+        self.mask_cond_hint_original = None
+        self.mask_cond_hint = None
+        self.tk_mask_cond_hint_original = None
+        self.tk_mask_cond_hint = None
+        self.weight_mask_cond_hint = None
+        # actual index values
+        self.sub_idxs = None
+        self.full_latent_length = 0
+        self.context_length = 0
+        # timesteps
+        self.t: Tensor = None
+        self.batched_number: int = None
+        # weights + override
+        self.weights: ControlWeights = None
+        self.weights_default: ControlWeights = weights_default
+        self.weights_override: ControlWeights = None
+        # latent keyframe + override
+        self.latent_keyframes: LatentKeyframeGroup = None
+        self.latent_keyframe_override: LatentKeyframeGroup = None
+        # initialize timestep_keyframes
+        self.set_timestep_keyframes(timestep_keyframes)
+        # override some functions
+        self.get_control = self.get_control_inject
+        self.control_merge = self.control_merge_inject#.__get__(self, type(self))
+        self.pre_run = self.pre_run_inject
+        self.cleanup = self.cleanup_inject
+
+    def add_compatible_weight(self, control_weight_type: str):
+        self.compatible_weights.append(control_weight_type)
+
+    def verify_all_weights(self, throw_error=True):
+        # first, check if override exists - if so, only need to check the override
+        if self.weights_override is not None:
+            if self.weights_override.weight_type not in self.compatible_weights:
+                msg = f"Weight override is type {self.weights_override.weight_type}, but loaded {type(self).__name__}" + \
+                    f"only supports {self.compatible_weights} weights."
+                raise WeightTypeException(msg)
+        # otherwise, check all timestep keyframe weights
+        else:
+            for tk in self.timestep_keyframes.keyframes:
+                if tk.has_control_weights() and tk.control_weights.weight_type not in self.compatible_weights:
+                    msg = f"Weight on Timestep Keyframe with start_percent={tk.start_percent} is type" + \
+                        f"{tk.control_weights.weight_type}, but loaded {type(self).__name__} only supports {self.compatible_weights} weights."
+                    raise WeightTypeException(msg)
+
+    def set_timestep_keyframes(self, timestep_keyframes: TimestepKeyframeGroup):
+        self.timestep_keyframes = timestep_keyframes if timestep_keyframes else TimestepKeyframeGroup()
+        # prepare first timestep_keyframe related stuff
+        self.current_timestep_keyframe = None
+        self.current_timestep_index = -1
+        self.next_timestep_keyframe = None
+        self.weights = None
+        self.latent_keyframes = None
+
+    def prepare_current_timestep(self, t: Tensor, batched_number: int):
+        self.t = t
+        self.batched_number = batched_number
+        # get current step percent
+        curr_t: float = t[0]
+        prev_index = self.current_timestep_index
+        # if has next index, loop through and see if need to switch
+        if self.timestep_keyframes.has_index(self.current_timestep_index+1):
+            for i in range(self.current_timestep_index+1, len(self.timestep_keyframes)):
+                eval_tk = self.timestep_keyframes[i]
+                # check if start percent is less or equal to curr_t
+                if eval_tk.start_t >= curr_t:
+                    self.current_timestep_index = i
+                    self.current_timestep_keyframe = eval_tk
+                    # keep track of control weights, latent keyframes, and masks,
+                    # accounting for inherit_missing
+                    if self.current_timestep_keyframe.has_control_weights():
+                        self.weights = self.current_timestep_keyframe.control_weights
+                    elif not self.current_timestep_keyframe.inherit_missing:
+                        self.weights = self.weights_default
+                    if self.current_timestep_keyframe.has_latent_keyframes():
+                        self.latent_keyframes = self.current_timestep_keyframe.latent_keyframes
+                    elif not self.current_timestep_keyframe.inherit_missing:
+                        self.latent_keyframes = None
+                    if self.current_timestep_keyframe.has_mask_hint():
+                        self.tk_mask_cond_hint_original = self.current_timestep_keyframe.mask_hint_orig
+                    elif not self.current_timestep_keyframe.inherit_missing:
+                        del self.tk_mask_cond_hint_original
+                        self.tk_mask_cond_hint_original = None
+                    # if guarantee_usage, stop searching for other TKs
+                    if self.current_timestep_keyframe.guarantee_usage:
+                        break
+                # if eval_tk is outside of percent range, stop looking further
+                else:
+                    break
+        
+        # if index changed, apply overrides
+        if prev_index != self.current_timestep_index:
+            if self.weights_override is not None:
+                self.weights = self.weights_override
+            if self.latent_keyframe_override is not None:
+                self.latent_keyframes = self.latent_keyframe_override
+
+        # make sure weights and latent_keyframes are in a workable state
+        # Note: each AdvancedControlBase should create their own get_universal_weights class
+        self.prepare_weights()
+    
+    def prepare_weights(self):
+        if self.weights is None or self.weights.weight_type == ControlWeightType.DEFAULT:
+            self.weights = self.weights_default
+        elif self.weights.weight_type == ControlWeightType.UNIVERSAL:
+            # if universal and weight_mask present, no need to convert
+            if self.weights.weight_mask is not None:
+                return
+            self.weights = self.get_universal_weights()
+    
+    def get_universal_weights(self) -> ControlWeights:
+        return self.weights
+
+    def set_cond_hint_mask(self, mask_hint):
+        self.mask_cond_hint_original = mask_hint
+        return self
+
+    def pre_run_inject(self, model, percent_to_timestep_function):
+        self.base.pre_run(model, percent_to_timestep_function)
+        self.pre_run_advanced(model, percent_to_timestep_function)
+    
+    def pre_run_advanced(self, model, percent_to_timestep_function):
+        # for each timestep keyframe, calculate the start_t
+        for tk in self.timestep_keyframes.keyframes:
+            tk.start_t = percent_to_timestep_function(tk.start_percent)
+        # clear variables
+        self.cleanup_advanced()
+
+    def get_control_inject(self, x_noisy, t, cond, batched_number):
+        # prepare timestep and everything related
+        self.prepare_current_timestep(t=t, batched_number=batched_number)
+        # if should not perform any actions for the controlnet, exit without doing any work
+        if self.strength == 0.0 or self.current_timestep_keyframe.strength == 0.0:
+            control_prev = None
+            if self.previous_controlnet is not None:
+                control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number)
+            if control_prev is not None:
+                return control_prev
+            else:
+                return None
+        # otherwise, perform normal function
+        return self.get_control_advanced(x_noisy, t, cond, batched_number)
+
+    def get_control_advanced(self, x_noisy, t, cond, batched_number):
+        pass
+
+    def calc_weight(self, idx: int, x: Tensor, layers: int) -> Union[float, Tensor]:
+        if self.weights.weight_mask is not None:
+            # prepare weight mask
+            self.prepare_weight_mask_cond_hint(x, self.batched_number)
+            # adjust mask for current layer and return
+            return torch.pow(self.weight_mask_cond_hint, self.get_calc_pow(idx=idx, layers=layers))
+        return self.weights.get(idx=idx)
+    
+    def get_calc_pow(self, idx: int, layers: int) -> int:
+        return (layers-1)-idx
+
+    def apply_advanced_strengths_and_masks(self, x: Tensor, batched_number: int):
+        # apply strengths, and get batch indeces to null out
+        # AKA latents that should not be influenced by ControlNet
+        if self.latent_keyframes is not None:
+            latent_count = x.size(0)//batched_number
+            indeces_to_null = set(range(latent_count))
+            mapped_indeces = None
+            # if expecting subdivision, will need to translate between subset and actual idx values
+            if self.sub_idxs:
+                mapped_indeces = {}
+                for i, actual in enumerate(self.sub_idxs):
+                    mapped_indeces[actual] = i
+            for keyframe in self.latent_keyframes:
+                real_index = keyframe.batch_index
+                # if negative, count from end
+                if real_index < 0:
+                    real_index += latent_count if self.sub_idxs is None else self.full_latent_length
+
+                # if not mapping indeces, what you see is what you get
+                if mapped_indeces is None:
+                    if real_index in indeces_to_null:
+                        indeces_to_null.remove(real_index)
+                # otherwise, see if batch_index is even included in this set of latents
+                else:
+                    real_index = mapped_indeces.get(real_index, None)
+                    if real_index is None:
+                        continue
+                    indeces_to_null.remove(real_index)
+
+                # if real_index is outside the bounds of latents, don't apply
+                if real_index >= latent_count or real_index < 0:
+                    continue
+
+                # apply strength for each batched cond/uncond
+                for b in range(batched_number):
+                    x[(latent_count*b)+real_index] = x[(latent_count*b)+real_index] * keyframe.strength
+
+            # null them out by multiplying by null_latent_kf_strength
+            for batch_index in indeces_to_null:
+                # apply null for each batched cond/uncond
+                for b in range(batched_number):
+                    x[(latent_count*b)+batch_index] = x[(latent_count*b)+batch_index] * self.current_timestep_keyframe.null_latent_kf_strength
+        # apply masks, resizing mask to required dims
+        if self.mask_cond_hint is not None:
+            masks = prepare_mask_batch(self.mask_cond_hint, x.shape)
+            x[:] = x[:] * masks
+        if self.tk_mask_cond_hint is not None:
+            masks = prepare_mask_batch(self.tk_mask_cond_hint, x.shape)
+            x[:] = x[:] * masks
+        # apply timestep keyframe strengths
+        if self.current_timestep_keyframe.strength != 1.0:
+            x[:] *= self.current_timestep_keyframe.strength
+    
+    def control_merge_inject(self: 'AdvancedControlBase', control_input, control_output, control_prev, output_dtype):
+        out = {'input':[], 'middle':[], 'output': []}
+
+        if control_input is not None:
+            for i in range(len(control_input)):
+                key = 'input'
+                x = control_input[i]
+                if x is not None:
+                    self.apply_advanced_strengths_and_masks(x, self.batched_number)
+
+                    x *= self.strength * self.calc_weight(i, x, len(control_input))
+                    if x.dtype != output_dtype:
+                        x = x.to(output_dtype)
+                out[key].insert(0, x)
+
+        if control_output is not None:
+            for i in range(len(control_output)):
+                if i == (len(control_output) - 1):
+                    key = 'middle'
+                    index = 0
+                else:
+                    key = 'output'
+                    index = i
+                x = control_output[i]
+                if x is not None:
+                    self.apply_advanced_strengths_and_masks(x, self.batched_number)
+
+                    if self.global_average_pooling:
+                        x = torch.mean(x, dim=(2, 3), keepdim=True).repeat(1, 1, x.shape[2], x.shape[3])
+
+                    x *= self.strength * self.calc_weight(i, x, len(control_output))
+                    if x.dtype != output_dtype:
+                        x = x.to(output_dtype)
+
+                out[key].append(x)
+        if control_prev is not None:
+            for x in ['input', 'middle', 'output']:
+                o = out[x]
+                for i in range(len(control_prev[x])):
+                    prev_val = control_prev[x][i]
+                    if i >= len(o):
+                        o.append(prev_val)
+                    elif prev_val is not None:
+                        if o[i] is None:
+                            o[i] = prev_val
+                        else:
+                            o[i] += prev_val
+        return out
+
+    def prepare_mask_cond_hint(self, x_noisy: Tensor, t, cond, batched_number, dtype=None):
+        self._prepare_mask("mask_cond_hint", self.mask_cond_hint_original, x_noisy, t, cond, batched_number, dtype)
+        self.prepare_tk_mask_cond_hint(x_noisy, t, cond, batched_number, dtype)
+
+    def prepare_tk_mask_cond_hint(self, x_noisy: Tensor, t, cond, batched_number, dtype=None):
+        return self._prepare_mask("tk_mask_cond_hint", self.current_timestep_keyframe.mask_hint_orig, x_noisy, t, cond, batched_number, dtype)
+
+    def prepare_weight_mask_cond_hint(self, x_noisy: Tensor, batched_number, dtype=None):
+        return self._prepare_mask("weight_mask_cond_hint", self.weights.weight_mask, x_noisy, t=None, cond=None, batched_number=batched_number, dtype=dtype, direct_attn=True)
+
+    def _prepare_mask(self, attr_name, orig_mask: Tensor, x_noisy: Tensor, t, cond, batched_number, dtype=None, direct_attn=False):
+        # make mask appropriate dimensions, if present
