[
  {
    "path": ".dockerignore",
    "content": "__pycache__\n*.ckpt\n*.safetensors\n*.pth\n*.pt\n*.bin\n*.patch\n*.backup\n*.corrupted\n*.partial\n*.onnx\nsorted_styles.json\n/input\n/cache\n/language/default.json\n/test_imgs\nconfig.txt\nconfig_modification_tutorial.txt\nuser_path_config.txt\nuser_path_config-deprecated.txt\n/modules/*.png\n/repositories\n/fooocus_env\n/venv\n/tmp\n/ui-config.json\n/outputs\n/config.json\n/log\n/webui.settings.bat\n/embeddings\n/styles.csv\n/params.txt\n/styles.csv.bak\n/webui-user.bat\n/webui-user.sh\n/interrogate\n/user.css\n/.idea\n/notification.ogg\n/notification.mp3\n/SwinIR\n/textual_inversion\n.vscode\n/extensions\n/test/stdout.txt\n/test/stderr.txt\n/cache.json*\n/config_states/\n/node_modules\n/package-lock.json\n/.coverage*\n/auth.json\n.DS_Store"
  },
  {
    "path": ".gitattributes",
    "content": "# Ensure that shell scripts always use lf line endings, e.g. entrypoint.sh for docker\n* text=auto\n*.sh text eol=lf"
  },
  {
    "path": ".github/CODEOWNERS",
    "content": "*       @lllyasviel\n"
  },
  {
    "path": ".github/ISSUE_TEMPLATE/bug_report.yml",
    "content": "name: Bug Report\ndescription: You think something is broken in Fooocus\ntitle: \"[Bug]: \"\nlabels: [\"bug\", \"triage\"]\n\nbody:\n  - type: markdown\n    attributes:\n      value: |\n        > The title of the bug report should be short and descriptive.\n        > Use relevant keywords for searchability.\n        > Do not leave it blank, but also do not put an entire error log in it.\n  - type: checkboxes\n    attributes:\n      label: Checklist\n      description: |\n        Please perform basic debugging to see if your configuration is the cause of the issue.\n        Basic debug procedure\n        　1. Update Fooocus - sometimes things just need to be updated\n        　2. Backup and remove your config.txt - check if the issue is caused by bad configuration\n        　3. Try a fresh installation of Fooocus in a different directory - see if a clean installation solves the issue\n        Before making a issue report please, check that the issue hasn't been reported recently.\n      options:\n        - label: The issue has not been resolved by following the [troubleshooting guide](https://github.com/lllyasviel/Fooocus/blob/main/troubleshoot.md)\n        - label: The issue exists on a clean installation of Fooocus\n        - label: The issue exists in the current version of Fooocus\n        - label: The issue has not been reported before recently\n        - label: The issue has been reported before but has not been fixed yet\n  - type: markdown\n    attributes:\n      value: |\n        > Please fill this form with as much information as possible. Don't forget to add information about \"What browsers\" and provide screenshots if possible\n  - type: textarea\n    id: what-did\n    attributes:\n      label: What happened?\n      description: Tell us what happened in a very clear and simple way\n      placeholder: |\n        image generation is not working as intended.\n    validations:\n      required: true\n  - type: textarea\n    id: steps\n    attributes:\n      label: Steps to reproduce the problem\n      description: Please provide us with precise step by step instructions on how to reproduce the bug\n      placeholder: |\n        1. Go to ...\n        2. Press ...\n        3. ...\n    validations:\n      required: true\n  - type: textarea\n    id: what-should\n    attributes:\n      label: What should have happened?\n      description: Tell us what you think the normal behavior should be\n      placeholder: |\n        Fooocus should ...\n    validations:\n      required: true\n  - type: dropdown\n    id: browsers\n    attributes:\n      label: What browsers do you use to access Fooocus?\n      multiple: true\n      options:\n        - Mozilla Firefox\n        - Google Chrome\n        - Brave\n        - Apple Safari\n        - Microsoft Edge\n        - Android\n        - iOS\n        - Other\n  - type: dropdown\n    id: hosting\n    attributes:\n      label: Where are you running Fooocus?\n      multiple: false\n      options:\n        - Locally\n        - Locally with virtualization (e.g. Docker)\n        - Cloud (Google Colab)\n        - Cloud (other)\n  - type: input\n    id: operating-system\n    attributes:\n      label: What operating system are you using?\n      placeholder: |\n        Windows 10\n  - type: textarea\n    id: logs\n    attributes:\n      label: Console logs\n      description: Please provide **full** cmd/terminal logs from the moment you started UI to the end of it, after the bug occured. If it's very long, provide a link to pastebin or similar service.\n      render: Shell\n    validations:\n      required: true\n  - type: textarea\n    id: misc\n    attributes:\n      label: Additional information\n      description: | \n        Please provide us with any relevant additional info or context.\n        Examples:\n        　I have updated my GPU driver recently."
  },
  {
    "path": ".github/ISSUE_TEMPLATE/config.yml",
    "content": "blank_issues_enabled: false\ncontact_links:\n  - name: Ask a question\n    url: https://github.com/lllyasviel/Fooocus/discussions/new?category=q-a\n    about: Ask the community for help"
  },
  {
    "path": ".github/ISSUE_TEMPLATE/feature_request.yml",
    "content": "name: Feature request\ndescription: Suggest an idea for this project\ntitle: \"[Feature Request]: \"\nlabels: [\"enhancement\", \"triage\"]\n\nbody:\n  - type: checkboxes\n    attributes:\n      label: Is there an existing issue for this?\n      description: Please search to see if an issue already exists for the feature you want, and that it's not implemented in a recent build/commit.\n      options:\n        - label: I have searched the existing issues and checked the recent builds/commits\n          required: true\n  - type: markdown\n    attributes:\n      value: |\n        *Please fill this form with as much information as possible, provide screenshots and/or illustrations of the feature if possible*\n  - type: textarea\n    id: feature\n    attributes:\n      label: What would your feature do?\n      description: Tell us about your feature in a very clear and simple way, and what problem it would solve\n    validations:\n      required: true\n  - type: textarea\n    id: workflow\n    attributes:\n      label: Proposed workflow\n      description: Please provide us with step by step information on how you'd like the feature to be accessed and used\n      value: |\n        1. Go to .... \n        2. Press ....\n        3. ...\n    validations:\n      required: true\n  - type: textarea\n    id: misc\n    attributes:\n      label: Additional information\n      description: Add any other context or screenshots about the feature request here."
  },
  {
    "path": ".github/dependabot.yml",
    "content": "version: 2\nupdates:\n  - package-ecosystem: \"github-actions\"\n    directory: \"/\"\n    schedule:\n      interval: \"monthly\""
  },
  {
    "path": ".github/workflows/build_container.yml",
    "content": "name: Docker image build\n\non:\n  push:\n    branches:\n      - main\n    tags:\n      - v*\n\njobs:\n  build-and-push-image:\n    runs-on: ubuntu-latest\n\n    permissions:\n      contents: read\n      packages: write\n\n    steps:\n      - name: Checkout repository\n        uses: actions/checkout@v5\n\n      - name: Log in to the Container registry\n        uses: docker/login-action@v3\n        with:\n          registry: ghcr.io\n          username: ${{ github.repository_owner }}\n          password: ${{ secrets.GITHUB_TOKEN }}\n\n      - name: Extract metadata (tags, labels) for Docker\n        id: meta\n        uses: docker/metadata-action@v5\n        with:\n          images: ghcr.io/${{ github.repository_owner }}/${{ github.event.repository.name }}\n          tags: |\n            type=semver,pattern={{version}}\n            type=semver,pattern={{major}}.{{minor}}\n            type=semver,pattern={{major}}\n            type=edge,branch=main\n\n      - name: Build and push Docker image\n        uses: docker/build-push-action@v6\n        with:\n          context: .\n          file: ./Dockerfile\n          push: true\n          tags: ${{ steps.meta.outputs.tags }}\n          labels: ${{ steps.meta.outputs.labels }}"
  },
  {
    "path": ".gitignore",
    "content": "__pycache__\n*.ckpt\n*.safetensors\n*.pth\n*.pt\n*.bin\n*.patch\n*.backup\n*.corrupted\n*.partial\n*.onnx\nsorted_styles.json\nhash_cache.txt\n/input\n/cache\n/language/default.json\n/test_imgs\nconfig.txt\nconfig_modification_tutorial.txt\nuser_path_config.txt\nuser_path_config-deprecated.txt\n/modules/*.png\n/repositories\n/fooocus_env\n/venv\n/tmp\n/ui-config.json\n/outputs\n/config.json\n/log\n/webui.settings.bat\n/embeddings\n/styles.csv\n/params.txt\n/styles.csv.bak\n/webui-user.bat\n/webui-user.sh\n/interrogate\n/user.css\n/.idea\n/notification.ogg\n/notification.mp3\n/SwinIR\n/textual_inversion\n.vscode\n/extensions\n/test/stdout.txt\n/test/stderr.txt\n/cache.json*\n/config_states/\n/node_modules\n/package-lock.json\n/.coverage*\n/auth.json\n.DS_Store\n"
  },
  {
    "path": "Dockerfile",
    "content": "FROM nvidia/cuda:12.4.1-base-ubuntu22.04\nENV DEBIAN_FRONTEND noninteractive\nENV CMDARGS --listen\n\nRUN apt-get update -y && \\\n\tapt-get install -y curl libgl1 libglib2.0-0 python3-pip python-is-python3 git && \\\n\tapt-get clean && \\\n\trm -rf /var/lib/apt/lists/*\n\nCOPY requirements_docker.txt requirements_versions.txt /tmp/\nRUN pip install --no-cache-dir -r /tmp/requirements_docker.txt -r /tmp/requirements_versions.txt && \\\n\trm -f /tmp/requirements_docker.txt /tmp/requirements_versions.txt\nRUN pip install --no-cache-dir xformers==0.0.23 --no-dependencies\nRUN curl -fsL -o /usr/local/lib/python3.10/dist-packages/gradio/frpc_linux_amd64_v0.2 https://cdn-media.huggingface.co/frpc-gradio-0.2/frpc_linux_amd64 && \\\n\tchmod +x /usr/local/lib/python3.10/dist-packages/gradio/frpc_linux_amd64_v0.2\n\nRUN adduser --disabled-password --gecos '' user && \\\n\tmkdir -p /content/app /content/data\n\nCOPY entrypoint.sh /content/\nRUN chown -R user:user /content\n\nWORKDIR /content\nUSER user\n\nCOPY --chown=user:user . /content/app\nRUN mv /content/app/models /content/app/models.org\n\nCMD [ \"sh\", \"-c\", \"/content/entrypoint.sh ${CMDARGS}\" ]\n"
  },
  {
    "path": "LICENSE",
    "content": "                    GNU GENERAL PUBLIC LICENSE\n                       Version 3, 29 June 2007\n\n Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>\n Everyone is permitted to copy and distribute verbatim copies\n of this license document, but changing it is not allowed.\n\n                            Preamble\n\n  The GNU General Public License is a free, copyleft license for\nsoftware and other kinds of works.\n\n  The licenses for most software and other practical works are designed\nto take away your freedom to share and change the works.  By contrast,\nthe GNU General Public License is intended to guarantee your freedom to\nshare and change all versions of a program--to make sure it remains free\nsoftware for all its users.  We, the Free Software Foundation, use the\nGNU General Public License for most of our software; it applies also to\nany other work released this way by its authors.  You can apply it to\nyour programs, too.\n\n  When we speak of free software, we are referring to freedom, not\nprice.  Our General Public Licenses are designed to make sure that you\nhave the freedom to distribute copies of free software (and charge for\nthem if you wish), that you receive source code or can get it if you\nwant it, that you can change the software or use pieces of it in new\nfree programs, and that you know you can do these things.\n\n  To protect your rights, we need to prevent others from denying you\nthese rights or asking you to surrender the rights.  Therefore, you have\ncertain responsibilities if you distribute copies of the software, or if\nyou modify it: responsibilities to respect the freedom of others.\n\n  For example, if you distribute copies of such a program, whether\ngratis or for a fee, you must pass on to the recipients the same\nfreedoms that you received.  You must make sure that they, too, receive\nor can get the source code.  And you must show them these terms so they\nknow their rights.\n\n  Developers that use the GNU GPL protect your rights with two steps:\n(1) assert copyright on the software, and (2) offer you this License\ngiving you legal permission to copy, distribute and/or modify it.\n\n  For the developers' and authors' protection, the GPL clearly explains\nthat there is no warranty for this free software.  For both users' and\nauthors' sake, the GPL requires that modified versions be marked as\nchanged, so that their problems will not be attributed erroneously to\nauthors of previous versions.\n\n  Some devices are designed to deny users access to install or run\nmodified versions of the software inside them, although the manufacturer\ncan do so.  This is fundamentally incompatible with the aim of\nprotecting users' freedom to change the software.  The systematic\npattern of such abuse occurs in the area of products for individuals to\nuse, which is precisely where it is most unacceptable.  Therefore, we\nhave designed this version of the GPL to prohibit the practice for those\nproducts.  If such problems arise substantially in other domains, we\nstand ready to extend this provision to those domains in future versions\nof the GPL, as needed to protect the freedom of users.\n\n  Finally, every program is threatened constantly by software patents.\nStates should not allow patents to restrict development and use of\nsoftware on general-purpose computers, but in those that do, we wish to\navoid the special danger that patents applied to a free program could\nmake it effectively proprietary.  To prevent this, the GPL assures that\npatents cannot be used to render the program non-free.\n\n  The precise terms and conditions for copying, distribution and\nmodification follow.\n\n                       TERMS AND CONDITIONS\n\n  0. Definitions.\n\n  \"This License\" refers to version 3 of the GNU General Public License.\n\n  \"Copyright\" also means copyright-like laws that apply to other kinds of\nworks, such as semiconductor masks.\n\n  \"The Program\" refers to any copyrightable work licensed under this\nLicense.  Each licensee is addressed as \"you\".  \"Licensees\" and\n\"recipients\" may be individuals or organizations.\n\n  To \"modify\" a work means to copy from or adapt all or part of the work\nin a fashion requiring copyright permission, other than the making of an\nexact copy.  The resulting work is called a \"modified version\" of the\nearlier work or a work \"based on\" the earlier work.\n\n  A \"covered work\" means either the unmodified Program or a work based\non the Program.\n\n  To \"propagate\" a work means to do anything with it that, without\npermission, would make you directly or secondarily liable for\ninfringement under applicable copyright law, except executing it on a\ncomputer or modifying a private copy.  Propagation includes copying,\ndistribution (with or without modification), making available to the\npublic, and in some countries other activities as well.\n\n  To \"convey\" a work means any kind of propagation that enables other\nparties to make or receive copies.  Mere interaction with a user through\na computer network, with no transfer of a copy, is not conveying.\n\n  An interactive user interface displays \"Appropriate Legal Notices\"\nto the extent that it includes a convenient and prominently visible\nfeature that (1) displays an appropriate copyright notice, and (2)\ntells the user that there is no warranty for the work (except to the\nextent that warranties are provided), that licensees may convey the\nwork under this License, and how to view a copy of this License.  If\nthe interface presents a list of user commands or options, such as a\nmenu, a prominent item in the list meets this criterion.\n\n  1. Source Code.\n\n  The \"source code\" for a work means the preferred form of the work\nfor making modifications to it.  \"Object code\" means any non-source\nform of a work.\n\n  A \"Standard Interface\" means an interface that either is an official\nstandard defined by a recognized standards body, or, in the case of\ninterfaces specified for a particular programming language, one that\nis widely used among developers working in that language.\n\n  The \"System Libraries\" of an executable work include anything, other\nthan the work as a whole, that (a) is included in the normal form of\npackaging a Major Component, but which is not part of that Major\nComponent, and (b) serves only to enable use of the work with that\nMajor Component, or to implement a Standard Interface for which an\nimplementation is available to the public in source code form.  A\n\"Major Component\", in this context, means a major essential component\n(kernel, window system, and so on) of the specific operating system\n(if any) on which the executable work runs, or a compiler used to\nproduce the work, or an object code interpreter used to run it.\n\n  The \"Corresponding Source\" for a work in object code form means all\nthe source code needed to generate, install, and (for an executable\nwork) run the object code and to modify the work, including scripts to\ncontrol those activities.  However, it does not include the work's\nSystem Libraries, or general-purpose tools or generally available free\nprograms which are used unmodified in performing those activities but\nwhich are not part of the work.  For example, Corresponding Source\nincludes interface definition files associated with source files for\nthe work, and the source code for shared libraries and dynamically\nlinked subprograms that the work is specifically designed to require,\nsuch as by intimate data communication or control flow between those\nsubprograms and other parts of the work.\n\n  The Corresponding Source need not include anything that users\ncan regenerate automatically from other parts of the Corresponding\nSource.\n\n  The Corresponding Source for a work in source code form is that\nsame work.\n\n  2. Basic Permissions.\n\n  All rights granted under this License are granted for the term of\ncopyright on the Program, and are irrevocable provided the stated\nconditions are met.  This License explicitly affirms your unlimited\npermission to run the unmodified Program.  The output from running a\ncovered work is covered by this License only if the output, given its\ncontent, constitutes a covered work.  This License acknowledges your\nrights of fair use or other equivalent, as provided by copyright law.\n\n  You may make, run and propagate covered works that you do not\nconvey, without conditions so long as your license otherwise remains\nin force.  You may convey covered works to others for the sole purpose\nof having them make modifications exclusively for you, or provide you\nwith facilities for running those works, provided that you comply with\nthe terms of this License in conveying all material for which you do\nnot control copyright.  Those thus making or running the covered works\nfor you must do so exclusively on your behalf, under your direction\nand control, on terms that prohibit them from making any copies of\nyour copyrighted material outside their relationship with you.\n\n  Conveying under any other circumstances is permitted solely under\nthe conditions stated below.  Sublicensing is not allowed; section 10\nmakes it unnecessary.\n\n  3. Protecting Users' Legal Rights From Anti-Circumvention Law.\n\n  No covered work shall be deemed part of an effective technological\nmeasure under any applicable law fulfilling obligations under article\n11 of the WIPO copyright treaty adopted on 20 December 1996, or\nsimilar laws prohibiting or restricting circumvention of such\nmeasures.\n\n  When you convey a covered work, you waive any legal power to forbid\ncircumvention of technological measures to the extent such circumvention\nis effected by exercising rights under this License with respect to\nthe covered work, and you disclaim any intention to limit operation or\nmodification of the work as a means of enforcing, against the work's\nusers, your or third parties' legal rights to forbid circumvention of\ntechnological measures.\n\n  4. Conveying Verbatim Copies.\n\n  You may convey verbatim copies of the Program's source code as you\nreceive it, in any medium, provided that you conspicuously and\nappropriately publish on each copy an appropriate copyright notice;\nkeep intact all notices stating that this License and any\nnon-permissive terms added in accord with section 7 apply to the code;\nkeep intact all notices of the absence of any warranty; and give all\nrecipients a copy of this License along with the Program.\n\n  You may charge any price or no price for each copy that you convey,\nand you may offer support or warranty protection for a fee.\n\n  5. Conveying Modified Source Versions.\n\n  You may convey a work based on the Program, or the modifications to\nproduce it from the Program, in the form of source code under the\nterms of section 4, provided that you also meet all of these conditions:\n\n    a) The work must carry prominent notices stating that you modified\n    it, and giving a relevant date.\n\n    b) The work must carry prominent notices stating that it is\n    released under this License and any conditions added under section\n    7.  This requirement modifies the requirement in section 4 to\n    \"keep intact all notices\".\n\n    c) You must license the entire work, as a whole, under this\n    License to anyone who comes into possession of a copy.  This\n    License will therefore apply, along with any applicable section 7\n    additional terms, to the whole of the work, and all its parts,\n    regardless of how they are packaged.  This License gives no\n    permission to license the work in any other way, but it does not\n    invalidate such permission if you have separately received it.\n\n    d) If the work has interactive user interfaces, each must display\n    Appropriate Legal Notices; however, if the Program has interactive\n    interfaces that do not display Appropriate Legal Notices, your\n    work need not make them do so.\n\n  A compilation of a covered work with other separate and independent\nworks, which are not by their nature extensions of the covered work,\nand which are not combined with it such as to form a larger program,\nin or on a volume of a storage or distribution medium, is called an\n\"aggregate\" if the compilation and its resulting copyright are not\nused to limit the access or legal rights of the compilation's users\nbeyond what the individual works permit.  Inclusion of a covered work\nin an aggregate does not cause this License to apply to the other\nparts of the aggregate.\n\n  6. Conveying Non-Source Forms.\n\n  You may convey a covered work in object code form under the terms\nof sections 4 and 5, provided that you also convey the\nmachine-readable Corresponding Source under the terms of this License,\nin one of these ways:\n\n    a) Convey the object code in, or embodied in, a physical product\n    (including a physical distribution medium), accompanied by the\n    Corresponding Source fixed on a durable physical medium\n    customarily used for software interchange.\n\n    b) Convey the object code in, or embodied in, a physical product\n    (including a physical distribution medium), accompanied by a\n    written offer, valid for at least three years and valid for as\n    long as you offer spare parts or customer support for that product\n    model, to give anyone who possesses the object code either (1) a\n    copy of the Corresponding Source for all the software in the\n    product that is covered by this License, on a durable physical\n    medium customarily used for software interchange, for a price no\n    more than your reasonable cost of physically performing this\n    conveying of source, or (2) access to copy the\n    Corresponding Source from a network server at no charge.\n\n    c) Convey individual copies of the object code with a copy of the\n    written offer to provide the Corresponding Source.  This\n    alternative is allowed only occasionally and noncommercially, and\n    only if you received the object code with such an offer, in accord\n    with subsection 6b.\n\n    d) Convey the object code by offering access from a designated\n    place (gratis or for a charge), and offer equivalent access to the\n    Corresponding Source in the same way through the same place at no\n    further charge.  You need not require recipients to copy the\n    Corresponding Source along with the object code.  If the place to\n    copy the object code is a network server, the Corresponding Source\n    may be on a different server (operated by you or a third party)\n    that supports equivalent copying facilities, provided you maintain\n    clear directions next to the object code saying where to find the\n    Corresponding Source.  Regardless of what server hosts the\n    Corresponding Source, you remain obligated to ensure that it is\n    available for as long as needed to satisfy these requirements.\n\n    e) Convey the object code using peer-to-peer transmission, provided\n    you inform other peers where the object code and Corresponding\n    Source of the work are being offered to the general public at no\n    charge under subsection 6d.\n\n  A separable portion of the object code, whose source code is excluded\nfrom the Corresponding Source as a System Library, need not be\nincluded in conveying the object code work.\n\n  A \"User Product\" is either (1) a \"consumer product\", which means any\ntangible personal property which is normally used for personal, family,\nor household purposes, or (2) anything designed or sold for incorporation\ninto a dwelling.  In determining whether a product is a consumer product,\ndoubtful cases shall be resolved in favor of coverage.  For a particular\nproduct received by a particular user, \"normally used\" refers to a\ntypical or common use of that class of product, regardless of the status\nof the particular user or of the way in which the particular user\nactually uses, or expects or is expected to use, the product.  A product\nis a consumer product regardless of whether the product has substantial\ncommercial, industrial or non-consumer uses, unless such uses represent\nthe only significant mode of use of the product.\n\n  \"Installation Information\" for a User Product means any methods,\nprocedures, authorization keys, or other information required to install\nand execute modified versions of a covered work in that User Product from\na modified version of its Corresponding Source.  The information must\nsuffice to ensure that the continued functioning of the modified object\ncode is in no case prevented or interfered with solely because\nmodification has been made.\n\n  If you convey an object code work under this section in, or with, or\nspecifically for use in, a User Product, and the conveying occurs as\npart of a transaction in which the right of possession and use of the\nUser Product is transferred to the recipient in perpetuity or for a\nfixed term (regardless of how the transaction is characterized), the\nCorresponding Source conveyed under this section must be accompanied\nby the Installation Information.  But this requirement does not apply\nif neither you nor any third party retains the ability to install\nmodified object code on the User Product (for example, the work has\nbeen installed in ROM).\n\n  The requirement to provide Installation Information does not include a\nrequirement to continue to provide support service, warranty, or updates\nfor a work that has been modified or installed by the recipient, or for\nthe User Product in which it has been modified or installed.  Access to a\nnetwork may be denied when the modification itself materially and\nadversely affects the operation of the network or violates the rules and\nprotocols for communication across the network.\n\n  Corresponding Source conveyed, and Installation Information provided,\nin accord with this section must be in a format that is publicly\ndocumented (and with an implementation available to the public in\nsource code form), and must require no special password or key for\nunpacking, reading or copying.\n\n  7. Additional Terms.\n\n  \"Additional permissions\" are terms that supplement the terms of this\nLicense by making exceptions from one or more of its conditions.\nAdditional permissions that are applicable to the entire Program shall\nbe treated as though they were included in this License, to the extent\nthat they are valid under applicable law.  If additional permissions\napply only to part of the Program, that part may be used separately\nunder those permissions, but the entire Program remains governed by\nthis License without regard to the additional permissions.\n\n  When you convey a copy of a covered work, you may at your option\nremove any additional permissions from that copy, or from any part of\nit.  (Additional permissions may be written to require their own\nremoval in certain cases when you modify the work.)  You may place\nadditional permissions on material, added by you to a covered work,\nfor which you have or can give appropriate copyright permission.\n\n  Notwithstanding any other provision of this License, for material you\nadd to a covered work, you may (if authorized by the copyright holders of\nthat material) supplement the terms of this License with terms:\n\n    a) Disclaiming warranty or limiting liability differently from the\n    terms of sections 15 and 16 of this License; or\n\n    b) Requiring preservation of specified reasonable legal notices or\n    author attributions in that material or in the Appropriate Legal\n    Notices displayed by works containing it; or\n\n    c) Prohibiting misrepresentation of the origin of that material, or\n    requiring that modified versions of such material be marked in\n    reasonable ways as different from the original version; or\n\n    d) Limiting the use for publicity purposes of names of licensors or\n    authors of the material; or\n\n    e) Declining to grant rights under trademark law for use of some\n    trade names, trademarks, or service marks; or\n\n    f) Requiring indemnification of licensors and authors of that\n    material by anyone who conveys the material (or modified versions of\n    it) with contractual assumptions of liability to the recipient, for\n    any liability that these contractual assumptions directly impose on\n    those licensors and authors.\n\n  All other non-permissive additional terms are considered \"further\nrestrictions\" within the meaning of section 10.  If the Program as you\nreceived it, or any part of it, contains a notice stating that it is\ngoverned by this License along with a term that is a further\nrestriction, you may remove that term.  If a license document contains\na further restriction but permits relicensing or conveying under this\nLicense, you may add to a covered work material governed by the terms\nof that license document, provided that the further restriction does\nnot survive such relicensing or conveying.\n\n  If you add terms to a covered work in accord with this section, you\nmust place, in the relevant source files, a statement of the\nadditional terms that apply to those files, or a notice indicating\nwhere to find the applicable terms.\n\n  Additional terms, permissive or non-permissive, may be stated in the\nform of a separately written license, or stated as exceptions;\nthe above requirements apply either way.\n\n  8. Termination.\n\n  You may not propagate or modify a covered work except as expressly\nprovided under this License.  Any attempt otherwise to propagate or\nmodify it is void, and will automatically terminate your rights under\nthis License (including any patent licenses granted under the third\nparagraph of section 11).\n\n  However, if you cease all violation of this License, then your\nlicense from a particular copyright holder is reinstated (a)\nprovisionally, unless and until the copyright holder explicitly and\nfinally terminates your license, and (b) permanently, if the copyright\nholder fails to notify you of the violation by some reasonable means\nprior to 60 days after the cessation.\n\n  Moreover, your license from a particular copyright holder is\nreinstated permanently if the copyright holder notifies you of the\nviolation by some reasonable means, this is the first time you have\nreceived notice of violation of this License (for any work) from that\ncopyright holder, and you cure the violation prior to 30 days after\nyour receipt of the notice.\n\n  Termination of your rights under this section does not terminate the\nlicenses of parties who have received copies or rights from you under\nthis License.  If your rights have been terminated and not permanently\nreinstated, you do not qualify to receive new licenses for the same\nmaterial under section 10.\n\n  9. Acceptance Not Required for Having Copies.\n\n  You are not required to accept this License in order to receive or\nrun a copy of the Program.  Ancillary propagation of a covered work\noccurring solely as a consequence of using peer-to-peer transmission\nto receive a copy likewise does not require acceptance.  However,\nnothing other than this License grants you permission to propagate or\nmodify any covered work.  These actions infringe copyright if you do\nnot accept this License.  Therefore, by modifying or propagating a\ncovered work, you indicate your acceptance of this License to do so.\n\n  10. Automatic Licensing of Downstream Recipients.\n\n  Each time you convey a covered work, the recipient automatically\nreceives a license from the original licensors, to run, modify and\npropagate that work, subject to this License.  You are not responsible\nfor enforcing compliance by third parties with this License.\n\n  An \"entity transaction\" is a transaction transferring control of an\norganization, or substantially all assets of one, or subdividing an\norganization, or merging organizations.  If propagation of a covered\nwork results from an entity transaction, each party to that\ntransaction who receives a copy of the work also receives whatever\nlicenses to the work the party's predecessor in interest had or could\ngive under the previous paragraph, plus a right to possession of the\nCorresponding Source of the work from the predecessor in interest, if\nthe predecessor has it or can get it with reasonable efforts.\n\n  You may not impose any further restrictions on the exercise of the\nrights granted or affirmed under this License.  For example, you may\nnot impose a license fee, royalty, or other charge for exercise of\nrights granted under this License, and you may not initiate litigation\n(including a cross-claim or counterclaim in a lawsuit) alleging that\nany patent claim is infringed by making, using, selling, offering for\nsale, or importing the Program or any portion of it.\n\n  11. Patents.\n\n  A \"contributor\" is a copyright holder who authorizes use under this\nLicense of the Program or a work on which the Program is based.  The\nwork thus licensed is called the contributor's \"contributor version\".\n\n  A contributor's \"essential patent claims\" are all patent claims\nowned or controlled by the contributor, whether already acquired or\nhereafter acquired, that would be infringed by some manner, permitted\nby this License, of making, using, or selling its contributor version,\nbut do not include claims that would be infringed only as a\nconsequence of further modification of the contributor version.  For\npurposes of this definition, \"control\" includes the right to grant\npatent sublicenses in a manner consistent with the requirements of\nthis License.\n\n  Each contributor grants you a non-exclusive, worldwide, royalty-free\npatent license under the contributor's essential patent claims, to\nmake, use, sell, offer for sale, import and otherwise run, modify and\npropagate the contents of its contributor version.\n\n  In the following three paragraphs, a \"patent license\" is any express\nagreement or commitment, however denominated, not to enforce a patent\n(such as an express permission to practice a patent or covenant not to\nsue for patent infringement).  To \"grant\" such a patent license to a\nparty means to make such an agreement or commitment not to enforce a\npatent against the party.\n\n  If you convey a covered work, knowingly relying on a patent license,\nand the Corresponding Source of the work is not available for anyone\nto copy, free of charge and under the terms of this License, through a\npublicly available network server or other readily accessible means,\nthen you must either (1) cause the Corresponding Source to be so\navailable, or (2) arrange to deprive yourself of the benefit of the\npatent license for this particular work, or (3) arrange, in a manner\nconsistent with the requirements of this License, to extend the patent\nlicense to downstream recipients.  \"Knowingly relying\" means you have\nactual knowledge that, but for the patent license, your conveying the\ncovered work in a country, or your recipient's use of the covered work\nin a country, would infringe one or more identifiable patents in that\ncountry that you have reason to believe are valid.\n\n  If, pursuant to or in connection with a single transaction or\narrangement, you convey, or propagate by procuring conveyance of, a\ncovered work, and grant a patent license to some of the parties\nreceiving the covered work authorizing them to use, propagate, modify\nor convey a specific copy of the covered work, then the patent license\nyou grant is automatically extended to all recipients of the covered\nwork and works based on it.\n\n  A patent license is \"discriminatory\" if it does not include within\nthe scope of its coverage, prohibits the exercise of, or is\nconditioned on the non-exercise of one or more of the rights that are\nspecifically granted under this License.  You may not convey a covered\nwork if you are a party to an arrangement with a third party that is\nin the business of distributing software, under which you make payment\nto the third party based on the extent of your activity of conveying\nthe work, and under which the third party grants, to any of the\nparties who would receive the covered work from you, a discriminatory\npatent license (a) in connection with copies of the covered work\nconveyed by you (or copies made from those copies), or (b) primarily\nfor and in connection with specific products or compilations that\ncontain the covered work, unless you entered into that arrangement,\nor that patent license was granted, prior to 28 March 2007.