Update version to 0.8.13; enhance recipe management and source tracking features in release notes

Dev

LICENSE

@@ -1,21 +1,674 @@
-MIT License
+GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
 
-Copyright (c) 2023 Will Miao
+ 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.
 
-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:
+                            Preamble
 
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
+  The GNU General Public License is a free, copyleft license for
+software and other kinds of works.
 
-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. 
+  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.
+
+    ComfyUI Lora Manager - A ComfyUI custom node for managing models
+    Copyright (C) 2025  Will Miao
+
+    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:
+
+    ComfyUI Lora Manager  Copyright (C) 2025  Will Miao
+    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>.

README.md

@@ -14,12 +14,30 @@ A comprehensive toolset that streamlines organizing, downloading, and applying L
 Watch this quick tutorial to learn how to use the new one-click LoRA integration feature:
 
 [![One-Click LoRA Integration Tutorial](https://img.youtube.com/vi/qS95OjX3e70/0.jpg)](https://youtu.be/qS95OjX3e70)
-[![LoRA Manager v0.8.0 - New Recipe Feature & Bulk Operations](https://img.youtube.com/vi/noN7f_ER7yo/0.jpg)](https://youtu.be/noN7f_ER7yo)
+[![LoRA Manager v0.8.10 - Checkpoint Management, Standalone Mode, and New Features!](https://img.youtube.com/vi/VKvTlCB78h4/0.jpg)](https://youtu.be/VKvTlCB78h4)
 
 ---
 
 ## Release Notes
 
+### v0.8.13
+* **Enhanced Recipe Management** - Added "Find duplicates" feature to identify and batch delete duplicate recipes with duplicate detection notifications during imports
+* **Improved Source Tracking** - Source URLs are now saved with recipes imported via URL, allowing users to view original content with one click or manually edit links
+* **Advanced LoRA Control** - Double-click LoRAs in Loader/Stacker nodes to access expanded CLIP strength controls for more precise adjustments of model and CLIP strength separately
+* **Lycoris Model Support** - Added compatibility with Lycoris models for expanded creative options
+* **Bug Fixes & UX Improvements** - Resolved various issues and enhanced overall user experience with numerous optimizations
+
+### v0.8.12
+* **Enhanced Model Discovery** - Added alphabetical navigation bar to LoRAs page for faster browsing through large collections
+* **Optimized Example Images** - Improved download logic to automatically refresh stale metadata before fetching example images
+* **Model Exclusion System** - New right-click option to exclude specific LoRAs or checkpoints from management
+* **Improved Showcase Experience** - Enhanced interaction in LoRA and checkpoint showcase areas for better usability
+
+### v0.8.11
+* **Offline Image Support** - Added functionality to download and save all model example images locally, ensuring access even when offline or if images are removed from CivitAI or the site is down
+* **Resilient Download System** - Implemented pause/resume capability with checkpoint recovery that persists through restarts or unexpected exits
+* **Bug Fixes & Stability** - Resolved various issues to enhance overall reliability and performance
+
 ### v0.8.10
 * **Standalone Mode** - Run LoRA Manager independently from ComfyUI for a lightweight experience that works even with other stable diffusion interfaces
 * **Portable Edition** - New one-click portable version for easy startup and updates in standalone mode
@@ -146,14 +164,21 @@ Watch this quick tutorial to learn how to use the new one-click LoRA integration
 
 ## Installation
 
-### Option 1: **ComfyUI Manager** (Recommended)
+### Option 1: **ComfyUI Manager** (Recommended for ComfyUI users)
 