+        if orig_mask is not None:
+            out_mask = getattr(self, attr_name)
+            if self.sub_idxs is not None or out_mask is None or x_noisy.shape[2] * 8 != out_mask.shape[1] or x_noisy.shape[3] * 8 != out_mask.shape[2]:
+                self._reset_attr(attr_name)
+                del out_mask
+                # TODO: perform upscale on only the sub_idxs masks at a time instead of all to conserve RAM
+                # resize mask and match batch count
+                multiplier = 1 if direct_attn else 8
+                out_mask = prepare_mask_batch(orig_mask, x_noisy.shape, multiplier=multiplier)
+                actual_latent_length = x_noisy.shape[0] // batched_number
+                out_mask = comfy.utils.repeat_to_batch_size(out_mask, actual_latent_length if self.sub_idxs is None else self.full_latent_length)
+                if self.sub_idxs is not None:
+                    out_mask = out_mask[self.sub_idxs]
+            # make cond_hint_mask length match x_noise
+            if x_noisy.shape[0] != out_mask.shape[0]:
+                out_mask = broadcast_image_to(out_mask, x_noisy.shape[0], batched_number)
+            # default dtype to be same as x_noisy
+            if dtype is None:
+                dtype = x_noisy.dtype
+            setattr(self, attr_name, out_mask.to(dtype=dtype).to(self.device))
+            del out_mask
+
+    def _reset_attr(self, attr_name, new_value=None):
+        if hasattr(self, attr_name):
+            delattr(self, attr_name)
+        setattr(self, attr_name, new_value)
+
+    def cleanup_inject(self):
+        self.base.cleanup()
+        self.cleanup_advanced()
+
+    def cleanup_advanced(self):
+        self.sub_idxs = None
+        self.full_latent_length = 0
+        self.context_length = 0
+        self.t = None
+        self.batched_number = None
+        self.weights = None
+        self.latent_keyframes = None
+        # timestep stuff
+        self.current_timestep_keyframe = None
+        self.next_timestep_keyframe = None
+        self.current_timestep_index = -1
+        # clear mask hints
+        if self.mask_cond_hint is not None:
+            del self.mask_cond_hint
+            self.mask_cond_hint = None
+        if self.tk_mask_cond_hint_original is not None:
+            del self.tk_mask_cond_hint_original
+            self.tk_mask_cond_hint_original = None
+        if self.tk_mask_cond_hint is not None:
+            del self.tk_mask_cond_hint
+            self.tk_mask_cond_hint = None
+        if self.weight_mask_cond_hint is not None:
+            del self.weight_mask_cond_hint
+            self.weight_mask_cond_hint = None
+    
+    def copy_to_advanced(self, copied: 'AdvancedControlBase'):
+        copied.mask_cond_hint_original = self.mask_cond_hint_original
+        copied.weights_override = self.weights_override
+        copied.latent_keyframe_override = self.latent_keyframe_override
+
+
+class ControlNetAdvanced(ControlNet, AdvancedControlBase):
+    def __init__(self, control_model, timestep_keyframes: TimestepKeyframeGroup, global_average_pooling=False, device=None):
+        super().__init__(control_model=control_model, global_average_pooling=global_average_pooling, device=device)
+        AdvancedControlBase.__init__(self, super(), timestep_keyframes=timestep_keyframes, weights_default=ControlWeights.controlnet())
+
+    def get_universal_weights(self) -> ControlWeights:
+        raw_weights = [(self.weights.base_multiplier ** float(12 - i)) for i in range(13)]
+        return ControlWeights.controlnet(raw_weights, self.weights.flip_weights)
+
+    def get_control_advanced(self, x_noisy, t, cond, batched_number):
+        # perform special version of get_control that supports sliding context and masks
+        return self.sliding_get_control(x_noisy, t, cond, batched_number)
+
+    def sliding_get_control(self, x_noisy: Tensor, t, cond, batched_number):
+        control_prev = None
+        if self.previous_controlnet is not None:
+            control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number)
+
+        if self.timestep_range is not None:
+            if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]:
+                if control_prev is not None:
+                    return control_prev
+                else:
+                    return None
+
+        output_dtype = x_noisy.dtype
+
+        # make cond_hint appropriate dimensions
+        # TODO: change this to not require cond_hint upscaling every step when self.sub_idxs are present
+        if self.sub_idxs is not None or self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]:
+            if self.cond_hint is not None:
+                del self.cond_hint
+            self.cond_hint = None
+            # if self.cond_hint_original length greater or equal to real latent count, subdivide it before scaling
+            if self.sub_idxs is not None and self.cond_hint_original.size(0) >= self.full_latent_length:
+                self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original[self.sub_idxs], x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(self.control_model.dtype).to(self.device)
+            else:
+                self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(self.control_model.dtype).to(self.device)
+        if x_noisy.shape[0] != self.cond_hint.shape[0]:
+            self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
+
+        # prepare mask_cond_hint
+        self.prepare_mask_cond_hint(x_noisy=x_noisy, t=t, cond=cond, batched_number=batched_number, dtype=self.control_model.dtype)
+
+        context = cond['c_crossattn']
+        # uses 'y' in new ComfyUI update
+        y = cond.get('y', None)
+        if y is None: # TODO: remove this in the future since no longer used by newest ComfyUI
+            y = cond.get('c_adm', None)
+        if y is not None:
+            y = y.to(self.control_model.dtype)
+        timestep = self.model_sampling_current.timestep(t)
+        x_noisy = self.model_sampling_current.calculate_input(t, x_noisy)
+
+        control = self.control_model(x=x_noisy.to(self.control_model.dtype), hint=self.cond_hint, timesteps=timestep.float(), context=context.to(self.control_model.dtype), y=y)
+        return self.control_merge(None, control, control_prev, output_dtype)
+
+    def copy(self):
+        c = ControlNetAdvanced(self.control_model, self.timestep_keyframes, global_average_pooling=self.global_average_pooling)
+        self.copy_to(c)
+        self.copy_to_advanced(c)
+        return c
+    
+    @staticmethod
+    def from_vanilla(v: ControlNet, timestep_keyframe: TimestepKeyframeGroup=None) -> 'ControlNetAdvanced':
+        return ControlNetAdvanced(control_model=v.control_model, timestep_keyframes=timestep_keyframe,
+                                  global_average_pooling=v.global_average_pooling, device=v.device)
+
+
+class T2IAdapterAdvanced(T2IAdapter, AdvancedControlBase):
+    def __init__(self, t2i_model, timestep_keyframes: TimestepKeyframeGroup, channels_in, device=None):
+        super().__init__(t2i_model=t2i_model, channels_in=channels_in, device=device)
+        AdvancedControlBase.__init__(self, super(), timestep_keyframes=timestep_keyframes, weights_default=ControlWeights.t2iadapter())
+
+    def get_universal_weights(self) -> ControlWeights:
+        raw_weights = [(self.weights.base_multiplier ** float(7 - i)) for i in range(8)]
+        raw_weights = [raw_weights[-8], raw_weights[-3], raw_weights[-2], raw_weights[-1]]
+        raw_weights = get_properly_arranged_t2i_weights(raw_weights)
+        return ControlWeights.t2iadapter(raw_weights, self.weights.flip_weights)
+
+    def get_calc_pow(self, idx: int, layers: int) -> int:
+        # match how T2IAdapterAdvanced deals with universal weights
+        indeces = [7 - i for i in range(8)]
+        indeces = [indeces[-8], indeces[-3], indeces[-2], indeces[-1]]
+        indeces = get_properly_arranged_t2i_weights(indeces)
+        return indeces[idx]
+
+    def get_control_advanced(self, x_noisy, t, cond, batched_number):
+        # prepare timestep and everything related
+        self.prepare_current_timestep(t=t, batched_number=batched_number)
+        try:
+            # if sub indexes present, replace original hint with subsection
+            if self.sub_idxs is not None:
+                # cond hints
+                full_cond_hint_original = self.cond_hint_original
+                del self.cond_hint
+                self.cond_hint = None
+                self.cond_hint_original = full_cond_hint_original[self.sub_idxs]
+            # mask hints
+            self.prepare_mask_cond_hint(x_noisy=x_noisy, t=t, cond=cond, batched_number=batched_number)
+            return super().get_control(x_noisy, t, cond, batched_number)
+        finally:
+            if self.sub_idxs is not None:
+                # replace original cond hint
+                self.cond_hint_original = full_cond_hint_original
+                del full_cond_hint_original
+
+    def copy(self):
+        c = T2IAdapterAdvanced(self.t2i_model, self.timestep_keyframes, self.channels_in)
+        self.copy_to(c)
+        self.copy_to_advanced(c)
+        return c
+    
+    def cleanup(self):
+        super().cleanup()
+        self.cleanup_advanced()
+
+    @staticmethod
+    def from_vanilla(v: T2IAdapter, timestep_keyframe: TimestepKeyframeGroup=None) -> 'T2IAdapterAdvanced':
+        return T2IAdapterAdvanced(t2i_model=v.t2i_model, timestep_keyframes=timestep_keyframe, channels_in=v.channels_in, device=v.device)
+
+
+class ControlLoraAdvanced(ControlLora, AdvancedControlBase):
+    def __init__(self, control_weights, timestep_keyframes: TimestepKeyframeGroup, global_average_pooling=False, device=None):
+        super().__init__(control_weights=control_weights, global_average_pooling=global_average_pooling, device=device)
+        AdvancedControlBase.__init__(self, super(), timestep_keyframes=timestep_keyframes, weights_default=ControlWeights.controllora())
+        # use some functions from ControlNetAdvanced
+        self.get_control_advanced = ControlNetAdvanced.get_control_advanced.__get__(self, type(self))
+        self.sliding_get_control = ControlNetAdvanced.sliding_get_control.__get__(self, type(self))
+    
+    def get_universal_weights(self) -> ControlWeights:
+        raw_weights = [(self.weights.base_multiplier ** float(9 - i)) for i in range(10)]
+        return ControlWeights.controllora(raw_weights, self.weights.flip_weights)
+
+    def copy(self):
+        c = ControlLoraAdvanced(self.control_weights, self.timestep_keyframes, global_average_pooling=self.global_average_pooling)
+        self.copy_to(c)
+        self.copy_to_advanced(c)
+        return c
+    
+    def cleanup(self):
+        super().cleanup()
+        self.cleanup_advanced()
+
+    @staticmethod
+    def from_vanilla(v: ControlLora, timestep_keyframe: TimestepKeyframeGroup=None) -> 'ControlLoraAdvanced':
+        return ControlLoraAdvanced(control_weights=v.control_weights, timestep_keyframes=timestep_keyframe,
+                                   global_average_pooling=v.global_average_pooling, device=v.device)
+
+
+class ControlLLLiteAdvanced(ControlNet, AdvancedControlBase):
+    def __init__(self, control_weights, timestep_keyframes: TimestepKeyframeGroup, device=None):
+        AdvancedControlBase.__init__(self, super(), timestep_keyframes=timestep_keyframes, weights_default=ControlWeights.controllllite())
+
+
+def load_controlnet(ckpt_path, timestep_keyframe: TimestepKeyframeGroup=None, model=None):
+    control = comfy_cn.load_controlnet(ckpt_path, model=model)
+    # TODO: support controlnet-lllite
+    # if is None, see if is a non-vanilla ControlNet
+    # if control is None:
+    #     controlnet_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
+    #     # check if lllite
+    #     if "lllite_unet" in controlnet_data:
+    #         pass
+    return convert_to_advanced(control, timestep_keyframe=timestep_keyframe)
+
+
+def convert_to_advanced(control, timestep_keyframe: TimestepKeyframeGroup=None):
+    # if already advanced, leave it be
+    if is_advanced_controlnet(control):
+        return control
+    # if exactly ControlNet returned, transform it into ControlNetAdvanced
+    if type(control) == ControlNet:
+        return ControlNetAdvanced.from_vanilla(v=control, timestep_keyframe=timestep_keyframe)
+    # if exactly ControlLora returned, transform it into ControlLoraAdvanced
+    elif type(control) == ControlLora:
+        return ControlLoraAdvanced.from_vanilla(v=control, timestep_keyframe=timestep_keyframe)
+    # if T2IAdapter returned, transform it into T2IAdapterAdvanced
+    elif isinstance(control, T2IAdapter):
+        return T2IAdapterAdvanced.from_vanilla(v=control, timestep_keyframe=timestep_keyframe)
+    # otherwise, leave it be - might be something I am not supporting yet
+    return control
+
+
+def is_advanced_controlnet(input_object):
+    return hasattr(input_object, "sub_idxs")
+
+
+# adapted from comfy/sample.py
+def prepare_mask_batch(mask: Tensor, shape: Tensor, multiplier: int=1, match_dim1=False):
+    mask = mask.clone()
+    mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[2]*multiplier, shape[3]*multiplier), mode="bilinear")
+    if match_dim1:
+        mask = torch.cat([mask] * shape[1], dim=1)
+    return mask
+
+
+# applies min-max normalization, from:
+# https://stackoverflow.com/questions/68791508/min-max-normalization-of-a-tensor-in-pytorch
+def normalize_min_max(x: Tensor, new_min = 0.0, new_max = 1.0):
+    x_min, x_max = x.min(), x.max()
+    return (((x - x_min)/(x_max - x_min)) * (new_max - new_min)) + new_min
+
+def linear_conversion(x, x_min=0.0, x_max=1.0, new_min=0.0, new_max=1.0):
+    return (((x - x_min)/(x_max - x_min)) * (new_max - new_min)) + new_min
+
+
+class WeightTypeException(TypeError):
+    "Raised when weight not compatible with AdvancedControlBase object"
+    pass