\n\n  Nothing in this License shall be construed as excluding or limiting\nany implied license or other defenses to infringement that may\notherwise be available to you under applicable patent law.\n\n  12. No Surrender of Others' Freedom.\n\n  If conditions are imposed on you (whether by court order, agreement or\notherwise) that contradict the conditions of this License, they do not\nexcuse you from the conditions of this License.  If you cannot convey a\ncovered work so as to satisfy simultaneously your obligations under this\nLicense and any other pertinent obligations, then as a consequence you may\nnot convey it at all.  For example, if you agree to terms that obligate you\nto collect a royalty for further conveying from those to whom you convey\nthe Program, the only way you could satisfy both those terms and this\nLicense would be to refrain entirely from conveying the Program.\n\n  13. Use with the GNU Affero General Public License.\n\n  Notwithstanding any other provision of this License, you have\npermission to link or combine any covered work with a work licensed\nunder version 3 of the GNU Affero General Public License into a single\ncombined work, and to convey the resulting work.  The terms of this\nLicense will continue to apply to the part which is the covered work,\nbut the special requirements of the GNU Affero General Public License,\nsection 13, concerning interaction through a network will apply to the\ncombination as such.\n\n  14. Revised Versions of this License.\n\n  The Free Software Foundation may publish revised and/or new versions of\nthe GNU General Public License from time to time.  Such new versions will\nbe similar in spirit to the present version, but may differ in detail to\naddress new problems or concerns.\n\n  Each version is given a distinguishing version number.  If the\nProgram specifies that a certain numbered version of the GNU General\nPublic License \"or any later version\" applies to it, you have the\noption of following the terms and conditions either of that numbered\nversion or of any later version published by the Free Software\nFoundation.  If the Program does not specify a version number of the\nGNU General Public License, you may choose any version ever published\nby the Free Software Foundation.\n\n  If the Program specifies that a proxy can decide which future\nversions of the GNU General Public License can be used, that proxy's\npublic statement of acceptance of a version permanently authorizes you\nto choose that version for the Program.\n\n  Later license versions may give you additional or different\npermissions.  However, no additional obligations are imposed on any\nauthor or copyright holder as a result of your choosing to follow a\nlater version.\n\n  15. Disclaimer of Warranty.\n\n  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY\nAPPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT\nHOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM \"AS IS\" WITHOUT WARRANTY\nOF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,\nTHE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR\nPURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM\nIS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF\nALL NECESSARY SERVICING, REPAIR OR CORRECTION.\n\n  16. Limitation of Liability.\n\n  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING\nWILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS\nTHE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY\nGENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE\nUSE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF\nDATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD\nPARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),\nEVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF\nSUCH DAMAGES.\n\n  17. Interpretation of Sections 15 and 16.\n\n  If the disclaimer of warranty and limitation of liability provided\nabove cannot be given local legal effect according to their terms,\nreviewing courts shall apply local law that most closely approximates\nan absolute waiver of all civil liability in connection with the\nProgram, unless a warranty or assumption of liability accompanies a\ncopy of the Program in return for a fee.\n\n                     END OF TERMS AND CONDITIONS\n\n            How to Apply These Terms to Your New Programs\n\n  If you develop a new program, and you want it to be of the greatest\npossible use to the public, the best way to achieve this is to make it\nfree software which everyone can redistribute and change under these terms.\n\n  To do so, attach the following notices to the program.  It is safest\nto attach them to the start of each source file to most effectively\nstate the exclusion of warranty; and each file should have at least\nthe \"copyright\" line and a pointer to where the full notice is found.\n\n    <one line to give the program's name and a brief idea of what it does.>\n    Copyright (C) <year>  <name of author>\n\n    This program is free software: you can redistribute it and/or modify\n    it under the terms of the GNU General Public License as published by\n    the Free Software Foundation, either version 3 of the License, or\n    (at your option) any later version.\n\n    This program is distributed in the hope that it will be useful,\n    but WITHOUT ANY WARRANTY; without even the implied warranty of\n    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n    GNU General Public License for more details.\n\n    You should have received a copy of the GNU General Public License\n    along with this program.  If not, see <https://www.gnu.org/licenses/>.\n\nAlso add information on how to contact you by electronic and paper mail.\n\n  If the program does terminal interaction, make it output a short\nnotice like this when it starts in an interactive mode:\n\n    <program>  Copyright (C) <year>  <name of author>\n    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.\n    This is free software, and you are welcome to redistribute it\n    under certain conditions; type `show c' for details.\n\nThe hypothetical commands `show w' and `show c' should show the appropriate\nparts of the General Public License.  Of course, your program's commands\nmight be different; for a GUI interface, you would use an \"about box\".\n\n  You should also get your employer (if you work as a programmer) or school,\nif any, to sign a \"copyright disclaimer\" for the program, if necessary.\nFor more information on this, and how to apply and follow the GNU GPL, see\n<https://www.gnu.org/licenses/>.\n\n  The GNU General Public License does not permit incorporating your program\ninto proprietary programs.  If your program is a subroutine library, you\nmay consider it more useful to permit linking proprietary applications with\nthe library.  If this is what you want to do, use the GNU Lesser General\nPublic License instead of this License.  But first, please read\n<https://www.gnu.org/licenses/why-not-lgpl.html>.\n"
  },
  {
    "path": "args_manager.py",
    "content": "import ldm_patched.modules.args_parser as args_parser\n\nargs_parser.parser.add_argument(\"--share\", action='store_true', help=\"Set whether to share on Gradio.\")\n\nargs_parser.parser.add_argument(\"--preset\", type=str, default=None, help=\"Apply specified UI preset.\")\nargs_parser.parser.add_argument(\"--disable-preset-selection\", action='store_true',\n                                help=\"Disables preset selection in Gradio.\")\n\nargs_parser.parser.add_argument(\"--language\", type=str, default='default',\n                                help=\"Translate UI using json files in [language] folder. \"\n                                  \"For example, [--language example] will use [language/example.json] for translation.\")\n\n# For example, https://github.com/lllyasviel/Fooocus/issues/849\nargs_parser.parser.add_argument(\"--disable-offload-from-vram\", action=\"store_true\",\n                                help=\"Force loading models to vram when the unload can be avoided. \"\n                                  \"Some Mac users may need this.\")\n\nargs_parser.parser.add_argument(\"--theme\", type=str, help=\"launches the UI with light or dark theme\", default=None)\nargs_parser.parser.add_argument(\"--disable-image-log\", action='store_true',\n                                help=\"Prevent writing images and logs to the outputs folder.\")\n\nargs_parser.parser.add_argument(\"--disable-analytics\", action='store_true',\n                                help=\"Disables analytics for Gradio.\")\n\nargs_parser.parser.add_argument(\"--disable-metadata\", action='store_true',\n                                help=\"Disables saving metadata to images.\")\n\nargs_parser.parser.add_argument(\"--disable-preset-download\", action='store_true',\n                                help=\"Disables downloading models for presets\", default=False)\n\nargs_parser.parser.add_argument(\"--disable-enhance-output-sorting\", action='store_true',\n                                help=\"Disables enhance output sorting for final image gallery.\")\n\nargs_parser.parser.add_argument(\"--enable-auto-describe-image\", action='store_true',\n                                help=\"Enables automatic description of uov and enhance image when prompt is empty\", default=False)\n\nargs_parser.parser.add_argument(\"--always-download-new-model\", action='store_true',\n                                help=\"Always download newer models\", default=False)\n\nargs_parser.parser.add_argument(\"--rebuild-hash-cache\", help=\"Generates missing model and LoRA hashes.\",\n                                type=int, nargs=\"?\", metavar=\"CPU_NUM_THREADS\", const=-1)\n\nargs_parser.parser.set_defaults(\n    disable_cuda_malloc=True,\n    in_browser=True,\n    port=None\n)\n\nargs_parser.args = args_parser.parser.parse_args()\n\n# (Disable by default because of issues like https://github.com/lllyasviel/Fooocus/issues/724)\nargs_parser.args.always_offload_from_vram = not args_parser.args.disable_offload_from_vram\n\nif args_parser.args.disable_analytics:\n    import os\n    os.environ[\"GRADIO_ANALYTICS_ENABLED\"] = \"False\"\n\nif args_parser.args.disable_in_browser:\n    args_parser.args.in_browser = False\n\nargs = args_parser.args\n"
  },
  {
    "path": "auth-example.json",
    "content": "[\n    {\n        \"user\": \"sitting-duck-1\",\n        \"pass\": \"very-bad-publicly-known-password-change-it\"\n    }\n]\n"
  },
  {
    "path": "build_launcher.py",
    "content": "import os\n\nwin32_root = os.path.dirname(os.path.dirname(__file__))\npython_embeded_path = os.path.join(win32_root, 'python_embeded')\n\nis_win32_standalone_build = os.path.exists(python_embeded_path) and os.path.isdir(python_embeded_path)\n\nwin32_cmd = '''\n.\\python_embeded\\python.exe -s Fooocus\\entry_with_update.py {cmds} %*\npause\n'''\n\n\ndef build_launcher():\n    if not is_win32_standalone_build:\n        return\n\n    presets = [None, 'anime', 'realistic']\n\n    for preset in presets:\n        win32_cmd_preset = win32_cmd.replace('{cmds}', '' if preset is None else f'--preset {preset}')\n        bat_path = os.path.join(win32_root, 'run.bat' if preset is None else f'run_{preset}.bat')\n        if not os.path.exists(bat_path):\n            with open(bat_path, \"w\", encoding=\"utf-8\") as f:\n                f.write(win32_cmd_preset)\n    return\n"
  },
  {
    "path": "css/style.css",
    "content": "/* based on https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/v1.6.0/style.css */\n\n.loader-container {\n  display: flex; /* Use flex to align items horizontally */\n  align-items: center; /* Center items vertically within the container */\n  white-space: nowrap; /* Prevent line breaks within the container */\n}\n\n.loader {\n  border: 8px solid #f3f3f3; /* Light grey */\n  border-top: 8px solid #3498db; /* Blue */\n  border-radius: 50%;\n  width: 30px;\n  height: 30px;\n  animation: spin 2s linear infinite;\n}\n\n@keyframes spin {\n  0% { transform: rotate(0deg); }\n  100% { transform: rotate(360deg); }\n}\n\n/* Style the progress bar */\nprogress {\n  appearance: none; /* Remove default styling */\n  height: 20px; /* Set the height of the progress bar */\n  border-radius: 5px; /* Round the corners of the progress bar */\n  background-color: #f3f3f3; /* Light grey background */\n  width: 100%;\n  vertical-align: middle !important;\n}\n\n/* Style the progress bar container */\n.progress-container {\n  margin-left: 20px;\n  margin-right: 20px;\n  flex-grow: 1; /* Allow the progress container to take up remaining space */\n}\n\n/* Set the color of the progress bar fill */\nprogress::-webkit-progress-value {\n  background-color: #3498db; /* Blue color for the fill */\n}\n\nprogress::-moz-progress-bar {\n  background-color: #3498db; /* Blue color for the fill in Firefox */\n}\n\n/* Style the text on the progress bar */\nprogress::after {\n  content: attr(value '%'); /* Display the progress value followed by '%' */\n  position: absolute;\n  top: 50%;\n  left: 50%;\n  transform: translate(-50%, -50%);\n  color: white; /* Set text color */\n  font-size: 14px; /* Set font size */\n}\n\n/* Style other texts */\n.loader-container > span {\n  margin-left: 5px; /* Add spacing between the progress bar and the text */\n}\n\n.progress-bar > .generating {\n  display: none !important;\n}\n\n.progress-bar{\n  height: 30px !important;\n}\n\n.progress-bar span {\n    text-align: right;\n    width: 215px;\n}\ndiv:has(> #positive_prompt) {\n    border: none;\n}\n\n#positive_prompt {\n    padding: 1px;\n    background: var(--background-fill-primary);\n}\n\n.type_row {\n  height: 84px !important;\n}\n\n.type_row_half {\n  height: 34px !important;\n}\n\n.refresh_button {\n  border: none !important;\n  background: none !important;\n  font-size: none !important;\n  box-shadow: none !important;\n}\n\n.advanced_check_row {\n  width: 330px !important;\n}\n\n.min_check {\n  min-width: min(1px, 100%) !important;\n}\n\n.resizable_area {\n  resize: vertical;\n  overflow: auto !important;\n}\n\n.performance_selection label {\n    width: 140px !important;\n}\n\n.aspect_ratios label {\n    flex: calc(50% - 5px) !important;\n}\n\n.aspect_ratios label span {\n    white-space: nowrap !important;\n}\n\n.aspect_ratios label input {\n    margin-left: -5px !important;\n}\n\n.lora_enable label {\n  height: 100%;\n}\n\n.lora_enable label input {\n  margin: auto;\n}\n\n.lora_enable label span {\n  display: none;\n}\n\n@-moz-document url-prefix() {\n  .lora_weight input[type=number] {\n    width: 80px;\n  }\n}\n\n#context-menu{\n    z-index:9999;\n    position:absolute;\n    display:block;\n    padding:0px 0;\n    border:2px solid #a55000;\n    border-radius:8px;\n    box-shadow:1px 1px 2px #CE6400;\n    width: 200px;\n}\n\n.context-menu-items{\n    list-style: none;\n    margin: 0;\n    padding: 0;\n}\n\n.context-menu-items a{\n    display:block;\n    padding:5px;\n    cursor:pointer;\n}\n\n.context-menu-items a:hover{\n    background: #a55000;\n}\n\n.canvas-tooltip-info {\n  position: absolute;\n  top: 28px;\n  left: 2px;\n  cursor: help;\n  background-color: rgba(0, 0, 0, 0.3);\n  width: 20px;\n  height: 20px;\n  border-radius: 50%;\n  display: flex;\n  align-items: center;\n  justify-content: center;\n  flex-direction: column;\n\n  z-index: 100;\n}\n\n.canvas-tooltip-info::after {\n  content: '';\n  display: block;\n  width: 2px;\n  height: 7px;\n  background-color: white;\n  margin-top: 2px;\n}\n\n.canvas-tooltip-info::before {\n  content: '';\n  display: block;\n  width: 2px;\n  height: 2px;\n  background-color: white;\n}\n\n.canvas-tooltip-content {\n  display: none;\n  background-color: #f9f9f9;\n  color: #333;\n  border: 1px solid #ddd;\n  padding: 15px;\n  position: absolute;\n  top: 40px;\n  left: 10px;\n  width: 250px;\n  font-size: 16px;\n  opacity: 0;\n  border-radius: 8px;\n  box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2);\n\n  z-index: 100;\n}\n\n.canvas-tooltip:hover .canvas-tooltip-content {\n  display: block;\n  animation: fadeIn 0.5s;\n  opacity: 1;\n}\n\n@keyframes fadeIn {\n  from {opacity: 0;}\n  to {opacity: 1;}\n}\n\n.styler {\n  overflow:inherit !important;\n}\n\n.gradio-container{\n  overflow: visible;\n}\n\n/* fullpage image viewer */\n\n#lightboxModal{\n    display: none;\n    position: fixed;\n    z-index: 1001;\n    left: 0;\n    top: 0;\n    width: 100%;\n    height: 100%;\n    overflow: auto;\n    background-color: rgba(20, 20, 20, 0.95);\n    user-select: none;\n    -webkit-user-select: none;\n    flex-direction: column;\n}\n\n.modalControls {\n    display: flex;\n    position: absolute;\n    right: 0px;\n    left: 0px;\n    gap: 1em;\n    padding: 1em;\n    background-color:rgba(0,0,0,0);\n    z-index: 1;\n    transition: 0.2s ease background-color;\n}\n.modalControls:hover {\n    background-color:rgba(0,0,0,0.9);\n}\n.modalClose {\n    margin-left: auto;\n}\n.modalControls span{\n    color: white;\n    text-shadow: 0px 0px 0.25em black;\n    font-size: 35px;\n    font-weight: bold;\n    cursor: pointer;\n    width: 1em;\n}\n\n.modalControls span:hover, .modalControls span:focus{\n    color: #999;\n    text-decoration: none;\n}\n\n#lightboxModal > img {\n    display: block;\n    margin: auto;\n    width: auto;\n}\n\n#lightboxModal > img.modalImageFullscreen{\n    object-fit: contain;\n    height: 100%;\n    width: 100%;\n    min-height: 0;\n}\n\n.modalPrev,\n.modalNext {\n  cursor: pointer;\n  position: absolute;\n  top: 50%;\n  width: auto;\n  padding: 16px;\n  margin-top: -50px;\n  color: white;\n  font-weight: bold;\n  font-size: 20px;\n  transition: 0.6s ease;\n  border-radius: 0 3px 3px 0;\n  user-select: none;\n  -webkit-user-select: none;\n}\n\n.modalNext {\n  right: 0;\n  border-radius: 3px 0 0 3px;\n}\n\n.modalPrev:hover,\n.modalNext:hover {\n  background-color: rgba(0, 0, 0, 0.8);\n}\n\n#imageARPreview {\n    position: absolute;\n    top: 0px;\n    left: 0px;\n    border: 2px solid red;\n    background: rgba(255, 0, 0, 0.3);\n    z-index: 900;\n    pointer-events: none;\n    display: none;\n}\n\n#stylePreviewOverlay {\n    opacity: 0;\n    pointer-events: none;\n    width: 128px;\n    height: 128px;\n    position: fixed;\n    top: 0px;\n    left: 0px;\n    border: solid 1px lightgrey;\n    transform: translate(-140px, 20px);\n    background-size: cover;\n    background-position: center;\n    background-color: rgba(0, 0, 0, 0.3);\n    border-radius: 5px;\n    z-index: 100;\n    transition: transform 0.1s ease, opacity 0.3s ease;\n}\n\n#stylePreviewOverlay.lower-half {\n    transform: translate(-140px, -140px);\n}\n\n/* scrollable box for style selections */\n.contain .tabs {\n  height: 100%;\n}\n\n.contain .tabs .tabitem.style_selections_tab {\n  height: 100%;\n}\n\n.contain .tabs .tabitem.style_selections_tab > div:first-child {\n  height: 100%;\n}\n\n.contain .tabs .tabitem.style_selections_tab .style_selections {\n  min-height: 200px;\n  height: 100%;\n}\n\n.contain .tabs .tabitem.style_selections_tab .style_selections .wrap[data-testid=\"checkbox-group\"] {\n  position: absolute; /* remove this to disable scrolling within the checkbox-group */\n  overflow: auto;\n  padding-right: 2px;\n  max-height: 100%;\n}\n\n.contain .tabs .tabitem.style_selections_tab .style_selections .wrap[data-testid=\"checkbox-group\"] label {\n  /* max-width: calc(35% - 15px) !important; */ /* add this to enable 3 columns layout */\n  flex: calc(50% - 5px) !important;\n}\n\n.contain .tabs .tabitem.style_selections_tab .style_selections .wrap[data-testid=\"checkbox-group\"] label span {\n  /* white-space:nowrap; */ /* add this to disable text wrapping (better choice for 3 columns layout) */\n  overflow: hidden;\n  text-overflow: ellipsis;\n}\n\n/* styles preview tooltip */\n.preview-tooltip {\n  background-color: #fff8;\n  font-family: monospace;\n  text-align: center;\n  border-radius: 5px 5px 0px 0px;\n  display: none; /* remove this to enable tooltip in preview image */\n}\n\n#inpaint_canvas .canvas-tooltip-info {\n  top: 2px;\n}\n\n#inpaint_brush_color input[type=color]{\n  background: none;\n}"
  },
  {
    "path": "development.md",
    "content": "## Running unit tests\n\nNative python:\n```\npython -m unittest tests/\n```\n\nEmbedded python (Windows zip file installation method):\n```\n..\\python_embeded\\python.exe -m unittest\n```\n"
  },
  {
    "path": "docker-compose.yml",
    "content": "volumes:\n  fooocus-data:\n\nservices:\n  app:\n    build: .\n    image: ghcr.io/lllyasviel/fooocus\n    ports:\n     - \"7865:7865\"\n    environment:\n      - CMDARGS=--listen    # Arguments for launch.py.\n      - DATADIR=/content/data   # Directory which stores models, outputs dir\n      - config_path=/content/data/config.txt\n      - config_example_path=/content/data/config_modification_tutorial.txt\n      - path_checkpoints=/content/data/models/checkpoints/\n      - path_loras=/content/data/models/loras/\n      - path_embeddings=/content/data/models/embeddings/\n      - path_vae_approx=/content/data/models/vae_approx/\n      - path_upscale_models=/content/data/models/upscale_models/\n      - path_inpaint=/content/data/models/inpaint/\n      - path_controlnet=/content/data/models/controlnet/\n      - path_clip_vision=/content/data/models/clip_vision/\n      - path_fooocus_expansion=/content/data/models/prompt_expansion/fooocus_expansion/\n      - path_outputs=/content/app/outputs/    # Warning: If it is not located under '/content/app', you can't see history log!\n    volumes:\n      - fooocus-data:/content/data\n      #- ./models:/import/models   # Once you import files, you don't need to mount again.\n      #- ./outputs:/import/outputs  # Once you import files, you don't need to mount again.\n    tty: true\n    deploy:\n      resources:\n        reservations:\n          devices:\n            - driver: nvidia\n              device_ids: ['0']\n              capabilities: [compute, utility]\n"
  },
  {
    "path": "docker.md",
    "content": "# Fooocus on Docker\n\nThe docker image is based on NVIDIA CUDA 12.4 and PyTorch 2.1, see [Dockerfile](Dockerfile) and [requirements_docker.txt](requirements_docker.txt) for details.\n\n## Requirements\n\n- A computer with specs good enough to run Fooocus, and proprietary Nvidia drivers\n- Docker, Docker Compose, or Podman\n\n## Quick start\n\n**More information in the [notes](#notes).**\n\n### Running with Docker Compose\n\n1. Clone this repository\n2. Run the docker container with `docker compose up`.\n\n### Running with Docker\n\n```sh\ndocker run -p 7865:7865 -v fooocus-data:/content/data -it \\\n--gpus all \\\n-e CMDARGS=--listen \\\n-e DATADIR=/content/data \\\n-e config_path=/content/data/config.txt \\\n-e config_example_path=/content/data/config_modification_tutorial.txt \\\n-e path_checkpoints=/content/data/models/checkpoints/ \\\n-e path_loras=/content/data/models/loras/ \\\n-e path_embeddings=/content/data/models/embeddings/ \\\n-e path_vae_approx=/content/data/models/vae_approx/ \\\n-e path_upscale_models=/content/data/models/upscale_models/ \\\n-e path_inpaint=/content/data/models/inpaint/ \\\n-e path_controlnet=/content/data/models/controlnet/ \\\n-e path_clip_vision=/content/data/models/clip_vision/ \\\n-e path_fooocus_expansion=/content/data/models/prompt_expansion/fooocus_expansion/ \\\n-e path_outputs=/content/app/outputs/ \\\nghcr.io/lllyasviel/fooocus\n```\n### Running with Podman\n\n```sh\npodman run -p 7865:7865 -v fooocus-data:/content/data -it \\\n--security-opt=no-new-privileges --cap-drop=ALL --security-opt label=type:nvidia_container_t --device=nvidia.com/gpu=all \\\n-e CMDARGS=--listen \\\n-e DATADIR=/content/data \\\n-e config_path=/content/data/config.txt \\\n-e config_example_path=/content/data/config_modification_tutorial.txt \\\n-e path_checkpoints=/content/data/models/checkpoints/ \\\n-e path_loras=/content/data/models/loras/ \\\n-e path_embeddings=/content/data/models/embeddings/ \\\n-e path_vae_approx=/content/data/models/vae_approx/ \\\n-e path_upscale_models=/content/data/models/upscale_models/ \\\n-e path_inpaint=/content/data/models/inpaint/ \\\n-e path_controlnet=/content/data/models/controlnet/ \\\n-e path_clip_vision=/content/data/models/clip_vision/ \\\n-e path_fooocus_expansion=/content/data/models/prompt_expansion/fooocus_expansion/ \\\n-e path_outputs=/content/app/outputs/ \\\nghcr.io/lllyasviel/fooocus\n```\n\nWhen you see the message  `Use the app with http://0.0.0.0:7865/` in the console, you can access the URL in your browser.\n\nYour models and outputs are stored in the `fooocus-data` volume, which, depending on OS, is stored in `/var/lib/docker/volumes/` (or `~/.local/share/containers/storage/volumes/` when using `podman`).\n\n## Building the container locally\n\nClone the repository first, and open a terminal in the folder.\n\nBuild with `docker`:\n```sh\ndocker build . -t fooocus\n```\n\nBuild with `podman`:\n```sh\npodman build . -t fooocus\n```\n\n## Details\n\n### Update the container manually (`docker compose`)\n\nWhen you are using `docker compose up` continuously, the container is not updated to the latest version of Fooocus automatically.\nRun `git pull` before executing `docker compose build --no-cache` to build an image with the latest Fooocus version.\nYou can then start it with `docker compose up`\n\n### Import models, outputs\n\nIf you want to import files from models or the outputs folder, you can add the following bind mounts in the [docker-compose.yml](docker-compose.yml) or your preferred method of running the container:\n```\n#- ./models:/import/models   # Once you import files, you don't need to mount again.\n#- ./outputs:/import/outputs  # Once you import files, you don't need to mount again.\n```\nAfter running the container, your files will be copied into `/content/data/models` and `/content/data/outputs`\nSince `/content/data` is a persistent volume folder, your files will be persisted even when you re-run the container without the above mounts.\n\n\n### Paths inside the container\n\n|Path|Details|\n|-|-|\n|/content/app|The application stored folder|\n|/content/app/models.org|Original 'models' folder.<br> Files are copied to the '/content/app/models' which is symlinked to '/content/data/models' every time the container boots. (Existing files will not be overwritten.) |\n|/content/data|Persistent volume mount point|\n|/content/data/models|The folder is symlinked to '/content/app/models'|\n|/content/data/outputs|The folder is symlinked to '/content/app/outputs'|\n\n### Environments\n\nYou can change `config.txt` parameters by using environment variables.\n**The priority of using the environments is higher than the values defined in `config.txt`, and they will be saved to the `config_modification_tutorial.txt`**\n\nDocker specified environments are there. They are used by 'entrypoint.sh'\n|Environment|Details|\n|-|-|\n|DATADIR|'/content/data' location.|\n|CMDARGS|Arguments for [entry_with_update.py](entry_with_update.py) which is called by [entrypoint.sh](entrypoint.sh)|\n|config_path|'config.txt' location|\n|config_example_path|'config_modification_tutorial.txt' location|\n|HF_MIRROR| huggingface mirror site domain| \n\nYou can also use the same json key names and values explained in the 'config_modification_tutorial.txt' as the environments.\nSee examples in the [docker-compose.yml](docker-compose.yml)\n\n## Notes\n\n- Please keep 'path_outputs' under '/content/app'. Otherwise, you may get an error when you open the history log.\n- Docker on Mac/Windows still has issues in the form of slow volume access when you use \"bind mount\" volumes. Please refer to [this article](https://docs.docker.com/storage/volumes/#use-a-volume-with-docker-compose) for not using \"bind mount\".\n- The MPS backend (Metal Performance Shaders, Apple Silicon M1/M2/etc.) is not yet supported in Docker, see https://github.com/pytorch/pytorch/issues/81224\n- You can also use `docker compose up -d` to start the container detached and connect to the logs with `docker compose logs -f`. This way you can also close the terminal and keep the container running."
  },
  {
    "path": "entry_with_update.py",
    "content": "import os\nimport sys\n\n\nroot = os.path.dirname(os.path.abspath(__file__))\nsys.path.append(root)\nos.chdir(root)\n\n\ntry:\n    import pygit2\n    pygit2.option(pygit2.GIT_OPT_SET_OWNER_VALIDATION, 0)\n\n    repo = pygit2.Repository(os.path.abspath(os.path.dirname(__file__)))\n\n    branch_name = repo.head.shorthand\n\n    remote_name = 'origin'\n    remote = repo.remotes[remote_name]\n\n    remote.fetch()\n\n    local_branch_ref = f'refs/heads/{branch_name}'\n    local_branch = repo.lookup_reference(local_branch_ref)\n\n    remote_reference = f'refs/remotes/{remote_name}/{branch_name}'\n    remote_commit = repo.revparse_single(remote_reference)\n\n    merge_result, _ = repo.merge_analysis(remote_commit.id)\n\n    if merge_result & pygit2.GIT_MERGE_ANALYSIS_UP_TO_DATE:\n        print(\"Already up-to-date\")\n    elif merge_result & pygit2.GIT_MERGE_ANALYSIS_FASTFORWARD:\n        local_branch.set_target(remote_commit.id)\n        repo.head.set_target(remote_commit.id)\n        repo.checkout_tree(repo.get(remote_commit.id))\n        repo.reset(local_branch.target, pygit2.GIT_RESET_HARD)\n        print(\"Fast-forward merge\")\n    elif merge_result & pygit2.GIT_MERGE_ANALYSIS_NORMAL:\n        print(\"Update failed - Did you modify any file?\")\nexcept Exception as e:\n    print('Update failed.')\n    print(str(e))\n\nprint('Update succeeded.')\nfrom launch import *\n"
  },
  {
    "path": "entrypoint.sh",
    "content": "#!/bin/bash\n\nORIGINALDIR=/content/app\n# Use predefined DATADIR if it is defined\n[[ x\"${DATADIR}\" == \"x\" ]] && DATADIR=/content/data\n\n# Make persistent dir from original dir\nfunction mklink () {\n\tmkdir -p $DATADIR/$1\n\tln -s $DATADIR/$1 $ORIGINALDIR\n}\n\n# Copy old files from import dir\nfunction import () {\n\t(test -d /import/$1 && cd /import/$1 && cp -Rpn . $DATADIR/$1/)\n}\n\ncd $ORIGINALDIR\n\n# models\nmklink models\n# Copy original files\n(cd $ORIGINALDIR/models.org && cp -Rpn . $ORIGINALDIR/models/)\n# Import old files\nimport models\n\n# outputs\nmklink outputs\n# Import old files\nimport outputs\n\n# Start application\npython launch.py $*\n"
  },
  {
    "path": "environment.yaml",
    "content": "name: fooocus\nchannels:\n  - defaults\ndependencies:\n  - python=3.10\n  - pip=23.0\n  - packaging\n"
  },
  {
    "path": "experiments_expansion.py",
    "content": "from modules.expansion import FooocusExpansion\n\nexpansion = FooocusExpansion()\n\ntext = 'a handsome man'\n\nfor i in range(64):\n    print(expansion(text, seed=i))\n"
  },
  {
    "path": "experiments_face.py",
    "content": "import cv2\nimport extras.face_crop as cropper\n\n\nimg = cv2.imread('lena.png')\nresult = cropper.crop_image(img)\ncv2.imwrite('lena_result.png', result)\n"
  },
  {
    "path": "experiments_interrogate.py",
    "content": "import cv2\nfrom extras.interrogate import default_interrogator as default_interrogator_photo\nfrom extras.wd14tagger import default_interrogator as default_interrogator_anime\n\nimg = cv2.imread('./test_imgs/red_box.jpg')[:, :, ::-1].copy()\nprint(default_interrogator_photo(img))\nimg = cv2.imread('./test_imgs/miku.jpg')[:, :, ::-1].copy()\nprint(default_interrogator_anime(img))\n"
  },
  {
    "path": "experiments_mask_generation.py",
    "content": "# https://github.com/sail-sg/EditAnything/blob/main/sam2groundingdino_edit.py\n\nimport numpy as np\nfrom PIL import Image\n\nfrom extras.inpaint_mask import SAMOptions, generate_mask_from_image\n\noriginal_image = Image.open('cat.webp')\nimage = np.array(original_image, dtype=np.uint8)\n\nsam_options = SAMOptions(\n    dino_prompt='eye',\n    dino_box_threshold=0.3,\n    dino_text_threshold=0.25,\n    dino_erode_or_dilate=0,\n    dino_debug=False,\n    max_detections=2,\n    model_type='vit_b'\n)\n\nmask_image, _, _, _ = generate_mask_from_image(image, sam_options=sam_options)\n\nmerged_masks_img = Image.fromarray(mask_image)\nmerged_masks_img.show()\n"
  },
  {
    "path": "extras/BLIP/configs/bert_config.json",
    "content": "{\n  \"architectures\": [\n    \"BertModel\"\n  ],\n  \"attention_probs_dropout_prob\": 0.1,\n  \"hidden_act\": \"gelu\",\n  \"hidden_dropout_prob\": 0.1,\n  \"hidden_size\": 768,\n  \"initializer_range\": 0.02,\n  \"intermediate_size\": 3072,\n  \"layer_norm_eps\": 1e-12,\n  \"max_position_embeddings\": 512,\n  \"model_type\": \"bert\",\n  \"num_attention_heads\": 12,\n  \"num_hidden_layers\": 12,\n  \"pad_token_id\": 0,\n  \"type_vocab_size\": 2,\n  \"vocab_size\": 30522,\n  \"encoder_width\": 768,\n  \"add_cross_attention\": true   \n}\n"
  },
  {
    "path": "extras/BLIP/configs/caption_coco.yaml",
    "content": "image_root: '/export/share/datasets/vision/coco/images/'\nann_root: 'annotation'\ncoco_gt_root: 'annotation/coco_gt'\n\n# set pretrained as a file path or an url\npretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth'\n\n# size of vit model; base or large\nvit: 'base'\nvit_grad_ckpt: False\nvit_ckpt_layer: 0\nbatch_size: 32\ninit_lr: 1e-5\n\n# vit: 'large'\n# vit_grad_ckpt: True\n# vit_ckpt_layer: 5\n# batch_size: 16\n# init_lr: 2e-6\n\nimage_size: 384\n\n# generation configs\nmax_length: 20  \nmin_length: 5\nnum_beams: 3\nprompt: 'a picture of '\n\n# optimizer\nweight_decay: 0.05\nmin_lr: 0\nmax_epoch: 5\n\n"
  },
  {
    "path": "extras/BLIP/configs/med_config.json",
    "content": "{\n  \"architectures\": [\n    \"BertModel\"\n  ],\n  \"attention_probs_dropout_prob\": 0.1,\n  \"hidden_act\": \"gelu\",\n  \"hidden_dropout_prob\": 0.1,\n  \"hidden_size\": 768,\n  \"initializer_range\": 0.02,\n  \"intermediate_size\": 3072,\n  \"layer_norm_eps\": 1e-12,\n  \"max_position_embeddings\": 512,\n  \"model_type\": \"bert\",\n  \"num_attention_heads\": 12,\n  \"num_hidden_layers\": 12,\n  \"pad_token_id\": 0,\n  \"type_vocab_size\": 2,\n  \"vocab_size\": 30524,\n  \"encoder_width\": 768,\n  \"add_cross_attention\": true   \n}\n"
  },
  {
    "path": "extras/BLIP/configs/nlvr.yaml",
    "content": "image_root: '/export/share/datasets/vision/NLVR2/' \nann_root: 'annotation'\n\n# set pretrained as a file path or an url\npretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_nlvr.pth'\n\n#size of vit model; base or large\nvit: 'base'\nbatch_size_train: 16 \nbatch_size_test: 64 \nvit_grad_ckpt: False\nvit_ckpt_layer: 0\nmax_epoch: 15\n\nimage_size: 384\n\n# optimizer\nweight_decay: 0.05\ninit_lr: 3e-5\nmin_lr: 0\n\n"
  },
  {
    "path": "extras/BLIP/configs/nocaps.yaml",
    "content": "image_root: '/export/share/datasets/vision/nocaps/'\nann_root: 'annotation'\n\n# set pretrained as a file path or an url\npretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth'\n\nvit: 'base'\nbatch_size: 32\n\nimage_size: 384\n\nmax_length: 20\nmin_length: 5\nnum_beams: 3\nprompt: 'a picture of '"
  },
  {
    "path": "extras/BLIP/configs/pretrain.yaml",
    "content": "train_file: ['/export/share/junnan-li/VL_pretrain/annotation/coco_karpathy_train.json',\n             '/export/share/junnan-li/VL_pretrain/annotation/vg_caption.json',\n             ]\nlaion_path: ''   \n\n# size of vit model; base or large\nvit: 'base'\nvit_grad_ckpt: False\nvit_ckpt_layer: 0\n\nimage_size: 224\nbatch_size: 75\n\nqueue_size: 57600\nalpha: 0.4\n\n# optimizer\nweight_decay: 0.05\ninit_lr: 3e-4\nmin_lr: 1e-6\nwarmup_lr: 1e-6\nlr_decay_rate: 0.9\nmax_epoch: 20\nwarmup_steps: 3000\n\n\n\n"
  },
  {
    "path": "extras/BLIP/configs/retrieval_coco.yaml",
    "content": "image_root: '/export/share/datasets/vision/coco/images/'\nann_root: 'annotation'\ndataset: 'coco'\n\n# set pretrained as a file path or an url\npretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'\n\n# size of vit model; base or large\n\nvit: 'base'\nbatch_size_train: 32\nbatch_size_test: 64\nvit_grad_ckpt: True\nvit_ckpt_layer: 4\ninit_lr: 1e-5\n\n# vit: 'large'\n# batch_size_train: 16\n# batch_size_test: 32\n# vit_grad_ckpt: True\n# vit_ckpt_layer: 12\n# init_lr: 5e-6\n\nimage_size: 384\nqueue_size: 57600\nalpha: 0.4\nk_test: 256\nnegative_all_rank: True\n\n# optimizer\nweight_decay: 0.05\nmin_lr: 0\nmax_epoch: 6\n\n"
  },
  {
    "path": "extras/BLIP/configs/retrieval_flickr.yaml",
    "content": "image_root: '/export/share/datasets/vision/flickr30k/'\nann_root: 'annotation'\ndataset: 'flickr'\n\n# set pretrained as a file path or an url\npretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_flickr.pth'\n\n# size of vit model; base or large\n\nvit: 'base'\nbatch_size_train: 32\nbatch_size_test: 64\nvit_grad_ckpt: True\nvit_ckpt_layer: 4\ninit_lr: 1e-5\n\n# vit: 'large'\n# batch_size_train: 16\n# batch_size_test: 32\n# vit_grad_ckpt: True\n# vit_ckpt_layer: 10\n# init_lr: 5e-6\n\nimage_size: 384\nqueue_size: 57600\nalpha: 0.4\nk_test: 128\nnegative_all_rank: False\n\n# optimizer\nweight_decay: 0.05\nmin_lr: 0\nmax_epoch: 6\n\n"
  },
  {
    "path": "extras/BLIP/configs/retrieval_msrvtt.yaml",
    "content": "video_root: '/export/share/dongxuli/data/msrvtt_retrieval/videos'\nann_root: 'annotation'\n\n# set pretrained as a file path or an url\npretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'\n\n# size of vit model; base or large\nvit: 'base'\nbatch_size: 64\nk_test: 128\nimage_size: 384\nnum_frm_test: 8"
  },
  {
    "path": "extras/BLIP/configs/vqa.yaml",
    "content": "vqa_root: '/export/share/datasets/vision/VQA/Images/mscoco/' #followed by train2014/\nvg_root: '/export/share/datasets/vision/visual-genome/'  #followed by image/\ntrain_files: ['vqa_train','vqa_val','vg_qa']\nann_root: 'annotation'\n\n# set pretrained as a file path or an url\npretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'\n\n# size of vit model; base or large\nvit: 'base'\nbatch_size_train: 16 \nbatch_size_test: 32 \nvit_grad_ckpt: False\nvit_ckpt_layer: 0\ninit_lr: 2e-5\n\nimage_size: 480\n\nk_test: 128\ninference: 'rank'\n\n# optimizer\nweight_decay: 0.05\nmin_lr: 0\nmax_epoch: 10"
  },
  {
    "path": "extras/BLIP/models/bert_tokenizer/config.json",
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    "content": 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  },
  {
    "path": "extras/BLIP/models/blip.py",