 1. Open **ComfyUI**.
 2. Go to **Manager > Custom Node Manager**.
 3. Search for `lora-manager`.
 4. Click **Install**.
 
-### Option 2: **Manual Installation**
+### Option 2: **Portable Standalone Edition** (No ComfyUI required)
+
+1. Download the [Portable Package](https://github.com/willmiao/ComfyUI-Lora-Manager/releases/download/v0.8.10/lora_manager_portable.7z)
+2. Copy the provided `settings.json.example` file to create a new file named `settings.json` in `comfyui-lora-manager` folder
+3. Edit `settings.json` to include your correct model folder paths and CivitAI API key
+4. Run run.bat
+
+### Option 3: **Manual Installation**
 
 ```bash
 git clone https://github.com/willmiao/ComfyUI-Lora-Manager.git
@@ -271,10 +296,11 @@ If you find this project helpful, consider supporting its development:
 
 [![ko-fi](https://ko-fi.com/img/githubbutton_sm.svg)](https://ko-fi.com/pixelpawsai)
 
+WeChat: [Click to view QR code](https://raw.githubusercontent.com/willmiao/ComfyUI-Lora-Manager/main/static/images/wechat-qr.webp)
+
 ## 💬 Community
 
 Join our Discord community for support, discussions, and updates:
 [Discord Server](https://discord.gg/vcqNrWVFvM)
 
 ---
-````

py/lora_manager.py

@@ -6,10 +6,12 @@ from .routes.api_routes import ApiRoutes
 from .routes.recipe_routes import RecipeRoutes
 from .routes.checkpoints_routes import CheckpointsRoutes
 from .routes.update_routes import UpdateRoutes
-from .routes.usage_stats_routes import UsageStatsRoutes
+from .routes.misc_routes import MiscRoutes
 from .services.service_registry import ServiceRegistry
+from .services.settings_manager import settings
 import logging
 import sys
+import os
 
 logger = logging.getLogger(__name__)
 
@@ -29,6 +31,13 @@ class LoraManager:
 
         added_targets = set()  # Track already added target paths
         
+        # Add static route for example images if the path exists in settings
+        example_images_path = settings.get('example_images_path')
+        logger.info(f"Example images path: {example_images_path}")
+        if example_images_path and os.path.exists(example_images_path):
+            app.router.add_static('/example_images_static', example_images_path)
+            logger.info(f"Added static route for example images: /example_images_static -> {example_images_path}")
+        
         # Add static routes for each lora root
         for idx, root in enumerate(config.loras_roots, start=1):
             preview_path = f'/loras_static/root{idx}/preview'
@@ -102,7 +111,7 @@ class LoraManager:
         ApiRoutes.setup_routes(app)
         RecipeRoutes.setup_routes(app)
         UpdateRoutes.setup_routes(app)  
-        UsageStatsRoutes.setup_routes(app)  # Register usage stats routes
+        MiscRoutes.setup_routes(app)  # Register miscellaneous routes
         
         # Schedule service initialization 
         app.on_startup.append(lambda app: cls._initialize_services())

py/metadata_collector/metadata_processor.py

@@ -11,9 +11,10 @@ class MetadataProcessor:
     
     @staticmethod
     def find_primary_sampler(metadata):
-        """Find the primary KSampler node (with denoise=1)"""
+        """Find the primary KSampler node (with highest denoise value)"""
         primary_sampler = None
         primary_sampler_id = None
+        max_denoise = -1  # Track the highest denoise value
         
         # First, check for SamplerCustomAdvanced
         prompt = metadata.get("current_prompt")
@@ -35,17 +36,17 @@ class MetadataProcessor:
                 primary_sampler_id = node_id
                 break
         
-        # If no specialized sampler found, fall back to traditional KSampler with denoise=1
+        # If no specialized sampler found, find the sampler with highest denoise value
         if primary_sampler is None:
             for node_id, sampler_info in metadata.get(SAMPLING, {}).items():
                 parameters = sampler_info.get("parameters", {})
                 denoise = parameters.get("denoise")
                 
-                # If denoise is 1.0, this is likely the primary sampler
-                if denoise == 1.0 or denoise == 1:
+                # If denoise exists and is higher than current max, use this sampler
+                if denoise is not None and denoise > max_denoise:
+                    max_denoise = denoise
                     primary_sampler = sampler_info
                     primary_sampler_id = node_id
-                    break
                 
         return primary_sampler_id, primary_sampler
     
@@ -206,6 +207,17 @@ class MetadataProcessor:
                     positive_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "positive", "CLIPTextEncode", max_depth=10)
                     if positive_node_id and positive_node_id in metadata.get(PROMPTS, {}):
                         params["prompt"] = metadata[PROMPTS][positive_node_id].get("text", "")
+                    else:
+                        # If CLIPTextEncode is not found, try to find CLIPTextEncodeFlux
+                        positive_flux_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "positive", "CLIPTextEncodeFlux", max_depth=10)
+                        if positive_flux_node_id and positive_flux_node_id in metadata.get(PROMPTS, {}):
+                            params["prompt"] = metadata[PROMPTS][positive_flux_node_id].get("text", "")
+                            
+                            # Also extract guidance value if present in the sampling data
+                            if positive_flux_node_id in metadata.get(SAMPLING, {}):
+                                flux_params = metadata[SAMPLING][positive_flux_node_id].get("parameters", {})
+                                if "guidance" in flux_params:
+                                    params["guidance"] = flux_params.get("guidance")
                     
                     # Find any FluxGuidance nodes in the positive conditioning path
                     flux_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "positive", "FluxGuidance", max_depth=5)
@@ -225,40 +237,6 @@ class MetadataProcessor:
                     height = metadata[SIZE][primary_sampler_id].get("height")
                     if width and height:
                         params["size"] = f"{width}x{height}"
-                else:
-                    # Fallback to the previous trace method if needed
-                    latent_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "latent_image")
-                    if latent_node_id:
-                        # Follow chain to find EmptyLatentImage node
-                        size_found = False
-                        current_node_id = latent_node_id
-                        
-                        # Limit depth to avoid infinite loops in complex workflows
-                        max_depth = 10
-                        for _ in range(max_depth):
-                            if current_node_id in metadata.get(SIZE, {}):
-                                width = metadata[SIZE][current_node_id].get("width")
-                                height = metadata[SIZE][current_node_id].get("height")
-                                if width and height:
-                                    params["size"] = f"{width}x{height}"
-                                    size_found = True
-                                    break
-                            
-                            # Try to follow the chain
-                            if prompt and prompt.original_prompt and current_node_id in prompt.original_prompt:
-                                node_info = prompt.original_prompt[current_node_id]
-                                if "inputs" in node_info:
-                                    # Look for a connection that might lead to size information
-                                    for input_name, input_value in node_info["inputs"].items():
-                                        if isinstance(input_value, list) and len(input_value) >= 2:
-                                            current_node_id = input_value[0]
-                                            break
-                                    else:
-                                        break  # No connections to follow
-                                else:
-                                    break  # No inputs to follow
-                            else:
-                                break  # Can't follow further
         
         # Extract LoRAs using the standardized format
         lora_parts = []

py/metadata_collector/node_extractors.py

@@ -327,6 +327,41 @@ class SamplerCustomAdvancedExtractor(NodeMetadataExtractor):
                         "node_id": node_id
                     }
 
+import json
+
+class CLIPTextEncodeFluxExtractor(NodeMetadataExtractor):
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs or "clip_l" not in inputs or "t5xxl" not in inputs:
+            return
+            
+        clip_l_text = inputs.get("clip_l", "")
+        t5xxl_text = inputs.get("t5xxl", "")
+        
+        # Create JSON string with T5 content first, then CLIP-L
+        combined_text = json.dumps({
+            "T5": t5xxl_text,
+            "CLIP-L": clip_l_text
+        })
+        
+        metadata[PROMPTS][node_id] = {
+            "text": combined_text,
+            "node_id": node_id
+        }
+        
+        # Extract guidance value if available
+        if "guidance" in inputs:
+            guidance_value = inputs.get("guidance")
+            
+            # Store the guidance value in SAMPLING category
+            if SAMPLING not in metadata:
+                metadata[SAMPLING] = {}
+                
+            if node_id not in metadata[SAMPLING]:
+                metadata[SAMPLING][node_id] = {"parameters": {}, "node_id": node_id}
+                
+            metadata[SAMPLING][node_id]["parameters"]["guidance"] = guidance_value
+
 # Registry of node-specific extractors
 NODE_EXTRACTORS = {
     # Sampling
@@ -343,6 +378,7 @@ NODE_EXTRACTORS = {
     "LoraManagerLoader": LoraLoaderManagerExtractor,
     # Conditioning
     "CLIPTextEncode": CLIPTextEncodeExtractor,
+    "CLIPTextEncodeFlux": CLIPTextEncodeFluxExtractor,  # Add CLIPTextEncodeFlux
     # Latent
     "EmptyLatentImage": ImageSizeExtractor,
     # Flux

py/nodes/lora_loader.py

@@ -1,10 +1,7 @@
 import logging
 from nodes import LoraLoader
 from comfy.comfy_types import IO # type: ignore
-from ..services.lora_scanner import LoraScanner
-from ..config import config
 import asyncio
-import os
 from .utils import FlexibleOptionalInputType, any_type, get_lora_info, extract_lora_name, get_loras_list
 
 logger = logging.getLogger(__name__)
@@ -51,7 +48,11 @@ class LoraManagerLoader:
                 _, trigger_words = asyncio.run(get_lora_info(lora_name))
                 
                 all_trigger_words.extend(trigger_words)
-                loaded_loras.append(f"{lora_name}: {model_strength}")
+                # Add clip strength to output if different from model strength
+                if abs(model_strength - clip_strength) > 0.001:
+                    loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
+                else:
+                    loaded_loras.append(f"{lora_name}: {model_strength}")
         
         # Then process loras from kwargs with support for both old and new formats
         loras_list = get_loras_list(kwargs)
@@ -60,14 +61,21 @@ class LoraManagerLoader:
                 continue
                 
             lora_name = lora['name']
-            strength = float(lora['strength'])
+            model_strength = float(lora['strength'])
+            # Get clip strength - use model strength as default if not specified
+            clip_strength = float(lora.get('clipStrength', model_strength))
             
             # Get lora path and trigger words
             lora_path, trigger_words = asyncio.run(get_lora_info(lora_name))
             
-            # Apply the LoRA using the resolved path
-            model, clip = LoraLoader().load_lora(model, clip, lora_path, strength, strength)
-            loaded_loras.append(f"{lora_name}: {strength}")
+            # Apply the LoRA using the resolved path with separate strengths
+            model, clip = LoraLoader().load_lora(model, clip, lora_path, model_strength, clip_strength)
+            
+            # Include clip strength in output if different from model strength
+            if abs(model_strength - clip_strength) > 0.001:
+                loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
+            else:
+                loaded_loras.append(f"{lora_name}: {model_strength}")
             
             # Add trigger words to collection
             all_trigger_words.extend(trigger_words)
@@ -75,8 +83,23 @@ class LoraManagerLoader:
         # use ',, ' to separate trigger words for group mode
         trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
         
-        # Format loaded_loras as <lora:lora_name:strength> separated by spaces
-        formatted_loras = " ".join([f"<lora:{name.split(':')[0].strip()}:{str(strength).strip()}>" 
-                                  for name, strength in [item.split(':') for item in loaded_loras]])
+        # Format loaded_loras with support for both formats
+        formatted_loras = []
+        for item in loaded_loras:
+            parts = item.split(":")
+            lora_name = parts[0].strip()
+            strength_parts = parts[1].strip().split(",")
+            
+            if len(strength_parts) > 1:
+                # Different model and clip strengths
+                model_str = strength_parts[0].strip()
+                clip_str = strength_parts[1].strip()
+                formatted_loras.append(f"<lora:{lora_name}:{model_str}:{clip_str}>")
+            else:
+                # Same strength for both
+                model_str = strength_parts[0].strip()
+                formatted_loras.append(f"<lora:{lora_name}:{model_str}>")
+                
+        formatted_loras_text = " ".join(formatted_loras)
 
-        return (model, clip, trigger_words_text, formatted_loras)
+        return (model, clip, trigger_words_text, formatted_loras_text)

py/nodes/lora_stacker.py

@@ -38,7 +38,7 @@ class LoraStacker:
         
         # Process existing lora_stack if available
         lora_stack = kwargs.get('lora_stack', None)
-        if lora_stack:
+        if (lora_stack):
             stack.extend(lora_stack)
             # Get trigger words from existing stack entries
             for lora_path, _, _ in lora_stack:
@@ -54,7 +54,8 @@ class LoraStacker:
                 
             lora_name = lora['name']
             model_strength = float(lora['strength'])
-            clip_strength = model_strength  # Using same strength for both as in the original loader
+            # Get clip strength - use model strength as default if not specified
+            clip_strength = float(lora.get('clipStrength', model_strength))
             
             # Get lora path and trigger words
             lora_path, trigger_words = asyncio.run(get_lora_info(lora_name))
@@ -62,15 +63,24 @@ class LoraStacker:
             # Add to stack without loading
             # replace '/' with os.sep to avoid different OS path format
             stack.append((lora_path.replace('/', os.sep), model_strength, clip_strength))
-            active_loras.append((lora_name, model_strength))
+            active_loras.append((lora_name, model_strength, clip_strength))
             
             # Add trigger words to collection
             all_trigger_words.extend(trigger_words)
         
         # use ',, ' to separate trigger words for group mode
         trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
-        # Format active_loras as <lora:lora_name:strength> separated by spaces
-        active_loras_text = " ".join([f"<lora:{name}:{str(strength).strip()}>" 
-                                  for name, strength in active_loras])
+        
+        # Format active_loras with support for both formats
+        formatted_loras = []
+        for name, model_strength, clip_strength in active_loras:
+            if abs(model_strength - clip_strength) > 0.001:
+                # Different model and clip strengths
+                formatted_loras.append(f"<lora:{name}:{str(model_strength).strip()}:{str(clip_strength).strip()}>")
+            else:
+                # Same strength for both
+                formatted_loras.append(f"<lora:{name}:{str(model_strength).strip()}>")
+                
+        active_loras_text = " ".join(formatted_loras)
 
         return (stack, trigger_words_text, active_loras_text)

py/recipes/__init__.py

@@ -0,0 +1,22 @@
+"""Recipe metadata parser package for ComfyUI-Lora-Manager."""
+
+from .base import RecipeMetadataParser
+from .factory import RecipeParserFactory
+from .constants import GEN_PARAM_KEYS, VALID_LORA_TYPES
+from .parsers import (
+    RecipeFormatParser,
+    ComfyMetadataParser,
+    MetaFormatParser,
+    AutomaticMetadataParser
+)
+
+__all__ = [
+    'RecipeMetadataParser',
+    'RecipeParserFactory',
+    'GEN_PARAM_KEYS',
+    'VALID_LORA_TYPES',
+    'RecipeFormatParser',
+    'ComfyMetadataParser',
+    'MetaFormatParser',
+    'AutomaticMetadataParser'
+]

py/recipes/base.py

@@ -0,0 +1,181 @@
+"""Base classes for recipe parsers."""
+
+import json
+import logging
+import os
+import re
+from typing import Dict, List, Any, Optional, Tuple
+from abc import ABC, abstractmethod
+from ..config import config
+from .constants import VALID_LORA_TYPES
+
+logger = logging.getLogger(__name__)
+
+class RecipeMetadataParser(ABC):
+    """Interface for parsing recipe metadata from image user comments"""
+
+    METADATA_MARKER = None
+
+    @abstractmethod
+    def is_metadata_matching(self, user_comment: str) -> bool:
+        """Check if the user comment matches the metadata format"""
+        pass
+    
+    @abstractmethod
+    async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
+        """
+        Parse metadata from user comment and return structured recipe data
+        
+        Args:
+            user_comment: The EXIF UserComment string from the image
+            recipe_scanner: Optional recipe scanner instance for local LoRA lookup
+            civitai_client: Optional Civitai client for fetching model information
+            
+        Returns:
+            Dict containing parsed recipe data with standardized format
+        """
+        pass
+    
+    async def populate_lora_from_civitai(self, lora_entry: Dict[str, Any], civitai_info_tuple: Tuple[Dict[str, Any], Optional[str]], 
+                                         recipe_scanner=None, base_model_counts=None, hash_value=None) -> Optional[Dict[str, Any]]:
+        """
+        Populate a lora entry with information from Civitai API response
+        
+        Args:
+            lora_entry: The lora entry to populate
+            civitai_info_tuple: The response tuple from Civitai API (data, error_msg)
+            recipe_scanner: Optional recipe scanner for local file lookup
+            base_model_counts: Optional dict to track base model counts
+            hash_value: Optional hash value to use if not available in civitai_info
+            
+        Returns:
+            The populated lora_entry dict if type is valid, None otherwise
+        """
+        try:
+            # Unpack the tuple to get the actual data
+            civitai_info, error_msg = civitai_info_tuple if isinstance(civitai_info_tuple, tuple) else (civitai_info_tuple, None)
+            
+            if not civitai_info or civitai_info.get("error") == "Model not found":
+                # Model not found or deleted
+                lora_entry['isDeleted'] = True
+                lora_entry['thumbnailUrl'] = '/loras_static/images/no-preview.png'
+                return lora_entry
+                
+            # Get model type and validate
+            model_type = civitai_info.get('model', {}).get('type', '').lower()
+            lora_entry['type'] = model_type
+            if model_type not in VALID_LORA_TYPES:
+                logger.debug(f"Skipping non-LoRA model type: {model_type}")
+                return None
+
+            # Check if this is an early access lora
+            if civitai_info.get('earlyAccessEndsAt'):
+                # Convert earlyAccessEndsAt to a human-readable date
+                early_access_date = civitai_info.get('earlyAccessEndsAt', '')
+                lora_entry['isEarlyAccess'] = True
+                lora_entry['earlyAccessEndsAt'] = early_access_date
+                
+            # Update model name if available
+            if 'model' in civitai_info and 'name' in civitai_info['model']:
+                lora_entry['name'] = civitai_info['model']['name']
+            
+            # Update version if available
+            if 'name' in civitai_info:
+                lora_entry['version'] = civitai_info.get('name', '')
+            
+            # Get thumbnail URL from first image
+            if 'images' in civitai_info and civitai_info['images']:
+                lora_entry['thumbnailUrl'] = civitai_info['images'][0].get('url', '')
+            
+            # Get base model
+            current_base_model = civitai_info.get('baseModel', '')
+            lora_entry['baseModel'] = current_base_model
+            
+            # Update base model counts if tracking them
+            if base_model_counts is not None and current_base_model:
+                base_model_counts[current_base_model] = base_model_counts.get(current_base_model, 0) + 1
+            
+            # Get download URL
+            lora_entry['downloadUrl'] = civitai_info.get('downloadUrl', '')
+            
+            # Process file information if available
+            if 'files' in civitai_info:
+                # Find the primary model file (type="Model" and primary=true) in the files list
+                model_file = next((file for file in civitai_info.get('files', []) 
+                                    if file.get('type') == 'Model' and file.get('primary') == True), None)
+                
+                if model_file:
+                    # Get size
+                    lora_entry['size'] = model_file.get('sizeKB', 0) * 1024
+                    
+                    # Get SHA256 hash
+                    sha256 = model_file.get('hashes', {}).get('SHA256', hash_value)
+                    if sha256:
+                        lora_entry['hash'] = sha256.lower()
+                    
+                    # Check if exists locally
+                    if recipe_scanner and lora_entry['hash']:
+                        lora_scanner = recipe_scanner._lora_scanner
+                        exists_locally = lora_scanner.has_lora_hash(lora_entry['hash'])
+                        if exists_locally:
+                            try:
+                                local_path = lora_scanner.get_lora_path_by_hash(lora_entry['hash'])
+                                lora_entry['existsLocally'] = True
+                                lora_entry['localPath'] = local_path
+                                lora_entry['file_name'] = os.path.splitext(os.path.basename(local_path))[0]
+                                
+                                # Get thumbnail from local preview if available
+                                lora_cache = await lora_scanner.get_cached_data()
+                                lora_item = next((item for item in lora_cache.raw_data 
+                                                    if item['sha256'].lower() == lora_entry['hash'].lower()), None)
+                                if lora_item and 'preview_url' in lora_item:
+                                    lora_entry['thumbnailUrl'] = config.get_preview_static_url(lora_item['preview_url'])
+                            except Exception as e:
+                                logger.error(f"Error getting local lora path: {e}")
+                        else:
+                            # For missing LoRAs, get file_name from model_file.name
+                            file_name = model_file.get('name', '')
+                            lora_entry['file_name'] = os.path.splitext(file_name)[0] if file_name else ''
+                
+        except Exception as e:
+            logger.error(f"Error populating lora from Civitai info: {e}")
+            
+        return lora_entry
+        
+    async def populate_checkpoint_from_civitai(self, checkpoint: Dict[str, Any], civitai_info: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Populate checkpoint information from Civitai API response
+        
+        Args:
+            checkpoint: The checkpoint entry to populate
+            civitai_info: The response from Civitai API
+            
+        Returns:
+            The populated checkpoint dict
+        """
+        try:
+            if civitai_info and civitai_info.get("error") != "Model not found":
+                # Update model name if available
+                if 'model' in civitai_info and 'name' in civitai_info['model']:
+                    checkpoint['name'] = civitai_info['model']['name']
+                
+                # Update version if available
+                if 'name' in civitai_info:
+                    checkpoint['version'] = civitai_info.get('name', '')
+                
+                # Get thumbnail URL from first image
+                if 'images' in civitai_info and civitai_info['images']:
+                    checkpoint['thumbnailUrl'] = civitai_info['images'][0].get('url', '')
+                
+                # Get base model
+                checkpoint['baseModel'] = civitai_info.get('baseModel', '')
+                
+                # Get download URL
+                checkpoint['downloadUrl'] = civitai_info.get('downloadUrl', '')
+            else:
+                # Model not found or deleted
+                checkpoint['isDeleted'] = True
+        except Exception as e:
+            logger.error(f"Error populating checkpoint from Civitai info: {e}")
+            
+        return checkpoint

py/recipes/constants.py

@@ -0,0 +1,16 @@
+"""Constants used across recipe parsers."""
+
+# Constants for generation parameters
+GEN_PARAM_KEYS = [
+    'prompt',
+    'negative_prompt', 
+    'steps',
+    'sampler',
+    'cfg_scale',
+    'seed',
+    'size',
+    'clip_skip',
+]
+
+# Valid Lora types
+VALID_LORA_TYPES = ['lora', 'locon']

py/recipes/factory.py

@@ -0,0 +1,43 @@
+"""Factory for creating recipe metadata parsers."""
+
+import logging
+from .parsers import (
+    RecipeFormatParser,
+    ComfyMetadataParser,
+    MetaFormatParser,
+    AutomaticMetadataParser
+)
+from .base import RecipeMetadataParser
+
+logger = logging.getLogger(__name__)
+
+class RecipeParserFactory:
+    """Factory for creating recipe metadata parsers"""
+    
+    @staticmethod
+    def create_parser(user_comment: str) -> RecipeMetadataParser:
+        """
+        Create appropriate parser based on the user comment content
+        
+        Args:
+            user_comment: The EXIF UserComment string from the image
+            
+        Returns:
+            Appropriate RecipeMetadataParser implementation
+        """
+        # Try ComfyMetadataParser first since it requires valid JSON
+        try:
+            if ComfyMetadataParser().is_metadata_matching(user_comment):
+                return ComfyMetadataParser()
+        except Exception:
+            # If JSON parsing fails, move on to other parsers
+            pass
+            
+        if RecipeFormatParser().is_metadata_matching(user_comment):
+            return RecipeFormatParser()
+        elif AutomaticMetadataParser().is_metadata_matching(user_comment):
+            return AutomaticMetadataParser()
+        elif MetaFormatParser().is_metadata_matching(user_comment):
+            return MetaFormatParser()
+        else:
+            return None

py/recipes/parsers/__init__.py

@@ -0,0 +1,13 @@
+"""Recipe parsers package."""
+
+from .recipe_format import RecipeFormatParser
+from .comfy import ComfyMetadataParser
+from .meta_format import MetaFormatParser
+from .automatic import AutomaticMetadataParser
+
+__all__ = [
+    'RecipeFormatParser',
+    'ComfyMetadataParser',
+    'MetaFormatParser',
+    'AutomaticMetadataParser',
+]

py/recipes/parsers/automatic.py

@@ -0,0 +1,304 @@
+"""Parser for Automatic1111 metadata format."""
+
+import re
+import json
+import logging
+from typing import Dict, Any
+from ..base import RecipeMetadataParser
+from ..constants import GEN_PARAM_KEYS
+
+logger = logging.getLogger(__name__)
+
+class AutomaticMetadataParser(RecipeMetadataParser):
+    """Parser for Automatic1111 metadata format"""
+    
+    METADATA_MARKER = r"Steps: \d+"
+    
+    # Regular expressions for extracting specific metadata
+    HASHES_REGEX = r', Hashes:\s*({[^}]+})'
+    LORA_HASHES_REGEX = r', Lora hashes:\s*"([^"]+)"'
+    CIVITAI_RESOURCES_REGEX = r', Civitai resources:\s*(\[\{.*?\}\])'
+    CIVITAI_METADATA_REGEX = r', Civitai metadata:\s*(\{.*?\})'
+    EXTRANETS_REGEX = r'<(lora|hypernet):([a-zA-Z0-9_\.\-]+):([0-9.]+)>'
+    MODEL_HASH_PATTERN = r'Model hash: ([a-zA-Z0-9]+)'
+    VAE_HASH_PATTERN = r'VAE hash: ([a-zA-Z0-9]+)'
+    
+    def is_metadata_matching(self, user_comment: str) -> bool:
+        """Check if the user comment matches the Automatic1111 format"""
+        return re.search(self.METADATA_MARKER, user_comment) is not None
+    
+    async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
+        """Parse metadata from Automatic1111 format"""
+        try:
+            # Split on Negative prompt if it exists
+            if "Negative prompt:" in user_comment:
+                parts = user_comment.split('Negative prompt:', 1)
+                prompt = parts[0].strip()
+                negative_and_params = parts[1] if len(parts) > 1 else ""
+            else:
+                # No negative prompt section
+                param_start = re.search(self.METADATA_MARKER, user_comment)
+                if param_start:
+                    prompt = user_comment[:param_start.start()].strip()
+                    negative_and_params = user_comment[param_start.start():]
+                else:
+                    prompt = user_comment.strip()
+                    negative_and_params = ""
+            
+            # Initialize metadata
+            metadata = {
+                "prompt": prompt,
+                "loras": []
+            }
+            
+            # Extract negative prompt and parameters
+            if negative_and_params:
+                # If we split on "Negative prompt:", check for params section
+                if "Negative prompt:" in user_comment:
+                    param_start = re.search(r'Steps: ', negative_and_params)
+                    if param_start:
+                        neg_prompt = negative_and_params[:param_start.start()].strip()
+                        metadata["negative_prompt"] = neg_prompt
+                        params_section = negative_and_params[param_start.start():]
+                    else:
+                        metadata["negative_prompt"] = negative_and_params.strip()
+                        params_section = ""
+                else:
+                    # No negative prompt, entire section is params
+                    params_section = negative_and_params
+                
+                # Extract generation parameters
+                if params_section:
+                    # Extract Civitai resources
+                    civitai_resources_match = re.search(self.CIVITAI_RESOURCES_REGEX, params_section)
+                    if civitai_resources_match:
+                        try:
+                            civitai_resources = json.loads(civitai_resources_match.group(1))
+                            metadata["civitai_resources"] = civitai_resources
+                            params_section = params_section.replace(civitai_resources_match.group(0), '')
+                        except json.JSONDecodeError:
+                            logger.error("Error parsing Civitai resources JSON")
+                    
+                    # Extract Hashes
+                    hashes_match = re.search(self.HASHES_REGEX, params_section)
+                    if hashes_match:
+                        try:
+                            hashes = json.loads(hashes_match.group(1))
+                            # Process hash keys
+                            processed_hashes = {}
+                            for key, value in hashes.items():
+                                # Convert Model: or LORA: prefix to lowercase if present
+                                if ':' in key:
+                                    prefix, name = key.split(':', 1)
+                                    prefix = prefix.lower()
+                                else:
+                                    prefix = ''
+                                    name = key
+
+                                # Clean up the name part
+                                if '/' in name:
+                                    name = name.split('/')[-1]  # Get last part after /
+                                if '.safetensors' in name:
+                                    name = name.split('.safetensors')[0]  # Remove .safetensors
+                                
+                                # Reconstruct the key
+                                new_key = f"{prefix}:{name}" if prefix else name
+                                processed_hashes[new_key] = value
+
+                            metadata["hashes"] = processed_hashes
+                            # Remove hashes from params section to not interfere with other parsing
+                            params_section = params_section.replace(hashes_match.group(0), '')
+                        except json.JSONDecodeError:
+                            logger.error("Error parsing hashes JSON")
+                    
+                    # Extract Lora hashes in alternative format
+                    lora_hashes_match = re.search(self.LORA_HASHES_REGEX, params_section)
+                    if not hashes_match and lora_hashes_match:
+                        try:
+                            lora_hashes_str = lora_hashes_match.group(1)
+                            lora_hash_entries = lora_hashes_str.split(', ')
+                            
+                            # Initialize hashes dict if it doesn't exist
+                            if "hashes" not in metadata:
+                                metadata["hashes"] = {}
+                                
+                            # Parse each lora hash entry (format: "name: hash")
+                            for entry in lora_hash_entries:
+                                if ': ' in entry:
+                                    lora_name, lora_hash = entry.split(': ', 1)
+                                    # Add as lora type in the same format as regular hashes
+                                    metadata["hashes"][f"lora:{lora_name}"] = lora_hash.strip()
+                            
+                            # Remove lora hashes from params section
+                            params_section = params_section.replace(lora_hashes_match.group(0), '')
+                        except Exception as e:
+                            logger.error(f"Error parsing Lora hashes: {e}")
+                    
+                    # Extract basic parameters
+                    param_pattern = r'([A-Za-z\s]+): ([^,]+)'
+                    params = re.findall(param_pattern, params_section)
+                    gen_params = {}
+                    
+                    for key, value in params:
+                        clean_key = key.strip().lower().replace(' ', '_')
+                        
+                        # Skip if not in recognized gen param keys
+                        if clean_key not in GEN_PARAM_KEYS:
+                            continue
+                            
+                        # Convert numeric values
+                        if clean_key in ['steps', 'seed']:
+                            try:
+                                gen_params[clean_key] = int(value.strip())
+                            except ValueError:
+                                gen_params[clean_key] = value.strip()
+                        elif clean_key in ['cfg_scale']:
+                            try:
+                                gen_params[clean_key] = float(value.strip())
+                            except ValueError:
+                                gen_params[clean_key] = value.strip()
+                        else:
+                            gen_params[clean_key] = value.strip()
+                    
+                    # Extract size if available and add to gen_params if a recognized key
+                    size_match = re.search(r'Size: (\d+)x(\d+)', params_section)
+                    if size_match and 'size' in GEN_PARAM_KEYS:
+                        width, height = size_match.groups()
+                        gen_params['size'] = f"{width}x{height}"
+                    
+                    # Add prompt and negative_prompt to gen_params if they're in GEN_PARAM_KEYS
+                    if 'prompt' in GEN_PARAM_KEYS and 'prompt' in metadata:
+                        gen_params['prompt'] = metadata['prompt']
+                    if 'negative_prompt' in GEN_PARAM_KEYS and 'negative_prompt' in metadata:
+                        gen_params['negative_prompt'] = metadata['negative_prompt']
+                    
+                    metadata["gen_params"] = gen_params
+            
+            # Extract LoRA information 
+            loras = []
+            base_model_counts = {}
+            
+            # First use Civitai resources if available (more reliable source)
+            if metadata.get("civitai_resources"):
+                for resource in metadata.get("civitai_resources", []):
+                    if resource.get("type") in ["lora", "lycoris", "hypernet"] and resource.get("modelVersionId"):
+                        # Initialize lora entry
+                        lora_entry = {
+                            'id': str(resource.get("modelVersionId")),
+                            'modelId': str(resource.get("modelId")) if resource.get("modelId") else None,
+                            'name': resource.get("modelName", "Unknown LoRA"),
+                            'version': resource.get("modelVersionName", ""),
+                            'type': resource.get("type", "lora"),
+                            'weight': round(float(resource.get("weight", 1.0)), 2),
+                            'existsLocally': False,
+                            'thumbnailUrl': '/loras_static/images/no-preview.png',
+                            'baseModel': '',
+                            'size': 0,
+                            'downloadUrl': '',
+                            'isDeleted': False
+                        }
+                        
+                        # Get additional info from Civitai
+                        if civitai_client:
+                            try:
+                                civitai_info = await civitai_client.get_model_version_info(resource.get("modelVersionId"))
+                                populated_entry = await self.populate_lora_from_civitai(
+                                    lora_entry,
+                                    civitai_info,
+                                    recipe_scanner,
+                                    base_model_counts
+                                )
+                                if populated_entry is None:
+                                    continue  # Skip invalid LoRA types
+                                lora_entry = populated_entry
+                            except Exception as e:
+                                logger.error(f"Error fetching Civitai info for LoRA {lora_entry['name']}: {e}")
+                        
+                        loras.append(lora_entry)
+            
+            # If no LoRAs from Civitai resources or to supplement, extract from metadata["hashes"]
+            if not loras or len(loras) == 0:
+                # Extract lora weights from extranet tags in prompt (for later use)
+                lora_weights = {}
+                lora_matches = re.findall(self.EXTRANETS_REGEX, prompt)
+                for lora_type, lora_name, lora_weight in lora_matches:
+                    key = f"{lora_type}:{lora_name}"
+                    lora_weights[key] = round(float(lora_weight), 2)
+                
+                # Use hashes from metadata as the primary source
+                if metadata.get("hashes"):
+                    for hash_key, lora_hash in metadata.get("hashes", {}).items():
+                        # Only process lora or hypernet types
+                        if not hash_key.startswith(("lora:", "hypernet:")):
+                            continue
+                            
+                        lora_type, lora_name = hash_key.split(':', 1)
+                        
+                        # Get weight from extranet tags if available, else default to 1.0
+                        weight = lora_weights.get(hash_key, 1.0)
+                        
+                        # Initialize lora entry
+                        lora_entry = {
+                            'name': lora_name,
+                            'type': lora_type,  # 'lora' or 'hypernet'
+                            'weight': weight,
+                            'hash': lora_hash,
+                            'existsLocally': False,
+                            'localPath': None,
+                            'file_name': lora_name,
+                            'thumbnailUrl': '/loras_static/images/no-preview.png',
+                            'baseModel': '',
+                            'size': 0,
+                            'downloadUrl': '',
+                            'isDeleted': False
+                        }
+                        
+                        # Try to get info from Civitai
+                        if civitai_client:
+                            try:
+                                if lora_hash:
+                                    # If we have hash, use it for lookup
+                                    civitai_info = await civitai_client.get_model_by_hash(lora_hash)
+                                else:
+                                    civitai_info = None
+                                
+                                populated_entry = await self.populate_lora_from_civitai(
+                                    lora_entry, 
+                                    civitai_info, 
+                                    recipe_scanner,
+                                    base_model_counts,
+                                    lora_hash
+                                )
+                                if populated_entry is None:
+                                    continue  # Skip invalid LoRA types
+                                lora_entry = populated_entry
+                            except Exception as e:
+                                logger.error(f"Error fetching Civitai info for LoRA {lora_name}: {e}")
+                        
+                        loras.append(lora_entry)
+                
+            # Try to get base model from resources or make educated guess
+            base_model = None
+            if base_model_counts:
+                # Use the most common base model from the loras
+                base_model = max(base_model_counts.items(), key=lambda x: x[1])[0]
+            
+            # Prepare final result structure
+            # Make sure gen_params only contains recognized keys
+            filtered_gen_params = {}
+            for key in GEN_PARAM_KEYS:
+                if key in metadata.get("gen_params", {}):
+                    filtered_gen_params[key] = metadata["gen_params"][key]
+            
+            result = {
+                'base_model': base_model,
+                'loras': loras,
+                'gen_params': filtered_gen_params,
+                'from_automatic_metadata': True
+            }
+            
+            return result
+            
+        except Exception as e:
+            logger.error(f"Error parsing Automatic1111 metadata: {e}", exc_info=True)
+            return {"error": str(e), "loras": []}

py/recipes/parsers/comfy.py

@@ -0,0 +1,216 @@
+"""Parser for ComfyUI metadata format."""
+
+import re
+import json
+import logging
+from typing import Dict, Any
+from ..base import RecipeMetadataParser
+from ..constants import GEN_PARAM_KEYS
+
+logger = logging.getLogger(__name__)
+
+class ComfyMetadataParser(RecipeMetadataParser):
+    """Parser for Civitai ComfyUI metadata JSON format"""
+    
+    METADATA_MARKER = r"class_type"
+    
+    def is_metadata_matching(self, user_comment: str) -> bool:
+        """Check if the user comment matches the ComfyUI metadata format"""
+        try:
+            data = json.loads(user_comment)
+            # Check if it contains class_type nodes typical of ComfyUI workflow
+            return isinstance(data, dict) and any(isinstance(v, dict) and 'class_type' in v for v in data.values())
+        except (json.JSONDecodeError, TypeError):
+            return False
+    
+    async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
+        """Parse metadata from Civitai ComfyUI metadata format"""
+        try:
+            data = json.loads(user_comment)
+            loras = []
+            
+            # Find all LoraLoader nodes
+            lora_nodes = {k: v for k, v in data.items() if isinstance(v, dict) and v.get('class_type') == 'LoraLoader'}
+            
+            if not lora_nodes:
+                return {"error": "No LoRA information found in this ComfyUI workflow", "loras": []}
+            
+            # Process each LoraLoader node
+            for node_id, node in lora_nodes.items():
+                if 'inputs' not in node or 'lora_name' not in node['inputs']:
+                    continue
+                    
+                lora_name = node['inputs'].get('lora_name', '')
+                
+                # Parse the URN to extract model ID and version ID
+                # Format: "urn:air:sdxl:lora:civitai:1107767@1253442"
+                lora_id_match = re.search(r'civitai:(\d+)@(\d+)', lora_name)
+                if not lora_id_match:
+                    continue
+                    
+                model_id = lora_id_match.group(1)
+                model_version_id = lora_id_match.group(2)
+                
+                # Get strength from node inputs
+                weight = node['inputs'].get('strength_model', 1.0)
+                
+                # Initialize lora entry with default values
+                lora_entry = {
+                    'id': model_version_id,
+                    'modelId': model_id,
+                    'name': f"Lora {model_id}",  # Default name
+                    'version': '',
+                    'type': 'lora',
+                    'weight': weight,
+                    'existsLocally': False,
+                    'localPath': None,
+                    'file_name': '',
+                    'hash': '',
+                    'thumbnailUrl': '/loras_static/images/no-preview.png',
+                    'baseModel': '',
+                    'size': 0,
+                    'downloadUrl': '',
+                    'isDeleted': False
+                }
+                
+                # Get additional info from Civitai if client is available
+                if civitai_client:
+                    try:
+                        civitai_info_tuple = await civitai_client.get_model_version_info(model_version_id)
+                        # Populate lora entry with Civitai info
+                        populated_entry = await self.populate_lora_from_civitai(
+                            lora_entry, 
+                            civitai_info_tuple, 
+                            recipe_scanner
+                        )
+                        if populated_entry is None:
+                            continue  # Skip invalid LoRA types
+                        lora_entry = populated_entry
+                    except Exception as e:
+                        logger.error(f"Error fetching Civitai info for LoRA: {e}")
+                
+                loras.append(lora_entry)
+            
+            # Find checkpoint info
+            checkpoint_nodes = {k: v for k, v in data.items() if isinstance(v, dict) and v.get('class_type') == 'CheckpointLoaderSimple'}
+            checkpoint = None
+            checkpoint_id = None
+            checkpoint_version_id = None
+            
+            if checkpoint_nodes:
+                # Get the first checkpoint node
+                checkpoint_node = next(iter(checkpoint_nodes.values()))
+                if 'inputs' in checkpoint_node and 'ckpt_name' in checkpoint_node['inputs']:
+                    checkpoint_name = checkpoint_node['inputs']['ckpt_name']
+                    # Parse checkpoint URN
+                    checkpoint_match = re.search(r'civitai:(\d+)@(\d+)', checkpoint_name)
+                    if checkpoint_match:
+                        checkpoint_id = checkpoint_match.group(1)
+                        checkpoint_version_id = checkpoint_match.group(2)
+                        checkpoint = {
+                            'id': checkpoint_version_id,
+                            'modelId': checkpoint_id,
+                            'name': f"Checkpoint {checkpoint_id}",
+                            'version': '',
+                            'type': 'checkpoint'
+                        }
+                        
+                        # Get additional checkpoint info from Civitai
+                        if civitai_client:
+                            try:
+                                civitai_info_tuple = await civitai_client.get_model_version_info(checkpoint_version_id)
+                                civitai_info, _ = civitai_info_tuple if isinstance(civitai_info_tuple, tuple) else (civitai_info_tuple, None)
+                                # Populate checkpoint with Civitai info
+                                checkpoint = await self.populate_checkpoint_from_civitai(checkpoint, civitai_info)
+                            except Exception as e:
+                                logger.error(f"Error fetching Civitai info for checkpoint: {e}")
+            
+            # Extract generation parameters
+            gen_params = {}
+            
+            # First try to get from extraMetadata
+            if 'extraMetadata' in data:
+                try:
+                    # extraMetadata is a JSON string that needs to be parsed
+                    extra_metadata = json.loads(data['extraMetadata'])
+                    
+                    # Map fields from extraMetadata to our standard format
+                    mapping = {
+                        'prompt': 'prompt',
+                        'negativePrompt': 'negative_prompt',
+                        'steps': 'steps',
+                        'sampler': 'sampler',
+                        'cfgScale': 'cfg_scale',
+                        'seed': 'seed'
+                    }
+                    
+                    for src_key, dest_key in mapping.items():
+                        if src_key in extra_metadata:
+                            gen_params[dest_key] = extra_metadata[src_key]
+                    
+                    # If size info is available, format as "width x height"
+                    if 'width' in extra_metadata and 'height' in extra_metadata:
+                        gen_params['size'] = f"{extra_metadata['width']}x{extra_metadata['height']}"
+                    
+                except Exception as e:
+                    logger.error(f"Error parsing extraMetadata: {e}")
+            
+            # If extraMetadata doesn't have all the info, try to get from nodes
+            if not gen_params or len(gen_params) < 3:  # At least we want prompt, negative_prompt, and steps
+                # Find positive prompt node
+                positive_nodes = {k: v for k, v in data.items() if isinstance(v, dict) and 
+                                v.get('class_type', '').endswith('CLIPTextEncode') and 
+                                v.get('_meta', {}).get('title') == 'Positive'}
+                
+                if positive_nodes:
+                    positive_node = next(iter(positive_nodes.values()))
+                    if 'inputs' in positive_node and 'text' in positive_node['inputs']:
+                        gen_params['prompt'] = positive_node['inputs']['text']
+                
+                # Find negative prompt node
+                negative_nodes = {k: v for k, v in data.items() if isinstance(v, dict) and 
+                                v.get('class_type', '').endswith('CLIPTextEncode') and 
+                                v.get('_meta', {}).get('title') == 'Negative'}
+                
+                if negative_nodes:
+                    negative_node = next(iter(negative_nodes.values()))
+                    if 'inputs' in negative_node and 'text' in negative_node['inputs']:
+                        gen_params['negative_prompt'] = negative_node['inputs']['text']
+                
+                # Find KSampler node for other parameters
+                ksampler_nodes = {k: v for k, v in data.items() if isinstance(v, dict) and v.get('class_type') == 'KSampler'}
+                
+                if ksampler_nodes:
+                    ksampler_node = next(iter(ksampler_nodes.values()))
+                    if 'inputs' in ksampler_node:
+                        inputs = ksampler_node['inputs']
+                        if 'sampler_name' in inputs:
+                            gen_params['sampler'] = inputs['sampler_name']
+                        if 'steps' in inputs:
+                            gen_params['steps'] = inputs['steps']
+                        if 'cfg' in inputs:
+                            gen_params['cfg_scale'] = inputs['cfg']
+                        if 'seed' in inputs:
+                            gen_params['seed'] = inputs['seed']
+            
+            # Determine base model from loras info
+            base_model = None
+            if loras:
+                # Use the most common base model from loras
+                base_models = [lora['baseModel'] for lora in loras if lora.get('baseModel')]
+                if base_models:
+                    from collections import Counter
+                    base_model_counts = Counter(base_models)
+                    base_model = base_model_counts.most_common(1)[0][0]
+            
+            return {
+                'base_model': base_model,
+                'loras': loras,
+                'checkpoint': checkpoint,
+                'gen_params': gen_params,
+                'from_comfy_metadata': True
+            }
+            
+        except Exception as e:
+            logger.error(f"Error parsing ComfyUI metadata: {e}", exc_info=True)
+            return {"error": str(e), "loras": []}

py/recipes/parsers/meta_format.py

@@ -0,0 +1,174 @@
+"""Parser for meta format (Lora_N Model hash) metadata."""
+
+import re
+import logging
+from typing import Dict, Any
+from ..base import RecipeMetadataParser
+from ..constants import GEN_PARAM_KEYS
+
+logger = logging.getLogger(__name__)
+
+class MetaFormatParser(RecipeMetadataParser):
+    """Parser for images with meta format metadata (Lora_N Model hash format)"""
+    
+    METADATA_MARKER = r'Lora_\d+ Model hash:'
+    
+    def is_metadata_matching(self, user_comment: str) -> bool:
+        """Check if the user comment matches the metadata format"""
+        return re.search(self.METADATA_MARKER, user_comment, re.IGNORECASE | re.DOTALL) is not None
+    