custom_nodes/ComfyUI-Advanced-ControlNet/control/control_lllite.py

@@ -0,0 +1 @@
+

custom_nodes/ComfyUI-Advanced-ControlNet/control/deprecated_nodes.py

@@ -0,0 +1,103 @@
+import os
+
+import torch
+
+import numpy as np
+from PIL import Image, ImageOps
+from .control import ControlWeights, LatentKeyframeGroup, TimestepKeyframeGroup, TimestepKeyframe
+from .logger import logger
+
+
+class LoadImagesFromDirectory:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "directory": ("STRING", {"default": ""}),
+            },
+            "optional": {
+                "image_load_cap": ("INT", {"default": 0, "min": 0, "step": 1}),
+                "start_index": ("INT", {"default": 0, "min": 0, "step": 1}),
+            }
+        }
+    
+    RETURN_TYPES = ("IMAGE", "MASK", "INT")
+    FUNCTION = "load_images"
+
+    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/deprecated"
+
+    def load_images(self, directory: str, image_load_cap: int = 0, start_index: int = 0):
+        if not os.path.isdir(directory):
+            raise FileNotFoundError(f"Directory '{directory} cannot be found.'")
+        dir_files = os.listdir(directory)
+        if len(dir_files) == 0:
+            raise FileNotFoundError(f"No files in directory '{directory}'.")
+
+        dir_files = sorted(dir_files)
+        dir_files = [os.path.join(directory, x) for x in dir_files]
+        # start at start_index
+        dir_files = dir_files[start_index:]
+
+        images = []
+        masks = []
+
+        limit_images = False
+        if image_load_cap > 0:
+            limit_images = True
+        image_count = 0
+
+        for image_path in dir_files:
+            if os.path.isdir(image_path):
+                continue
+            if limit_images and image_count >= image_load_cap:
+                break
+            i = Image.open(image_path)
+            i = ImageOps.exif_transpose(i)
+            image = i.convert("RGB")
+            image = np.array(image).astype(np.float32) / 255.0
+            image = torch.from_numpy(image)[None,]
+            if 'A' in i.getbands():
+                mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
+                mask = 1. - torch.from_numpy(mask)
+            else:
+                mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
+            images.append(image)
+            masks.append(mask)
+            image_count += 1
+        
+        if len(images) == 0:
+            raise FileNotFoundError(f"No images could be loaded from directory '{directory}'.")
+
+        return (torch.cat(images, dim=0), torch.stack(masks, dim=0), image_count)
+
+
+class TimestepKeyframeNodeDeprecated:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}, ),
+            },
+            "optional": {
+                "control_net_weights": ("CONTROL_NET_WEIGHTS", ),
+                "t2i_adapter_weights": ("T2I_ADAPTER_WEIGHTS", ),
+                "latent_keyframe": ("LATENT_KEYFRAME", ),
+                "prev_timestep_keyframe": ("TIMESTEP_KEYFRAME", ),
+            }
+        }
+    
+    RETURN_TYPES = ("TIMESTEP_KEYFRAME", )
+    FUNCTION = "load_keyframe"
+
+    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
+
+    def load_keyframe(self,
+                      start_percent: float,
+                      control_net_weights: ControlWeights=None,
+                      latent_keyframe: LatentKeyframeGroup=None,
+                      prev_timestep_keyframe: TimestepKeyframeGroup=None):
+        if not prev_timestep_keyframe:
+            prev_timestep_keyframe = TimestepKeyframeGroup()
+        keyframe = TimestepKeyframe(start_percent, control_net_weights, latent_keyframe)
+        prev_timestep_keyframe.add(keyframe)
+        return (prev_timestep_keyframe,)