    "content": "'''\n * Copyright (c) 2022, salesforce.com, inc.\n * All rights reserved.\n * SPDX-License-Identifier: BSD-3-Clause\n * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause\n * By Junnan Li\n'''\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\nfrom extras.BLIP.models.vit import VisionTransformer, interpolate_pos_embed\nfrom extras.BLIP.models.med import BertConfig, BertModel, BertLMHeadModel\nfrom transformers import BertTokenizer\n\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\n\nimport os\nfrom urllib.parse import urlparse\nfrom timm.models.hub import download_cached_file\n\nclass BLIP_Base(nn.Module):\n    def __init__(self,                 \n                 med_config = 'configs/med_config.json',  \n                 image_size = 224,\n                 vit = 'base',\n                 vit_grad_ckpt = False,\n                 vit_ckpt_layer = 0,                 \n                 ):\n        \"\"\"\n        Args:\n            med_config (str): path for the mixture of encoder-decoder model's configuration file\n            image_size (int): input image size\n            vit (str): model size of vision transformer\n        \"\"\"               \n        super().__init__()\n        \n        self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer)\n        self.tokenizer = init_tokenizer()   \n        med_config = BertConfig.from_json_file(med_config)\n        med_config.encoder_width = vision_width\n        self.text_encoder = BertModel(config=med_config, add_pooling_layer=False)  \n\n        \n    def forward(self, image, caption, mode):\n        \n        assert mode in ['image', 'text', 'multimodal'], \"mode parameter must be image, text, or multimodal\"\n        text = self.tokenizer(caption, return_tensors=\"pt\").to(image.device) \n        \n        if mode=='image':    \n            # return image features\n            image_embeds = self.visual_encoder(image)             \n            return image_embeds\n        \n        elif mode=='text':\n            # return text features\n            text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask,                      \n                                            return_dict = True, mode = 'text')  \n            return text_output.last_hidden_state\n        \n        elif mode=='multimodal':\n            # return multimodel features\n            image_embeds = self.visual_encoder(image)    \n            image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)      \n            \n            text.input_ids[:,0] = self.tokenizer.enc_token_id\n            output = self.text_encoder(text.input_ids,\n                                       attention_mask = text.attention_mask,\n                                       encoder_hidden_states = image_embeds,\n                                       encoder_attention_mask = image_atts,      \n                                       return_dict = True,\n                                      )              \n            return output.last_hidden_state\n        \n        \n        \nclass BLIP_Decoder(nn.Module):\n    def __init__(self,                 \n                 med_config = 'configs/med_config.json',  \n                 image_size = 384,\n                 vit = 'base',\n                 vit_grad_ckpt = False,\n                 vit_ckpt_layer = 0,\n                 prompt = 'a picture of ',\n                 ):\n        \"\"\"\n        Args:\n            med_config (str): path for the mixture of encoder-decoder model's configuration file\n            image_size (int): input image size\n            vit (str): model size of vision transformer\n        \"\"\"            \n        super().__init__()\n        \n        self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer)\n        self.tokenizer = init_tokenizer()   \n        med_config = BertConfig.from_json_file(med_config)\n        med_config.encoder_width = vision_width\n        self.text_decoder = BertLMHeadModel(config=med_config)    \n        \n        self.prompt = prompt\n        self.prompt_length = len(self.tokenizer(self.prompt).input_ids)-1\n\n        \n    def forward(self, image, caption):\n        \n        image_embeds = self.visual_encoder(image) \n        image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)\n        \n        text = self.tokenizer(caption, padding='longest', truncation=True, max_length=40, return_tensors=\"pt\").to(image.device) \n        \n        text.input_ids[:,0] = self.tokenizer.bos_token_id\n        \n        decoder_targets = text.input_ids.masked_fill(text.input_ids == self.tokenizer.pad_token_id, -100)         \n        decoder_targets[:,:self.prompt_length] = -100\n     \n        decoder_output = self.text_decoder(text.input_ids, \n                                           attention_mask = text.attention_mask, \n                                           encoder_hidden_states = image_embeds,\n                                           encoder_attention_mask = image_atts,                  \n                                           labels = decoder_targets,\n                                           return_dict = True,   \n                                          )   \n        loss_lm = decoder_output.loss\n        \n        return loss_lm\n        \n    def generate(self, image, sample=False, num_beams=3, max_length=30, min_length=10, top_p=0.9, repetition_penalty=1.0):\n        image_embeds = self.visual_encoder(image)\n\n        if not sample:\n            image_embeds = image_embeds.repeat_interleave(num_beams,dim=0)\n            \n        image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)\n        model_kwargs = {\"encoder_hidden_states\": image_embeds, \"encoder_attention_mask\":image_atts}\n        \n        prompt = [self.prompt] * image.size(0)\n        input_ids = self.tokenizer(prompt, return_tensors=\"pt\").input_ids.to(image.device) \n        input_ids[:,0] = self.tokenizer.bos_token_id\n        input_ids = input_ids[:, :-1] \n\n        if sample:\n            #nucleus sampling\n            outputs = self.text_decoder.generate(input_ids=input_ids,\n                                                  max_length=max_length,\n                                                  min_length=min_length,\n                                                  do_sample=True,\n                                                  top_p=top_p,\n                                                  num_return_sequences=1,\n                                                  eos_token_id=self.tokenizer.sep_token_id,\n                                                  pad_token_id=self.tokenizer.pad_token_id, \n                                                  repetition_penalty=1.1,                                            \n                                                  **model_kwargs)\n        else:\n            #beam search\n            outputs = self.text_decoder.generate(input_ids=input_ids,\n                                                  max_length=max_length,\n                                                  min_length=min_length,\n                                                  num_beams=num_beams,\n                                                  eos_token_id=self.tokenizer.sep_token_id,\n                                                  pad_token_id=self.tokenizer.pad_token_id,     \n                                                  repetition_penalty=repetition_penalty,\n                                                  **model_kwargs)            \n            \n        captions = []    \n        for output in outputs:\n            caption = self.tokenizer.decode(output, skip_special_tokens=True)    \n            captions.append(caption[len(self.prompt):])\n        return captions\n    \n\ndef blip_decoder(pretrained='',**kwargs):\n    model = BLIP_Decoder(**kwargs)\n    if pretrained:\n        model,msg = load_checkpoint(model,pretrained)\n        assert(len(msg.missing_keys)==0)\n    return model    \n    \ndef blip_feature_extractor(pretrained='',**kwargs):\n    model = BLIP_Base(**kwargs)\n    if pretrained:\n        model,msg = load_checkpoint(model,pretrained)\n        assert(len(msg.missing_keys)==0)\n    return model        \n\ndef init_tokenizer():\n    tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"bert_tokenizer\")\n    tokenizer = BertTokenizer.from_pretrained(tokenizer_path)\n    tokenizer.add_special_tokens({'bos_token':'[DEC]'})\n    tokenizer.add_special_tokens({'additional_special_tokens':['[ENC]']})       \n    tokenizer.enc_token_id = tokenizer.additional_special_tokens_ids[0]  \n    return tokenizer\n\n\ndef create_vit(vit, image_size, use_grad_checkpointing=False, ckpt_layer=0, drop_path_rate=0):\n        \n    assert vit in ['base', 'large'], \"vit parameter must be base or large\"\n    if vit=='base':\n        vision_width = 768\n        visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=12, \n                                           num_heads=12, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer,\n                                           drop_path_rate=0 or drop_path_rate\n                                          )   \n    elif vit=='large':\n        vision_width = 1024\n        visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=24, \n                                           num_heads=16, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer,\n                                           drop_path_rate=0.1 or drop_path_rate\n                                          )   \n    return visual_encoder, vision_width\n\ndef is_url(url_or_filename):\n    parsed = urlparse(url_or_filename)\n    return parsed.scheme in (\"http\", \"https\")\n\ndef load_checkpoint(model,url_or_filename):\n    if is_url(url_or_filename):\n        cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True)\n        checkpoint = torch.load(cached_file, map_location='cpu', weights_only=True) \n    elif os.path.isfile(url_or_filename):        \n        checkpoint = torch.load(url_or_filename, map_location='cpu', weights_only=True) \n    else:\n        raise RuntimeError('checkpoint url or path is invalid')\n        \n    state_dict = checkpoint['model']\n    \n    state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder) \n    if 'visual_encoder_m.pos_embed' in model.state_dict().keys():\n        state_dict['visual_encoder_m.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder_m.pos_embed'],\n                                                                         model.visual_encoder_m)    \n    for key in model.state_dict().keys():\n        if key in state_dict.keys():\n            if state_dict[key].shape!=model.state_dict()[key].shape:\n                del state_dict[key]\n    \n    msg = model.load_state_dict(state_dict,strict=False)\n    print('load checkpoint from %s'%url_or_filename)  \n    return model,msg\n    \n"
  },
  {
    "path": "extras/BLIP/models/blip_itm.py",
    "content": "from extras.BLIP.models.med import BertConfig, BertModel\nfrom transformers import BertTokenizer\n\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\n\nfrom extras.BLIP.models.blip import create_vit, init_tokenizer, load_checkpoint\n\nclass BLIP_ITM(nn.Module):\n    def __init__(self,                 \n                 med_config = 'configs/med_config.json',  \n                 image_size = 384,\n                 vit = 'base',\n                 vit_grad_ckpt = False,\n                 vit_ckpt_layer = 0,                      \n                 embed_dim = 256,     \n                 ):\n        \"\"\"\n        Args:\n            med_config (str): path for the mixture of encoder-decoder model's configuration file\n            image_size (int): input image size\n            vit (str): model size of vision transformer\n        \"\"\"               \n        super().__init__()\n        \n        self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer)\n        self.tokenizer = init_tokenizer()   \n        med_config = BertConfig.from_json_file(med_config)\n        med_config.encoder_width = vision_width\n        self.text_encoder = BertModel(config=med_config, add_pooling_layer=False)          \n\n        text_width = self.text_encoder.config.hidden_size\n        \n        self.vision_proj = nn.Linear(vision_width, embed_dim)\n        self.text_proj = nn.Linear(text_width, embed_dim)\n\n        self.itm_head = nn.Linear(text_width, 2) \n        \n        \n    def forward(self, image, caption, match_head='itm'):\n\n        image_embeds = self.visual_encoder(image) \n        image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)        \n      \n        text = self.tokenizer(caption, padding='max_length', truncation=True, max_length=35, \n                              return_tensors=\"pt\").to(image.device) \n\n                 \n        if match_head=='itm':\n            output = self.text_encoder(text.input_ids,\n                                       attention_mask = text.attention_mask,\n                                       encoder_hidden_states = image_embeds,\n                                       encoder_attention_mask = image_atts,      \n                                       return_dict = True,\n                                      )\n            itm_output = self.itm_head(output.last_hidden_state[:,0,:])     \n            return itm_output\n            \n        elif match_head=='itc':\n            text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask,                      \n                                            return_dict = True, mode = 'text')                     \n            image_feat = F.normalize(self.vision_proj(image_embeds[:,0,:]),dim=-1)   \n            text_feat = F.normalize(self.text_proj(text_output.last_hidden_state[:,0,:]),dim=-1)    \n            \n            sim = image_feat @ text_feat.t()\n            return sim\n        \n        \ndef blip_itm(pretrained='',**kwargs):\n    model = BLIP_ITM(**kwargs)\n    if pretrained:\n        model,msg = load_checkpoint(model,pretrained)\n        assert(len(msg.missing_keys)==0)\n    return model         \n            "
  },
  {
    "path": "extras/BLIP/models/blip_nlvr.py",
    "content": "from extras.BLIP.models.med import BertConfig\nfrom extras.BLIP.models.nlvr_encoder import BertModel\nfrom extras.BLIP.models.vit import interpolate_pos_embed\nfrom extras.BLIP.models.blip import create_vit, init_tokenizer, is_url\n\nfrom timm.models.hub import download_cached_file\n\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\nfrom transformers import BertTokenizer\nimport numpy as np\nimport os\n\n\nclass BLIP_NLVR(nn.Module):\n    def __init__(self,                 \n                 med_config = 'configs/med_config.json',  \n                 image_size = 480,\n                 vit = 'base',\n                 vit_grad_ckpt = False,\n                 vit_ckpt_layer = 0,                   \n                 ):\n        \"\"\"\n        Args:\n            med_config (str): path for the mixture of encoder-decoder model's configuration file\n            image_size (int): input image size\n            vit (str): model size of vision transformer\n        \"\"\"               \n        super().__init__()\n        \n        self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer, drop_path_rate=0.1)\n        self.tokenizer = init_tokenizer()   \n        med_config = BertConfig.from_json_file(med_config)\n        med_config.encoder_width = vision_width\n        self.text_encoder = BertModel(config=med_config, add_pooling_layer=False) \n                    \n        self.cls_head = nn.Sequential(\n                  nn.Linear(self.text_encoder.config.hidden_size, self.text_encoder.config.hidden_size),\n                  nn.ReLU(),\n                  nn.Linear(self.text_encoder.config.hidden_size, 2)\n                )  \n\n    def forward(self, image, text, targets, train=True):\n        \n        image_embeds = self.visual_encoder(image) \n        image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)        \n        image0_embeds, image1_embeds = torch.split(image_embeds,targets.size(0))     \n\n        text = self.tokenizer(text, padding='longest', return_tensors=\"pt\").to(image.device) \n        text.input_ids[:,0] = self.tokenizer.enc_token_id        \n\n        output = self.text_encoder(text.input_ids, \n                                   attention_mask = text.attention_mask, \n                                   encoder_hidden_states = [image0_embeds,image1_embeds],\n                                   encoder_attention_mask = [image_atts[:image0_embeds.size(0)],\n                                                             image_atts[image0_embeds.size(0):]],        \n                                   return_dict = True,\n                                  )  \n        hidden_state = output.last_hidden_state[:,0,:]        \n        prediction = self.cls_head(hidden_state)\n\n        if train:            \n            loss = F.cross_entropy(prediction, targets)   \n            return loss\n        else:\n            return prediction\n    \ndef blip_nlvr(pretrained='',**kwargs):\n    model = BLIP_NLVR(**kwargs)\n    if pretrained:\n        model,msg = load_checkpoint(model,pretrained)\n        print(\"missing keys:\")\n        print(msg.missing_keys)\n    return model  \n\n        \ndef load_checkpoint(model,url_or_filename):\n    if is_url(url_or_filename):\n        cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True)\n        checkpoint = torch.load(cached_file, map_location='cpu', weights_only=True) \n    elif os.path.isfile(url_or_filename):        \n        checkpoint = torch.load(url_or_filename, map_location='cpu', weights_only=True) \n    else:\n        raise RuntimeError('checkpoint url or path is invalid')\n    state_dict = checkpoint['model']\n    \n    state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder) \n    \n    for key in list(state_dict.keys()):\n        if 'crossattention.self.' in key:\n            new_key0 = key.replace('self','self0')\n            new_key1 = key.replace('self','self1')\n            state_dict[new_key0] = state_dict[key]\n            state_dict[new_key1] = state_dict[key]\n        elif 'crossattention.output.dense.' in key:\n            new_key0 = key.replace('dense','dense0')\n            new_key1 = key.replace('dense','dense1')\n            state_dict[new_key0] = state_dict[key]\n            state_dict[new_key1] = state_dict[key]  \n                \n    msg = model.load_state_dict(state_dict,strict=False)\n    print('load checkpoint from %s'%url_or_filename)  \n    return model,msg\n            "
  },
  {
    "path": "extras/BLIP/models/blip_pretrain.py",
    "content": "'''\n * Copyright (c) 2022, salesforce.com, inc.\n * All rights reserved.\n * SPDX-License-Identifier: BSD-3-Clause\n * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause\n * By Junnan Li\n'''\nfrom extras.BLIP.models.med import BertConfig, BertModel, BertLMHeadModel\nfrom transformers import BertTokenizer\nimport transformers\ntransformers.logging.set_verbosity_error()\n\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\n\nfrom extras.BLIP.models.blip import create_vit, init_tokenizer, load_checkpoint\n\nclass BLIP_Pretrain(nn.Module):\n    def __init__(self,                 \n                 med_config = 'configs/bert_config.json',  \n                 image_size = 224,\n                 vit = 'base',\n                 vit_grad_ckpt = False,\n                 vit_ckpt_layer = 0,                    \n                 embed_dim = 256,     \n                 queue_size = 57600,\n                 momentum = 0.995,\n                 ):\n        \"\"\"\n        Args:\n            med_config (str): path for the mixture of encoder-decoder model's configuration file\n            image_size (int): input image size\n            vit (str): model size of vision transformer\n        \"\"\"               \n        super().__init__()\n        \n        self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer, 0)\n        \n        if vit=='base':\n            checkpoint = torch.hub.load_state_dict_from_url(\n                url=\"https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth\",\n                map_location=\"cpu\", check_hash=True)\n            state_dict = checkpoint[\"model\"]     \n            msg = self.visual_encoder.load_state_dict(state_dict,strict=False)\n        elif vit=='large':\n            from timm.models.helpers import load_custom_pretrained\n            from timm.models.vision_transformer import default_cfgs\n            load_custom_pretrained(self.visual_encoder,default_cfgs['vit_large_patch16_224_in21k'])        \n               \n        self.tokenizer = init_tokenizer()   \n        encoder_config = BertConfig.from_json_file(med_config)\n        encoder_config.encoder_width = vision_width\n        self.text_encoder = BertModel.from_pretrained('bert-base-uncased',config=encoder_config, add_pooling_layer=False)\n        self.text_encoder.resize_token_embeddings(len(self.tokenizer)) \n\n        text_width = self.text_encoder.config.hidden_size\n        \n        self.vision_proj = nn.Linear(vision_width, embed_dim)\n        self.text_proj = nn.Linear(text_width, embed_dim)\n\n        self.itm_head = nn.Linear(text_width, 2) \n        \n        # create momentum encoders  \n        self.visual_encoder_m, vision_width = create_vit(vit,image_size)              \n        self.vision_proj_m = nn.Linear(vision_width, embed_dim)\n        self.text_encoder_m = BertModel(config=encoder_config, add_pooling_layer=False)      \n        self.text_proj_m = nn.Linear(text_width, embed_dim)\n        \n        self.model_pairs = [[self.visual_encoder,self.visual_encoder_m],\n                            [self.vision_proj,self.vision_proj_m],\n                            [self.text_encoder,self.text_encoder_m],\n                            [self.text_proj,self.text_proj_m],\n                           ]       \n        self.copy_params()\n\n        # create the queue\n        self.register_buffer(\"image_queue\", torch.randn(embed_dim, queue_size))\n        self.register_buffer(\"text_queue\", torch.randn(embed_dim, queue_size))\n        self.register_buffer(\"queue_ptr\", torch.zeros(1, dtype=torch.long))  \n\n        self.image_queue = nn.functional.normalize(self.image_queue, dim=0)\n        self.text_queue = nn.functional.normalize(self.text_queue, dim=0)\n        \n        self.queue_size = queue_size\n        self.momentum = momentum\n        self.temp = nn.Parameter(0.07*torch.ones([]))   \n        \n        # create the decoder\n        decoder_config = BertConfig.from_json_file(med_config)\n        decoder_config.encoder_width = vision_width        \n        self.text_decoder = BertLMHeadModel.from_pretrained('bert-base-uncased',config=decoder_config)    \n        self.text_decoder.resize_token_embeddings(len(self.tokenizer)) \n        tie_encoder_decoder_weights(self.text_encoder,self.text_decoder.bert,'','/attention')\n        \n        \n    def forward(self, image, caption, alpha):\n        with torch.no_grad():\n            self.temp.clamp_(0.001,0.5)\n        \n        image_embeds = self.visual_encoder(image) \n        image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)        \n        image_feat = F.normalize(self.vision_proj(image_embeds[:,0,:]),dim=-1)          \n        \n        text = self.tokenizer(caption, padding='max_length', truncation=True, max_length=30, \n                              return_tensors=\"pt\").to(image.device)  \n        text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask,                      \n                                        return_dict = True, mode = 'text')            \n        text_feat = F.normalize(self.text_proj(text_output.last_hidden_state[:,0,:]),dim=-1)                 \n             \n        # get momentum features\n        with torch.no_grad():\n            self._momentum_update()\n            image_embeds_m = self.visual_encoder_m(image) \n            image_feat_m = F.normalize(self.vision_proj_m(image_embeds_m[:,0,:]),dim=-1)  \n            image_feat_all = torch.cat([image_feat_m.t(),self.image_queue.clone().detach()],dim=1)                   \n            \n            text_output_m = self.text_encoder_m(text.input_ids, attention_mask = text.attention_mask,                      \n                                                return_dict = True, mode = 'text')    \n            text_feat_m = F.normalize(self.text_proj_m(text_output_m.last_hidden_state[:,0,:]),dim=-1) \n            text_feat_all = torch.cat([text_feat_m.t(),self.text_queue.clone().detach()],dim=1)\n\n            sim_i2t_m = image_feat_m @ text_feat_all / self.temp  \n            sim_t2i_m = text_feat_m @ image_feat_all / self.temp \n\n            sim_targets = torch.zeros(sim_i2t_m.size()).to(image.device)\n            sim_targets.fill_diagonal_(1)          \n\n            sim_i2t_targets = alpha * F.softmax(sim_i2t_m, dim=1) + (1 - alpha) * sim_targets\n            sim_t2i_targets = alpha * F.softmax(sim_t2i_m, dim=1) + (1 - alpha) * sim_targets        \n\n        sim_i2t = image_feat @ text_feat_all / self.temp\n        sim_t2i = text_feat @ image_feat_all / self.temp\n                             \n        loss_i2t = -torch.sum(F.log_softmax(sim_i2t, dim=1)*sim_i2t_targets,dim=1).mean()\n        loss_t2i = -torch.sum(F.log_softmax(sim_t2i, dim=1)*sim_t2i_targets,dim=1).mean() \n\n        loss_ita = (loss_i2t+loss_t2i)/2\n\n        self._dequeue_and_enqueue(image_feat_m, text_feat_m)        \n\n        ###============== Image-text Matching ===================###\n        encoder_input_ids = text.input_ids.clone()\n        encoder_input_ids[:,0] = self.tokenizer.enc_token_id\n        \n        # forward the positve image-text pair\n        bs = image.size(0)\n        output_pos = self.text_encoder(encoder_input_ids,\n                                       attention_mask = text.attention_mask,\n                                       encoder_hidden_states = image_embeds,\n                                       encoder_attention_mask = image_atts,      \n                                       return_dict = True,\n                                      )            \n        with torch.no_grad():       \n            weights_t2i = F.softmax(sim_t2i[:,:bs],dim=1)+1e-4 \n            weights_t2i.fill_diagonal_(0)            \n            weights_i2t = F.softmax(sim_i2t[:,:bs],dim=1)+1e-4  \n            weights_i2t.fill_diagonal_(0)   \n            \n        # select a negative image for each text\n        image_embeds_neg = []    \n        for b in range(bs):\n            neg_idx = torch.multinomial(weights_t2i[b], 1).item()\n            image_embeds_neg.append(image_embeds[neg_idx])\n        image_embeds_neg = torch.stack(image_embeds_neg,dim=0)   \n\n        # select a negative text for each image\n        text_ids_neg = []\n        text_atts_neg = []\n        for b in range(bs):\n            neg_idx = torch.multinomial(weights_i2t[b], 1).item()\n            text_ids_neg.append(encoder_input_ids[neg_idx])\n            text_atts_neg.append(text.attention_mask[neg_idx])\n\n        text_ids_neg = torch.stack(text_ids_neg,dim=0)   \n        text_atts_neg = torch.stack(text_atts_neg,dim=0)      \n\n        text_ids_all = torch.cat([encoder_input_ids, text_ids_neg],dim=0)     \n        text_atts_all = torch.cat([text.attention_mask, text_atts_neg],dim=0)     \n\n        image_embeds_all = torch.cat([image_embeds_neg,image_embeds],dim=0)\n        image_atts_all = torch.cat([image_atts,image_atts],dim=0)\n\n        output_neg = self.text_encoder(text_ids_all,\n                                       attention_mask = text_atts_all,\n                                       encoder_hidden_states = image_embeds_all,\n                                       encoder_attention_mask = image_atts_all,      \n                                       return_dict = True,\n                                      )                            \n\n        vl_embeddings = torch.cat([output_pos.last_hidden_state[:,0,:], output_neg.last_hidden_state[:,0,:]],dim=0)\n        vl_output = self.itm_head(vl_embeddings)            \n\n        itm_labels = torch.cat([torch.ones(bs,dtype=torch.long),torch.zeros(2*bs,dtype=torch.long)],\n                               dim=0).to(image.device)\n        loss_itm = F.cross_entropy(vl_output, itm_labels)  \n        \n        ##================= LM ========================##     \n        decoder_input_ids = text.input_ids.clone()      \n        decoder_input_ids[:,0] = self.tokenizer.bos_token_id\n        decoder_targets = decoder_input_ids.masked_fill(decoder_input_ids == self.tokenizer.pad_token_id, -100) \n\n        decoder_output = self.text_decoder(decoder_input_ids, \n                                           attention_mask = text.attention_mask, \n                                           encoder_hidden_states = image_embeds,\n                                           encoder_attention_mask = image_atts,                  \n                                           labels = decoder_targets,\n                                           return_dict = True,   \n                                          )   \n          \n        loss_lm = decoder_output.loss                \n        return loss_ita, loss_itm, loss_lm\n \n\n\n    @torch.no_grad()    \n    def copy_params(self):\n        for model_pair in self.model_pairs:           \n            for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()):\n                param_m.data.copy_(param.data)  # initialize\n                param_m.requires_grad = False  # not update by gradient    \n\n            \n    @torch.no_grad()        \n    def _momentum_update(self):\n        for model_pair in self.model_pairs:           \n            for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()):\n                param_m.data = param_m.data * self.momentum + param.data * (1. - self.momentum)\n\n                        \n    @torch.no_grad()\n    def _dequeue_and_enqueue(self, image_feat, text_feat):\n        # gather keys before updating queue\n        image_feats = concat_all_gather(image_feat)\n        text_feats = concat_all_gather(text_feat)\n\n        batch_size = image_feats.shape[0]\n\n        ptr = int(self.queue_ptr)\n        assert self.queue_size % batch_size == 0  # for simplicity\n\n        # replace the keys at ptr (dequeue and enqueue)\n        self.image_queue[:, ptr:ptr + batch_size] = image_feats.T\n        self.text_queue[:, ptr:ptr + batch_size] = text_feats.T\n        ptr = (ptr + batch_size) % self.queue_size  # move pointer\n\n        self.queue_ptr[0] = ptr \n\n\ndef blip_pretrain(**kwargs):\n    model = BLIP_Pretrain(**kwargs)\n    return model \n\n\n@torch.no_grad()\ndef concat_all_gather(tensor):\n    \"\"\"\n    Performs all_gather operation on the provided tensors.\n    *** Warning ***: torch.distributed.all_gather has no gradient.\n    \"\"\"\n    tensors_gather = [torch.ones_like(tensor)\n        for _ in range(torch.distributed.get_world_size())]\n    torch.distributed.all_gather(tensors_gather, tensor, async_op=False)\n\n    output = torch.cat(tensors_gather, dim=0)\n    return output     \n\n\nfrom typing import List\ndef tie_encoder_decoder_weights(encoder: nn.Module, decoder: nn.Module, base_model_prefix: str, skip_key:str):\n    uninitialized_encoder_weights: List[str] = []\n    if decoder.__class__ != encoder.__class__:\n        print(\n            f\"{decoder.__class__} and {encoder.__class__} are not equal. In this case make sure that all encoder weights are correctly initialized.\"\n        )\n\n    def tie_encoder_to_decoder_recursively(\n        decoder_pointer: nn.Module,\n        encoder_pointer: nn.Module,\n        module_name: str,\n        uninitialized_encoder_weights: List[str],\n        skip_key: str,\n        depth=0,\n    ):\n        assert isinstance(decoder_pointer, nn.Module) and isinstance(\n            encoder_pointer, nn.Module\n        ), f\"{decoder_pointer} and {encoder_pointer} have to be of type torch.nn.Module\"\n        if hasattr(decoder_pointer, \"weight\") and skip_key not in module_name:\n            assert hasattr(encoder_pointer, \"weight\")\n            encoder_pointer.weight = decoder_pointer.weight\n            if hasattr(decoder_pointer, \"bias\"):\n                assert hasattr(encoder_pointer, \"bias\")\n                encoder_pointer.bias = decoder_pointer.bias                \n            print(module_name+' is tied')    \n            return\n\n        encoder_modules = encoder_pointer._modules\n        decoder_modules = decoder_pointer._modules\n        if len(decoder_modules) > 0:\n            assert (\n                len(encoder_modules) > 0\n            ), f\"Encoder module {encoder_pointer} does not match decoder module {decoder_pointer}\"\n\n            all_encoder_weights = set([module_name + \"/\" + sub_name for sub_name in encoder_modules.keys()])\n            encoder_layer_pos = 0\n            for name, module in decoder_modules.items():\n                if name.isdigit():\n                    encoder_name = str(int(name) + encoder_layer_pos)\n                    decoder_name = name\n                    if not isinstance(decoder_modules[decoder_name], type(encoder_modules[encoder_name])) and len(\n                        encoder_modules\n                    ) != len(decoder_modules):\n                        # this can happen if the name corresponds to the position in a list module list of layers\n                        # in this case the decoder has added a cross-attention that the encoder does not have\n                        # thus skip this step and subtract one layer pos from encoder\n                        encoder_layer_pos -= 1\n                        continue\n                elif name not in encoder_modules:\n                    continue\n                elif depth > 500:\n                    raise ValueError(\n                        \"Max depth of recursive function `tie_encoder_to_decoder` reached. It seems that there is a circular dependency between two or more `nn.Modules` of your model.\"\n                    )\n                else:\n                    decoder_name = encoder_name = name\n                tie_encoder_to_decoder_recursively(\n                    decoder_modules[decoder_name],\n                    encoder_modules[encoder_name],\n                    module_name + \"/\" + name,\n                    uninitialized_encoder_weights,\n                    skip_key,\n                    depth=depth + 1,\n                )\n                all_encoder_weights.remove(module_name + \"/\" + encoder_name)\n\n            uninitialized_encoder_weights += list(all_encoder_weights)\n\n    # tie weights recursively\n    tie_encoder_to_decoder_recursively(decoder, encoder, base_model_prefix, uninitialized_encoder_weights, skip_key)  \n"
  },
  {
    "path": "extras/BLIP/models/blip_retrieval.py",