+    async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
+        """Parse metadata from images with meta format metadata"""
+        try:
+            # Extract prompt and negative prompt
+            parts = user_comment.split('Negative prompt:', 1)
+            prompt = parts[0].strip()
+            
+            # Initialize metadata
+            metadata = {"prompt": prompt, "loras": []}
+            
+            # Extract negative prompt and parameters if available
+            if len(parts) > 1:
+                negative_and_params = parts[1]
+                
+                # Extract negative prompt - everything until the first parameter (usually "Steps:")
+                param_start = re.search(r'([A-Za-z]+): ', negative_and_params)
+                if param_start:
+                    neg_prompt = negative_and_params[:param_start.start()].strip()
+                    metadata["negative_prompt"] = neg_prompt
+                    params_section = negative_and_params[param_start.start():]
+                else:
+                    params_section = negative_and_params
+                
+                # Extract key-value parameters (Steps, Sampler, Seed, etc.)
+                param_pattern = r'([A-Za-z_0-9 ]+): ([^,]+)'
+                params = re.findall(param_pattern, params_section)
+                for key, value in params:
+                    clean_key = key.strip().lower().replace(' ', '_')
+                    metadata[clean_key] = value.strip()
+            
+            # Extract LoRA information
+            # Pattern to match lora entries: Lora_0 Model name: ArtVador I.safetensors, Lora_0 Model hash: 08f7133a58, etc.
+            lora_pattern = r'Lora_(\d+) Model name: ([^,]+), Lora_\1 Model hash: ([^,]+), Lora_\1 Strength model: ([^,]+), Lora_\1 Strength clip: ([^,]+)'
+            lora_matches = re.findall(lora_pattern, user_comment)
+            
+            # If the regular pattern doesn't match, try a more flexible approach
+            if not lora_matches:
+                # First find all Lora indices
+                lora_indices = set(re.findall(r'Lora_(\d+)', user_comment))
+                
+                # For each index, extract the information
+                for idx in lora_indices:
+                    lora_info = {}
+                    
+                    # Extract model name
+                    name_match = re.search(f'Lora_{idx} Model name: ([^,]+)', user_comment)
+                    if name_match:
+                        lora_info['name'] = name_match.group(1).strip()
+                    
+                    # Extract model hash
+                    hash_match = re.search(f'Lora_{idx} Model hash: ([^,]+)', user_comment)
+                    if hash_match:
+                        lora_info['hash'] = hash_match.group(1).strip()
+                    
+                    # Extract strength model
+                    strength_model_match = re.search(f'Lora_{idx} Strength model: ([^,]+)', user_comment)
+                    if strength_model_match:
+                        lora_info['strength_model'] = float(strength_model_match.group(1).strip())
+                    
+                    # Extract strength clip
+                    strength_clip_match = re.search(f'Lora_{idx} Strength clip: ([^,]+)', user_comment)
+                    if strength_clip_match:
+                        lora_info['strength_clip'] = float(strength_clip_match.group(1).strip())
+                    
+                    # Only add if we have at least name and hash
+                    if 'name' in lora_info and 'hash' in lora_info:
+                        lora_matches.append((idx, lora_info['name'], lora_info['hash'], 
+                                            str(lora_info.get('strength_model', 1.0)), 
+                                            str(lora_info.get('strength_clip', 1.0))))
+            
+            # Process LoRAs
+            base_model_counts = {}
+            loras = []
+            
+            for match in lora_matches:
+                if len(match) == 5:  # Regular pattern match
+                    idx, name, hash_value, strength_model, strength_clip = match
+                else:  # Flexible approach match
+                    continue  # Should not happen now
+                
+                # Clean up the values
+                name = name.strip()
+                if name.endswith('.safetensors'):
+                    name = name[:-12]  # Remove .safetensors extension
+                    
+                hash_value = hash_value.strip()
+                weight = float(strength_model)  # Use model strength as weight
+                
+                # Initialize lora entry with default values
+                lora_entry = {
+                    'name': name,
+                    'type': 'lora',
+                    'weight': weight,
+                    'existsLocally': False,
+                    'localPath': None,
+                    'file_name': name,
+                    'hash': hash_value,
+                    'thumbnailUrl': '/loras_static/images/no-preview.png',
+                    'baseModel': '',
+                    'size': 0,
+                    'downloadUrl': '',
+                    'isDeleted': False
+                }
+                
+                # Get info from Civitai by hash if available
+                if civitai_client and hash_value:
+                    try:
+                        civitai_info = await civitai_client.get_model_by_hash(hash_value)
+                        # Populate lora entry with Civitai info
+                        populated_entry = await self.populate_lora_from_civitai(
+                            lora_entry, 
+                            civitai_info, 
+                            recipe_scanner,
+                            base_model_counts,
+                            hash_value
+                        )
+                        if populated_entry is None:
+                            continue  # Skip invalid LoRA types
+                        lora_entry = populated_entry
+                    except Exception as e:
+                        logger.error(f"Error fetching Civitai info for LoRA hash {hash_value}: {e}")
+                
+                loras.append(lora_entry)
+            
+            # Extract model information
+            model = None
+            if 'model' in metadata:
+                model = metadata['model']
+            
+            # Set base_model to the most common one from civitai_info
+            base_model = None
+            if base_model_counts:
+                base_model = max(base_model_counts.items(), key=lambda x: x[1])[0]
+            
+            # Extract generation parameters for recipe metadata
+            gen_params = {}
+            for key in GEN_PARAM_KEYS:
+                if key in metadata:
+                    gen_params[key] = metadata.get(key, '')
+            
+            # Try to extract size information if available
+            if 'width' in metadata and 'height' in metadata:
+                gen_params['size'] = f"{metadata['width']}x{metadata['height']}"
+            
+            return {
+                'base_model': base_model,
+                'loras': loras,
+                'gen_params': gen_params,
+                'raw_metadata': metadata,
+                'from_meta_format': True
+            }
+            
+        except Exception as e:
+            logger.error(f"Error parsing meta format metadata: {e}", exc_info=True)
+            return {"error": str(e), "loras": []}

py/recipes/parsers/recipe_format.py

@@ -0,0 +1,114 @@
+"""Parser for dedicated recipe metadata format."""
+
+import re
+import json
+import logging
+from typing import Dict, Any
+from ...config import config
+from ..base import RecipeMetadataParser
+from ..constants import GEN_PARAM_KEYS
+
+logger = logging.getLogger(__name__)
+
+class RecipeFormatParser(RecipeMetadataParser):
+    """Parser for images with dedicated recipe metadata format"""
+    
+    # Regular expression pattern for extracting recipe metadata
+    METADATA_MARKER = r'Recipe metadata: (\{.*\})'
+    
+    def is_metadata_matching(self, user_comment: str) -> bool:
+        """Check if the user comment matches the metadata format"""
+        return re.search(self.METADATA_MARKER, user_comment, re.IGNORECASE | re.DOTALL) is not None
+    
+    async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
+        """Parse metadata from images with dedicated recipe metadata format"""
+        try:
+            # Extract recipe metadata from user comment
+            try:
+                # Look for recipe metadata section
+                recipe_match = re.search(self.METADATA_MARKER, user_comment, re.IGNORECASE | re.DOTALL)
+                if not recipe_match:
+                    recipe_metadata = None
+                else:
+                    recipe_json = recipe_match.group(1)
+                    recipe_metadata = json.loads(recipe_json)
+            except Exception as e:
+                logger.error(f"Error extracting recipe metadata: {e}")
+                recipe_metadata = None
+            if not recipe_metadata:
+                return {"error": "No recipe metadata found", "loras": []}
+                
+            # Process the recipe metadata
+            loras = []
+            for lora in recipe_metadata.get('loras', []):
+                # Convert recipe lora format to frontend format
+                lora_entry = {
+                    'id': lora.get('modelVersionId', ''),
+                    'name': lora.get('modelName', ''),
+                    'version': lora.get('modelVersionName', ''),
+                    'type': 'lora',
+                    'weight': lora.get('strength', 1.0),
+                    'file_name': lora.get('file_name', ''),
+                    'hash': lora.get('hash', '')
+                }
+                
+                # Check if this LoRA exists locally by SHA256 hash
+                if lora.get('hash') and recipe_scanner:
+                    lora_scanner = recipe_scanner._lora_scanner
+                    exists_locally = lora_scanner.has_lora_hash(lora['hash'])
+                    if exists_locally:
+                        lora_cache = await lora_scanner.get_cached_data()
+                        lora_item = next((item for item in lora_cache.raw_data if item['sha256'].lower() == lora['hash'].lower()), None)
+                        if lora_item:
+                            lora_entry['existsLocally'] = True
+                            lora_entry['localPath'] = lora_item['file_path']
+                            lora_entry['file_name'] = lora_item['file_name']
+                            lora_entry['size'] = lora_item['size']
+                            lora_entry['thumbnailUrl'] = config.get_preview_static_url(lora_item['preview_url'])
+                            
+                    else:
+                        lora_entry['existsLocally'] = False
+                        lora_entry['localPath'] = None
+                        
+                        # Try to get additional info from Civitai if we have a model version ID
+                        if lora.get('modelVersionId') and civitai_client:
+                            try:
+                                civitai_info_tuple = await civitai_client.get_model_version_info(lora['modelVersionId'])
+                                # Populate lora entry with Civitai info
+                                populated_entry = await self.populate_lora_from_civitai(
+                                    lora_entry, 
+                                    civitai_info_tuple, 
+                                    recipe_scanner,
+                                    None,  # No need to track base model counts
+                                    lora['hash']
+                                )
+                                if populated_entry is None:
+                                    continue  # Skip invalid LoRA types
+                                lora_entry = populated_entry
+                            except Exception as e:
+                                logger.error(f"Error fetching Civitai info for LoRA: {e}")
+                                lora_entry['thumbnailUrl'] = '/loras_static/images/no-preview.png'
+                
+                loras.append(lora_entry)
+
+            logger.info(f"Found {len(loras)} loras in recipe metadata")
+            
+            # Filter gen_params to only include recognized keys
+            filtered_gen_params = {}
+            if 'gen_params' in recipe_metadata:
+                for key, value in recipe_metadata['gen_params'].items():
+                    if key in GEN_PARAM_KEYS:
+                        filtered_gen_params[key] = value
+            
+            return {
+                'base_model': recipe_metadata.get('base_model', ''),
+                'loras': loras,
+                'gen_params': filtered_gen_params,
+                'tags': recipe_metadata.get('tags', []),
+                'title': recipe_metadata.get('title', ''),
+                'from_recipe_metadata': True
+            }
+            
+        except Exception as e:
+            logger.error(f"Error parsing recipe format metadata: {e}", exc_info=True)
+            return {"error": str(e), "loras": []}

py/routes/api_routes.py

@@ -43,6 +43,7 @@ class ApiRoutes:
         app.on_startup.append(lambda _: routes.initialize_services())
         
         app.router.add_post('/api/delete_model', routes.delete_model)
+        app.router.add_post('/api/loras/exclude', routes.exclude_model)  # Add new exclude endpoint
         app.router.add_post('/api/fetch-civitai', routes.fetch_civitai)
         app.router.add_post('/api/replace_preview', routes.replace_preview)
         app.router.add_get('/api/loras', routes.get_loras)
@@ -55,7 +56,6 @@ class ApiRoutes:
         app.router.add_get('/api/civitai/model/version/{modelVersionId}', routes.get_civitai_model_by_version)
         app.router.add_get('/api/civitai/model/hash/{hash}', routes.get_civitai_model_by_hash)
         app.router.add_post('/api/download-lora', routes.download_lora)
-        app.router.add_post('/api/settings', routes.update_settings)
         app.router.add_post('/api/move_model', routes.move_model)
         app.router.add_get('/api/lora-model-description', routes.get_lora_model_description)  # Add new route
         app.router.add_post('/api/loras/save-metadata', routes.save_metadata)
@@ -70,6 +70,9 @@ class ApiRoutes:
         # Add the new trigger words route
         app.router.add_post('/loramanager/get_trigger_words', routes.get_trigger_words)
 
+        # Add new endpoint for letter counts
+        app.router.add_get('/api/loras/letter-counts', routes.get_letter_counts)
+
         # Add update check routes
         UpdateRoutes.setup_routes(app)
 
@@ -79,6 +82,12 @@ class ApiRoutes:
             self.scanner = await ServiceRegistry.get_lora_scanner()
         return await ModelRouteUtils.handle_delete_model(request, self.scanner)
 
+    async def exclude_model(self, request: web.Request) -> web.Response:
+        """Handle model exclusion request"""
+        if self.scanner is None:
+            self.scanner = await ServiceRegistry.get_lora_scanner()
+        return await ModelRouteUtils.handle_exclude_model(request, self.scanner)
+
     async def fetch_civitai(self, request: web.Request) -> web.Response:
         """Handle CivitAI metadata fetch request"""
         if self.scanner is None:
@@ -127,6 +136,9 @@ class ApiRoutes:
             tags = request.query.get('tags', None)
             favorites_only = request.query.get('favorites_only', 'false').lower() == 'true'  # New parameter
             
+            # New parameter for alphabet filtering
+            first_letter = request.query.get('first_letter', None)
+            
             # New parameters for recipe filtering
             lora_hash = request.query.get('lora_hash', None)
             lora_hashes = request.query.get('lora_hashes', None)
@@ -157,7 +169,8 @@ class ApiRoutes:
                 tags=filters.get('tags', None),
                 search_options=search_options,
                 hash_filters=hash_filters,
-                favorites_only=favorites_only  # Pass favorites_only parameter
+                favorites_only=favorites_only,  # Pass favorites_only parameter
+                first_letter=first_letter  # Pass the new first_letter parameter
             )
 
             # Get all available folders from cache
@@ -373,10 +386,10 @@ class ApiRoutes:
             versions = response.get('modelVersions', [])
             model_type = response.get('type', '')
             
-            # Check model type - should be LORA
-            if model_type.lower() != 'lora':
+            # Check model type - should be LORA or LoCon
+            if model_type.lower() not in ['lora', 'locon']:
                 return web.json_response({
-                    'error': f"Model type mismatch. Expected LORA, got {model_type}"
+                    'error': f"Model type mismatch. Expected LORA or LoCon, got {model_type}"
                 }, status=400)
             
             # Check local availability for each version
@@ -515,21 +528,6 @@ class ApiRoutes:
                 logger.error(f"Error downloading LoRA: {error_message}")
                 return web.Response(status=500, text=error_message)
 
-    async def update_settings(self, request: web.Request) -> web.Response:
-        """Update application settings"""
-        try:
-            data = await request.json()
-            
-            # Validate and update settings
-            if 'civitai_api_key' in data:
-                settings.set('civitai_api_key', data['civitai_api_key'])
-            if 'show_only_sfw' in data:
-                settings.set('show_only_sfw', data['show_only_sfw'])
-            
-            return web.json_response({'success': True})
-        except Exception as e:
-            logger.error(f"Error updating settings: {e}", exc_info=True)
-            return web.Response(status=500, text=str(e))
 
     async def move_model(self, request: web.Request) -> web.Response:
         """Handle model move request"""
@@ -797,11 +795,13 @@ class ApiRoutes:
             # Check if we already have the description stored in metadata
             description = None
             tags = []
+            creator = {}
             if file_path:
                 metadata_path = os.path.splitext(file_path)[0] + '.metadata.json'
                 metadata = await ModelRouteUtils.load_local_metadata(metadata_path)
                 description = metadata.get('modelDescription')
                 tags = metadata.get('tags', [])
+                creator = metadata.get('creator', {})
             
             # If description is not in metadata, fetch from CivitAI
             if not description:
@@ -811,6 +811,7 @@ class ApiRoutes:
                 if (model_metadata):
                     description = model_metadata.get('description')
                     tags = model_metadata.get('tags', [])
+                    creator = model_metadata.get('creator', {})
                 
                     # Save the metadata to file if we have a file path and got metadata
                     if file_path:
@@ -820,6 +821,7 @@ class ApiRoutes:
                             
                             metadata['modelDescription'] = description
                             metadata['tags'] = tags
+                            metadata['creator'] = creator
                             
                             with open(metadata_path, 'w', encoding='utf-8') as f:
                                 json.dump(metadata, f, indent=2, ensure_ascii=False)
@@ -830,7 +832,8 @@ class ApiRoutes:
             return web.json_response({
                 'success': True,
                 'description': description or "<p>No model description available.</p>",
-                'tags': tags
+                'tags': tags,
+                'creator': creator
             })
             
         except Exception as e:
@@ -1060,4 +1063,24 @@ class ApiRoutes:
             return web.json_response({
                 "success": False,
                 "error": str(e)
-            }, status=500)
+            }, status=500)
+
+    async def get_letter_counts(self, request: web.Request) -> web.Response:
+        """Get count of loras for each letter of the alphabet"""
+        try:
+            if self.scanner is None:
+                self.scanner = await ServiceRegistry.get_lora_scanner()
+                
+            # Get letter counts
+            letter_counts = await self.scanner.get_letter_counts()
+            
+            return web.json_response({
+                'success': True,
+                'letter_counts': letter_counts
+            })
+        except Exception as e:
+            logger.error(f"Error getting letter counts: {e}")
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)

py/routes/checkpoints_routes.py

@@ -49,6 +49,7 @@ class CheckpointsRoutes:
         
         # Add new routes for model management similar to LoRA routes
         app.router.add_post('/api/checkpoints/delete', self.delete_model)
+        app.router.add_post('/api/checkpoints/exclude', self.exclude_model)  # Add new exclude endpoint
         app.router.add_post('/api/checkpoints/fetch-civitai', self.fetch_civitai)
         app.router.add_post('/api/checkpoints/replace-preview', self.replace_preview)
         app.router.add_post('/api/checkpoints/download', self.download_checkpoint)
@@ -499,6 +500,10 @@ class CheckpointsRoutes:
     async def delete_model(self, request: web.Request) -> web.Response:
         """Handle checkpoint model deletion request"""
         return await ModelRouteUtils.handle_delete_model(request, self.scanner)
+
+    async def exclude_model(self, request: web.Request) -> web.Response:
+        """Handle checkpoint model exclusion request"""
+        return await ModelRouteUtils.handle_exclude_model(request, self.scanner)
     
     async def fetch_civitai(self, request: web.Request) -> web.Response:
         """Handle CivitAI metadata fetch request for checkpoints"""
@@ -653,7 +658,7 @@ class CheckpointsRoutes:
             model_type = response.get('type', '')
             
             # Check model type - should be Checkpoint
-            if model_type.lower() != 'checkpoint':
+            if (model_type.lower() != 'checkpoint'):
                 return web.json_response({
                     'error': f"Model type mismatch. Expected Checkpoint, got {model_type}"
                 }, status=400)

py/routes/misc_routes.py

@@ -0,0 +1,767 @@
+import logging
+import os
+import asyncio
+import json
+import time
+import aiohttp
+from aiohttp import web
+from ..services.settings_manager import settings
+from ..utils.usage_stats import UsageStats
+from ..services.service_registry import ServiceRegistry
+from ..utils.exif_utils import ExifUtils
+from ..utils.constants import EXAMPLE_IMAGE_WIDTH, SUPPORTED_MEDIA_EXTENSIONS
+from ..services.civitai_client import CivitaiClient
+from ..utils.routes_common import ModelRouteUtils
+
+logger = logging.getLogger(__name__)
+
+# Download status tracking
+download_task = None
+is_downloading = False
+download_progress = {
+    'total': 0,
+    'completed': 0,
+    'current_model': '',
+    'status': 'idle',  # idle, running, paused, completed, error
+    'errors': [],
+    'last_error': None,
+    'start_time': None,
+    'end_time': None,
+    'processed_models': set(),  # Track models that have been processed
+    'refreshed_models': set()  # Track models that had metadata refreshed
+}
+
+class MiscRoutes:
+    """Miscellaneous routes for various utility functions"""
+    
+    @staticmethod
+    def setup_routes(app):
+        """Register miscellaneous routes"""
+        app.router.add_post('/api/settings', MiscRoutes.update_settings)
+
+        # Usage stats routes
+        app.router.add_post('/api/update-usage-stats', MiscRoutes.update_usage_stats)
+        app.router.add_get('/api/get-usage-stats', MiscRoutes.get_usage_stats)
+        
+        # Example images download routes
+        app.router.add_post('/api/download-example-images', MiscRoutes.download_example_images)
+        app.router.add_get('/api/example-images-status', MiscRoutes.get_example_images_status)
+        app.router.add_post('/api/pause-example-images', MiscRoutes.pause_example_images)
+        app.router.add_post('/api/resume-example-images', MiscRoutes.resume_example_images)
+        
+    @staticmethod
+    async def update_settings(request):
+        """Update application settings"""
+        try:
+            data = await request.json()
+            
+            # Validate and update settings
+            for key, value in data.items():
+                # Special handling for example_images_path - verify path exists
+                if key == 'example_images_path' and value:
+                    if not os.path.exists(value):
+                        return web.json_response({
+                            'success': False,
+                            'error': f"Path does not exist: {value}"
+                        })
+                    
+                    # Path changed - server restart required for new path to take effect
+                    old_path = settings.get('example_images_path')
+                    if old_path != value:
+                        logger.info(f"Example images path changed to {value} - server restart required")
+                
+                # Save to settings
+                settings.set(key, value)
+            
+            return web.json_response({'success': True})
+        except Exception as e:
+            logger.error(f"Error updating settings: {e}", exc_info=True)
+            return web.Response(status=500, text=str(e))
+    
+    @staticmethod
+    async def update_usage_stats(request):
+        """
+        Update usage statistics based on a prompt_id
+        
+        Expects a JSON body with:
+        {
+            "prompt_id": "string"
+        }
+        """
+        try:
+            # Parse the request body
+            data = await request.json()
+            prompt_id = data.get('prompt_id')
+            
+            if not prompt_id:
+                return web.json_response({
+                    'success': False,
+                    'error': 'Missing prompt_id'
+                }, status=400)
+            
+            # Call the UsageStats to process this prompt_id synchronously
+            usage_stats = UsageStats()
+            await usage_stats.process_execution(prompt_id)
+            
+            return web.json_response({
+                'success': True
+            })
+            
+        except Exception as e:
+            logger.error(f"Failed to update usage stats: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+    
+    @staticmethod
+    async def get_usage_stats(request):
+        """Get current usage statistics"""
+        try:
+            usage_stats = UsageStats()
+            stats = await usage_stats.get_stats()
+            
+            return web.json_response({
+                'success': True,
+                'data': stats
+            })
+            
+        except Exception as e:
+            logger.error(f"Failed to get usage stats: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+    
+    @staticmethod
+    async def download_example_images(request):
+        """
+        Download example images for models from Civitai
+        
+        Expects a JSON body with:
+        {
+            "output_dir": "path/to/output",  # Base directory to save example images
+            "optimize": true,                # Whether to optimize images (default: true)
+            "model_types": ["lora", "checkpoint"], # Model types to process (default: both)
+            "delay": 1.0                     # Delay between downloads to avoid rate limiting (default: 1.0)
+        }
+        """
+        global download_task, is_downloading, download_progress
+        
+        if is_downloading:
+            # Create a copy for JSON serialization
+            response_progress = download_progress.copy()
+            response_progress['processed_models'] = list(download_progress['processed_models'])
+            response_progress['refreshed_models'] = list(download_progress['refreshed_models'])
+            
+            return web.json_response({
+                'success': False,
+                'error': 'Download already in progress',
+                'status': response_progress
+            }, status=400)
+        
+        try:
+            # Parse the request body
+            data = await request.json()
+            output_dir = data.get('output_dir')
+            optimize = data.get('optimize', True)
+            model_types = data.get('model_types', ['lora', 'checkpoint'])
+            delay = float(data.get('delay', 0.2))
+            
+            if not output_dir:
+                return web.json_response({
+                    'success': False,
+                    'error': 'Missing output_dir parameter'
+                }, status=400)
+            
+            # Create the output directory
+            os.makedirs(output_dir, exist_ok=True)
+            
+            # Initialize progress tracking
+            download_progress['total'] = 0
+            download_progress['completed'] = 0
+            download_progress['current_model'] = ''
+            download_progress['status'] = 'running'
+            download_progress['errors'] = []
+            download_progress['last_error'] = None
+            download_progress['start_time'] = time.time()
+            download_progress['end_time'] = None
+            
+            # Get the processed models list from a file if it exists
+            progress_file = os.path.join(output_dir, '.download_progress.json')
+            if os.path.exists(progress_file):
+                try:
+                    with open(progress_file, 'r', encoding='utf-8') as f:
+                        saved_progress = json.load(f)
+                        download_progress['processed_models'] = set(saved_progress.get('processed_models', []))
+                        logger.info(f"Loaded previous progress, {len(download_progress['processed_models'])} models already processed")
+                except Exception as e:
+                    logger.error(f"Failed to load progress file: {e}")
+                    download_progress['processed_models'] = set()
+            else:
+                download_progress['processed_models'] = set()
+            
+            # Start the download task
+            is_downloading = True
+            download_task = asyncio.create_task(
+                MiscRoutes._download_all_example_images(
+                    output_dir, 
+                    optimize, 
+                    model_types,
+                    delay
+                )
+            )
+            
+            # Create a copy for JSON serialization
+            response_progress = download_progress.copy()
+            response_progress['processed_models'] = list(download_progress['processed_models'])
+            response_progress['refreshed_models'] = list(download_progress['refreshed_models'])
+            
+            return web.json_response({
+                'success': True,
+                'message': 'Download started',
+                'status': response_progress
+            })
+            
+        except Exception as e:
+            logger.error(f"Failed to start example images download: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+
+    @staticmethod
+    async def get_example_images_status(request):
+        """Get the current status of example images download"""
+        global download_progress
+        
+        # Create a copy of the progress dict with the set converted to a list for JSON serialization
+        response_progress = download_progress.copy()
+        response_progress['processed_models'] = list(download_progress['processed_models'])
+        response_progress['refreshed_models'] = list(download_progress['refreshed_models'])
+        
+        return web.json_response({
+            'success': True,
+            'is_downloading': is_downloading,
+            'status': response_progress
+        })
+
+    @staticmethod
+    async def pause_example_images(request):
+        """Pause the example images download"""
+        global download_progress
+        
+        if not is_downloading:
+            return web.json_response({
+                'success': False,
+                'error': 'No download in progress'
+            }, status=400)
+        
+        download_progress['status'] = 'paused'
+        
+        return web.json_response({
+            'success': True,
+            'message': 'Download paused'
+        })
+
+    @staticmethod
+    async def resume_example_images(request):
+        """Resume the example images download"""
+        global download_progress
+        
+        if not is_downloading:
+            return web.json_response({
+                'success': False,
+                'error': 'No download in progress'
+            }, status=400)
+        
+        if download_progress['status'] == 'paused':
+            download_progress['status'] = 'running'
+            
+            return web.json_response({
+                'success': True,
+                'message': 'Download resumed'
+            })
+        else:
+            return web.json_response({
+                'success': False,
+                'error': f"Download is in '{download_progress['status']}' state, cannot resume"
+            }, status=400)
+
+    @staticmethod
+    async def _refresh_model_metadata(model_hash, model_name, scanner_type, scanner):
+        """Refresh model metadata from CivitAI
+        
+        Args:
+            model_hash: SHA256 hash of the model
+            model_name: Name of the model (for logging)
+            scanner_type: Type of scanner ('lora' or 'checkpoint')
+            scanner: Scanner instance for this model type
+            
+        Returns:
+            bool: True if metadata was successfully refreshed, False otherwise
+        """
+        global download_progress
+        
+        try:
+            # Find the model in the scanner cache
+            cache = await scanner.get_cached_data()
+            model_data = None
+            
+            for item in cache.raw_data:
+                if item.get('sha256') == model_hash:
+                    model_data = item
+                    break
+            
+            if not model_data:
+                logger.warning(f"Model {model_name} with hash {model_hash} not found in cache")
+                return False
+            
+            file_path = model_data.get('file_path')
+            if not file_path:
+                logger.warning(f"Model {model_name} has no file path")
+                return False
+            
+            # Track that we're refreshing this model
+            download_progress['refreshed_models'].add(model_hash)
+            
+            # Use ModelRouteUtils to refresh the metadata
+            async def update_cache_func(old_path, new_path, metadata):
+                return await scanner.update_single_model_cache(old_path, new_path, metadata)
+            
+            success = await ModelRouteUtils.fetch_and_update_model(
+                model_hash, 
+                file_path, 
+                model_data,
+                update_cache_func
+            )
+            
+            if success:
+                logger.info(f"Successfully refreshed metadata for {model_name}")
+                return True
+            else:
+                logger.warning(f"Failed to refresh metadata for {model_name}")
+                return False
+                
+        except Exception as e:
+            error_msg = f"Error refreshing metadata for {model_name}: {str(e)}"
+            logger.error(error_msg, exc_info=True)
+            download_progress['errors'].append(error_msg)
+            download_progress['last_error'] = error_msg
+            return False
+    
+    @staticmethod
+    async def _process_model_images(model_hash, model_name, model_images, model_dir, optimize, independent_session, delay):
+        """Process and download images for a single model
+        
+        Args:
+            model_hash: SHA256 hash of the model
+            model_name: Name of the model
+            model_images: List of image objects from CivitAI
+            model_dir: Directory to save images to
+            optimize: Whether to optimize images
+            independent_session: aiohttp session for downloads
+            delay: Delay between downloads
+            
+        Returns:
+            bool: True if all images were processed successfully, False otherwise
+        """
+        global download_progress
+        
+        model_success = True
+        
+        for i, image in enumerate(model_images, 1):
+            image_url = image.get('url')
+            if not image_url:
+                continue
+            
+            # Get image filename from URL
+            image_filename = os.path.basename(image_url.split('?')[0])
+            image_ext = os.path.splitext(image_filename)[1].lower()
+            
+            # Handle both images and videos
+            is_image = image_ext in SUPPORTED_MEDIA_EXTENSIONS['images']
+            is_video = image_ext in SUPPORTED_MEDIA_EXTENSIONS['videos']
+            
+            if not (is_image or is_video):
+                logger.debug(f"Skipping unsupported file type: {image_filename}")
+                continue
+            
+            save_filename = f"image_{i}{image_ext}"
+            
+            # Check if already downloaded
+            save_path = os.path.join(model_dir, save_filename)
+            if os.path.exists(save_path):
+                logger.debug(f"File already exists: {save_path}")
+                continue
+            
+            # Download the file
+            try:
+                logger.debug(f"Downloading {save_filename} for {model_name}")
+                
+                # Direct download using the independent session
+                async with independent_session.get(image_url, timeout=60) as response:
+                    if response.status == 200:
+                        if is_image and optimize:
+                            # For images, optimize if requested
+                            image_data = await response.read()
+                            optimized_data, ext = ExifUtils.optimize_image(
+                                image_data, 
+                                target_width=EXAMPLE_IMAGE_WIDTH, 
+                                format='webp', 
+                                quality=85, 
+                                preserve_metadata=False
+                            )
+                            
+                            # Update save filename if format changed
+                            if ext == '.webp':
+                                save_filename = os.path.splitext(save_filename)[0] + '.webp'
+                                save_path = os.path.join(model_dir, save_filename)
+                            
+                            # Save the optimized image
+                            with open(save_path, 'wb') as f:
+                                f.write(optimized_data)
+                        else:
+                            # For videos or unoptimized images, save directly
+                            with open(save_path, 'wb') as f:
+                                async for chunk in response.content.iter_chunked(8192):
+                                    if chunk:
+                                        f.write(chunk)
+                    elif response.status == 404:
+                        error_msg = f"Failed to download file: {image_url}, status code: 404 - Model metadata might be stale"
+                        logger.warning(error_msg)
+                        download_progress['errors'].append(error_msg)
+                        download_progress['last_error'] = error_msg
+                        model_success = False  # Mark model as failed due to 404
+                        # Return early to trigger metadata refresh attempt
+                        return False, True  # (success, is_stale_metadata)
+                    else:
+                        error_msg = f"Failed to download file: {image_url}, status code: {response.status}"
+                        logger.warning(error_msg)
+                        download_progress['errors'].append(error_msg)
+                        download_progress['last_error'] = error_msg
+                        model_success = False  # Mark model as failed
+                
+                # Add a delay between downloads for remote files only
+                await asyncio.sleep(delay)
+            except Exception as e:
+                error_msg = f"Error downloading file {image_url}: {str(e)}"
+                logger.error(error_msg)
+                download_progress['errors'].append(error_msg)
+                download_progress['last_error'] = error_msg
+                model_success = False  # Mark model as failed
+        
+        return model_success, False  # (success, is_stale_metadata)
+    
+    @staticmethod
+    async def _process_local_example_images(model_file_path, model_file_name, model_name, model_dir, optimize):
+        """Process local example images for a model
+        
+        Args:
+            model_file_path: Path to the model file
+            model_file_name: Filename of the model
+            model_name: Name of the model
+            model_dir: Directory to save processed images to
+            optimize: Whether to optimize images
+            
+        Returns:
+            bool: True if local images were processed successfully, False otherwise
+        """
+        global download_progress
+        
+        try:
+            model_dir_path = os.path.dirname(model_file_path)
+            local_images = []
+            
+            # Look for files with pattern: filename.example.*.ext
+            if model_file_name:
+                example_prefix = f"{model_file_name}.example."
+                
+                if os.path.exists(model_dir_path):
+                    for file in os.listdir(model_dir_path):
+                        file_lower = file.lower()
+                        if file_lower.startswith(example_prefix.lower()):
+                            file_ext = os.path.splitext(file_lower)[1]
+                            is_supported = (file_ext in SUPPORTED_MEDIA_EXTENSIONS['images'] or 
+                                           file_ext in SUPPORTED_MEDIA_EXTENSIONS['videos'])
+                            
+                            if is_supported:
+                                local_images.append(os.path.join(model_dir_path, file))
+            
+            # Process local images if found
+            if local_images:
+                logger.info(f"Found {len(local_images)} local example images for {model_name}")
+                
+                for i, local_image_path in enumerate(local_images, 1):
+                    local_ext = os.path.splitext(local_image_path)[1].lower()
+                    save_filename = f"image_{i}{local_ext}"
+                    save_path = os.path.join(model_dir, save_filename)
+                    
+                    # Skip if already exists in output directory
+                    if os.path.exists(save_path):
+                        logger.debug(f"File already exists in output: {save_path}")
+                        continue
+                    
+                    # Handle image processing based on file type and optimize setting
+                    is_image = local_ext in SUPPORTED_MEDIA_EXTENSIONS['images']
+                    
+                    if is_image and optimize:
+                        # Optimize the image
+                        with open(local_image_path, 'rb') as img_file:
+                            image_data = img_file.read()
+                        
+                        optimized_data, ext = ExifUtils.optimize_image(
+                            image_data, 
+                            target_width=EXAMPLE_IMAGE_WIDTH, 
+                            format='webp', 
+                            quality=85, 
+                            preserve_metadata=False
+                        )
+                        
+                        # Update save filename if format changed
+                        if ext == '.webp':
+                            save_filename = os.path.splitext(save_filename)[0] + '.webp'
+                            save_path = os.path.join(model_dir, save_filename)
+                        
+                        # Save the optimized image
+                        with open(save_path, 'wb') as f:
+                            f.write(optimized_data)
+                    else:
+                        # For videos or unoptimized images, copy directly
+                        with open(local_image_path, 'rb') as src_file:
+                            with open(save_path, 'wb') as dst_file:
+                                dst_file.write(src_file.read())
+                
+                return True
+            return False
+        except Exception as e:
+            error_msg = f"Error processing local examples for {model_name}: {str(e)}"
+            logger.error(error_msg)
+            download_progress['errors'].append(error_msg)
+            download_progress['last_error'] = error_msg
+            return False
+
+    @staticmethod
+    async def _download_all_example_images(output_dir, optimize, model_types, delay):
+        """Download example images for all models
+        
+        Args:
+            output_dir: Base directory to save example images
+            optimize: Whether to optimize images
+            model_types: List of model types to process
+            delay: Delay between downloads to avoid rate limiting
+        """
+        global is_downloading, download_progress
+        
+        # Create an independent session for downloading example images
+        # This avoids interference with the CivitAI client's session
+        connector = aiohttp.TCPConnector(
+            ssl=True,
+            limit=3,
+            force_close=False,
+            enable_cleanup_closed=True
+        )
+        timeout = aiohttp.ClientTimeout(total=None, connect=60, sock_read=60)
+        
+        # Create a dedicated session just for this download task
+        independent_session = aiohttp.ClientSession(
+            connector=connector,
+            trust_env=True,
+            timeout=timeout
+        )
+        
+        try:
+            # Get the scanners
+            scanners = []
+            if 'lora' in model_types:
+                lora_scanner = await ServiceRegistry.get_lora_scanner()
+                scanners.append(('lora', lora_scanner))
+            
+            if 'checkpoint' in model_types:
+                checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
+                scanners.append(('checkpoint', checkpoint_scanner))
+            
+            # Get all models from all scanners
+            all_models = []
+            for scanner_type, scanner in scanners:
+                cache = await scanner.get_cached_data()
+                if cache and cache.raw_data:
+                    for model in cache.raw_data:
+                        # Only process models with images and a valid sha256
+                        if model.get('civitai') and model.get('civitai', {}).get('images') and model.get('sha256'):
+                            all_models.append((scanner_type, model, scanner))
+            
+            # Update total count
+            download_progress['total'] = len(all_models)
+            logger.info(f"Found {download_progress['total']} models with example images")
+            
+            # Process each model
+            for scanner_type, model, scanner in all_models:
+                # Check if download is paused
+                while download_progress['status'] == 'paused':
+                    await asyncio.sleep(1)
+                
+                # Check if download should continue
+                if download_progress['status'] != 'running':
+                    logger.info(f"Download stopped: {download_progress['status']}")
+                    break
+                
+                model_hash = model.get('sha256', '').lower()
+                model_name = model.get('model_name', 'Unknown')
+                model_file_path = model.get('file_path', '')
+                model_file_name = model.get('file_name', '')
+                
+                try:
+                    # Update current model info
+                    download_progress['current_model'] = f"{model_name} ({model_hash[:8]})"
+                    
+                    # Skip if already processed
+                    if model_hash in download_progress['processed_models']:
+                        logger.debug(f"Skipping already processed model: {model_name}")
+                        download_progress['completed'] += 1
+                        continue
+                    
+                    # Create model directory
+                    model_dir = os.path.join(output_dir, model_hash)
+                    os.makedirs(model_dir, exist_ok=True)
+                    
+                    # Process images for this model
+                    images = model.get('civitai', {}).get('images', [])
+                    
+                    if not images:
+                        logger.debug(f"No images found for model: {model_name}")
+                        download_progress['processed_models'].add(model_hash)
+                        download_progress['completed'] += 1
+                        continue
+                    
+                    # First check if we have local example images for this model
+                    local_images_processed = False
+                    if model_file_path:
+                        local_images_processed = await MiscRoutes._process_local_example_images(
+                            model_file_path, 
+                            model_file_name, 
+                            model_name, 
+                            model_dir, 
+                            optimize
+                        )
+                        
+                        if local_images_processed:
+                            # Mark as successfully processed if all local images were processed
+                            download_progress['processed_models'].add(model_hash)
+                            logger.info(f"Successfully processed local examples for {model_name}")
+                    
+                    # If we didn't process local images, download from remote
+                    if not local_images_processed:
+                        # Try to download images
+                        model_success, is_stale_metadata = await MiscRoutes._process_model_images(
+                            model_hash, 
+                            model_name, 
+                            images, 
+                            model_dir, 
+                            optimize, 
+                            independent_session, 
+                            delay
+                        )
+                        
+                        # If metadata is stale (404 error), try to refresh it and download again
+                        if is_stale_metadata and model_hash not in download_progress['refreshed_models']:
+                            logger.info(f"Metadata seems stale for {model_name}, attempting to refresh...")
+                            
+                            # Refresh metadata from CivitAI
+                            refresh_success = await MiscRoutes._refresh_model_metadata(
+                                model_hash, 
+                                model_name, 
+                                scanner_type, 
+                                scanner
+                            )
+                            
+                            if refresh_success:
+                                # Get updated model data
+                                updated_cache = await scanner.get_cached_data()
+                                updated_model = None
+                                
+                                for item in updated_cache.raw_data:
+                                    if item.get('sha256') == model_hash:
+                                        updated_model = item
+                                        break
+                                
+                                if updated_model and updated_model.get('civitai', {}).get('images'):
+                                    # Try downloading with updated metadata
+                                    logger.info(f"Retrying download with refreshed metadata for {model_name}")
+                                    updated_images = updated_model.get('civitai', {}).get('images', [])
+                                    
+                                    # Retry download with new images
+                                    model_success, _ = await MiscRoutes._process_model_images(
+                                        model_hash, 
+                                        model_name, 
+                                        updated_images, 
+                                        model_dir, 
+                                        optimize, 
+                                        independent_session, 
+                                        delay
+                                    )
+                        
+                        # Only mark model as processed if all images downloaded successfully
+                        if model_success:
+                            download_progress['processed_models'].add(model_hash)
+                        else:
+                            logger.warning(f"Model {model_name} had download errors, will not mark as completed")
+                    
+                    # Save progress to file periodically
+                    if download_progress['completed'] % 10 == 0 or download_progress['completed'] == download_progress['total'] - 1:
+                        progress_file = os.path.join(output_dir, '.download_progress.json')
+                        with open(progress_file, 'w', encoding='utf-8') as f:
+                            json.dump({
+                                'processed_models': list(download_progress['processed_models']),
+                                'refreshed_models': list(download_progress['refreshed_models']),
+                                'completed': download_progress['completed'],
+                                'total': download_progress['total'],
+                                'last_update': time.time()
+                            }, f, indent=2)
+                
+                except Exception as e:
+                    error_msg = f"Error processing model {model.get('model_name')}: {str(e)}"
+                    logger.error(error_msg, exc_info=True)
+                    download_progress['errors'].append(error_msg)
+                    download_progress['last_error'] = error_msg
+                
+                # Update progress
+                download_progress['completed'] += 1
+            
+            # Mark as completed
+            download_progress['status'] = 'completed'
+            download_progress['end_time'] = time.time()
+            logger.info(f"Example images download completed: {download_progress['completed']}/{download_progress['total']} models processed")
+        
+        except Exception as e:
+            error_msg = f"Error during example images download: {str(e)}"