custom_nodes/ComfyUI-Advanced-ControlNet/control/latent_keyframe_nodes.py

@@ -0,0 +1,283 @@
+from typing import Union
+import numpy as np
+from collections.abc import Iterable
+
+from .control import LatentKeyframe, LatentKeyframeGroup
+from .control import StrengthInterpolation as SI
+from .logger import logger
+
+
+class LatentKeyframeNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "batch_index": ("INT", {"default": 0, "min": -1000, "max": 1000, "step": 1}),
+                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            },
+            "optional": {
+                "prev_latent_kf": ("LATENT_KEYFRAME", ),
+            }
+        }
+
+    RETURN_NAMES = ("LATENT_KF", )
+    RETURN_TYPES = ("LATENT_KEYFRAME", )
+    FUNCTION = "load_keyframe"
+
+    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
+
+    def load_keyframe(self,
+                      batch_index: int,
+                      strength: float,
+                      prev_latent_kf: LatentKeyframeGroup=None,
+                      prev_latent_keyframe: LatentKeyframeGroup=None, # old name
+                      ):
+        prev_latent_keyframe = prev_latent_keyframe if prev_latent_keyframe else prev_latent_kf
+        if not prev_latent_keyframe:
+            prev_latent_keyframe = LatentKeyframeGroup()
+        else:
+            prev_latent_keyframe = prev_latent_keyframe.clone()
+        keyframe = LatentKeyframe(batch_index, strength)
+        prev_latent_keyframe.add(keyframe)
+        return (prev_latent_keyframe,)
+
+
+class LatentKeyframeGroupNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "index_strengths": ("STRING", {"multiline": True, "default": ""}),
+            },
+            "optional": {
+                "prev_latent_kf": ("LATENT_KEYFRAME", ),
+                "latent_optional": ("LATENT", ),
+                "print_keyframes": ("BOOLEAN", {"default": False})
+            }
+        }
+    
+    RETURN_NAMES = ("LATENT_KF", )
+    RETURN_TYPES = ("LATENT_KEYFRAME", )
+    FUNCTION = "load_keyframes"
+
+    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
+
+    def validate_index(self, index: int, latent_count: int = 0, is_range: bool = False, allow_negative = False) -> int:
+        # if part of range, do nothing
+        if is_range:
+            return index
+        # otherwise, validate index
+        # validate not out of range - only when latent_count is passed in
+        if latent_count > 0 and index > latent_count-1:
+            raise IndexError(f"Index '{index}' out of range for the total {latent_count} latents.")
+        # if negative, validate not out of range
+        if index < 0:
+            if not allow_negative:
+                raise IndexError(f"Negative indeces not allowed, but was {index}.")
+            conv_index = latent_count+index
+            if conv_index < 0:
+                raise IndexError(f"Index '{index}', converted to '{conv_index}' out of range for the total {latent_count} latents.")
+            index = conv_index
+        return index
+
+    def convert_to_index_int(self, raw_index: str, latent_count: int = 0, is_range: bool = False, allow_negative = False) -> int:
+        try:
+            return self.validate_index(int(raw_index), latent_count=latent_count, is_range=is_range, allow_negative=allow_negative)
+        except ValueError as e:
+            raise ValueError(f"index '{raw_index}' must be an integer.", e)
+
+    def convert_to_latent_keyframes(self, latent_indeces: str, latent_count: int) -> set[LatentKeyframe]:
+        if not latent_indeces:
+            return set()
+        int_latent_indeces = [i for i in range(0, latent_count)]
+        allow_negative = latent_count > 0
+        chosen_indeces = set()
+        # parse string - allow positive ints, negative ints, and ranges separated by ':'
+        groups = latent_indeces.split(",")
+        groups = [g.strip() for g in groups]
+        for g in groups:
+            # parse strengths - default to 1.0 if no strength given
+            strength = 1.0
+            if '=' in g:
+                g, strength_str = g.split("=", 1)
+                g = g.strip()
+                try:
+                    strength = float(strength_str.strip())
+                except ValueError as e:
+                    raise ValueError(f"strength '{strength_str}' must be a float.", e)
+                if strength < 0:
+                    raise ValueError(f"Strength '{strength}' cannot be negative.")
+            # parse range of indeces (e.g. 2:16)
+            if ':' in g:
+                index_range = g.split(":", 1)
+                index_range = [r.strip() for r in index_range]
+                start_index = self.convert_to_index_int(index_range[0], latent_count=latent_count, is_range=True, allow_negative=allow_negative)
+                end_index = self.convert_to_index_int(index_range[1], latent_count=latent_count, is_range=True, allow_negative=allow_negative)
+                # if latents were passed in, base indeces on known latent count
+                if len(int_latent_indeces) > 0:
+                    for i in int_latent_indeces[start_index:end_index]:
+                        chosen_indeces.add(LatentKeyframe(i, strength))
+                # otherwise, assume indeces are valid
+                else:
+                    for i in range(start_index, end_index):
+                        chosen_indeces.add(LatentKeyframe(i, strength))
+            # parse individual indeces
+            else:
+                chosen_indeces.add(LatentKeyframe(self.convert_to_index_int(g, latent_count=latent_count, allow_negative=allow_negative), strength))
+        return chosen_indeces
+
+    def load_keyframes(self,
+                       index_strengths: str,
+                       prev_latent_kf: LatentKeyframeGroup=None,
+                       prev_latent_keyframe: LatentKeyframeGroup=None, # old name
+                       latent_image_opt=None,
+                       print_keyframes=False):
+        prev_latent_keyframe = prev_latent_keyframe if prev_latent_keyframe else prev_latent_kf
+        if not prev_latent_keyframe:
+            prev_latent_keyframe = LatentKeyframeGroup()
+        else:
+            prev_latent_keyframe = prev_latent_keyframe.clone()
+        curr_latent_keyframe = LatentKeyframeGroup()
+
+        latent_count = -1
+        if latent_image_opt:
+            latent_count = latent_image_opt['samples'].size()[0]
+        latent_keyframes = self.convert_to_latent_keyframes(index_strengths, latent_count=latent_count)
+
+        for latent_keyframe in latent_keyframes:
+            curr_latent_keyframe.add(latent_keyframe)
+        
+        if print_keyframes:
+            for keyframe in curr_latent_keyframe.keyframes:
+                logger.info(f"keyframe {keyframe.batch_index}:{keyframe.strength}")
+
+        # replace values with prev_latent_keyframes
+        for latent_keyframe in prev_latent_keyframe.keyframes:
+            curr_latent_keyframe.add(latent_keyframe)
+
+        return (curr_latent_keyframe,)
+
+        
+class LatentKeyframeInterpolationNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "batch_index_from": ("INT", {"default": 0, "min": -10000, "max": 10000, "step": 1}),
+                "batch_index_to_excl": ("INT", {"default": 0, "min": -10000, "max": 10000, "step": 1}),
+                "strength_from": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "strength_to": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "interpolation": ([SI.LINEAR, SI.EASE_IN, SI.EASE_OUT, SI.EASE_IN_OUT], ),
+            },
+            "optional": {
+                "prev_latent_kf": ("LATENT_KEYFRAME", ),
+                "print_keyframes": ("BOOLEAN", {"default": False})
+            }
+        }
+
+    RETURN_NAMES = ("LATENT_KF", )
+    RETURN_TYPES = ("LATENT_KEYFRAME", )
+    FUNCTION = "load_keyframe"
+    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
+
+    def load_keyframe(self,
+                        batch_index_from: int,
+                        strength_from: float,
+                        batch_index_to_excl: int,
+                        strength_to: float,
+                        interpolation: str,
+                        prev_latent_kf: LatentKeyframeGroup=None,
+                        prev_latent_keyframe: LatentKeyframeGroup=None, # old name
+                        print_keyframes=False):
+
+        if (batch_index_from > batch_index_to_excl):
+            raise ValueError("batch_index_from must be less than or equal to batch_index_to.")
+
+        if (batch_index_from < 0 and batch_index_to_excl >= 0):
+            raise ValueError("batch_index_from and batch_index_to must be either both positive or both negative.")
+
+        prev_latent_keyframe = prev_latent_keyframe if prev_latent_keyframe else prev_latent_kf
+        if not prev_latent_keyframe:
+            prev_latent_keyframe = LatentKeyframeGroup()
+        else:
+            prev_latent_keyframe = prev_latent_keyframe.clone()
+        curr_latent_keyframe = LatentKeyframeGroup()
+
+        steps = batch_index_to_excl - batch_index_from
+        diff = strength_to - strength_from
+        if interpolation == SI.LINEAR:
+            weights = np.linspace(strength_from, strength_to, steps)
+        elif interpolation == SI.EASE_IN:
+            index = np.linspace(0, 1, steps)
+            weights = diff * np.power(index, 2) + strength_from
+        elif interpolation == SI.EASE_OUT:
+            index = np.linspace(0, 1, steps)
+            weights = diff * (1 - np.power(1 - index, 2)) + strength_from
+        elif interpolation == SI.EASE_IN_OUT:
+            index = np.linspace(0, 1, steps)
+            weights = diff * ((1 - np.cos(index * np.pi)) / 2) + strength_from
+
+        for i in range(steps):
+            keyframe = LatentKeyframe(batch_index_from + i, float(weights[i]))
+            curr_latent_keyframe.add(keyframe)
+        
+        if print_keyframes:
+            for keyframe in curr_latent_keyframe.keyframes:
+                logger.info(f"keyframe {keyframe.batch_index}:{keyframe.strength}")
+
+        # replace values with prev_latent_keyframes
+        for latent_keyframe in prev_latent_keyframe.keyframes:
+            curr_latent_keyframe.add(latent_keyframe)
+
+        return (curr_latent_keyframe,)
+
+
+class LatentKeyframeBatchedGroupNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "float_strengths": ("FLOAT", {"default": -1, "min": -1, "step": 0.001, "forceInput": True}),
+            },
+            "optional": {
+                "prev_latent_kf": ("LATENT_KEYFRAME", ),
+                "print_keyframes": ("BOOLEAN", {"default": False})
+            }
+        }
+
+    RETURN_NAMES = ("LATENT_KF", )
+    RETURN_TYPES = ("LATENT_KEYFRAME", )
+    FUNCTION = "load_keyframe"
+    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
+
+    def load_keyframe(self, float_strengths: Union[float, list[float]],
+                      prev_latent_kf: LatentKeyframeGroup=None,
+                      prev_latent_keyframe: LatentKeyframeGroup=None, # old name
+                      print_keyframes=False):
+        prev_latent_keyframe = prev_latent_keyframe if prev_latent_keyframe else prev_latent_kf
+        if not prev_latent_keyframe:
+            prev_latent_keyframe = LatentKeyframeGroup()
+        else:
+            prev_latent_keyframe = prev_latent_keyframe.clone()
+        curr_latent_keyframe = LatentKeyframeGroup()
+
+        # if received a normal float input, do nothing
+        if type(float_strengths) in (float, int):
+            logger.info("No batched float_strengths passed into Latent Keyframe Batch Group node; will not create any new keyframes.")
+        # if iterable, attempt to create LatentKeyframes with chosen strengths
+        elif isinstance(float_strengths, Iterable):
+            for idx, strength in enumerate(float_strengths):
+                keyframe = LatentKeyframe(idx, strength)
+                curr_latent_keyframe.add(keyframe)
+        else:
+            raise ValueError(f"Expected strengths to be an iterable input, but was {type(float_strengths).__repr__}.")    
+
+        if print_keyframes:
+            for keyframe in curr_latent_keyframe.keyframes:
+                logger.info(f"keyframe {keyframe.batch_index}:{keyframe.strength}")
+
+        # replace values with prev_latent_keyframes
+        for latent_keyframe in prev_latent_keyframe.keyframes:
+            curr_latent_keyframe.add(latent_keyframe)
+
+        return (curr_latent_keyframe,)