    "content": "from extras.BLIP.models.med import BertConfig, BertModel\nfrom transformers import BertTokenizer\n\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\n\nfrom extras.BLIP.models.blip import create_vit, init_tokenizer, load_checkpoint\n\nclass BLIP_Retrieval(nn.Module):\n    def __init__(self,                 \n                 med_config = 'configs/med_config.json',  \n                 image_size = 384,\n                 vit = 'base',\n                 vit_grad_ckpt = False,\n                 vit_ckpt_layer = 0,                      \n                 embed_dim = 256,     \n                 queue_size = 57600,\n                 momentum = 0.995,\n                 negative_all_rank = False,\n                 ):\n        \"\"\"\n        Args:\n            med_config (str): path for the mixture of encoder-decoder model's configuration file\n            image_size (int): input image size\n            vit (str): model size of vision transformer\n        \"\"\"               \n        super().__init__()\n        \n        self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer)\n        self.tokenizer = init_tokenizer()   \n        med_config = BertConfig.from_json_file(med_config)\n        med_config.encoder_width = vision_width\n        self.text_encoder = BertModel(config=med_config, add_pooling_layer=False)          \n\n        text_width = self.text_encoder.config.hidden_size\n        \n        self.vision_proj = nn.Linear(vision_width, embed_dim)\n        self.text_proj = nn.Linear(text_width, embed_dim)\n\n        self.itm_head = nn.Linear(text_width, 2) \n        \n        # create momentum encoders  \n        self.visual_encoder_m, vision_width = create_vit(vit,image_size)              \n        self.vision_proj_m = nn.Linear(vision_width, embed_dim)\n        self.text_encoder_m = BertModel(config=med_config, add_pooling_layer=False)    \n        self.text_proj_m = nn.Linear(text_width, embed_dim)\n        \n        self.model_pairs = [[self.visual_encoder,self.visual_encoder_m],\n                            [self.vision_proj,self.vision_proj_m],\n                            [self.text_encoder,self.text_encoder_m],\n                            [self.text_proj,self.text_proj_m],\n                           ]       \n        self.copy_params()\n\n        # create the queue\n        self.register_buffer(\"image_queue\", torch.randn(embed_dim, queue_size))\n        self.register_buffer(\"text_queue\", torch.randn(embed_dim, queue_size))\n        self.register_buffer(\"idx_queue\", torch.full((1,queue_size),-100))\n        self.register_buffer(\"ptr_queue\", torch.zeros(1, dtype=torch.long))  \n\n        self.image_queue = nn.functional.normalize(self.image_queue, dim=0)\n        self.text_queue = nn.functional.normalize(self.text_queue, dim=0)\n        \n        self.queue_size = queue_size\n        self.momentum = momentum\n        self.temp = nn.Parameter(0.07*torch.ones([]))   \n        \n        self.negative_all_rank = negative_all_rank\n        \n        \n    def forward(self, image, caption, alpha, idx):\n        with torch.no_grad():\n            self.temp.clamp_(0.001,0.5)\n        \n        image_embeds = self.visual_encoder(image) \n        image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)        \n        image_feat = F.normalize(self.vision_proj(image_embeds[:,0,:]),dim=-1)    \n        \n        text = self.tokenizer(caption, padding='max_length', truncation=True, max_length=35, \n                              return_tensors=\"pt\").to(image.device) \n        \n        text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask,                      \n                                        return_dict = True, mode = 'text')            \n        text_feat = F.normalize(self.text_proj(text_output.last_hidden_state[:,0,:]),dim=-1)        \n        \n        ###============== Image-text Contrastive Learning ===================###\n        idx = idx.view(-1,1)\n        idx_all = torch.cat([idx.t(), self.idx_queue.clone().detach()],dim=1)  \n        pos_idx = torch.eq(idx, idx_all).float()       \n        sim_targets = pos_idx / pos_idx.sum(1,keepdim=True)   \n        \n        # get momentum features\n        with torch.no_grad():\n            self._momentum_update()\n            image_embeds_m = self.visual_encoder_m(image) \n            image_feat_m = F.normalize(self.vision_proj_m(image_embeds_m[:,0,:]),dim=-1)  \n            image_feat_m_all = torch.cat([image_feat_m.t(),self.image_queue.clone().detach()],dim=1)                   \n            \n            text_output_m = self.text_encoder_m(text.input_ids, attention_mask = text.attention_mask,                      \n                                                return_dict = True, mode = 'text')    \n            text_feat_m = F.normalize(self.text_proj_m(text_output_m.last_hidden_state[:,0,:]),dim=-1) \n            text_feat_m_all = torch.cat([text_feat_m.t(),self.text_queue.clone().detach()],dim=1)\n\n            sim_i2t_m = image_feat_m @ text_feat_m_all / self.temp  \n            sim_t2i_m = text_feat_m @ image_feat_m_all / self.temp   \n\n            sim_i2t_targets = alpha * F.softmax(sim_i2t_m, dim=1) + (1 - alpha) * sim_targets\n            sim_t2i_targets = alpha * F.softmax(sim_t2i_m, dim=1) + (1 - alpha) * sim_targets        \n\n        sim_i2t = image_feat @ text_feat_m_all / self.temp \n        sim_t2i = text_feat @ image_feat_m_all / self.temp \n                             \n        loss_i2t = -torch.sum(F.log_softmax(sim_i2t, dim=1)*sim_i2t_targets,dim=1).mean()\n        loss_t2i = -torch.sum(F.log_softmax(sim_t2i, dim=1)*sim_t2i_targets,dim=1).mean() \n\n        loss_ita = (loss_i2t+loss_t2i)/2\n        \n        idxs = concat_all_gather(idx)\n        self._dequeue_and_enqueue(image_feat_m, text_feat_m, idxs)        \n\n        ###============== Image-text Matching ===================###\n        encoder_input_ids = text.input_ids.clone()\n        encoder_input_ids[:,0] = self.tokenizer.enc_token_id\n\n        # forward the positve image-text pair\n        bs = image.size(0)\n        output_pos = self.text_encoder(encoder_input_ids,\n                                       attention_mask = text.attention_mask,\n                                       encoder_hidden_states = image_embeds,\n                                       encoder_attention_mask = image_atts,      \n                                       return_dict = True,\n                                      )  \n        \n        \n        if self.negative_all_rank:    \n            # compute sample similarity\n            with torch.no_grad():                \n                mask = torch.eq(idx, idxs.t())\n\n                image_feat_world = concat_all_gather(image_feat)\n                text_feat_world = concat_all_gather(text_feat)\n\n                sim_i2t = image_feat @ text_feat_world.t() / self.temp \n                sim_t2i = text_feat @ image_feat_world.t() / self.temp \n\n                weights_i2t = F.softmax(sim_i2t,dim=1)\n                weights_i2t.masked_fill_(mask, 0)            \n\n                weights_t2i = F.softmax(sim_t2i,dim=1)\n                weights_t2i.masked_fill_(mask, 0)     \n\n            image_embeds_world = all_gather_with_grad(image_embeds) \n\n            # select a negative image (from all ranks) for each text\n            image_embeds_neg = []    \n            for b in range(bs):\n                neg_idx = torch.multinomial(weights_t2i[b], 1).item()\n                image_embeds_neg.append(image_embeds_world[neg_idx])\n            image_embeds_neg = torch.stack(image_embeds_neg,dim=0)   \n\n            # select a negative text (from all ranks) for each image\n            input_ids_world = concat_all_gather(encoder_input_ids)\n            att_mask_world = concat_all_gather(text.attention_mask)        \n\n            text_ids_neg = []\n            text_atts_neg = []\n            for b in range(bs):\n                neg_idx = torch.multinomial(weights_i2t[b], 1).item()\n                text_ids_neg.append(input_ids_world[neg_idx])\n                text_atts_neg.append(att_mask_world[neg_idx])\n                \n        else:\n            with torch.no_grad():                \n                mask = torch.eq(idx, idx.t())\n                \n                sim_i2t = image_feat @ text_feat.t() / self.temp \n                sim_t2i = text_feat @ image_feat.t() / self.temp \n\n                weights_i2t = F.softmax(sim_i2t,dim=1)\n                weights_i2t.masked_fill_(mask, 0)            \n\n                weights_t2i = F.softmax(sim_t2i,dim=1)\n                weights_t2i.masked_fill_(mask, 0)     \n\n            # select a negative image (from same rank) for each text\n            image_embeds_neg = []    \n            for b in range(bs):\n                neg_idx = torch.multinomial(weights_t2i[b], 1).item()\n                image_embeds_neg.append(image_embeds[neg_idx])\n            image_embeds_neg = torch.stack(image_embeds_neg,dim=0)   \n\n            # select a negative text (from same rank) for each image    \n            text_ids_neg = []\n            text_atts_neg = []\n            for b in range(bs):\n                neg_idx = torch.multinomial(weights_i2t[b], 1).item()\n                text_ids_neg.append(encoder_input_ids[neg_idx])\n                text_atts_neg.append(text.attention_mask[neg_idx])            \n            \n        text_ids_neg = torch.stack(text_ids_neg,dim=0)   \n        text_atts_neg = torch.stack(text_atts_neg,dim=0)      \n\n        text_ids_all = torch.cat([encoder_input_ids, text_ids_neg],dim=0)     \n        text_atts_all = torch.cat([text.attention_mask, text_atts_neg],dim=0)     \n\n        image_embeds_all = torch.cat([image_embeds_neg,image_embeds],dim=0)\n        image_atts_all = torch.cat([image_atts,image_atts],dim=0)\n\n        output_neg = self.text_encoder(text_ids_all,\n                                       attention_mask = text_atts_all,\n                                       encoder_hidden_states = image_embeds_all,\n                                       encoder_attention_mask = image_atts_all,      \n                                       return_dict = True,\n                                      )                         \n          \n\n        vl_embeddings = torch.cat([output_pos.last_hidden_state[:,0,:], output_neg.last_hidden_state[:,0,:]],dim=0)\n        vl_output = self.itm_head(vl_embeddings)            \n\n        itm_labels = torch.cat([torch.ones(bs,dtype=torch.long),torch.zeros(2*bs,dtype=torch.long)],\n                               dim=0).to(image.device)\n        loss_itm = F.cross_entropy(vl_output, itm_labels)     \n\n        return loss_ita, loss_itm \n \n\n    @torch.no_grad()    \n    def copy_params(self):\n        for model_pair in self.model_pairs:           \n            for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()):\n                param_m.data.copy_(param.data)  # initialize\n                param_m.requires_grad = False  # not update by gradient    \n\n            \n    @torch.no_grad()        \n    def _momentum_update(self):\n        for model_pair in self.model_pairs:           \n            for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()):\n                param_m.data = param_m.data * self.momentum + param.data * (1. - self.momentum)\n                \n                \n    @torch.no_grad()\n    def _dequeue_and_enqueue(self, image_feat, text_feat, idxs):\n        # gather keys before updating queue\n        image_feats = concat_all_gather(image_feat)\n        text_feats = concat_all_gather(text_feat)\n        \n\n        batch_size = image_feats.shape[0]\n\n        ptr = int(self.ptr_queue)\n        assert self.queue_size % batch_size == 0  # for simplicity\n\n        # replace the keys at ptr (dequeue and enqueue)\n        self.image_queue[:, ptr:ptr + batch_size] = image_feats.T\n        self.text_queue[:, ptr:ptr + batch_size] = text_feats.T\n        self.idx_queue[:, ptr:ptr + batch_size] = idxs.T\n        ptr = (ptr + batch_size) % self.queue_size # move pointer\n\n        self.ptr_queue[0] = ptr  \n\n\ndef blip_retrieval(pretrained='',**kwargs):\n    model = BLIP_Retrieval(**kwargs)\n    if pretrained:\n        model,msg = load_checkpoint(model,pretrained)\n        print(\"missing keys:\")\n        print(msg.missing_keys)\n    return model \n\n\n@torch.no_grad()\ndef concat_all_gather(tensor):\n    \"\"\"\n    Performs all_gather operation on the provided tensors.\n    *** Warning ***: torch.distributed.all_gather has no gradient.\n    \"\"\"\n    tensors_gather = [torch.ones_like(tensor)\n        for _ in range(torch.distributed.get_world_size())]\n    torch.distributed.all_gather(tensors_gather, tensor, async_op=False)\n\n    output = torch.cat(tensors_gather, dim=0)\n    return output      \n\n\nclass GatherLayer(torch.autograd.Function):\n    \"\"\"\n    Gather tensors from all workers with support for backward propagation:\n    This implementation does not cut the gradients as torch.distributed.all_gather does.\n    \"\"\"\n\n    @staticmethod\n    def forward(ctx, x):\n        output = [torch.zeros_like(x) for _ in range(torch.distributed.get_world_size())]\n        torch.distributed.all_gather(output, x)\n        return tuple(output)\n\n    @staticmethod\n    def backward(ctx, *grads):\n        all_gradients = torch.stack(grads)\n        torch.distributed.all_reduce(all_gradients)\n        return all_gradients[torch.distributed.get_rank()]\n\n\ndef all_gather_with_grad(tensors):\n    \"\"\"\n    Performs all_gather operation on the provided tensors.\n    Graph remains connected for backward grad computation.\n    \"\"\"\n    # Queue the gathered tensors\n    world_size = torch.distributed.get_world_size()\n    # There is no need for reduction in the single-proc case\n    if world_size == 1:\n        return tensors\n\n    tensor_all = GatherLayer.apply(tensors)\n\n    return torch.cat(tensor_all, dim=0)\n"
  },
  {
    "path": "extras/BLIP/models/blip_vqa.py",
    "content": "from extras.BLIP.models.med import BertConfig, BertModel, BertLMHeadModel\nfrom extras.BLIP.models.blip import create_vit, init_tokenizer, load_checkpoint\n\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\nfrom transformers import BertTokenizer\nimport numpy as np\n\nclass BLIP_VQA(nn.Module):\n    def __init__(self,                 \n                 med_config = 'configs/med_config.json',  \n                 image_size = 480,\n                 vit = 'base',\n                 vit_grad_ckpt = False,\n                 vit_ckpt_layer = 0,                   \n                 ):\n        \"\"\"\n        Args:\n            med_config (str): path for the mixture of encoder-decoder model's configuration file\n            image_size (int): input image size\n            vit (str): model size of vision transformer\n        \"\"\"               \n        super().__init__()\n        \n        self.visual_encoder, vision_width = create_vit(vit, image_size, vit_grad_ckpt, vit_ckpt_layer, drop_path_rate=0.1)\n        self.tokenizer = init_tokenizer()  \n        \n        encoder_config = BertConfig.from_json_file(med_config)\n        encoder_config.encoder_width = vision_width\n        self.text_encoder = BertModel(config=encoder_config, add_pooling_layer=False) \n        \n        decoder_config = BertConfig.from_json_file(med_config)        \n        self.text_decoder = BertLMHeadModel(config=decoder_config)          \n\n\n    def forward(self, image, question, answer=None, n=None, weights=None, train=True, inference='rank', k_test=128):\n        \n        image_embeds = self.visual_encoder(image) \n        image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)\n        \n        question = self.tokenizer(question, padding='longest', truncation=True, max_length=35, \n                                  return_tensors=\"pt\").to(image.device) \n        question.input_ids[:,0] = self.tokenizer.enc_token_id\n        \n        if train:               \n            '''\n            n: number of answers for each question\n            weights: weight for each answer\n            '''                     \n            answer = self.tokenizer(answer, padding='longest', return_tensors=\"pt\").to(image.device) \n            answer.input_ids[:,0] = self.tokenizer.bos_token_id\n            answer_targets = answer.input_ids.masked_fill(answer.input_ids == self.tokenizer.pad_token_id, -100)      \n\n            question_output = self.text_encoder(question.input_ids, \n                                                attention_mask = question.attention_mask, \n                                                encoder_hidden_states = image_embeds,\n                                                encoder_attention_mask = image_atts,                             \n                                                return_dict = True)    \n\n            question_states = []                \n            question_atts = []  \n            for b, n in enumerate(n):\n                question_states += [question_output.last_hidden_state[b]]*n\n                question_atts += [question.attention_mask[b]]*n                \n            question_states = torch.stack(question_states,0)    \n            question_atts = torch.stack(question_atts,0)     \n\n            answer_output = self.text_decoder(answer.input_ids, \n                                              attention_mask = answer.attention_mask, \n                                              encoder_hidden_states = question_states,\n                                              encoder_attention_mask = question_atts,                  \n                                              labels = answer_targets,\n                                              return_dict = True,   \n                                              reduction = 'none',\n                                             )      \n            \n            loss = weights * answer_output.loss\n            loss = loss.sum()/image.size(0)\n\n            return loss\n            \n\n        else: \n            question_output = self.text_encoder(question.input_ids, \n                                                attention_mask = question.attention_mask, \n                                                encoder_hidden_states = image_embeds,\n                                                encoder_attention_mask = image_atts,                                    \n                                                return_dict = True) \n            \n            if inference=='generate':\n                num_beams = 3\n                question_states = question_output.last_hidden_state.repeat_interleave(num_beams,dim=0)\n                question_atts = torch.ones(question_states.size()[:-1],dtype=torch.long).to(question_states.device)\n                model_kwargs = {\"encoder_hidden_states\": question_states, \"encoder_attention_mask\":question_atts}\n                \n                bos_ids = torch.full((image.size(0),1),fill_value=self.tokenizer.bos_token_id,device=image.device)\n                \n                outputs = self.text_decoder.generate(input_ids=bos_ids,\n                                                     max_length=10,\n                                                     min_length=1,\n                                                     num_beams=num_beams,\n                                                     eos_token_id=self.tokenizer.sep_token_id,\n                                                     pad_token_id=self.tokenizer.pad_token_id, \n                                                     **model_kwargs)\n                \n                answers = []    \n                for output in outputs:\n                    answer = self.tokenizer.decode(output, skip_special_tokens=True)    \n                    answers.append(answer)\n                return answers\n            \n            elif inference=='rank':\n                max_ids = self.rank_answer(question_output.last_hidden_state, question.attention_mask, \n                                           answer.input_ids, answer.attention_mask, k_test) \n                return max_ids\n \n                \n                \n    def rank_answer(self, question_states, question_atts, answer_ids, answer_atts, k):\n        \n        num_ques = question_states.size(0)\n        start_ids = answer_ids[0,0].repeat(num_ques,1) # bos token\n        \n        start_output = self.text_decoder(start_ids, \n                                         encoder_hidden_states = question_states,\n                                         encoder_attention_mask = question_atts,                                      \n                                         return_dict = True,\n                                         reduction = 'none')              \n        logits = start_output.logits[:,0,:] # first token's logit\n        \n        # topk_probs: top-k probability \n        # topk_ids: [num_question, k]        \n        answer_first_token = answer_ids[:,1]\n        prob_first_token = F.softmax(logits,dim=1).index_select(dim=1, index=answer_first_token) \n        topk_probs, topk_ids = prob_first_token.topk(k,dim=1) \n        \n        # answer input: [num_question*k, answer_len]                 \n        input_ids = []\n        input_atts = []\n        for b, topk_id in enumerate(topk_ids):\n            input_ids.append(answer_ids.index_select(dim=0, index=topk_id))\n            input_atts.append(answer_atts.index_select(dim=0, index=topk_id))\n        input_ids = torch.cat(input_ids,dim=0)  \n        input_atts = torch.cat(input_atts,dim=0)  \n\n        targets_ids = input_ids.masked_fill(input_ids == self.tokenizer.pad_token_id, -100)\n\n        # repeat encoder's output for top-k answers\n        question_states = tile(question_states, 0, k)\n        question_atts = tile(question_atts, 0, k)\n        \n        output = self.text_decoder(input_ids, \n                                   attention_mask = input_atts, \n                                   encoder_hidden_states = question_states,\n                                   encoder_attention_mask = question_atts,     \n                                   labels = targets_ids,\n                                   return_dict = True, \n                                   reduction = 'none')   \n        \n        log_probs_sum = -output.loss\n        log_probs_sum = log_probs_sum.view(num_ques,k)\n\n        max_topk_ids = log_probs_sum.argmax(dim=1) \n        max_ids = topk_ids[max_topk_ids>=0,max_topk_ids]\n\n        return max_ids\n    \n    \ndef blip_vqa(pretrained='',**kwargs):\n    model = BLIP_VQA(**kwargs)\n    if pretrained:\n        model,msg = load_checkpoint(model,pretrained)\n#         assert(len(msg.missing_keys)==0)\n    return model  \n\n\ndef tile(x, dim, n_tile):\n    init_dim = x.size(dim)\n    repeat_idx = [1] * x.dim()\n    repeat_idx[dim] = n_tile\n    x = x.repeat(*(repeat_idx))\n    order_index = torch.LongTensor(np.concatenate([init_dim * np.arange(n_tile) + i for i in range(init_dim)]))\n    return torch.index_select(x, dim, order_index.to(x.device))    \n        \n        "
  },
  {
    "path": "extras/BLIP/models/med.py",
    "content": "'''\n * Copyright (c) 2022, salesforce.com, inc.\n * All rights reserved.\n * SPDX-License-Identifier: BSD-3-Clause\n * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause\n * By Junnan Li\n * Based on huggingface code base\n * https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert\n'''\n\nimport math\nimport os\nimport warnings\nfrom dataclasses import dataclass\nfrom typing import Optional, Tuple\n\nimport torch\nfrom torch import Tensor, device, dtype, nn\nimport torch.utils.checkpoint\nfrom torch import nn\nfrom torch.nn import CrossEntropyLoss\nimport torch.nn.functional as F\n\nfrom transformers.activations import ACT2FN\nfrom transformers.file_utils import (\n    ModelOutput,\n)\nfrom transformers.modeling_outputs import (\n    BaseModelOutputWithPastAndCrossAttentions,\n    BaseModelOutputWithPoolingAndCrossAttentions,\n    CausalLMOutputWithCrossAttentions,\n    MaskedLMOutput,\n    MultipleChoiceModelOutput,\n    NextSentencePredictorOutput,\n    QuestionAnsweringModelOutput,\n    SequenceClassifierOutput,\n    TokenClassifierOutput,\n)\nfrom transformers.modeling_utils import (\n    PreTrainedModel,\n    apply_chunking_to_forward,\n    find_pruneable_heads_and_indices,\n    prune_linear_layer,\n)\nfrom transformers.utils import logging\nfrom transformers.models.bert.configuration_bert import BertConfig\n\n\nlogger = logging.get_logger(__name__)\n\n\nclass BertEmbeddings(nn.Module):\n    \"\"\"Construct the embeddings from word and position embeddings.\"\"\"\n\n    def __init__(self, config):\n        super().__init__()\n        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)\n        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)\n\n        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load\n        # any TensorFlow checkpoint file\n        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)\n        self.dropout = nn.Dropout(config.hidden_dropout_prob)\n\n        # position_ids (1, len position emb) is contiguous in memory and exported when serialized\n        self.register_buffer(\"position_ids\", torch.arange(config.max_position_embeddings).expand((1, -1)))\n        self.position_embedding_type = getattr(config, \"position_embedding_type\", \"absolute\")\n        \n        self.config = config\n\n    def forward(\n        self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0\n    ):\n        if input_ids is not None:\n            input_shape = input_ids.size()\n        else:\n            input_shape = inputs_embeds.size()[:-1]\n\n        seq_length = input_shape[1]\n\n        if position_ids is None:\n            position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]\n\n        if inputs_embeds is None:\n            inputs_embeds = self.word_embeddings(input_ids)\n\n        embeddings = inputs_embeds\n\n        if self.position_embedding_type == \"absolute\":\n            position_embeddings = self.position_embeddings(position_ids)\n            embeddings += position_embeddings\n        embeddings = self.LayerNorm(embeddings)\n        embeddings = self.dropout(embeddings)\n        return embeddings\n\n\nclass BertSelfAttention(nn.Module):\n    def __init__(self, config, is_cross_attention):\n        super().__init__()\n        self.config = config\n        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, \"embedding_size\"):\n            raise ValueError(\n                \"The hidden size (%d) is not a multiple of the number of attention \"\n                \"heads (%d)\" % (config.hidden_size, config.num_attention_heads)\n            )\n        \n        self.num_attention_heads = config.num_attention_heads\n        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)\n        self.all_head_size = self.num_attention_heads * self.attention_head_size\n\n        self.query = nn.Linear(config.hidden_size, self.all_head_size)\n        if is_cross_attention:\n            self.key = nn.Linear(config.encoder_width, self.all_head_size)\n            self.value = nn.Linear(config.encoder_width, self.all_head_size)\n        else:\n            self.key = nn.Linear(config.hidden_size, self.all_head_size)\n            self.value = nn.Linear(config.hidden_size, self.all_head_size)\n\n        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)\n        self.position_embedding_type = getattr(config, \"position_embedding_type\", \"absolute\")\n        if self.position_embedding_type == \"relative_key\" or self.position_embedding_type == \"relative_key_query\":\n            self.max_position_embeddings = config.max_position_embeddings\n            self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)\n        self.save_attention = False   \n            \n    def save_attn_gradients(self, attn_gradients):\n        self.attn_gradients = attn_gradients\n        \n    def get_attn_gradients(self):\n        return self.attn_gradients\n    \n    def save_attention_map(self, attention_map):\n        self.attention_map = attention_map\n        \n    def get_attention_map(self):\n        return self.attention_map\n    \n    def transpose_for_scores(self, x):\n        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)\n        x = x.view(*new_x_shape)\n        return x.permute(0, 2, 1, 3)\n\n    def forward(\n        self,\n        hidden_states,\n        attention_mask=None,\n        head_mask=None,\n        encoder_hidden_states=None,\n        encoder_attention_mask=None,\n        past_key_value=None,\n        output_attentions=False,\n    ):\n        mixed_query_layer = self.query(hidden_states)\n\n        # If this is instantiated as a cross-attention module, the keys\n        # and values come from an encoder; the attention mask needs to be\n        # such that the encoder's padding tokens are not attended to.\n        is_cross_attention = encoder_hidden_states is not None\n\n        if is_cross_attention:\n            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))\n            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))\n            attention_mask = encoder_attention_mask\n        elif past_key_value is not None:\n            key_layer = self.transpose_for_scores(self.key(hidden_states))\n            value_layer = self.transpose_for_scores(self.value(hidden_states))\n            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)\n            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)\n        else:\n            key_layer = self.transpose_for_scores(self.key(hidden_states))\n            value_layer = self.transpose_for_scores(self.value(hidden_states))\n\n        query_layer = self.transpose_for_scores(mixed_query_layer)\n\n        past_key_value = (key_layer, value_layer)\n\n        # Take the dot product between \"query\" and \"key\" to get the raw attention scores.\n        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))\n\n        if self.position_embedding_type == \"relative_key\" or self.position_embedding_type == \"relative_key_query\":\n            seq_length = hidden_states.size()[1]\n            position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)\n            position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)\n            distance = position_ids_l - position_ids_r\n            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)\n            positional_embedding = positional_embedding.to(dtype=query_layer.dtype)  # fp16 compatibility\n\n            if self.position_embedding_type == \"relative_key\":\n                relative_position_scores = torch.einsum(\"bhld,lrd->bhlr\", query_layer, positional_embedding)\n                attention_scores = attention_scores + relative_position_scores\n            elif self.position_embedding_type == \"relative_key_query\":\n                relative_position_scores_query = torch.einsum(\"bhld,lrd->bhlr\", query_layer, positional_embedding)\n                relative_position_scores_key = torch.einsum(\"bhrd,lrd->bhlr\", key_layer, positional_embedding)\n                attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key\n\n        attention_scores = attention_scores / math.sqrt(self.attention_head_size)\n        if attention_mask is not None:\n            # Apply the attention mask is (precomputed for all layers in BertModel forward() function)\n            attention_scores = attention_scores + attention_mask\n\n        # Normalize the attention scores to probabilities.\n        attention_probs = nn.Softmax(dim=-1)(attention_scores)\n        \n        if is_cross_attention and self.save_attention:\n            self.save_attention_map(attention_probs)\n            attention_probs.register_hook(self.save_attn_gradients)         \n\n        # This is actually dropping out entire tokens to attend to, which might\n        # seem a bit unusual, but is taken from the original Transformer paper.\n        attention_probs_dropped = self.dropout(attention_probs)\n\n        # Mask heads if we want to\n        if head_mask is not None:\n            attention_probs_dropped = attention_probs_dropped * head_mask\n\n        context_layer = torch.matmul(attention_probs_dropped, value_layer)\n\n        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()\n        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)\n        context_layer = context_layer.view(*new_context_layer_shape)\n\n        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)\n\n        outputs = outputs + (past_key_value,)\n        return outputs\n\n\nclass BertSelfOutput(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.dense = nn.Linear(config.hidden_size, config.hidden_size)\n        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)\n        self.dropout = nn.Dropout(config.hidden_dropout_prob)\n\n    def forward(self, hidden_states, input_tensor):\n        hidden_states = self.dense(hidden_states)\n        hidden_states = self.dropout(hidden_states)\n        hidden_states = self.LayerNorm(hidden_states + input_tensor)\n        return hidden_states\n\n\nclass BertAttention(nn.Module):\n    def __init__(self, config, is_cross_attention=False):\n        super().__init__()\n        self.self = BertSelfAttention(config, is_cross_attention)\n        self.output = BertSelfOutput(config)\n        self.pruned_heads = set()\n\n    def prune_heads(self, heads):\n        if len(heads) == 0:\n            return\n        heads, index = find_pruneable_heads_and_indices(\n            heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads\n        )\n\n        # Prune linear layers\n        self.self.query = prune_linear_layer(self.self.query, index)\n        self.self.key = prune_linear_layer(self.self.key, index)\n        self.self.value = prune_linear_layer(self.self.value, index)\n        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)\n\n        # Update hyper params and store pruned heads\n        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)\n        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads\n        self.pruned_heads = self.pruned_heads.union(heads)\n\n    def forward(\n        self,\n        hidden_states,\n        attention_mask=None,\n        head_mask=None,\n        encoder_hidden_states=None,\n        encoder_attention_mask=None,\n        past_key_value=None,\n        output_attentions=False,\n    ):\n        self_outputs = self.self(\n            hidden_states,\n            attention_mask,\n            head_mask,\n            encoder_hidden_states,\n            encoder_attention_mask,\n            past_key_value,\n            output_attentions,\n        )\n        attention_output = self.output(self_outputs[0], hidden_states)\n        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them\n        return outputs\n\n\nclass BertIntermediate(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)\n        if isinstance(config.hidden_act, str):\n            self.intermediate_act_fn = ACT2FN[config.hidden_act]\n        else:\n            self.intermediate_act_fn = config.hidden_act\n\n    def forward(self, hidden_states):\n        hidden_states = self.dense(hidden_states)\n        hidden_states = self.intermediate_act_fn(hidden_states)\n        return hidden_states\n\n\nclass BertOutput(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)\n        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)\n        self.dropout = nn.Dropout(config.hidden_dropout_prob)\n\n    def forward(self, hidden_states, input_tensor):\n        hidden_states = self.dense(hidden_states)\n        hidden_states = self.dropout(hidden_states)\n        hidden_states = self.LayerNorm(hidden_states + input_tensor)\n        return hidden_states\n\n\nclass BertLayer(nn.Module):\n    def __init__(self, config, layer_num):\n        super().__init__()\n        self.config = config\n        self.chunk_size_feed_forward = config.chunk_size_feed_forward\n        self.seq_len_dim = 1\n        self.attention = BertAttention(config)      \n        self.layer_num = layer_num          \n        if self.config.add_cross_attention:\n            self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention)\n        self.intermediate = BertIntermediate(config)\n        self.output = BertOutput(config)\n\n    def forward(\n        self,\n        hidden_states,\n        attention_mask=None,\n        head_mask=None,\n        encoder_hidden_states=None,\n        encoder_attention_mask=None,\n        past_key_value=None,\n        output_attentions=False,\n        mode=None,\n    ):\n        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2\n        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None\n        self_attention_outputs = self.attention(\n            hidden_states,\n            attention_mask,\n            head_mask,\n            output_attentions=output_attentions,\n            past_key_value=self_attn_past_key_value,\n        )\n        attention_output = self_attention_outputs[0]\n\n        outputs = self_attention_outputs[1:-1]\n        present_key_value = self_attention_outputs[-1]\n\n        if mode=='multimodal':\n            assert encoder_hidden_states is not None, \"encoder_hidden_states must be given for cross-attention layers\"\n\n            cross_attention_outputs = self.crossattention(\n                attention_output,\n                attention_mask,\n                head_mask,\n                encoder_hidden_states,\n                encoder_attention_mask,\n                output_attentions=output_attentions,\n            )\n            attention_output = cross_attention_outputs[0]\n            outputs = outputs + cross_attention_outputs[1:-1]  # add cross attentions if we output attention weights                               \n        layer_output = apply_chunking_to_forward(\n            self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output\n        )\n        outputs = (layer_output,) + outputs\n\n        outputs = outputs + (present_key_value,)\n\n        return outputs\n\n    def feed_forward_chunk(self, attention_output):\n        intermediate_output = self.intermediate(attention_output)\n        layer_output = self.output(intermediate_output, attention_output)\n        return layer_output\n\n\nclass BertEncoder(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.config = config\n        self.layer = nn.ModuleList([BertLayer(config,i) for i in range(config.num_hidden_layers)])\n        self.gradient_checkpointing = False\n\n    def forward(\n        self,\n        hidden_states,\n        attention_mask=None,\n        head_mask=None,\n        encoder_hidden_states=None,\n        encoder_attention_mask=None,\n        past_key_values=None,\n        use_cache=None,\n        output_attentions=False,\n        output_hidden_states=False,\n        return_dict=True,\n        mode='multimodal',\n    ):\n        all_hidden_states = () if output_hidden_states else None\n        all_self_attentions = () if output_attentions else None\n        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None\n\n        next_decoder_cache = () if use_cache else None\n               \n        for i in range(self.config.num_hidden_layers):\n            layer_module = self.layer[i]\n            if output_hidden_states:\n                all_hidden_states = all_hidden_states + (hidden_states,)\n\n            layer_head_mask = head_mask[i] if head_mask is not None else None\n            past_key_value = past_key_values[i] if past_key_values is not None else None\n\n            if self.gradient_checkpointing and self.training:\n\n                if use_cache:\n                    logger.warn(\n                        \"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\"\n                    )\n                    use_cache = False\n\n                def create_custom_forward(module):\n                    def custom_forward(*inputs):\n                        return module(*inputs, past_key_value, output_attentions)\n\n                    return custom_forward\n\n                layer_outputs = torch.utils.checkpoint.checkpoint(\n                    create_custom_forward(layer_module),\n                    hidden_states,\n                    attention_mask,\n                    layer_head_mask,\n                    encoder_hidden_states,\n                    encoder_attention_mask,\n                    mode=mode,\n                )\n            else:\n                layer_outputs = layer_module(\n                    hidden_states,\n                    attention_mask,\n                    layer_head_mask,\n                    encoder_hidden_states,\n                    encoder_attention_mask,\n                    past_key_value,\n                    output_attentions,\n                    mode=mode,\n                )\n\n            hidden_states = layer_outputs[0]\n            if use_cache:\n                next_decoder_cache += (layer_outputs[-1],)\n            if output_attentions:\n                all_self_attentions = all_self_attentions + (layer_outputs[1],)\n\n        if output_hidden_states:\n            all_hidden_states = all_hidden_states + (hidden_states,)\n\n        if not return_dict:\n            return tuple(\n                v\n                for v in [\n                    hidden_states,\n                    next_decoder_cache,\n                    all_hidden_states,\n                    all_self_attentions,\n                    all_cross_attentions,\n                ]\n                if v is not None\n            )\n        return BaseModelOutputWithPastAndCrossAttentions(\n            last_hidden_state=hidden_states,\n            past_key_values=next_decoder_cache,\n            hidden_states=all_hidden_states,\n            attentions=all_self_attentions,\n            cross_attentions=all_cross_attentions,\n        )\n\n\nclass BertPooler(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.dense = nn.Linear(config.hidden_size, config.hidden_size)\n        self.activation = nn.Tanh()\n\n    def forward(self, hidden_states):\n        # We \"pool\" the model by simply taking the hidden state corresponding\n        # to the first token.\n        first_token_tensor = hidden_states[:, 0]\n        pooled_output = self.dense(first_token_tensor)\n        pooled_output = self.activation(pooled_output)\n        return pooled_output\n\n\nclass BertPredictionHeadTransform(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.dense = nn.Linear(config.hidden_size, config.hidden_size)\n        if isinstance(config.hidden_act, str):\n            self.transform_act_fn = ACT2FN[config.hidden_act]\n        else:\n            self.transform_act_fn = config.hidden_act\n        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)\n\n    def forward(self, hidden_states):\n        hidden_states = self.dense(hidden_states)\n        hidden_states = self.transform_act_fn(hidden_states)\n        hidden_states = self.LayerNorm(hidden_states)\n        return hidden_states\n\n\nclass BertLMPredictionHead(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n\n\nclass BertOnlyMLMHead(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.predictions = BertLMPredictionHead(config)\n\n    def forward(self, sequence_output):\n        prediction_scores = self.predictions(sequence_output)\n        return prediction_scores\n\n\nclass BertPreTrainedModel(PreTrainedModel):\n    \"\"\"\n    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained\n    models.