+            logger.error(error_msg, exc_info=True)
+            download_progress['errors'].append(error_msg)
+            download_progress['last_error'] = error_msg
+            download_progress['status'] = 'error'
+            download_progress['end_time'] = time.time()
+        
+        finally:
+            # Close the independent session
+            try:
+                await independent_session.close()
+            except Exception as e:
+                logger.error(f"Error closing download session: {e}")
+                
+            # Save final progress to file
+            try:
+                progress_file = os.path.join(output_dir, '.download_progress.json')
+                with open(progress_file, 'w', encoding='utf-8') as f:
+                    json.dump({
+                        'processed_models': list(download_progress['processed_models']),
+                        'refreshed_models': list(download_progress['refreshed_models']),
+                        'completed': download_progress['completed'],
+                        'total': download_progress['total'],
+                        'last_update': time.time(),
+                        'status': download_progress['status']
+                    }, f, indent=2)
+            except Exception as e:
+                logger.error(f"Failed to save progress file: {e}")
+            
+            # Set download status to not downloading
+            is_downloading = False

py/routes/recipe_routes.py

@@ -1,5 +1,6 @@
 import os
 import time
+import base64
 import numpy as np
 from PIL import Image
 import torch
@@ -12,7 +13,7 @@ import json
 import asyncio
 import sys
 from ..utils.exif_utils import ExifUtils
-from ..utils.recipe_parsers import RecipeParserFactory
+from ..recipes import RecipeParserFactory
 from ..utils.constants import CARD_PREVIEW_WIDTH
 
 from ..config import config
@@ -56,6 +57,7 @@ class RecipeRoutes:
         app.router.add_get('/api/recipes', routes.get_recipes)
         app.router.add_get('/api/recipe/{recipe_id}', routes.get_recipe_detail)
         app.router.add_post('/api/recipes/analyze-image', routes.analyze_recipe_image)
+        app.router.add_post('/api/recipes/analyze-local-image', routes.analyze_local_image)
         app.router.add_post('/api/recipes/save', routes.save_recipe)
         app.router.add_delete('/api/recipe/{recipe_id}', routes.delete_recipe)
         
@@ -70,12 +72,18 @@ class RecipeRoutes:
         # Add new endpoint for getting recipe syntax
         app.router.add_get('/api/recipe/{recipe_id}/syntax', routes.get_recipe_syntax)
         
-        # Add new endpoint for updating recipe metadata (name and tags)
+        # Add new endpoint for updating recipe metadata (name, tags and source_path)
         app.router.add_put('/api/recipe/{recipe_id}/update', routes.update_recipe)
         
         # Add new endpoint for reconnecting deleted LoRAs
         app.router.add_post('/api/recipe/lora/reconnect', routes.reconnect_lora)
         
+        # Add new endpoint for finding duplicate recipes
+        app.router.add_get('/api/recipes/find-duplicates', routes.find_duplicates)
+        
+        # Add new endpoint for bulk deletion of recipes
+        app.router.add_post('/api/recipes/bulk-delete', routes.bulk_delete)
+        
         # Start cache initialization
         app.on_startup.append(routes._init_cache)
         
@@ -300,7 +308,6 @@ class RecipeRoutes:
                 
                 # For URL mode, include the image data as base64
                 if is_url_mode and temp_path:
-                    import base64
                     with open(temp_path, "rb") as image_file:
                         result["image_base64"] = base64.b64encode(image_file.read()).decode('utf-8')
                     
@@ -317,7 +324,6 @@ class RecipeRoutes:
                 
                 # For URL mode, include the image data as base64
                 if is_url_mode and temp_path:
-                    import base64
                     with open(temp_path, "rb") as image_file:
                         result["image_base64"] = base64.b64encode(image_file.read()).decode('utf-8')
                     
@@ -332,7 +338,6 @@ class RecipeRoutes:
             
             # For URL mode, include the image data as base64
             if is_url_mode and temp_path:
-                import base64
                 with open(temp_path, "rb") as image_file:
                     result["image_base64"] = base64.b64encode(image_file.read()).decode('utf-8')
             
@@ -340,6 +345,21 @@ class RecipeRoutes:
             if "error" in result and not result.get("loras"):
                 return web.json_response(result, status=200)
             
+            # Calculate fingerprint from parsed loras
+            from ..utils.utils import calculate_recipe_fingerprint
+            fingerprint = calculate_recipe_fingerprint(result.get("loras", []))
+            
+            # Add fingerprint to result
+            result["fingerprint"] = fingerprint
+            
+            # Find matching recipes with the same fingerprint
+            matching_recipes = []
+            if fingerprint:
+                matching_recipes = await self.recipe_scanner.find_recipes_by_fingerprint(fingerprint)
+                
+            # Add matching recipes to result
+            result["matching_recipes"] = matching_recipes
+            
             return web.json_response(result)
             
         except Exception as e:
@@ -355,7 +375,100 @@ class RecipeRoutes:
                     os.unlink(temp_path)
                 except Exception as e:
                     logger.error(f"Error deleting temporary file: {e}")
+    
+    async def analyze_local_image(self, request: web.Request) -> web.Response:
+        """Analyze a local image file for recipe metadata"""
+        try:
+            # Ensure services are initialized 
+            await self.init_services()
+            
+            # Get JSON data from request
+            data = await request.json()
+            file_path = data.get('path')
+            
+            if not file_path:
+                return web.json_response({
+                    'error': 'No file path provided',
+                    'loras': []
+                }, status=400)
+                
+            # Normalize file path for cross-platform compatibility
+            file_path = os.path.normpath(file_path.strip('"').strip("'"))
+            
+            # Validate that the file exists
+            if not os.path.isfile(file_path):
+                return web.json_response({
+                    'error': 'File not found',
+                    'loras': []
+                }, status=404)
+                
+            # Extract metadata from the image using ExifUtils
+            metadata = ExifUtils.extract_image_metadata(file_path)
+            
+            # If no metadata found, return error
+            if not metadata:
+                # Get base64 image data
+                with open(file_path, "rb") as image_file:
+                    image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
+                    
+                return web.json_response({
+                    "error": "No metadata found in this image",
+                    "loras": [],  # Return empty loras array to prevent client-side errors
+                    "image_base64": image_base64
+                }, status=200)
+            
+            # Use the parser factory to get the appropriate parser
+            parser = RecipeParserFactory.create_parser(metadata)
 
+            if parser is None:
+                # Get base64 image data
+                with open(file_path, "rb") as image_file:
+                    image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
+                    
+                return web.json_response({
+                    "error": "No parser found for this image",
+                    "loras": [],  # Return empty loras array to prevent client-side errors
+                    "image_base64": image_base64
+                }, status=200)
+            
+            # Parse the metadata
+            result = await parser.parse_metadata(
+                metadata, 
+                recipe_scanner=self.recipe_scanner, 
+                civitai_client=self.civitai_client
+            )
+            
+            # Add base64 image data to result
+            with open(file_path, "rb") as image_file:
+                result["image_base64"] = base64.b64encode(image_file.read()).decode('utf-8')
+            
+            # Check for errors
+            if "error" in result and not result.get("loras"):
+                return web.json_response(result, status=200)
+            
+            # Calculate fingerprint from parsed loras
+            from ..utils.utils import calculate_recipe_fingerprint
+            fingerprint = calculate_recipe_fingerprint(result.get("loras", []))
+            
+            # Add fingerprint to result
+            result["fingerprint"] = fingerprint
+            
+            # Find matching recipes with the same fingerprint
+            matching_recipes = []
+            if fingerprint:
+                matching_recipes = await self.recipe_scanner.find_recipes_by_fingerprint(fingerprint)
+                
+            # Add matching recipes to result
+            result["matching_recipes"] = matching_recipes
+            
+            return web.json_response(result)
+            
+        except Exception as e:
+            logger.error(f"Error analyzing local image: {e}", exc_info=True)
+            return web.json_response({
+                'error': str(e),
+                'loras': []  # Return empty loras array to prevent client-side errors
+            }, status=500)
 
     async def save_recipe(self, request: web.Request) -> web.Response:
         """Save a recipe to the recipes folder"""
@@ -425,7 +538,6 @@ class RecipeRoutes:
             if not image:
                 if image_base64:
                     # Convert base64 to binary
-                    import base64
                     try:
                         # Remove potential data URL prefix
                         if ',' in image_base64:
@@ -474,7 +586,7 @@ class RecipeRoutes:
             with open(image_path, 'wb') as f:
                 f.write(optimized_image)
             
-            # Create the recipe JSON
+            # Create the recipe data structure
             current_time = time.time()
             
             # Format loras data according to the recipe.json format
@@ -514,6 +626,10 @@ class RecipeRoutes:
                     "clip_skip": raw_metadata.get("clip_skip", "")
                 }
             
+            # Calculate recipe fingerprint 
+            from ..utils.utils import calculate_recipe_fingerprint
+            fingerprint = calculate_recipe_fingerprint(loras_data)
+            
             # Create the recipe data structure
             recipe_data = {
                 "id": recipe_id,
@@ -523,13 +639,18 @@ class RecipeRoutes:
                 "created_date": current_time,
                 "base_model": metadata.get("base_model", ""),
                 "loras": loras_data,
-                "gen_params": gen_params
+                "gen_params": gen_params,
+                "fingerprint": fingerprint
             }
             
             # Add tags if provided
             if tags:
                 recipe_data["tags"] = tags
             
+            # Add source_path if provided in metadata
+            if metadata.get("source_path"):
+                recipe_data["source_path"] = metadata.get("source_path")
+            
             # Save the recipe JSON
             json_filename = f"{recipe_id}.recipe.json"
             json_path = os.path.join(recipes_dir, json_filename)
@@ -539,6 +660,14 @@ class RecipeRoutes:
             # Add recipe metadata to the image
             ExifUtils.append_recipe_metadata(image_path, recipe_data)
             
+            # Check for duplicates
+            matching_recipes = []
+            if fingerprint:
+                matching_recipes = await self.recipe_scanner.find_recipes_by_fingerprint(fingerprint)
+                # Remove current recipe from matches
+                if recipe_id in matching_recipes:
+                    matching_recipes.remove(recipe_id)
+            
             # Simplified cache update approach
             # Instead of trying to update the cache directly, just set it to None
             # to force a refresh on the next get_cached_data call
@@ -554,7 +683,8 @@ class RecipeRoutes:
                 'success': True,
                 'recipe_id': recipe_id,
                 'image_path': image_path,
-                'json_path': json_path
+                'json_path': json_path,
+                'matching_recipes': matching_recipes
             })
             
         except Exception as e:
@@ -1089,9 +1219,9 @@ class RecipeRoutes:
             data = await request.json()
             
             # Validate required fields
-            if 'title' not in data and 'tags' not in data:
+            if 'title' not in data and 'tags' not in data and 'source_path' not in data:
                 return web.json_response({
-                    "error": "At least one field to update must be provided (title or tags)"
+                    "error": "At least one field to update must be provided (title or tags or source_path)"
                 }, status=400)
             
             # Use the recipe scanner's update method
@@ -1186,6 +1316,10 @@ class RecipeRoutes:
                     
             if not found:
                 return web.json_response({"error": "Could not find matching deleted LoRA in recipe"}, status=404)
+            
+            # Recalculate recipe fingerprint after updating LoRA
+            from ..utils.utils import calculate_recipe_fingerprint
+            recipe_data['fingerprint'] = calculate_recipe_fingerprint(recipe_data.get('loras', []))
                 
             # Save updated recipe
             with open(recipe_path, 'w', encoding='utf-8') as f:
@@ -1201,6 +1335,8 @@ class RecipeRoutes:
                     if cache_item.get('id') == recipe_id:
                         # Replace loras array with updated version
                         cache_item['loras'] = recipe_data['loras']
+                        # Update fingerprint in cache
+                        cache_item['fingerprint'] = recipe_data['fingerprint']
                         
                         # Resort the cache
                         asyncio.create_task(scanner._cache.resort())
@@ -1211,11 +1347,20 @@ class RecipeRoutes:
             if image_path and os.path.exists(image_path):
                 from ..utils.exif_utils import ExifUtils
                 ExifUtils.append_recipe_metadata(image_path, recipe_data)
+            
+            # Find other recipes with the same fingerprint
+            matching_recipes = []
+            if 'fingerprint' in recipe_data:
+                matching_recipes = await scanner.find_recipes_by_fingerprint(recipe_data['fingerprint'])
+                # Remove current recipe from matches
+                if recipe_id in matching_recipes:
+                    matching_recipes.remove(recipe_id)
                 
             return web.json_response({
                 "success": True,
                 "recipe_id": recipe_id,
-                "updated_lora": updated_lora
+                "updated_lora": updated_lora,
+                "matching_recipes": matching_recipes
             })
             
         except Exception as e:
@@ -1291,3 +1436,150 @@ class RecipeRoutes:
                 'success': False,
                 'error': str(e)
             }, status=500)
+
+    async def find_duplicates(self, request: web.Request) -> web.Response:
+        """Find all duplicate recipes based on fingerprints"""
+        try:
+            # Ensure services are initialized
+            await self.init_services()
+            
+            # Get all duplicate recipes
+            duplicate_groups = await self.recipe_scanner.find_all_duplicate_recipes()
+            
+            # Create response data with additional recipe information
+            response_data = []
+            
+            for fingerprint, recipe_ids in duplicate_groups.items():
+                # Skip groups with only one recipe (not duplicates)
+                if len(recipe_ids) <= 1:
+                    continue
+                    
+                # Get recipe details for each recipe in the group
+                recipes = []
+                for recipe_id in recipe_ids:
+                    recipe = await self.recipe_scanner.get_recipe_by_id(recipe_id)
+                    if recipe:
+                        # Add only needed fields to keep response size manageable
+                        recipes.append({
+                            'id': recipe.get('id'),
+                            'title': recipe.get('title'),
+                            'file_url': recipe.get('file_url') or self._format_recipe_file_url(recipe.get('file_path', '')),
+                            'modified': recipe.get('modified'),
+                            'created_date': recipe.get('created_date'),
+                            'lora_count': len(recipe.get('loras', [])),
+                        })
+                        
+                # Only include groups with at least 2 valid recipes
+                if len(recipes) >= 2:
+                    # Sort recipes by modified date (newest first)
+                    recipes.sort(key=lambda x: x.get('modified', 0), reverse=True)
+                    
+                    response_data.append({
+                        'fingerprint': fingerprint,
+                        'count': len(recipes),
+                        'recipes': recipes
+                    })
+            
+            # Sort groups by count (highest first)
+            response_data.sort(key=lambda x: x['count'], reverse=True)
+            
+            return web.json_response({
+                'success': True,
+                'duplicate_groups': response_data
+            })
+            
+        except Exception as e:
+            logger.error(f"Error finding duplicate recipes: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+    
+    async def bulk_delete(self, request: web.Request) -> web.Response:
+        """Delete multiple recipes by ID"""
+        try:
+            # Ensure services are initialized
+            await self.init_services()
+            
+            # Parse request data
+            data = await request.json()
+            recipe_ids = data.get('recipe_ids', [])
+            
+            if not recipe_ids:
+                return web.json_response({
+                    'success': False,
+                    'error': 'No recipe IDs provided'
+                }, status=400)
+                
+            # Get recipes directory
+            recipes_dir = self.recipe_scanner.recipes_dir
+            if not recipes_dir or not os.path.exists(recipes_dir):
+                return web.json_response({
+                    'success': False,
+                    'error': 'Recipes directory not found'
+                }, status=404)
+            
+            # Track deleted and failed recipes
+            deleted_recipes = []
+            failed_recipes = []
+            
+            # Process each recipe ID
+            for recipe_id in recipe_ids:
+                # Find recipe JSON file
+                recipe_json_path = os.path.join(recipes_dir, f"{recipe_id}.recipe.json")
+                
+                if not os.path.exists(recipe_json_path):
+                    failed_recipes.append({
+                        'id': recipe_id,
+                        'reason': 'Recipe not found'
+                    })
+                    continue
+                
+                try:
+                    # Load recipe data to get image path
+                    with open(recipe_json_path, 'r', encoding='utf-8') as f:
+                        recipe_data = json.load(f)
+                    
+                    # Get image path
+                    image_path = recipe_data.get('file_path')
+                    
+                    # Delete recipe JSON file
+                    os.remove(recipe_json_path)
+                    
+                    # Delete recipe image if it exists
+                    if image_path and os.path.exists(image_path):
+                        os.remove(image_path)
+                        
+                    deleted_recipes.append(recipe_id)
+                    
+                except Exception as e:
+                    failed_recipes.append({
+                        'id': recipe_id,
+                        'reason': str(e)
+                    })
+            
+            # Update cache if any recipes were deleted
+            if deleted_recipes and self.recipe_scanner._cache is not None:
+                # Remove deleted recipes from raw_data
+                self.recipe_scanner._cache.raw_data = [
+                    r for r in self.recipe_scanner._cache.raw_data 
+                    if r.get('id') not in deleted_recipes
+                ]
+                # Resort the cache
+                asyncio.create_task(self.recipe_scanner._cache.resort())
+                logger.info(f"Removed {len(deleted_recipes)} recipes from cache")
+            
+            return web.json_response({
+                'success': True,
+                'deleted': deleted_recipes,
+                'failed': failed_recipes,
+                'total_deleted': len(deleted_recipes),
+                'total_failed': len(failed_recipes)
+            })
+            
+        except Exception as e:
+            logger.error(f"Error performing bulk delete: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)

py/routes/update_routes.py

@@ -150,11 +150,16 @@ class UpdateRoutes:
         """
         Compare two semantic version strings
         Returns True if version2 is newer than version1
+        Ignores any suffixes after '-' (e.g., -bugfix, -alpha)
         """
         try:
+            # Clean version strings - remove any suffix after '-'
+            v1_clean = version1.split('-')[0]
+            v2_clean = version2.split('-')[0]
+            
             # Split versions into components
-            v1_parts = [int(x) for x in version1.split('.')]
-            v2_parts = [int(x) for x in version2.split('.')]
+            v1_parts = [int(x) for x in v1_clean.split('.')]
+            v2_parts = [int(x) for x in v2_clean.split('.')]
             
             # Ensure both have 3 components (major.minor.patch)
             while len(v1_parts) < 3:

py/routes/usage_stats_routes.py

@@ -1,69 +0,0 @@
-import logging
-from aiohttp import web
-from ..utils.usage_stats import UsageStats
-
-logger = logging.getLogger(__name__)
-
-class UsageStatsRoutes:
-    """Routes for handling usage statistics updates"""
-    
-    @staticmethod
-    def setup_routes(app):
-        """Register usage stats routes"""
-        app.router.add_post('/loras/api/update-usage-stats', UsageStatsRoutes.update_usage_stats)
-        app.router.add_get('/loras/api/get-usage-stats', UsageStatsRoutes.get_usage_stats)
-    
-    @staticmethod
-    async def update_usage_stats(request):
-        """
-        Update usage statistics based on a prompt_id
-        
-        Expects a JSON body with:
-        {
-            "prompt_id": "string"
-        }
-        """
-        try:
-            # Parse the request body
-            data = await request.json()
-            prompt_id = data.get('prompt_id')
-            
-            if not prompt_id:
-                return web.json_response({
-                    'success': False,
-                    'error': 'Missing prompt_id'
-                }, status=400)
-            
-            # Call the UsageStats to process this prompt_id synchronously
-            usage_stats = UsageStats()
-            await usage_stats.process_execution(prompt_id)
-            
-            return web.json_response({
-                'success': True
-            })
-            
-        except Exception as e:
-            logger.error(f"Failed to update usage stats: {e}", exc_info=True)
-            return web.json_response({
-                'success': False,
-                'error': str(e)
-            }, status=500)
-    
-    @staticmethod
-    async def get_usage_stats(request):
-        """Get current usage statistics"""
-        try:
-            usage_stats = UsageStats()
-            stats = await usage_stats.get_stats()
-            
-            return web.json_response({
-                'success': True,
-                'data': stats
-            })
-            
-        except Exception as e:
-            logger.error(f"Failed to get usage stats: {e}", exc_info=True)
-            return web.json_response({
-                'success': False,
-                'error': str(e)
-            }, status=500)

py/server_routes.py

@@ -1,26 +0,0 @@
-from aiohttp import web
-from server import PromptServer
-from .nodes.utils import get_lora_info
-
-@PromptServer.instance.routes.post("/loramanager/get_trigger_words")
-async def get_trigger_words(request):
-    json_data = await request.json()
-    lora_names = json_data.get("lora_names", [])
-    node_ids = json_data.get("node_ids", [])
-    
-    all_trigger_words = []
-    for lora_name in lora_names:
-        _, trigger_words = await get_lora_info(lora_name)
-        all_trigger_words.extend(trigger_words)
-    
-    # Format the trigger words
-    trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
-    
-    # Send update to all connected trigger word toggle nodes
-    for node_id in node_ids:
-        PromptServer.instance.send_sync("trigger_word_update", {
-            "id": node_id,
-            "message": trigger_words_text
-        })
-    
-    return web.json_response({"success": True})

py/services/civitai_client.py

@@ -267,7 +267,7 @@ class CivitaiClient:
             return None, error_msg
 
     async def get_model_metadata(self, model_id: str) -> Tuple[Optional[Dict], int]:
-        """Fetch model metadata (description and tags) from Civitai API
+        """Fetch model metadata (description, tags, and creator info) from Civitai API
         
         Args:
             model_id: The Civitai model ID
@@ -294,10 +294,14 @@ class CivitaiClient:
                 # Extract relevant metadata
                 metadata = {
                     "description": data.get("description") or "No model description available",
-                    "tags": data.get("tags", [])
+                    "tags": data.get("tags", []),
+                    "creator": {
+                        "username": data.get("creator", {}).get("username"),
+                        "image": data.get("creator", {}).get("image")
+                    }
                 }
                 
-                if metadata["description"] or metadata["tags"]:
+                if metadata["description"] or metadata["tags"] or metadata["creator"]["username"]:
                     return metadata, status_code
                 else:
                     logger.warning(f"No metadata found for model {model_id}")

py/services/download_manager.py

@@ -136,15 +136,9 @@ class DownloadManager:
             # 3. Prepare download
             file_name = file_info['name']
             save_path = os.path.join(save_dir, file_name)
-            file_size = file_info.get('sizeKB', 0) * 1024
 
             # 4. Notify file monitor - use normalized path and file size
-            file_monitor = await self._get_lora_monitor() if model_type == "lora" else await self._get_checkpoint_monitor()
-            if file_monitor and file_monitor.handler:
-                file_monitor.handler.add_ignore_path(
-                    save_path.replace(os.sep, '/'),
-                    file_size
-                )
+            # file monitor is despreted, so we don't need to use it
 
             # 5. Prepare metadata based on model type
             if model_type == "checkpoint":
@@ -154,7 +148,7 @@ class DownloadManager:
                 metadata = LoraMetadata.from_civitai_info(version_info, file_info, save_path)
                 logger.info(f"Creating LoraMetadata for {file_name}")
             
-            # 5.1 Get and update model tags and description
+            # 5.1 Get and update model tags, description and creator info
             model_id = version_info.get('modelId')
             if model_id:
                 model_metadata, _ = await civitai_client.get_model_metadata(str(model_id))
@@ -163,6 +157,8 @@ class DownloadManager:
                         metadata.tags = model_metadata.get("tags", [])
                     if model_metadata.get("description"):
                         metadata.modelDescription = model_metadata.get("description", "")
+                    if model_metadata.get("creator"):
+                        metadata.civitai["creator"] = model_metadata.get("creator")
             
             # 6. Start download process
             result = await self._execute_download(

py/services/lora_cache.py

@@ -2,6 +2,7 @@ import asyncio
 from typing import List, Dict
 from dataclasses import dataclass
 from operator import itemgetter
+from natsort import natsorted
 
 @dataclass
 class LoraCache:
@@ -17,7 +18,7 @@ class LoraCache:
     async def resort(self, name_only: bool = False):
         """Resort all cached data views"""
         async with self._lock:
-            self.sorted_by_name = sorted(
+            self.sorted_by_name = natsorted(
                 self.raw_data, 
                 key=lambda x: x['model_name'].lower()  # Case-insensitive sort
             )

py/services/lora_scanner.py

@@ -4,6 +4,7 @@ import logging
 import asyncio
 import shutil
 import time
+import re
 from typing import List, Dict, Optional, Set
 
 from ..utils.models import LoraMetadata
@@ -123,7 +124,7 @@ class LoraScanner(ModelScanner):
                                folder: str = None, search: str = None, fuzzy_search: bool = False,
                                base_models: list = None, tags: list = None,
                                search_options: dict = None, hash_filters: dict = None,
-                               favorites_only: bool = False) -> Dict:
+                               favorites_only: bool = False, first_letter: str = None) -> Dict:
         """Get paginated and filtered lora data
         
         Args:
@@ -138,6 +139,7 @@ class LoraScanner(ModelScanner):
             search_options: Dictionary with search options (filename, modelname, tags, recursive)
             hash_filters: Dictionary with hash filtering options (single_hash or multiple_hashes)
             favorites_only: Filter for favorite models only
+            first_letter: Filter by first letter of model name
         """
         cache = await self.get_cached_data()
 
@@ -202,6 +204,10 @@ class LoraScanner(ModelScanner):
                 lora for lora in filtered_data
                 if lora.get('favorite', False) is True
             ]
+            
+        # Apply first letter filtering
+        if first_letter:
+            filtered_data = self._filter_by_first_letter(filtered_data, first_letter)
         
         # Apply folder filtering
         if folder is not None:
@@ -273,6 +279,101 @@ class LoraScanner(ModelScanner):
         
         return result
 
+    def _filter_by_first_letter(self, data, letter):
+        """Filter data by first letter of model name
+        
+        Special handling:
+        - '#': Numbers (0-9)
+        - '@': Special characters (not alphanumeric)
+        - '漢': CJK characters
+        """
+        filtered_data = []
+        
+        for lora in data:
+            model_name = lora.get('model_name', '')
+            if not model_name:
+                continue
+                
+            first_char = model_name[0].upper()
+            
+            if letter == '#' and first_char.isdigit():
+                filtered_data.append(lora)
+            elif letter == '@' and not first_char.isalnum():
+                # Special characters (not alphanumeric)
+                filtered_data.append(lora)
+            elif letter == '漢' and self._is_cjk_character(first_char):
+                # CJK characters
+                filtered_data.append(lora)
+            elif letter.upper() == first_char:
+                # Regular alphabet matching
+                filtered_data.append(lora)
+                
+        return filtered_data
+    
+    def _is_cjk_character(self, char):
+        """Check if character is a CJK character"""
+        # Define Unicode ranges for CJK characters
+        cjk_ranges = [
+            (0x4E00, 0x9FFF),   # CJK Unified Ideographs
+            (0x3400, 0x4DBF),   # CJK Unified Ideographs Extension A
+            (0x20000, 0x2A6DF), # CJK Unified Ideographs Extension B
+            (0x2A700, 0x2B73F), # CJK Unified Ideographs Extension C
+            (0x2B740, 0x2B81F), # CJK Unified Ideographs Extension D
+            (0x2B820, 0x2CEAF), # CJK Unified Ideographs Extension E
+            (0x2CEB0, 0x2EBEF), # CJK Unified Ideographs Extension F
+            (0x30000, 0x3134F), # CJK Unified Ideographs Extension G
+            (0xF900, 0xFAFF),   # CJK Compatibility Ideographs
+            (0x3300, 0x33FF),   # CJK Compatibility
+            (0x3200, 0x32FF),   # Enclosed CJK Letters and Months
+            (0x3100, 0x312F),   # Bopomofo
+            (0x31A0, 0x31BF),   # Bopomofo Extended
+            (0x3040, 0x309F),   # Hiragana
+            (0x30A0, 0x30FF),   # Katakana
+            (0x31F0, 0x31FF),   # Katakana Phonetic Extensions
+            (0xAC00, 0xD7AF),   # Hangul Syllables
+            (0x1100, 0x11FF),   # Hangul Jamo
+            (0xA960, 0xA97F),   # Hangul Jamo Extended-A
+            (0xD7B0, 0xD7FF),   # Hangul Jamo Extended-B
+        ]
+        
+        code_point = ord(char)
+        return any(start <= code_point <= end for start, end in cjk_ranges)
+    
+    async def get_letter_counts(self):
+        """Get count of models for each letter of the alphabet"""
+        cache = await self.get_cached_data()
+        data = cache.sorted_by_name
+        
+        # Define letter categories
+        letters = {
+            '#': 0,  # Numbers
+            'A': 0, 'B': 0, 'C': 0, 'D': 0, 'E': 0, 'F': 0, 'G': 0, 'H': 0,
+            'I': 0, 'J': 0, 'K': 0, 'L': 0, 'M': 0, 'N': 0, 'O': 0, 'P': 0,
+            'Q': 0, 'R': 0, 'S': 0, 'T': 0, 'U': 0, 'V': 0, 'W': 0, 'X': 0,
+            'Y': 0, 'Z': 0,
+            '@': 0,  # Special characters
+            '漢': 0   # CJK characters
+        }
+        
+        # Count models for each letter
+        for lora in data:
+            model_name = lora.get('model_name', '')
+            if not model_name:
+                continue
+                
+            first_char = model_name[0].upper()
+            
+            if first_char.isdigit():
+                letters['#'] += 1
+            elif first_char in letters:
+                letters[first_char] += 1
+            elif self._is_cjk_character(first_char):
+                letters['漢'] += 1
+            elif not first_char.isalnum():
+                letters['@'] += 1
+                
+        return letters
+
     async def _update_metadata_paths(self, metadata_path: str, lora_path: str) -> Dict:
         """Update file paths in metadata file"""
         try:

py/services/model_cache.py

@@ -2,6 +2,7 @@ import asyncio
 from typing import List, Dict
 from dataclasses import dataclass
 from operator import itemgetter
+from natsort import natsorted
 
 @dataclass
 class ModelCache:
@@ -17,7 +18,7 @@ class ModelCache:
     async def resort(self, name_only: bool = False):
         """Resort all cached data views"""
         async with self._lock:
-            self.sorted_by_name = sorted(
+            self.sorted_by_name = natsorted(
                 self.raw_data, 
                 key=lambda x: x['model_name'].lower()  # Case-insensitive sort
             )

py/services/model_scanner.py

@@ -38,6 +38,7 @@ class ModelScanner:
         self._hash_index = hash_index or ModelHashIndex()
         self._tags_count = {}  # Dictionary to store tag counts
         self._is_initializing = False  # Flag to track initialization state
+        self._excluded_models = []  # List to track excluded models
         
         # Register this service
         asyncio.create_task(self._register_service())
@@ -394,6 +395,9 @@ class ModelScanner:
                             if file_path in cached_paths:
                                 found_paths.add(file_path)
                                 continue
+
+                            if file_path in self._excluded_models:
+                                continue
                                 
                             # Try case-insensitive match on Windows
                             if os.name == 'nt':
@@ -406,7 +410,7 @@ class ModelScanner:
                                         break
                                 if matched:
                                     continue
-                            
+                                
                             # This is a new file to process
                             new_files.append(file_path)
                     
@@ -586,6 +590,11 @@ class ModelScanner:
         
         model_data = metadata.to_dict()
         
+        # Skip excluded models
+        if model_data.get('exclude', False):
+            self._excluded_models.append(model_data['file_path'])
+            return None
+            
         await self._fetch_missing_metadata(file_path, model_data)
         rel_path = os.path.relpath(file_path, root_path)
         folder = os.path.dirname(rel_path)
@@ -610,7 +619,10 @@ class ModelScanner:
                     model_id = str(model_id)
                     tags_missing = not model_data.get('tags') or len(model_data.get('tags', [])) == 0
                     desc_missing = not model_data.get('modelDescription') or model_data.get('modelDescription') in (None, "")
-                    needs_metadata_update = tags_missing or desc_missing
+                    # TODO: not for now, but later we should check if the creator is missing
+                    # creator_missing = not model_data.get('civitai', {}).get('creator')
+                    creator_missing = False
+                    needs_metadata_update = tags_missing or desc_missing or creator_missing
             
             if needs_metadata_update and model_id:
                 logger.debug(f"Fetching missing metadata for {file_path} with model ID {model_id}")
@@ -636,6 +648,8 @@ class ModelScanner:
                     
                     if model_metadata.get('description') and (not model_data.get('modelDescription') or model_data.get('modelDescription') in (None, "")):
                         model_data['modelDescription'] = model_metadata['description']
+
+                    model_data['civitai']['creator'] = model_metadata['creator']
                     
                     metadata_path = os.path.splitext(file_path)[0] + '.metadata.json'
                     with open(metadata_path, 'w', encoding='utf-8') as f:
@@ -900,6 +914,10 @@ class ModelScanner:
             logger.error(f"Error getting model info by name: {e}", exc_info=True)
             return None
         
+    def get_excluded_models(self) -> List[str]:
+        """Get list of excluded model file paths"""
+        return self._excluded_models.copy()
+
     async def update_preview_in_cache(self, file_path: str, preview_url: str) -> bool:
         """Update preview URL in cache for a specific lora
         
@@ -913,4 +931,4 @@ class ModelScanner:
         if self._cache is None:
             return False
 
-        return await self._cache.update_preview_url(file_path, preview_url)
+        return await self._cache.update_preview_url(file_path, preview_url)

py/services/recipe_cache.py

@@ -2,6 +2,7 @@ import asyncio
 from typing import List, Dict
 from dataclasses import dataclass
 from operator import itemgetter
+from natsort import natsorted
 
 @dataclass
 class RecipeCache:
@@ -16,7 +17,7 @@ class RecipeCache:
     async def resort(self, name_only: bool = False):
         """Resort all cached data views"""
         async with self._lock:
-            self.sorted_by_name = sorted(
+            self.sorted_by_name = natsorted(
                 self.raw_data, 
                 key=lambda x: x.get('title', '').lower()  # Case-insensitive sort
             )

py/services/recipe_scanner.py

@@ -9,6 +9,7 @@ from .recipe_cache import RecipeCache
 from .service_registry import ServiceRegistry
 from .lora_scanner import LoraScanner
 from ..utils.utils import fuzzy_match
+from natsort import natsorted
 import sys
 
 logger = logging.getLogger(__name__)
@@ -164,7 +165,7 @@ class RecipeScanner:
                 if hasattr(self._cache, "resort"):
                     try:
                         # Sort by name
-                        self._cache.sorted_by_name = sorted(
+                        self._cache.sorted_by_name = natsorted(
                             self._cache.raw_data,
                             key=lambda x: x.get('title', '').lower()
                         )
@@ -321,6 +322,20 @@ class RecipeScanner:
             
             # Update lora information with local paths and availability
             await self._update_lora_information(recipe_data)
+
+            # Calculate and update fingerprint if missing
+            if 'loras' in recipe_data and 'fingerprint' not in recipe_data:
+                from ..utils.utils import calculate_recipe_fingerprint
+                fingerprint = calculate_recipe_fingerprint(recipe_data['loras'])
+                recipe_data['fingerprint'] = fingerprint
+                
+                # Write updated recipe data back to file
+                try:
+                    with open(recipe_path, 'w', encoding='utf-8') as f:
+                        json.dump(recipe_data, f, indent=4, ensure_ascii=False)
+                    logger.info(f"Added fingerprint to recipe: {recipe_path}")
+                except Exception as e:
+                    logger.error(f"Error writing updated recipe with fingerprint: {e}")
             
             return recipe_data
         except Exception as e:
@@ -801,3 +816,60 @@ class RecipeScanner:
             logger.info(f"Resorted recipe cache after updating {cache_updated_count} items")
             
         return file_updated_count, cache_updated_count
+
+    async def find_recipes_by_fingerprint(self, fingerprint: str) -> list:
+        """Find recipes with a matching fingerprint
+        
+        Args:
+            fingerprint: The recipe fingerprint to search for
+            
+        Returns:
+            List of recipe details that match the fingerprint
+        """
+        if not fingerprint:
+            return []
+            
+        # Get all recipes from cache
+        cache = await self.get_cached_data()
+        
+        # Find recipes with matching fingerprint
+        matching_recipes = []
+        for recipe in cache.raw_data:
+            if recipe.get('fingerprint') == fingerprint:
+                recipe_details = {
+                    'id': recipe.get('id'),
+                    'title': recipe.get('title'),
+                    'file_url': self._format_file_url(recipe.get('file_path')),
+                    'modified': recipe.get('modified'),
+                    'created_date': recipe.get('created_date'),
+                    'lora_count': len(recipe.get('loras', []))
+                }
+                matching_recipes.append(recipe_details)
+        
+        return matching_recipes
+        
+    async def find_all_duplicate_recipes(self) -> dict:
+        """Find all recipe duplicates based on fingerprints
+        
+        Returns:
+            Dictionary where keys are fingerprints and values are lists of recipe IDs
+        """
+        # Get all recipes from cache
+        cache = await self.get_cached_data()
+        
+        # Group recipes by fingerprint
+        fingerprint_groups = {}
+        for recipe in cache.raw_data:
+            fingerprint = recipe.get('fingerprint')
+            if not fingerprint:
+                continue
+                
+            if fingerprint not in fingerprint_groups:
+                fingerprint_groups[fingerprint] = []
+                
+            fingerprint_groups[fingerprint].append(recipe.get('id'))
+        
+        # Filter to only include groups with more than one recipe
+        duplicate_groups = {k: v for k, v in fingerprint_groups.items() if len(v) > 1}
+        
+        return duplicate_groups

py/utils/constants.py

@@ -11,15 +11,24 @@ NSFW_LEVELS = {
 PREVIEW_EXTENSIONS = [
     '.webp',
     '.preview.webp',
-    '.preview.png', 
-    '.preview.jpeg', 
-    '.preview.jpg', 
+    '.preview.png',
+    '.preview.jpeg',
+    '.preview.jpg',
     '.preview.mp4',
-    '.png', 
-    '.jpeg', 
-    '.jpg', 
+    '.png',
+    '.jpeg',
+    '.jpg',
     '.mp4'
 ]
 
 # Card preview image width
-CARD_PREVIEW_WIDTH = 480
+CARD_PREVIEW_WIDTH = 480
+
+# Width for optimized example images
+EXAMPLE_IMAGE_WIDTH = 832
+
+# Supported media extensions for example downloads
+SUPPORTED_MEDIA_EXTENSIONS = {
+    'images': ['.jpg', '.jpeg', '.png', '.webp', '.gif'],
+    'videos': ['.mp4', '.webm']
+}

py/utils/file_utils.py

@@ -233,6 +233,17 @@ async def load_metadata(file_path: str, model_class: Type[BaseModelMetadata] = L
                     data['usage_tips'] = "{}"
                     needs_update = True
                 
+                # Update preview_nsfw_level if needed
+                civitai_data = data.get('civitai', {})
+                civitai_images = civitai_data.get('images', []) if civitai_data else []
+                if (data.get('preview_url') and 
+                    data.get('preview_nsfw_level', 0) == 0 and 
+                    civitai_images and 
+                    civitai_images[0].get('nsfwLevel', 0) != 0):
+                    data['preview_nsfw_level'] = civitai_images[0]['nsfwLevel']
+                    # TODO: write to metadata file
+                    # needs_update = True
+
                 if needs_update:
                     with open(metadata_path, 'w', encoding='utf-8') as f:
                         json.dump(data, f, indent=2, ensure_ascii=False)

py/utils/lora_metadata.py

@@ -2,6 +2,9 @@ from safetensors import safe_open
 from typing import Dict
 from .model_utils import determine_base_model
 import os
+import logging
+
+logger = logging.getLogger(__name__)
 
 async def extract_lora_metadata(file_path: str) -> Dict:
     """Extract essential metadata from safetensors file"""

py/utils/models.py

@@ -23,6 +23,7 @@ class BaseModelMetadata:
     modelDescription: str = ""  # Full model description
     civitai_deleted: bool = False  # Whether deleted from Civitai
     favorite: bool = False      # Whether the model is a favorite
+    exclude: bool = False       # Whether to exclude this model from the cache
 
     def __post_init__(self):
         # Initialize empty lists to avoid mutable default parameter issue

py/utils/routes_common.py

@@ -53,6 +53,7 @@ class ModelRouteUtils:
                 if model_metadata:
                     local_metadata['modelDescription'] = model_metadata.get('description', '')
                     local_metadata['tags'] = model_metadata.get('tags', [])
+                    local_metadata['civitai']['creator'] = model_metadata['creator']
         
         # Update base model
         local_metadata['base_model'] = determine_base_model(civitai_metadata.get('baseModel'))
@@ -424,6 +425,65 @@ class ModelRouteUtils:
             logger.error(f"Error replacing preview: {e}", exc_info=True)
             return web.Response(text=str(e), status=500)
 
+    @staticmethod
+    async def handle_exclude_model(request: web.Request, scanner) -> web.Response:
+        """Handle model exclusion request
+        
+        Args:
+            request: The aiohttp request
+            scanner: The model scanner instance with cache management methods
+            
+        Returns:
+            web.Response: The HTTP response
+        """
+        try:
+            data = await request.json()
+            file_path = data.get('file_path')
+            if not file_path:
+                return web.Response(text='Model path is required', status=400)
+
+            # Update metadata to mark as excluded
+            metadata_path = os.path.splitext(file_path)[0] + '.metadata.json'
+            metadata = await ModelRouteUtils.load_local_metadata(metadata_path)
+            metadata['exclude'] = True
+            
+            # Save updated metadata
+            with open(metadata_path, 'w', encoding='utf-8') as f:
+                json.dump(metadata, f, indent=2, ensure_ascii=False)
+
+            # Update cache
+            cache = await scanner.get_cached_data()
+
+            # Find and remove model from cache
+            model_to_remove = next((item for item in cache.raw_data if item['file_path'] == file_path), None)
+            if model_to_remove:
+                # Update tags count
+                for tag in model_to_remove.get('tags', []):
+                    if tag in scanner._tags_count:
+                        scanner._tags_count[tag] = max(0, scanner._tags_count[tag] - 1)
+                        if scanner._tags_count[tag] == 0:
+                            del scanner._tags_count[tag]
+
+                # Remove from hash index if available
+                if hasattr(scanner, '_hash_index') and scanner._hash_index:
+                    scanner._hash_index.remove_by_path(file_path)
+
+                # Remove from cache data
+                cache.raw_data = [item for item in cache.raw_data if item['file_path'] != file_path]
+                await cache.resort()
+            
+            # Add to excluded models list
+            scanner._excluded_models.append(file_path)
+            
+            return web.json_response({
+                'success': True,
+                'message': f"Model {os.path.basename(file_path)} excluded"
+            })
+            
+        except Exception as e:
+            logger.error(f"Error excluding model: {e}", exc_info=True)
+            return web.Response(text=str(e), status=500)
+
     @staticmethod
     async def handle_download_model(request: web.Request, download_manager: DownloadManager, model_type="lora") -> web.Response:
         """Handle model download request
@@ -500,4 +560,4 @@ class ModelRouteUtils:
                 )
             
             logger.error(f"Error downloading {model_type}: {error_message}")
-            return web.Response(status=500, text=error_message)
+            return web.Response(status=500, text=error_message)

py/utils/utils.py

@@ -114,3 +114,49 @@ def fuzzy_match(text: str, pattern: str, threshold: float = 0.7) -> bool:
         
         # All words found either as substrings or fuzzy matches
         return True
+
+def calculate_recipe_fingerprint(loras):
+    """
+    Calculate a unique fingerprint for a recipe based on its LoRAs.
+    
+    The fingerprint is created by sorting LoRA hashes, filtering invalid entries,
+    normalizing strength values to 2 decimal places, and joining in format:
+    hash1:strength1|hash2:strength2|...
+    
+    Args:
+        loras (list): List of LoRA dictionaries with hash and strength values
+        
+    Returns:
+        str: The calculated fingerprint
+    """
+    if not loras:
+        return ""
+    
+    # Filter valid entries and extract hash and strength
+    valid_loras = []
+    for lora in loras:
+        # Skip excluded loras
+        if lora.get("exclude", False):
+            continue
+            
+        # Get the hash - use modelVersionId as fallback if hash is empty
+        hash_value = lora.get("hash", "").lower()
+        if not hash_value and lora.get("isDeleted", False) and lora.get("modelVersionId"):
+            hash_value = lora.get("modelVersionId")
+            
+        # Skip entries without a valid hash
+        if not hash_value:
+            continue
+            
+        # Normalize strength to 2 decimal places (check both strength and weight fields)
+        strength = round(float(lora.get("strength", lora.get("weight", 1.0))), 2)
+        
+        valid_loras.append((hash_value, strength))
+    
+    # Sort by hash
+    valid_loras.sort()
+    
+    # Join in format hash1:strength1|hash2:strength2|...
+    fingerprint = "|".join([f"{hash_value}:{strength}" for hash_value, strength in valid_loras])
+    
+    return fingerprint

py/workflow/__init__.py

@@ -1,3 +0,0 @@
-"""
-ComfyUI workflow parsing module to extract generation parameters
-""" 

py/workflow/cli.py

@@ -1,58 +0,0 @@
-"""
-Command-line interface for the ComfyUI workflow parser
-"""
-import argparse
-import json
-import os
-import logging
-import sys
-from .parser import parse_workflow
-
-logging.basicConfig(
-    level=logging.INFO,
-    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
-    handlers=[logging.StreamHandler()]
-)
-logger = logging.getLogger(__name__)
-
-def main():
-    """Entry point for the CLI"""
-    parser = argparse.ArgumentParser(description='Parse ComfyUI workflow files')
-    parser.add_argument('input', help='Input workflow JSON file path')
-    parser.add_argument('-o', '--output', help='Output JSON file path')
-    parser.add_argument('-p', '--pretty', action='store_true', help='Pretty print JSON output')
-    parser.add_argument('--debug', action='store_true', help='Enable debug logging')
-    
-    args = parser.parse_args()
-    
-    # Set logging level
-    if args.debug:
-        logging.getLogger().setLevel(logging.DEBUG)
-    
-    # Validate input file
-    if not os.path.isfile(args.input):
-        logger.error(f"Input file not found: {args.input}")
-        sys.exit(1)
-    
-    # Parse workflow
-    try:
-        result = parse_workflow(args.input, args.output)
-        
-        # Print result to console if output file not specified
-        if not args.output:
-            if args.pretty:
-                print(json.dumps(result, indent=4))
-            else:
-                print(json.dumps(result))
-        else:
-            logger.info(f"Output saved to: {args.output}")
-            
-    except Exception as e:
-        logger.error(f"Error parsing workflow: {e}")
-        if args.debug:
-            import traceback
-            traceback.print_exc()
-        sys.exit(1)
-
-if __name__ == "__main__":
-    main() 

py/workflow/ext/__init__.py

@@ -1,3 +0,0 @@
-"""
-Extension directory for custom node mappers
-""" 

py/workflow/ext/comfyui_core.py

@@ -1,285 +0,0 @@
-"""
-ComfyUI Core nodes mappers extension for workflow parsing
-"""
-import logging
-from typing import Dict, Any, List
-
-logger = logging.getLogger(__name__)
-
-# =============================================================================
-# Transform Functions
-# =============================================================================
-
-def transform_random_noise(inputs: Dict) -> Dict:
-    """Transform function for RandomNoise node"""
-    return {"seed": str(inputs.get("noise_seed", ""))}
-
-def transform_ksampler_select(inputs: Dict) -> Dict:
-    """Transform function for KSamplerSelect node"""
-    return {"sampler": inputs.get("sampler_name", "")}
-
-def transform_basic_scheduler(inputs: Dict) -> Dict:
-    """Transform function for BasicScheduler node"""
-    result = {
-        "scheduler": inputs.get("scheduler", ""),
-        "denoise": str(inputs.get("denoise", "1.0"))
-    }
-    
-    # Get steps from inputs or steps input
-    if "steps" in inputs:
-        if isinstance(inputs["steps"], str):
-            result["steps"] = inputs["steps"]