custom_nodes/ComfyUI-Advanced-ControlNet/control/logger.py

@@ -0,0 +1,36 @@
+import sys
+import copy
+import logging
+
+
+class ColoredFormatter(logging.Formatter):
+    COLORS = {
+        "DEBUG": "\033[0;36m",  # CYAN
+        "INFO": "\033[0;32m",  # GREEN
+        "WARNING": "\033[0;33m",  # YELLOW
+        "ERROR": "\033[0;31m",  # RED
+        "CRITICAL": "\033[0;37;41m",  # WHITE ON RED
+        "RESET": "\033[0m",  # RESET COLOR
+    }
+
+    def format(self, record):
+        colored_record = copy.copy(record)
+        levelname = colored_record.levelname
+        seq = self.COLORS.get(levelname, self.COLORS["RESET"])
+        colored_record.levelname = f"{seq}{levelname}{self.COLORS['RESET']}"
+        return super().format(colored_record)
+
+
+# Create a new logger
+logger = logging.getLogger("Advanced-ControlNet")
+logger.propagate = False
+
+# Add handler if we don't have one.
+if not logger.handlers:
+    handler = logging.StreamHandler(sys.stdout)
+    handler.setFormatter(ColoredFormatter("[%(name)s] - %(levelname)s - %(message)s"))
+    logger.addHandler(handler)
+
+# Configure logger
+loglevel = logging.INFO
+logger.setLevel(loglevel)

custom_nodes/ComfyUI-Advanced-ControlNet/control/nodes.py

@@ -0,0 +1,243 @@
+import numpy as np
+from torch import Tensor
+
+import folder_paths
+
+from .control import load_controlnet, convert_to_advanced, ControlWeights, ControlWeightType,\
+    LatentKeyframeGroup, TimestepKeyframe, TimestepKeyframeGroup, is_advanced_controlnet
+from .control import StrengthInterpolation as SI
+from .weight_nodes import DefaultWeights, ScaledSoftMaskedUniversalWeights, ScaledSoftUniversalWeights, SoftControlNetWeights, CustomControlNetWeights, \
+    SoftT2IAdapterWeights, CustomT2IAdapterWeights
+from .latent_keyframe_nodes import LatentKeyframeGroupNode, LatentKeyframeInterpolationNode, LatentKeyframeBatchedGroupNode, LatentKeyframeNode
+from .deprecated_nodes import LoadImagesFromDirectory
+from .logger import logger
+
+
+class TimestepKeyframeNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}, ),
+            },
+            "optional": {
+                "prev_timestep_kf": ("TIMESTEP_KEYFRAME", ),
+                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "cn_weights": ("CONTROL_NET_WEIGHTS", ),
+                "latent_keyframe": ("LATENT_KEYFRAME", ),
+                "null_latent_kf_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "inherit_missing": ("BOOLEAN", {"default": True}, ),
+                "guarantee_usage": ("BOOLEAN", {"default": True}, ),
+                "mask_optional": ("MASK", ),
+                #"interpolation": ([SI.LINEAR, SI.EASE_IN, SI.EASE_OUT, SI.EASE_IN_OUT, SI.NONE], {"default": SI.NONE}, ),
+            }
+        }
+    
+    RETURN_NAMES = ("TIMESTEP_KF", )
+    RETURN_TYPES = ("TIMESTEP_KEYFRAME", )
+    FUNCTION = "load_keyframe"
+
+    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
+
+    def load_keyframe(self,
+                      start_percent: float,
+                      strength: float=1.0,
+                      cn_weights: ControlWeights=None, control_net_weights: ControlWeights=None, # old name
+                      latent_keyframe: LatentKeyframeGroup=None,
+                      prev_timestep_kf: TimestepKeyframeGroup=None, prev_timestep_keyframe: TimestepKeyframeGroup=None, # old name
+                      null_latent_kf_strength: float=0.0,
+                      inherit_missing=True,
+                      guarantee_usage=True,
+                      mask_optional=None,
+                      interpolation: str=SI.NONE,):
+        control_net_weights = control_net_weights if control_net_weights else cn_weights
+        prev_timestep_keyframe = prev_timestep_keyframe if prev_timestep_keyframe else prev_timestep_kf
+        if not prev_timestep_keyframe:
+            prev_timestep_keyframe = TimestepKeyframeGroup()
+        else:
+            prev_timestep_keyframe = prev_timestep_keyframe.clone()
+        keyframe = TimestepKeyframe(start_percent=start_percent, strength=strength, interpolation=interpolation, null_latent_kf_strength=null_latent_kf_strength,
+                                    control_weights=control_net_weights, latent_keyframes=latent_keyframe, inherit_missing=inherit_missing, guarantee_usage=guarantee_usage,
+                                    mask_hint_orig=mask_optional)
+        prev_timestep_keyframe.add(keyframe)
+        return (prev_timestep_keyframe,)
+
+
+class ControlNetLoaderAdvanced:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "control_net_name": (folder_paths.get_filename_list("controlnet"), ),
+            },
+            "optional": {
+                "timestep_keyframe": ("TIMESTEP_KEYFRAME", ),
+            }
+        }
+
+    RETURN_TYPES = ("CONTROL_NET", )
+    FUNCTION = "load_controlnet"
+
+    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝"
+
+    def load_controlnet(self, control_net_name,
+                        timestep_keyframe: TimestepKeyframeGroup=None
+                        ):
+        controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
+        controlnet = load_controlnet(controlnet_path, timestep_keyframe)
+        return (controlnet,)
+    
+
+class DiffControlNetLoaderAdvanced:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "control_net_name": (folder_paths.get_filename_list("controlnet"), )
+            },
+            "optional": {
+                "timestep_keyframe": ("TIMESTEP_KEYFRAME", ),
+            }
+        }
+    
+    RETURN_TYPES = ("CONTROL_NET", )
+    FUNCTION = "load_controlnet"
+
+    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝"
+
+    def load_controlnet(self, control_net_name, model,
+                        timestep_keyframe: TimestepKeyframeGroup=None
+                        ):
+        controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
+        controlnet = load_controlnet(controlnet_path, timestep_keyframe, model)
+        if is_advanced_controlnet(controlnet):
+            controlnet.verify_all_weights()
+        return (controlnet,)
+
+
+class AdvancedControlNetApply:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "positive": ("CONDITIONING", ),
+                "negative": ("CONDITIONING", ),
+                "control_net": ("CONTROL_NET", ),
+                "image": ("IMAGE", ),
+                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
+            },
+            "optional": {
+                "mask_optional": ("MASK", ),
+                "timestep_kf": ("TIMESTEP_KEYFRAME", ),
+                "latent_kf_override": ("LATENT_KEYFRAME", ),
+                "weights_override": ("CONTROL_NET_WEIGHTS", ),
+            }
+        }
+
+    RETURN_TYPES = ("CONDITIONING","CONDITIONING")
+    RETURN_NAMES = ("positive", "negative")
+    FUNCTION = "apply_controlnet"
+
+    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝"
+
+    def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent,
+                         mask_optional: Tensor=None,
+                         timestep_kf: TimestepKeyframeGroup=None, latent_kf_override: LatentKeyframeGroup=None,
+                         weights_override: ControlWeights=None):
+        if strength == 0:
+            return (positive, negative)
+
+        control_hint = image.movedim(-1,1)
+        cnets = {}
+
+        out = []
+        for conditioning in [positive, negative]:
+            c = []
+            for t in conditioning:
+                d = t[1].copy()
+
+                prev_cnet = d.get('control', None)
+                if prev_cnet in cnets:
+                    c_net = cnets[prev_cnet]
+                else:
+                    # copy, convert to advanced if needed, and set cond
+                    c_net = convert_to_advanced(control_net.copy()).set_cond_hint(control_hint, strength, (start_percent, end_percent))
+                    if is_advanced_controlnet(c_net):
+                        # apply optional parameters and overrides, if provided
+                        if timestep_kf is not None:
+                            c_net.set_timestep_keyframes(timestep_kf)
+                        if latent_kf_override is not None:
+                            c_net.latent_keyframe_override = latent_kf_override
+                        if weights_override is not None:
+                            c_net.weights_override = weights_override
+                        # verify weights are compatible
+                        c_net.verify_all_weights()
+                        # set cond hint mask
+                        if mask_optional is not None:
+                            mask_optional = mask_optional.clone()
+                            # if not in the form of a batch, make it so
+                            if len(mask_optional.shape) < 3:
+                                mask_optional = mask_optional.unsqueeze(0)
+                            c_net.set_cond_hint_mask(mask_optional)
+                    c_net.set_previous_controlnet(prev_cnet)
+                    cnets[prev_cnet] = c_net
+
+                d['control'] = c_net
+                d['control_apply_to_uncond'] = False
+                n = [t[0], d]
+                c.append(n)
+            out.append(c)
+        return (out[0], out[1])
+
+
+# NODE MAPPING
+NODE_CLASS_MAPPINGS = {
+    # Keyframes
+    "TimestepKeyframe": TimestepKeyframeNode,
+    "LatentKeyframe": LatentKeyframeNode,
+    "LatentKeyframeGroup": LatentKeyframeGroupNode,
+    "LatentKeyframeBatchedGroup": LatentKeyframeBatchedGroupNode,
+    "LatentKeyframeTiming": LatentKeyframeInterpolationNode,
+    # Conditioning
+    "ACN_AdvancedControlNetApply": AdvancedControlNetApply,
+    # Loaders
+    "ControlNetLoaderAdvanced": ControlNetLoaderAdvanced,
+    "DiffControlNetLoaderAdvanced": DiffControlNetLoaderAdvanced,
+    # Weights
+    "ScaledSoftControlNetWeights": ScaledSoftUniversalWeights,
+    "ScaledSoftMaskedUniversalWeights": ScaledSoftMaskedUniversalWeights,
+    "SoftControlNetWeights": SoftControlNetWeights,
+    "CustomControlNetWeights": CustomControlNetWeights,
+    "SoftT2IAdapterWeights": SoftT2IAdapterWeights,
+    "CustomT2IAdapterWeights": CustomT2IAdapterWeights,
+    "ACN_DefaultUniversalWeights": DefaultWeights,
+    # Image
+    "LoadImagesFromDirectory": LoadImagesFromDirectory
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    # Keyframes
+    "TimestepKeyframe": "Timestep Keyframe 🛂🅐🅒🅝",
+    "LatentKeyframe": "Latent Keyframe 🛂🅐🅒🅝",
+    "LatentKeyframeGroup": "Latent Keyframe Group 🛂🅐🅒🅝",
+    "LatentKeyframeBatchedGroup": "Latent Keyframe Batched Group 🛂🅐🅒🅝",
+    "LatentKeyframeTiming": "Latent Keyframe Interpolation 🛂🅐🅒🅝",
+    # Conditioning
+    "ACN_AdvancedControlNetApply": "Apply Advanced ControlNet 🛂🅐🅒🅝",
+    # Loaders
+    "ControlNetLoaderAdvanced": "Load Advanced ControlNet Model 🛂🅐🅒🅝",
+    "DiffControlNetLoaderAdvanced": "Load Advanced ControlNet Model (diff) 🛂🅐🅒🅝",
+    # Weights
+    "ScaledSoftControlNetWeights": "Scaled Soft Weights 🛂🅐🅒🅝",
+    "ScaledSoftMaskedUniversalWeights": "Scaled Soft Masked Weights 🛂🅐🅒🅝",
+    "SoftControlNetWeights": "ControlNet Soft Weights 🛂🅐🅒🅝",
+    "CustomControlNetWeights": "ControlNet Custom Weights 🛂🅐🅒🅝",
+    "SoftT2IAdapterWeights": "T2IAdapter Soft Weights 🛂🅐🅒🅝",
+    "CustomT2IAdapterWeights": "T2IAdapter Custom Weights 🛂🅐🅒🅝",
+    "ACN_DefaultUniversalWeights": "Force Default Weights 🛂🅐🅒🅝",
+    # Image
+    "LoadImagesFromDirectory": "Load Images [DEPRECATED] 🛂🅐🅒🅝"
+}