\n    \"\"\"\n\n    config_class = BertConfig\n    base_model_prefix = \"bert\"\n    _keys_to_ignore_on_load_missing = [r\"position_ids\"]\n\n    def _init_weights(self, module):\n        \"\"\" Initialize the weights \"\"\"\n        if isinstance(module, (nn.Linear, nn.Embedding)):\n            # Slightly different from the TF version which uses truncated_normal for initialization\n            # cf https://github.com/pytorch/pytorch/pull/5617\n            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)\n        elif isinstance(module, nn.LayerNorm):\n            module.bias.data.zero_()\n            module.weight.data.fill_(1.0)\n        if isinstance(module, nn.Linear) and module.bias is not None:\n            module.bias.data.zero_()\n\n\nclass BertModel(BertPreTrainedModel):\n    \"\"\"\n    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of\n    cross-attention is added between the self-attention layers, following the architecture described in `Attention is\n    all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,\n    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.\n    argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an\n    input to the forward pass.\n    \"\"\"\n\n    def __init__(self, config, add_pooling_layer=True):\n        super().__init__(config)\n        self.config = config\n\n        self.embeddings = BertEmbeddings(config)\n        \n        self.encoder = BertEncoder(config)\n\n        self.pooler = BertPooler(config) if add_pooling_layer else None\n\n        self.init_weights()\n \n\n    def get_input_embeddings(self):\n        return self.embeddings.word_embeddings\n\n    def set_input_embeddings(self, value):\n        self.embeddings.word_embeddings = value\n\n    def _prune_heads(self, heads_to_prune):\n        \"\"\"\n        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base\n        class PreTrainedModel\n        \"\"\"\n        for layer, heads in heads_to_prune.items():\n            self.encoder.layer[layer].attention.prune_heads(heads)\n\n    \n    def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device, is_decoder: bool) -> Tensor:\n        \"\"\"\n        Makes broadcastable attention and causal masks so that future and masked tokens are ignored.\n\n        Arguments:\n            attention_mask (:obj:`torch.Tensor`):\n                Mask with ones indicating tokens to attend to, zeros for tokens to ignore.\n            input_shape (:obj:`Tuple[int]`):\n                The shape of the input to the model.\n            device: (:obj:`torch.device`):\n                The device of the input to the model.\n\n        Returns:\n            :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`.\n        \"\"\"\n        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]\n        # ourselves in which case we just need to make it broadcastable to all heads.\n        if attention_mask.dim() == 3:\n            extended_attention_mask = attention_mask[:, None, :, :]\n        elif attention_mask.dim() == 2:\n            # Provided a padding mask of dimensions [batch_size, seq_length]\n            # - if the model is a decoder, apply a causal mask in addition to the padding mask\n            # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]\n            if is_decoder:\n                batch_size, seq_length = input_shape\n\n                seq_ids = torch.arange(seq_length, device=device)\n                causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]\n                # in case past_key_values are used we need to add a prefix ones mask to the causal mask\n                # causal and attention masks must have same type with pytorch version < 1.3\n                causal_mask = causal_mask.to(attention_mask.dtype)\n   \n                if causal_mask.shape[1] < attention_mask.shape[1]:\n                    prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1]\n                    causal_mask = torch.cat(\n                        [\n                            torch.ones((batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype),\n                            causal_mask,\n                        ],\n                        axis=-1,\n                    )                     \n\n                extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]\n            else:\n                extended_attention_mask = attention_mask[:, None, None, :]\n        else:\n            raise ValueError(\n                \"Wrong shape for input_ids (shape {}) or attention_mask (shape {})\".format(\n                    input_shape, attention_mask.shape\n                )\n            )\n\n        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for\n        # masked positions, this operation will create a tensor which is 0.0 for\n        # positions we want to attend and -10000.0 for masked positions.\n        # Since we are adding it to the raw scores before the softmax, this is\n        # effectively the same as removing these entirely.\n        extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility\n        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0\n        return extended_attention_mask\n    \n    def forward(\n        self,\n        input_ids=None,\n        attention_mask=None,\n        position_ids=None,\n        head_mask=None,\n        inputs_embeds=None,\n        encoder_embeds=None,\n        encoder_hidden_states=None,\n        encoder_attention_mask=None,\n        past_key_values=None,\n        use_cache=None,\n        output_attentions=None,\n        output_hidden_states=None,\n        return_dict=None,\n        is_decoder=False,\n        mode='multimodal',\n    ):\n        r\"\"\"\n        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):\n            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if\n            the model is configured as a decoder.\n        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):\n            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in\n            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:\n            - 1 for tokens that are **not masked**,\n            - 0 for tokens that are **masked**.\n        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):\n            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.\n            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`\n            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`\n            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.\n        use_cache (:obj:`bool`, `optional`):\n            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up\n            decoding (see :obj:`past_key_values`).\n        \"\"\"\n        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n        output_hidden_states = (\n            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n        )\n        return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n        if is_decoder:\n            use_cache = use_cache if use_cache is not None else self.config.use_cache\n        else:\n            use_cache = False\n\n        if input_ids is not None and inputs_embeds is not None:\n            raise ValueError(\"You cannot specify both input_ids and inputs_embeds at the same time\")\n        elif input_ids is not None:\n            input_shape = input_ids.size()\n            batch_size, seq_length = input_shape\n            device = input_ids.device\n        elif inputs_embeds is not None:\n            input_shape = inputs_embeds.size()[:-1]\n            batch_size, seq_length = input_shape\n            device = inputs_embeds.device\n        elif encoder_embeds is not None:    \n            input_shape = encoder_embeds.size()[:-1]\n            batch_size, seq_length = input_shape \n            device = encoder_embeds.device\n        else:\n            raise ValueError(\"You have to specify either input_ids or inputs_embeds or encoder_embeds\")\n\n        # past_key_values_length\n        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0\n\n        if attention_mask is None:\n            attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)\n            \n        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]\n        # ourselves in which case we just need to make it broadcastable to all heads.\n        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, \n                                                                                 device, is_decoder)\n\n        # If a 2D or 3D attention mask is provided for the cross-attention\n        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]\n        if encoder_hidden_states is not None:\n            if type(encoder_hidden_states) == list:\n                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size()\n            else:\n                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()\n            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)\n            \n            if type(encoder_attention_mask) == list:\n                encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask]\n            elif encoder_attention_mask is None:\n                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)\n                encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)\n            else:    \n                encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)\n        else:\n            encoder_extended_attention_mask = None\n\n        # Prepare head mask if needed\n        # 1.0 in head_mask indicate we keep the head\n        # attention_probs has shape bsz x n_heads x N x N\n        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]\n        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]\n        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)\n        \n        if encoder_embeds is None:\n            embedding_output = self.embeddings(\n                input_ids=input_ids,\n                position_ids=position_ids,\n                inputs_embeds=inputs_embeds,\n                past_key_values_length=past_key_values_length,\n            )\n        else:\n            embedding_output = encoder_embeds\n            \n        encoder_outputs = self.encoder(\n            embedding_output,\n            attention_mask=extended_attention_mask,\n            head_mask=head_mask,\n            encoder_hidden_states=encoder_hidden_states,\n            encoder_attention_mask=encoder_extended_attention_mask,\n            past_key_values=past_key_values,\n            use_cache=use_cache,\n            output_attentions=output_attentions,\n            output_hidden_states=output_hidden_states,\n            return_dict=return_dict,\n            mode=mode,\n        )\n        sequence_output = encoder_outputs[0]\n        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None\n\n        if not return_dict:\n            return (sequence_output, pooled_output) + encoder_outputs[1:]\n\n        return BaseModelOutputWithPoolingAndCrossAttentions(\n            last_hidden_state=sequence_output,\n            pooler_output=pooled_output,\n            past_key_values=encoder_outputs.past_key_values,\n            hidden_states=encoder_outputs.hidden_states,\n            attentions=encoder_outputs.attentions,\n            cross_attentions=encoder_outputs.cross_attentions,\n        )\n\n\n\nclass BertLMHeadModel(BertPreTrainedModel):\n\n    _keys_to_ignore_on_load_unexpected = [r\"pooler\"]\n    _keys_to_ignore_on_load_missing = [r\"position_ids\", r\"predictions.decoder.bias\"]\n\n    def __init__(self, config):\n        super().__init__(config)\n\n        self.bert = BertModel(config, add_pooling_layer=False)\n        self.cls = BertOnlyMLMHead(config)\n\n        self.init_weights()\n\n    def get_output_embeddings(self):\n        return self.cls.predictions.decoder\n\n    def set_output_embeddings(self, new_embeddings):\n        self.cls.predictions.decoder = new_embeddings\n\n    def forward(\n        self,\n        input_ids=None,\n        attention_mask=None,\n        position_ids=None,\n        head_mask=None,\n        inputs_embeds=None,\n        encoder_hidden_states=None,\n        encoder_attention_mask=None,\n        labels=None,\n        past_key_values=None,\n        use_cache=None,\n        output_attentions=None,\n        output_hidden_states=None,\n        return_dict=None,\n        return_logits=False,            \n        is_decoder=True,\n        reduction='mean',\n        mode='multimodal', \n    ):\n        r\"\"\"\n        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):\n            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if\n            the model is configured as a decoder.\n        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):\n            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in\n            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:\n            - 1 for tokens that are **not masked**,\n            - 0 for tokens that are **masked**.\n        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):\n            Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in\n            ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are\n            ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]``\n        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):\n            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.\n            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`\n            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`\n            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.\n        use_cache (:obj:`bool`, `optional`):\n            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up\n            decoding (see :obj:`past_key_values`).\n        Returns:\n        Example::\n            >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig\n            >>> import torch\n            >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased')\n            >>> config = BertConfig.from_pretrained(\"bert-base-cased\")\n            >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config)\n            >>> inputs = tokenizer(\"Hello, my dog is cute\", return_tensors=\"pt\")\n            >>> outputs = model(**inputs)\n            >>> prediction_logits = outputs.logits\n        \"\"\"\n        return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n        if labels is not None:\n            use_cache = False\n\n        outputs = self.bert(\n            input_ids,\n            attention_mask=attention_mask,\n            position_ids=position_ids,\n            head_mask=head_mask,\n            inputs_embeds=inputs_embeds,\n            encoder_hidden_states=encoder_hidden_states,\n            encoder_attention_mask=encoder_attention_mask,\n            past_key_values=past_key_values,\n            use_cache=use_cache,\n            output_attentions=output_attentions,\n            output_hidden_states=output_hidden_states,\n            return_dict=return_dict,\n            is_decoder=is_decoder,\n            mode=mode,\n        )\n        \n        sequence_output = outputs[0]\n        prediction_scores = self.cls(sequence_output)\n        \n        if return_logits:\n            return prediction_scores[:, :-1, :].contiguous()  \n\n        lm_loss = None\n        if labels is not None:\n            # we are doing next-token prediction; shift prediction scores and input ids by one\n            shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()\n            labels = labels[:, 1:].contiguous()\n            loss_fct = CrossEntropyLoss(reduction=reduction, label_smoothing=0.1) \n            lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))\n            if reduction=='none':\n                lm_loss = lm_loss.view(prediction_scores.size(0),-1).sum(1)               \n\n        if not return_dict:\n            output = (prediction_scores,) + outputs[2:]\n            return ((lm_loss,) + output) if lm_loss is not None else output\n\n        return CausalLMOutputWithCrossAttentions(\n            loss=lm_loss,\n            logits=prediction_scores,\n            past_key_values=outputs.past_key_values,\n            hidden_states=outputs.hidden_states,\n            attentions=outputs.attentions,\n            cross_attentions=outputs.cross_attentions,\n        )\n\n    def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs):\n        input_shape = input_ids.shape\n        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly\n        if attention_mask is None:\n            attention_mask = input_ids.new_ones(input_shape)\n\n        # cut decoder_input_ids if past is used\n        if past is not None:\n            input_ids = input_ids[:, -1:]\n\n        return {\n            \"input_ids\": input_ids, \n            \"attention_mask\": attention_mask, \n            \"past_key_values\": past,\n            \"encoder_hidden_states\": model_kwargs.get(\"encoder_hidden_states\", None),\n            \"encoder_attention_mask\": model_kwargs.get(\"encoder_attention_mask\", None),\n            \"is_decoder\": True,\n        }\n\n    def _reorder_cache(self, past, beam_idx):\n        reordered_past = ()\n        for layer_past in past:\n            reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)\n        return reordered_past\n"
  },
  {
    "path": "extras/BLIP/models/nlvr_encoder.py",
    "content": "import math\nimport os\nimport warnings\nfrom dataclasses import dataclass\nfrom typing import Optional, Tuple\n\nimport torch\nfrom torch import Tensor, device, dtype, nn\nimport torch.utils.checkpoint\nfrom torch import nn\nfrom torch.nn import CrossEntropyLoss\nimport torch.nn.functional as F\n\nfrom transformers.activations import ACT2FN\nfrom transformers.file_utils import (\n    ModelOutput,\n)\nfrom transformers.modeling_outputs import (\n    BaseModelOutputWithPastAndCrossAttentions,\n    BaseModelOutputWithPoolingAndCrossAttentions,\n    CausalLMOutputWithCrossAttentions,\n    MaskedLMOutput,\n    MultipleChoiceModelOutput,\n    NextSentencePredictorOutput,\n    QuestionAnsweringModelOutput,\n    SequenceClassifierOutput,\n    TokenClassifierOutput,\n)\nfrom transformers.modeling_utils import (\n    PreTrainedModel,\n    apply_chunking_to_forward,\n    find_pruneable_heads_and_indices,\n    prune_linear_layer,\n)\nfrom transformers.utils import logging\nfrom transformers.models.bert.configuration_bert import BertConfig\n\n\nlogger = logging.get_logger(__name__)\n\n\nclass BertEmbeddings(nn.Module):\n    \"\"\"Construct the embeddings from word and position embeddings.\"\"\"\n\n    def __init__(self, config):\n        super().__init__()\n        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)\n        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)\n\n        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load\n        # any TensorFlow checkpoint file\n        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)\n        self.dropout = nn.Dropout(config.hidden_dropout_prob)\n\n        # position_ids (1, len position emb) is contiguous in memory and exported when serialized\n        self.register_buffer(\"position_ids\", torch.arange(config.max_position_embeddings).expand((1, -1)))\n        self.position_embedding_type = getattr(config, \"position_embedding_type\", \"absolute\")\n        \n        self.config = config\n\n    def forward(\n        self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0\n    ):\n        if input_ids is not None:\n            input_shape = input_ids.size()\n        else:\n            input_shape = inputs_embeds.size()[:-1]\n\n        seq_length = input_shape[1]\n\n        if position_ids is None:\n            position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]\n\n        if inputs_embeds is None:\n            inputs_embeds = self.word_embeddings(input_ids)\n\n        embeddings = inputs_embeds\n\n        if self.position_embedding_type == \"absolute\":\n            position_embeddings = self.position_embeddings(position_ids)\n            embeddings += position_embeddings\n        embeddings = self.LayerNorm(embeddings)\n        embeddings = self.dropout(embeddings)\n        return embeddings\n\n\nclass BertSelfAttention(nn.Module):\n    def __init__(self, config, is_cross_attention):\n        super().__init__()\n        self.config = config\n        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, \"embedding_size\"):\n            raise ValueError(\n                \"The hidden size (%d) is not a multiple of the number of attention \"\n                \"heads (%d)\" % (config.hidden_size, config.num_attention_heads)\n            )\n        \n        self.num_attention_heads = config.num_attention_heads\n        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)\n        self.all_head_size = self.num_attention_heads * self.attention_head_size\n\n        self.query = nn.Linear(config.hidden_size, self.all_head_size)\n        if is_cross_attention:\n            self.key = nn.Linear(config.encoder_width, self.all_head_size)\n            self.value = nn.Linear(config.encoder_width, self.all_head_size)\n        else:\n            self.key = nn.Linear(config.hidden_size, self.all_head_size)\n            self.value = nn.Linear(config.hidden_size, self.all_head_size)\n\n        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)\n        self.position_embedding_type = getattr(config, \"position_embedding_type\", \"absolute\")\n        if self.position_embedding_type == \"relative_key\" or self.position_embedding_type == \"relative_key_query\":\n            self.max_position_embeddings = config.max_position_embeddings\n            self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)\n        self.save_attention = False   \n            \n    def save_attn_gradients(self, attn_gradients):\n        self.attn_gradients = attn_gradients\n        \n    def get_attn_gradients(self):\n        return self.attn_gradients\n    \n    def save_attention_map(self, attention_map):\n        self.attention_map = attention_map\n        \n    def get_attention_map(self):\n        return self.attention_map\n    \n    def transpose_for_scores(self, x):\n        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)\n        x = x.view(*new_x_shape)\n        return x.permute(0, 2, 1, 3)\n\n    def forward(\n        self,\n        hidden_states,\n        attention_mask=None,\n        head_mask=None,\n        encoder_hidden_states=None,\n        encoder_attention_mask=None,\n        past_key_value=None,\n        output_attentions=False,\n    ):\n        mixed_query_layer = self.query(hidden_states)\n\n        # If this is instantiated as a cross-attention module, the keys\n        # and values come from an encoder; the attention mask needs to be\n        # such that the encoder's padding tokens are not attended to.\n        is_cross_attention = encoder_hidden_states is not None\n\n        if is_cross_attention:\n            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))\n            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))\n            attention_mask = encoder_attention_mask\n        elif past_key_value is not None:\n            key_layer = self.transpose_for_scores(self.key(hidden_states))\n            value_layer = self.transpose_for_scores(self.value(hidden_states))\n            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)\n            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)\n        else:\n            key_layer = self.transpose_for_scores(self.key(hidden_states))\n            value_layer = self.transpose_for_scores(self.value(hidden_states))\n\n        query_layer = self.transpose_for_scores(mixed_query_layer)\n\n        past_key_value = (key_layer, value_layer)\n\n        # Take the dot product between \"query\" and \"key\" to get the raw attention scores.\n        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))\n\n        if self.position_embedding_type == \"relative_key\" or self.position_embedding_type == \"relative_key_query\":\n            seq_length = hidden_states.size()[1]\n            position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)\n            position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)\n            distance = position_ids_l - position_ids_r\n            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)\n            positional_embedding = positional_embedding.to(dtype=query_layer.dtype)  # fp16 compatibility\n\n            if self.position_embedding_type == \"relative_key\":\n                relative_position_scores = torch.einsum(\"bhld,lrd->bhlr\", query_layer, positional_embedding)\n                attention_scores = attention_scores + relative_position_scores\n            elif self.position_embedding_type == \"relative_key_query\":\n                relative_position_scores_query = torch.einsum(\"bhld,lrd->bhlr\", query_layer, positional_embedding)\n                relative_position_scores_key = torch.einsum(\"bhrd,lrd->bhlr\", key_layer, positional_embedding)\n                attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key\n\n        attention_scores = attention_scores / math.sqrt(self.attention_head_size)\n        if attention_mask is not None:\n            # Apply the attention mask is (precomputed for all layers in BertModel forward() function)\n            attention_scores = attention_scores + attention_mask\n\n        # Normalize the attention scores to probabilities.\n        attention_probs = nn.Softmax(dim=-1)(attention_scores)\n        \n        if is_cross_attention and self.save_attention:\n            self.save_attention_map(attention_probs)\n            attention_probs.register_hook(self.save_attn_gradients)         \n\n        # This is actually dropping out entire tokens to attend to, which might\n        # seem a bit unusual, but is taken from the original Transformer paper.\n        attention_probs_dropped = self.dropout(attention_probs)\n\n        # Mask heads if we want to\n        if head_mask is not None:\n            attention_probs_dropped = attention_probs_dropped * head_mask\n\n        context_layer = torch.matmul(attention_probs_dropped, value_layer)\n\n        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()\n        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)\n        context_layer = context_layer.view(*new_context_layer_shape)\n\n        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)\n\n        outputs = outputs + (past_key_value,)\n        return outputs\n\n\nclass BertSelfOutput(nn.Module):\n    def __init__(self, config, twin=False, merge=False):     \n        super().__init__()\n        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)\n        self.dropout = nn.Dropout(config.hidden_dropout_prob)        \n        if twin:\n            self.dense0 = nn.Linear(config.hidden_size, config.hidden_size)\n            self.dense1 = nn.Linear(config.hidden_size, config.hidden_size)         \n        else:\n            self.dense = nn.Linear(config.hidden_size, config.hidden_size)\n        if merge:\n            self.act =  ACT2FN[config.hidden_act]\n            self.merge_layer = nn.Linear(config.hidden_size * 2, config.hidden_size)\n            self.merge = True\n        else:\n            self.merge = False\n\n    def forward(self, hidden_states, input_tensor):\n        if type(hidden_states) == list:\n            hidden_states0 = self.dense0(hidden_states[0])\n            hidden_states1 = self.dense1(hidden_states[1])        \n            if self.merge:  \n                #hidden_states = self.merge_layer(self.act(torch.cat([hidden_states0,hidden_states1],dim=-1)))\n                hidden_states = self.merge_layer(torch.cat([hidden_states0,hidden_states1],dim=-1))\n            else:\n                hidden_states = (hidden_states0+hidden_states1)/2\n        else:    \n            hidden_states = self.dense(hidden_states)\n        hidden_states = self.dropout(hidden_states)\n        hidden_states = self.LayerNorm(hidden_states + input_tensor)\n        return hidden_states\n\n\nclass BertAttention(nn.Module):\n    def __init__(self, config, is_cross_attention=False, layer_num=-1):\n        super().__init__()\n        if is_cross_attention:\n            self.self0 = BertSelfAttention(config, is_cross_attention)\n            self.self1 = BertSelfAttention(config, is_cross_attention)\n        else:    \n            self.self = BertSelfAttention(config, is_cross_attention)\n        self.output = BertSelfOutput(config, twin=is_cross_attention, merge=(is_cross_attention and layer_num>=6))\n        self.pruned_heads = set()\n\n    def prune_heads(self, heads):\n        if len(heads) == 0:\n            return\n        heads, index = find_pruneable_heads_and_indices(\n            heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads\n        )\n\n        # Prune linear layers\n        self.self.query = prune_linear_layer(self.self.query, index)\n        self.self.key = prune_linear_layer(self.self.key, index)\n        self.self.value = prune_linear_layer(self.self.value, index)\n        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)\n\n        # Update hyper params and store pruned heads\n        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)\n        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads\n        self.pruned_heads = self.pruned_heads.union(heads)\n\n    def forward(\n        self,\n        hidden_states,\n        attention_mask=None,\n        head_mask=None,\n        encoder_hidden_states=None,\n        encoder_attention_mask=None,\n        past_key_value=None,\n        output_attentions=False,\n    ):        \n        if type(encoder_hidden_states)==list:   \n            self_outputs0 = self.self0(\n                hidden_states,\n                attention_mask,\n                head_mask,\n                encoder_hidden_states[0],\n                encoder_attention_mask[0],\n                past_key_value,\n                output_attentions,\n            )\n            self_outputs1 = self.self1(\n                hidden_states,\n                attention_mask,\n                head_mask,\n                encoder_hidden_states[1],\n                encoder_attention_mask[1],\n                past_key_value,\n                output_attentions,\n            )                        \n            attention_output = self.output([self_outputs0[0],self_outputs1[0]], hidden_states)\n    \n            outputs = (attention_output,) + self_outputs0[1:]  # add attentions if we output them\n        else:        \n            self_outputs = self.self(\n                hidden_states,\n                attention_mask,\n                head_mask,\n                encoder_hidden_states,\n                encoder_attention_mask,\n                past_key_value,\n                output_attentions,\n            )\n            attention_output = self.output(self_outputs[0], hidden_states)\n            outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them\n        return outputs\n\n\nclass BertIntermediate(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)\n        if isinstance(config.hidden_act, str):\n            self.intermediate_act_fn = ACT2FN[config.hidden_act]\n        else:\n            self.intermediate_act_fn = config.hidden_act\n\n    def forward(self, hidden_states):\n        hidden_states = self.dense(hidden_states)\n        hidden_states = self.intermediate_act_fn(hidden_states)\n        return hidden_states\n\n\nclass BertOutput(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)\n        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)\n        self.dropout = nn.Dropout(config.hidden_dropout_prob)\n\n    def forward(self, hidden_states, input_tensor):\n        hidden_states = self.dense(hidden_states)\n        hidden_states = self.dropout(hidden_states)\n        hidden_states = self.LayerNorm(hidden_states + input_tensor)\n        return hidden_states\n\n\nclass BertLayer(nn.Module):\n    def __init__(self, config, layer_num):\n        super().__init__()\n        self.config = config\n        self.chunk_size_feed_forward = config.chunk_size_feed_forward\n        self.seq_len_dim = 1\n        self.attention = BertAttention(config)      \n        self.layer_num = layer_num          \n        if self.config.add_cross_attention:\n            self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention, layer_num=layer_num)\n        self.intermediate = BertIntermediate(config)\n        self.output = BertOutput(config)\n\n    def forward(\n        self,\n        hidden_states,\n        attention_mask=None,\n        head_mask=None,\n        encoder_hidden_states=None,\n        encoder_attention_mask=None,\n        past_key_value=None,\n        output_attentions=False,\n        mode=None,\n    ):\n        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2\n        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None\n        self_attention_outputs = self.attention(\n            hidden_states,\n            attention_mask,\n            head_mask,\n            output_attentions=output_attentions,\n            past_key_value=self_attn_past_key_value,\n        )\n        attention_output = self_attention_outputs[0]\n\n        outputs = self_attention_outputs[1:-1]\n        present_key_value = self_attention_outputs[-1]\n\n        if mode=='multimodal':\n            assert encoder_hidden_states is not None, \"encoder_hidden_states must be given for cross-attention layers\"\n            cross_attention_outputs = self.crossattention(\n                attention_output,\n                attention_mask,\n                head_mask,\n                encoder_hidden_states,\n                encoder_attention_mask,\n                output_attentions=output_attentions,\n            )\n            attention_output = cross_attention_outputs[0]\n            outputs = outputs + cross_attention_outputs[1:-1]  # add cross attentions if we output attention weights                               \n        layer_output = apply_chunking_to_forward(\n            self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output\n        )\n        outputs = (layer_output,) + outputs\n\n        outputs = outputs + (present_key_value,)\n\n        return outputs\n\n    def feed_forward_chunk(self, attention_output):\n        intermediate_output = self.intermediate(attention_output)\n        layer_output = self.output(intermediate_output, attention_output)\n        return layer_output\n\n\nclass BertEncoder(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.config = config\n        self.layer = nn.ModuleList([BertLayer(config,i) for i in range(config.num_hidden_layers)])\n        self.gradient_checkpointing = False\n\n    def forward(\n        self,\n        hidden_states,\n        attention_mask=None,\n        head_mask=None,\n        encoder_hidden_states=None,\n        encoder_attention_mask=None,\n        past_key_values=None,\n        use_cache=None,\n        output_attentions=False,\n        output_hidden_states=False,\n        return_dict=True,\n        mode='multimodal',\n    ):\n        all_hidden_states = () if output_hidden_states else None\n        all_self_attentions = () if output_attentions else None\n        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None\n\n        next_decoder_cache = () if use_cache else None\n               \n        for i in range(self.config.num_hidden_layers):\n            layer_module = self.layer[i]\n            if output_hidden_states:\n                all_hidden_states = all_hidden_states + (hidden_states,)\n\n            layer_head_mask = head_mask[i] if head_mask is not None else None\n            past_key_value = past_key_values[i] if past_key_values is not None else None\n\n            if self.gradient_checkpointing and self.training:\n\n                if use_cache:\n                    logger.warn(\n                        \"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\"\n                    )\n                    use_cache = False\n\n                def create_custom_forward(module):\n                    def custom_forward(*inputs):\n                        return module(*inputs, past_key_value, output_attentions)\n\n                    return custom_forward\n\n                layer_outputs = torch.utils.checkpoint.checkpoint(\n                    create_custom_forward(layer_module),\n                    hidden_states,\n                    attention_mask,\n                    layer_head_mask,\n                    encoder_hidden_states,\n                    encoder_attention_mask,\n                    mode=mode,\n                )\n            else:\n                layer_outputs = layer_module(\n                    hidden_states,\n                    attention_mask,\n                    layer_head_mask,\n                    encoder_hidden_states,\n                    encoder_attention_mask,\n                    past_key_value,\n                    output_attentions,\n                    mode=mode,\n                )\n\n            hidden_states = layer_outputs[0]\n            if use_cache:\n                next_decoder_cache += (layer_outputs[-1],)\n            if output_attentions:\n                all_self_attentions = all_self_attentions + (layer_outputs[1],)\n\n        if output_hidden_states:\n            all_hidden_states = all_hidden_states + (hidden_states,)\n\n        if not return_dict:\n            return tuple(\n                v\n                for v in [\n                    hidden_states,\n                    next_decoder_cache,\n                    all_hidden_states,\n                    all_self_attentions,\n                    all_cross_attentions,\n                ]\n                if v is not None\n            )\n        return BaseModelOutputWithPastAndCrossAttentions(\n            last_hidden_state=hidden_states,\n            past_key_values=next_decoder_cache,\n            hidden_states=all_hidden_states,\n            attentions=all_self_attentions,\n            cross_attentions=all_cross_attentions,\n        )\n\n\nclass BertPooler(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.dense = nn.Linear(config.hidden_size, config.hidden_size)\n        self.activation = nn.Tanh()\n\n    def forward(self, hidden_states):\n        # We \"pool\" the model by simply taking the hidden state corresponding\n        # to the first token.