-        elif isinstance(inputs["steps"], dict) and "value" in inputs["steps"]:
-            result["steps"] = str(inputs["steps"]["value"])
-        else:
-            result["steps"] = str(inputs["steps"])
-    
-    return result
-
-def transform_basic_guider(inputs: Dict) -> Dict:
-    """Transform function for BasicGuider node"""
-    result = {}
-    
-    # Process conditioning
-    if "conditioning" in inputs:
-        if isinstance(inputs["conditioning"], str):
-            result["prompt"] = inputs["conditioning"]
-        elif isinstance(inputs["conditioning"], dict):
-            result["conditioning"] = inputs["conditioning"]
-    
-    # Get model information if needed
-    if "model" in inputs and isinstance(inputs["model"], dict):
-        result["model"] = inputs["model"]
-    
-    return result
-
-def transform_model_sampling_flux(inputs: Dict) -> Dict:
-    """Transform function for ModelSamplingFlux - mostly a pass-through node"""
-    # This node is primarily used for routing, so we mostly pass through values
-    
-    return inputs["model"]
-
-def transform_sampler_custom_advanced(inputs: Dict) -> Dict:
-    """Transform function for SamplerCustomAdvanced node"""
-    result = {}
-    
-    # Extract seed from noise
-    if "noise" in inputs and isinstance(inputs["noise"], dict):
-        result["seed"] = str(inputs["noise"].get("seed", ""))
-    
-    # Extract sampler info
-    if "sampler" in inputs and isinstance(inputs["sampler"], dict):
-        sampler = inputs["sampler"].get("sampler", "")
-        if sampler:
-            result["sampler"] = sampler
-    
-    # Extract scheduler, steps, denoise from sigmas
-    if "sigmas" in inputs and isinstance(inputs["sigmas"], dict):
-        sigmas = inputs["sigmas"]
-        result["scheduler"] = sigmas.get("scheduler", "")
-        result["steps"] = str(sigmas.get("steps", ""))
-        result["denoise"] = str(sigmas.get("denoise", "1.0"))
-    
-    # Extract prompt and guidance from guider
-    if "guider" in inputs and isinstance(inputs["guider"], dict):
-        guider = inputs["guider"]
-        
-        # Get prompt from conditioning
-        if "conditioning" in guider and isinstance(guider["conditioning"], str):
-            result["prompt"] = guider["conditioning"]
-        elif "conditioning" in guider and isinstance(guider["conditioning"], dict):
-            result["guidance"] = guider["conditioning"].get("guidance", "")
-            result["prompt"] = guider["conditioning"].get("prompt", "")
-
-        if "model" in guider and isinstance(guider["model"], dict):
-            result["checkpoint"] = guider["model"].get("checkpoint", "")
-            result["loras"] = guider["model"].get("loras", "")
-            result["clip_skip"] = str(int(guider["model"].get("clip_skip", "-1")) * -1)
-    
-    # Extract dimensions from latent_image
-    if "latent_image" in inputs and isinstance(inputs["latent_image"], dict):
-        latent = inputs["latent_image"]
-        width = latent.get("width", 0)
-        height = latent.get("height", 0)
-        if width and height:
-            result["width"] = width
-            result["height"] = height
-            result["size"] = f"{width}x{height}"
-    
-    return result
-
-def transform_ksampler(inputs: Dict) -> Dict:
-    """Transform function for KSampler nodes"""
-    result = {
-        "seed": str(inputs.get("seed", "")),
-        "steps": str(inputs.get("steps", "")),
-        "cfg": str(inputs.get("cfg", "")),
-        "sampler": inputs.get("sampler_name", ""),
-        "scheduler": inputs.get("scheduler", ""),
-    }
-    
-    # Process positive prompt
-    if "positive" in inputs:
-        result["prompt"] = inputs["positive"]
-    
-    # Process negative prompt
-    if "negative" in inputs:
-        result["negative_prompt"] = inputs["negative"]
-    
-    # Get dimensions from latent image
-    if "latent_image" in inputs and isinstance(inputs["latent_image"], dict):
-        width = inputs["latent_image"].get("width", 0)
-        height = inputs["latent_image"].get("height", 0)
-        if width and height:
-            result["size"] = f"{width}x{height}"
-    
-    # Add clip_skip if present
-    if "clip_skip" in inputs:
-        result["clip_skip"] = str(inputs.get("clip_skip", ""))
-
-    # Add guidance if present
-    if "guidance" in inputs:
-        result["guidance"] = str(inputs.get("guidance", ""))
-
-    # Add model if present
-    if "model" in inputs:
-        result["checkpoint"] = inputs.get("model", {}).get("checkpoint", "")
-        result["loras"] = inputs.get("model", {}).get("loras", "")
-        result["clip_skip"] = str(inputs.get("model", {}).get("clip_skip", -1) * -1)
-        
-    return result
-
-def transform_empty_latent(inputs: Dict) -> Dict:
-    """Transform function for EmptyLatentImage nodes"""
-    width = inputs.get("width", 0)
-    height = inputs.get("height", 0)
-    return {"width": width, "height": height, "size": f"{width}x{height}"}
-
-def transform_clip_text(inputs: Dict) -> Any:
-    """Transform function for CLIPTextEncode nodes"""
-    return inputs.get("text", "")
-
-def transform_flux_guidance(inputs: Dict) -> Dict:
-    """Transform function for FluxGuidance nodes"""
-    result = {}
-    
-    if "guidance" in inputs:
-        result["guidance"] = inputs["guidance"]
-    
-    if "conditioning" in inputs:
-        conditioning = inputs["conditioning"]
-        if isinstance(conditioning, str):
-            result["prompt"] = conditioning
-        else:
-            result["prompt"] = "Unknown prompt"
-    
-    return result
-
-def transform_unet_loader(inputs: Dict) -> Dict:
-    """Transform function for UNETLoader node"""
-    unet_name = inputs.get("unet_name", "")
-    return {"checkpoint": unet_name} if unet_name else {}
-
-def transform_checkpoint_loader(inputs: Dict) -> Dict:
-    """Transform function for CheckpointLoaderSimple node"""
-    ckpt_name = inputs.get("ckpt_name", "")
-    return {"checkpoint": ckpt_name} if ckpt_name else {}
-
-def transform_latent_upscale_by(inputs: Dict) -> Dict:
-    """Transform function for LatentUpscaleBy node"""
-    result = {}
-    
-    width = inputs["samples"].get("width", 0) * inputs["scale_by"]
-    height = inputs["samples"].get("height", 0) * inputs["scale_by"]
-    result["width"] = width
-    result["height"] = height
-    result["size"] = f"{width}x{height}"
-    
-    return result
-
-def transform_clip_set_last_layer(inputs: Dict) -> Dict:
-    """Transform function for CLIPSetLastLayer node"""
-    result = {}
-    
-    if "stop_at_clip_layer" in inputs:
-        result["clip_skip"] = inputs["stop_at_clip_layer"]
-    
-    return result
-
-# =============================================================================
-# Node Mapper Definitions
-# =============================================================================
-
-# Define the mappers for ComfyUI core nodes not in main mapper
-NODE_MAPPERS_EXT = {
-    # KSamplers
-    "SamplerCustomAdvanced": {
-        "inputs_to_track": ["noise", "guider", "sampler", "sigmas", "latent_image"],
-        "transform_func": transform_sampler_custom_advanced
-    },
-    "KSampler": {
-        "inputs_to_track": [
-            "seed", "steps", "cfg", "sampler_name", "scheduler", 
-            "denoise", "positive", "negative", "latent_image",
-            "model", "clip_skip"
-        ],
-        "transform_func": transform_ksampler
-    },
-    # ComfyUI core nodes
-    "EmptyLatentImage": {
-        "inputs_to_track": ["width", "height", "batch_size"],
-        "transform_func": transform_empty_latent
-    },
-    "EmptySD3LatentImage": {
-        "inputs_to_track": ["width", "height", "batch_size"],
-        "transform_func": transform_empty_latent
-    },
-    "CLIPTextEncode": {
-        "inputs_to_track": ["text", "clip"],
-        "transform_func": transform_clip_text
-    },
-    "FluxGuidance": {
-        "inputs_to_track": ["guidance", "conditioning"],
-        "transform_func": transform_flux_guidance
-    },
-    "RandomNoise": {
-        "inputs_to_track": ["noise_seed"],
-        "transform_func": transform_random_noise
-    },
-    "KSamplerSelect": {
-        "inputs_to_track": ["sampler_name"],
-        "transform_func": transform_ksampler_select
-    },
-    "BasicScheduler": {
-        "inputs_to_track": ["scheduler", "steps", "denoise", "model"],
-        "transform_func": transform_basic_scheduler
-    },
-    "BasicGuider": {
-        "inputs_to_track": ["model", "conditioning"],
-        "transform_func": transform_basic_guider
-    },
-    "ModelSamplingFlux": {
-        "inputs_to_track": ["max_shift", "base_shift", "width", "height", "model"],
-        "transform_func": transform_model_sampling_flux
-    },
-    "UNETLoader": {
-        "inputs_to_track": ["unet_name"],
-        "transform_func": transform_unet_loader
-    },
-    "CheckpointLoaderSimple": {
-        "inputs_to_track": ["ckpt_name"],
-        "transform_func": transform_checkpoint_loader
-    },
-    "LatentUpscale": {
-        "inputs_to_track": ["width", "height"],
-        "transform_func": transform_empty_latent
-    },
-    "LatentUpscaleBy": {
-        "inputs_to_track": ["samples", "scale_by"],
-        "transform_func": transform_latent_upscale_by
-    },
-    "CLIPSetLastLayer": {
-        "inputs_to_track": ["clip", "stop_at_clip_layer"],
-        "transform_func": transform_clip_set_last_layer
-    }
-} 

py/workflow/ext/kjnodes.py

@@ -1,74 +0,0 @@
-"""
-KJNodes mappers extension for ComfyUI workflow parsing
-"""
-import logging
-import re
-from typing import Dict, Any
-
-logger = logging.getLogger(__name__)
-
-# =============================================================================
-# Transform Functions
-# =============================================================================
-
-def transform_join_strings(inputs: Dict) -> str:
-    """Transform function for JoinStrings nodes"""
-    string1 = inputs.get("string1", "")
-    string2 = inputs.get("string2", "")
-    delimiter = inputs.get("delimiter", "")
-    return f"{string1}{delimiter}{string2}"
-
-def transform_string_constant(inputs: Dict) -> str:
-    """Transform function for StringConstant nodes"""
-    return inputs.get("string", "")
-
-def transform_empty_latent_presets(inputs: Dict) -> Dict:
-    """Transform function for EmptyLatentImagePresets nodes"""
-    dimensions = inputs.get("dimensions", "")
-    invert = inputs.get("invert", False)
-    
-    # Extract width and height from dimensions string
-    # Expected format: "width x height (ratio)" or similar
-    width = 0
-    height = 0
-    
-    if dimensions:
-        # Try to extract dimensions using regex
-        match = re.search(r'(\d+)\s*x\s*(\d+)', dimensions)
-        if match:
-            width = int(match.group(1))
-            height = int(match.group(2))
-    
-    # If invert is True, swap width and height
-    if invert and width and height:
-        width, height = height, width
-    
-    return {"width": width, "height": height, "size": f"{width}x{height}"}
-
-def transform_int_constant(inputs: Dict) -> int:
-    """Transform function for INTConstant nodes"""
-    return inputs.get("value", 0)
-
-# =============================================================================
-# Node Mapper Definitions
-# =============================================================================
-
-# Define the mappers for KJNodes
-NODE_MAPPERS_EXT = {
-    "JoinStrings": {
-        "inputs_to_track": ["string1", "string2", "delimiter"],
-        "transform_func": transform_join_strings
-    },
-    "StringConstantMultiline": {
-        "inputs_to_track": ["string"],
-        "transform_func": transform_string_constant
-    },
-    "EmptyLatentImagePresets": {
-        "inputs_to_track": ["dimensions", "invert", "batch_size"],
-        "transform_func": transform_empty_latent_presets
-    },
-    "INTConstant": {
-        "inputs_to_track": ["value"],
-        "transform_func": transform_int_constant
-    }
-} 

py/workflow/main.py

@@ -1,37 +0,0 @@
-"""
-Main entry point for the workflow parser module
-"""
-import os
-import sys
-import logging
-from typing import Dict, Optional, Union
-
-# Add the parent directory to sys.path to enable imports
-SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
-ROOT_DIR = os.path.abspath(os.path.join(SCRIPT_DIR, '..', '..'))
-sys.path.insert(0, os.path.dirname(SCRIPT_DIR))
-
-from .parser import parse_workflow
-
-logger = logging.getLogger(__name__)
-
-def parse_comfyui_workflow(
-    workflow_path: str, 
-    output_path: Optional[str] = None
-) -> Dict:
-    """
-    Parse a ComfyUI workflow file and extract generation parameters
-    
-    Args:
-        workflow_path: Path to the workflow JSON file
-        output_path: Optional path to save the output JSON
-        
-    Returns:
-        Dictionary containing extracted parameters
-    """
-    return parse_workflow(workflow_path, output_path)
-
-if __name__ == "__main__":
-    # If run directly, use the CLI
-    from .cli import main
-    main() 

py/workflow/mappers.py

@@ -1,282 +0,0 @@
-"""
-Node mappers for ComfyUI workflow parsing
-"""
-import logging
-import os
-import importlib.util
-import inspect
-from typing import Dict, List, Any, Optional, Union, Type, Callable, Tuple
-
-logger = logging.getLogger(__name__)
-
-# Global mapper registry
-_MAPPER_REGISTRY: Dict[str, Dict] = {}
-
-# =============================================================================
-# Mapper Definition Functions
-# =============================================================================
-
-def create_mapper(
-    node_type: str,
-    inputs_to_track: List[str],
-    transform_func: Callable[[Dict], Any] = None
-) -> Dict:
-    """Create a mapper definition for a node type"""
-    mapper = {
-        "node_type": node_type,
-        "inputs_to_track": inputs_to_track,
-        "transform": transform_func or (lambda inputs: inputs)
-    }
-    return mapper
-
-def register_mapper(mapper: Dict) -> None:
-    """Register a node mapper in the global registry"""
-    _MAPPER_REGISTRY[mapper["node_type"]] = mapper
-    logger.debug(f"Registered mapper for node type: {mapper['node_type']}")
-
-def get_mapper(node_type: str) -> Optional[Dict]:
-    """Get a mapper for the specified node type"""
-    return _MAPPER_REGISTRY.get(node_type)
-
-def get_all_mappers() -> Dict[str, Dict]:
-    """Get all registered mappers"""
-    return _MAPPER_REGISTRY.copy()
-
-# =============================================================================
-# Node Processing Function
-# =============================================================================
-
-def process_node(node_id: str, node_data: Dict, workflow: Dict, parser: 'WorkflowParser') -> Any: # type: ignore
-    """Process a node using its mapper and extract relevant information"""
-    node_type = node_data.get("class_type")
-    mapper = get_mapper(node_type)
-    
-    if not mapper:
-        logger.warning(f"No mapper found for node type: {node_type}")
-        return None
-        
-    result = {}
-    
-    # Extract inputs based on the mapper's tracked inputs
-    for input_name in mapper["inputs_to_track"]:
-        if input_name in node_data.get("inputs", {}):
-            input_value = node_data["inputs"][input_name]
-            
-            # Check if input is a reference to another node's output
-            if isinstance(input_value, list) and len(input_value) == 2:
-                try:
-                    # Format is [node_id, output_slot]
-                    ref_node_id, output_slot = input_value
-                    # Convert node_id to string if it's an integer
-                    if isinstance(ref_node_id, int):
-                        ref_node_id = str(ref_node_id)
-                    
-                    # Recursively process the referenced node
-                    ref_value = parser.process_node(ref_node_id, workflow)
-                    
-                    if ref_value is not None:
-                        result[input_name] = ref_value
-                    else:
-                        # If we couldn't get a value from the reference, store the raw value
-                        result[input_name] = input_value
-                except Exception as e:
-                    logger.error(f"Error processing reference in node {node_id}, input {input_name}: {e}")
-                    result[input_name] = input_value
-            else:
-                # Direct value
-                result[input_name] = input_value
-    
-    # Apply the transform function
-    try:
-        return mapper["transform"](result)
-    except Exception as e:
-        logger.error(f"Error in transform function for node {node_id} of type {node_type}: {e}")
-        return result
-
-# =============================================================================
-# Transform Functions
-# =============================================================================
-
-
-
-def transform_lora_loader(inputs: Dict) -> Dict:
-    """Transform function for LoraLoader nodes"""
-    loras_data = inputs.get("loras", [])
-    lora_stack = inputs.get("lora_stack", {}).get("lora_stack", [])
-    
-    lora_texts = []
-    
-    # Process loras array
-    if isinstance(loras_data, dict) and "__value__" in loras_data:
-        loras_list = loras_data["__value__"]
-    elif isinstance(loras_data, list):
-        loras_list = loras_data
-    else:
-        loras_list = []
-        
-    # Process each active lora entry
-    for lora in loras_list:
-        if isinstance(lora, dict) and lora.get("active", False):
-            lora_name = lora.get("name", "")
-            strength = lora.get("strength", 1.0)
-            lora_texts.append(f"<lora:{lora_name}:{strength}>")
-    
-    # Process lora_stack if valid
-    if lora_stack and isinstance(lora_stack, list):
-        if not (len(lora_stack) == 2 and isinstance(lora_stack[0], (str, int)) and isinstance(lora_stack[1], int)):
-            for stack_entry in lora_stack:
-                lora_name = stack_entry[0]
-                strength = stack_entry[1]
-                lora_texts.append(f"<lora:{lora_name}:{strength}>")
-
-    result = {
-        "checkpoint": inputs.get("model", {}).get("checkpoint", ""),
-        "loras": " ".join(lora_texts)
-    }
-
-    if "clip" in inputs and isinstance(inputs["clip"], dict):
-        result["clip_skip"] = inputs["clip"].get("clip_skip", "-1")
-    
-    return result
-
-def transform_lora_stacker(inputs: Dict) -> Dict:
-    """Transform function for LoraStacker nodes"""
-    loras_data = inputs.get("loras", [])
-    result_stack = []
-    
-    # Handle existing stack entries
-    existing_stack = []
-    lora_stack_input = inputs.get("lora_stack", [])
-    
-    if isinstance(lora_stack_input, dict) and "lora_stack" in lora_stack_input:
-        existing_stack = lora_stack_input["lora_stack"]
-    elif isinstance(lora_stack_input, list):
-        if not (len(lora_stack_input) == 2 and isinstance(lora_stack_input[0], (str, int)) and 
-               isinstance(lora_stack_input[1], int)):
-            existing_stack = lora_stack_input
-    
-    # Add existing entries
-    if existing_stack:
-        result_stack.extend(existing_stack)
-    
-    # Process new loras
-    if isinstance(loras_data, dict) and "__value__" in loras_data:
-        loras_list = loras_data["__value__"]
-    elif isinstance(loras_data, list):
-        loras_list = loras_data
-    else:
-        loras_list = []
-        
-    for lora in loras_list:
-        if isinstance(lora, dict) and lora.get("active", False):
-            lora_name = lora.get("name", "")
-            strength = float(lora.get("strength", 1.0))
-            result_stack.append((lora_name, strength))
-    
-    return {"lora_stack": result_stack}
-
-def transform_trigger_word_toggle(inputs: Dict) -> str:
-    """Transform function for TriggerWordToggle nodes"""
-    toggle_data = inputs.get("toggle_trigger_words", [])
-
-    if isinstance(toggle_data, dict) and "__value__" in toggle_data:
-        toggle_words = toggle_data["__value__"]
-    elif isinstance(toggle_data, list):
-        toggle_words = toggle_data
-    else:
-        toggle_words = []
-    
-    # Filter active trigger words
-    active_words = []
-    for item in toggle_words:
-        if isinstance(item, dict) and item.get("active", False):
-            word = item.get("text", "")
-            if word and not word.startswith("__dummy"):
-                active_words.append(word)
-    
-    return ", ".join(active_words)
-
-# =============================================================================
-# Node Mapper Definitions
-# =============================================================================
-
-# Central definition of all supported node types and their configurations
-NODE_MAPPERS = {
-    
-    # LoraManager nodes
-    "Lora Loader (LoraManager)": {
-        "inputs_to_track": ["model", "clip", "loras", "lora_stack"],
-        "transform_func": transform_lora_loader
-    },
-    "Lora Stacker (LoraManager)": {
-        "inputs_to_track": ["loras", "lora_stack"],
-        "transform_func": transform_lora_stacker
-    },
-    "TriggerWord Toggle (LoraManager)": {
-        "inputs_to_track": ["toggle_trigger_words"],
-        "transform_func": transform_trigger_word_toggle
-    }   
-}
-
-def register_all_mappers() -> None:
-    """Register all mappers from the NODE_MAPPERS dictionary"""
-    for node_type, config in NODE_MAPPERS.items():
-        mapper = create_mapper(
-            node_type=node_type,
-            inputs_to_track=config["inputs_to_track"],
-            transform_func=config["transform_func"]
-        )
-        register_mapper(mapper)
-    logger.info(f"Registered {len(NODE_MAPPERS)} node mappers")
-
-# =============================================================================
-# Extension Loading
-# =============================================================================
-
-def load_extensions(ext_dir: str = None) -> None:
-    """
-    Load mapper extensions from the specified directory
-    
-    Extension files should define a NODE_MAPPERS_EXT dictionary containing mapper configurations.
-    These will be added to the global NODE_MAPPERS dictionary and registered automatically.
-    """
-    # Use default path if none provided
-    if ext_dir is None:
-        # Get the directory of this file
-        current_dir = os.path.dirname(os.path.abspath(__file__))
-        ext_dir = os.path.join(current_dir, 'ext')
-    
-    # Ensure the extension directory exists
-    if not os.path.exists(ext_dir):
-        os.makedirs(ext_dir, exist_ok=True)
-        logger.info(f"Created extension directory: {ext_dir}")
-        return
-    
-    # Load each Python file in the extension directory
-    for filename in os.listdir(ext_dir):
-        if filename.endswith('.py') and not filename.startswith('_'):
-            module_path = os.path.join(ext_dir, filename)
-            module_name = f"workflow.ext.{filename[:-3]}"  # Remove .py
-            
-            try:
-                # Load the module
-                spec = importlib.util.spec_from_file_location(module_name, module_path)
-                if spec and spec.loader:
-                    module = importlib.util.module_from_spec(spec)
-                    spec.loader.exec_module(module)
-                    
-                    # Check if the module defines NODE_MAPPERS_EXT
-                    if hasattr(module, 'NODE_MAPPERS_EXT'):
-                        # Add the extension mappers to the global NODE_MAPPERS dictionary
-                        NODE_MAPPERS.update(module.NODE_MAPPERS_EXT)
-                        logger.info(f"Added {len(module.NODE_MAPPERS_EXT)} mappers from extension: {filename}")
-                    else:
-                        logger.warning(f"Extension {filename} does not define NODE_MAPPERS_EXT dictionary")
-            except Exception as e:
-                logger.warning(f"Error loading extension {filename}: {e}")
-    
-    # Re-register all mappers after loading extensions
-    register_all_mappers()
-
-# Initialize the registry with default mappers
-# register_default_mappers() 

py/workflow/parser.py

@@ -1,181 +0,0 @@
-"""
-Main workflow parser implementation for ComfyUI
-"""
-import json
-import logging
-from typing import Dict, List, Any, Optional, Union, Set
-from .mappers import get_mapper, get_all_mappers, load_extensions, process_node
-from .utils import (
-    load_workflow, save_output, find_node_by_type,
-    trace_model_path
-)
-
-logger = logging.getLogger(__name__)
-
-class WorkflowParser:
-    """Parser for ComfyUI workflows"""
-    
-    def __init__(self):
-        """Initialize the parser with mappers"""
-        self.processed_nodes: Set[str] = set()  # Track processed nodes to avoid cycles
-        self.node_results_cache: Dict[str, Any] = {}  # Cache for processed node results
-        
-        # Load extensions
-        load_extensions()
-    
-    def process_node(self, node_id: str, workflow: Dict) -> Any:
-        """Process a single node and extract relevant information"""
-        # Return cached result if available
-        if node_id in self.node_results_cache:
-            return self.node_results_cache[node_id]
-            
-        # Check if we're in a cycle
-        if node_id in self.processed_nodes:
-            return None
-        
-        # Mark this node as being processed (to detect cycles)
-        self.processed_nodes.add(node_id)
-        
-        if node_id not in workflow:
-            self.processed_nodes.remove(node_id)
-            return None
-        
-        node_data = workflow[node_id]
-        node_type = node_data.get("class_type")
-        
-        result = None
-        if get_mapper(node_type):
-            try:
-                result = process_node(node_id, node_data, workflow, self)
-                # Cache the result
-                self.node_results_cache[node_id] = result
-            except Exception as e:
-                logger.error(f"Error processing node {node_id} of type {node_type}: {e}", exc_info=True)
-                # Return a partial result or None depending on how we want to handle errors
-                result = {}
-        
-        # Remove node from processed set to allow it to be processed again in a different context
-        self.processed_nodes.remove(node_id)
-        return result
-    
-    def find_primary_sampler_node(self, workflow: Dict) -> Optional[str]:
-        """
-        Find the primary sampler node in the workflow.
-        
-        Priority:
-        1. First try to find a SamplerCustomAdvanced node
-        2. If not found, look for KSampler nodes with denoise=1.0
-        3. If still not found, use the first KSampler node
-        
-        Args:
-            workflow: The workflow data as a dictionary
-            
-        Returns:
-            The node ID of the primary sampler node, or None if not found
-        """
-        # First check for SamplerCustomAdvanced nodes
-        sampler_advanced_nodes = []
-        ksampler_nodes = []
-        
-        # Scan workflow for sampler nodes
-        for node_id, node_data in workflow.items():
-            node_type = node_data.get("class_type")
-            
-            if node_type == "SamplerCustomAdvanced":
-                sampler_advanced_nodes.append(node_id)
-            elif node_type == "KSampler":
-                ksampler_nodes.append(node_id)
-        
-        # If we found SamplerCustomAdvanced nodes, return the first one
-        if sampler_advanced_nodes:
-            logger.debug(f"Found SamplerCustomAdvanced node: {sampler_advanced_nodes[0]}")
-            return sampler_advanced_nodes[0]
-        
-        # If we have KSampler nodes, look for one with denoise=1.0
-        if ksampler_nodes:
-            for node_id in ksampler_nodes:
-                node_data = workflow[node_id]
-                inputs = node_data.get("inputs", {})
-                denoise = inputs.get("denoise", 0)
-                
-                # Check if denoise is 1.0 (allowing for small floating point differences)
-                if abs(float(denoise) - 1.0) < 0.001:
-                    logger.debug(f"Found KSampler node with denoise=1.0: {node_id}")
-                    return node_id
-            
-            # If no KSampler with denoise=1.0 found, use the first one
-            logger.debug(f"No KSampler with denoise=1.0 found, using first KSampler: {ksampler_nodes[0]}")
-            return ksampler_nodes[0]
-        
-        # No sampler nodes found
-        logger.warning("No sampler nodes found in workflow")
-        return None
-    
-    def parse_workflow(self, workflow_data: Union[str, Dict], output_path: Optional[str] = None) -> Dict:
-        """
-        Parse the workflow and extract generation parameters
-        
-        Args:
-            workflow_data: The workflow data as a dictionary or a file path
-            output_path: Optional path to save the output JSON
-            
-        Returns:
-            Dictionary containing extracted parameters
-        """
-        # Load workflow from file if needed
-        if isinstance(workflow_data, str):
-            workflow = load_workflow(workflow_data)
-        else:
-            workflow = workflow_data
-            
-        # Reset the processed nodes tracker and cache
-        self.processed_nodes = set()
-        self.node_results_cache = {}
-        
-        # Find the primary sampler node
-        sampler_node_id = self.find_primary_sampler_node(workflow)
-        if not sampler_node_id:
-            logger.warning("No suitable sampler node found in workflow")
-            return {}
-        
-        # Process sampler node to extract parameters
-        sampler_result = self.process_node(sampler_node_id, workflow)
-        if not sampler_result:
-            return {}
-        
-        # Return the sampler result directly - it's already in the format we need
-        # This simplifies the structure and makes it easier to use in recipe_routes.py
-        
-        # Handle standard ComfyUI names vs our output format
-        if "cfg" in sampler_result:
-            sampler_result["cfg_scale"] = sampler_result.pop("cfg")
-            
-        # Add clip_skip = 1 to match reference output if not already present
-        if "clip_skip" not in sampler_result:
-            sampler_result["clip_skip"] = "1"
-        
-        # Ensure the prompt is a string and not a nested dictionary
-        if "prompt" in sampler_result and isinstance(sampler_result["prompt"], dict):
-            if "prompt" in sampler_result["prompt"]:
-                sampler_result["prompt"] = sampler_result["prompt"]["prompt"]
-        
-        # Save the result if requested
-        if output_path:
-            save_output(sampler_result, output_path)
-            
-        return sampler_result
-
-
-def parse_workflow(workflow_path: str, output_path: Optional[str] = None) -> Dict:
-    """
-    Parse a ComfyUI workflow file and extract generation parameters
-    
-    Args:
-        workflow_path: Path to the workflow JSON file
-        output_path: Optional path to save the output JSON
-        
-    Returns:
-        Dictionary containing extracted parameters
-    """
-    parser = WorkflowParser()
-    return parser.parse_workflow(workflow_path, output_path)

py/workflow/test.py

@@ -1,63 +0,0 @@
-"""
-Test script for the ComfyUI workflow parser
-"""
-import os
-import json
-import logging
-from .parser import parse_workflow
-
-logging.basicConfig(
-    level=logging.INFO,
-    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
-    handlers=[logging.StreamHandler()]
-)
-logger = logging.getLogger(__name__)
-
-# Configure paths
-SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
-ROOT_DIR = os.path.abspath(os.path.join(SCRIPT_DIR, '..', '..'))
-REFS_DIR = os.path.join(ROOT_DIR, 'refs')
-OUTPUT_DIR = os.path.join(ROOT_DIR, 'output')
-
-def test_parse_flux_workflow():
-    """Test parsing the flux example workflow"""
-    # Ensure output directory exists
-    os.makedirs(OUTPUT_DIR, exist_ok=True)
-    
-    # Define input and output paths
-    input_path = os.path.join(REFS_DIR, 'flux_prompt.json')
-    output_path = os.path.join(OUTPUT_DIR, 'parsed_flux_output.json')
-    
-    # Parse workflow
-    logger.info(f"Parsing workflow: {input_path}")
-    result = parse_workflow(input_path, output_path)
-    
-    # Print result summary
-    logger.info(f"Output saved to: {output_path}")
-    logger.info(f"Parsing completed. Result summary:")
-    logger.info(f"  LoRAs: {result.get('loras', '')}")
-    
-    gen_params = result.get('gen_params', {})
-    logger.info(f"  Prompt: {gen_params.get('prompt', '')[:50]}...")
-    logger.info(f"  Steps: {gen_params.get('steps', '')}")
-    logger.info(f"  Sampler: {gen_params.get('sampler', '')}")
-    logger.info(f"  Size: {gen_params.get('size', '')}")
-    
-    # Compare with reference output
-    ref_output_path = os.path.join(REFS_DIR, 'flux_output.json')
-    try:
-        with open(ref_output_path, 'r') as f:
-            ref_output = json.load(f)
-            
-        # Simple validation
-        loras_match = result.get('loras', '') == ref_output.get('loras', '')
-        prompt_match = gen_params.get('prompt', '') == ref_output.get('gen_params', {}).get('prompt', '')
-        
-        logger.info(f"Validation against reference:")
-        logger.info(f"  LoRAs match: {loras_match}")
-        logger.info(f"  Prompt match: {prompt_match}")
-    except Exception as e:
-        logger.warning(f"Failed to compare with reference output: {e}")
-
-if __name__ == "__main__":
-    test_parse_flux_workflow() 

py/workflow/utils.py

@@ -1,120 +0,0 @@
-"""
-Utility functions for ComfyUI workflow parsing
-"""
-import json
-import os
-import logging
-from typing import Dict, List, Any, Optional, Union, Set, Tuple
-
-logger = logging.getLogger(__name__)
-
-def load_workflow(workflow_path: str) -> Dict:
-    """Load a workflow from a JSON file"""
-    try:
-        with open(workflow_path, 'r', encoding='utf-8') as f:
-            return json.load(f)
-    except Exception as e:
-        logger.error(f"Error loading workflow from {workflow_path}: {e}")
-        raise
-
-def save_output(output: Dict, output_path: str) -> None:
-    """Save the parsed output to a JSON file"""
-    os.makedirs(os.path.dirname(os.path.abspath(output_path)), exist_ok=True)
-    try:
-        with open(output_path, 'w', encoding='utf-8') as f:
-            json.dump(output, f, indent=4)
-    except Exception as e:
-        logger.error(f"Error saving output to {output_path}: {e}")
-        raise
-
-def find_node_by_type(workflow: Dict, node_type: str) -> Optional[str]:
-    """Find a node of the specified type in the workflow"""
-    for node_id, node_data in workflow.items():
-        if node_data.get("class_type") == node_type:
-            return node_id
-    return None
-
-def find_nodes_by_type(workflow: Dict, node_type: str) -> List[str]:
-    """Find all nodes of the specified type in the workflow"""
-    return [node_id for node_id, node_data in workflow.items() 
-            if node_data.get("class_type") == node_type]
-
-def get_input_node_ids(workflow: Dict, node_id: str) -> Dict[str, Tuple[str, int]]:
-    """
-    Get the node IDs for all inputs of the given node
-    
-    Returns a dictionary mapping input names to (node_id, output_slot) tuples
-    """
-    result = {}
-    if node_id not in workflow:
-        return result
-        
-    node_data = workflow[node_id]
-    for input_name, input_value in node_data.get("inputs", {}).items():
-        # Check if this input is connected to another node
-        if isinstance(input_value, list) and len(input_value) == 2:
-            # Input is connected to another node's output
-            # Format: [node_id, output_slot]
-            ref_node_id, output_slot = input_value
-            result[input_name] = (str(ref_node_id), output_slot)
-            
-    return result
-
-def trace_model_path(workflow: Dict, start_node_id: str) -> List[str]:
-    """
-    Trace the model path backward from KSampler to find all LoRA nodes
-    
-    Args:
-        workflow: The workflow data
-        start_node_id: The starting node ID (usually KSampler)
-        
-    Returns:
-        List of node IDs in the model path
-    """
-    model_path_nodes = []
-    
-    # Get the model input from the start node
-    if start_node_id not in workflow:
-        return model_path_nodes
-    
-    # Track visited nodes to avoid cycles
-    visited = set()
-    
-    # Stack for depth-first search
-    stack = []
-    
-    # Get model input reference if available
-    start_node = workflow[start_node_id]
-    if "inputs" in start_node and "model" in start_node["inputs"] and isinstance(start_node["inputs"]["model"], list):
-        model_ref = start_node["inputs"]["model"]
-        stack.append(str(model_ref[0]))
-    
-    # Perform depth-first search
-    while stack:
-        node_id = stack.pop()
-        
-        # Skip if already visited
-        if node_id in visited:
-            continue
-        
-        # Mark as visited
-        visited.add(node_id)
-        
-        # Skip if node doesn't exist
-        if node_id not in workflow:
-            continue
-        
-        node = workflow[node_id]
-        node_type = node.get("class_type", "")
-        
-        # Add current node to result list if it's a LoRA node
-        if "Lora" in node_type:
-            model_path_nodes.append(node_id)
-        
-        # Add all input nodes that have a "model" or "lora_stack" output to the stack
-        if "inputs" in node:
-            for input_name, input_value in node["inputs"].items():
-                if input_name in ["model", "lora_stack"] and isinstance(input_value, list) and len(input_value) == 2:
-                    stack.append(str(input_value[0]))
-    
-    return model_path_nodes 

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-lora-manager"
 description = "LoRA Manager for ComfyUI - Access it at http://localhost:8188/loras for managing LoRA models with previews and metadata integration."
-version = "0.8.10"
+version = "0.8.13"
 license = {file = "LICENSE"}
 dependencies = [
     "aiohttp",
@@ -13,7 +13,8 @@ dependencies = [
     "Pillow",
     "olefile", # for getting rid of warning message
     "requests",
-    "toml"
+    "toml",
+    "natsort"
 ]
 
 [project.urls]

refs/test_output.txt

@@ -1,294 +0,0 @@
-Loading workflow from D:\Workspace\ComfyUI\custom_nodes\ComfyUI-Lora-Manager\refs\prompt.json
-Expected output from D:\Workspace\ComfyUI\custom_nodes\ComfyUI-Lora-Manager\refs\output.json
-
-Expected output:
-{
-  "loras": "<lora:ck-neon-retrowave-IL-000012:0.8> <lora:aorunIllstrious:1> <lora:ck-shadow-circuit-IL-000012:0.78> <lora:MoriiMee_Gothic_Niji_Style_Illustrious_r1:0.45> <lora:ck-nc-cyberpunk-IL-000011:0.4>",
-  "gen_params": {
-    "prompt": "in the style of ck-rw, aorun, scales, makeup, bare shoulders, pointy ears, dress, claws, in the style of cksc, artist:moriimee, in the style of cknc, masterpiece, best quality, good quality, very aesthetic, absurdres, newest, 8K, depth of field, focused subject, close up, stylized, in gold and neon shades, wabi sabi, 1girl, rainbow angel wings, looking at viewer, dynamic angle, from below, from side, relaxing",
-    "negative_prompt": "bad quality, worst quality, worst detail, sketch ,signature, watermark, patreon logo, nsfw",
-    "steps": "20",
-    "sampler": "euler_ancestral",
-    "cfg_scale": "8",
-    "seed": "241",
-    "size": "832x1216",
-    "clip_skip": "2"
-  }
-}
-
-Sampler node:
-{
-  "inputs": {
-    "seed": 241,
-    "steps": 20,
-    "cfg": 8,
-    "sampler_name": "euler_ancestral",
-    "scheduler": "karras",
-    "denoise": 1,
-    "model": [
-      "56",
-      0
-    ],
-    "positive": [
-      "6",
-      0
-    ],
-    "negative": [
-      "7",
-      0
-    ],
-    "latent_image": [
-      "5",
-      0
-    ]
-  },
-  "class_type": "KSampler",
-  "_meta": {
-    "title": "KSampler"
-  }
-}
-
-Extracted parameters:
-seed: 241
-steps: 20
-cfg_scale: 8
-
-Positive node (6):
-{
-  "inputs": {
-    "text": [
-      "22",
-      0
-    ],
-    "clip": [
-      "56",
-      1
-    ]
-  },
-  "class_type": "CLIPTextEncode",
-  "_meta": {
-    "title": "CLIP Text Encode (Prompt)"
-  }
-}
-
-Text node (22):
-{
-  "inputs": {
-    "string1": [
-      "55",
-      0
-    ],
-    "string2": [
-      "21",
-      0
-    ],
-    "delimiter": ", "
-  },
-  "class_type": "JoinStrings",
-  "_meta": {
-    "title": "Join Strings"
-  }
-}
-
-String1 node (55):
-{
-  "inputs": {
-    "group_mode": true,
-    "toggle_trigger_words": [
-      {
-        "text": "in the style of ck-rw",
-        "active": true
-      },
-      {
-        "text": "aorun, scales, makeup, bare shoulders, pointy ears",
-        "active": true
-      },
-      {
-        "text": "dress",
-        "active": true
-      },
-      {
-        "text": "claws",
-        "active": true
-      },
-      {
-        "text": "in the style of cksc",
-        "active": true
-      },
-      {
-        "text": "artist:moriimee",
-        "active": true
-      },
-      {
-        "text": "in the style of cknc",
-        "active": true
-      },
-      {
-        "text": "__dummy_item__",
-        "active": false,
-        "_isDummy": true
-      },
-      {
-        "text": "__dummy_item__",
-        "active": false,
-        "_isDummy": true
-      }
-    ],
-    "orinalMessage": "in the style of ck-rw,, aorun, scales, makeup, bare shoulders, pointy ears,, dress,, claws,, in the style of cksc,, artist:moriimee,, in the style of cknc",
-    "trigger_words": [
-      "56",
-      2
-    ]
-  },
-  "class_type": "TriggerWord Toggle (LoraManager)",
-  "_meta": {
-    "title": "TriggerWord Toggle (LoraManager)"
-  }
-}
-
-String2 node (21):
-{
-  "inputs": {
-    "string": "masterpiece, best quality, good quality, very aesthetic, absurdres, newest, 8K, depth of field, focused subject, close up, stylized, in gold and neon shades, wabi sabi, 1girl, rainbow angel wings, looking at viewer, dynamic angle, from below, from side, relaxing",
-    "strip_newlines": false
-  },
-  "class_type": "StringConstantMultiline",
-  "_meta": {
-    "title": "positive"
-  }
-}
-
-Negative node (7):
-{
-  "inputs": {
-    "text": "bad quality, worst quality, worst detail, sketch ,signature, watermark, patreon logo, nsfw",
-    "clip": [
-      "56",
-      1
-    ]
-  },
-  "class_type": "CLIPTextEncode",
-  "_meta": {
-    "title": "CLIP Text Encode (Prompt)"
-  }
-}
-
-LoRA nodes (3):
-
-LoRA node 56:
-{
-  "inputs": {
-    "text": "<lora:ck-shadow-circuit-IL-000012:0.78> <lora:MoriiMee_Gothic_Niji_Style_Illustrious_r1:0.45> <lora:ck-nc-cyberpunk-IL-000011:0.4>",
-    "loras": [
-      {
-        "name": "ck-shadow-circuit-IL-000012",
-        "strength": 0.78,
-        "active": true
-      },
-      {
-        "name": "MoriiMee_Gothic_Niji_Style_Illustrious_r1",
-        "strength": 0.45,
-        "active": true
-      },
-      {
-        "name": "ck-nc-cyberpunk-IL-000011",
-        "strength": 0.4,
-        "active": true
-      },
-      {
-        "name": "__dummy_item1__",
-        "strength": 0,
-        "active": false,
-        "_isDummy": true
-      },
-      {
-        "name": "__dummy_item2__",
-        "strength": 0,
-        "active": false,
-        "_isDummy": true
-      }
-    ],
-    "model": [
-      "4",
-      0
-    ],
-    "clip": [
-      "4",
-      1
-    ],
-    "lora_stack": [
-      "57",
-      0
-    ]
-  },
-  "class_type": "Lora Loader (LoraManager)",
-  "_meta": {
-    "title": "Lora Loader (LoraManager)"
-  }
-}
-
-LoRA node 57:
-{
-  "inputs": {
-    "text": "<lora:aorunIllstrious:1>",
-    "loras": [
-      {
-        "name": "aorunIllstrious",
-        "strength": "0.90",
-        "active": true
-      },
-      {
-        "name": "__dummy_item1__",
-        "strength": 0,
-        "active": false,
-        "_isDummy": true
-      },
-      {
-        "name": "__dummy_item2__",
-        "strength": 0,
-        "active": false,
-        "_isDummy": true
-      }
-    ],
-    "lora_stack": [
-      "59",
-      0
-    ]
-  },
-  "class_type": "Lora Stacker (LoraManager)",
-  "_meta": {
-    "title": "Lora Stacker (LoraManager)"
-  }
-}
-
-LoRA node 59:
-{
-  "inputs": {
-    "text": "<lora:ck-neon-retrowave-IL-000012:0.8>",
-    "loras": [
-      {
-        "name": "ck-neon-retrowave-IL-000012",
-        "strength": 0.8,
-        "active": true
-      },
-      {
-        "name": "__dummy_item1__",
-        "strength": 0,
-        "active": false,
-        "_isDummy": true
-      },
-      {
-        "name": "__dummy_item2__",
-        "strength": 0,
-        "active": false,
-        "_isDummy": true
-      }
-    ]
-  },
-  "class_type": "Lora Stacker (LoraManager)",
-  "_meta": {
-    "title": "Lora Stacker (LoraManager)"
-  }
-}
-
-Test completed.

requirements.txt

@@ -9,4 +9,5 @@ olefile
 requests
 toml
 numpy
-torch
+torch
+natsort

standalone.py

@@ -127,6 +127,17 @@ class StandaloneServer:
         """Set up basic routes"""
         # Add a simple status endpoint
         self.app.router.add_get('/', self.handle_status)
+        
+        # Add static route for example images if the path exists in settings
+        settings_path = os.path.join(os.path.dirname(__file__), 'settings.json')
+        if os.path.exists(settings_path):
+            with open(settings_path, 'r', encoding='utf-8') as f:
+                settings = json.load(f)
+            example_images_path = settings.get('example_images_path')
+            logger.info(f"Example images path: {example_images_path}")
+            if example_images_path and os.path.exists(example_images_path):
+                self.app.router.add_static('/example_images_static', example_images_path)
+                logger.info(f"Added static route for example images: /example_images_static -> {example_images_path}")
     
     async def handle_status(self, request):
         """Handle status request by redirecting to loras page"""
@@ -283,7 +294,7 @@ class StandaloneLoraManager(LoraManager):
         from py.routes.recipe_routes import RecipeRoutes
         from py.routes.checkpoints_routes import CheckpointsRoutes
         from py.routes.update_routes import UpdateRoutes
-        from py.routes.usage_stats_routes import UsageStatsRoutes
+        from py.routes.misc_routes import MiscRoutes
         
         lora_routes = LoraRoutes()
         checkpoints_routes = CheckpointsRoutes()
@@ -294,7 +305,7 @@ class StandaloneLoraManager(LoraManager):
         ApiRoutes.setup_routes(app)
         RecipeRoutes.setup_routes(app)
         UpdateRoutes.setup_routes(app)
-        UsageStatsRoutes.setup_routes(app)
+        MiscRoutes.setup_routes(app)
         
         # Schedule service initialization
         app.on_startup.append(lambda app: cls._initialize_services())
@@ -344,4 +355,4 @@ if __name__ == "__main__":
         # Run the main function
         asyncio.run(main())
     except KeyboardInterrupt:
-        logger.info("Server stopped by user")
+        logger.info("Server stopped by user")

static/css/base.css

@@ -38,7 +38,7 @@ html, body {
     --lora-border: oklch(90% 0.02 256 / 0.15);
     --lora-text: oklch(95% 0.02 256);
     --lora-error: oklch(75% 0.32 29);
-    --lora-warning: oklch(75% 0.25 80); /* Add warning color for deleted LoRAs */
+    --lora-warning: oklch(75% 0.25 80); /* Modified to be used with oklch() */
     
     /* Spacing Scale */
     --space-1: calc(8px * 1);
@@ -79,7 +79,7 @@ html[data-theme="light"] {
     --lora-surface: oklch(25% 0.02 256 / 0.98);
     --lora-border: oklch(90% 0.02 256 / 0.15);
     --lora-text: oklch(98% 0.02 256);
-    --lora-warning: oklch(75% 0.25 80); /* Add warning color for dark theme too */
+    --lora-warning: oklch(75% 0.25 80); /* Modified to be used with oklch() */
 }
 
 body {

static/css/components/alphabet-bar.css

@@ -0,0 +1,165 @@
+/* Alphabet Bar Component */
+.alphabet-bar-container {
+    position: fixed;
+    left: 0;
+    top: 50%;
+    transform: translateY(-50%);
+    z-index: 100;
+    display: flex;
+    transition: transform 0.3s ease;
+}
+
+.alphabet-bar-container.collapsed {
+    transform: translateY(-50%) translateX(-90%);
+}
+
+/* New visual indicator for when a letter is active and bar is collapsed */
+.alphabet-bar-container.collapsed .toggle-alphabet-bar.has-active-letter {
+    border-color: var(--lora-accent);
+    background: oklch(var(--lora-accent) / 0.15);
+}
+
+.alphabet-bar-container.collapsed .toggle-alphabet-bar.has-active-letter::after {
+    content: '';
+    position: absolute;
+    top: 7px;
+    right: 7px;
+    width: 8px;
+    height: 8px;
+    background-color: var(--lora-accent);
+    border-radius: 50%;
+    animation: pulse-active 2s infinite;
+}
+
+@keyframes pulse-active {
+    0% { transform: scale(0.8); opacity: 0.7; }
+    50% { transform: scale(1.1); opacity: 1; }
+    100% { transform: scale(0.8); opacity: 0.7; }
+}
+
+.alphabet-bar {
+    background: var(--card-bg);
+    border: 1px solid var(--border-color);
+    border-radius: 0 var(--border-radius-xs) var(--border-radius-xs) 0;
+    padding: 8px 4px;
+    display: flex;
+    flex-direction: column;
+    gap: 6px;
+    align-items: center;
+    box-shadow: 2px 0 8px rgba(0, 0, 0, 0.1);
+    max-height: 80vh;
+    overflow-y: auto;
+    scrollbar-width: thin;
+}
+
+.alphabet-bar::-webkit-scrollbar {
+    width: 4px;
+}
+
+.alphabet-bar::-webkit-scrollbar-thumb {
+    background: var(--border-color);
+    border-radius: 4px;
+}
+
+.toggle-alphabet-bar {
+    background: var(--card-bg);
+    border: 1px solid var(--border-color);
+    border-left: none;
+    border-radius: 0 var(--border-radius-xs) var(--border-radius-xs) 0;
+    padding: 8px 4px;
+    cursor: pointer;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    color: var(--text-color);
+    width: 20px;
+    height: 40px;
+    align-self: center;
+    box-shadow: 2px 0 8px rgba(0, 0, 0, 0.1);
+}
+
+.toggle-alphabet-bar:hover {
+    background: var(--bg-hover);
+}
+
+.toggle-alphabet-bar i {
+    transition: transform 0.3s ease;
+}
+
+.alphabet-bar-container.collapsed .toggle-alphabet-bar i {
+    transform: rotate(180deg);
+}
+
+.letter-chip {
+    padding: 4px 2px;
+    border-radius: var(--border-radius-xs);
+    background: var(--bg-color);
+    color: var(--text-color);
+    cursor: pointer;
+    min-width: 24px;
+    text-align: center;
+    font-size: 0.85em;
+    transition: all 0.2s ease;
+    border: 1px solid var(--border-color);
+}
+
+.letter-chip:hover {
+    background: var(--lora-accent);
+    color: white;
+    transform: scale(1.1);
+    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
+}
+
+.letter-chip.active {
+    background: var(--lora-accent);
+    color: white;
+    border-color: var(--lora-accent);
+}
+
+.letter-chip.disabled {
+    opacity: 0.5;
+    pointer-events: none;
+    cursor: default;
+}
+
+/* Hide the count by default, only show in tooltip */
+.letter-chip .count {
+    display: none;
+}
+
+.alphabet-bar-title {
+    font-size: 0.75em;
+    color: var(--text-color);
+    opacity: 0.7;
+    margin-bottom: 6px;
+    writing-mode: vertical-lr;
+    transform: rotate(180deg);
+    white-space: nowrap;
+}
+
+@media (max-width: 768px) {
+    .alphabet-bar-container {
+        transform: translateY(-50%) translateX(-90%);
+    }
+    
+    .alphabet-bar-container.active {
+        transform: translateY(-50%) translateX(0);
+    }
+    
+    .letter-chip {
+        padding: 3px 1px;
+        min-width: 20px;
+        font-size: 0.75em;
+    }
+}
+
+/* Keyframe animations for the active letter */
+@keyframes pulse {
+    0% { transform: scale(1); }
+    50% { transform: scale(1.1); }
+    100% { transform: scale(1); }
+}
+
+.letter-chip.active {
+    animation: pulse 1s ease-in-out 1;
+}

static/css/components/download-modal.css

@@ -190,14 +190,6 @@
     border-color: var(--lora-border);
 }
 
-/* Add disabled button styles */
-.primary-btn.disabled {
-    background-color: var(--border-color);
-    color: var(--text-color);
-    opacity: 0.7;
-    cursor: not-allowed;
-}
-
 /* Enhance the local badge to make it more noticeable */
 .version-item.exists-locally {
     background: oklch(var(--lora-accent) / 0.05);

static/css/components/duplicates.css

@@ -0,0 +1,259 @@
+/* Duplicates Management Styles */
+
+/* Duplicates banner */
+.duplicates-banner {
+    position: sticky;
+    top: 48px; /* Match header height */
+    left: 0;
+    width: 100%;
+    background-color: var(--card-bg);
+    color: var(--text-color);
+    border-bottom: 1px solid var(--border-color);
+    z-index: var(--z-overlay);
+    padding: 12px 16px;
+    box-shadow: 0 2px 8px rgba(0, 0, 0, 0.15);
+    transition: all 0.3s ease;
+}
+
+.duplicates-banner .banner-content {
+    max-width: 1400px;
+    margin: 0 auto;
+    display: flex;
+    align-items: center;
+    gap: 12px;
+}
+
+.duplicates-banner i.fa-exclamation-triangle {
+    font-size: 18px;
+    color: oklch(var(--lora-warning));
+}
+
+.duplicates-banner .banner-actions {
+    margin-left: auto;
+    display: flex;
+    gap: 8px;
+    align-items: center;
+}
+
+.duplicates-banner button {
+    min-width: 100px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    gap: 4px;
+    border-radius: var(--border-radius-xs);
+    padding: 4px 10px;
+    border: 1px solid var(--border-color);
+    background: var(--card-bg);
+    color: var(--text-color);
+    font-size: 0.85em;
+    transition: all 0.2s ease;
+    cursor: pointer;
+    box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
+}
+
+.duplicates-banner button:hover {
+    border-color: var(--lora-accent);
+    background: var(--bg-color);
+    transform: translateY(-1px);
+    box-shadow: 0 3px 5px rgba(0, 0, 0, 0.08);
+}
+
+.duplicates-banner button.btn-exit {
+    min-width: unset;
+    width: 28px;
+    height: 28px;
+    padding: 0;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    border-radius: 50%;
+}
+
+.duplicates-banner button.disabled {
+    opacity: 0.5;
+    cursor: not-allowed;
+}
+
+/* Duplicate groups */
+.duplicate-group {
+    position: relative;
+    border: 2px solid oklch(var(--lora-warning));
+    border-radius: var(--border-radius-base);
+    padding: 16px;
+    margin-bottom: 24px;
+    background: var(--card-bg);
+}
+
+.duplicate-group-header {
+    background-color: var(--bg-color);
+    color: var(--text-color);
+    border: 1px solid var(--border-color);
+    padding: 8px 16px;
+    border-radius: var(--border-radius-xs);
+    margin-bottom: 16px;
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+}
+
+.duplicate-group-header span:last-child {
+    display: flex;
+    gap: 8px;
+    align-items: center;
+}
+
+.duplicate-group-header button {
+    min-width: 80px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    gap: 4px;
+    border-radius: var(--border-radius-xs);
+    padding: 4px 8px;
+    border: 1px solid var(--border-color);
+    background: var(--card-bg);
+    color: var(--text-color);
+    font-size: 0.85em;
+    transition: all 0.2s ease;
+    cursor: pointer;
+    box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
+    margin-left: 8px;
+}
+
+.duplicate-group-header button:hover {
+    border-color: var(--lora-accent);
+    background: var(--bg-color);
+    transform: translateY(-1px);
+    box-shadow: 0 3px 5px rgba(0, 0, 0, 0.08);
+}
+
+.card-group-container {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 16px;
+    justify-content: flex-start;
+    align-items: flex-start;
+}
+
+/* Make cards in duplicate groups have consistent width */
+.card-group-container .lora-card {
+    flex: 0 0 auto;
+    width: 240px;
+    margin: 0;
+    cursor: pointer; /* Indicate the card is clickable */
+}
+
+/* Ensure the grid layout is only applied to the main recipe grid, not duplicate groups */