custom_nodes/ComfyUI-Advanced-ControlNet/control/reference_nodes.py

@@ -0,0 +1,12 @@
+class AnimateDiffLoaderWithContext:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "image": ("IMAGE",),
+            },
+        }
+    
+    RETURN_TYPES = ("MODEL",)
+    CATEGORY = ""

custom_nodes/ComfyUI-Advanced-ControlNet/control/weight_nodes.py

@@ -0,0 +1,201 @@
+from torch import Tensor
+import torch
+from .control import TimestepKeyframe, TimestepKeyframeGroup, ControlWeights, get_properly_arranged_t2i_weights, linear_conversion
+from .logger import logger
+
+
+WEIGHTS_RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT")
+
+
+class DefaultWeights:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+        }
+    
+    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
+    RETURN_NAMES = WEIGHTS_RETURN_NAMES
+    FUNCTION = "load_weights"
+
+    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
+
+    def load_weights(self):
+        weights = ControlWeights.default()
+        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights))) 
+
+
+class ScaledSoftMaskedUniversalWeights:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "mask": ("MASK", ),
+                "min_base_multiplier": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}, ),
+                "max_base_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}, ),
+                #"lock_min": ("BOOLEAN", {"default": False}, ),
+                #"lock_max": ("BOOLEAN", {"default": False}, ),
+            },
+        }
+    
+    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
+    RETURN_NAMES = WEIGHTS_RETURN_NAMES
+    FUNCTION = "load_weights"
+
+    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
+
+    def load_weights(self, mask: Tensor, min_base_multiplier: float, max_base_multiplier: float, lock_min=False, lock_max=False):
+        # normalize mask
+        mask = mask.clone()
+        x_min = 0.0 if lock_min else mask.min()
+        x_max = 1.0 if lock_max else mask.max()
+        if x_min == x_max:
+            mask = torch.ones_like(mask) * max_base_multiplier
+        else:
+            mask = linear_conversion(mask, x_min, x_max, min_base_multiplier, max_base_multiplier)
+        weights = ControlWeights.universal_mask(weight_mask=mask)
+        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
+
+
+class ScaledSoftUniversalWeights:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "base_multiplier": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 1.0, "step": 0.001}, ),
+                "flip_weights": ("BOOLEAN", {"default": False}),
+            },
+        }
+    
+    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
+    RETURN_NAMES = WEIGHTS_RETURN_NAMES
+    FUNCTION = "load_weights"
+
+    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
+
+    def load_weights(self, base_multiplier, flip_weights):
+        weights = ControlWeights.universal(base_multiplier=base_multiplier, flip_weights=flip_weights)
+        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights))) 
+
+
+class SoftControlNetWeights:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "weight_00": ("FLOAT", {"default": 0.09941396206337118, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "weight_01": ("FLOAT", {"default": 0.12050177219802567, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "weight_02": ("FLOAT", {"default": 0.14606275417942507, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "weight_03": ("FLOAT", {"default": 0.17704576264172736, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "weight_04": ("FLOAT", {"default": 0.214600924414215, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "weight_05": ("FLOAT", {"default": 0.26012233262329093, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "weight_06": ("FLOAT", {"default": 0.3152997971191405, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "weight_07": ("FLOAT", {"default": 0.3821815722656249, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "weight_08": ("FLOAT", {"default": 0.4632503906249999, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "weight_09": ("FLOAT", {"default": 0.561515625, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "weight_10": ("FLOAT", {"default": 0.6806249999999999, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "weight_11": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "weight_12": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "flip_weights": ("BOOLEAN", {"default": False}),
+            },
+        }
+    
+    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
+    RETURN_NAMES = WEIGHTS_RETURN_NAMES
+    FUNCTION = "load_weights"
+
+    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet"
+
+    def load_weights(self, weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06, 
+                     weight_07, weight_08, weight_09, weight_10, weight_11, weight_12, flip_weights):
+        weights = [weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06, 
+                   weight_07, weight_08, weight_09, weight_10, weight_11, weight_12]
+        weights = ControlWeights.controlnet(weights, flip_weights=flip_weights)
+        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
+
+
+class CustomControlNetWeights:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "weight_00": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "weight_01": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "weight_02": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "weight_03": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "weight_04": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "weight_05": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "weight_06": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "weight_07": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "weight_08": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "weight_09": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "weight_10": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "weight_11": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "weight_12": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "flip_weights": ("BOOLEAN", {"default": False}),
+            }
+        }
+    
+    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
+    RETURN_NAMES = WEIGHTS_RETURN_NAMES
+    FUNCTION = "load_weights"
+
+    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet"
+
+    def load_weights(self, weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06, 
+                     weight_07, weight_08, weight_09, weight_10, weight_11, weight_12, flip_weights):
+        weights = [weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06, 
+                   weight_07, weight_08, weight_09, weight_10, weight_11, weight_12]
+        weights = ControlWeights.controlnet(weights, flip_weights=flip_weights)
+        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
+
+
+class SoftT2IAdapterWeights:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "weight_00": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "weight_01": ("FLOAT", {"default": 0.62, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "weight_02": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "weight_03": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "flip_weights": ("BOOLEAN", {"default": False}),
+            },
+        }
+    
+    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
+    RETURN_NAMES = WEIGHTS_RETURN_NAMES
+    FUNCTION = "load_weights"
+
+    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/T2IAdapter"
+
+    def load_weights(self, weight_00, weight_01, weight_02, weight_03, flip_weights):
+        weights = [weight_00, weight_01, weight_02, weight_03]
+        weights = get_properly_arranged_t2i_weights(weights)
+        weights = ControlWeights.t2iadapter(weights, flip_weights=flip_weights)
+        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
+
+
+class CustomT2IAdapterWeights:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "weight_00": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "weight_01": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "weight_02": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "weight_03": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                "flip_weights": ("BOOLEAN", {"default": False}),
+            },
+        }
+    
+    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
+    RETURN_NAMES = WEIGHTS_RETURN_NAMES
+    FUNCTION = "load_weights"
+
+    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/T2IAdapter"
+
+    def load_weights(self, weight_00, weight_01, weight_02, weight_03, flip_weights):
+        weights = [weight_00, weight_01, weight_02, weight_03]
+        weights = get_properly_arranged_t2i_weights(weights)
+        weights = ControlWeights.t2iadapter(weights, flip_weights=flip_weights)
+        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))