\n        first_token_tensor = hidden_states[:, 0]\n        pooled_output = self.dense(first_token_tensor)\n        pooled_output = self.activation(pooled_output)\n        return pooled_output\n\n\nclass BertPredictionHeadTransform(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.dense = nn.Linear(config.hidden_size, config.hidden_size)\n        if isinstance(config.hidden_act, str):\n            self.transform_act_fn = ACT2FN[config.hidden_act]\n        else:\n            self.transform_act_fn = config.hidden_act\n        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)\n\n    def forward(self, hidden_states):\n        hidden_states = self.dense(hidden_states)\n        hidden_states = self.transform_act_fn(hidden_states)\n        hidden_states = self.LayerNorm(hidden_states)\n        return hidden_states\n\n\nclass BertLMPredictionHead(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n\n\nclass BertOnlyMLMHead(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.predictions = BertLMPredictionHead(config)\n\n    def forward(self, sequence_output):\n        prediction_scores = self.predictions(sequence_output)\n        return prediction_scores\n\n\nclass BertPreTrainedModel(PreTrainedModel):\n    \"\"\"\n    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained\n    models.\n    \"\"\"\n\n    config_class = BertConfig\n    base_model_prefix = \"bert\"\n    _keys_to_ignore_on_load_missing = [r\"position_ids\"]\n\n    def _init_weights(self, module):\n        \"\"\" Initialize the weights \"\"\"\n        if isinstance(module, (nn.Linear, nn.Embedding)):\n            # Slightly different from the TF version which uses truncated_normal for initialization\n            # cf https://github.com/pytorch/pytorch/pull/5617\n            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)\n        elif isinstance(module, nn.LayerNorm):\n            module.bias.data.zero_()\n            module.weight.data.fill_(1.0)\n        if isinstance(module, nn.Linear) and module.bias is not None:\n            module.bias.data.zero_()\n\n\nclass BertModel(BertPreTrainedModel):\n    \"\"\"\n    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of\n    cross-attention is added between the self-attention layers, following the architecture described in `Attention is\n    all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,\n    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.\n    argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an\n    input to the forward pass.\n    \"\"\"\n\n    def __init__(self, config, add_pooling_layer=True):\n        super().__init__(config)\n        self.config = config\n\n        self.embeddings = BertEmbeddings(config)\n        \n        self.encoder = BertEncoder(config)\n\n        self.pooler = BertPooler(config) if add_pooling_layer else None\n\n        self.init_weights()\n \n\n    def get_input_embeddings(self):\n        return self.embeddings.word_embeddings\n\n    def set_input_embeddings(self, value):\n        self.embeddings.word_embeddings = value\n\n    def _prune_heads(self, heads_to_prune):\n        \"\"\"\n        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base\n        class PreTrainedModel\n        \"\"\"\n        for layer, heads in heads_to_prune.items():\n            self.encoder.layer[layer].attention.prune_heads(heads)\n\n    \n    def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device, is_decoder: bool) -> Tensor:\n        \"\"\"\n        Makes broadcastable attention and causal masks so that future and masked tokens are ignored.\n\n        Arguments:\n            attention_mask (:obj:`torch.Tensor`):\n                Mask with ones indicating tokens to attend to, zeros for tokens to ignore.\n            input_shape (:obj:`Tuple[int]`):\n                The shape of the input to the model.\n            device: (:obj:`torch.device`):\n                The device of the input to the model.\n\n        Returns:\n            :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`.\n        \"\"\"\n        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]\n        # ourselves in which case we just need to make it broadcastable to all heads.\n        if attention_mask.dim() == 3:\n            extended_attention_mask = attention_mask[:, None, :, :]\n        elif attention_mask.dim() == 2:\n            # Provided a padding mask of dimensions [batch_size, seq_length]\n            # - if the model is a decoder, apply a causal mask in addition to the padding mask\n            # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]\n            if is_decoder:\n                batch_size, seq_length = input_shape\n\n                seq_ids = torch.arange(seq_length, device=device)\n                causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]\n                # in case past_key_values are used we need to add a prefix ones mask to the causal mask\n                # causal and attention masks must have same type with pytorch version < 1.3\n                causal_mask = causal_mask.to(attention_mask.dtype)\n   \n                if causal_mask.shape[1] < attention_mask.shape[1]:\n                    prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1]\n                    causal_mask = torch.cat(\n                        [\n                            torch.ones((batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype),\n                            causal_mask,\n                        ],\n                        axis=-1,\n                    )                     \n\n                extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]\n            else:\n                extended_attention_mask = attention_mask[:, None, None, :]\n        else:\n            raise ValueError(\n                \"Wrong shape for input_ids (shape {}) or attention_mask (shape {})\".format(\n                    input_shape, attention_mask.shape\n                )\n            )\n\n        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for\n        # masked positions, this operation will create a tensor which is 0.0 for\n        # positions we want to attend and -10000.0 for masked positions.\n        # Since we are adding it to the raw scores before the softmax, this is\n        # effectively the same as removing these entirely.\n        extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility\n        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0\n        return extended_attention_mask\n    \n    def forward(\n        self,\n        input_ids=None,\n        attention_mask=None,\n        position_ids=None,\n        head_mask=None,\n        inputs_embeds=None,\n        encoder_embeds=None,\n        encoder_hidden_states=None,\n        encoder_attention_mask=None,\n        past_key_values=None,\n        use_cache=None,\n        output_attentions=None,\n        output_hidden_states=None,\n        return_dict=None,\n        is_decoder=False,\n        mode='multimodal',\n    ):\n        r\"\"\"\n        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):\n            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if\n            the model is configured as a decoder.\n        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):\n            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in\n            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:\n            - 1 for tokens that are **not masked**,\n            - 0 for tokens that are **masked**.\n        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):\n            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.\n            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`\n            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`\n            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.\n        use_cache (:obj:`bool`, `optional`):\n            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up\n            decoding (see :obj:`past_key_values`).\n        \"\"\"\n        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n        output_hidden_states = (\n            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n        )\n        return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n        if is_decoder:\n            use_cache = use_cache if use_cache is not None else self.config.use_cache\n        else:\n            use_cache = False\n\n        if input_ids is not None and inputs_embeds is not None:\n            raise ValueError(\"You cannot specify both input_ids and inputs_embeds at the same time\")\n        elif input_ids is not None:\n            input_shape = input_ids.size()\n            batch_size, seq_length = input_shape\n            device = input_ids.device\n        elif inputs_embeds is not None:\n            input_shape = inputs_embeds.size()[:-1]\n            batch_size, seq_length = input_shape\n            device = inputs_embeds.device\n        elif encoder_embeds is not None:    \n            input_shape = encoder_embeds.size()[:-1]\n            batch_size, seq_length = input_shape \n            device = encoder_embeds.device\n        else:\n            raise ValueError(\"You have to specify either input_ids or inputs_embeds or encoder_embeds\")\n\n        # past_key_values_length\n        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0\n\n        if attention_mask is None:\n            attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)\n            \n        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]\n        # ourselves in which case we just need to make it broadcastable to all heads.\n        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, \n                                                                                 device, is_decoder)\n\n        # If a 2D or 3D attention mask is provided for the cross-attention\n        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]\n        if encoder_hidden_states is not None:\n            if type(encoder_hidden_states) == list:\n                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size()\n            else:\n                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()\n            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)\n            \n            if type(encoder_attention_mask) == list:\n                encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask]\n            elif encoder_attention_mask is None:\n                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)\n                encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)\n            else:    \n                encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)\n        else:\n            encoder_extended_attention_mask = None\n\n        # Prepare head mask if needed\n        # 1.0 in head_mask indicate we keep the head\n        # attention_probs has shape bsz x n_heads x N x N\n        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]\n        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]\n        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)\n        \n        if encoder_embeds is None:\n            embedding_output = self.embeddings(\n                input_ids=input_ids,\n                position_ids=position_ids,\n                inputs_embeds=inputs_embeds,\n                past_key_values_length=past_key_values_length,\n            )\n        else:\n            embedding_output = encoder_embeds\n            \n        encoder_outputs = self.encoder(\n            embedding_output,\n            attention_mask=extended_attention_mask,\n            head_mask=head_mask,\n            encoder_hidden_states=encoder_hidden_states,\n            encoder_attention_mask=encoder_extended_attention_mask,\n            past_key_values=past_key_values,\n            use_cache=use_cache,\n            output_attentions=output_attentions,\n            output_hidden_states=output_hidden_states,\n            return_dict=return_dict,\n            mode=mode,\n        )\n        sequence_output = encoder_outputs[0]\n        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None\n\n        if not return_dict:\n            return (sequence_output, pooled_output) + encoder_outputs[1:]\n\n        return BaseModelOutputWithPoolingAndCrossAttentions(\n            last_hidden_state=sequence_output,\n            pooler_output=pooled_output,\n            past_key_values=encoder_outputs.past_key_values,\n            hidden_states=encoder_outputs.hidden_states,\n            attentions=encoder_outputs.attentions,\n            cross_attentions=encoder_outputs.cross_attentions,\n        )\n\n"
  },
  {
    "path": "extras/BLIP/models/vit.py",
    "content": "'''\n * Copyright (c) 2022, salesforce.com, inc.\n * All rights reserved.\n * SPDX-License-Identifier: BSD-3-Clause\n * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause\n * By Junnan Li\n * Based on timm code base\n * https://github.com/rwightman/pytorch-image-models/tree/master/timm\n'''\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom functools import partial\n\nfrom timm.models.vision_transformer import _cfg, PatchEmbed\nfrom timm.models.registry import register_model\nfrom timm.models.layers import trunc_normal_, DropPath\nfrom timm.models.helpers import named_apply, adapt_input_conv\n\n\ndef checkpoint_wrapper(x):\n    return x\n\n\nclass Mlp(nn.Module):\n    \"\"\" MLP as used in Vision Transformer, MLP-Mixer and related networks\n    \"\"\"\n    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):\n        super().__init__()\n        out_features = out_features or in_features\n        hidden_features = hidden_features or in_features\n        self.fc1 = nn.Linear(in_features, hidden_features)\n        self.act = act_layer()\n        self.fc2 = nn.Linear(hidden_features, out_features)\n        self.drop = nn.Dropout(drop)\n\n    def forward(self, x):\n        x = self.fc1(x)\n        x = self.act(x)\n        x = self.drop(x)\n        x = self.fc2(x)\n        x = self.drop(x)\n        return x\n\n\nclass Attention(nn.Module):\n    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):\n        super().__init__()\n        self.num_heads = num_heads\n        head_dim = dim // num_heads\n        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights\n        self.scale = qk_scale or head_dim ** -0.5\n        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)\n        self.attn_drop = nn.Dropout(attn_drop)\n        self.proj = nn.Linear(dim, dim)\n        self.proj_drop = nn.Dropout(proj_drop)\n        self.attn_gradients = None\n        self.attention_map = None\n        \n    def save_attn_gradients(self, attn_gradients):\n        self.attn_gradients = attn_gradients\n        \n    def get_attn_gradients(self):\n        return self.attn_gradients\n    \n    def save_attention_map(self, attention_map):\n        self.attention_map = attention_map\n        \n    def get_attention_map(self):\n        return self.attention_map\n    \n    def forward(self, x, register_hook=False):\n        B, N, C = x.shape\n        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)\n        q, k, v = qkv[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)\n\n        attn = (q @ k.transpose(-2, -1)) * self.scale\n        attn = attn.softmax(dim=-1)\n        attn = self.attn_drop(attn)\n                \n        if register_hook:\n            self.save_attention_map(attn)\n            attn.register_hook(self.save_attn_gradients)        \n\n        x = (attn @ v).transpose(1, 2).reshape(B, N, C)\n        x = self.proj(x)\n        x = self.proj_drop(x)\n        return x\n\n\nclass Block(nn.Module):\n\n    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,\n                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, use_grad_checkpointing=False):\n        super().__init__()\n        self.norm1 = norm_layer(dim)\n        self.attn = Attention(\n            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)\n        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here\n        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()\n        self.norm2 = norm_layer(dim)\n        mlp_hidden_dim = int(dim * mlp_ratio)\n        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)\n\n        if use_grad_checkpointing:\n            self.attn = checkpoint_wrapper(self.attn)\n            self.mlp = checkpoint_wrapper(self.mlp)\n\n    def forward(self, x, register_hook=False):\n        x = x + self.drop_path(self.attn(self.norm1(x), register_hook=register_hook))\n        x = x + self.drop_path(self.mlp(self.norm2(x)))\n        return x\n\n    \nclass VisionTransformer(nn.Module):\n    \"\"\" Vision Transformer\n    A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`  -\n        https://arxiv.org/abs/2010.11929\n    \"\"\"\n    def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,\n                 num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None,\n                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=None, \n                 use_grad_checkpointing=False, ckpt_layer=0):\n        \"\"\"\n        Args:\n            img_size (int, tuple): input image size\n            patch_size (int, tuple): patch size\n            in_chans (int): number of input channels\n            num_classes (int): number of classes for classification head\n            embed_dim (int): embedding dimension\n            depth (int): depth of transformer\n            num_heads (int): number of attention heads\n            mlp_ratio (int): ratio of mlp hidden dim to embedding dim\n            qkv_bias (bool): enable bias for qkv if True\n            qk_scale (float): override default qk scale of head_dim ** -0.5 if set\n            representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set\n            drop_rate (float): dropout rate\n            attn_drop_rate (float): attention dropout rate\n            drop_path_rate (float): stochastic depth rate\n            norm_layer: (nn.Module): normalization layer\n        \"\"\"\n        super().__init__()\n        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models\n        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)\n\n        self.patch_embed = PatchEmbed(\n            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)\n\n        num_patches = self.patch_embed.num_patches\n\n        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))\n        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))\n        self.pos_drop = nn.Dropout(p=drop_rate)\n\n        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule\n        self.blocks = nn.ModuleList([\n            Block(\n                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,\n                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,\n                use_grad_checkpointing=(use_grad_checkpointing and i>=depth-ckpt_layer)\n            )\n            for i in range(depth)])\n        self.norm = norm_layer(embed_dim)\n\n        trunc_normal_(self.pos_embed, std=.02)\n        trunc_normal_(self.cls_token, std=.02)\n        self.apply(self._init_weights)\n\n    def _init_weights(self, m):\n        if isinstance(m, nn.Linear):\n            trunc_normal_(m.weight, std=.02)\n            if isinstance(m, nn.Linear) and m.bias is not None:\n                nn.init.constant_(m.bias, 0)\n        elif isinstance(m, nn.LayerNorm):\n            nn.init.constant_(m.bias, 0)\n            nn.init.constant_(m.weight, 1.0)\n\n    @torch.jit.ignore\n    def no_weight_decay(self):\n        return {'pos_embed', 'cls_token'}\n\n    def forward(self, x, register_blk=-1):\n        B = x.shape[0]\n        x = self.patch_embed(x)\n\n        cls_tokens = self.cls_token.expand(B, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks\n        x = torch.cat((cls_tokens, x), dim=1)\n  \n        x = x + self.pos_embed[:,:x.size(1),:]\n        x = self.pos_drop(x)\n\n        for i,blk in enumerate(self.blocks):\n            x = blk(x, register_blk==i)\n        x = self.norm(x)\n        \n        return x\n\n    @torch.jit.ignore()\n    def load_pretrained(self, checkpoint_path, prefix=''):\n        _load_weights(self, checkpoint_path, prefix)\n        \n\n@torch.no_grad()\ndef _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ''):\n    \"\"\" Load weights from .npz checkpoints for official Google Brain Flax implementation\n    \"\"\"\n    import numpy as np\n\n    def _n2p(w, t=True):\n        if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1:\n            w = w.flatten()\n        if t:\n            if w.ndim == 4:\n                w = w.transpose([3, 2, 0, 1])\n            elif w.ndim == 3:\n                w = w.transpose([2, 0, 1])\n            elif w.ndim == 2:\n                w = w.transpose([1, 0])\n        return torch.from_numpy(w)\n\n    w = np.load(checkpoint_path)\n    if not prefix and 'opt/target/embedding/kernel' in w:\n        prefix = 'opt/target/'\n\n    if hasattr(model.patch_embed, 'backbone'):\n        # hybrid\n        backbone = model.patch_embed.backbone\n        stem_only = not hasattr(backbone, 'stem')\n        stem = backbone if stem_only else backbone.stem\n        stem.conv.weight.copy_(adapt_input_conv(stem.conv.weight.shape[1], _n2p(w[f'{prefix}conv_root/kernel'])))\n        stem.norm.weight.copy_(_n2p(w[f'{prefix}gn_root/scale']))\n        stem.norm.bias.copy_(_n2p(w[f'{prefix}gn_root/bias']))\n        if not stem_only:\n            for i, stage in enumerate(backbone.stages):\n                for j, block in enumerate(stage.blocks):\n                    bp = f'{prefix}block{i + 1}/unit{j + 1}/'\n                    for r in range(3):\n                        getattr(block, f'conv{r + 1}').weight.copy_(_n2p(w[f'{bp}conv{r + 1}/kernel']))\n                        getattr(block, f'norm{r + 1}').weight.copy_(_n2p(w[f'{bp}gn{r + 1}/scale']))\n                        getattr(block, f'norm{r + 1}').bias.copy_(_n2p(w[f'{bp}gn{r + 1}/bias']))\n                    if block.downsample is not None:\n                        block.downsample.conv.weight.copy_(_n2p(w[f'{bp}conv_proj/kernel']))\n                        block.downsample.norm.weight.copy_(_n2p(w[f'{bp}gn_proj/scale']))\n                        block.downsample.norm.bias.copy_(_n2p(w[f'{bp}gn_proj/bias']))\n        embed_conv_w = _n2p(w[f'{prefix}embedding/kernel'])\n    else:\n        embed_conv_w = adapt_input_conv(\n            model.patch_embed.proj.weight.shape[1], _n2p(w[f'{prefix}embedding/kernel']))\n    model.patch_embed.proj.weight.copy_(embed_conv_w)\n    model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias']))\n    model.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False))\n    pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False)\n    if pos_embed_w.shape != model.pos_embed.shape:\n        pos_embed_w = resize_pos_embed(  # resize pos embedding when different size from pretrained weights\n            pos_embed_w, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size)\n    model.pos_embed.copy_(pos_embed_w)\n    model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale']))\n    model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias']))\n#     if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]:\n#         model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel']))\n#         model.head.bias.copy_(_n2p(w[f'{prefix}head/bias']))\n#     if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w:\n#         model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel']))\n#         model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias']))\n    for i, block in enumerate(model.blocks.children()):\n        block_prefix = f'{prefix}Transformer/encoderblock_{i}/'\n        mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/'\n        block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale']))\n        block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias']))\n        block.attn.qkv.weight.copy_(torch.cat([\n            _n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')]))\n        block.attn.qkv.bias.copy_(torch.cat([\n            _n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')]))\n        block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1))\n        block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias']))\n        for r in range(2):\n            getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/kernel']))\n            getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/bias']))\n        block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/scale']))\n        block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/bias']))\n\n            \ndef interpolate_pos_embed(pos_embed_checkpoint, visual_encoder):        \n    # interpolate position embedding\n    embedding_size = pos_embed_checkpoint.shape[-1]\n    num_patches = visual_encoder.patch_embed.num_patches\n    num_extra_tokens = visual_encoder.pos_embed.shape[-2] - num_patches\n    # height (== width) for the checkpoint position embedding\n    orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)\n    # height (== width) for the new position embedding\n    new_size = int(num_patches ** 0.5)\n\n    if orig_size!=new_size:\n        # class_token and dist_token are kept unchanged\n        extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]\n        # only the position tokens are interpolated\n        pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]\n        pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)\n        pos_tokens = torch.nn.functional.interpolate(\n            pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)\n        pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)\n        new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)\n        print('reshape position embedding from %d to %d'%(orig_size ** 2,new_size ** 2))\n        \n        return new_pos_embed    \n    else:\n        return pos_embed_checkpoint"
  },
  {
    "path": "extras/GroundingDINO/config/GroundingDINO_SwinT_OGC.py",
    "content": "batch_size = 1\nmodelname = \"groundingdino\"\nbackbone = \"swin_T_224_1k\"\nposition_embedding = \"sine\"\npe_temperatureH = 20\npe_temperatureW = 20\nreturn_interm_indices = [1, 2, 3]\nbackbone_freeze_keywords = None\nenc_layers = 6\ndec_layers = 6\npre_norm = False\ndim_feedforward = 2048\nhidden_dim = 256\ndropout = 0.0\nnheads = 8\nnum_queries = 900\nquery_dim = 4\nnum_patterns = 0\nnum_feature_levels = 4\nenc_n_points = 4\ndec_n_points = 4\ntwo_stage_type = \"standard\"\ntwo_stage_bbox_embed_share = False\ntwo_stage_class_embed_share = False\ntransformer_activation = \"relu\"\ndec_pred_bbox_embed_share = True\ndn_box_noise_scale = 1.0\ndn_label_noise_ratio = 0.5\ndn_label_coef = 1.0\ndn_bbox_coef = 1.0\nembed_init_tgt = True\ndn_labelbook_size = 2000\nmax_text_len = 256\ntext_encoder_type = \"bert-base-uncased\"\nuse_text_enhancer = True\nuse_fusion_layer = True\nuse_checkpoint = True\nuse_transformer_ckpt = True\nuse_text_cross_attention = True\ntext_dropout = 0.0\nfusion_dropout = 0.0\nfusion_droppath = 0.1\nsub_sentence_present = True"
  },
  {
    "path": "extras/GroundingDINO/util/inference.py",
    "content": "from typing import Tuple, List\n\nimport ldm_patched.modules.model_management as model_management\nfrom ldm_patched.modules.model_patcher import ModelPatcher\nfrom modules.config import path_inpaint\nfrom modules.model_loader import load_file_from_url\n\nimport numpy as np\nimport supervision as sv\nimport torch\nfrom groundingdino.util.inference import Model\nfrom groundingdino.util.inference import load_model, preprocess_caption, get_phrases_from_posmap\n\n\nclass GroundingDinoModel(Model):\n    def __init__(self):\n        self.config_file = 'extras/GroundingDINO/config/GroundingDINO_SwinT_OGC.py'\n        self.model = None\n        self.load_device = torch.device('cpu')\n        self.offload_device = torch.device('cpu')\n\n    @torch.no_grad()\n    @torch.inference_mode()\n    def predict_with_caption(\n            self,\n            image: np.ndarray,\n            caption: str,\n            box_threshold: float = 0.35,\n            text_threshold: float = 0.25\n    ) -> Tuple[sv.Detections, torch.Tensor, torch.Tensor, List[str]]:\n        if self.model is None:\n            filename = load_file_from_url(\n                url=\"https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth\",\n                file_name='groundingdino_swint_ogc.pth',\n                model_dir=path_inpaint)\n            model = load_model(model_config_path=self.config_file, model_checkpoint_path=filename)\n\n            self.load_device = model_management.text_encoder_device()\n            self.offload_device = model_management.text_encoder_offload_device()\n\n            model.to(self.offload_device)\n\n            self.model = ModelPatcher(model, load_device=self.load_device, offload_device=self.offload_device)\n\n        model_management.load_model_gpu(self.model)\n\n        processed_image = GroundingDinoModel.preprocess_image(image_bgr=image).to(self.load_device)\n        boxes, logits, phrases = predict(\n            model=self.model,\n            image=processed_image,\n            caption=caption,\n            box_threshold=box_threshold,\n            text_threshold=text_threshold,\n            device=self.load_device)\n        source_h, source_w, _ = image.shape\n        detections = GroundingDinoModel.post_process_result(\n            source_h=source_h,\n            source_w=source_w,\n            boxes=boxes,\n            logits=logits)\n        return detections, boxes, logits, phrases\n\n\ndef predict(\n        model,\n        image: torch.Tensor,\n        caption: str,\n        box_threshold: float,\n        text_threshold: float,\n        device: str = \"cuda\"\n) -> Tuple[torch.Tensor, torch.Tensor, List[str]]:\n    caption = preprocess_caption(caption=caption)\n\n    # override to use model wrapped by patcher\n    model = model.model.to(device)\n    image = image.to(device)\n\n    with torch.no_grad():\n        outputs = model(image[None], captions=[caption])\n\n    prediction_logits = outputs[\"pred_logits\"].cpu().sigmoid()[0]  # prediction_logits.shape = (nq, 256)\n    prediction_boxes = outputs[\"pred_boxes\"].cpu()[0]  # prediction_boxes.shape = (nq, 4)\n\n    mask = prediction_logits.max(dim=1)[0] > box_threshold\n    logits = prediction_logits[mask]  # logits.shape = (n, 256)\n    boxes = prediction_boxes[mask]  # boxes.shape = (n, 4)\n\n    tokenizer = model.tokenizer\n    tokenized = tokenizer(caption)\n\n    phrases = [\n        get_phrases_from_posmap(logit > text_threshold, tokenized, tokenizer).replace('.', '')\n        for logit\n        in logits\n    ]\n\n    return boxes, logits.max(dim=1)[0], phrases\n\n\ndefault_groundingdino = GroundingDinoModel().predict_with_caption\n"
  },
  {
    "path": "extras/censor.py",
    "content": "import os\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPConfig, CLIPImageProcessor\n\nimport ldm_patched.modules.model_management as model_management\nimport modules.config\nfrom extras.safety_checker.models.safety_checker import StableDiffusionSafetyChecker\nfrom ldm_patched.modules.model_patcher import ModelPatcher\n\nsafety_checker_repo_root = os.path.join(os.path.dirname(__file__), 'safety_checker')\nconfig_path = os.path.join(safety_checker_repo_root, \"configs\", \"config.json\")\npreprocessor_config_path = os.path.join(safety_checker_repo_root, \"configs\", \"preprocessor_config.json\")\n\n\nclass Censor:\n    def __init__(self):\n        self.safety_checker_model: ModelPatcher | None = None\n        self.clip_image_processor: CLIPImageProcessor | None = None\n        self.load_device = torch.device('cpu')\n        self.offload_device = torch.device('cpu')\n\n    def init(self):\n        if self.safety_checker_model is None and self.clip_image_processor is None:\n            safety_checker_model = modules.config.downloading_safety_checker_model()\n            self.clip_image_processor = CLIPImageProcessor.from_json_file(preprocessor_config_path)\n            clip_config = CLIPConfig.from_json_file(config_path)\n            model = StableDiffusionSafetyChecker.from_pretrained(safety_checker_model, config=clip_config)\n            model.eval()\n\n            self.load_device = model_management.text_encoder_device()\n            self.offload_device = model_management.text_encoder_offload_device()\n\n            model.to(self.offload_device)\n\n            self.safety_checker_model = ModelPatcher(model, load_device=self.load_device, offload_device=self.offload_device)\n\n    def censor(self, images: list | np.ndarray) -> list | np.ndarray:\n        self.init()\n        model_management.load_model_gpu(self.safety_checker_model)\n\n        single = False\n        if not isinstance(images, (list, np.ndarray)):\n            images = [images]\n            single = True\n\n        safety_checker_input = self.clip_image_processor(images, return_tensors=\"pt\")\n        safety_checker_input.to(device=self.load_device)\n        checked_images, has_nsfw_concept = self.safety_checker_model.model(images=images,\n                                                                           clip_input=safety_checker_input.pixel_values)\n        checked_images = [image.astype(np.uint8) for image in checked_images]\n\n        if single:\n            checked_images = checked_images[0]\n\n        return checked_images\n\n\ndefault_censor = Censor().censor\n"
  },
  {
    "path": "extras/expansion.py",
    "content": "# Fooocus GPT2 Expansion\n# Algorithm created by Lvmin Zhang at 2023, Stanford\n# If used inside Fooocus, any use is permitted.\n# If used outside Fooocus, only non-commercial use is permitted (CC-By NC 4.0).\n# This applies to the word list, vocab, model, and algorithm.\n\n\nimport os\nimport torch\nimport math\nimport ldm_patched.modules.model_management as model_management\n\nfrom transformers.generation.logits_process import LogitsProcessorList\nfrom transformers import AutoTokenizer, AutoModelForCausalLM, set_seed\nfrom modules.config import path_fooocus_expansion\nfrom ldm_patched.modules.model_patcher import ModelPatcher\n\n\n# limitation of np.random.seed(), called from transformers.set_seed()\nSEED_LIMIT_NUMPY = 2**32\nneg_inf = - 8192.0\n\n\ndef safe_str(x):\n    x = str(x)\n    for _ in range(16):\n        x = x.replace('  ', ' ')\n    return x.strip(\",. \\r\\n\")\n\n\ndef remove_pattern(x, pattern):\n    for p in pattern:\n        x = x.replace(p, '')\n    return x\n\n\nclass FooocusExpansion:\n    def __init__(self):\n        self.tokenizer = AutoTokenizer.from_pretrained(path_fooocus_expansion)\n\n        positive_words = open(os.path.join(path_fooocus_expansion, 'positive.txt'),\n                              encoding='utf-8').read().splitlines()\n        positive_words = ['Ġ' + x.lower() for x in positive_words if x != '']\n\n        self.logits_bias = torch.zeros((1, len(self.tokenizer.vocab)), dtype=torch.float32) + neg_inf\n\n        debug_list = []\n        for k, v in self.tokenizer.vocab.items():\n            if k in positive_words:\n                self.logits_bias[0, v] = 0\n                debug_list.append(k[1:])\n\n        print(f'Fooocus V2 Expansion: Vocab with {len(debug_list)} words.')\n\n        # debug_list = '\\n'.join(sorted(debug_list))\n        # print(debug_list)\n\n        # t11 = self.tokenizer(',', return_tensors=\"np\")\n        # t198 = self.tokenizer('\\n', return_tensors=\"np\")\n        # eos = self.tokenizer.eos_token_id\n\n        self.model = AutoModelForCausalLM.from_pretrained(path_fooocus_expansion)\n        self.model.eval()\n\n        load_device = model_management.text_encoder_device()\n        offload_device = model_management.text_encoder_offload_device()\n\n        # MPS hack\n        if model_management.is_device_mps(load_device):\n            load_device = torch.device('cpu')\n            offload_device = torch.device('cpu')\n\n        use_fp16 = model_management.should_use_fp16(device=load_device)\n\n        if use_fp16:\n            self.model.half()\n\n        self.patcher = ModelPatcher(self.model, load_device=load_device, offload_device=offload_device)\n        print(f'Fooocus Expansion engine loaded for {load_device}, use_fp16 = {use_fp16}.')\n\n    @torch.no_grad()\n    @torch.inference_mode()\n    def logits_processor(self, input_ids, scores):\n        assert scores.ndim == 2 and scores.shape[0] == 1\n        self.logits_bias = self.logits_bias.to(scores)\n\n        bias = self.logits_bias.clone()\n        bias[0, input_ids[0].to(bias.device).long()] = neg_inf\n        bias[0, 11] = 0\n\n        return scores + bias\n\n    @torch.no_grad()\n    @torch.inference_mode()\n    def __call__(self, prompt, seed):\n        if prompt == '':\n            return ''\n\n        if self.patcher.current_device != self.patcher.load_device:\n            print('Fooocus Expansion loaded by itself.')\n            model_management.load_model_gpu(self.patcher)\n\n        seed = int(seed) % SEED_LIMIT_NUMPY\n        set_seed(seed)\n        prompt = safe_str(prompt) + ','\n\n        tokenized_kwargs = self.tokenizer(prompt, return_tensors=\"pt\")\n        tokenized_kwargs.data['input_ids'] = tokenized_kwargs.data['input_ids'].to(self.patcher.load_device)\n        tokenized_kwargs.data['attention_mask'] = tokenized_kwargs.data['attention_mask'].to(self.patcher.load_device)\n\n        current_token_length = int(tokenized_kwargs.data['input_ids'].shape[1])\n        max_token_length = 75 * int(math.ceil(float(current_token_length) / 75.0))\n        max_new_tokens = max_token_length - current_token_length\n\n        if max_new_tokens == 0:\n            return prompt[:-1]\n\n        # https://huggingface.co/blog/introducing-csearch\n        # https://huggingface.co/docs/transformers/generation_strategies\n        features = self.model.generate(**tokenized_kwargs,\n                                       top_k=100,\n                                       max_new_tokens=max_new_tokens,\n                                       do_sample=True,\n                                       logits_processor=LogitsProcessorList([self.logits_processor]))\n\n        response = self.tokenizer.batch_decode(features, skip_special_tokens=True)\n        result = safe_str(response[0])\n\n        return result\n"
  },
  {
    "path": "extras/face_crop.py",
    "content": "import cv2\nimport numpy as np\nimport modules.config\n\n\nfaceRestoreHelper = None\n\n\ndef align_warp_face(self, landmark, border_mode='constant'):\n    affine_matrix = cv2.estimateAffinePartial2D(landmark, self.face_template, method=cv2.LMEDS)[0]\n    self.affine_matrices.append(affine_matrix)\n    if border_mode == 'constant':\n        border_mode = cv2.BORDER_CONSTANT\n    elif border_mode == 'reflect101':\n        border_mode = cv2.BORDER_REFLECT101\n    elif border_mode == 'reflect':\n        border_mode = cv2.BORDER_REFLECT\n    input_img = self.input_img\n    cropped_face = cv2.warpAffine(input_img, affine_matrix, self.face_size,\n                                  borderMode=border_mode, borderValue=(135, 133, 132))\n    return cropped_face\n\n\ndef crop_image(img_rgb):\n    global faceRestoreHelper\n    \n    if faceRestoreHelper is None:\n        from extras.facexlib.utils.face_restoration_helper import FaceRestoreHelper\n        faceRestoreHelper = FaceRestoreHelper(\n            upscale_factor=1,\n            model_rootpath=modules.config.path_controlnet,\n            device='cpu'  # use cpu is safer since we are out of memory management\n        )\n\n    faceRestoreHelper.clean_all()\n    faceRestoreHelper.read_image(np.ascontiguousarray(img_rgb[:, :, ::-1].copy()))\n    faceRestoreHelper.get_face_landmarks_5()\n\n    landmarks = faceRestoreHelper.all_landmarks_5\n    # landmarks are already sorted with confidence.\n\n    if len(landmarks) == 0:\n        print('No face detected')\n        return img_rgb\n    else:\n        print(f'Detected {len(landmarks)} faces')\n\n    result = align_warp_face(faceRestoreHelper, landmarks[0])\n\n    return np.ascontiguousarray(result[:, :, ::-1].copy())\n"