+.duplicate-mode .card-grid {
+    display: block;
+}
+
+/* Scrollable container for large duplicate groups */
+.card-group-container.scrollable {
+    max-height: 450px;
+    overflow-y: auto;
+    padding-right: 8px;
+}
+
+/* Add a toggle button to expand/collapse large duplicate groups */
+.group-toggle-btn {
+    position: absolute;
+    right: 16px;
+    bottom: -12px;
+    background: var(--card-bg);
+    color: var(--text-color);
+    border: 1px solid var(--border-color);
+    border-radius: 50%;
+    width: 24px;
+    height: 24px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    cursor: pointer;
+    z-index: 1;
+    box-shadow: 0 1px 3px rgba(0, 0, 0, 0.1);
+    transition: all 0.2s ease;
+}
+
+.group-toggle-btn:hover {
+    border-color: var(--lora-accent);
+    transform: translateY(-1px);
+    box-shadow: 0 3px 5px rgba(0, 0, 0, 0.08);
+}
+
+/* Duplicate card styling */
+.lora-card.duplicate {
+    position: relative;
+    transition: all 0.2s ease;
+}
+
+.lora-card.duplicate:hover {
+    border-color: var(--lora-accent);
+}
+
+.lora-card.duplicate.latest {
+    border-style: solid;
+    border-color: oklch(var(--lora-warning));
+}
+
+.lora-card.duplicate-selected {
+    border: 2px solid oklch(var(--lora-accent));
+    box-shadow: 0 0 8px rgba(0, 0, 0, 0.2);
+}
+
+.lora-card .selector-checkbox {
+    position: absolute;
+    top: 10px;
+    right: 10px;
+    z-index: 10;
+    width: 20px;
+    height: 20px;
+    cursor: pointer;
+}
+
+/* Latest indicator */
+.lora-card.duplicate.latest::after {
+    content: "Latest";
+    position: absolute;
+    top: 10px;
+    left: 10px;
+    background: oklch(var(--lora-accent));
+    color: white;
+    font-size: 12px;
+    padding: 2px 6px;
+    border-radius: var(--border-radius-xs);
+    z-index: 5;
+}
+
+/* Responsive adjustments */
+@media (max-width: 768px) {
+    .duplicates-banner .banner-content {
+        flex-direction: column;
+        align-items: flex-start;
+        gap: 8px;
+    }
+    
+    .duplicates-banner .banner-actions {
+        width: 100%;
+        margin-left: 0;
+        justify-content: space-between;
+    }
+    
+    .duplicate-group-header {
+        flex-direction: column;
+        gap: 8px;
+        align-items: flex-start;
+    }
+    
+    .duplicate-group-header span:last-child {
+        display: flex;
+        gap: 8px;
+        width: 100%;
+    }
+    
+    .duplicate-group-header button {
+        margin-left: 0;
+        flex: 1;
+    }
+}

static/css/components/import-modal.css

@@ -291,7 +291,7 @@
     gap: 8px;
     padding: var(--space-1);
     border: 1px solid var(--border-color);
-    border-radius: var(--border-radius-sm);
+    border-radius: var (--border-radius-sm);
     background: var(--lora-surface);
 }
 
@@ -733,3 +733,150 @@
     font-size: 0.9em;
     line-height: 1.4;
 }
+
+/* Duplicate Recipes Styles */
+.duplicate-recipes-container {
+    margin-bottom: var(--space-3);
+    border-radius: var(--border-radius-sm);
+    overflow: hidden;
+    animation: fadeIn 0.3s ease-in-out;
+}
+
+@keyframes fadeIn {
+    from { opacity: 0; transform: translateY(-10px); }
+    to { opacity: 1; transform: translateY(0); }
+}
+
+.duplicate-warning {
+    display: flex;
+    align-items: flex-start;
+    gap: 12px;
+    padding: 12px 16px;
+    background: oklch(var(--lora-warning) / 0.1);
+    border: 1px solid var(--lora-warning);
+    border-radius: var(--border-radius-sm) var(--border-radius-sm) 0 0;
+    color: var(--text-color);
+}
+
+.duplicate-warning .warning-icon {
+    color: var(--lora-warning);
+    font-size: 1.2em;
+    padding-top: 2px;
+}
+
+.duplicate-warning .warning-content {
+    flex: 1;
+}
+
+.duplicate-warning .warning-title {
+    font-weight: 600;
+    margin-bottom: 4px;
+}
+
+.duplicate-warning .warning-text {
+    font-size: 0.9em;
+    line-height: 1.4;
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    flex-wrap: wrap;
+    gap: 8px;
+}
+
+.toggle-duplicates-btn {
+    background: none;
+    border: none;
+    color: var(--lora-warning);
+    cursor: pointer;
+    font-size: 0.9em;
+    display: flex;
+    align-items: center;
+    gap: 6px;
+    padding: 4px 8px;
+    border-radius: var(--border-radius-xs);
+}
+
+.toggle-duplicates-btn:hover {
+    background: oklch(var(--lora-warning) / 0.1);
+}
+
+.duplicate-recipes-list {
+    display: grid;
+    grid-template-columns: repeat(auto-fill, minmax(150px, 1fr));
+    gap: 12px;
+    padding: 16px;
+    border: 1px solid var(--border-color);
+    border-top: none;
+    border-radius: 0 0 var(--border-radius-sm) var(--border-radius-sm);
+    background: var(--bg-color);
+    max-height: 300px;
+    overflow-y: auto;
+    transition: max-height 0.3s ease, padding 0.3s ease;
+}
+
+.duplicate-recipes-list.collapsed {
+    max-height: 0;
+    padding: 0 16px;
+    overflow: hidden;
+}
+
+.duplicate-recipe-card {
+    position: relative;
+    border-radius: var(--border-radius-sm);
+    overflow: hidden;
+    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
+    transition: transform 0.2s ease;
+}
+
+.duplicate-recipe-card:hover {
+    transform: translateY(-2px);
+}
+
+.duplicate-recipe-preview {
+    width: 100%;
+    position: relative;
+    aspect-ratio: 2/3;
+    background: var(--bg-color);
+}
+
+.duplicate-recipe-preview img {
+    width: 100%;
+    height: 100%;
+    object-fit: cover;
+}
+
+.duplicate-recipe-title {
+    position: absolute;
+    bottom: 0;
+    left: 0;
+    right: 0;
+    padding: 8px;
+    background: rgba(0, 0, 0, 0.7);
+    color: white;
+    font-size: 0.85em;
+    line-height: 1.3;
+    max-height: 50%;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    display: -webkit-box;
+    -webkit-line-clamp: 2;
+    -webkit-box-orient: vertical;
+}
+
+.duplicate-recipe-details {
+    padding: 8px;
+    background: var(--bg-color);
+    font-size: 0.75em;
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    color: var(--text-color);
+    opacity: 0.8;
+}
+
+.duplicate-recipe-date, 
+.duplicate-recipe-lora-count {
+    display: flex;
+    align-items: center;
+    gap: 4px;
+}

static/css/components/lora-modal.css

@@ -1133,8 +1133,8 @@
     pointer-events: none;
 }
 
-/* Show metadata panel only on hover */
-.media-wrapper:hover .image-metadata-panel {
+/* Show metadata panel only when the 'visible' class is added */
+.media-wrapper .image-metadata-panel.visible {
     transform: translateY(0);
     opacity: 0.98;
     pointer-events: auto;

static/css/components/modal.css

@@ -44,26 +44,12 @@ body.modal-open {
 }
 
 /* Delete Modal specific styles */
-.delete-modal-content {
-    max-width: 500px;
-    text-align: center;
-}
 
 .delete-message {
     color: var(--text-color);
     margin: var(--space-2) 0;
 }
 
-.delete-model-info {
-    background: var(--lora-surface);
-    border: 1px solid var(--lora-border);
-    border-radius: var(--border-radius-sm);
-    padding: var(--space-2);
-    margin: var(--space-2) 0;
-    color: var(--text-color);
-    word-break: break-all;
-}
-
 /* Update delete modal styles */
 .delete-modal {
     display: none;  /* Set initial display to none */
@@ -92,7 +78,8 @@ body.modal-open {
     animation: modalFadeIn 0.2s ease-out;
 }
 
-.delete-model-info {
+.delete-model-info,
+.exclude-model-info {
     /* Update info display styling */
     background: var(--lora-surface);
     border: 1px solid var(--lora-border);
@@ -123,7 +110,7 @@ body.modal-open {
     margin-top: var(--space-3);
 }
 
-.cancel-btn, .delete-btn {
+.cancel-btn, .delete-btn, .exclude-btn {
     padding: 8px var(--space-2);
     border-radius: 6px;
     border: none;
@@ -143,6 +130,12 @@ body.modal-open {
     color: white;
 }
 
+/* Style for exclude button - different from delete button */
+.exclude-btn {
+    background: var(--lora-accent, #4f46e5);
+    color: white;
+}
+
 .cancel-btn:hover {
     background: var(--lora-border);
 }
@@ -151,6 +144,11 @@ body.modal-open {
     opacity: 0.9;
 }
 
+.exclude-btn:hover {
+    opacity: 0.9;
+    background: oklch(from var(--lora-accent, #4f46e5) l c h / 85%);
+}
+
 .modal-content h2 {
     color: var(--text-color);
     margin-bottom: var(--space-2);
@@ -496,6 +494,107 @@ input:checked + .toggle-slider:before {
     filter: blur(8px);
 }
 
+/* Example Images Settings Styles */
+.download-buttons {
+    justify-content: flex-start;
+    gap: var(--space-2);
+}
+
+.primary-btn {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    padding: 8px 16px;
+    background-color: var(--lora-accent);
+    color: var(--lora-text);
+    border: none;
+    border-radius: var(--border-radius-sm);
+    cursor: pointer;
+    transition: background-color 0.2s;
+    font-size: 0.95em;
+}
+
+.primary-btn:hover {
+    background-color: oklch(from var(--lora-accent) l c h / 85%);
+    color: var(--lora-text);
+}
+
+/* Secondary button styles */
+.secondary-btn {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    padding: 8px 16px;
+    background-color: var(--card-bg);
+    color: var(--text-color);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-sm);
+    cursor: pointer;
+    transition: all 0.2s;
+    font-size: 0.95em;
+}
+
+.secondary-btn:hover {
+    background-color: var(--border-color);
+    color: var(--text-color);
+}
+
+/* Disabled button styles */
+.primary-btn.disabled {
+    opacity: 0.5;
+    cursor: not-allowed;
+    background-color: var(--lora-accent);
+    color: var(--lora-text);
+    pointer-events: none;
+}
+
+.secondary-btn.disabled {
+    opacity: 0.5;
+    cursor: not-allowed;
+    pointer-events: none;
+}
+
+/* Dark theme specific button adjustments */
+[data-theme="dark"] .primary-btn:hover {
+    background-color: oklch(from var(--lora-accent) l c h / 75%);
+}
+
+[data-theme="dark"] .secondary-btn {
+    background-color: var(--lora-surface);
+}
+
+[data-theme="dark"] .secondary-btn:hover {
+    background-color: oklch(35% 0.02 256 / 0.98);
+}
+
+.primary-btn.disabled {
+    opacity: 0.5;
+    cursor: not-allowed;
+}
+
+.path-control {
+    display: flex;
+    gap: 8px;
+    align-items: center;
+    width: 100%;
+}
+
+.path-control input[type="text"] {
+    flex: 1;
+    padding: 6px 10px;
+    border-radius: var(--border-radius-xs);
+    border: 1px solid var(--border-color);
+    background-color: var(--lora-surface);
+    color: var (--text-color);
+    font-size: 0.95em;
+    height: 32px;
+}
+
+.primary-btn.disabled {
+    opacity: 0.5;
+    cursor: not-allowed;
+}
+
 /* Add styles for delete preview image */
 .delete-preview {
     max-width: 150px;

static/css/components/progress-panel.css

@@ -0,0 +1,217 @@
+/* Progress Panel Styles */
+.progress-panel {
+    position: fixed;
+    bottom: 20px;
+    right: 20px;
+    width: 350px;
+    background: var(--lora-surface);
+    border: 1px solid var(--lora-border);
+    border-radius: var(--border-radius-sm);
+    box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1);
+    z-index: calc(var(--z-modal) - 1);
+    transition: transform 0.3s ease, opacity 0.3s ease;
+    opacity: 0;
+    transform: translateY(20px);
+    pointer-events: none; /* Ignore mouse events when invisible */
+}
+
+.progress-panel.visible {
+    opacity: 1;
+    transform: translateY(0);
+    pointer-events: auto; /* Capture mouse events when visible */
+}
+
+.progress-panel.collapsed .progress-panel-content {
+    display: none;
+}
+
+.progress-panel.collapsed .progress-panel-header {
+    border-bottom: none;
+    padding-bottom: calc(var(--space-2) + 12px);
+}
+
+.progress-panel-header {
+    padding: var(--space-2);
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    border-bottom: 1px solid var(--lora-border);
+}
+
+.progress-panel-title {
+    font-weight: 500;
+    color: var(--text-color);
+    display: flex;
+    align-items: center;
+    gap: 8px;
+}
+
+.progress-panel-actions {
+    display: flex;
+    gap: 6px;
+}
+
+.icon-button {
+    background: none;
+    border: none;
+    color: var(--text-color);
+    width: 24px;
+    height: 24px;
+    border-radius: 50%;
+    cursor: pointer;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    opacity: 0.6;
+    transition: all 0.2s;
+    position: relative;
+}
+
+.icon-button:hover {
+    opacity: 1;
+    background: rgba(0, 0, 0, 0.05);
+}
+
+[data-theme="dark"] .icon-button:hover {
+    background: rgba(255, 255, 255, 0.1);
+}
+
+.progress-panel-content {
+    padding: var(--space-2);
+}
+
+.download-progress-info {
+    margin-bottom: var(--space-2);
+}
+
+.progress-status {
+    display: flex;
+    justify-content: space-between;
+    margin-bottom: 8px;
+    font-size: 0.9em;
+    color: var(--text-color);
+}
+
+/* Use specific selectors to avoid conflicts with loading.css */
+.progress-panel .progress-container {
+    width: 100%;
+    background-color: var(--lora-border);
+    border-radius: 4px;
+    overflow: hidden;
+    height: var(--space-1);
+}
+
+.progress-panel .progress-bar {
+    width: 0%;
+    height: 100%;
+    background-color: var(--lora-accent);
+    transition: width 0.5s ease;
+}
+
+.current-model-info {
+    background: var(--bg-color);
+    border-radius: var(--border-radius-xs);
+    padding: 8px;
+    margin-bottom: var(--space-2);
+    font-size: 0.95em;
+}
+
+.current-label {
+    font-size: 0.85em;
+    color: var(--text-color);
+    opacity: 0.7;
+    margin-bottom: 4px;
+}
+
+.current-model-name {
+    white-space: nowrap;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    color: var(--text-color);
+}
+
+.download-stats {
+    display: flex;
+    justify-content: space-between;
+    margin-bottom: var(--space-2);
+}
+
+.stat-item {
+    font-size: 0.9em;
+    color: var(--text-color);
+}
+
+.stat-label {
+    opacity: 0.7;
+    margin-right: 4px;
+}
+
+.download-errors {
+    background: oklch(var(--lora-warning) / 0.1);
+    border: 1px solid var(--lora-warning);
+    border-radius: var(--border-radius-xs);
+    padding: var(--space-1);
+    max-height: 100px;
+    overflow-y: auto;
+    font-size: 0.85em;
+}
+
+.error-header {
+    color: var(--lora-warning);
+    font-weight: 500;
+    margin-bottom: 4px;
+}
+
+.error-list {
+    color: var(--text-color);
+    opacity: 0.85;
+}
+
+.hidden {
+    display: none !important;
+}
+
+/* Mini progress indicator on pause button when panel collapsed */
+.mini-progress-container {
+    position: absolute;
+    top: 0;
+    left: 0;
+    width: 100%;
+    height: 100%;
+    border-radius: 50%;
+    pointer-events: none;
+    opacity: 0; /* Hide by default */
+    transition: opacity 0.2s ease;
+}
+
+/* Show mini progress when panel is collapsed */
+.progress-panel.collapsed .mini-progress-container {
+    opacity: 1;
+}
+
+.mini-progress-circle {
+    stroke: var(--lora-accent);
+    fill: none;
+    stroke-width: 2.5;
+    stroke-linecap: round;
+    transform: rotate(-90deg);
+    transform-origin: center;
+    transition: stroke-dashoffset 0.3s ease;
+}
+
+.mini-progress-background {
+    stroke: var(--lora-border);
+    fill: none;
+    stroke-width: 2;
+}
+
+.progress-percent {
+    position: absolute;
+    top: 100%;
+    left: 50%;
+    transform: translateX(-50%);
+    font-size: 0.65em;
+    color: var(--text-color);
+    opacity: 0.8;
+    white-space: nowrap;
+}

static/css/components/recipe-modal.css

@@ -229,8 +229,10 @@
     background: var(--lora-surface);
     border: 1px solid var(--border-color);
     display: flex;
+    flex-direction: column;
     align-items: center;
     justify-content: center;
+    position: relative;
 }
 
 .recipe-preview-container img,
@@ -246,6 +248,133 @@
     object-fit: contain;
 }
 
+/* Source URL container */
+.source-url-container {
+    position: absolute;
+    bottom: 0;
+    left: 0;
+    right: 0;
+    background: rgba(0, 0, 0, 0.5);
+    padding: 8px 12px;
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    transition: transform 0.3s ease;
+    transform: translateY(100%);
+}
+
+.recipe-preview-container:hover .source-url-container {
+    transform: translateY(0);
+}
+
+.source-url-container.active {
+    transform: translateY(0);
+}
+
+.source-url-content {
+    display: flex;
+    align-items: center;
+    color: #fff;
+    flex: 1;
+    overflow: hidden;
+    font-size: 0.85em;
+}
+
+.source-url-icon {
+    margin-right: 8px;
+    flex-shrink: 0;
+}
+
+.source-url-text {
+    white-space: nowrap;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    cursor: pointer;
+    flex: 1;
+}
+
+.source-url-edit-btn {
+    background: none;
+    border: none;
+    color: #fff;
+    cursor: pointer;
+    padding: 4px;
+    margin-left: 8px;
+    border-radius: var(--border-radius-xs);
+    opacity: 0.7;
+    transition: opacity 0.2s ease;
+    flex-shrink: 0;
+}
+
+.source-url-edit-btn:hover {
+    opacity: 1;
+    background: rgba(255, 255, 255, 0.1);
+}
+
+/* Source URL editor */
+.source-url-editor {
+    display: none;
+    position: absolute;
+    bottom: 0;
+    left: 0;
+    right: 0;
+    background: var(--bg-color);
+    border-top: 1px solid var(--border-color);
+    padding: 12px;
+    flex-direction: column;
+    gap: 10px;
+    z-index: 5;
+}
+
+.source-url-editor.active {
+    display: flex;
+}
+
+.source-url-input {
+    width: 100%;
+    padding: 8px 10px;
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-xs);
+    background: var(--bg-color);
+    color: var(--text-color);
+    font-size: 0.9em;
+}
+
+.source-url-actions {
+    display: flex;
+    justify-content: flex-end;
+    gap: 8px;
+}
+
+.source-url-cancel-btn, 
+.source-url-save-btn {
+    padding: 6px 12px;
+    border-radius: var(--border-radius-xs);
+    font-size: 0.85em;
+    cursor: pointer;
+    border: none;
+    transition: all 0.2s;
+}
+
+.source-url-cancel-btn {
+    background: var(--bg-color);
+    color: var(--text-color);
+    border: 1px solid var(--border-color);
+}
+
+.source-url-save-btn {
+    background: var(--lora-accent);
+    color: white;
+}
+
+.source-url-cancel-btn:hover {
+    background: var(--lora-surface);
+}
+
+.source-url-save-btn:hover {
+    background: color-mix(in oklch, var(--lora-accent), black 10%);
+}
+
 /* Generation Parameters */
 .recipe-gen-params {
     height: 360px;

static/css/components/support-modal.css

@@ -117,9 +117,50 @@
     box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
 }
 
+/* QR Code section styles */
+.qrcode-toggle {
+    width: 100%;
+    margin-top: var(--space-2);
+    justify-content: center;
+    position: relative;
+}
+
+.qrcode-toggle .toggle-icon {
+    margin-left: 8px;
+    transition: transform 0.3s ease;
+}
+
+.qrcode-toggle.active .toggle-icon {
+    transform: rotate(180deg);
+}
+
+.qrcode-container {
+    max-height: 0;
+    overflow: hidden;
+    transition: max-height 0.4s ease, opacity 0.3s ease;
+    opacity: 0;
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+}
+
+.qrcode-container.show {
+    max-height: 500px;
+    opacity: 1;
+    margin-top: var(--space-3);
+}
+
+.qrcode-image {
+    max-width: 80%;
+    height: auto;
+    border-radius: var(--border-radius-sm);
+    box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);
+    border: 1px solid var(--lora-border);
+    aspect-ratio: 1/1; /* Ensure proper aspect ratio for the square QR code */
+}
+
 .support-footer {
     text-align: center;
-    margin-top: var(--space-1);
     font-style: italic;
     color: var(--text-color);
 }

static/css/layout.css

@@ -260,8 +260,8 @@
 /* Back to Top Button */
 .back-to-top {
     position: fixed;
-    bottom: 20px;
-    right: 20px;
+    bottom: 85px;
+    right: 30px;
     width: 36px;
     height: 36px;
     border-radius: 50%;

static/css/style.css

@@ -20,6 +20,9 @@
 @import 'components/shared.css';
 @import 'components/filter-indicator.css';
 @import 'components/initialization.css';
+@import 'components/progress-panel.css';
+@import 'components/alphabet-bar.css'; /* Add alphabet bar component */
+@import 'components/duplicates.css'; /* Add duplicates component */
 
 .initialization-notice {
     display: flex;

static/js/api/baseModelApi.js

@@ -49,6 +49,11 @@ export async function loadMoreModels(options = {}) {
         if (pageState.showFavoritesOnly) {
             params.append('favorites_only', 'true');
         }
+        
+        // Add active letter filter if set
+        if (pageState.activeLetterFilter) {
+            params.append('first_letter', pageState.activeLetterFilter);
+        }
 
         // Add search parameters if there's a search term
         if (pageState.filters?.search) {
@@ -203,13 +208,44 @@ export function replaceModelPreview(filePath, modelType = 'lora') {
 }
 
 // Delete a model (generic)
-export function deleteModel(filePath, modelType = 'lora') {
-    if (modelType === 'checkpoint') {
-        confirmDelete('Are you sure you want to delete this checkpoint?', () => {
-            performDelete(filePath, modelType);
+export async function deleteModel(filePath, modelType = 'lora') {
+    try {
+        const endpoint = modelType === 'checkpoint' 
+            ? '/api/checkpoints/delete' 
+            : '/api/delete_model';
+            
+        const response = await fetch(endpoint, {
+            method: 'POST',
+            headers: {
+                'Content-Type': 'application/json',
+            },
+            body: JSON.stringify({
+                file_path: filePath
+            })
         });
-    } else {
-        showDeleteModal(filePath);
+        
+        if (!response.ok) {
+            throw new Error(`Failed to delete ${modelType}: ${response.statusText}`);
+        }
+        
+        const data = await response.json();
+        
+        if (data.success) {
+            // Remove the card from UI
+            const card = document.querySelector(`.lora-card[data-filepath="${filePath}"]`);
+            if (card) {
+                card.remove();
+            }
+            
+            showToast(`${modelType} deleted successfully`, 'success');
+            return true;
+        } else {
+            throw new Error(data.error || `Failed to delete ${modelType}`);
+        }
+    } catch (error) {
+        console.error(`Error deleting ${modelType}:`, error);
+        showToast(`Failed to delete ${modelType}: ${error.message}`, 'error');
+        return false;
     }
 }
 
@@ -389,6 +425,48 @@ export async function refreshSingleModelMetadata(filePath, modelType = 'lora') {
     }
 }
 
+// Generic function to exclude a model
+export async function excludeModel(filePath, modelType = 'lora') {
+    try {
+        const endpoint = modelType === 'checkpoint' 
+            ? '/api/checkpoints/exclude' 
+            : '/api/loras/exclude';
+            
+        const response = await fetch(endpoint, {
+            method: 'POST',
+            headers: {
+                'Content-Type': 'application/json',
+            },
+            body: JSON.stringify({
+                file_path: filePath
+            })
+        });
+        
+        if (!response.ok) {
+            throw new Error(`Failed to exclude ${modelType}: ${response.statusText}`);
+        }
+        
+        const data = await response.json();
+        
+        if (data.success) {
+            // Remove the card from UI
+            const card = document.querySelector(`.lora-card[data-filepath="${filePath}"]`);
+            if (card) {
+                card.remove();
+            }
+            
+            showToast(`${modelType} excluded successfully`, 'success');
+            return true;
+        } else {
+            throw new Error(data.error || `Failed to exclude ${modelType}`);
+        }
+    } catch (error) {
+        console.error(`Error excluding ${modelType}:`, error);
+        showToast(`Failed to exclude ${modelType}: ${error.message}`, 'error');
+        return false;
+    }
+}
+
 // Private methods
 
 // Upload a preview image

static/js/api/checkpointApi.js

@@ -6,7 +6,8 @@ import {
     deleteModel as baseDeleteModel,
     replaceModelPreview,
     fetchCivitaiMetadata,
-    refreshSingleModelMetadata
+    refreshSingleModelMetadata,
+    excludeModel as baseExcludeModel
 } from './baseModelApi.js';
 
 // Load more checkpoints with pagination
@@ -85,4 +86,13 @@ export async function saveModelMetadata(filePath, data) {
     }
     
     return response.json();
+}
+
+/**
+ * Exclude a checkpoint model from being shown in the UI
+ * @param {string} filePath - File path of the checkpoint to exclude
+ * @returns {Promise<boolean>} Promise resolving to success status
+ */
+export function excludeCheckpoint(filePath) {
+    return baseExcludeModel(filePath, 'checkpoint');
 }

static/js/api/loraApi.js

@@ -6,7 +6,8 @@ import {
     deleteModel as baseDeleteModel,
     replaceModelPreview,
     fetchCivitaiMetadata,
-    refreshSingleModelMetadata
+    refreshSingleModelMetadata,
+    excludeModel as baseExcludeModel
 } from './baseModelApi.js';
 
 /**
@@ -34,6 +35,15 @@ export async function saveModelMetadata(filePath, data) {
     return response.json();
 }
 
+/**
+ * Exclude a lora model from being shown in the UI
+ * @param {string} filePath - File path of the model to exclude
+ * @returns {Promise<boolean>} Promise resolving to success status
+ */
+export async function excludeLora(filePath) {
+    return baseExcludeModel(filePath, 'lora');
+}
+
 export async function loadMoreLoras(resetPage = false, updateFolders = false) {
     return loadMoreModels({
         resetPage,

static/js/checkpoints.js

@@ -1,6 +1,6 @@
 import { appCore } from './core.js';
 import { initializeInfiniteScroll } from './utils/infiniteScroll.js';
-import { confirmDelete, closeDeleteModal } from './utils/modalUtils.js';
+import { confirmDelete, closeDeleteModal, confirmExclude, closeExcludeModal } from './utils/modalUtils.js';
 import { createPageControls } from './components/controls/index.js';
 import { loadMoreCheckpoints } from './api/checkpointApi.js';
 import { CheckpointDownloadManager } from './managers/CheckpointDownloadManager.js';
@@ -23,6 +23,8 @@ class CheckpointsPageManager {
         // Minimal set of functions that need to remain global
         window.confirmDelete = confirmDelete;
         window.closeDeleteModal = closeDeleteModal;
+        window.confirmExclude = confirmExclude;
+        window.closeExcludeModal = closeExcludeModal;
         
         // Add loadCheckpoints function to window for FilterManager compatibility
         window.checkpointManager = {

static/js/components/CheckpointCard.js

@@ -3,6 +3,7 @@ import { state } from '../state/index.js';
 import { showCheckpointModal } from './checkpointModal/index.js';
 import { NSFW_LEVELS } from '../utils/constants.js';
 import { replaceCheckpointPreview as apiReplaceCheckpointPreview, saveModelMetadata } from '../api/checkpointApi.js';
+import { showDeleteModal } from '../utils/modalUtils.js';
 
 export function createCheckpointCard(checkpoint) {
     const card = document.createElement('div');
@@ -262,7 +263,7 @@ export function createCheckpointCard(checkpoint) {
     // Delete button click event
     card.querySelector('.fa-trash')?.addEventListener('click', e => {
         e.stopPropagation();
-        deleteCheckpoint(checkpoint.file_path);
+        showDeleteModal(checkpoint.file_path);
     });
 
     // Replace preview button click event
@@ -322,17 +323,6 @@ function openCivitai(modelName) {
     }
 }
 
-function deleteCheckpoint(filePath) {
-    if (window.deleteCheckpoint) {
-        window.deleteCheckpoint(filePath);
-    } else {
-        // Use the modal delete functionality
-        import('../utils/modalUtils.js').then(({ showDeleteModal }) => {
-            showDeleteModal(filePath, 'checkpoint');
-        });
-    }
-}
-
 function replaceCheckpointPreview(filePath) {
     if (window.replaceCheckpointPreview) {
         window.replaceCheckpointPreview(filePath);

static/js/components/ContextMenu/CheckpointContextMenu.js

@@ -3,6 +3,7 @@ import { refreshSingleCheckpointMetadata, saveModelMetadata } from '../../api/ch
 import { showToast, getNSFWLevelName } from '../../utils/uiHelpers.js';
 import { NSFW_LEVELS } from '../../utils/constants.js';
 import { getStorageItem } from '../../utils/storageHelpers.js';
+import { showExcludeModal } from '../../utils/modalUtils.js';
 
 export class CheckpointContextMenu extends BaseContextMenu {
     constructor() {
@@ -61,6 +62,10 @@ export class CheckpointContextMenu extends BaseContextMenu {
                 // Move to folder (placeholder)
                 showToast('Move to folder feature coming soon', 'info');
                 break;
+            case 'exclude':
+                showExcludeModal(this.currentCard.dataset.filepath, 'checkpoint');
+                break;
+
         }
     }
 

static/js/components/ContextMenu/LoraContextMenu.js

@@ -3,6 +3,7 @@ import { refreshSingleLoraMetadata, saveModelMetadata } from '../../api/loraApi.
 import { showToast, getNSFWLevelName } from '../../utils/uiHelpers.js';
 import { NSFW_LEVELS } from '../../utils/constants.js';
 import { getStorageItem } from '../../utils/storageHelpers.js';
+import { showExcludeModal } from '../../utils/modalUtils.js';
 
 export class LoraContextMenu extends BaseContextMenu {
     constructor() {
@@ -51,6 +52,9 @@ export class LoraContextMenu extends BaseContextMenu {
             case 'set-nsfw':
                 this.showNSFWLevelSelector(null, null, this.currentCard);
                 break;
+            case 'exclude':
+                showExcludeModal(this.currentCard.dataset.filepath);
+                break;
         }
     }
 

static/js/components/DuplicatesManager.js

@@ -0,0 +1,395 @@
+// Duplicates Manager Component
+import { showToast } from '../utils/uiHelpers.js';
+import { RecipeCard } from './RecipeCard.js';
+import { getCurrentPageState } from '../state/index.js';
+import { initializeInfiniteScroll } from '../utils/infiniteScroll.js';
+
+export class DuplicatesManager {
+    constructor(recipeManager) {
+        this.recipeManager = recipeManager;
+        this.duplicateGroups = [];
+        this.inDuplicateMode = false;
+        this.selectedForDeletion = new Set();
+    }
+    
+    async findDuplicates() {
+        try {
+            document.body.classList.add('loading');
+            
+            const response = await fetch('/api/recipes/find-duplicates');
+            if (!response.ok) {
+                throw new Error('Failed to find duplicates');
+            }
+            
+            const data = await response.json();
+            if (!data.success) {
+                throw new Error(data.error || 'Unknown error finding duplicates');
+            }
+            
+            this.duplicateGroups = data.duplicate_groups || [];
+            
+            if (this.duplicateGroups.length === 0) {
+                showToast('No duplicate recipes found', 'info');
+                return false;
+            }
+            
+            this.enterDuplicateMode();
+            return true;
+        } catch (error) {
+            console.error('Error finding duplicates:', error);
+            showToast('Failed to find duplicates: ' + error.message, 'error');
+            return false;
+        } finally {
+            document.body.classList.remove('loading');
+        }
+    }
+    
+    enterDuplicateMode() {
+        this.inDuplicateMode = true;
+        this.selectedForDeletion.clear();
+        
+        // Update state
+        const pageState = getCurrentPageState();
+        pageState.duplicatesMode = true;
+        
+        // Show duplicates banner
+        const banner = document.getElementById('duplicatesBanner');
+        const countSpan = document.getElementById('duplicatesCount');
+        
+        if (banner && countSpan) {
+            countSpan.textContent = `Found ${this.duplicateGroups.length} duplicate group${this.duplicateGroups.length !== 1 ? 's' : ''}`;
+            banner.style.display = 'block';
+        }
+        
+        // Disable infinite scroll
+        if (this.recipeManager.observer) {
+            this.recipeManager.observer.disconnect();
+            this.recipeManager.observer = null;
+        }
+        
+        // Add duplicate-mode class to the body
+        document.body.classList.add('duplicate-mode');
+        
+        // Render duplicate groups
+        this.renderDuplicateGroups();
+        
+        // Update selected count
+        this.updateSelectedCount();
+    }
+    
+    exitDuplicateMode() {
+        this.inDuplicateMode = false;
+        this.selectedForDeletion.clear();
+        
+        // Update state
+        const pageState = getCurrentPageState();
+        pageState.duplicatesMode = false;
+        
+        // Hide duplicates banner
+        const banner = document.getElementById('duplicatesBanner');
+        if (banner) {
+            banner.style.display = 'none';
+        }
+        
+        // Remove duplicate-mode class from the body
+        document.body.classList.remove('duplicate-mode');
+        
+        // Reload normal recipes view
+        this.recipeManager.loadRecipes();
+        
+        // Reinitialize infinite scroll
+        setTimeout(() => {
+            initializeInfiniteScroll('recipes');
+        }, 500);
+    }
+    
+    renderDuplicateGroups() {
+        const recipeGrid = document.getElementById('recipeGrid');
+        if (!recipeGrid) return;
+        
+        // Clear existing content
+        recipeGrid.innerHTML = '';
+        
+        // Render each duplicate group
+        this.duplicateGroups.forEach((group, groupIndex) => {
+            const groupDiv = document.createElement('div');
+            groupDiv.className = 'duplicate-group';
+            groupDiv.dataset.fingerprint = group.fingerprint;
+            
+            // Create group header
+            const header = document.createElement('div');
+            header.className = 'duplicate-group-header';
+            header.innerHTML = `
+                <span>Duplicate Group #${groupIndex + 1} (${group.recipes.length} recipes)</span>
+                <span>
+                    <button class="btn-select-all" onclick="recipeManager.duplicatesManager.toggleSelectAllInGroup('${group.fingerprint}')">
+                        Select All
+                    </button>
+                    <button class="btn-select-latest" onclick="recipeManager.duplicatesManager.selectLatestInGroup('${group.fingerprint}')">
+                        Keep Latest
+                    </button>
+                </span>
+            `;
+            groupDiv.appendChild(header);
+            
+            // Create cards container
+            const cardsDiv = document.createElement('div');
+            cardsDiv.className = 'card-group-container';
+            
+            // Add scrollable class if there are many recipes in the group
+            if (group.recipes.length > 6) {
+                cardsDiv.classList.add('scrollable');
+                
+                // Add expand/collapse toggle button
+                const toggleBtn = document.createElement('button');
+                toggleBtn.className = 'group-toggle-btn';
+                toggleBtn.innerHTML = '<i class="fas fa-chevron-down"></i>';
+                toggleBtn.title = "Expand/Collapse";
+                toggleBtn.onclick = function() {
+                    cardsDiv.classList.toggle('scrollable');
+                    this.innerHTML = cardsDiv.classList.contains('scrollable') ? 
+                        '<i class="fas fa-chevron-down"></i>' : 
+                        '<i class="fas fa-chevron-up"></i>';
+                };
+                groupDiv.appendChild(toggleBtn);
+            }
+            
+            // Sort recipes by date (newest first)
+            const sortedRecipes = [...group.recipes].sort((a, b) => b.modified - a.modified);
+            
+            // Add all recipe cards in this group
+            sortedRecipes.forEach((recipe, index) => {
+                // Create recipe card
+                const recipeCard = new RecipeCard(recipe, (recipe) => {
+                    this.recipeManager.showRecipeDetails(recipe);
+                });
+                const card = recipeCard.element;
+                
+                // Add duplicate class
+                card.classList.add('duplicate');
+                
+                // Mark the latest one
+                if (index === 0) {
+                    card.classList.add('latest');
+                }
+                
+                // Add selection checkbox
+                const checkbox = document.createElement('input');
+                checkbox.type = 'checkbox';
+                checkbox.className = 'selector-checkbox';
+                checkbox.dataset.recipeId = recipe.id;
+                checkbox.dataset.groupFingerprint = group.fingerprint;
+                
+                // Check if already selected
+                if (this.selectedForDeletion.has(recipe.id)) {
+                    checkbox.checked = true;
+                    card.classList.add('duplicate-selected');
+                }
+                
+                // Add change event to checkbox
+                checkbox.addEventListener('change', (e) => {
+                    e.stopPropagation();
+                    this.toggleCardSelection(recipe.id, card, checkbox);
+                });
+                
+                // Make the entire card clickable for selection
+                card.addEventListener('click', (e) => {
+                    // Don't toggle if clicking on the checkbox directly or card actions
+                    if (e.target === checkbox || e.target.closest('.card-actions')) {
+                        return;
+                    }
+                    
+                    // Toggle checkbox state
+                    checkbox.checked = !checkbox.checked;
+                    this.toggleCardSelection(recipe.id, card, checkbox);
+                });
+                
+                card.appendChild(checkbox);
+                cardsDiv.appendChild(card);
+            });
+            
+            groupDiv.appendChild(cardsDiv);
+            recipeGrid.appendChild(groupDiv);
+        });
+    }
+    
+    // Helper method to toggle card selection state
+    toggleCardSelection(recipeId, card, checkbox) {
+        if (checkbox.checked) {
+            this.selectedForDeletion.add(recipeId);
+            card.classList.add('duplicate-selected');
+        } else {
+            this.selectedForDeletion.delete(recipeId);
+            card.classList.remove('duplicate-selected');
+        }
+        
+        this.updateSelectedCount();
+    }
+    
+    updateSelectedCount() {
+        const selectedCountEl = document.getElementById('selectedCount');
+        if (selectedCountEl) {
+            selectedCountEl.textContent = this.selectedForDeletion.size;
+        }
+        
+        // Update delete button state
+        const deleteBtn = document.querySelector('.btn-delete-selected');
+        if (deleteBtn) {
+            deleteBtn.disabled = this.selectedForDeletion.size === 0;
+            deleteBtn.classList.toggle('disabled', this.selectedForDeletion.size === 0);
+        }
+    }
+    
+    toggleSelectAllInGroup(fingerprint) {
+        const checkboxes = document.querySelectorAll(`.selector-checkbox[data-group-fingerprint="${fingerprint}"]`);
+        const allSelected = Array.from(checkboxes).every(checkbox => checkbox.checked);
+        
+        // If all are selected, deselect all; otherwise select all
+        checkboxes.forEach(checkbox => {
+            checkbox.checked = !allSelected;
+            const recipeId = checkbox.dataset.recipeId;
+            const card = checkbox.closest('.lora-card');
+            
+            if (!allSelected) {
+                this.selectedForDeletion.add(recipeId);
+                card.classList.add('duplicate-selected');
+            } else {
+                this.selectedForDeletion.delete(recipeId);
+                card.classList.remove('duplicate-selected');
+            }
+        });
+        
+        // Update the button text
+        const button = document.querySelector(`.duplicate-group[data-fingerprint="${fingerprint}"] .btn-select-all`);
+        if (button) {
+            button.textContent = !allSelected ? "Deselect All" : "Select All";
+        }
+        
+        this.updateSelectedCount();
+    }
+    
+    selectAllInGroup(fingerprint) {
+        const checkboxes = document.querySelectorAll(`.selector-checkbox[data-group-fingerprint="${fingerprint}"]`);
+        checkboxes.forEach(checkbox => {
+            checkbox.checked = true;
+            this.selectedForDeletion.add(checkbox.dataset.recipeId);
+            checkbox.closest('.lora-card').classList.add('duplicate-selected');
+        });
+        
+        // Update the button text
+        const button = document.querySelector(`.duplicate-group[data-fingerprint="${fingerprint}"] .btn-select-all`);
+        if (button) {
+            button.textContent = "Deselect All";
+        }
+        
+        this.updateSelectedCount();
+    }
+    
+    selectLatestInGroup(fingerprint) {
+        // Find all checkboxes in this group
+        const checkboxes = document.querySelectorAll(`.selector-checkbox[data-group-fingerprint="${fingerprint}"]`);
+        
+        // Get all the recipes in this group
+        const group = this.duplicateGroups.find(g => g.fingerprint === fingerprint);
+        if (!group) return;
+        
+        // Sort recipes by date (newest first)
+        const sortedRecipes = [...group.recipes].sort((a, b) => b.modified - a.modified);
+        
+        // Skip the first (latest) one and select the rest for deletion
+        for (let i = 1; i < sortedRecipes.length; i++) {
+            const recipeId = sortedRecipes[i].id;
+            const checkbox = document.querySelector(`.selector-checkbox[data-recipe-id="${recipeId}"]`);
+            
+            if (checkbox) {
+                checkbox.checked = true;
+                this.selectedForDeletion.add(recipeId);
+                checkbox.closest('.lora-card').classList.add('duplicate-selected');
+            }
+        }
+        
+        // Make sure the latest one is not selected
+        const latestId = sortedRecipes[0].id;
+        const latestCheckbox = document.querySelector(`.selector-checkbox[data-recipe-id="${latestId}"]`);
+        
+        if (latestCheckbox) {
+            latestCheckbox.checked = false;
+            this.selectedForDeletion.delete(latestId);
+            latestCheckbox.closest('.lora-card').classList.remove('duplicate-selected');
+        }
+        
+        this.updateSelectedCount();
+    }
+    
+    selectLatestDuplicates() {
+        // For each duplicate group, select all but the latest recipe
+        this.duplicateGroups.forEach(group => {
+            this.selectLatestInGroup(group.fingerprint);
+        });
+    }
+    
+    async deleteSelectedDuplicates() {
+        if (this.selectedForDeletion.size === 0) {
+            showToast('No recipes selected for deletion', 'info');
+            return;
+        }
+        
+        try {
+            // Show the delete confirmation modal instead of a simple confirm
+            const duplicateDeleteCount = document.getElementById('duplicateDeleteCount');
+            if (duplicateDeleteCount) {
+                duplicateDeleteCount.textContent = this.selectedForDeletion.size;
+            }
+            
+            // Use the modal manager to show the confirmation modal
+            modalManager.showModal('duplicateDeleteModal');
+        } catch (error) {
+            console.error('Error preparing delete:', error);
+            showToast('Error: ' + error.message, 'error');
+        }
+    }
+    
+    // Add new method to execute deletion after confirmation
+    async confirmDeleteDuplicates() {
+        try {
+            document.body.classList.add('loading');
+            
+            // Close the modal
+            modalManager.closeModal('duplicateDeleteModal');
+            
+            // Prepare recipe IDs for deletion
+            const recipeIds = Array.from(this.selectedForDeletion);
+            
+            // Call API to bulk delete
+            const response = await fetch('/api/recipes/bulk-delete', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json'
+                },
+                body: JSON.stringify({ recipe_ids: recipeIds })
+            });
+            
+            if (!response.ok) {
+                throw new Error('Failed to delete selected recipes');
+            }
+            
+            const data = await response.json();
+            if (!data.success) {
+                throw new Error(data.error || 'Unknown error deleting recipes');
+            }
+            
+            showToast(`Successfully deleted ${data.total_deleted} recipes`, 'success');
+            
+            // Exit duplicate mode if deletions were successful
+            if (data.total_deleted > 0) {
+                this.exitDuplicateMode();
+            }
+            
+        } catch (error) {
+            console.error('Error deleting recipes:', error);
+            showToast('Failed to delete recipes: ' + error.message, 'error');
+        } finally {
+            document.body.classList.remove('loading');
+        }
+    }
+}

static/js/components/Header.js

@@ -78,5 +78,33 @@ export class HeaderManager {
           // Handle support panel logic
         });
       }
+
+      // Handle QR code toggle
+      const qrToggle = document.getElementById('toggleQRCode');
+      const qrContainer = document.getElementById('qrCodeContainer');
+      
+      if (qrToggle && qrContainer) {
+          qrToggle.addEventListener('click', function() {
+              qrContainer.classList.toggle('show');
+              qrToggle.classList.toggle('active');
+              
+              const toggleText = qrToggle.querySelector('.toggle-text');
+              if (qrContainer.classList.contains('show')) {
+                  toggleText.textContent = 'Hide WeChat QR Code';
+                  // Add small delay to ensure DOM is updated before scrolling
+                  setTimeout(() => {
+                      const supportModal = document.querySelector('.support-modal');
+                      if (supportModal) {
+                          supportModal.scrollTo({
+                              top: supportModal.scrollHeight,
+                              behavior: 'smooth'
+                          });
+                      }
+                  }, 250);
+              } else {
+                  toggleText.textContent = 'Show WeChat QR Code';
+              }
+          });
+      }
     }
 }

static/js/components/LoraCard.js

@@ -3,7 +3,8 @@ import { state } from '../state/index.js';
 import { showLoraModal } from './loraModal/index.js';
 import { bulkManager } from '../managers/BulkManager.js';
 import { NSFW_LEVELS } from '../utils/constants.js';
-import { replacePreview, deleteModel, saveModelMetadata } from '../api/loraApi.js'
+import { replacePreview, saveModelMetadata } from '../api/loraApi.js'
+import { showDeleteModal } from '../utils/modalUtils.js';
 
 export function createLoraCard(lora) {
     const card = document.createElement('div');
@@ -260,7 +261,7 @@ export function createLoraCard(lora) {
     // Delete button click event
     card.querySelector('.fa-trash')?.addEventListener('click', e => {
         e.stopPropagation();
-        deleteModel(lora.file_path);
+        showDeleteModal(lora.file_path);
     });
 
     // Replace preview button click event

static/js/components/RecipeCard.js

@@ -1,12 +1,16 @@
 // Recipe Card Component
 import { showToast, copyToClipboard } from '../utils/uiHelpers.js';
 import { modalManager } from '../managers/ModalManager.js';
+import { getCurrentPageState } from '../state/index.js';
 
 class RecipeCard {
     constructor(recipe, clickHandler) {
         this.recipe = recipe;
         this.clickHandler = clickHandler;
         this.element = this.createCardElement();
+        
+        // Store reference to this instance on the DOM element for updates
+        this.element._recipeCardInstance = this;
     }
     
     createCardElement() {
@@ -33,10 +37,15 @@ class RecipeCard {
                          (this.recipe.file_path ? `/loras_static/root1/preview/${this.recipe.file_path.split('/').pop()}` : 
                          '/loras_static/images/no-preview.png');
 
+        // Check if in duplicates mode
+        const pageState = getCurrentPageState();
+        const isDuplicatesMode = pageState.duplicatesMode;
+
         card.innerHTML = `
-            <div class="recipe-indicator" title="Recipe">R</div>
+            ${!isDuplicatesMode ? `<div class="recipe-indicator" title="Recipe">R</div>` : ''}
             <div class="card-preview">
                 <img src="${imageUrl}" alt="${this.recipe.title}">
+                ${!isDuplicatesMode ? `
                 <div class="card-header">
                     <div class="base-model-wrapper">
                         ${baseModel ? `<span class="base-model-label" title="${baseModel}">${baseModel}</span>` : ''}
@@ -47,19 +56,22 @@ class RecipeCard {
                         <i class="fas fa-trash" title="Delete Recipe"></i>
                     </div>
                 </div>
+                ` : ''}
                 <div class="card-footer">
                     <div class="model-info">
                         <span class="model-name">${this.recipe.title}</span>
                     </div>
+                    ${!isDuplicatesMode ? `
                     <div class="lora-count ${allLorasAvailable ? 'ready' : (lorasCount > 0 ? 'missing' : '')}" 
                          title="${this.getLoraStatusTitle(lorasCount, missingLorasCount)}">
                         <i class="fas fa-layer-group"></i> ${lorasCount}
                     </div>
+                    ` : ''}
                 </div>
             </div>
         `;
         
-        this.attachEventListeners(card);
+        this.attachEventListeners(card, isDuplicatesMode);
         return card;
     }
     
@@ -69,29 +81,31 @@ class RecipeCard {
         return `${missingCount} of ${totalCount} LoRAs missing`;
     }
     
-    attachEventListeners(card) {
-        // Recipe card click event
-        card.addEventListener('click', () => {
-            this.clickHandler(this.recipe);
-        });
-        
-        // Share button click event - prevent propagation to card
-        card.querySelector('.fa-share-alt')?.addEventListener('click', (e) => {
-            e.stopPropagation();
-            this.shareRecipe();
-        });
-        
-        // Copy button click event - prevent propagation to card
-        card.querySelector('.fa-copy')?.addEventListener('click', (e) => {
-            e.stopPropagation();
-            this.copyRecipeSyntax();
-        });
-        
-        // Delete button click event - prevent propagation to card
-        card.querySelector('.fa-trash')?.addEventListener('click', (e) => {
-            e.stopPropagation();
-            this.showDeleteConfirmation();
-        });
+    attachEventListeners(card, isDuplicatesMode) {
+        // Recipe card click event - only attach if not in duplicates mode
+        if (!isDuplicatesMode) {
+            card.addEventListener('click', () => {
+                this.clickHandler(this.recipe);
+            });
+            
+            // Share button click event - prevent propagation to card
+            card.querySelector('.fa-share-alt')?.addEventListener('click', (e) => {
+                e.stopPropagation();
+                this.shareRecipe();
+            });
+            
+            // Copy button click event - prevent propagation to card
+            card.querySelector('.fa-copy')?.addEventListener('click', (e) => {
+                e.stopPropagation();
+                this.copyRecipeSyntax();
+            });
+            
+            // Delete button click event - prevent propagation to card
+            card.querySelector('.fa-trash')?.addEventListener('click', (e) => {
+                e.stopPropagation();
+                this.showDeleteConfirmation();
+            });
+        }
     }
     
     copyRecipeSyntax() {

static/js/components/RecipeModal.js

@@ -2,6 +2,7 @@
 import { showToast, copyToClipboard } from '../utils/uiHelpers.js';
 import { state } from '../state/index.js';
 import { setSessionItem, removeSessionItem } from '../utils/storageHelpers.js';
+import { updateRecipeCard } from '../utils/cardUpdater.js';
 
 class RecipeModal {
     constructor() {
@@ -82,7 +83,7 @@ class RecipeModal {
     
     showRecipeDetails(recipe) {
         // Store the full recipe for editing
-        this.currentRecipe = JSON.parse(JSON.stringify(recipe)); // 深拷贝以避免对原始对象的修改
+        this.currentRecipe = recipe;
         
         // Set modal title with edit icon
         const modalTitle = document.getElementById('recipeModalTitle');
@@ -245,6 +246,45 @@ class RecipeModal {
                 imgElement.alt = recipe.title || 'Recipe Preview';
                 mediaContainer.appendChild(imgElement);
             }
+
+            // Add source URL container if the recipe has a source_path
+            const sourceUrlContainer = document.createElement('div');
+            sourceUrlContainer.className = 'source-url-container';
+            const hasSourceUrl = recipe.source_path && recipe.source_path.trim().length > 0;
+            const sourceUrl = hasSourceUrl ? recipe.source_path : '';
+            const isValidUrl = hasSourceUrl && (sourceUrl.startsWith('http://') || sourceUrl.startsWith('https://'));
+            
+            sourceUrlContainer.innerHTML = `
+                <div class="source-url-content">
+                    <span class="source-url-icon"><i class="fas fa-link"></i></span>
+                    <span class="source-url-text" title="${isValidUrl ? 'Click to open source URL' : 'No valid URL'}">${
+                        hasSourceUrl ? sourceUrl : 'No source URL'
+                    }</span>
+                </div>
+                <button class="source-url-edit-btn" title="Edit source URL">
+                    <i class="fas fa-pencil-alt"></i>
+                </button>
+            `;
+            
+            // Add source URL editor
+            const sourceUrlEditor = document.createElement('div');
+            sourceUrlEditor.className = 'source-url-editor';
+            sourceUrlEditor.innerHTML = `
+                <input type="text" class="source-url-input" placeholder="Enter source URL (e.g., https://civitai.com/...)" value="${sourceUrl}">
+                <div class="source-url-actions">
+                    <button class="source-url-cancel-btn">Cancel</button>
+                    <button class="source-url-save-btn">Save</button>
+                </div>
+            `;
+            
+            // Append both containers to the media container
+            mediaContainer.appendChild(sourceUrlContainer);
+            mediaContainer.appendChild(sourceUrlEditor);
+            
+            // Set up event listeners for source URL functionality
+            setTimeout(() => {
+                this.setupSourceUrlHandlers();
+            }, 50);
         }
         
         // Set generation parameters
@@ -451,8 +491,6 @@ class RecipeModal {
             lorasListElement.innerHTML = '<div class="no-loras">No LoRAs associated with this recipe</div>';
             this.recipeLorasSyntax = '';
         }
-
-        console.log(this.currentRecipe.loras);
         
         // Show the modal
         modalManager.showModal('recipeModal');
@@ -648,50 +686,8 @@ class RecipeModal {
                 // 更新当前recipe对象的属性
                 Object.assign(this.currentRecipe, updates);
                 
-                // 确保这个更新也传播到卡片视图
-                // 尝试找到可能显示这个recipe的卡片并更新它
-                try {
-                    const recipeCards = document.querySelectorAll('.recipe-card');
-                    recipeCards.forEach(card => {
-                        if (card.dataset.recipeId === this.recipeId) {
-                            // 更新卡片标题
-                            if (updates.title) {