custom_nodes/ComfyUI-Custom-Scripts/.gitignore

@@ -0,0 +1 @@
+__pycache__

custom_nodes/ComfyUI-Custom-Scripts/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 pythongosssss
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

custom_nodes/ComfyUI-Custom-Scripts/README.md

@@ -0,0 +1,394 @@
+# ComfyUI-Custom-Scripts
+
+# Installation
+
+1. Clone the repository:
+`git clone https://github.com/pythongosssss/ComfyUI-Custom-Scripts.git`  
+to your ComfyUI `custom_nodes` directory
+
+   The script will then automatically install all custom scripts and nodes.  
+   It will attempt to use symlinks and junctions to prevent having to copy files and keep them up to date.
+
+- For uninstallation:
+  - Delete the cloned repo in `custom_nodes`
+  - Ensure `web/extensions/pysssss/CustomScripts` has also been removed
+
+# Update
+1. Navigate to the cloned repo e.g. `custom_nodes/ComfyUI-Custom-Scripts`
+2. `git pull`
+
+# Features
+
+## Autocomplete
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/b5971135-414f-4f4e-a6cf-2650dc01085f)  
+Provides embedding and custom word autocomplete. You can view embedding details by clicking on the info icon on the list.  
+Define your list of custom words via the settings.  
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/160ef61c-7d7e-49d0-b60f-5a1501b74c9d)  
+You can quickly default to danbooru tags using the Load button, or load/manage other custom word lists.  
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/cc180b35-5f45-442f-9285-3ddf3fa320d0)
+
+## Auto Arrange Graph
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/04b06081-ca6f-4c0f-8584-d0a157c36747)  
+Adds a menu option to auto arrange the graph in order of execution, this makes very wide graphs!
+
+## Always Snap to Grid
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/66f36d1f-e579-4959-9880-9a9624922e3a)  
+Adds a setting to make moving nodes always snap to grid.
+
+## [Testing] "Better" Loader Lists
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/664caa71-f25f-4a96-a04a-1466d6b2b8b4)  
+Adds custom Lora and Checkpoint loader nodes, these have the ability to show preview images, just place a png or jpg next to the file and it'll display in the list on hover (e.g. sdxl.safetensors and sdxl.png).  
+Optionally enable subfolders via the settings:  
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/e15b5e83-4f9d-4d57-8324-742bedf75439)   
+Adds an "examples" widget to load sample prompts, triggerwords, etc:  
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/ad1751e4-4c85-42e7-9490-e94fb1cbc8e7)  
+These should be stored in a folder matching the name of the model, e.g. if it is `loras/add_detail.safetensors` put your files in as  `loras/add_detail/*.txt`  
+To quickly save a generated image as the preview to use for the model, you can right click on an image on a node, and select Save as Preview and choose the model to save the preview for:  
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/9fa8e9db-27b3-45cb-85c2-0860a238fd3a)
+
+## Checkpoint/LoRA/Embedding Info
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/6b67bf40-ee17-4fa6-a0c1-7947066bafc2)
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/32405df6-b367-404f-a5df-2d4347089a9e)  
+Adds "View Info" menu option to view details about the selected LoRA or Checkpoint. To view embedding details, click the info button when using embedding autocomplete.
+
+## Constrain Image
+Adds a node for resizing an image to a max & min size optionally cropping if required.
+
+## Custom Colors
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/fa7883f3-f81c-49f6-9ab6-9526e4debab6)  
+Adds a custom color picker to nodes & groups
+
+## Favicon Status
+![image](https://user-images.githubusercontent.com/125205205/230171227-31f061a6-6324-4976-bed9-723a87500cf3.png)
+![image](https://user-images.githubusercontent.com/125205205/230171445-c7202a45-b511-4d69-87fa-945ad44c063f.png)  
+Adds a favicon and title to the window, favicon changes color while generating and the window title includes the number of prompts in the queue
+
+## Image Feed
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/caea0d48-85b9-4ca9-9771-5c795db35fbc)
+Adds a panel showing images that have been generated in the current session, you can control the direction that images are added and the position of the panel via the ComfyUI settings screen and the size of the panel and the images via the sliders at the top of the panel.  
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/ca093d38-41a3-4647-9223-5bd0b9ee4f1e)
+
+## KSampler (Advanced) denoise helper
+Provides a simple method to set custom denoise on the advanced sampler  
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/42946bd8-0078-4c7a-bfe9-7adb1382b5e2)
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/7cfccb22-f155-4848-934b-a2b2a6efe16f)
+
+## Lock Nodes & Groups
+![image](https://user-images.githubusercontent.com/125205205/230172868-5c5a943c-ade1-4799-bf80-cc931da5d4b2.png)  
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/cfca09d9-38e5-4ecd-8b73-1455009fcd67)  
+Adds a lock option to nodes & groups that prevents you from moving them until unlocked
+
+## Math Expression
+Allows for evaluating complex expressions using values from the graph. You can input `INT`, `FLOAT`, `IMAGE` and `LATENT` values.  
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/1593edde-67b8-45d8-88cb-e75f52dba039)  
+Other nodes values can be referenced via the `Node name for S&R` via the `Properties` menu item on a node, or the node title.  
+Supported operators: `+ - * /` (basic ops) `//` (floor division) `**` (power) `^` (xor) `%` (mod)  
+Supported functions `floor(num, dp?)` `floor(num)` `ceil(num)` `randomint(min,max)`  
+If using a `LATENT` or `IMAGE` you can get the dimensions using `a.width` or `a.height` where `a` is the input name.
+
+## Node Finder
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/177d2b67-acbc-4ec3-ab31-7c295a98c194)  
+Adds a menu item for following/jumping to the executing node, and a menu to quickly go to a node of a specific type.
+
+## Preset Text
+![image](https://user-images.githubusercontent.com/125205205/230173939-08459efc-785b-46da-93d1-b02f0300c6f4.png)  
+Adds a node that lets you save and use text presets (e.g. for your 'normal' negatives)
+
+## Quick Nodes
+![image](https://user-images.githubusercontent.com/125205205/230174266-5232831a-a03b-4bf7-bc8b-c45466a0bc64.png)  
+Adds various menu items to some nodes for quickly setting up common parts of graphs
+
+## Play Sound
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/9bcf9fb3-5898-4432-a974-fb1e17d3b7e8)  
+Plays a sound when the node is executed, either after each prompt or only when the queue is empty for queuing multiple prompts.  
+You can customize the sound by replacing the mp3 file `web/extensions/pysssss/CustomScripts/assets\notify.mp3`
+
+## [WIP] Repeater
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/ec0dac25-14e4-4d44-b975-52193656709d)
+Node allows you to either create a list of N repeats of the input node, or create N outputs from the input node.  
+You can optionally decide if you want to reuse the input node, or create a new instance each time (e.g. a Checkpoint Loader would want to be re-used, but a random number would want to be unique)
+TODO: Type safety on the wildcard outputs to require match with input
+
+## Show Text
+![image](https://user-images.githubusercontent.com/125205205/230174888-c004fd48-da78-4de9-81c2-93a866fcfcd1.png)  
+Takes input from a node that produces a string and displays it, useful for things like interrogator, prompt generators, etc.
+
+## Show Image on Menu
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/b6ab58f2-583b-448c-bcfc-f93f5cdab0fc)  
+Shows the current generating image on the menu at the bottom, you can disable this via the settings menu.
+
+## String Function
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/01107137-8a93-4765-bae0-fcc110a09091)  
+Supports appending and replacing text  
+`tidy_tags` will add commas between parts when in `append` mode.  
+`replace` mode supports regex replace by using `/your regex here/` and you can reference capturing groups using `\number` e.g. `\1`
+
+## Touch Support
+Provides basic support for touch screen devices, its not perfect but better than nothing
+
+## Widget Defaults
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/3d675032-2b19-4da8-a7d7-fa2d7c555daa)  
+Allows you to specify default values for widgets when adding new nodes, the values are configured via the settings menu  
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/7b57a3d8-98d3-46e9-9b33-6645c0da41e7)
+
+## Workflows
+Adds options to the menu for saving + loading workflows:  
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/7b5a3012-4c59-47c6-8eea-85cf534403ea)
+
+## Workflow Images
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/06453fd2-c020-46ee-a7db-2b8bf5bcba7e)  
+Adds menu options for importing/exporting the graph as SVG and PNG showing a view of the nodes
+
+## (Testing) Reroute Primitive
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/8b870eef-d572-43f9-b394-cfa7abbd2f98)  Provides a node that allows rerouting primitives.  
+The node can also be collapsed to a single point that you can drag around.  
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/a9bd0112-cf8f-44f3-af6d-f9a8fed152a7)  
+Warning: Don't use normal reroutes or primitives with these nodes, it isn't tested and this node replaces their functionality.
+
+<br>
+<br>
+
+
+## WD14 Tagger
+Moved to: https://github.com/pythongosssss/ComfyUI-WD14-Tagger
+
+## Link Render Mode  
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/ad3be76b-43b1-455e-a64a-bf2a6571facf)  
+Allows you to control the rendering of the links between nodes between straight, linear & spline, e.g. Straight. 
+
+<br>
+<br>
+
+
+# Changelog
+
+## 2023-09-22
+### Minor
+- ✨ Use Civitai image as preview
+- 🐛 CTRL+Enter on autocomplete will no longer accept the suggestions as it is the shortcut for queuing a prompt.
+- 🐛 Fix using numbers in widget defaults
+- ✨ Support setting node properties (e.g. title, colors) via widget defaults
+
+## 2023-09-13
+### New
+- ✨ Ability to "send" an image to a Load Image node in either the current or a different workflow
+### Minor
+- ✨ Add support for A1111 autocomplete CSV format
+- ✨ Allow setting custom node for middle click to add node
+
+## 2023-09-10
+### Minor
+- 🐛 Fix rendering new lines in workflow image exports
+
+## 2023-09-08
+### New
+- ✨ Add Load + Save Text file nodes, you can configure the allowed directories in the `user/text_file_dirs.json` file
+### Minor
+- 🎨 Show autocomplete alias word on popup
+- ✨ Add setting to disable middle click from adding a reroute node
+- 🎨 Add prompt for setting custom column count on image feed  (click the column count label)
+
+## 2023-09-07
+### New
+- ✨ Support Unicode (e.g. Chinese) and word aliases in autocomplete.
+
+## 2023-09-05
+### Minor
+- 🎨 Disable autocomplete on math node
+- 🐛 Fix Show Text node always resizing on update
+
+### Minor
+- 🎨 Better adding of preview image to menu (thanks to @zeroeightysix)
+- 🎨 UX improvements for image feed (thanks to @birdddev)
+- 🐛 Fix Math Expression expression not showing on updated ComfyUI
+- 
+## 2023-08-30
+### Minor
+- 🎨 Allow jpeg lora/checkpoint preview images
+- ✨ Save ShowText value to embedded image metadata
+  
+## 2023-08-29
+### Minor
+- ✨ Option to auto insert `, ` after autocomplete
+- 🎨 Exclude arrow keys from triggering autocomplete
+- 🐛 Split paths by `\` and `/` on Windows for submenus
+
+## 2023-08-28
+### New
+- ✨ Add custom autocomplete word list setting
+- ✨ Support autocomplete word priority sorting
+- ✨ Support autocomplete matching anywhere in word rather than requiring starts with
+
+## 2023-08-27
+### New
+- ✨ Add Checkpoint info
+- ✨ Add embedding autocomplete
+- ✨ Add embedding info
+### Major
+- ♻️ Refactor LoRA info
+
+## 2023-08-26
+### Minor
+- 🐛 Fix using text widget values in Math Expression not casting to number
+- 🎨 Fix padding on lightbox next arrow
+ 
+## 2023-08-25
+### Minor
+- ♻️ Support older versions of python
+  
+## 2023-08-24
+### Minor
+- 🐛 Fix extracting links from LoRA info notes
+
+## 2023-08-23
+### Major
+- 🚨 Update to use `WEB_DIRECTORY` feature instead of manual linking/copying web files
+
+## 2023-08-22
+### New
+- ✨ Math Expression now supports IMAGE and LATENT inputs, to access the dimensions use `a.width`, `b.height`
+- 🎨 Removed STRING output on Math Expression, now draws the result onto the node
+
+## 2023-08-21
+### New
+- ✨ Allow custom note (named {file}.txt) to show in LoRA info
+- ✨ Query Civita API using the model hash to provide link
+  
+## 2023-08-20
+### New
+- ✨ Add LoRA Info menu option for displaying LoRA metadata
+### Minor
+- 🐛 Fix crash on preset text replacement (thanks to @sjuxax)
+
+## 2023-08-19
+### New
+- ✨ Add support for importing JPG files with embedded metadata (e.g. from Civitai)
+### Minor
+- 🐛 Fix crash on graph arrange where LiteGraph sometimes stores links to deleted nodes
+- 🐛 Fix a couple of rendering issues in workflow export
+
+## 2023-08-18
+### New
+- ✨ Add "example" widget to custom LoRA + Checkpoint loader allowing you to quickly view saved prompts, triggers, etc
+- ✨ Add quick "Save as Preview" option on images to save generated images for models
+
+## 2023-08-16
+### New
+- ✨ Add repeater node for generating lists or quickly duplicating nodes
+### Minor
+- 🐛 Support quick Add LoRA on custom Checkpoint Loader
+- ✨ Support `randomint(min,max)` function in math node
+- 🎨 Use relative imports to support proxied urls not on root path (thanks to @mcmonkey4eva)
+
+## 2023-08-13
+### Minor
+- ✨ Support `round` `floor` `ceil` functions in math node
+- 🐛 Fix floor division in math node
+
+## 2023-08-12
+### New
+- 🎨 Image feed now uses a lightbox for showing images
+### Minor
+- 🎨 Better loader lists now supports images named `{name}.preview.png`
+
+## 2023-08-11
+### Minor
+- ✨ Enable filter box on submenus
+  
+## 2023-08-05
+### Major
+- 🚨 The ComfyUI Lora Loader no longer has subfolders, due to compatibility issues you need to use my Lora Loader if you want subfolers, these can be enabled/disabled on the node via a setting (🐍 Enable submenu in custom nodes)
+### New
+- ✨ Add custom Checkpoint Loader supporting images & subfolders
+- ✨ Add Play Sound node for notifying when a prompt is finished
+### Minor
+- ✨ Quick Nodes supports new LoRA loader ("Add 🐍 LoRA")
+- ♻️ Disable link render mode if ComfyUI has native support
+
+## 2023-08-04
+### Minor
+- ✨ Always snap to grid now applies on node resize
+- 🐛 Fix reroute primitive widget value not being restored on reload
+- ✨ Workflows now reuse last filename from load & save - save must be done by the submenu
+
+## 2023-08-02
+### New
+- ✨ Add "Always snap to grid" setting that does the same as holding shift, aligning nodes to the grid
+### Minor
+- 🚨 No longer populates image feed when its closed
+- 🐛 Allow lock/unlock of multiple selected nodes
+
+## 2023-08-01
+### Minor
+- 🎨 Image feed now uses comfy theme variables for colors
+- 🐛 Link render mode redraws graph on change of setting instead of requiring mouse move
+
+## 2023-07-30
+- 🎨 Update to image feed to make more user friendly, change image size to column count, various other tweaks (thanks @DrJKL)
+
+## 2023-07-30
+### Major
+- 🐛 Fix issue with context menu (right click) not working for some users after Lora script updates
+### New
+- ✨ Add "Custom" option to color menu for nodes & groups
+### Minor
+- 🐛 Fix String Function values converted to unconnected inputs outputting the text "undefined"
+
+## 2023-07-29
+### New
+- ✨ Added Reroute Primitive combining the functionality of reroutes + primitives, also allowing collapsing to a single point.
+- ✨ Add support for exporting workflow images as PNGs and optional embedding of metadata in PNG and SVG
+### Minor
+- ✨ Remove new lines in Math Expression node
+- ✨ String function is now an output node
+- 🐛 Fix conflict between Lora Loader + Lora submenu causing the context menu to be have strangely (#23, #24)
+- 🎨 Rename "SVG -> Import/Export" to "Workflow Image" -> Import/Export
+
+## 2023-07-27
+### New
+- ✨ Added custom Lora Loader that includes image previews
+### Minor
+- ✨ Add preview output to string function node
+- 📄 Updated missing/out of date parts of readme
+- 🐛 Fix crash on show image on menu when set to not show (thanks @DrJKL)
+- 🐛 Fix incorrect category (util vs utils) for math node (thanks @DrJKL)
+
+## 2023-07-27
+### Minor
+- ✨ Save Image Feed close state
+- 🐛 Fix unlocked group size calculation
+
+## 2023-07-21 + 22
+### Minor
+- 🐛 Fix preset text incompatibility with Impact Pack (thanks @ltdrdata)
+
+## 2023-07-13
+### New
+- ✨ Add Math Expression node for evaluating expressions using values from the graph
+### Minor
+- ✨ Add settings for image feed location + image order
+
+## 2023-06-27
+### Minor
+- 🐛 Fix unlocking group using incorrect size
+- ✨ Save visibility of image feed
+
+## 2023-06-18
+### Major Changes
+- ✨ Added auto installation of scripts and `__init__` (thanks @TashaSkyUp)
+- ♻️ Reworked folder structure
+- 🚨 Renamed a number of nodes to include `pysssss` to prevent name conflicts
+- 🚨 Remove Latent Upscale By as it is now a built in node in ComfyUI
+- 🚨 Removed Anime Segmentation to own repo
+### New
+- ✨ Add Link Render Mode setting to choose how links are rendered
+- ✨ Add Constrain Image node for resizing nodes to a min/max resolution with optional cropping
+- ✨ Add Show Image On Menu to include the latest image output on the menu
+- ✨ Add KSamplerAdvanced simple denoise prompt for configuring the node using steps + denoise
+- 🎨 Add sizing options to Image Feed
+
+### Other
+- ♻️ Include [canvas2svg](https://gliffy.github.io/canvas2svg/) for SVG export in assets to prevent downloading at runtime
+- 🎨 Add background color (using theme color) to exported SVG
+- 🐛 Fix Manage Widget Defaults to work with new ComfyUI settings dialog
+- 🐛 Increase Image Feed z-index to prevent node text overlapping