  },
  {
    "path": "extras/facexlib/detection/__init__.py",
    "content": "import torch\nfrom copy import deepcopy\n\nfrom extras.facexlib.utils import load_file_from_url\nfrom .retinaface import RetinaFace\n\n\ndef init_detection_model(model_name, half=False, device='cuda', model_rootpath=None):\n    if model_name == 'retinaface_resnet50':\n        model = RetinaFace(network_name='resnet50', half=half, device=device)\n        model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth'\n    elif model_name == 'retinaface_mobile0.25':\n        model = RetinaFace(network_name='mobile0.25', half=half, device=device)\n        model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_mobilenet0.25_Final.pth'\n    else:\n        raise NotImplementedError(f'{model_name} is not implemented.')\n\n    model_path = load_file_from_url(\n        url=model_url, model_dir='facexlib/weights', progress=True, file_name=None, save_dir=model_rootpath)\n\n    # TODO: clean pretrained model\n    load_net = torch.load(model_path, map_location=lambda storage, loc: storage, weights_only=True)\n    # remove unnecessary 'module.'\n    for k, v in deepcopy(load_net).items():\n        if k.startswith('module.'):\n            load_net[k[7:]] = v\n            load_net.pop(k)\n    model.load_state_dict(load_net, strict=True)\n    model.eval()\n    model = model.to(device)\n    return model\n"
  },
  {
    "path": "extras/facexlib/detection/align_trans.py",
    "content": "import cv2\nimport numpy as np\n\nfrom .matlab_cp2tform import get_similarity_transform_for_cv2\n\n# reference facial points, a list of coordinates (x,y)\nREFERENCE_FACIAL_POINTS = [[30.29459953, 51.69630051], [65.53179932, 51.50139999], [48.02519989, 71.73660278],\n                           [33.54930115, 92.3655014], [62.72990036, 92.20410156]]\n\nDEFAULT_CROP_SIZE = (96, 112)\n\n\nclass FaceWarpException(Exception):\n\n    def __str__(self):\n        return 'In File {}:{}'.format(__file__, super.__str__(self))\n\n\ndef get_reference_facial_points(output_size=None, inner_padding_factor=0.0, outer_padding=(0, 0), default_square=False):\n    \"\"\"\n    Function:\n    ----------\n        get reference 5 key points according to crop settings:\n        0. Set default crop_size:\n            if default_square:\n                crop_size = (112, 112)\n            else:\n                crop_size = (96, 112)\n        1. Pad the crop_size by inner_padding_factor in each side;\n        2. Resize crop_size into (output_size - outer_padding*2),\n            pad into output_size with outer_padding;\n        3. Output reference_5point;\n    Parameters:\n    ----------\n        @output_size: (w, h) or None\n            size of aligned face image\n        @inner_padding_factor: (w_factor, h_factor)\n            padding factor for inner (w, h)\n        @outer_padding: (w_pad, h_pad)\n            each row is a pair of coordinates (x, y)\n        @default_square: True or False\n            if True:\n                default crop_size = (112, 112)\n            else:\n                default crop_size = (96, 112);\n        !!! make sure, if output_size is not None:\n                (output_size - outer_padding)\n                = some_scale * (default crop_size * (1.0 +\n                inner_padding_factor))\n    Returns:\n    ----------\n        @reference_5point: 5x2 np.array\n            each row is a pair of transformed coordinates (x, y)\n    \"\"\"\n\n    tmp_5pts = np.array(REFERENCE_FACIAL_POINTS)\n    tmp_crop_size = np.array(DEFAULT_CROP_SIZE)\n\n    # 0) make the inner region a square\n    if default_square:\n        size_diff = max(tmp_crop_size) - tmp_crop_size\n        tmp_5pts += size_diff / 2\n        tmp_crop_size += size_diff\n\n    if (output_size and output_size[0] == tmp_crop_size[0] and output_size[1] == tmp_crop_size[1]):\n\n        return tmp_5pts\n\n    if (inner_padding_factor == 0 and outer_padding == (0, 0)):\n        if output_size is None:\n            return tmp_5pts\n        else:\n            raise FaceWarpException('No paddings to do, output_size must be None or {}'.format(tmp_crop_size))\n\n    # check output size\n    if not (0 <= inner_padding_factor <= 1.0):\n        raise FaceWarpException('Not (0 <= inner_padding_factor <= 1.0)')\n\n    if ((inner_padding_factor > 0 or outer_padding[0] > 0 or outer_padding[1] > 0) and output_size is None):\n        output_size = tmp_crop_size * \\\n            (1 + inner_padding_factor * 2).astype(np.int32)\n        output_size += np.array(outer_padding)\n    if not (outer_padding[0] < output_size[0] and outer_padding[1] < output_size[1]):\n        raise FaceWarpException('Not (outer_padding[0] < output_size[0] and outer_padding[1] < output_size[1])')\n\n    # 1) pad the inner region according inner_padding_factor\n    if inner_padding_factor > 0:\n        size_diff = tmp_crop_size * inner_padding_factor * 2\n        tmp_5pts += size_diff / 2\n        tmp_crop_size += np.round(size_diff).astype(np.int32)\n\n    # 2) resize the padded inner region\n    size_bf_outer_pad = np.array(output_size) - np.array(outer_padding) * 2\n\n    if size_bf_outer_pad[0] * tmp_crop_size[1] != size_bf_outer_pad[1] * tmp_crop_size[0]:\n        raise FaceWarpException('Must have (output_size - outer_padding)'\n                                '= some_scale * (crop_size * (1.0 + inner_padding_factor)')\n\n    scale_factor = size_bf_outer_pad[0].astype(np.float32) / tmp_crop_size[0]\n    tmp_5pts = tmp_5pts * scale_factor\n    #    size_diff = tmp_crop_size * (scale_factor - min(scale_factor))\n    #    tmp_5pts = tmp_5pts + size_diff / 2\n    tmp_crop_size = size_bf_outer_pad\n\n    # 3) add outer_padding to make output_size\n    reference_5point = tmp_5pts + np.array(outer_padding)\n    tmp_crop_size = output_size\n\n    return reference_5point\n\n\ndef get_affine_transform_matrix(src_pts, dst_pts):\n    \"\"\"\n    Function:\n    ----------\n        get affine transform matrix 'tfm' from src_pts to dst_pts\n    Parameters:\n    ----------\n        @src_pts: Kx2 np.array\n            source points matrix, each row is a pair of coordinates (x, y)\n        @dst_pts: Kx2 np.array\n            destination points matrix, each row is a pair of coordinates (x, y)\n    Returns:\n    ----------\n        @tfm: 2x3 np.array\n            transform matrix from src_pts to dst_pts\n    \"\"\"\n\n    tfm = np.float32([[1, 0, 0], [0, 1, 0]])\n    n_pts = src_pts.shape[0]\n    ones = np.ones((n_pts, 1), src_pts.dtype)\n    src_pts_ = np.hstack([src_pts, ones])\n    dst_pts_ = np.hstack([dst_pts, ones])\n\n    A, res, rank, s = np.linalg.lstsq(src_pts_, dst_pts_)\n\n    if rank == 3:\n        tfm = np.float32([[A[0, 0], A[1, 0], A[2, 0]], [A[0, 1], A[1, 1], A[2, 1]]])\n    elif rank == 2:\n        tfm = np.float32([[A[0, 0], A[1, 0], 0], [A[0, 1], A[1, 1], 0]])\n\n    return tfm\n\n\ndef warp_and_crop_face(src_img, facial_pts, reference_pts=None, crop_size=(96, 112), align_type='smilarity'):\n    \"\"\"\n    Function:\n    ----------\n        apply affine transform 'trans' to uv\n    Parameters:\n    ----------\n        @src_img: 3x3 np.array\n            input image\n        @facial_pts: could be\n            1)a list of K coordinates (x,y)\n        or\n            2) Kx2 or 2xK np.array\n            each row or col is a pair of coordinates (x, y)\n        @reference_pts: could be\n            1) a list of K coordinates (x,y)\n        or\n            2) Kx2 or 2xK np.array\n            each row or col is a pair of coordinates (x, y)\n        or\n            3) None\n            if None, use default reference facial points\n        @crop_size: (w, h)\n            output face image size\n        @align_type: transform type, could be one of\n            1) 'similarity': use similarity transform\n            2) 'cv2_affine': use the first 3 points to do affine transform,\n                    by calling cv2.getAffineTransform()\n            3) 'affine': use all points to do affine transform\n    Returns:\n    ----------\n        @face_img: output face image with size (w, h) = @crop_size\n    \"\"\"\n\n    if reference_pts is None:\n        if crop_size[0] == 96 and crop_size[1] == 112:\n            reference_pts = REFERENCE_FACIAL_POINTS\n        else:\n            default_square = False\n            inner_padding_factor = 0\n            outer_padding = (0, 0)\n            output_size = crop_size\n\n            reference_pts = get_reference_facial_points(output_size, inner_padding_factor, outer_padding,\n                                                        default_square)\n\n    ref_pts = np.float32(reference_pts)\n    ref_pts_shp = ref_pts.shape\n    if max(ref_pts_shp) < 3 or min(ref_pts_shp) != 2:\n        raise FaceWarpException('reference_pts.shape must be (K,2) or (2,K) and K>2')\n\n    if ref_pts_shp[0] == 2:\n        ref_pts = ref_pts.T\n\n    src_pts = np.float32(facial_pts)\n    src_pts_shp = src_pts.shape\n    if max(src_pts_shp) < 3 or min(src_pts_shp) != 2:\n        raise FaceWarpException('facial_pts.shape must be (K,2) or (2,K) and K>2')\n\n    if src_pts_shp[0] == 2:\n        src_pts = src_pts.T\n\n    if src_pts.shape != ref_pts.shape:\n        raise FaceWarpException('facial_pts and reference_pts must have the same shape')\n\n    if align_type == 'cv2_affine':\n        tfm = cv2.getAffineTransform(src_pts[0:3], ref_pts[0:3])\n    elif align_type == 'affine':\n        tfm = get_affine_transform_matrix(src_pts, ref_pts)\n    else:\n        tfm = get_similarity_transform_for_cv2(src_pts, ref_pts)\n\n    face_img = cv2.warpAffine(src_img, tfm, (crop_size[0], crop_size[1]))\n\n    return face_img\n"
  },
  {
    "path": "extras/facexlib/detection/matlab_cp2tform.py",
    "content": "import numpy as np\nfrom numpy.linalg import inv, lstsq\nfrom numpy.linalg import matrix_rank as rank\nfrom numpy.linalg import norm\n\n\nclass MatlabCp2tormException(Exception):\n\n    def __str__(self):\n        return 'In File {}:{}'.format(__file__, super.__str__(self))\n\n\ndef tformfwd(trans, uv):\n    \"\"\"\n    Function:\n    ----------\n        apply affine transform 'trans' to uv\n\n    Parameters:\n    ----------\n        @trans: 3x3 np.array\n            transform matrix\n        @uv: Kx2 np.array\n            each row is a pair of coordinates (x, y)\n\n    Returns:\n    ----------\n        @xy: Kx2 np.array\n            each row is a pair of transformed coordinates (x, y)\n    \"\"\"\n    uv = np.hstack((uv, np.ones((uv.shape[0], 1))))\n    xy = np.dot(uv, trans)\n    xy = xy[:, 0:-1]\n    return xy\n\n\ndef tforminv(trans, uv):\n    \"\"\"\n    Function:\n    ----------\n        apply the inverse of affine transform 'trans' to uv\n\n    Parameters:\n    ----------\n        @trans: 3x3 np.array\n            transform matrix\n        @uv: Kx2 np.array\n            each row is a pair of coordinates (x, y)\n\n    Returns:\n    ----------\n        @xy: Kx2 np.array\n            each row is a pair of inverse-transformed coordinates (x, y)\n    \"\"\"\n    Tinv = inv(trans)\n    xy = tformfwd(Tinv, uv)\n    return xy\n\n\ndef findNonreflectiveSimilarity(uv, xy, options=None):\n    options = {'K': 2}\n\n    K = options['K']\n    M = xy.shape[0]\n    x = xy[:, 0].reshape((-1, 1))  # use reshape to keep a column vector\n    y = xy[:, 1].reshape((-1, 1))  # use reshape to keep a column vector\n\n    tmp1 = np.hstack((x, y, np.ones((M, 1)), np.zeros((M, 1))))\n    tmp2 = np.hstack((y, -x, np.zeros((M, 1)), np.ones((M, 1))))\n    X = np.vstack((tmp1, tmp2))\n\n    u = uv[:, 0].reshape((-1, 1))  # use reshape to keep a column vector\n    v = uv[:, 1].reshape((-1, 1))  # use reshape to keep a column vector\n    U = np.vstack((u, v))\n\n    # We know that X * r = U\n    if rank(X) >= 2 * K:\n        r, _, _, _ = lstsq(X, U, rcond=-1)\n        r = np.squeeze(r)\n    else:\n        raise Exception('cp2tform:twoUniquePointsReq')\n    sc = r[0]\n    ss = r[1]\n    tx = r[2]\n    ty = r[3]\n\n    Tinv = np.array([[sc, -ss, 0], [ss, sc, 0], [tx, ty, 1]])\n    T = inv(Tinv)\n    T[:, 2] = np.array([0, 0, 1])\n\n    return T, Tinv\n\n\ndef findSimilarity(uv, xy, options=None):\n    options = {'K': 2}\n\n    #    uv = np.array(uv)\n    #    xy = np.array(xy)\n\n    # Solve for trans1\n    trans1, trans1_inv = findNonreflectiveSimilarity(uv, xy, options)\n\n    # Solve for trans2\n\n    # manually reflect the xy data across the Y-axis\n    xyR = xy\n    xyR[:, 0] = -1 * xyR[:, 0]\n\n    trans2r, trans2r_inv = findNonreflectiveSimilarity(uv, xyR, options)\n\n    # manually reflect the tform to undo the reflection done on xyR\n    TreflectY = np.array([[-1, 0, 0], [0, 1, 0], [0, 0, 1]])\n\n    trans2 = np.dot(trans2r, TreflectY)\n\n    # Figure out if trans1 or trans2 is better\n    xy1 = tformfwd(trans1, uv)\n    norm1 = norm(xy1 - xy)\n\n    xy2 = tformfwd(trans2, uv)\n    norm2 = norm(xy2 - xy)\n\n    if norm1 <= norm2:\n        return trans1, trans1_inv\n    else:\n        trans2_inv = inv(trans2)\n        return trans2, trans2_inv\n\n\ndef get_similarity_transform(src_pts, dst_pts, reflective=True):\n    \"\"\"\n    Function:\n    ----------\n        Find Similarity Transform Matrix 'trans':\n            u = src_pts[:, 0]\n            v = src_pts[:, 1]\n            x = dst_pts[:, 0]\n            y = dst_pts[:, 1]\n            [x, y, 1] = [u, v, 1] * trans\n\n    Parameters:\n    ----------\n        @src_pts: Kx2 np.array\n            source points, each row is a pair of coordinates (x, y)\n        @dst_pts: Kx2 np.array\n            destination points, each row is a pair of transformed\n            coordinates (x, y)\n        @reflective: True or False\n            if True:\n                use reflective similarity transform\n            else:\n                use non-reflective similarity transform\n\n    Returns:\n    ----------\n       @trans: 3x3 np.array\n            transform matrix from uv to xy\n        trans_inv: 3x3 np.array\n            inverse of trans, transform matrix from xy to uv\n    \"\"\"\n\n    if reflective:\n        trans, trans_inv = findSimilarity(src_pts, dst_pts)\n    else:\n        trans, trans_inv = findNonreflectiveSimilarity(src_pts, dst_pts)\n\n    return trans, trans_inv\n\n\ndef cvt_tform_mat_for_cv2(trans):\n    \"\"\"\n    Function:\n    ----------\n        Convert Transform Matrix 'trans' into 'cv2_trans' which could be\n        directly used by cv2.warpAffine():\n            u = src_pts[:, 0]\n            v = src_pts[:, 1]\n            x = dst_pts[:, 0]\n            y = dst_pts[:, 1]\n            [x, y].T = cv_trans * [u, v, 1].T\n\n    Parameters:\n    ----------\n        @trans: 3x3 np.array\n            transform matrix from uv to xy\n\n    Returns:\n    ----------\n        @cv2_trans: 2x3 np.array\n            transform matrix from src_pts to dst_pts, could be directly used\n            for cv2.warpAffine()\n    \"\"\"\n    cv2_trans = trans[:, 0:2].T\n\n    return cv2_trans\n\n\ndef get_similarity_transform_for_cv2(src_pts, dst_pts, reflective=True):\n    \"\"\"\n    Function:\n    ----------\n        Find Similarity Transform Matrix 'cv2_trans' which could be\n        directly used by cv2.warpAffine():\n            u = src_pts[:, 0]\n            v = src_pts[:, 1]\n            x = dst_pts[:, 0]\n            y = dst_pts[:, 1]\n            [x, y].T = cv_trans * [u, v, 1].T\n\n    Parameters:\n    ----------\n        @src_pts: Kx2 np.array\n            source points, each row is a pair of coordinates (x, y)\n        @dst_pts: Kx2 np.array\n            destination points, each row is a pair of transformed\n            coordinates (x, y)\n        reflective: True or False\n            if True:\n                use reflective similarity transform\n            else:\n                use non-reflective similarity transform\n\n    Returns:\n    ----------\n        @cv2_trans: 2x3 np.array\n            transform matrix from src_pts to dst_pts, could be directly used\n            for cv2.warpAffine()\n    \"\"\"\n    trans, trans_inv = get_similarity_transform(src_pts, dst_pts, reflective)\n    cv2_trans = cvt_tform_mat_for_cv2(trans)\n\n    return cv2_trans\n\n\nif __name__ == '__main__':\n    \"\"\"\n    u = [0, 6, -2]\n    v = [0, 3, 5]\n    x = [-1, 0, 4]\n    y = [-1, -10, 4]\n\n    # In Matlab, run:\n    #\n    #   uv = [u'; v'];\n    #   xy = [x'; y'];\n    #   tform_sim=cp2tform(uv,xy,'similarity');\n    #\n    #   trans = tform_sim.tdata.T\n    #   ans =\n    #       -0.0764   -1.6190         0\n    #        1.6190   -0.0764         0\n    #       -3.2156    0.0290    1.0000\n    #   trans_inv = tform_sim.tdata.Tinv\n    #    ans =\n    #\n    #       -0.0291    0.6163         0\n    #       -0.6163   -0.0291         0\n    #       -0.0756    1.9826    1.0000\n    #    xy_m=tformfwd(tform_sim, u,v)\n    #\n    #    xy_m =\n    #\n    #       -3.2156    0.0290\n    #        1.1833   -9.9143\n    #        5.0323    2.8853\n    #    uv_m=tforminv(tform_sim, x,y)\n    #\n    #    uv_m =\n    #\n    #        0.5698    1.3953\n    #        6.0872    2.2733\n    #       -2.6570    4.3314\n    \"\"\"\n    u = [0, 6, -2]\n    v = [0, 3, 5]\n    x = [-1, 0, 4]\n    y = [-1, -10, 4]\n\n    uv = np.array((u, v)).T\n    xy = np.array((x, y)).T\n\n    print('\\n--->uv:')\n    print(uv)\n    print('\\n--->xy:')\n    print(xy)\n\n    trans, trans_inv = get_similarity_transform(uv, xy)\n\n    print('\\n--->trans matrix:')\n    print(trans)\n\n    print('\\n--->trans_inv matrix:')\n    print(trans_inv)\n\n    print('\\n---> apply transform to uv')\n    print('\\nxy_m = uv_augmented * trans')\n    uv_aug = np.hstack((uv, np.ones((uv.shape[0], 1))))\n    xy_m = np.dot(uv_aug, trans)\n    print(xy_m)\n\n    print('\\nxy_m = tformfwd(trans, uv)')\n    xy_m = tformfwd(trans, uv)\n    print(xy_m)\n\n    print('\\n---> apply inverse transform to xy')\n    print('\\nuv_m = xy_augmented * trans_inv')\n    xy_aug = np.hstack((xy, np.ones((xy.shape[0], 1))))\n    uv_m = np.dot(xy_aug, trans_inv)\n    print(uv_m)\n\n    print('\\nuv_m = tformfwd(trans_inv, xy)')\n    uv_m = tformfwd(trans_inv, xy)\n    print(uv_m)\n\n    uv_m = tforminv(trans, xy)\n    print('\\nuv_m = tforminv(trans, xy)')\n    print(uv_m)\n"
  },
  {
    "path": "extras/facexlib/detection/retinaface.py",
    "content": "import cv2\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom PIL import Image\nfrom torchvision.models._utils import IntermediateLayerGetter as IntermediateLayerGetter\n\nfrom extras.facexlib.detection.align_trans import get_reference_facial_points, warp_and_crop_face\nfrom extras.facexlib.detection.retinaface_net import FPN, SSH, MobileNetV1, make_bbox_head, make_class_head, make_landmark_head\nfrom extras.facexlib.detection.retinaface_utils import (PriorBox, batched_decode, batched_decode_landm, decode, decode_landm,\n                                                 py_cpu_nms)\n\n\ndef generate_config(network_name):\n\n    cfg_mnet = {\n        'name': 'mobilenet0.25',\n        'min_sizes': [[16, 32], [64, 128], [256, 512]],\n        'steps': [8, 16, 32],\n        'variance': [0.1, 0.2],\n        'clip': False,\n        'loc_weight': 2.0,\n        'gpu_train': True,\n        'batch_size': 32,\n        'ngpu': 1,\n        'epoch': 250,\n        'decay1': 190,\n        'decay2': 220,\n        'image_size': 640,\n        'return_layers': {\n            'stage1': 1,\n            'stage2': 2,\n            'stage3': 3\n        },\n        'in_channel': 32,\n        'out_channel': 64\n    }\n\n    cfg_re50 = {\n        'name': 'Resnet50',\n        'min_sizes': [[16, 32], [64, 128], [256, 512]],\n        'steps': [8, 16, 32],\n        'variance': [0.1, 0.2],\n        'clip': False,\n        'loc_weight': 2.0,\n        'gpu_train': True,\n        'batch_size': 24,\n        'ngpu': 4,\n        'epoch': 100,\n        'decay1': 70,\n        'decay2': 90,\n        'image_size': 840,\n        'return_layers': {\n            'layer2': 1,\n            'layer3': 2,\n            'layer4': 3\n        },\n        'in_channel': 256,\n        'out_channel': 256\n    }\n\n    if network_name == 'mobile0.25':\n        return cfg_mnet\n    elif network_name == 'resnet50':\n        return cfg_re50\n    else:\n        raise NotImplementedError(f'network_name={network_name}')\n\n\nclass RetinaFace(nn.Module):\n\n    def __init__(self, network_name='resnet50', half=False, phase='test', device=None):\n        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if device is None else device\n\n        super(RetinaFace, self).__init__()\n        self.half_inference = half\n        cfg = generate_config(network_name)\n        self.backbone = cfg['name']\n\n        self.model_name = f'retinaface_{network_name}'\n        self.cfg = cfg\n        self.phase = phase\n        self.target_size, self.max_size = 1600, 2150\n        self.resize, self.scale, self.scale1 = 1., None, None\n        self.mean_tensor = torch.tensor([[[[104.]], [[117.]], [[123.]]]], device=self.device)\n        self.reference = get_reference_facial_points(default_square=True)\n        # Build network.\n        backbone = None\n        if cfg['name'] == 'mobilenet0.25':\n            backbone = MobileNetV1()\n            self.body = IntermediateLayerGetter(backbone, cfg['return_layers'])\n        elif cfg['name'] == 'Resnet50':\n            import torchvision.models as models\n            backbone = models.resnet50(weights=None)\n            self.body = IntermediateLayerGetter(backbone, cfg['return_layers'])\n\n        in_channels_stage2 = cfg['in_channel']\n        in_channels_list = [\n            in_channels_stage2 * 2,\n            in_channels_stage2 * 4,\n            in_channels_stage2 * 8,\n        ]\n\n        out_channels = cfg['out_channel']\n        self.fpn = FPN(in_channels_list, out_channels)\n        self.ssh1 = SSH(out_channels, out_channels)\n        self.ssh2 = SSH(out_channels, out_channels)\n        self.ssh3 = SSH(out_channels, out_channels)\n\n        self.ClassHead = make_class_head(fpn_num=3, inchannels=cfg['out_channel'])\n        self.BboxHead = make_bbox_head(fpn_num=3, inchannels=cfg['out_channel'])\n        self.LandmarkHead = make_landmark_head(fpn_num=3, inchannels=cfg['out_channel'])\n\n        self.to(self.device)\n        self.eval()\n        if self.half_inference:\n            self.half()\n\n    def forward(self, inputs):\n        out = self.body(inputs)\n\n        if self.backbone == 'mobilenet0.25' or self.backbone == 'Resnet50':\n            out = list(out.values())\n        # FPN\n        fpn = self.fpn(out)\n\n        # SSH\n        feature1 = self.ssh1(fpn[0])\n        feature2 = self.ssh2(fpn[1])\n        feature3 = self.ssh3(fpn[2])\n        features = [feature1, feature2, feature3]\n\n        bbox_regressions = torch.cat([self.BboxHead[i](feature) for i, feature in enumerate(features)], dim=1)\n        classifications = torch.cat([self.ClassHead[i](feature) for i, feature in enumerate(features)], dim=1)\n        tmp = [self.LandmarkHead[i](feature) for i, feature in enumerate(features)]\n        ldm_regressions = (torch.cat(tmp, dim=1))\n\n        if self.phase == 'train':\n            output = (bbox_regressions, classifications, ldm_regressions)\n        else:\n            output = (bbox_regressions, F.softmax(classifications, dim=-1), ldm_regressions)\n        return output\n\n    def __detect_faces(self, inputs):\n        # get scale\n        height, width = inputs.shape[2:]\n        self.scale = torch.tensor([width, height, width, height], dtype=torch.float32, device=self.device)\n        tmp = [width, height, width, height, width, height, width, height, width, height]\n        self.scale1 = torch.tensor(tmp, dtype=torch.float32, device=self.device)\n\n        # forawrd\n        inputs = inputs.to(self.device)\n        if self.half_inference:\n            inputs = inputs.half()\n        loc, conf, landmarks = self(inputs)\n\n        # get priorbox\n        priorbox = PriorBox(self.cfg, image_size=inputs.shape[2:])\n        priors = priorbox.forward().to(self.device)\n\n        return loc, conf, landmarks, priors\n\n    # single image detection\n    def transform(self, image, use_origin_size):\n        # convert to opencv format\n        if isinstance(image, Image.Image):\n            image = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)\n        image = image.astype(np.float32)\n\n        # testing scale\n        im_size_min = np.min(image.shape[0:2])\n        im_size_max = np.max(image.shape[0:2])\n        resize = float(self.target_size) / float(im_size_min)\n\n        # prevent bigger axis from being more than max_size\n        if np.round(resize * im_size_max) > self.max_size:\n            resize = float(self.max_size) / float(im_size_max)\n        resize = 1 if use_origin_size else resize\n\n        # resize\n        if resize != 1:\n            image = cv2.resize(image, None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR)\n\n        # convert to torch.tensor format\n        # image -= (104, 117, 123)\n        image = image.transpose(2, 0, 1)\n        image = torch.from_numpy(image).unsqueeze(0)\n\n        return image, resize\n\n    def detect_faces(\n        self,\n        image,\n        conf_threshold=0.8,\n        nms_threshold=0.4,\n        use_origin_size=True,\n    ):\n        image, self.resize = self.transform(image, use_origin_size)\n        image = image.to(self.device)\n        if self.half_inference:\n            image = image.half()\n        image = image - self.mean_tensor\n\n        loc, conf, landmarks, priors = self.__detect_faces(image)\n\n        boxes = decode(loc.data.squeeze(0), priors.data, self.cfg['variance'])\n        boxes = boxes * self.scale / self.resize\n        boxes = boxes.cpu().numpy()\n\n        scores = conf.squeeze(0).data.cpu().numpy()[:, 1]\n\n        landmarks = decode_landm(landmarks.squeeze(0), priors, self.cfg['variance'])\n        landmarks = landmarks * self.scale1 / self.resize\n        landmarks = landmarks.cpu().numpy()\n\n        # ignore low scores\n        inds = np.where(scores > conf_threshold)[0]\n        boxes, landmarks, scores = boxes[inds], landmarks[inds], scores[inds]\n\n        # sort\n        order = scores.argsort()[::-1]\n        boxes, landmarks, scores = boxes[order], landmarks[order], scores[order]\n\n        # do NMS\n        bounding_boxes = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)\n        keep = py_cpu_nms(bounding_boxes, nms_threshold)\n        bounding_boxes, landmarks = bounding_boxes[keep, :], landmarks[keep]\n        # self.t['forward_pass'].toc()\n        # print(self.t['forward_pass'].average_time)\n        # import sys\n        # sys.stdout.flush()\n        return np.concatenate((bounding_boxes, landmarks), axis=1)\n\n    def __align_multi(self, image, boxes, landmarks, limit=None):\n\n        if len(boxes) < 1:\n            return [], []\n\n        if limit:\n            boxes = boxes[:limit]\n            landmarks = landmarks[:limit]\n\n        faces = []\n        for landmark in landmarks:\n            facial5points = [[landmark[2 * j], landmark[2 * j + 1]] for j in range(5)]\n\n            warped_face = warp_and_crop_face(np.array(image), facial5points, self.reference, crop_size=(112, 112))\n            faces.append(warped_face)\n\n        return np.concatenate((boxes, landmarks), axis=1), faces\n\n    def align_multi(self, img, conf_threshold=0.8, limit=None):\n\n        rlt = self.detect_faces(img, conf_threshold=conf_threshold)\n        boxes, landmarks = rlt[:, 0:5], rlt[:, 5:]\n\n        return self.__align_multi(img, boxes, landmarks, limit)\n\n    # batched detection\n    def batched_transform(self, frames, use_origin_size):\n        \"\"\"\n        Arguments:\n            frames: a list of PIL.Image, or torch.Tensor(shape=[n, h, w, c],\n                type=np.float32, BGR format).\n            use_origin_size: whether to use origin size.\n        \"\"\"\n        from_PIL = True if isinstance(frames[0], Image.Image) else False\n\n        # convert to opencv format\n        if from_PIL:\n            frames = [cv2.cvtColor(np.asarray(frame), cv2.COLOR_RGB2BGR) for frame in frames]\n            frames = np.asarray(frames, dtype=np.float32)\n\n        # testing scale\n        im_size_min = np.min(frames[0].shape[0:2])\n        im_size_max = np.max(frames[0].shape[0:2])\n        resize = float(self.target_size) / float(im_size_min)\n\n        # prevent bigger axis from being more than max_size\n        if np.round(resize * im_size_max) > self.max_size:\n            resize = float(self.max_size) / float(im_size_max)\n        resize = 1 if use_origin_size else resize\n\n        # resize\n        if resize != 1:\n            if not from_PIL:\n                frames = F.interpolate(frames, scale_factor=resize)\n            else:\n                frames = [\n                    cv2.resize(frame, None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR)\n                    for frame in frames\n                ]\n\n        # convert to torch.tensor format\n        if not from_PIL:\n            frames = frames.transpose(1, 2).transpose(1, 3).contiguous()\n        else:\n            frames = frames.transpose((0, 3, 1, 2))\n            frames = torch.from_numpy(frames)\n\n        return frames, resize\n\n    def batched_detect_faces(self, frames, conf_threshold=0.8, nms_threshold=0.4, use_origin_size=True):\n        \"\"\"\n        Arguments:\n            frames: a list of PIL.Image, or np.array(shape=[n, h, w, c],\n                type=np.uint8, BGR format).\n            conf_threshold: confidence threshold.\n            nms_threshold: nms threshold.\n            use_origin_size: whether to use origin size.\n        Returns:\n            final_bounding_boxes: list of np.array ([n_boxes, 5],\n                type=np.float32).\n            final_landmarks: list of np.array ([n_boxes, 10], type=np.float32).\n        \"\"\"\n        # self.t['forward_pass'].tic()\n        frames, self.resize = self.batched_transform(frames, use_origin_size)\n        frames = frames.to(self.device)\n        frames = frames - self.mean_tensor\n\n        b_loc, b_conf, b_landmarks, priors = self.__detect_faces(frames)\n\n        final_bounding_boxes, final_landmarks = [], []\n\n        # decode\n        priors = priors.unsqueeze(0)\n        b_loc = batched_decode(b_loc, priors, self.cfg['variance']) * self.scale / self.resize\n        b_landmarks = batched_decode_landm(b_landmarks, priors, self.cfg['variance']) * self.scale1 / self.resize\n        b_conf = b_conf[:, :, 1]\n\n        # index for selection\n        b_indice = b_conf > conf_threshold\n\n        # concat\n        b_loc_and_conf = torch.cat((b_loc, b_conf.unsqueeze(-1)), dim=2).float()\n\n        for pred, landm, inds in zip(b_loc_and_conf, b_landmarks, b_indice):\n\n            # ignore low scores\n            pred, landm = pred[inds, :], landm[inds, :]\n            if pred.shape[0] == 0:\n                final_bounding_boxes.append(np.array([], dtype=np.float32))\n                final_landmarks.append(np.array([], dtype=np.float32))\n                continue\n\n            # sort\n            # order = score.argsort(descending=True)\n            # box, landm, score = box[order], landm[order], score[order]\n\n            # to CPU\n            bounding_boxes, landm = pred.cpu().numpy(), landm.cpu().numpy()\n\n            # NMS\n            keep = py_cpu_nms(bounding_boxes, nms_threshold)\n            bounding_boxes, landmarks = bounding_boxes[keep, :], landm[keep]\n\n            # append\n            final_bounding_boxes.append(bounding_boxes)\n            final_landmarks.append(landmarks)\n        # self.t['forward_pass'].toc(average=True)\n        # self.batch_time += self.t['forward_pass'].diff\n        # self.total_frame += len(frames)\n        # print(self.batch_time / self.total_frame)\n\n        return final_bounding_boxes, final_landmarks\n"
  },
  {
    "path": "extras/facexlib/detection/retinaface_net.py",
    "content": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\ndef conv_bn(inp, oup, stride=1, leaky=0):\n    return nn.Sequential(\n        nn.Conv2d(inp, oup, 3, stride, 1, bias=False), nn.BatchNorm2d(oup),\n        nn.LeakyReLU(negative_slope=leaky, inplace=True))\n\n\ndef conv_bn_no_relu(inp, oup, stride):\n    return nn.Sequential(\n        nn.Conv2d(inp, oup, 3, stride, 1, bias=False),\n        nn.BatchNorm2d(oup),\n    )\n\n\ndef conv_bn1X1(inp, oup, stride, leaky=0):\n    return nn.Sequential(\n        nn.Conv2d(inp, oup, 1, stride, padding=0, bias=False), nn.BatchNorm2d(oup),\n        nn.LeakyReLU(negative_slope=leaky, inplace=True))\n\n\ndef conv_dw(inp, oup, stride, leaky=0.1):\n    return nn.Sequential(\n        nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),\n        nn.BatchNorm2d(inp),\n        nn.LeakyReLU(negative_slope=leaky, inplace=True),\n        nn.Conv2d(inp, oup, 1, 1, 0, bias=False),\n        nn.BatchNorm2d(oup),\n        nn.LeakyReLU(negative_slope=leaky, inplace=True),\n    )\n\n\nclass SSH(nn.Module):\n\n    def __init__(self, in_channel, out_channel):\n        super(SSH, self).__init__()\n        assert out_channel % 4 == 0\n        leaky = 0\n        if (out_channel <= 64):\n            leaky = 0.1\n        self.conv3X3 = conv_bn_no_relu(in_channel, out_channel // 2, stride=1)\n\n        self.conv5X5_1 = conv_bn(in_channel, out_channel // 4, stride=1, leaky=leaky)\n        self.conv5X5_2 = conv_bn_no_relu(out_channel // 4, out_channel // 4, stride=1)\n\n        self.conv7X7_2 = conv_bn(out_channel // 4, out_channel // 4, stride=1, leaky=leaky)\n        self.conv7x7_3 = conv_bn_no_relu(out_channel // 4, out_channel // 4, stride=1)\n\n    def forward(self, input):\n        conv3X3 = self.conv3X3(input)\n\n        conv5X5_1 = self.conv5X5_1(input)\n        conv5X5 = self.conv5X5_2(conv5X5_1)\n\n        conv7X7_2 = self.conv7X7_2(conv5X5_1)\n        conv7X7 = self.conv7x7_3(conv7X7_2)\n\n        out = torch.cat([conv3X3, conv5X5, conv7X7], dim=1)\n        out = F.relu(out)\n        return out\n\n\nclass FPN(nn.Module):\n\n    def __init__(self, in_channels_list, out_channels):\n        super(FPN, self).__init__()\n        leaky = 0\n        if (out_channels <= 64):\n            leaky = 0.1\n        self.output1 = conv_bn1X1(in_channels_list[0], out_channels, stride=1, leaky=leaky)\n        self.output2 = conv_bn1X1(in_channels_list[1], out_channels, stride=1, leaky=leaky)\n        self.output3 = conv_bn1X1(in_channels_list[2], out_channels, stride=1, leaky=leaky)\n\n        self.merge1 = conv_bn(out_channels, out_channels, leaky=leaky)\n        self.merge2 = conv_bn(out_channels, out_channels, leaky=leaky)\n\n    def forward(self, input):\n        # names = list(input.keys())\n        # input = list(input.values())\n\n        output1 = self.output1(input[0])\n        output2 = self.output2(input[1])\n        output3 = self.output3(input[2])\n\n        up3 = F.interpolate(output3, size=[output2.size(2), output2.size(3)], mode='nearest')\n        output2 = output2 + up3\n        output2 = self.merge2(output2)\n\n        up2 = F.interpolate(output2, size=[output1.size(2), output1.size(3)], mode='nearest')\n        output1 = output1 + up2\n        output1 = self.merge1(output1)\n\n        out = [output1, output2, output3]\n        return out\n\n\nclass MobileNetV1(nn.Module):\n\n    def __init__(self):\n        super(MobileNetV1, self).__init__()\n        self.stage1 = nn.Sequential(\n            conv_bn(3, 8, 2, leaky=0.1),  # 3\n            conv_dw(8, 16, 1),  # 7\n            conv_dw(16, 32, 2),  # 11\n            conv_dw(32, 32, 1),  # 19\n            conv_dw(32, 64, 2),  # 27\n            conv_dw(64, 64, 1),  # 43\n        )\n        self.stage2 = nn.Sequential(\n            conv_dw(64, 128, 2),  # 43 + 16 = 59\n            conv_dw(128, 128, 1),  # 59 + 32 = 91\n            conv_dw(128, 128, 1),  # 91 + 32 = 123\n            conv_dw(128, 128, 1),  # 123 + 32 = 155\n            conv_dw(128, 128, 1),  # 155 + 32 = 187\n            conv_dw(128, 128, 1),  # 187 + 32 = 219\n        )\n        self.stage3 = nn.Sequential(\n            conv_dw(128, 256, 2),  # 219 +3 2 = 241\n            conv_dw(256, 256, 1),  # 241 + 64 = 301\n        )\n        self.avg = nn.AdaptiveAvgPool2d((1, 1))\n        self.fc = nn.Linear(256, 1000)\n\n    def forward(self, x):\n        x = self.stage1(x)\n        x = self.stage2(x)\n        x = self.stage3(x)\n        x = self.avg(x)\n        # x = self.model(x)\n        x = x.view(-1, 256)\n        x = self.fc(x)\n        return x\n\n\nclass ClassHead(nn.Module):\n\n    def __init__(self, inchannels=512, num_anchors=3):\n        super(ClassHead, self).__init__()\n        self.num_anchors = num_anchors\n        self.conv1x1 = nn.Conv2d(inchannels, self.num_anchors * 2, kernel_size=(1, 1), stride=1, padding=0)\n\n    def forward(self, x):\n        out = self.conv1x1(x)\n        out = out.permute(0, 2, 3, 1).contiguous()\n\n        return out.view(out.shape[0], -1, 2)\n\n\nclass BboxHead(nn.Module):\n\n    def __init__(self, inchannels=512, num_anchors=3):\n        super(BboxHead, self).__init__()\n        self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 4, kernel_size=(1, 1), stride=1, padding=0)\n\n    def forward(self, x):\n        out = self.conv1x1(x)\n        out = out.permute(0, 2, 3, 1).contiguous()\n\n        return out.view(out.shape[0], -1, 4)\n\n\nclass LandmarkHead(nn.Module):\n\n    def __init__(self, inchannels=512, num_anchors=3):\n        super(LandmarkHead, self).__init__()\n        self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 10, kernel_size=(1, 1), stride=1, padding=0)\n\n    def forward(self, x):\n        out = self.conv1x1(x)\n        out = out.permute(0, 2, 3, 1).contiguous()\n\n        return out.view(out.shape[0], -1, 10)\n\n\ndef make_class_head(fpn_num=3, inchannels=64, anchor_num=2):\n    classhead = nn.ModuleList()\n    for i in range(fpn_num):\n        classhead.append(ClassHead(inchannels, anchor_num))\n    return classhead\n\n\ndef make_bbox_head(fpn_num=3, inchannels=64, anchor_num=2):\n    bboxhead = nn.ModuleList()\n    for i in range(fpn_num):\n        bboxhead.append(BboxHead(inchannels, anchor_num))\n    return bboxhead\n\n\ndef make_landmark_head(fpn_num=3, inchannels=64, anchor_num=2):\n    landmarkhead = nn.ModuleList()\n    for i in range(fpn_num):\n        landmarkhead.append(LandmarkHead(inchannels, anchor_num))\n    return landmarkhead\n"