-                                const titleElement = card.querySelector('.recipe-title');
-                                if (titleElement) {
-                                    titleElement.textContent = updates.title;
-                                }
-                            }
-                            
-                            // 更新卡片标签
-                            if (updates.tags) {
-                                const tagsElement = card.querySelector('.recipe-tags');
-                                if (tagsElement) {
-                                    if (updates.tags.length > 0) {
-                                        tagsElement.innerHTML = updates.tags.map(
-                                            tag => `<div class="recipe-tag">${tag}</div>`
-                                        ).join('');
-                                    } else {
-                                        tagsElement.innerHTML = '';
-                                    }
-                                }
-                            }
-                        }
-                    });
-                } catch (err) {
-                    console.log("Non-critical error updating recipe cards:", err);
-                }
-                
-                // 重要：强制刷新recipes列表，确保从服务器获取最新数据
-                try {
-                    if (window.recipeManager && typeof window.recipeManager.loadRecipes === 'function') {
-                        // 异步刷新recipes列表，不阻塞用户界面
-                        setTimeout(() => {
-                            window.recipeManager.loadRecipes(true);
-                        }, 500);
-                    }
-                } catch (err) {
-                    console.log("Error refreshing recipes list:", err);
-                }
+                // Update the recipe card in the UI
+                updateRecipeCard(this.recipeId, updates);
             } else {
                 showToast(`Failed to update recipe: ${data.error}`, 'error');
             }
@@ -951,8 +947,8 @@ class RecipeModal {
             let loraSyntaxMatch = inputValue.match(/<lora:([^:>]+)(?::[^>]+)?>/);
             let fileName = loraSyntaxMatch ? loraSyntaxMatch[1] : inputValue.trim();
             
-            // Remove any file extension if present
-            fileName = fileName.replace(/\.\w+$/, '');
+            // Remove .safetensors extension if present
+            fileName = fileName.replace(/\.safetensors$/, '');
             
             // Get the deleted lora data
             const deletedLora = this.currentRecipe.loras[loraIndex];
@@ -1069,6 +1065,56 @@ class RecipeModal {
             });
         });
     }
+
+    // New method to set up source URL handlers
+    setupSourceUrlHandlers() {
+        const sourceUrlContainer = document.querySelector('.source-url-container');
+        const sourceUrlEditor = document.querySelector('.source-url-editor');
+        const sourceUrlText = sourceUrlContainer.querySelector('.source-url-text');
+        const sourceUrlEditBtn = sourceUrlContainer.querySelector('.source-url-edit-btn');
+        const sourceUrlCancelBtn = sourceUrlEditor.querySelector('.source-url-cancel-btn');
+        const sourceUrlSaveBtn = sourceUrlEditor.querySelector('.source-url-save-btn');
+        const sourceUrlInput = sourceUrlEditor.querySelector('.source-url-input');
+        
+        // Show editor on edit button click
+        sourceUrlEditBtn.addEventListener('click', () => {
+            sourceUrlContainer.classList.add('hide');
+            sourceUrlEditor.classList.add('active');
+            sourceUrlInput.focus();
+        });
+        
+        // Cancel editing
+        sourceUrlCancelBtn.addEventListener('click', () => {
+            sourceUrlEditor.classList.remove('active');
+            sourceUrlContainer.classList.remove('hide');
+            sourceUrlInput.value = this.currentRecipe.source_path || '';
+        });
+        
+        // Save new source URL
+        sourceUrlSaveBtn.addEventListener('click', () => {
+            const newSourceUrl = sourceUrlInput.value.trim();
+            if (newSourceUrl && newSourceUrl !== this.currentRecipe.source_path) {
+                // Update source URL in the UI
+                sourceUrlText.textContent = newSourceUrl;
+                sourceUrlText.title = newSourceUrl.startsWith('http://') || newSourceUrl.startsWith('https://') ? 'Click to open source URL' : 'No valid URL';
+                
+                // Update the recipe on the server
+                this.updateRecipeMetadata({ source_path: newSourceUrl });
+            }
+            
+            // Hide editor
+            sourceUrlEditor.classList.remove('active');
+            sourceUrlContainer.classList.remove('hide');
+        });
+        
+        // Open source URL in a new tab if it's valid
+        sourceUrlText.addEventListener('click', () => {
+            const url = sourceUrlText.textContent.trim();
+            if (url.startsWith('http://') || url.startsWith('https://')) {
+                window.open(url, '_blank');
+            }
+        });
+    }
 }
 
 export { RecipeModal };

static/js/components/alphabet/AlphabetBar.js

@@ -0,0 +1,319 @@
+// AlphabetBar.js - Component for alphabet filtering
+import { getCurrentPageState, setCurrentPageType } from '../../state/index.js';
+import { getStorageItem, setStorageItem } from '../../utils/storageHelpers.js';
+import { resetAndReload } from '../../api/loraApi.js';
+
+/**
+ * AlphabetBar class - Handles the alphabet filtering UI and interactions
+ */
+export class AlphabetBar {
+    constructor(pageType = 'loras') {
+        // Store the page type
+        this.pageType = pageType;
+        
+        // Get the current page state
+        this.pageState = getCurrentPageState();
+        
+        // Initialize letter counts
+        this.letterCounts = {};
+        
+        // Initialize the component
+        this.initializeComponent();
+    }
+    
+    /**
+     * Initialize the alphabet bar component
+     */
+    async initializeComponent() {
+        // Get letter counts from API
+        await this.fetchLetterCounts();
+        
+        // Initialize event listeners
+        this.initEventListeners();
+        
+        // Restore the active letter filter from storage if available
+        this.restoreActiveLetterFilter();
+        
+        // Restore collapse state from storage
+        this.restoreCollapseState();
+
+        // Update the toggle button indicator if there's an active letter filter
+        this.updateToggleIndicator();
+    }
+    
+    /**
+     * Fetch letter counts from the API
+     */
+    async fetchLetterCounts() {
+        try {
+            const response = await fetch('/api/loras/letter-counts');
+            
+            if (!response.ok) {
+                throw new Error(`Failed to fetch letter counts: ${response.statusText}`);
+            }
+            
+            const data = await response.json();
+            
+            if (data.success && data.letter_counts) {
+                this.letterCounts = data.letter_counts;
+                
+                // Update the count display in the UI
+                this.updateLetterCountsDisplay();
+            }
+        } catch (error) {
+            console.error('Error fetching letter counts:', error);
+        }
+    }
+    
+    /**
+     * Update the letter counts display in the UI
+     */
+    updateLetterCountsDisplay() {
+        const letterChips = document.querySelectorAll('.letter-chip');
+        
+        letterChips.forEach(chip => {
+            const letter = chip.dataset.letter;
+            const count = this.letterCounts[letter] || 0;
+            
+            // Update the title attribute for tooltip display
+            if (count > 0) {
+                chip.title = `${letter}: ${count} LoRAs`;
+                chip.classList.remove('disabled');
+            } else {
+                chip.title = `${letter}: No LoRAs`;
+                chip.classList.add('disabled');
+            }
+            
+            // Keep the count span for backward compatibility
+            const countSpan = chip.querySelector('.count');
+            if (countSpan) {
+                countSpan.textContent = ` (${count})`;
+            }
+        });
+    }
+    
+    /**
+     * Initialize event listeners for the alphabet bar
+     */
+    initEventListeners() {
+        const alphabetBar = document.querySelector('.alphabet-bar');
+        const toggleButton = document.querySelector('.toggle-alphabet-bar');
+        const alphabetBarContainer = document.querySelector('.alphabet-bar-container');
+        
+        if (alphabetBar) {
+            // Use event delegation for letter chips
+            alphabetBar.addEventListener('click', (e) => {
+                const letterChip = e.target.closest('.letter-chip');
+                
+                if (letterChip && !letterChip.classList.contains('disabled')) {
+                    this.handleLetterClick(letterChip);
+                }
+            });
+            
+            // Add toggle button listener
+            if (toggleButton && alphabetBarContainer) {
+                toggleButton.addEventListener('click', () => {
+                    alphabetBarContainer.classList.toggle('collapsed');
+                    
+                    // If expanding and there's an active letter, scroll it into view
+                    if (!alphabetBarContainer.classList.contains('collapsed')) {
+                        this.scrollActiveLetterIntoView();
+                    }
+                    
+                    // Save collapse state to storage
+                    setStorageItem(`${this.pageType}_alphabetBarCollapsed`, 
+                        alphabetBarContainer.classList.contains('collapsed'));
+
+                    // Update toggle indicator
+                    this.updateToggleIndicator();
+                });
+            }
+            
+            // Add keyboard shortcut listeners
+            document.addEventListener('keydown', (e) => {
+                // Alt + letter shortcuts
+                if (e.altKey && !e.ctrlKey && !e.metaKey) {
+                    const key = e.key.toUpperCase();
+                    
+                    // Check if it's a letter A-Z
+                    if (/^[A-Z]$/.test(key)) {
+                        const letterChip = document.querySelector(`.letter-chip[data-letter="${key}"]`);
+                        
+                        if (letterChip && !letterChip.classList.contains('disabled')) {
+                            this.handleLetterClick(letterChip);
+                            e.preventDefault();
+                        }
+                    } 
+                    // Special cases for non-letter filters
+                    else if (e.key === '0' || e.key === ')') {
+                        // Alt+0 for numbers (#)
+                        const letterChip = document.querySelector('.letter-chip[data-letter="#"]');
+                        
+                        if (letterChip && !letterChip.classList.contains('disabled')) {
+                            this.handleLetterClick(letterChip);
+                            e.preventDefault();
+                        }
+                    } else if (e.key === '2' || e.key === '@') {
+                        // Alt+@ for special characters
+                        const letterChip = document.querySelector('.letter-chip[data-letter="@"]');
+                        
+                        if (letterChip && !letterChip.classList.contains('disabled')) {
+                            this.handleLetterClick(letterChip);
+                            e.preventDefault();
+                        }
+                    } else if (e.key === 'c' || e.key === 'C') {
+                        // Alt+C for CJK characters
+                        const letterChip = document.querySelector('.letter-chip[data-letter="漢"]');
+                        
+                        if (letterChip && !letterChip.classList.contains('disabled')) {
+                            this.handleLetterClick(letterChip);
+                            e.preventDefault();
+                        }
+                    }
+                }
+            });
+        }
+    }
+    
+    /**
+     * Restore the collapse state from storage
+     */
+    restoreCollapseState() {
+        const alphabetBarContainer = document.querySelector('.alphabet-bar-container');
+        
+        if (alphabetBarContainer) {
+            const isCollapsed = getStorageItem(`${this.pageType}_alphabetBarCollapsed`);
+            
+            // If there's a stored preference, apply it
+            if (isCollapsed !== null) {
+                if (isCollapsed) {
+                    alphabetBarContainer.classList.add('collapsed');
+                } else {
+                    alphabetBarContainer.classList.remove('collapsed');
+                }
+            }
+        }
+    }
+    
+    /**
+     * Handle letter chip click
+     * @param {HTMLElement} letterChip - The letter chip that was clicked
+     */
+    handleLetterClick(letterChip) {
+        const letter = letterChip.dataset.letter;
+        const wasActive = letterChip.classList.contains('active');
+        
+        // Remove active class from all letter chips
+        document.querySelectorAll('.letter-chip').forEach(chip => {
+            chip.classList.remove('active');
+        });
+        
+        if (!wasActive) {
+            // Set the new active letter
+            letterChip.classList.add('active');
+            this.pageState.activeLetterFilter = letter;
+            
+            // Save to storage
+            setStorageItem(`${this.pageType}_activeLetterFilter`, letter);
+        } else {
+            // Clear the active letter filter
+            this.pageState.activeLetterFilter = null;
+            
+            // Remove from storage
+            setStorageItem(`${this.pageType}_activeLetterFilter`, null);
+        }
+        
+        // Update visual indicator on toggle button
+        this.updateToggleIndicator();
+        
+        // Trigger a reload with the new filter
+        resetAndReload(true);
+    }
+    
+    /**
+     * Restore the active letter filter from storage
+     */
+    restoreActiveLetterFilter() {
+        const activeLetterFilter = getStorageItem(`${this.pageType}_activeLetterFilter`);
+        
+        if (activeLetterFilter) {
+            const letterChip = document.querySelector(`.letter-chip[data-letter="${activeLetterFilter}"]`);
+            
+            if (letterChip && !letterChip.classList.contains('disabled')) {
+                letterChip.classList.add('active');
+                this.pageState.activeLetterFilter = activeLetterFilter;
+                
+                // Scroll the active letter into view if the alphabet bar is expanded
+                this.scrollActiveLetterIntoView();
+            }
+        }
+    }
+    
+    /**
+     * Clear the active letter filter
+     */
+    clearActiveLetterFilter() {
+        // Remove active class from all letter chips
+        document.querySelectorAll('.letter-chip').forEach(chip => {
+            chip.classList.remove('active');
+        });
+        
+        // Clear the active letter filter
+        this.pageState.activeLetterFilter = null;
+        
+        // Remove from storage
+        setStorageItem(`${this.pageType}_activeLetterFilter`, null);
+        
+        // Update the toggle button indicator
+        this.updateToggleIndicator();
+    }
+    
+    /**
+     * Update letter counts with new data
+     * @param {Object} newCounts - New letter count data
+     */
+    updateCounts(newCounts) {
+        this.letterCounts = { ...newCounts };
+        this.updateLetterCountsDisplay();
+    }
+    
+    /**
+     * Update the toggle button visual indicator based on active filter
+     */
+    updateToggleIndicator() {
+        const toggleButton = document.querySelector('.toggle-alphabet-bar');
+        const hasActiveFilter = this.pageState.activeLetterFilter !== null;
+        
+        if (toggleButton) {
+            if (hasActiveFilter) {
+                toggleButton.classList.add('has-active-letter');
+            } else {
+                toggleButton.classList.remove('has-active-letter');
+            }
+        }
+    }
+    
+    /**
+     * Scroll the active letter into view if the alphabet bar is expanded
+     */
+    scrollActiveLetterIntoView() {
+        if (!this.pageState.activeLetterFilter) return;
+
+        
+        const alphabetBarContainer = document.querySelector('.alphabet-bar-container');
+        if (alphabetBarContainer) {
+            const activeLetterChip = document.querySelector(`.letter-chip.active`);
+            
+            if (activeLetterChip) {
+                // Use a small timeout to ensure the alphabet bar is fully expanded
+                setTimeout(() => {
+                    activeLetterChip.scrollIntoView({
+                        behavior: 'smooth',
+                        block: 'center',
+                        inline: 'center'
+                    });
+                }, 300);
+            }
+        }
+    }
+}

static/js/components/alphabet/index.js

@@ -0,0 +1,14 @@
+// Alphabet component index file
+import { AlphabetBar } from './AlphabetBar.js';
+
+// Export the class
+export { AlphabetBar };
+
+/**
+ * Factory function to create the appropriate alphabet bar
+ * @param {string} pageType - The type of page ('loras' or 'checkpoints')
+ * @returns {AlphabetBar} - The alphabet bar instance
+ */
+export function createAlphabetBar(pageType) {
+    return new AlphabetBar(pageType);
+}

static/js/components/checkpointModal/ModelMetadata.js

@@ -171,12 +171,13 @@ export function setupBaseModelEditing(filePath) {
             'Stable Diffusion 2.x': [BASE_MODELS.SD_2_0, BASE_MODELS.SD_2_1],
             'Stable Diffusion 3.x': [BASE_MODELS.SD_3, BASE_MODELS.SD_3_5, BASE_MODELS.SD_3_5_MEDIUM, BASE_MODELS.SD_3_5_LARGE, BASE_MODELS.SD_3_5_LARGE_TURBO],
             'SDXL': [BASE_MODELS.SDXL, BASE_MODELS.SDXL_LIGHTNING, BASE_MODELS.SDXL_HYPER],
-            'Video Models': [BASE_MODELS.SVD, BASE_MODELS.WAN_VIDEO, BASE_MODELS.HUNYUAN_VIDEO],
+            'Video Models': [BASE_MODELS.SVD, BASE_MODELS.LTXV, BASE_MODELS.WAN_VIDEO, BASE_MODELS.HUNYUAN_VIDEO],
             'Other Models': [
                 BASE_MODELS.FLUX_1_D, BASE_MODELS.FLUX_1_S, BASE_MODELS.AURAFLOW,
                 BASE_MODELS.PIXART_A, BASE_MODELS.PIXART_E, BASE_MODELS.HUNYUAN_1,
                 BASE_MODELS.LUMINA, BASE_MODELS.KOLORS, BASE_MODELS.NOOBAI,
-                BASE_MODELS.ILLUSTRIOUS, BASE_MODELS.PONY, BASE_MODELS.UNKNOWN
+                BASE_MODELS.ILLUSTRIOUS, BASE_MODELS.PONY, BASE_MODELS.HIDREAM,
+                BASE_MODELS.UNKNOWN
             ]
         };
         

static/js/components/checkpointModal/ShowcaseView.js

@@ -6,12 +6,40 @@ import { showToast, copyToClipboard } from '../../utils/uiHelpers.js';
 import { state } from '../../state/index.js';
 import { NSFW_LEVELS } from '../../utils/constants.js';
 
+/**
+ * Get the local URL for an example image if available
+ * @param {Object} img - Image object
+ * @param {number} index - Image index
+ * @param {string} modelHash - Model hash
+ * @returns {string|null} - Local URL or null if not available
+ */
+function getLocalExampleImageUrl(img, index, modelHash) {
+    if (!modelHash) return null;
+    
+    // Get remote extension
+    const remoteExt = (img.url || '').split('?')[0].split('.').pop().toLowerCase();
+    
+    // If it's a video (mp4), use that extension
+    if (remoteExt === 'mp4') {
+        return `/example_images_static/${modelHash}/image_${index + 1}.mp4`;
+    }
+    
+    // For images, check if optimization is enabled (defaults to true) 
+    const optimizeImages = state.settings.optimizeExampleImages !== false;
+    
+    // Use .webp for images if optimization enabled, otherwise use original extension
+    const extension = optimizeImages ? 'webp' : remoteExt;
+    
+    return `/example_images_static/${modelHash}/image_${index + 1}.${extension}`;
+}
+
 /**
  * Render showcase content
  * @param {Array} images - Array of images/videos to show
+ * @param {string} modelHash - Model hash for identifying local files
  * @returns {string} HTML content
  */
-export function renderShowcaseContent(images) {
+export function renderShowcaseContent(images, modelHash) {
     if (!images?.length) return '<div class="no-examples">No example images available</div>';
     
     // Filter images based on SFW setting
@@ -53,7 +81,11 @@ export function renderShowcaseContent(images) {
         <div class="carousel collapsed">
             ${hiddenNotification}
             <div class="carousel-container">
-                ${filteredImages.map(img => generateMediaWrapper(img)).join('')}
+                ${filteredImages.map((img, index) => {
+                    // Try to get local URL for the example image
+                    const localUrl = getLocalExampleImageUrl(img, index, modelHash);
+                    return generateMediaWrapper(img, localUrl);
+                }).join('')}
             </div>
         </div>
     `;
@@ -64,7 +96,7 @@ export function renderShowcaseContent(images) {
  * @param {Object} media - Media object with image or video data
  * @returns {string} HTML content
  */
-function generateMediaWrapper(media) {
+function generateMediaWrapper(media, localUrl = null) {
     // Calculate appropriate aspect ratio:
     // 1. Keep original aspect ratio
     // 2. Limit maximum height to 60% of viewport height
@@ -117,10 +149,10 @@ function generateMediaWrapper(media) {
     
     // Check if this is a video or image
     if (media.type === 'video') {
-        return generateVideoWrapper(media, heightPercent, shouldBlur, nsfwText, metadataPanel);
+        return generateVideoWrapper(media, heightPercent, shouldBlur, nsfwText, metadataPanel, localUrl);
     }
     
-    return generateImageWrapper(media, heightPercent, shouldBlur, nsfwText, metadataPanel);
+    return generateImageWrapper(media, heightPercent, shouldBlur, nsfwText, metadataPanel, localUrl);
 }
 
 /**
@@ -193,7 +225,7 @@ function generateMetadataPanel(hasParams, hasPrompts, prompt, negativePrompt, si
 /**
  * Generate video wrapper HTML
  */
-function generateVideoWrapper(media, heightPercent, shouldBlur, nsfwText, metadataPanel) {
+function generateVideoWrapper(media, heightPercent, shouldBlur, nsfwText, metadataPanel, localUrl = null) {
     return `
         <div class="media-wrapper ${shouldBlur ? 'nsfw-media-wrapper' : ''}" style="padding-bottom: ${heightPercent}%">
             ${shouldBlur ? `
@@ -202,9 +234,11 @@ function generateVideoWrapper(media, heightPercent, shouldBlur, nsfwText, metada
                 </button>
             ` : ''}
             <video controls autoplay muted loop crossorigin="anonymous" 
-                referrerpolicy="no-referrer" data-src="${media.url}"
+                referrerpolicy="no-referrer" 
+                data-local-src="${localUrl || ''}"
+                data-remote-src="${media.url}"
                 class="lazy ${shouldBlur ? 'blurred' : ''}">
-                <source data-src="${media.url}" type="video/mp4">
+                <source data-local-src="${localUrl || ''}" data-remote-src="${media.url}" type="video/mp4">
                 Your browser does not support video playback
             </video>
             ${shouldBlur ? `
@@ -223,7 +257,7 @@ function generateVideoWrapper(media, heightPercent, shouldBlur, nsfwText, metada
 /**
  * Generate image wrapper HTML
  */
-function generateImageWrapper(media, heightPercent, shouldBlur, nsfwText, metadataPanel) {
+function generateImageWrapper(media, heightPercent, shouldBlur, nsfwText, metadataPanel, localUrl = null) {
     return `
         <div class="media-wrapper ${shouldBlur ? 'nsfw-media-wrapper' : ''}" style="padding-bottom: ${heightPercent}%">
             ${shouldBlur ? `
@@ -231,7 +265,8 @@ function generateImageWrapper(media, heightPercent, shouldBlur, nsfwText, metada
                     <i class="fas fa-eye"></i>
                 </button>
             ` : ''}
-            <img data-src="${media.url}" 
+            <img data-local-src="${localUrl || ''}" 
+                data-remote-src="${media.url}"
                 alt="Preview" 
                 crossorigin="anonymous" 
                 referrerpolicy="no-referrer"
@@ -287,8 +322,72 @@ function initMetadataPanelHandlers(container) {
     const mediaWrappers = container.querySelectorAll('.media-wrapper');
     
     mediaWrappers.forEach(wrapper => {
+        // Get the metadata panel and media element (img or video)
         const metadataPanel = wrapper.querySelector('.image-metadata-panel');
-        if (!metadataPanel) return;
+        const mediaElement = wrapper.querySelector('img, video');
+        
+        if (!metadataPanel || !mediaElement) return;
+        
+        let isOverMetadataPanel = false;
+        
+        // Add event listeners to the wrapper for mouse tracking
+        wrapper.addEventListener('mousemove', (e) => {
+            // Get mouse position relative to wrapper
+            const rect = wrapper.getBoundingClientRect();
+            const mouseX = e.clientX - rect.left;
+            const mouseY = e.clientY - rect.top;
+            
+            // Get the actual displayed dimensions of the media element
+            const mediaRect = getRenderedMediaRect(mediaElement, rect.width, rect.height);
+            
+            // Check if mouse is over the actual media content
+            const isOverMedia = (
+                mouseX >= mediaRect.left && 
+                mouseX <= mediaRect.right && 
+                mouseY >= mediaRect.top && 
+                mouseY <= mediaRect.bottom
+            );
+            
+            // Show metadata panel when over media content or metadata panel itself
+            if (isOverMedia || isOverMetadataPanel) {
+                metadataPanel.classList.add('visible');
+            } else {
+                metadataPanel.classList.remove('visible');
+            }
+        });
+        
+        wrapper.addEventListener('mouseleave', () => {
+            // Only hide panel when mouse leaves the wrapper and not over the metadata panel
+            if (!isOverMetadataPanel) {
+                metadataPanel.classList.remove('visible');
+            }
+        });
+        
+        // Add mouse enter/leave events for the metadata panel itself
+        metadataPanel.addEventListener('mouseenter', () => {
+            isOverMetadataPanel = true;
+            metadataPanel.classList.add('visible');
+        });
+        
+        metadataPanel.addEventListener('mouseleave', () => {
+            isOverMetadataPanel = false;
+            // Only hide if mouse is not over the media
+            const rect = wrapper.getBoundingClientRect();
+            const mediaRect = getRenderedMediaRect(mediaElement, rect.width, rect.height);
+            const mouseX = event.clientX - rect.left;
+            const mouseY = event.clientY - rect.top;
+            
+            const isOverMedia = (
+                mouseX >= mediaRect.left && 
+                mouseX <= mediaRect.right && 
+                mouseY >= mediaRect.top && 
+                mouseY <= mediaRect.bottom
+            );
+            
+            if (!isOverMedia) {
+                metadataPanel.classList.remove('visible');
+            }
+        });
         
         // Prevent events from bubbling
         metadataPanel.addEventListener('click', (e) => {
@@ -317,11 +416,61 @@ function initMetadataPanelHandlers(container) {
         
         // Prevent panel scroll from causing modal scroll
         metadataPanel.addEventListener('wheel', (e) => {
-            e.stopPropagation();
-        });
+            const isAtTop = metadataPanel.scrollTop === 0;
+            const isAtBottom = metadataPanel.scrollHeight - metadataPanel.scrollTop === metadataPanel.clientHeight;
+            
+            // Only prevent default if scrolling would cause the panel to scroll
+            if ((e.deltaY < 0 && !isAtTop) || (e.deltaY > 0 && !isAtBottom)) {
+                e.stopPropagation();
+            }
+        }, { passive: true });
     });
 }
 
+/**
+ * Get the actual rendered rectangle of a media element with object-fit: contain
+ * @param {HTMLElement} mediaElement - The img or video element
+ * @param {number} containerWidth - Width of the container
+ * @param {number} containerHeight - Height of the container
+ * @returns {Object} - Rect with left, top, right, bottom coordinates
+ */
+function getRenderedMediaRect(mediaElement, containerWidth, containerHeight) {
+    // Get natural dimensions of the media
+    const naturalWidth = mediaElement.naturalWidth || mediaElement.videoWidth || mediaElement.clientWidth;
+    const naturalHeight = mediaElement.naturalHeight || mediaElement.videoHeight || mediaElement.clientHeight;
+    
+    if (!naturalWidth || !naturalHeight) {
+        // Fallback if dimensions cannot be determined
+        return { left: 0, top: 0, right: containerWidth, bottom: containerHeight };
+    }
+    
+    // Calculate aspect ratios
+    const containerRatio = containerWidth / containerHeight;
+    const mediaRatio = naturalWidth / naturalHeight;
+    
+    let renderedWidth, renderedHeight, left = 0, top = 0;
+    
+    // Apply object-fit: contain logic
+    if (containerRatio > mediaRatio) {
+        // Container is wider than media - will have empty space on sides
+        renderedHeight = containerHeight;
+        renderedWidth = renderedHeight * mediaRatio;
+        left = (containerWidth - renderedWidth) / 2;
+    } else {
+        // Container is taller than media - will have empty space top/bottom
+        renderedWidth = containerWidth;
+        renderedHeight = renderedWidth / mediaRatio;
+        top = (containerHeight - renderedHeight) / 2;
+    }
+    
+    return {
+        left,
+        top,
+        right: left + renderedWidth,
+        bottom: top + renderedHeight
+    };
+}
+
 /**
  * Initialize blur toggle handlers
  */
@@ -382,15 +531,73 @@ function initLazyLoading(container) {
     const lazyElements = container.querySelectorAll('.lazy');
     
     const lazyLoad = (element) => {
+        const localSrc = element.dataset.localSrc;
+        const remoteSrc = element.dataset.remoteSrc;
+        
+        // Check if element is an image or video
         if (element.tagName.toLowerCase() === 'video') {
-            element.src = element.dataset.src;
-            element.querySelector('source').src = element.dataset.src;
-            element.load();
+            // Try local first, then remote
+            tryLocalOrFallbackToRemote(element, localSrc, remoteSrc);
         } else {
-            element.src = element.dataset.src;
+            // For images, we'll use an Image object to test if local file exists
+            tryLocalImageOrFallbackToRemote(element, localSrc, remoteSrc);
         }
+        
         element.classList.remove('lazy');
     };
+    
+    // Try to load local image first, fall back to remote if local fails
+    const tryLocalImageOrFallbackToRemote = (imgElement, localSrc, remoteSrc) => {
+        // Only try local if we have a local path
+        if (localSrc) {
+            const testImg = new Image();
+            testImg.onload = () => {
+                // Local image loaded successfully
+                imgElement.src = localSrc;
+            };
+            testImg.onerror = () => {
+                // Local image failed, use remote
+                imgElement.src = remoteSrc;
+            };
+            // Start loading test image
+            testImg.src = localSrc;
+        } else {
+            // No local path, use remote directly
+            imgElement.src = remoteSrc;
+        }
+    };
+    
+    // Try to load local video first, fall back to remote if local fails
+    const tryLocalOrFallbackToRemote = (videoElement, localSrc, remoteSrc) => {
+        // Only try local if we have a local path
+        if (localSrc) {
+            // Try to fetch local file headers to see if it exists
+            fetch(localSrc, { method: 'HEAD' })
+                .then(response => {
+                    if (response.ok) {
+                        // Local video exists, use it
+                        videoElement.src = localSrc;
+                        videoElement.querySelector('source').src = localSrc;
+                    } else {
+                        // Local video doesn't exist, use remote
+                        videoElement.src = remoteSrc;
+                        videoElement.querySelector('source').src = remoteSrc;
+                    }
+                    videoElement.load();
+                })
+                .catch(() => {
+                    // Error fetching, use remote
+                    videoElement.src = remoteSrc;
+                    videoElement.querySelector('source').src = remoteSrc;
+                    videoElement.load();
+                });
+        } else {
+            // No local path, use remote directly
+            videoElement.src = remoteSrc;
+            videoElement.querySelector('source').src = remoteSrc;
+            videoElement.load();
+        }
+    };
 
     const observer = new IntersectionObserver((entries) => {
         entries.forEach(entry => {
@@ -485,4 +692,4 @@ export function scrollToTop(button) {
             behavior: 'smooth'
         });
     }
-}
+}

static/js/components/checkpointModal/index.js

@@ -96,7 +96,7 @@ export function showCheckpointModal(checkpoint) {
                     
                     <div class="tab-content">
                         <div id="showcase-tab" class="tab-pane active">
-                            ${renderShowcaseContent(checkpoint.civitai?.images || [])}
+                            ${renderShowcaseContent(checkpoint.civitai?.images || [], checkpoint.sha256)}
                         </div>
                         
                         <div id="description-tab" class="tab-pane">

static/js/components/controls/LorasControls.js

@@ -2,6 +2,7 @@
 import { PageControls } from './PageControls.js';
 import { loadMoreLoras, fetchCivitai, resetAndReload, refreshLoras } from '../../api/loraApi.js';
 import { getSessionItem, removeSessionItem } from '../../utils/storageHelpers.js';
+import { createAlphabetBar } from '../alphabet/index.js';
 
 /**
  * LorasControls class - Extends PageControls for LoRA-specific functionality
@@ -16,6 +17,9 @@ export class LorasControls extends PageControls {
         
         // Check for custom filters (e.g., from recipe navigation)
         this.checkCustomFilters();
+        
+        // Initialize alphabet bar component
+        this.initAlphabetBar();
     }
     
     /**
@@ -142,4 +146,15 @@ export class LorasControls extends PageControls {
     _truncateText(text, maxLength) {
         return text.length > maxLength ? text.substring(0, maxLength - 3) + '...' : text;
     }
+    
+    /**
+     * Initialize the alphabet bar component
+     */
+    initAlphabetBar() {
+        // Create the alphabet bar component
+        this.alphabetBar = createAlphabetBar('loras');
+        
+        // Expose the alphabet bar to the global scope for debugging
+        window.alphabetBar = this.alphabetBar;
+    }
 }

static/js/components/loraModal/ModelMetadata.js

@@ -173,12 +173,13 @@ export function setupBaseModelEditing(filePath) {
             'Stable Diffusion 2.x': [BASE_MODELS.SD_2_0, BASE_MODELS.SD_2_1],
             'Stable Diffusion 3.x': [BASE_MODELS.SD_3, BASE_MODELS.SD_3_5, BASE_MODELS.SD_3_5_MEDIUM, BASE_MODELS.SD_3_5_LARGE, BASE_MODELS.SD_3_5_LARGE_TURBO],
             'SDXL': [BASE_MODELS.SDXL, BASE_MODELS.SDXL_LIGHTNING, BASE_MODELS.SDXL_HYPER],
-            'Video Models': [BASE_MODELS.SVD, BASE_MODELS.WAN_VIDEO, BASE_MODELS.HUNYUAN_VIDEO],
+            'Video Models': [BASE_MODELS.SVD, BASE_MODELS.LTXV, BASE_MODELS.WAN_VIDEO, BASE_MODELS.HUNYUAN_VIDEO],
             'Other Models': [
                 BASE_MODELS.FLUX_1_D, BASE_MODELS.FLUX_1_S, BASE_MODELS.AURAFLOW,
                 BASE_MODELS.PIXART_A, BASE_MODELS.PIXART_E, BASE_MODELS.HUNYUAN_1,
                 BASE_MODELS.LUMINA, BASE_MODELS.KOLORS, BASE_MODELS.NOOBAI,
-                BASE_MODELS.ILLUSTRIOUS, BASE_MODELS.PONY, BASE_MODELS.UNKNOWN
+                BASE_MODELS.ILLUSTRIOUS, BASE_MODELS.PONY, BASE_MODELS.HIDREAM,
+                BASE_MODELS.UNKNOWN
             ]
         };
         

static/js/components/loraModal/ShowcaseView.js

@@ -6,12 +6,40 @@ import { showToast, copyToClipboard } from '../../utils/uiHelpers.js';
 import { state } from '../../state/index.js';
 import { NSFW_LEVELS } from '../../utils/constants.js';
 
+/**
+ * Get the local URL for an example image if available
+ * @param {Object} img - Image object
+ * @param {number} index - Image index
+ * @param {string} modelHash - Model hash
+ * @returns {string|null} - Local URL or null if not available
+ */
+function getLocalExampleImageUrl(img, index, modelHash) {
+    if (!modelHash) return null;
+    
+    // Get remote extension
+    const remoteExt = (img.url || '').split('?')[0].split('.').pop().toLowerCase();
+    
+    // If it's a video (mp4), use that extension
+    if (remoteExt === 'mp4') {
+        return `/example_images_static/${modelHash}/image_${index + 1}.mp4`;
+    }
+    
+    // For images, check if optimization is enabled (defaults to true) 
+    const optimizeImages = state.settings.optimizeExampleImages !== false;
+    
+    // Use .webp for images if optimization enabled, otherwise use original extension
+    const extension = optimizeImages ? 'webp' : remoteExt;
+    
+    return `/example_images_static/${modelHash}/image_${index + 1}.${extension}`;
+}
+
 /**
  * 渲染展示内容
  * @param {Array} images - 要展示的图片/视频数组
+ * @param {string} modelHash - Model hash for identifying local files
  * @returns {string} HTML内容
  */
-export function renderShowcaseContent(images) {
+export function renderShowcaseContent(images, modelHash) {
     if (!images?.length) return '<div class="no-examples">No example images available</div>';
     
     // Filter images based on SFW setting
@@ -53,7 +81,15 @@ export function renderShowcaseContent(images) {
         <div class="carousel collapsed">
             ${hiddenNotification}
             <div class="carousel-container">
-                ${filteredImages.map(img => {
+                ${filteredImages.map((img, index) => {
+                    // Try to get local URL for the example image
+                    const localUrl = getLocalExampleImageUrl(img, index, modelHash);
+                    
+                    // Create data attributes for both remote and local URLs
+                    const remoteUrl = img.url;
+                    const dataRemoteSrc = remoteUrl;
+                    const dataLocalSrc = localUrl;
+                    
                     // 计算适当的展示高度：
                     // 1. 保持原始宽高比
                     // 2. 限制最大高度为视窗高度的60%
@@ -111,9 +147,9 @@ export function renderShowcaseContent(images) {
                         `;
                         
                         if (img.type === 'video') {
-                            return generateVideoWrapper(img, heightPercent, shouldBlur, nsfwText, metadataPanel);
+                            return generateVideoWrapper(img, heightPercent, shouldBlur, nsfwText, metadataPanel, dataLocalSrc, dataRemoteSrc);
                         }
-                        return generateImageWrapper(img, heightPercent, shouldBlur, nsfwText, metadataPanel);
+                        return generateImageWrapper(img, heightPercent, shouldBlur, nsfwText, metadataPanel, dataLocalSrc, dataRemoteSrc);
                     }
                     
                     // Create a data attribute with the prompt for copying instead of trying to handle it in the onclick
@@ -174,9 +210,9 @@ export function renderShowcaseContent(images) {
                     `;
                     
                     if (img.type === 'video') {
-                        return generateVideoWrapper(img, heightPercent, shouldBlur, nsfwText, metadataPanel);
+                        return generateVideoWrapper(img, heightPercent, shouldBlur, nsfwText, metadataPanel, dataLocalSrc, dataRemoteSrc);
                     }
-                    return generateImageWrapper(img, heightPercent, shouldBlur, nsfwText, metadataPanel);
+                    return generateImageWrapper(img, heightPercent, shouldBlur, nsfwText, metadataPanel, dataLocalSrc, dataRemoteSrc);
                 }).join('')}
             </div>
         </div>
@@ -186,7 +222,7 @@ export function renderShowcaseContent(images) {
 /**
  * 生成视频包装HTML
  */
-function generateVideoWrapper(img, heightPercent, shouldBlur, nsfwText, metadataPanel) {
+function generateVideoWrapper(img, heightPercent, shouldBlur, nsfwText, metadataPanel, localUrl, remoteUrl) {
     return `
         <div class="media-wrapper ${shouldBlur ? 'nsfw-media-wrapper' : ''}" style="padding-bottom: ${heightPercent}%">
             ${shouldBlur ? `
@@ -195,9 +231,11 @@ function generateVideoWrapper(img, heightPercent, shouldBlur, nsfwText, metadata
                 </button>
             ` : ''}
             <video controls autoplay muted loop crossorigin="anonymous" 
-                referrerpolicy="no-referrer" data-src="${img.url}"
+                referrerpolicy="no-referrer" 
+                data-local-src="${localUrl || ''}"
+                data-remote-src="${remoteUrl}"
                 class="lazy ${shouldBlur ? 'blurred' : ''}">
-                <source data-src="${img.url}" type="video/mp4">
+                <source data-local-src="${localUrl || ''}" data-remote-src="${remoteUrl}" type="video/mp4">
                 Your browser does not support video playback
             </video>
             ${shouldBlur ? `
@@ -216,7 +254,7 @@ function generateVideoWrapper(img, heightPercent, shouldBlur, nsfwText, metadata
 /**
  * 生成图片包装HTML
  */
-function generateImageWrapper(img, heightPercent, shouldBlur, nsfwText, metadataPanel) {
+function generateImageWrapper(img, heightPercent, shouldBlur, nsfwText, metadataPanel, localUrl, remoteUrl) {
     return `
         <div class="media-wrapper ${shouldBlur ? 'nsfw-media-wrapper' : ''}" style="padding-bottom: ${heightPercent}%">
             ${shouldBlur ? `
@@ -224,7 +262,8 @@ function generateImageWrapper(img, heightPercent, shouldBlur, nsfwText, metadata
                     <i class="fas fa-eye"></i>
                 </button>
             ` : ''}
-            <img data-src="${img.url}" 
+            <img data-local-src="${localUrl || ''}" 
+                data-remote-src="${remoteUrl}"
                 alt="Preview" 
                 crossorigin="anonymous" 
                 referrerpolicy="no-referrer"
@@ -290,9 +329,72 @@ function initMetadataPanelHandlers(container) {
     const mediaWrappers = container.querySelectorAll('.media-wrapper');
     
     mediaWrappers.forEach(wrapper => {
-        // Get the metadata panel
+        // Get the metadata panel and media element (img or video)
         const metadataPanel = wrapper.querySelector('.image-metadata-panel');
-        if (!metadataPanel) return;
+        const mediaElement = wrapper.querySelector('img, video');
+        
+        if (!metadataPanel || !mediaElement) return;
+        
+        let isOverMetadataPanel = false;
+        
+        // Add event listeners to the wrapper for mouse tracking
+        wrapper.addEventListener('mousemove', (e) => {
+            // Get mouse position relative to wrapper
+            const rect = wrapper.getBoundingClientRect();
+            const mouseX = e.clientX - rect.left;
+            const mouseY = e.clientY - rect.top;
+            
+            // Get the actual displayed dimensions of the media element
+            const mediaRect = getRenderedMediaRect(mediaElement, rect.width, rect.height);
+            
+            // Check if mouse is over the actual media content
+            const isOverMedia = (
+                mouseX >= mediaRect.left && 
+                mouseX <= mediaRect.right && 
+                mouseY >= mediaRect.top && 
+                mouseY <= mediaRect.bottom
+            );
+            
+            // Show metadata panel when over media content
+            if (isOverMedia || isOverMetadataPanel) {
+                metadataPanel.classList.add('visible');
+            } else {
+                metadataPanel.classList.remove('visible');
+            }
+        });
+        
+        wrapper.addEventListener('mouseleave', () => {
+            // Only hide panel when mouse leaves the wrapper and not over the metadata panel
+            if (!isOverMetadataPanel) {
+                metadataPanel.classList.remove('visible');
+            }
+        });
+        
+        // Add mouse enter/leave events for the metadata panel itself
+        metadataPanel.addEventListener('mouseenter', () => {
+            isOverMetadataPanel = true;
+            metadataPanel.classList.add('visible');
+        });
+        
+        metadataPanel.addEventListener('mouseleave', () => {
+            isOverMetadataPanel = false;
+            // Only hide if mouse is not over the media
+            const rect = wrapper.getBoundingClientRect();
+            const mediaRect = getRenderedMediaRect(mediaElement, rect.width, rect.height);
+            const mouseX = event.clientX - rect.left;
+            const mouseY = event.clientY - rect.top;
+            
+            const isOverMedia = (
+                mouseX >= mediaRect.left && 
+                mouseX <= mediaRect.right && 
+                mouseY >= mediaRect.top && 
+                mouseY <= mediaRect.bottom
+            );
+            
+            if (!isOverMedia) {
+                metadataPanel.classList.remove('visible');
+            }
+        });
         
         // Prevent events from the metadata panel from bubbling
         metadataPanel.addEventListener('click', (e) => {
@@ -332,6 +434,50 @@ function initMetadataPanelHandlers(container) {
     });
 }
 
+/**
+ * Get the actual rendered rectangle of a media element with object-fit: contain
+ * @param {HTMLElement} mediaElement - The img or video element
+ * @param {number} containerWidth - Width of the container
+ * @param {number} containerHeight - Height of the container
+ * @returns {Object} - Rect with left, top, right, bottom coordinates
+ */
+function getRenderedMediaRect(mediaElement, containerWidth, containerHeight) {
+    // Get natural dimensions of the media
+    const naturalWidth = mediaElement.naturalWidth || mediaElement.videoWidth || mediaElement.clientWidth;
+    const naturalHeight = mediaElement.naturalHeight || mediaElement.videoHeight || mediaElement.clientHeight;
+    
+    if (!naturalWidth || !naturalHeight) {
+        // Fallback if dimensions cannot be determined
+        return { left: 0, top: 0, right: containerWidth, bottom: containerHeight };
+    }
+    
+    // Calculate aspect ratios
+    const containerRatio = containerWidth / containerHeight;
+    const mediaRatio = naturalWidth / naturalHeight;
+    
+    let renderedWidth, renderedHeight, left = 0, top = 0;
+    
+    // Apply object-fit: contain logic
+    if (containerRatio > mediaRatio) {
+        // Container is wider than media - will have empty space on sides
+        renderedHeight = containerHeight;
+        renderedWidth = renderedHeight * mediaRatio;
+        left = (containerWidth - renderedWidth) / 2;
+    } else {
+        // Container is taller than media - will have empty space top/bottom
+        renderedWidth = containerWidth;
+        renderedHeight = renderedWidth / mediaRatio;
+        top = (containerHeight - renderedHeight) / 2;
+    }
+    
+    return {
+        left,
+        top,
+        right: left + renderedWidth,
+        bottom: top + renderedHeight
+    };
+}
+
 /**
  * 初始化模糊切换处理
  */
@@ -392,15 +538,73 @@ function initLazyLoading(container) {
     const lazyElements = container.querySelectorAll('.lazy');
     
     const lazyLoad = (element) => {
+        const localSrc = element.dataset.localSrc;
+        const remoteSrc = element.dataset.remoteSrc;
+        
+        // Check if element is an image or video
         if (element.tagName.toLowerCase() === 'video') {
-            element.src = element.dataset.src;
-            element.querySelector('source').src = element.dataset.src;
-            element.load();
+            // Try local first, then remote
+            tryLocalOrFallbackToRemote(element, localSrc, remoteSrc);
         } else {
-            element.src = element.dataset.src;
+            // For images, we'll use an Image object to test if local file exists
+            tryLocalImageOrFallbackToRemote(element, localSrc, remoteSrc);
         }
+        
         element.classList.remove('lazy');
     };
+    
+    // Try to load local image first, fall back to remote if local fails
+    const tryLocalImageOrFallbackToRemote = (imgElement, localSrc, remoteSrc) => {
+        // Only try local if we have a local path
+        if (localSrc) {
+            const testImg = new Image();
+            testImg.onload = () => {
+                // Local image loaded successfully
+                imgElement.src = localSrc;
+            };
+            testImg.onerror = () => {
+                // Local image failed, use remote
+                imgElement.src = remoteSrc;
+            };
+            // Start loading test image
+            testImg.src = localSrc;
+        } else {
+            // No local path, use remote directly
+            imgElement.src = remoteSrc;
+        }
+    };
+    
+    // Try to load local video first, fall back to remote if local fails
+    const tryLocalOrFallbackToRemote = (videoElement, localSrc, remoteSrc) => {
+        // Only try local if we have a local path
+        if (localSrc) {
+            // Try to fetch local file headers to see if it exists
+            fetch(localSrc, { method: 'HEAD' })
+                .then(response => {
+                    if (response.ok) {
+                        // Local video exists, use it
+                        videoElement.src = localSrc;
+                        videoElement.querySelector('source').src = localSrc;
+                    } else {
+                        // Local video doesn't exist, use remote
+                        videoElement.src = remoteSrc;
+                        videoElement.querySelector('source').src = remoteSrc;
+                    }
+                    videoElement.load();
+                })
+                .catch(() => {
+                    // Error fetching, use remote
+                    videoElement.src = remoteSrc;
+                    videoElement.querySelector('source').src = remoteSrc;
+                    videoElement.load();
+                });
+        } else {
+            // No local path, use remote directly
+            videoElement.src = remoteSrc;
+            videoElement.querySelector('source').src = remoteSrc;
+            videoElement.load();
+        }
+    };
 
     const observer = new IntersectionObserver((entries) => {
         entries.forEach(entry => {
@@ -497,4 +701,4 @@ export function scrollToTop(button) {
             behavior: 'smooth'
         });
     }
-}
+}

static/js/components/loraModal/index.js

@@ -122,7 +122,7 @@ export function showLoraModal(lora) {
                     
                     <div class="tab-content">
                         <div id="showcase-tab" class="tab-pane active">
-                            ${renderShowcaseContent(lora.civitai?.images)}
+                            ${renderShowcaseContent(lora.civitai?.images, lora.sha256)}
                         </div>
                         
                         <div id="description-tab" class="tab-pane">

static/js/core.js

@@ -5,6 +5,7 @@ import { modalManager } from './managers/ModalManager.js';
 import { updateService } from './managers/UpdateService.js';
 import { HeaderManager } from './components/Header.js';
 import { settingsManager } from './managers/SettingsManager.js';
+import { exampleImagesManager } from './managers/ExampleImagesManager.js';
 import { showToast, initTheme, initBackToTop, lazyLoadImages } from './utils/uiHelpers.js';
 import { initializeInfiniteScroll } from './utils/infiniteScroll.js';
 import { migrateStorageItems } from './utils/storageHelpers.js';
@@ -27,12 +28,16 @@ export class AppCore {
         updateService.initialize();
         window.modalManager = modalManager;
         window.settingsManager = settingsManager;
+        window.exampleImagesManager = exampleImagesManager;
         
         // Initialize UI components
         window.headerManager = new HeaderManager();
         initTheme();
         initBackToTop();
         
+        // Initialize the example images manager
+        exampleImagesManager.initialize();
+        
         // Mark as initialized
         this.initialized = true;
         

static/js/loras.js

@@ -8,7 +8,7 @@ import { DownloadManager } from './managers/DownloadManager.js';
 import { moveManager } from './managers/MoveManager.js';
 import { LoraContextMenu } from './components/ContextMenu/index.js';
 import { createPageControls } from './components/controls/index.js';
-import { confirmDelete, closeDeleteModal } from './utils/modalUtils.js';
+import { confirmDelete, closeDeleteModal, confirmExclude, closeExcludeModal } from './utils/modalUtils.js';
 
 // Initialize the LoRA page
 class LoraPageManager {
@@ -35,6 +35,8 @@ class LoraPageManager {
         window.showLoraModal = showLoraModal;
         window.confirmDelete = confirmDelete;
         window.closeDeleteModal = closeDeleteModal;
+        window.confirmExclude = confirmExclude;
+        window.closeExcludeModal = closeExcludeModal;
         window.downloadManager = this.downloadManager;
         window.moveManager = moveManager;
         window.toggleShowcase = toggleShowcase;

static/js/managers/ExampleImagesManager.js

@@ -0,0 +1,602 @@
+import { showToast } from '../utils/uiHelpers.js';
+import { getStorageItem, setStorageItem } from '../utils/storageHelpers.js';
+
+// ExampleImagesManager.js
+class ExampleImagesManager {
+    constructor() {
+        this.isDownloading = false;
+        this.isPaused = false;
+        this.progressUpdateInterval = null;
+        this.startTime = null;
+        this.progressPanel = null;
+        this.isProgressPanelCollapsed = false;
+        this.pauseButton = null; // Store reference to the pause button
+        
+        // Initialize download path field and check download status
+        this.initializePathOptions();
+        this.checkDownloadStatus();
+    }
+    
+    // Initialize the manager
+    initialize() {
+        // Initialize event listeners
+        this.initEventListeners();
+        
+        // Initialize progress panel reference
+        this.progressPanel = document.getElementById('exampleImagesProgress');
+        
+        // Load collapse state from storage
+        this.isProgressPanelCollapsed = getStorageItem('progress_panel_collapsed', false);
+        if (this.progressPanel && this.isProgressPanelCollapsed) {
+            this.progressPanel.classList.add('collapsed');
+            const icon = document.querySelector('#collapseProgressBtn i');
+            if (icon) {
+                icon.className = 'fas fa-chevron-up';
+            }
+        }
+        
+        // Initialize progress panel button handlers
+        this.pauseButton = document.getElementById('pauseExampleDownloadBtn');
+        const collapseBtn = document.getElementById('collapseProgressBtn');
+        
+        if (this.pauseButton) {
+            this.pauseButton.onclick = () => this.pauseDownload();
+        }
+        
+        if (collapseBtn) {
+            collapseBtn.onclick = () => this.toggleProgressPanel();
+        }
+    }