custom_nodes/ComfyUI-Custom-Scripts/__init__.py

@@ -0,0 +1,25 @@
+import importlib.util
+import glob
+import os
+import sys
+from .pysssss import init, get_ext_dir
+
+NODE_CLASS_MAPPINGS = {}
+NODE_DISPLAY_NAME_MAPPINGS = {}
+
+if init():
+    py = get_ext_dir("py")
+    files = glob.glob(os.path.join(py, "*.py"), recursive=False)
+    for file in files:
+        name = os.path.splitext(file)[0]
+        spec = importlib.util.spec_from_file_location(name, file)
+        module = importlib.util.module_from_spec(spec)
+        sys.modules[name] = module
+        spec.loader.exec_module(module)
+        if hasattr(module, "NODE_CLASS_MAPPINGS") and getattr(module, "NODE_CLASS_MAPPINGS") is not None:
+            NODE_CLASS_MAPPINGS.update(module.NODE_CLASS_MAPPINGS)
+            if hasattr(module, "NODE_DISPLAY_NAME_MAPPINGS") and getattr(module, "NODE_DISPLAY_NAME_MAPPINGS") is not None:
+                NODE_DISPLAY_NAME_MAPPINGS.update(module.NODE_DISPLAY_NAME_MAPPINGS)
+
+WEB_DIRECTORY = "./web"
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"]