+    
+    // Initialize event listeners for buttons
+    initEventListeners() {
+        const downloadBtn = document.getElementById('exampleImagesDownloadBtn');
+        if (downloadBtn) {
+            downloadBtn.onclick = () => this.handleDownloadButton();
+        }
+    }
+    
+    async initializePathOptions() {
+        try {
+            // Get custom path input element
+            const pathInput = document.getElementById('exampleImagesPath');
+
+            // Set path from storage if available
+            const savedPath = getStorageItem('example_images_path', '');
+            if (savedPath) {
+                pathInput.value = savedPath;
+                // Enable download button if path is set
+                this.updateDownloadButtonState(true);
+            } else {
+                // Disable download button if no path is set
+                this.updateDownloadButtonState(false);
+            }
+            
+            // Add event listener to validate path input
+            pathInput.addEventListener('input', async () => {
+                const hasPath = pathInput.value.trim() !== '';
+                this.updateDownloadButtonState(hasPath);
+                
+                // Save path to storage when changed
+                if (hasPath) {
+                    setStorageItem('example_images_path', pathInput.value);
+                    
+                    // Update path in backend settings
+                    try {
+                        const response = await fetch('/api/settings', {
+                            method: 'POST',
+                            headers: {
+                                'Content-Type': 'application/json'
+                            },
+                            body: JSON.stringify({
+                                example_images_path: pathInput.value
+                            })
+                        });
+                        
+                        if (!response.ok) {
+                            throw new Error(`HTTP error! Status: ${response.status}`);
+                        }
+                        
+                        const data = await response.json();
+                        if (!data.success) {
+                            console.error('Failed to update example images path in backend:', data.error);
+                        } else {
+                            showToast('Example images path updated successfully', 'success');
+                        }
+                    } catch (error) {
+                        console.error('Failed to update example images path:', error);
+                    }
+                }
+            });
+        } catch (error) {
+            console.error('Failed to initialize path options:', error);
+        }
+    }
+    
+    // Method to update download button state
+    updateDownloadButtonState(enabled) {
+        const downloadBtn = document.getElementById('exampleImagesDownloadBtn');
+        if (downloadBtn) {
+            if (enabled) {
+                downloadBtn.classList.remove('disabled');
+                downloadBtn.disabled = false;
+            } else {
+                downloadBtn.classList.add('disabled');
+                downloadBtn.disabled = true;
+            }
+        }
+    }
+    
+    // Method to handle download button click based on current state
+    async handleDownloadButton() {
+        if (this.isDownloading && this.isPaused) {
+            // If download is paused, resume it
+            this.resumeDownload();
+        } else if (!this.isDownloading) {
+            // If no download in progress, start a new one
+            this.startDownload();
+        } else {
+            // If download is in progress, show info toast
+            showToast('Download already in progress', 'info');
+        }
+    }
+    
+    async checkDownloadStatus() {
+        try {
+            const response = await fetch('/api/example-images-status');
+            const data = await response.json();
+            
+            if (data.success) {
+                this.isDownloading = data.is_downloading;
+                this.isPaused = data.status.status === 'paused';
+                
+                // Update download button text based on status
+                this.updateDownloadButtonText();
+                
+                if (this.isDownloading) {
+                    // Ensure progress panel exists before updating UI
+                    if (!this.progressPanel) {
+                        this.progressPanel = document.getElementById('exampleImagesProgress');
+                    }
+                    
+                    if (this.progressPanel) {
+                        this.updateUI(data.status);
+                        this.showProgressPanel();
+                        
+                        // Start the progress update interval if downloading
+                        if (!this.progressUpdateInterval) {
+                            this.startProgressUpdates();
+                        }
+                    } else {
+                        console.warn('Progress panel not found, will retry on next update');
+                        // Set a shorter timeout to try again
+                        setTimeout(() => this.checkDownloadStatus(), 500);
+                    }
+                }
+            }
+        } catch (error) {
+            console.error('Failed to check download status:', error);
+        }
+    }
+    
+    // Update download button text based on current state
+    updateDownloadButtonText() {
+        const btnTextElement = document.getElementById('exampleDownloadBtnText');
+        if (btnTextElement) {
+            if (this.isDownloading && this.isPaused) {
+                btnTextElement.textContent = "Resume";
+            } else if (!this.isDownloading) {
+                btnTextElement.textContent = "Download";
+            }
+        }
+    }
+    
+    async startDownload() {
+        if (this.isDownloading) {
+            showToast('Download already in progress', 'warning');
+            return;
+        }
+        
+        try {
+            const outputDir = document.getElementById('exampleImagesPath').value || '';
+            
+            if (!outputDir) {
+                showToast('Please enter a download location first', 'warning');
+                return;
+            }
+            
+            const optimize = document.getElementById('optimizeExampleImages').checked;
+            
+            const response = await fetch('/api/download-example-images', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json'
+                },
+                body: JSON.stringify({
+                    output_dir: outputDir,
+                    optimize: optimize,
+                    model_types: ['lora', 'checkpoint']
+                })
+            });
+            
+            const data = await response.json();
+            
+            if (data.success) {
+                this.isDownloading = true;
+                this.isPaused = false;
+                this.startTime = new Date();
+                this.updateUI(data.status);
+                this.showProgressPanel();
+                this.startProgressUpdates();
+                this.updateDownloadButtonText();
+                showToast('Example images download started', 'success');
+                
+                // Close settings modal
+                modalManager.closeModal('settingsModal');
+            } else {
+                showToast(data.error || 'Failed to start download', 'error');
+            }
+        } catch (error) {
+            console.error('Failed to start download:', error);
+            showToast('Failed to start download', 'error');
+        }
+    }
+    
+    async pauseDownload() {
+        if (!this.isDownloading || this.isPaused) {
+            return;
+        }
+        
+        try {
+            const response = await fetch('/api/pause-example-images', {
+                method: 'POST'
+            });
+            
+            const data = await response.json();
+            
+            if (data.success) {
+                this.isPaused = true;
+                document.getElementById('downloadStatusText').textContent = 'Paused';
+                
+                // Only update the icon element, not the entire innerHTML
+                if (this.pauseButton) {
+                    const iconElement = this.pauseButton.querySelector('i');
+                    if (iconElement) {
+                        iconElement.className = 'fas fa-play';
+                    }
+                    this.pauseButton.onclick = () => this.resumeDownload();
+                }
+                
+                this.updateDownloadButtonText();
+                showToast('Download paused', 'info');
+            } else {
+                showToast(data.error || 'Failed to pause download', 'error');
+            }
+        } catch (error) {
+            console.error('Failed to pause download:', error);
+            showToast('Failed to pause download', 'error');
+        }
+    }
+    
+    async resumeDownload() {
+        if (!this.isDownloading || !this.isPaused) {
+            return;
+        }
+        
+        try {
+            const response = await fetch('/api/resume-example-images', {
+                method: 'POST'
+            });
+            
+            const data = await response.json();
+            
+            if (data.success) {
+                this.isPaused = false;
+                document.getElementById('downloadStatusText').textContent = 'Downloading';
+                
+                // Only update the icon element, not the entire innerHTML
+                if (this.pauseButton) {
+                    const iconElement = this.pauseButton.querySelector('i');
+                    if (iconElement) {
+                        iconElement.className = 'fas fa-pause';
+                    }
+                    this.pauseButton.onclick = () => this.pauseDownload();
+                }
+                
+                this.updateDownloadButtonText();
+                showToast('Download resumed', 'success');
+            } else {
+                showToast(data.error || 'Failed to resume download', 'error');
+            }
+        } catch (error) {
+            console.error('Failed to resume download:', error);
+            showToast('Failed to resume download', 'error');
+        }
+    }
+    
+    startProgressUpdates() {
+        // Clear any existing interval
+        if (this.progressUpdateInterval) {
+            clearInterval(this.progressUpdateInterval);
+        }
+        
+        // Set new interval to update progress every 2 seconds
+        this.progressUpdateInterval = setInterval(async () => {
+            await this.updateProgress();
+        }, 2000);
+    }
+    
+    async updateProgress() {
+        try {
+            const response = await fetch('/api/example-images-status');
+            const data = await response.json();
+            
+            if (data.success) {
+                this.isDownloading = data.is_downloading;
+                this.isPaused = data.status.status === 'paused';
+                
+                // Update download button text
+                this.updateDownloadButtonText();
+                
+                if (this.isDownloading) {
+                    this.updateUI(data.status);
+                } else {
+                    // Download completed or failed
+                    clearInterval(this.progressUpdateInterval);
+                    this.progressUpdateInterval = null;
+                    
+                    if (data.status.status === 'completed') {
+                        showToast('Example images download completed', 'success');
+                        // Hide the panel after a delay
+                        setTimeout(() => this.hideProgressPanel(), 5000);
+                    } else if (data.status.status === 'error') {
+                        showToast('Example images download failed', 'error');
+                    }
+                }
+            }
+        } catch (error) {
+            console.error('Failed to update progress:', error);
+        }
+    }
+    
+    updateUI(status) {
+        // Ensure progress panel exists
+        if (!this.progressPanel) {
+            this.progressPanel = document.getElementById('exampleImagesProgress');
+            if (!this.progressPanel) {
+                console.error('Progress panel element not found in DOM');
+                return;
+            }
+        }
+        
+        // Update status text
+        const statusText = document.getElementById('downloadStatusText');
+        if (statusText) {
+            statusText.textContent = this.getStatusText(status.status);
+        }
+        
+        // Update progress counts and bar
+        const progressCounts = document.getElementById('downloadProgressCounts');
+        if (progressCounts) {
+            progressCounts.textContent = `${status.completed}/${status.total}`;
+        }
+        
+        const progressBar = document.getElementById('downloadProgressBar');
+        if (progressBar) {
+            const progressPercent = status.total > 0 ? (status.completed / status.total) * 100 : 0;
+            progressBar.style.width = `${progressPercent}%`;
+            
+            // Update mini progress circle
+            this.updateMiniProgress(progressPercent);
+        }
+        
+        // Update current model
+        const currentModel = document.getElementById('currentModelName');
+        if (currentModel) {
+            currentModel.textContent = status.current_model || '-';
+        }
+        
+        // Update time stats
+        this.updateTimeStats(status);
+        
+        // Update errors
+        this.updateErrors(status);
+        
+        // Update pause/resume button
+        if (!this.pauseButton) {
+            this.pauseButton = document.getElementById('pauseExampleDownloadBtn');
+        }
+        
+        if (this.pauseButton) {
+            // Check if the button already has the SVG elements
+            let hasProgressElements = !!this.pauseButton.querySelector('.mini-progress-circle');
+            
+            if (!hasProgressElements) {
+                // If elements don't exist, add them
+                this.pauseButton.innerHTML = `
+                    <i class="${status.status === 'paused' ? 'fas fa-play' : 'fas fa-pause'}"></i>
+                    <svg class="mini-progress-container" width="24" height="24" viewBox="0 0 24 24">
+                        <circle class="mini-progress-background" cx="12" cy="12" r="10"></circle>
+                        <circle class="mini-progress-circle" cx="12" cy="12" r="10" stroke-dasharray="62.8" stroke-dashoffset="62.8"></circle>
+                    </svg>
+                    <span class="progress-percent"></span>
+                `;
+            } else {
+                // If elements exist, just update the icon
+                const iconElement = this.pauseButton.querySelector('i');
+                if (iconElement) {
+                    iconElement.className = status.status === 'paused' ? 'fas fa-play' : 'fas fa-pause';
+                }
+            }
+            
+            // Update click handler
+            this.pauseButton.onclick = status.status === 'paused' 
+                ? () => this.resumeDownload() 
+                : () => this.pauseDownload();
+            
+            // Update progress immediately
+            const progressBar = document.getElementById('downloadProgressBar');
+            if (progressBar) {
+                const progressPercent = status.total > 0 ? (status.completed / status.total) * 100 : 0;
+                this.updateMiniProgress(progressPercent);
+            }
+        }
+    }
+    
+    // Update the mini progress circle in the pause button
+    updateMiniProgress(percent) {
+        // Ensure we have the pause button reference
+        if (!this.pauseButton) {
+            this.pauseButton = document.getElementById('pauseExampleDownloadBtn');
+            if (!this.pauseButton) {
+                console.error('Pause button not found');
+                return;
+            }
+        }
+        
+        // Query elements within the context of the pause button
+        const miniProgressCircle = this.pauseButton.querySelector('.mini-progress-circle');
+        const percentText = this.pauseButton.querySelector('.progress-percent');
+        
+        if (miniProgressCircle && percentText) {
+            // Circle circumference = 2πr = 2 * π * 10 = 62.8
+            const circumference = 62.8;
+            const offset = circumference - (percent / 100) * circumference;
+            
+            miniProgressCircle.style.strokeDashoffset = offset;
+            percentText.textContent = `${Math.round(percent)}%`;
+            
+            // Only show percent text when panel is collapsed
+            percentText.style.display = this.isProgressPanelCollapsed ? 'block' : 'none';
+        } else {
+            console.warn('Mini progress elements not found within pause button', 
+                         this.pauseButton,
+                         'mini-progress-circle:', !!miniProgressCircle, 
+                         'progress-percent:', !!percentText);
+        }
+    }
+    
+    updateTimeStats(status) {
+        const elapsedTime = document.getElementById('elapsedTime');
+        const remainingTime = document.getElementById('remainingTime');
+        
+        if (!elapsedTime || !remainingTime) return;
+        
+        // Calculate elapsed time
+        let elapsed;
+        if (status.start_time) {
+            const now = new Date();
+            const startTime = new Date(status.start_time * 1000);
+            elapsed = Math.floor((now - startTime) / 1000);
+        } else {
+            elapsed = 0;
+        }
+        
+        elapsedTime.textContent = this.formatTime(elapsed);
+        
+        // Calculate remaining time
+        if (status.total > 0 && status.completed > 0 && status.status === 'running') {
+            const rate = status.completed / elapsed; // models per second
+            const remaining = Math.floor((status.total - status.completed) / rate);
+            remainingTime.textContent = this.formatTime(remaining);
+        } else {
+            remainingTime.textContent = '--:--:--';
+        }
+    }
+    
+    updateErrors(status) {
+        const errorContainer = document.getElementById('downloadErrorContainer');
+        const errorList = document.getElementById('downloadErrors');
+        
+        if (!errorContainer || !errorList) return;
+        
+        if (status.errors && status.errors.length > 0) {
+            // Show only the last 3 errors
+            const recentErrors = status.errors.slice(-3);
+            errorList.innerHTML = recentErrors.map(error => 
+                `<div class="error-item">${error}</div>`
+            ).join('');
+            
+            errorContainer.classList.remove('hidden');
+        } else {
+            errorContainer.classList.add('hidden');
+        }
+    }
+    
+    formatTime(seconds) {
+        const hours = Math.floor(seconds / 3600);
+        const minutes = Math.floor((seconds % 3600) / 60);
+        const secs = seconds % 60;
+        
+        return [
+            hours.toString().padStart(2, '0'),
+            minutes.toString().padStart(2, '0'),
+            secs.toString().padStart(2, '0')
+        ].join(':');
+    }
+    
+    getStatusText(status) {
+        switch (status) {
+            case 'running': return 'Downloading';
+            case 'paused': return 'Paused';
+            case 'completed': return 'Completed';
+            case 'error': return 'Error';
+            default: return 'Initializing';
+        }
+    }
+    
+    showProgressPanel() {
+        // Ensure progress panel exists
+        if (!this.progressPanel) {
+            this.progressPanel = document.getElementById('exampleImagesProgress');
+            if (!this.progressPanel) {
+                console.error('Progress panel element not found in DOM');
+                return;
+            }
+        }
+        this.progressPanel.classList.add('visible');
+    }
+    
+    hideProgressPanel() {
+        if (!this.progressPanel) {
+            this.progressPanel = document.getElementById('exampleImagesProgress');
+            if (!this.progressPanel) return;
+        }
+        this.progressPanel.classList.remove('visible');
+    }
+    
+    toggleProgressPanel() {
+        if (!this.progressPanel) {
+            this.progressPanel = document.getElementById('exampleImagesProgress');
+            if (!this.progressPanel) return;
+        }
+        
+        this.isProgressPanelCollapsed = !this.isProgressPanelCollapsed;
+        this.progressPanel.classList.toggle('collapsed');
+        
+        // Save collapsed state to storage
+        setStorageItem('progress_panel_collapsed', this.isProgressPanelCollapsed);
+        
+        // Update icon
+        const icon = document.querySelector('#collapseProgressBtn i');
+        if (icon) {
+            if (this.isProgressPanelCollapsed) {
+                icon.className = 'fas fa-chevron-up';
+            } else {
+                icon.className = 'fas fa-chevron-down';
+            }
+        }
+        
+        // Force update mini progress if panel is collapsed
+        if (this.isProgressPanelCollapsed) {
+            const progressBar = document.getElementById('downloadProgressBar');
+            if (progressBar) {
+                const progressPercent = parseFloat(progressBar.style.width) || 0;
+                this.updateMiniProgress(progressPercent);
+            }
+        }
+    }
+}
+
+// Create singleton instance
+export const exampleImagesManager = new ExampleImagesManager();

static/js/managers/ModalManager.js

@@ -59,6 +59,19 @@ export class ModalManager {
                 }
             });
         }
+        
+        // Add excludeModal registration
+        const excludeModal = document.getElementById('excludeModal');
+        if (excludeModal) {
+            this.registerModal('excludeModal', {
+                element: excludeModal,
+                onClose: () => {
+                    this.getModal('excludeModal').element.classList.remove('show');
+                    document.body.classList.remove('modal-open');
+                },
+                closeOnOutsideClick: true
+            });
+        }
 
         // Add downloadModal registration
         const downloadModal = document.getElementById('downloadModal');
@@ -145,6 +158,18 @@ export class ModalManager {
             });
         }
 
+        // Add duplicateDeleteModal registration
+        const duplicateDeleteModal = document.getElementById('duplicateDeleteModal');
+        if (duplicateDeleteModal) {
+            this.registerModal('duplicateDeleteModal', {
+                element: duplicateDeleteModal,
+                onClose: () => {
+                    this.getModal('duplicateDeleteModal').element.classList.remove('show');
+                    document.body.classList.remove('modal-open');
+                }
+            });
+        }
+        
         // Set up event listeners for modal toggles
         const supportToggle = document.getElementById('supportToggleBtn');
         if (supportToggle) {
@@ -208,7 +233,7 @@ export class ModalManager {
         // Store current scroll position before showing modal
         this.scrollPosition = window.scrollY;
 
-        if (id === 'deleteModal') {
+        if (id === 'deleteModal' || id === 'excludeModal' || id === 'duplicateDeleteModal') {
             modal.element.classList.add('show');
         } else {
             modal.element.style.display = 'block';

static/js/managers/SettingsManager.js

@@ -147,6 +147,8 @@ export class SettingsManager {
             state.global.settings.show_only_sfw = value;
         } else if (settingKey === 'autoplay_on_hover') {
             state.global.settings.autoplayOnHover = value;
+        } else if (settingKey === 'optimize_example_images') {
+            state.global.settings.optimizeExampleImages = value;
         } else {
             // For any other settings that might be added in the future
             state.global.settings[settingKey] = value;

static/js/managers/import/DownloadManager.js

@@ -0,0 +1,256 @@
+import { showToast } from '../../utils/uiHelpers.js';
+
+export class DownloadManager {
+    constructor(importManager) {
+        this.importManager = importManager;
+    }
+
+    async saveRecipe() {
+        // Check if we're in download-only mode (for existing recipe)
+        const isDownloadOnly = !!this.importManager.recipeId;
+        
+        if (!isDownloadOnly && !this.importManager.recipeName) {
+            showToast('Please enter a recipe name', 'error');
+            return;
+        }
+        
+        try {
+            // Show progress indicator
+            this.importManager.loadingManager.showSimpleLoading(isDownloadOnly ? 'Downloading LoRAs...' : 'Saving recipe...');
+            
+            // Only send the complete recipe to save if not in download-only mode
+            if (!isDownloadOnly) {
+                // Create FormData object for saving recipe
+                const formData = new FormData();
+                
+                // Add image data - depends on import mode
+                if (this.importManager.recipeImage) {
+                    // Direct upload
+                    formData.append('image', this.importManager.recipeImage);
+                } else if (this.importManager.recipeData && this.importManager.recipeData.image_base64) {
+                    // URL mode with base64 data
+                    formData.append('image_base64', this.importManager.recipeData.image_base64);
+                } else if (this.importManager.importMode === 'url') {
+                    // Fallback for URL mode - tell backend to fetch the image again
+                    const urlInput = document.getElementById('imageUrlInput');
+                    if (urlInput && urlInput.value) {
+                        formData.append('image_url', urlInput.value);
+                    } else {
+                        throw new Error('No image data available');
+                    }
+                } else {
+                    throw new Error('No image data available');
+                }
+                
+                formData.append('name', this.importManager.recipeName);
+                formData.append('tags', JSON.stringify(this.importManager.recipeTags));
+                
+                // Prepare complete metadata including generation parameters
+                const completeMetadata = {
+                    base_model: this.importManager.recipeData.base_model || "",
+                    loras: this.importManager.recipeData.loras || [],
+                    gen_params: this.importManager.recipeData.gen_params || {},
+                    raw_metadata: this.importManager.recipeData.raw_metadata || {}
+                };
+                
+                // Add source_path to metadata to track where the recipe was imported from
+                if (this.importManager.importMode === 'url') {
+                    const urlInput = document.getElementById('imageUrlInput');
+                    if (urlInput && urlInput.value) {
+                        completeMetadata.source_path = urlInput.value;
+                    }
+                }
+                
+                formData.append('metadata', JSON.stringify(completeMetadata));
+            
+                // Send save request
+                const response = await fetch('/api/recipes/save', {
+                    method: 'POST',
+                    body: formData
+                });
+                
+                const result = await response.json();
+
+                if (!result.success) {
+                    // Handle save error
+                    console.error("Failed to save recipe:", result.error);
+                    showToast(result.error, 'error');
+                    // Close modal
+                    modalManager.closeModal('importModal');
+                    return;
+                }
+            }
+
+            // Check if we need to download LoRAs
+            let failedDownloads = 0;
+            if (this.importManager.downloadableLoRAs && this.importManager.downloadableLoRAs.length > 0) {
+                await this.downloadMissingLoras();
+            }
+
+            // Show success message
+            if (isDownloadOnly) {
+                if (failedDownloads === 0) {    
+                    showToast('LoRAs downloaded successfully', 'success');
+                }
+            } else {
+                showToast(`Recipe "${this.importManager.recipeName}" saved successfully`, 'success');
+            }
+            
+            // Close modal
+            modalManager.closeModal('importModal');
+            
+            // Refresh the recipe
+            window.recipeManager.loadRecipes();
+            
+        } catch (error) {
+            console.error('Error:', error);
+            showToast(error.message, 'error');
+        } finally {
+            this.importManager.loadingManager.hide();
+        }
+    }
+
+    async downloadMissingLoras() {
+        // For download, we need to validate the target path
+        const loraRoot = document.getElementById('importLoraRoot')?.value;
+        if (!loraRoot) {
+            throw new Error('Please select a LoRA root directory');
+        }
+        
+        // Build target path
+        let targetPath = loraRoot;
+        if (this.importManager.selectedFolder) {
+            targetPath += '/' + this.importManager.selectedFolder;
+        }
+        
+        const newFolder = document.getElementById('importNewFolder')?.value?.trim();
+        if (newFolder) {
+            targetPath += '/' + newFolder;
+        }
+        
+        // Set up WebSocket for progress updates
+        const wsProtocol = window.location.protocol === 'https:' ? 'wss://' : 'ws://';
+        const ws = new WebSocket(`${wsProtocol}${window.location.host}/ws/fetch-progress`);
+        
+        // Show enhanced loading with progress details for multiple items
+        const updateProgress = this.importManager.loadingManager.showDownloadProgress(
+            this.importManager.downloadableLoRAs.length
+        );
+        
+        let completedDownloads = 0;
+        let failedDownloads = 0;
+        let accessFailures = 0;
+        let currentLoraProgress = 0;
+        
+        // Set up progress tracking for current download
+        ws.onmessage = (event) => {
+            const data = JSON.parse(event.data);
+            if (data.status === 'progress') {
+                // Update current LoRA progress
+                currentLoraProgress = data.progress;
+                
+                // Get current LoRA name
+                const currentLora = this.importManager.downloadableLoRAs[completedDownloads + failedDownloads];
+                const loraName = currentLora ? currentLora.name : '';
+                
+                // Update progress display
+                updateProgress(currentLoraProgress, completedDownloads, loraName);
+                
+                // Add more detailed status messages based on progress
+                if (currentLoraProgress < 3) {
+                    this.importManager.loadingManager.setStatus(
+                        `Preparing download for LoRA ${completedDownloads + failedDownloads + 1}/${this.importManager.downloadableLoRAs.length}`
+                    );
+                } else if (currentLoraProgress === 3) {
+                    this.importManager.loadingManager.setStatus(
+                        `Downloaded preview for LoRA ${completedDownloads + failedDownloads + 1}/${this.importManager.downloadableLoRAs.length}`
+                    );
+                } else if (currentLoraProgress > 3 && currentLoraProgress < 100) {
+                    this.importManager.loadingManager.setStatus(
+                        `Downloading LoRA ${completedDownloads + failedDownloads + 1}/${this.importManager.downloadableLoRAs.length}`
+                    );
+                } else {
+                    this.importManager.loadingManager.setStatus(
+                        `Finalizing LoRA ${completedDownloads + failedDownloads + 1}/${this.importManager.downloadableLoRAs.length}`
+                    );
+                }
+            }
+        };
+        
+        for (let i = 0; i < this.importManager.downloadableLoRAs.length; i++) {
+            const lora = this.importManager.downloadableLoRAs[i];
+            
+            // Reset current LoRA progress for new download
+            currentLoraProgress = 0;
+            
+            // Initial status update for new LoRA
+            this.importManager.loadingManager.setStatus(`Starting download for LoRA ${i+1}/${this.importManager.downloadableLoRAs.length}`);
+            updateProgress(0, completedDownloads, lora.name);
+            
+            try {
+                // Download the LoRA
+                const response = await fetch('/api/download-lora', {
+                    method: 'POST',
+                    headers: { 'Content-Type': 'application/json' },
+                    body: JSON.stringify({
+                        download_url: lora.downloadUrl,
+                        model_version_id: lora.modelVersionId,
+                        model_hash: lora.hash,
+                        lora_root: loraRoot,
+                        relative_path: targetPath.replace(loraRoot + '/', '')
+                    })
+                });
+                
+                if (!response.ok) {
+                    const errorText = await response.text();
+                    console.error(`Failed to download LoRA ${lora.name}: ${errorText}`);
+                    
+                    // Check if this is an early access error (status 401 is the key indicator)
+                    if (response.status === 401) {
+                        accessFailures++;
+                        this.importManager.loadingManager.setStatus(
+                            `Failed to download ${lora.name}: Access restricted`
+                        );
+                    }
+                    
+                    failedDownloads++;
+                    // Continue with next download
+                } else {
+                    completedDownloads++;
+                    
+                    // Update progress to show completion of current LoRA
+                    updateProgress(100, completedDownloads, '');
+                    
+                    if (completedDownloads + failedDownloads < this.importManager.downloadableLoRAs.length) {
+                        this.importManager.loadingManager.setStatus(
+                            `Completed ${completedDownloads}/${this.importManager.downloadableLoRAs.length} LoRAs. Starting next download...`
+                        );
+                    }
+                }
+            } catch (downloadError) {
+                console.error(`Error downloading LoRA ${lora.name}:`, downloadError);
+                failedDownloads++;
+                // Continue with next download
+            }
+        }
+        
+        // Close WebSocket
+        ws.close();
+        
+        // Show appropriate completion message based on results
+        if (failedDownloads === 0) {
+            showToast(`All ${completedDownloads} LoRAs downloaded successfully`, 'success');
+        } else {
+            if (accessFailures > 0) {
+                showToast(
+                    `Downloaded ${completedDownloads} of ${this.importManager.downloadableLoRAs.length} LoRAs. ${accessFailures} failed due to access restrictions. Check your API key in settings or early access status.`,
+                    'error'
+                );
+            } else {
+                showToast(`Downloaded ${completedDownloads} of ${this.importManager.downloadableLoRAs.length} LoRAs`, 'error');
+            }
+        }
+        
+        return failedDownloads;
+    }
+}

static/js/managers/import/FolderBrowser.js

@@ -0,0 +1,220 @@
+import { showToast } from '../../utils/uiHelpers.js';
+import { getStorageItem } from '../../utils/storageHelpers.js';
+
+export class FolderBrowser {
+    constructor(importManager) {
+        this.importManager = importManager;
+        this.folderClickHandler = null;
+        this.updateTargetPath = this.updateTargetPath.bind(this);
+    }
+
+    async proceedToLocation() {
+        // Show the location step with special handling
+        this.importManager.stepManager.showStep('locationStep');
+        
+        // Double-check after a short delay to ensure the step is visible
+        setTimeout(() => {
+            const locationStep = document.getElementById('locationStep');
+            if (locationStep.style.display !== 'block' || 
+                window.getComputedStyle(locationStep).display !== 'block') {
+                // Force display again
+                locationStep.style.display = 'block';
+                
+                // If still not visible, try with injected style
+                if (window.getComputedStyle(locationStep).display !== 'block') {
+                    this.importManager.stepManager.injectedStyles = document.createElement('style');
+                    this.importManager.stepManager.injectedStyles.innerHTML = `
+                        #locationStep {
+                            display: block !important;
+                            opacity: 1 !important;
+                            visibility: visible !important;
+                        }
+                    `;
+                    document.head.appendChild(this.importManager.stepManager.injectedStyles);
+                }
+            }
+        }, 100);
+        
+        try {
+            // Display missing LoRAs that will be downloaded
+            const missingLorasList = document.getElementById('missingLorasList');
+            if (missingLorasList && this.importManager.downloadableLoRAs.length > 0) {
+                // Calculate total size
+                const totalSize = this.importManager.downloadableLoRAs.reduce((sum, lora) => {
+                    return sum + (lora.size ? parseInt(lora.size) : 0);
+                }, 0);
+                
+                // Update total size display
+                const totalSizeDisplay = document.getElementById('totalDownloadSize');
+                if (totalSizeDisplay) {
+                    totalSizeDisplay.textContent = this.importManager.formatFileSize(totalSize);
+                }
+                
+                // Update header to include count of missing LoRAs
+                const missingLorasHeader = document.querySelector('.summary-header h3');
+                if (missingLorasHeader) {
+                    missingLorasHeader.innerHTML = `Missing LoRAs <span class="lora-count-badge">(${this.importManager.downloadableLoRAs.length})</span> <span id="totalDownloadSize" class="total-size-badge">${this.importManager.formatFileSize(totalSize)}</span>`;
+                }
+                
+                // Generate missing LoRAs list
+                missingLorasList.innerHTML = this.importManager.downloadableLoRAs.map(lora => {
+                    const sizeDisplay = lora.size ? 
+                        this.importManager.formatFileSize(lora.size) : 'Unknown size';
+                    const baseModel = lora.baseModel ? 
+                        `<span class="lora-base-model">${lora.baseModel}</span>` : '';
+                    const isEarlyAccess = lora.isEarlyAccess;
+                    
+                    // Early access badge
+                    let earlyAccessBadge = '';
+                    if (isEarlyAccess) {
+                        earlyAccessBadge = `<span class="early-access-badge">
+                            <i class="fas fa-clock"></i> Early Access
+                        </span>`;
+                    }
+                    
+                    return `
+                        <div class="missing-lora-item ${isEarlyAccess ? 'is-early-access' : ''}">
+                            <div class="missing-lora-info">
+                                <div class="missing-lora-name">${lora.name}</div>
+                                ${baseModel}
+                                ${earlyAccessBadge}
+                            </div>
+                            <div class="missing-lora-size">${sizeDisplay}</div>
+                        </div>
+                    `;
+                }).join('');
+                
+                // Set up toggle for missing LoRAs list
+                const toggleBtn = document.getElementById('toggleMissingLorasList');
+                if (toggleBtn) {
+                    toggleBtn.addEventListener('click', () => {
+                        missingLorasList.classList.toggle('collapsed');
+                        const icon = toggleBtn.querySelector('i');
+                        if (icon) {
+                            icon.classList.toggle('fa-chevron-down');
+                            icon.classList.toggle('fa-chevron-up');
+                        }
+                    });
+                }
+            }
+            
+            // Fetch LoRA roots
+            const rootsResponse = await fetch('/api/lora-roots');
+            if (!rootsResponse.ok) {
+                throw new Error(`Failed to fetch LoRA roots: ${rootsResponse.status}`);
+            }
+            
+            const rootsData = await rootsResponse.json();
+            const loraRoot = document.getElementById('importLoraRoot');
+            if (loraRoot) {
+                loraRoot.innerHTML = rootsData.roots.map(root => 
+                    `<option value="${root}">${root}</option>`
+                ).join('');
+                
+                // Set default lora root if available
+                const defaultRoot = getStorageItem('settings', {}).default_loras_root;
+                if (defaultRoot && rootsData.roots.includes(defaultRoot)) {
+                    loraRoot.value = defaultRoot;
+                }
+            }
+            
+            // Fetch folders
+            const foldersResponse = await fetch('/api/folders');
+            if (!foldersResponse.ok) {
+                throw new Error(`Failed to fetch folders: ${foldersResponse.status}`);
+            }
+            
+            const foldersData = await foldersResponse.json();
+            const folderBrowser = document.getElementById('importFolderBrowser');
+            if (folderBrowser) {
+                folderBrowser.innerHTML = foldersData.folders.map(folder => 
+                    folder ? `<div class="folder-item" data-folder="${folder}">${folder}</div>` : ''
+                ).join('');
+            }
+
+            // Initialize folder browser after loading data
+            this.initializeFolderBrowser();
+        } catch (error) {
+            console.error('Error in API calls:', error);
+            showToast(error.message, 'error');
+        }
+    }
+
+    initializeFolderBrowser() {
+        const folderBrowser = document.getElementById('importFolderBrowser');
+        if (!folderBrowser) return;
+
+        // Cleanup existing handler if any
+        this.cleanup();
+
+        // Create new handler
+        this.folderClickHandler = (event) => {
+            const folderItem = event.target.closest('.folder-item');
+            if (!folderItem) return;
+
+            if (folderItem.classList.contains('selected')) {
+                folderItem.classList.remove('selected');
+                this.importManager.selectedFolder = '';
+            } else {
+                folderBrowser.querySelectorAll('.folder-item').forEach(f => 
+                    f.classList.remove('selected'));
+                folderItem.classList.add('selected');
+                this.importManager.selectedFolder = folderItem.dataset.folder;
+            }
+            
+            // Update path display after folder selection
+            this.updateTargetPath();
+        };
+
+        // Add the new handler
+        folderBrowser.addEventListener('click', this.folderClickHandler);
+        
+        // Add event listeners for path updates
+        const loraRoot = document.getElementById('importLoraRoot');
+        const newFolder = document.getElementById('importNewFolder');
+        
+        if (loraRoot) loraRoot.addEventListener('change', this.updateTargetPath);
+        if (newFolder) newFolder.addEventListener('input', this.updateTargetPath);
+        
+        // Update initial path
+        this.updateTargetPath();
+    }
+
+    cleanup() {
+        if (this.folderClickHandler) {
+            const folderBrowser = document.getElementById('importFolderBrowser');
+            if (folderBrowser) {
+                folderBrowser.removeEventListener('click', this.folderClickHandler);
+                this.folderClickHandler = null;
+            }
+        }
+        
+        // Remove path update listeners
+        const loraRoot = document.getElementById('importLoraRoot');
+        const newFolder = document.getElementById('importNewFolder');
+        
+        if (loraRoot) loraRoot.removeEventListener('change', this.updateTargetPath);
+        if (newFolder) newFolder.removeEventListener('input', this.updateTargetPath);
+    }
+    
+    updateTargetPath() {
+        const pathDisplay = document.getElementById('importTargetPathDisplay');
+        if (!pathDisplay) return;
+        
+        const loraRoot = document.getElementById('importLoraRoot')?.value || '';
+        const newFolder = document.getElementById('importNewFolder')?.value?.trim() || '';
+        
+        let fullPath = loraRoot || 'Select a LoRA root directory'; 
+        
+        if (loraRoot) {
+            if (this.importManager.selectedFolder) {
+                fullPath += '/' + this.importManager.selectedFolder;
+            }
+            if (newFolder) {
+                fullPath += '/' + newFolder;
+            }
+        }
+    
+        pathDisplay.innerHTML = `<span class="path-text">${fullPath}</span>`;
+    }
+}

static/js/managers/import/ImageProcessor.js

@@ -0,0 +1,208 @@
+import { showToast } from '../../utils/uiHelpers.js';
+
+export class ImageProcessor {
+    constructor(importManager) {
+        this.importManager = importManager;
+    }
+
+    handleFileUpload(event) {
+        const file = event.target.files[0];
+        const errorElement = document.getElementById('uploadError');
+        
+        if (!file) return;
+        
+        // Validate file type
+        if (!file.type.match('image.*')) {
+            errorElement.textContent = 'Please select an image file';
+            return;
+        }
+        
+        // Reset error
+        errorElement.textContent = '';
+        this.importManager.recipeImage = file;
+        
+        // Auto-proceed to next step if file is selected
+        this.importManager.uploadAndAnalyzeImage();
+    }
+
+    async handleUrlInput() {
+        const urlInput = document.getElementById('imageUrlInput');
+        const errorElement = document.getElementById('urlError');
+        const input = urlInput.value.trim();
+        
+        // Validate input
+        if (!input) {
+            errorElement.textContent = 'Please enter a URL or file path';
+            return;
+        }
+        
+        // Reset error
+        errorElement.textContent = '';
+        
+        // Show loading indicator
+        this.importManager.loadingManager.showSimpleLoading('Processing input...');
+        
+        try {
+            // Check if it's a URL or a local file path
+            if (input.startsWith('http://') || input.startsWith('https://')) {
+                // Handle as URL
+                await this.analyzeImageFromUrl(input);
+            } else {
+                // Handle as local file path
+                await this.analyzeImageFromLocalPath(input);
+            }
+        } catch (error) {
+            errorElement.textContent = error.message || 'Failed to process input';
+        } finally {
+            this.importManager.loadingManager.hide();
+        }
+    }
+
+    async analyzeImageFromUrl(url) {
+        try {
+            // Call the API with URL data
+            const response = await fetch('/api/recipes/analyze-image', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json'
+                },
+                body: JSON.stringify({ url: url })
+            });
+            
+            if (!response.ok) {
+                const errorData = await response.json();
+                throw new Error(errorData.error || 'Failed to analyze image from URL');
+            }
+            
+            // Get recipe data from response
+            this.importManager.recipeData = await response.json();
+            
+            // Check if we have an error message
+            if (this.importManager.recipeData.error) {
+                throw new Error(this.importManager.recipeData.error);
+            }
+            
+            // Check if we have valid recipe data
+            if (!this.importManager.recipeData || 
+                !this.importManager.recipeData.loras || 
+                this.importManager.recipeData.loras.length === 0) {
+                throw new Error('No LoRA information found in this image');
+            }
+            
+            // Find missing LoRAs
+            this.importManager.missingLoras = this.importManager.recipeData.loras.filter(
+                lora => !lora.existsLocally
+            );
+            
+            // Reset import as new flag
+            this.importManager.importAsNew = false;
+            
+            // Proceed to recipe details step
+            this.importManager.showRecipeDetailsStep();
+            
+        } catch (error) {
+            console.error('Error analyzing URL:', error);
+            throw error;
+        }
+    }
+
+    async analyzeImageFromLocalPath(path) {
+        try {
+            // Call the API with local path data
+            const response = await fetch('/api/recipes/analyze-local-image', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json'
+                },
+                body: JSON.stringify({ path: path })
+            });
+            
+            if (!response.ok) {
+                const errorData = await response.json();
+                throw new Error(errorData.error || 'Failed to load image from local path');
+            }
+            
+            // Get recipe data from response
+            this.importManager.recipeData = await response.json();
+            
+            // Check if we have an error message
+            if (this.importManager.recipeData.error) {
+                throw new Error(this.importManager.recipeData.error);
+            }
+            
+            // Check if we have valid recipe data
+            if (!this.importManager.recipeData || 
+                !this.importManager.recipeData.loras || 
+                this.importManager.recipeData.loras.length === 0) {
+                throw new Error('No LoRA information found in this image');
+            }
+            
+            // Find missing LoRAs
+            this.importManager.missingLoras = this.importManager.recipeData.loras.filter(
+                lora => !lora.existsLocally
+            );
+            
+            // Reset import as new flag
+            this.importManager.importAsNew = false;
+            
+            // Proceed to recipe details step
+            this.importManager.showRecipeDetailsStep();
+            
+        } catch (error) {
+            console.error('Error analyzing local path:', error);
+            throw error;
+        }
+    }
+
+    async uploadAndAnalyzeImage() {
+        if (!this.importManager.recipeImage) {
+            showToast('Please select an image first', 'error');
+            return;
+        }
+        
+        try {
+            this.importManager.loadingManager.showSimpleLoading('Analyzing image metadata...');
+            
+            // Create form data for upload
+            const formData = new FormData();
+            formData.append('image', this.importManager.recipeImage);
+            
+            // Upload image for analysis
+            const response = await fetch('/api/recipes/analyze-image', {
+                method: 'POST',
+                body: formData
+            });
+             
+            // Get recipe data from response
+            this.importManager.recipeData = await response.json();
+
+            // Check if we have an error message
+            if (this.importManager.recipeData.error) {
+                throw new Error(this.importManager.recipeData.error);
+            }
+            
+            // Check if we have valid recipe data
+            if (!this.importManager.recipeData || 
+                !this.importManager.recipeData.loras || 
+                this.importManager.recipeData.loras.length === 0) {
+                throw new Error('No LoRA information found in this image');
+            }
+            
+            // Find missing LoRAs
+            this.importManager.missingLoras = this.importManager.recipeData.loras.filter(
+                lora => !lora.existsLocally
+            );
+            
+            // Reset import as new flag
+            this.importManager.importAsNew = false;
+            
+            // Proceed to recipe details step
+            this.importManager.showRecipeDetailsStep();
+            
+        } catch (error) {
+            document.getElementById('uploadError').textContent = error.message;
+        } finally {
+            this.importManager.loadingManager.hide();
+        }
+    }
+}

static/js/managers/import/ImportStepManager.js

@@ -0,0 +1,57 @@
+export class ImportStepManager {
+    constructor() {
+        this.injectedStyles = null;
+    }
+
+    removeInjectedStyles() {
+        if (this.injectedStyles && this.injectedStyles.parentNode) {
+            this.injectedStyles.parentNode.removeChild(this.injectedStyles);
+            this.injectedStyles = null;
+        }
+        
+        // Reset inline styles
+        document.querySelectorAll('.import-step').forEach(step => {
+            step.style.cssText = '';
+        });
+    }
+
+    showStep(stepId) {
+        // Remove any injected styles to prevent conflicts
+        this.removeInjectedStyles();
+        
+        // Hide all steps first
+        document.querySelectorAll('.import-step').forEach(step => {
+            step.style.display = 'none';
+        });
+        
+        // Show target step with a monitoring mechanism
+        const targetStep = document.getElementById(stepId);
+        if (targetStep) {
+            // Use direct style setting
+            targetStep.style.display = 'block';
+            
+            // For the locationStep specifically, we need additional measures
+            if (stepId === 'locationStep') {
+                // Create a more persistent style to override any potential conflicts
+                this.injectedStyles = document.createElement('style');
+                this.injectedStyles.innerHTML = `
+                    #locationStep {
+                        display: block !important;
+                        opacity: 1 !important;
+                        visibility: visible !important;
+                    }
+                `;
+                document.head.appendChild(this.injectedStyles);
+                
+                // Force layout recalculation
+                targetStep.offsetHeight;
+            }
+            
+            // Scroll modal content to top
+            const modalContent = document.querySelector('#importModal .modal-content');
+            if (modalContent) {
+                modalContent.scrollTop = 0;
+            }
+        }
+    }
+}