  },
  {
    "path": "extras/facexlib/detection/retinaface_utils.py",
    "content": "import numpy as np\nimport torch\nimport torchvision\nfrom itertools import product as product\nfrom math import ceil\n\n\nclass PriorBox(object):\n\n    def __init__(self, cfg, image_size=None, phase='train'):\n        super(PriorBox, self).__init__()\n        self.min_sizes = cfg['min_sizes']\n        self.steps = cfg['steps']\n        self.clip = cfg['clip']\n        self.image_size = image_size\n        self.feature_maps = [[ceil(self.image_size[0] / step), ceil(self.image_size[1] / step)] for step in self.steps]\n        self.name = 's'\n\n    def forward(self):\n        anchors = []\n        for k, f in enumerate(self.feature_maps):\n            min_sizes = self.min_sizes[k]\n            for i, j in product(range(f[0]), range(f[1])):\n                for min_size in min_sizes:\n                    s_kx = min_size / self.image_size[1]\n                    s_ky = min_size / self.image_size[0]\n                    dense_cx = [x * self.steps[k] / self.image_size[1] for x in [j + 0.5]]\n                    dense_cy = [y * self.steps[k] / self.image_size[0] for y in [i + 0.5]]\n                    for cy, cx in product(dense_cy, dense_cx):\n                        anchors += [cx, cy, s_kx, s_ky]\n\n        # back to torch land\n        output = torch.Tensor(anchors).view(-1, 4)\n        if self.clip:\n            output.clamp_(max=1, min=0)\n        return output\n\n\ndef py_cpu_nms(dets, thresh):\n    \"\"\"Pure Python NMS baseline.\"\"\"\n    keep = torchvision.ops.nms(\n        boxes=torch.Tensor(dets[:, :4]),\n        scores=torch.Tensor(dets[:, 4]),\n        iou_threshold=thresh,\n    )\n\n    return list(keep)\n\n\ndef point_form(boxes):\n    \"\"\" Convert prior_boxes to (xmin, ymin, xmax, ymax)\n    representation for comparison to point form ground truth data.\n    Args:\n        boxes: (tensor) center-size default boxes from priorbox layers.\n    Return:\n        boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes.\n    \"\"\"\n    return torch.cat(\n        (\n            boxes[:, :2] - boxes[:, 2:] / 2,  # xmin, ymin\n            boxes[:, :2] + boxes[:, 2:] / 2),\n        1)  # xmax, ymax\n\n\ndef center_size(boxes):\n    \"\"\" Convert prior_boxes to (cx, cy, w, h)\n    representation for comparison to center-size form ground truth data.\n    Args:\n        boxes: (tensor) point_form boxes\n    Return:\n        boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes.\n    \"\"\"\n    return torch.cat(\n        (boxes[:, 2:] + boxes[:, :2]) / 2,  # cx, cy\n        boxes[:, 2:] - boxes[:, :2],\n        1)  # w, h\n\n\ndef intersect(box_a, box_b):\n    \"\"\" We resize both tensors to [A,B,2] without new malloc:\n    [A,2] -> [A,1,2] -> [A,B,2]\n    [B,2] -> [1,B,2] -> [A,B,2]\n    Then we compute the area of intersect between box_a and box_b.\n    Args:\n      box_a: (tensor) bounding boxes, Shape: [A,4].\n      box_b: (tensor) bounding boxes, Shape: [B,4].\n    Return:\n      (tensor) intersection area, Shape: [A,B].\n    \"\"\"\n    A = box_a.size(0)\n    B = box_b.size(0)\n    max_xy = torch.min(box_a[:, 2:].unsqueeze(1).expand(A, B, 2), box_b[:, 2:].unsqueeze(0).expand(A, B, 2))\n    min_xy = torch.max(box_a[:, :2].unsqueeze(1).expand(A, B, 2), box_b[:, :2].unsqueeze(0).expand(A, B, 2))\n    inter = torch.clamp((max_xy - min_xy), min=0)\n    return inter[:, :, 0] * inter[:, :, 1]\n\n\ndef jaccard(box_a, box_b):\n    \"\"\"Compute the jaccard overlap of two sets of boxes.  The jaccard overlap\n    is simply the intersection over union of two boxes.  Here we operate on\n    ground truth boxes and default boxes.\n    E.g.:\n        A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B)\n    Args:\n        box_a: (tensor) Ground truth bounding boxes, Shape: [num_objects,4]\n        box_b: (tensor) Prior boxes from priorbox layers, Shape: [num_priors,4]\n    Return:\n        jaccard overlap: (tensor) Shape: [box_a.size(0), box_b.size(0)]\n    \"\"\"\n    inter = intersect(box_a, box_b)\n    area_a = ((box_a[:, 2] - box_a[:, 0]) * (box_a[:, 3] - box_a[:, 1])).unsqueeze(1).expand_as(inter)  # [A,B]\n    area_b = ((box_b[:, 2] - box_b[:, 0]) * (box_b[:, 3] - box_b[:, 1])).unsqueeze(0).expand_as(inter)  # [A,B]\n    union = area_a + area_b - inter\n    return inter / union  # [A,B]\n\n\ndef matrix_iou(a, b):\n    \"\"\"\n    return iou of a and b, numpy version for data augenmentation\n    \"\"\"\n    lt = np.maximum(a[:, np.newaxis, :2], b[:, :2])\n    rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:])\n\n    area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2)\n    area_a = np.prod(a[:, 2:] - a[:, :2], axis=1)\n    area_b = np.prod(b[:, 2:] - b[:, :2], axis=1)\n    return area_i / (area_a[:, np.newaxis] + area_b - area_i)\n\n\ndef matrix_iof(a, b):\n    \"\"\"\n    return iof of a and b, numpy version for data augenmentation\n    \"\"\"\n    lt = np.maximum(a[:, np.newaxis, :2], b[:, :2])\n    rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:])\n\n    area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2)\n    area_a = np.prod(a[:, 2:] - a[:, :2], axis=1)\n    return area_i / np.maximum(area_a[:, np.newaxis], 1)\n\n\ndef match(threshold, truths, priors, variances, labels, landms, loc_t, conf_t, landm_t, idx):\n    \"\"\"Match each prior box with the ground truth box of the highest jaccard\n    overlap, encode the bounding boxes, then return the matched indices\n    corresponding to both confidence and location preds.\n    Args:\n        threshold: (float) The overlap threshold used when matching boxes.\n        truths: (tensor) Ground truth boxes, Shape: [num_obj, 4].\n        priors: (tensor) Prior boxes from priorbox layers, Shape: [n_priors,4].\n        variances: (tensor) Variances corresponding to each prior coord,\n            Shape: [num_priors, 4].\n        labels: (tensor) All the class labels for the image, Shape: [num_obj].\n        landms: (tensor) Ground truth landms, Shape [num_obj, 10].\n        loc_t: (tensor) Tensor to be filled w/ encoded location targets.\n        conf_t: (tensor) Tensor to be filled w/ matched indices for conf preds.\n        landm_t: (tensor) Tensor to be filled w/ encoded landm targets.\n        idx: (int) current batch index\n    Return:\n        The matched indices corresponding to 1)location 2)confidence\n        3)landm preds.\n    \"\"\"\n    # jaccard index\n    overlaps = jaccard(truths, point_form(priors))\n    # (Bipartite Matching)\n    # [1,num_objects] best prior for each ground truth\n    best_prior_overlap, best_prior_idx = overlaps.max(1, keepdim=True)\n\n    # ignore hard gt\n    valid_gt_idx = best_prior_overlap[:, 0] >= 0.2\n    best_prior_idx_filter = best_prior_idx[valid_gt_idx, :]\n    if best_prior_idx_filter.shape[0] <= 0:\n        loc_t[idx] = 0\n        conf_t[idx] = 0\n        return\n\n    # [1,num_priors] best ground truth for each prior\n    best_truth_overlap, best_truth_idx = overlaps.max(0, keepdim=True)\n    best_truth_idx.squeeze_(0)\n    best_truth_overlap.squeeze_(0)\n    best_prior_idx.squeeze_(1)\n    best_prior_idx_filter.squeeze_(1)\n    best_prior_overlap.squeeze_(1)\n    best_truth_overlap.index_fill_(0, best_prior_idx_filter, 2)  # ensure best prior\n    # TODO refactor: index  best_prior_idx with long tensor\n    # ensure every gt matches with its prior of max overlap\n    for j in range(best_prior_idx.size(0)):  # 判别此anchor是预测哪一个boxes\n        best_truth_idx[best_prior_idx[j]] = j\n    matches = truths[best_truth_idx]  # Shape: [num_priors,4] 此处为每一个anchor对应的bbox取出来\n    conf = labels[best_truth_idx]  # Shape: [num_priors]      此处为每一个anchor对应的label取出来\n    conf[best_truth_overlap < threshold] = 0  # label as background   overlap<0.35的全部作为负样本\n    loc = encode(matches, priors, variances)\n\n    matches_landm = landms[best_truth_idx]\n    landm = encode_landm(matches_landm, priors, variances)\n    loc_t[idx] = loc  # [num_priors,4] encoded offsets to learn\n    conf_t[idx] = conf  # [num_priors] top class label for each prior\n    landm_t[idx] = landm\n\n\ndef encode(matched, priors, variances):\n    \"\"\"Encode the variances from the priorbox layers into the ground truth boxes\n    we have matched (based on jaccard overlap) with the prior boxes.\n    Args:\n        matched: (tensor) Coords of ground truth for each prior in point-form\n            Shape: [num_priors, 4].\n        priors: (tensor) Prior boxes in center-offset form\n            Shape: [num_priors,4].\n        variances: (list[float]) Variances of priorboxes\n    Return:\n        encoded boxes (tensor), Shape: [num_priors, 4]\n    \"\"\"\n\n    # dist b/t match center and prior's center\n    g_cxcy = (matched[:, :2] + matched[:, 2:]) / 2 - priors[:, :2]\n    # encode variance\n    g_cxcy /= (variances[0] * priors[:, 2:])\n    # match wh / prior wh\n    g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:]\n    g_wh = torch.log(g_wh) / variances[1]\n    # return target for smooth_l1_loss\n    return torch.cat([g_cxcy, g_wh], 1)  # [num_priors,4]\n\n\ndef encode_landm(matched, priors, variances):\n    \"\"\"Encode the variances from the priorbox layers into the ground truth boxes\n    we have matched (based on jaccard overlap) with the prior boxes.\n    Args:\n        matched: (tensor) Coords of ground truth for each prior in point-form\n            Shape: [num_priors, 10].\n        priors: (tensor) Prior boxes in center-offset form\n            Shape: [num_priors,4].\n        variances: (list[float]) Variances of priorboxes\n    Return:\n        encoded landm (tensor), Shape: [num_priors, 10]\n    \"\"\"\n\n    # dist b/t match center and prior's center\n    matched = torch.reshape(matched, (matched.size(0), 5, 2))\n    priors_cx = priors[:, 0].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)\n    priors_cy = priors[:, 1].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)\n    priors_w = priors[:, 2].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)\n    priors_h = priors[:, 3].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)\n    priors = torch.cat([priors_cx, priors_cy, priors_w, priors_h], dim=2)\n    g_cxcy = matched[:, :, :2] - priors[:, :, :2]\n    # encode variance\n    g_cxcy /= (variances[0] * priors[:, :, 2:])\n    # g_cxcy /= priors[:, :, 2:]\n    g_cxcy = g_cxcy.reshape(g_cxcy.size(0), -1)\n    # return target for smooth_l1_loss\n    return g_cxcy\n\n\n# Adapted from https://github.com/Hakuyume/chainer-ssd\ndef decode(loc, priors, variances):\n    \"\"\"Decode locations from predictions using priors to undo\n    the encoding we did for offset regression at train time.\n    Args:\n        loc (tensor): location predictions for loc layers,\n            Shape: [num_priors,4]\n        priors (tensor): Prior boxes in center-offset form.\n            Shape: [num_priors,4].\n        variances: (list[float]) Variances of priorboxes\n    Return:\n        decoded bounding box predictions\n    \"\"\"\n\n    boxes = torch.cat((priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:],\n                       priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1)\n    boxes[:, :2] -= boxes[:, 2:] / 2\n    boxes[:, 2:] += boxes[:, :2]\n    return boxes\n\n\ndef decode_landm(pre, priors, variances):\n    \"\"\"Decode landm from predictions using priors to undo\n    the encoding we did for offset regression at train time.\n    Args:\n        pre (tensor): landm predictions for loc layers,\n            Shape: [num_priors,10]\n        priors (tensor): Prior boxes in center-offset form.\n            Shape: [num_priors,4].\n        variances: (list[float]) Variances of priorboxes\n    Return:\n        decoded landm predictions\n    \"\"\"\n    tmp = (\n        priors[:, :2] + pre[:, :2] * variances[0] * priors[:, 2:],\n        priors[:, :2] + pre[:, 2:4] * variances[0] * priors[:, 2:],\n        priors[:, :2] + pre[:, 4:6] * variances[0] * priors[:, 2:],\n        priors[:, :2] + pre[:, 6:8] * variances[0] * priors[:, 2:],\n        priors[:, :2] + pre[:, 8:10] * variances[0] * priors[:, 2:],\n    )\n    landms = torch.cat(tmp, dim=1)\n    return landms\n\n\ndef batched_decode(b_loc, priors, variances):\n    \"\"\"Decode locations from predictions using priors to undo\n    the encoding we did for offset regression at train time.\n    Args:\n        b_loc (tensor): location predictions for loc layers,\n            Shape: [num_batches,num_priors,4]\n        priors (tensor): Prior boxes in center-offset form.\n            Shape: [1,num_priors,4].\n        variances: (list[float]) Variances of priorboxes\n    Return:\n        decoded bounding box predictions\n    \"\"\"\n    boxes = (\n        priors[:, :, :2] + b_loc[:, :, :2] * variances[0] * priors[:, :, 2:],\n        priors[:, :, 2:] * torch.exp(b_loc[:, :, 2:] * variances[1]),\n    )\n    boxes = torch.cat(boxes, dim=2)\n\n    boxes[:, :, :2] -= boxes[:, :, 2:] / 2\n    boxes[:, :, 2:] += boxes[:, :, :2]\n    return boxes\n\n\ndef batched_decode_landm(pre, priors, variances):\n    \"\"\"Decode landm from predictions using priors to undo\n    the encoding we did for offset regression at train time.\n    Args:\n        pre (tensor): landm predictions for loc layers,\n            Shape: [num_batches,num_priors,10]\n        priors (tensor): Prior boxes in center-offset form.\n            Shape: [1,num_priors,4].\n        variances: (list[float]) Variances of priorboxes\n    Return:\n        decoded landm predictions\n    \"\"\"\n    landms = (\n        priors[:, :, :2] + pre[:, :, :2] * variances[0] * priors[:, :, 2:],\n        priors[:, :, :2] + pre[:, :, 2:4] * variances[0] * priors[:, :, 2:],\n        priors[:, :, :2] + pre[:, :, 4:6] * variances[0] * priors[:, :, 2:],\n        priors[:, :, :2] + pre[:, :, 6:8] * variances[0] * priors[:, :, 2:],\n        priors[:, :, :2] + pre[:, :, 8:10] * variances[0] * priors[:, :, 2:],\n    )\n    landms = torch.cat(landms, dim=2)\n    return landms\n\n\ndef log_sum_exp(x):\n    \"\"\"Utility function for computing log_sum_exp while determining\n    This will be used to determine unaveraged confidence loss across\n    all examples in a batch.\n    Args:\n        x (Variable(tensor)): conf_preds from conf layers\n    \"\"\"\n    x_max = x.data.max()\n    return torch.log(torch.sum(torch.exp(x - x_max), 1, keepdim=True)) + x_max\n\n\n# Original author: Francisco Massa:\n# https://github.com/fmassa/object-detection.torch\n# Ported to PyTorch by Max deGroot (02/01/2017)\ndef nms(boxes, scores, overlap=0.5, top_k=200):\n    \"\"\"Apply non-maximum suppression at test time to avoid detecting too many\n    overlapping bounding boxes for a given object.\n    Args:\n        boxes: (tensor) The location preds for the img, Shape: [num_priors,4].\n        scores: (tensor) The class predscores for the img, Shape:[num_priors].\n        overlap: (float) The overlap thresh for suppressing unnecessary boxes.\n        top_k: (int) The Maximum number of box preds to consider.\n    Return:\n        The indices of the kept boxes with respect to num_priors.\n    \"\"\"\n\n    keep = torch.Tensor(scores.size(0)).fill_(0).long()\n    if boxes.numel() == 0:\n        return keep\n    x1 = boxes[:, 0]\n    y1 = boxes[:, 1]\n    x2 = boxes[:, 2]\n    y2 = boxes[:, 3]\n    area = torch.mul(x2 - x1, y2 - y1)\n    v, idx = scores.sort(0)  # sort in ascending order\n    # I = I[v >= 0.01]\n    idx = idx[-top_k:]  # indices of the top-k largest vals\n    xx1 = boxes.new()\n    yy1 = boxes.new()\n    xx2 = boxes.new()\n    yy2 = boxes.new()\n    w = boxes.new()\n    h = boxes.new()\n\n    # keep = torch.Tensor()\n    count = 0\n    while idx.numel() > 0:\n        i = idx[-1]  # index of current largest val\n        # keep.append(i)\n        keep[count] = i\n        count += 1\n        if idx.size(0) == 1:\n            break\n        idx = idx[:-1]  # remove kept element from view\n        # load bboxes of next highest vals\n        torch.index_select(x1, 0, idx, out=xx1)\n        torch.index_select(y1, 0, idx, out=yy1)\n        torch.index_select(x2, 0, idx, out=xx2)\n        torch.index_select(y2, 0, idx, out=yy2)\n        # store element-wise max with next highest score\n        xx1 = torch.clamp(xx1, min=x1[i])\n        yy1 = torch.clamp(yy1, min=y1[i])\n        xx2 = torch.clamp(xx2, max=x2[i])\n        yy2 = torch.clamp(yy2, max=y2[i])\n        w.resize_as_(xx2)\n        h.resize_as_(yy2)\n        w = xx2 - xx1\n        h = yy2 - yy1\n        # check sizes of xx1 and xx2.. after each iteration\n        w = torch.clamp(w, min=0.0)\n        h = torch.clamp(h, min=0.0)\n        inter = w * h\n        # IoU = i / (area(a) + area(b) - i)\n        rem_areas = torch.index_select(area, 0, idx)  # load remaining areas)\n        union = (rem_areas - inter) + area[i]\n        IoU = inter / union  # store result in iou\n        # keep only elements with an IoU <= overlap\n        idx = idx[IoU.le(overlap)]\n    return keep, count\n"
  },
  {
    "path": "extras/facexlib/parsing/__init__.py",
    "content": "import torch\n\nfrom extras.facexlib.utils import load_file_from_url\nfrom .bisenet import BiSeNet\nfrom .parsenet import ParseNet\n\n\ndef init_parsing_model(model_name='bisenet', half=False, device='cuda', model_rootpath=None):\n    if model_name == 'bisenet':\n        model = BiSeNet(num_class=19)\n        model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.2.0/parsing_bisenet.pth'\n    elif model_name == 'parsenet':\n        model = ParseNet(in_size=512, out_size=512, parsing_ch=19)\n        model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth'\n    else:\n        raise NotImplementedError(f'{model_name} is not implemented.')\n\n    model_path = load_file_from_url(\n        url=model_url, model_dir='facexlib/weights', progress=True, file_name=None, save_dir=model_rootpath)\n    load_net = torch.load(model_path, map_location=lambda storage, loc: storage, weights_only=True)\n    model.load_state_dict(load_net, strict=True)\n    model.eval()\n    model = model.to(device)\n    return model\n"
  },
  {
    "path": "extras/facexlib/parsing/bisenet.py",
    "content": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom .resnet import ResNet18\n\n\nclass ConvBNReLU(nn.Module):\n\n    def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1):\n        super(ConvBNReLU, self).__init__()\n        self.conv = nn.Conv2d(in_chan, out_chan, kernel_size=ks, stride=stride, padding=padding, bias=False)\n        self.bn = nn.BatchNorm2d(out_chan)\n\n    def forward(self, x):\n        x = self.conv(x)\n        x = F.relu(self.bn(x))\n        return x\n\n\nclass BiSeNetOutput(nn.Module):\n\n    def __init__(self, in_chan, mid_chan, num_class):\n        super(BiSeNetOutput, self).__init__()\n        self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1)\n        self.conv_out = nn.Conv2d(mid_chan, num_class, kernel_size=1, bias=False)\n\n    def forward(self, x):\n        feat = self.conv(x)\n        out = self.conv_out(feat)\n        return out, feat\n\n\nclass AttentionRefinementModule(nn.Module):\n\n    def __init__(self, in_chan, out_chan):\n        super(AttentionRefinementModule, self).__init__()\n        self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1)\n        self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size=1, bias=False)\n        self.bn_atten = nn.BatchNorm2d(out_chan)\n        self.sigmoid_atten = nn.Sigmoid()\n\n    def forward(self, x):\n        feat = self.conv(x)\n        atten = F.avg_pool2d(feat, feat.size()[2:])\n        atten = self.conv_atten(atten)\n        atten = self.bn_atten(atten)\n        atten = self.sigmoid_atten(atten)\n        out = torch.mul(feat, atten)\n        return out\n\n\nclass ContextPath(nn.Module):\n\n    def __init__(self):\n        super(ContextPath, self).__init__()\n        self.resnet = ResNet18()\n        self.arm16 = AttentionRefinementModule(256, 128)\n        self.arm32 = AttentionRefinementModule(512, 128)\n        self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)\n        self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)\n        self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0)\n\n    def forward(self, x):\n        feat8, feat16, feat32 = self.resnet(x)\n        h8, w8 = feat8.size()[2:]\n        h16, w16 = feat16.size()[2:]\n        h32, w32 = feat32.size()[2:]\n\n        avg = F.avg_pool2d(feat32, feat32.size()[2:])\n        avg = self.conv_avg(avg)\n        avg_up = F.interpolate(avg, (h32, w32), mode='nearest')\n\n        feat32_arm = self.arm32(feat32)\n        feat32_sum = feat32_arm + avg_up\n        feat32_up = F.interpolate(feat32_sum, (h16, w16), mode='nearest')\n        feat32_up = self.conv_head32(feat32_up)\n\n        feat16_arm = self.arm16(feat16)\n        feat16_sum = feat16_arm + feat32_up\n        feat16_up = F.interpolate(feat16_sum, (h8, w8), mode='nearest')\n        feat16_up = self.conv_head16(feat16_up)\n\n        return feat8, feat16_up, feat32_up  # x8, x8, x16\n\n\nclass FeatureFusionModule(nn.Module):\n\n    def __init__(self, in_chan, out_chan):\n        super(FeatureFusionModule, self).__init__()\n        self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0)\n        self.conv1 = nn.Conv2d(out_chan, out_chan // 4, kernel_size=1, stride=1, padding=0, bias=False)\n        self.conv2 = nn.Conv2d(out_chan // 4, out_chan, kernel_size=1, stride=1, padding=0, bias=False)\n        self.relu = nn.ReLU(inplace=True)\n        self.sigmoid = nn.Sigmoid()\n\n    def forward(self, fsp, fcp):\n        fcat = torch.cat([fsp, fcp], dim=1)\n        feat = self.convblk(fcat)\n        atten = F.avg_pool2d(feat, feat.size()[2:])\n        atten = self.conv1(atten)\n        atten = self.relu(atten)\n        atten = self.conv2(atten)\n        atten = self.sigmoid(atten)\n        feat_atten = torch.mul(feat, atten)\n        feat_out = feat_atten + feat\n        return feat_out\n\n\nclass BiSeNet(nn.Module):\n\n    def __init__(self, num_class):\n        super(BiSeNet, self).__init__()\n        self.cp = ContextPath()\n        self.ffm = FeatureFusionModule(256, 256)\n        self.conv_out = BiSeNetOutput(256, 256, num_class)\n        self.conv_out16 = BiSeNetOutput(128, 64, num_class)\n        self.conv_out32 = BiSeNetOutput(128, 64, num_class)\n\n    def forward(self, x, return_feat=False):\n        h, w = x.size()[2:]\n        feat_res8, feat_cp8, feat_cp16 = self.cp(x)  # return res3b1 feature\n        feat_sp = feat_res8  # replace spatial path feature with res3b1 feature\n        feat_fuse = self.ffm(feat_sp, feat_cp8)\n\n        out, feat = self.conv_out(feat_fuse)\n        out16, feat16 = self.conv_out16(feat_cp8)\n        out32, feat32 = self.conv_out32(feat_cp16)\n\n        out = F.interpolate(out, (h, w), mode='bilinear', align_corners=True)\n        out16 = F.interpolate(out16, (h, w), mode='bilinear', align_corners=True)\n        out32 = F.interpolate(out32, (h, w), mode='bilinear', align_corners=True)\n\n        if return_feat:\n            feat = F.interpolate(feat, (h, w), mode='bilinear', align_corners=True)\n            feat16 = F.interpolate(feat16, (h, w), mode='bilinear', align_corners=True)\n            feat32 = F.interpolate(feat32, (h, w), mode='bilinear', align_corners=True)\n            return out, out16, out32, feat, feat16, feat32\n        else:\n            return out, out16, out32\n"
  },
  {
    "path": "extras/facexlib/parsing/parsenet.py",
    "content": "\"\"\"Modified from https://github.com/chaofengc/PSFRGAN\n\"\"\"\nimport numpy as np\nimport torch.nn as nn\nfrom torch.nn import functional as F\n\n\nclass NormLayer(nn.Module):\n    \"\"\"Normalization Layers.\n\n    Args:\n        channels: input channels, for batch norm and instance norm.\n        input_size: input shape without batch size, for layer norm.\n    \"\"\"\n\n    def __init__(self, channels, normalize_shape=None, norm_type='bn'):\n        super(NormLayer, self).__init__()\n        norm_type = norm_type.lower()\n        self.norm_type = norm_type\n        if norm_type == 'bn':\n            self.norm = nn.BatchNorm2d(channels, affine=True)\n        elif norm_type == 'in':\n            self.norm = nn.InstanceNorm2d(channels, affine=False)\n        elif norm_type == 'gn':\n            self.norm = nn.GroupNorm(32, channels, affine=True)\n        elif norm_type == 'pixel':\n            self.norm = lambda x: F.normalize(x, p=2, dim=1)\n        elif norm_type == 'layer':\n            self.norm = nn.LayerNorm(normalize_shape)\n        elif norm_type == 'none':\n            self.norm = lambda x: x * 1.0\n        else:\n            assert 1 == 0, f'Norm type {norm_type} not support.'\n\n    def forward(self, x, ref=None):\n        if self.norm_type == 'spade':\n            return self.norm(x, ref)\n        else:\n            return self.norm(x)\n\n\nclass ReluLayer(nn.Module):\n    \"\"\"Relu Layer.\n\n    Args:\n        relu type: type of relu layer, candidates are\n            - ReLU\n            - LeakyReLU: default relu slope 0.2\n            - PRelu\n            - SELU\n            - none: direct pass\n    \"\"\"\n\n    def __init__(self, channels, relu_type='relu'):\n        super(ReluLayer, self).__init__()\n        relu_type = relu_type.lower()\n        if relu_type == 'relu':\n            self.func = nn.ReLU(True)\n        elif relu_type == 'leakyrelu':\n            self.func = nn.LeakyReLU(0.2, inplace=True)\n        elif relu_type == 'prelu':\n            self.func = nn.PReLU(channels)\n        elif relu_type == 'selu':\n            self.func = nn.SELU(True)\n        elif relu_type == 'none':\n            self.func = lambda x: x * 1.0\n        else:\n            assert 1 == 0, f'Relu type {relu_type} not support.'\n\n    def forward(self, x):\n        return self.func(x)\n\n\nclass ConvLayer(nn.Module):\n\n    def __init__(self,\n                 in_channels,\n                 out_channels,\n                 kernel_size=3,\n                 scale='none',\n                 norm_type='none',\n                 relu_type='none',\n                 use_pad=True,\n                 bias=True):\n        super(ConvLayer, self).__init__()\n        self.use_pad = use_pad\n        self.norm_type = norm_type\n        if norm_type in ['bn']:\n            bias = False\n\n        stride = 2 if scale == 'down' else 1\n\n        self.scale_func = lambda x: x\n        if scale == 'up':\n            self.scale_func = lambda x: nn.functional.interpolate(x, scale_factor=2, mode='nearest')\n\n        self.reflection_pad = nn.ReflectionPad2d(int(np.ceil((kernel_size - 1.) / 2)))\n        self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, stride, bias=bias)\n\n        self.relu = ReluLayer(out_channels, relu_type)\n        self.norm = NormLayer(out_channels, norm_type=norm_type)\n\n    def forward(self, x):\n        out = self.scale_func(x)\n        if self.use_pad:\n            out = self.reflection_pad(out)\n        out = self.conv2d(out)\n        out = self.norm(out)\n        out = self.relu(out)\n        return out\n\n\nclass ResidualBlock(nn.Module):\n    \"\"\"\n    Residual block recommended in: http://torch.ch/blog/2016/02/04/resnets.html\n    \"\"\"\n\n    def __init__(self, c_in, c_out, relu_type='prelu', norm_type='bn', scale='none'):\n        super(ResidualBlock, self).__init__()\n\n        if scale == 'none' and c_in == c_out:\n            self.shortcut_func = lambda x: x\n        else:\n            self.shortcut_func = ConvLayer(c_in, c_out, 3, scale)\n\n        scale_config_dict = {'down': ['none', 'down'], 'up': ['up', 'none'], 'none': ['none', 'none']}\n        scale_conf = scale_config_dict[scale]\n\n        self.conv1 = ConvLayer(c_in, c_out, 3, scale_conf[0], norm_type=norm_type, relu_type=relu_type)\n        self.conv2 = ConvLayer(c_out, c_out, 3, scale_conf[1], norm_type=norm_type, relu_type='none')\n\n    def forward(self, x):\n        identity = self.shortcut_func(x)\n\n        res = self.conv1(x)\n        res = self.conv2(res)\n        return identity + res\n\n\nclass ParseNet(nn.Module):\n\n    def __init__(self,\n                 in_size=128,\n                 out_size=128,\n                 min_feat_size=32,\n                 base_ch=64,\n                 parsing_ch=19,\n                 res_depth=10,\n                 relu_type='LeakyReLU',\n                 norm_type='bn',\n                 ch_range=[32, 256]):\n        super().__init__()\n        self.res_depth = res_depth\n        act_args = {'norm_type': norm_type, 'relu_type': relu_type}\n        min_ch, max_ch = ch_range\n\n        ch_clip = lambda x: max(min_ch, min(x, max_ch))  # noqa: E731\n        min_feat_size = min(in_size, min_feat_size)\n\n        down_steps = int(np.log2(in_size // min_feat_size))\n        up_steps = int(np.log2(out_size // min_feat_size))\n\n        # =============== define encoder-body-decoder ====================\n        self.encoder = []\n        self.encoder.append(ConvLayer(3, base_ch, 3, 1))\n        head_ch = base_ch\n        for i in range(down_steps):\n            cin, cout = ch_clip(head_ch), ch_clip(head_ch * 2)\n            self.encoder.append(ResidualBlock(cin, cout, scale='down', **act_args))\n            head_ch = head_ch * 2\n\n        self.body = []\n        for i in range(res_depth):\n            self.body.append(ResidualBlock(ch_clip(head_ch), ch_clip(head_ch), **act_args))\n\n        self.decoder = []\n        for i in range(up_steps):\n            cin, cout = ch_clip(head_ch), ch_clip(head_ch // 2)\n            self.decoder.append(ResidualBlock(cin, cout, scale='up', **act_args))\n            head_ch = head_ch // 2\n\n        self.encoder = nn.Sequential(*self.encoder)\n        self.body = nn.Sequential(*self.body)\n        self.decoder = nn.Sequential(*self.decoder)\n        self.out_img_conv = ConvLayer(ch_clip(head_ch), 3)\n        self.out_mask_conv = ConvLayer(ch_clip(head_ch), parsing_ch)\n\n    def forward(self, x):\n        feat = self.encoder(x)\n        x = feat + self.body(feat)\n        x = self.decoder(x)\n        out_img = self.out_img_conv(x)\n        out_mask = self.out_mask_conv(x)\n        return out_mask, out_img\n"
  },
  {
    "path": "extras/facexlib/parsing/resnet.py",
    "content": "import torch.nn as nn\nimport torch.nn.functional as F\n\n\ndef conv3x3(in_planes, out_planes, stride=1):\n    \"\"\"3x3 convolution with padding\"\"\"\n    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)\n\n\nclass BasicBlock(nn.Module):\n\n    def __init__(self, in_chan, out_chan, stride=1):\n        super(BasicBlock, self).__init__()\n        self.conv1 = conv3x3(in_chan, out_chan, stride)\n        self.bn1 = nn.BatchNorm2d(out_chan)\n        self.conv2 = conv3x3(out_chan, out_chan)\n        self.bn2 = nn.BatchNorm2d(out_chan)\n        self.relu = nn.ReLU(inplace=True)\n        self.downsample = None\n        if in_chan != out_chan or stride != 1:\n            self.downsample = nn.Sequential(\n                nn.Conv2d(in_chan, out_chan, kernel_size=1, stride=stride, bias=False),\n                nn.BatchNorm2d(out_chan),\n            )\n\n    def forward(self, x):\n        residual = self.conv1(x)\n        residual = F.relu(self.bn1(residual))\n        residual = self.conv2(residual)\n        residual = self.bn2(residual)\n\n        shortcut = x\n        if self.downsample is not None:\n            shortcut = self.downsample(x)\n\n        out = shortcut + residual\n        out = self.relu(out)\n        return out\n\n\ndef create_layer_basic(in_chan, out_chan, bnum, stride=1):\n    layers = [BasicBlock(in_chan, out_chan, stride=stride)]\n    for i in range(bnum - 1):\n        layers.append(BasicBlock(out_chan, out_chan, stride=1))\n    return nn.Sequential(*layers)\n\n\nclass ResNet18(nn.Module):\n\n    def __init__(self):\n        super(ResNet18, self).__init__()\n        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)\n        self.bn1 = nn.BatchNorm2d(64)\n        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n        self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1)\n        self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2)\n        self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2)\n        self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2)\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = F.relu(self.bn1(x))\n        x = self.maxpool(x)\n\n        x = self.layer1(x)\n        feat8 = self.layer2(x)  # 1/8\n        feat16 = self.layer3(feat8)  # 1/16\n        feat32 = self.layer4(feat16)  # 1/32\n        return feat8, feat16, feat32\n"
  },
  {
    "path": "extras/facexlib/utils/__init__.py",
    "content": "from .face_utils import align_crop_face_landmarks, compute_increased_bbox, get_valid_bboxes, paste_face_back\nfrom .misc import img2tensor, load_file_from_url, scandir\n\n__all__ = [\n    'align_crop_face_landmarks', 'compute_increased_bbox', 'get_valid_bboxes', 'load_file_from_url', 'paste_face_back',\n    'img2tensor', 'scandir'\n]\n"
  },
  {
    "path": "extras/facexlib/utils/face_restoration_helper.py",
    "content": "import cv2\nimport numpy as np\nimport os\nimport torch\nfrom torchvision.transforms.functional import normalize\n\nfrom extras.facexlib.detection import init_detection_model\nfrom extras.facexlib.parsing import init_parsing_model\nfrom extras.facexlib.utils.misc import img2tensor, imwrite\n\n\ndef get_largest_face(det_faces, h, w):\n\n    def get_location(val, length):\n        if val < 0:\n            return 0\n        elif val > length:\n            return length\n        else:\n            return val\n\n    face_areas = []\n    for det_face in det_faces:\n        left = get_location(det_face[0], w)\n        right = get_location(det_face[2], w)\n        top = get_location(det_face[1], h)\n        bottom = get_location(det_face[3], h)\n        face_area = (right - left) * (bottom - top)\n        face_areas.append(face_area)\n    largest_idx = face_areas.index(max(face_areas))\n    return det_faces[largest_idx], largest_idx\n\n\ndef get_center_face(det_faces, h=0, w=0, center=None):\n    if center is not None:\n        center = np.array(center)\n    else:\n        center = np.array([w / 2, h / 2])\n    center_dist = []\n    for det_face in det_faces:\n        face_center = np.array([(det_face[0] + det_face[2]) / 2, (det_face[1] + det_face[3]) / 2])\n        dist = np.linalg.norm(face_center - center)\n        center_dist.append(dist)\n    center_idx = center_dist.index(min(center_dist))\n    return det_faces[center_idx], center_idx\n\n\nclass FaceRestoreHelper(object):\n    \"\"\"Helper for the face restoration pipeline (base class).\"\"\"\n\n    def __init__(self,\n                 upscale_factor,\n                 face_size=512,\n                 crop_ratio=(1, 1),\n                 det_model='retinaface_resnet50',\n                 save_ext='png',\n                 template_3points=False,\n                 pad_blur=False,\n                 use_parse=False,\n                 device=None,\n                 model_rootpath=None):\n        self.template_3points = template_3points  # improve robustness\n        self.upscale_factor = upscale_factor\n        # the cropped face ratio based on the square face\n        self.crop_ratio = crop_ratio  # (h, w)\n        assert (self.crop_ratio[0] >= 1 and self.crop_ratio[1] >= 1), 'crop ration only supports >=1'\n        self.face_size = (int(face_size * self.crop_ratio[1]), int(face_size * self.crop_ratio[0]))\n\n        if self.template_3points:\n            self.face_template = np.array([[192, 240], [319, 240], [257, 371]])\n        else:\n            # standard 5 landmarks for FFHQ faces with 512 x 512\n            self.face_template = np.array([[192.98138, 239.94708], [318.90277, 240.1936], [256.63416, 314.01935],\n                                           [201.26117, 371.41043], [313.08905, 371.15118]])\n        self.face_template = self.face_template * (face_size / 512.0)\n        if self.crop_ratio[0] > 1:\n            self.face_template[:, 1] += face_size * (self.crop_ratio[0] - 1) / 2\n        if self.crop_ratio[1] > 1:\n            self.face_template[:, 0] += face_size * (self.crop_ratio[1] - 1) / 2\n        self.save_ext = save_ext\n        self.pad_blur = pad_blur\n        if self.pad_blur is True:\n            self.template_3points = False\n\n        self.all_landmarks_5 = []\n        self.det_faces = []\n        self.affine_matrices = []\n        self.inverse_affine_matrices = []\n        self.cropped_faces = []\n        self.restored_faces = []\n        self.pad_input_imgs = []\n\n        if device is None:\n            self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n        else:\n            self.device = device\n\n        # init face detection model\n        self.face_det = init_detection_model(det_model, half=False, device=self.device, model_rootpath=model_rootpath)\n\n        # init face parsing model\n        self.use_parse = use_parse\n        self.face_parse = init_parsing_model(model_name='parsenet', device=self.device, model_rootpath=model_rootpath)\n\n    def set_upscale_factor(self, upscale_factor):\n        self.upscale_factor = upscale_factor\n\n    def read_image(self, img):\n        \"\"\"img can be image path or cv2 loaded image.\"\"\"\n        # self.input_img is Numpy array, (h, w, c), BGR, uint8, [0, 255]\n        if isinstance(img, str):\n            img = cv2.imread(img)\n\n        if np.max(img) > 256:  # 16-bit image\n            img = img / 65535 * 255\n        if len(img.shape) == 2:  # gray image\n            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)\n        elif img.shape[2] == 4:  # RGBA image with alpha channel\n            img = img[:, :, 0:3]\n\n        self.input_img = img\n\n    def get_face_landmarks_5(self,\n                             only_keep_largest=False,\n                             only_center_face=False,\n                             resize=None,\n                             blur_ratio=0.01,\n                             eye_dist_threshold=None):\n        if resize is None:\n            scale = 1\n            input_img = self.input_img\n        else:\n            h, w = self.input_img.shape[0:2]\n            scale = min(h, w) / resize\n            h, w = int(h / scale), int(w / scale)\n            input_img = cv2.resize(self.input_img, (w, h), interpolation=cv2.INTER_LANCZOS4)\n\n        with torch.no_grad():\n            bboxes = self.face_det.detect_faces(input_img, 0.97) * scale\n        for bbox in bboxes:\n            # remove faces with too small eye distance: side faces or too small faces\n            eye_dist = np.linalg.norm([bbox[5] - bbox[7], bbox[6] - bbox[8]])\n            if eye_dist_threshold is not None and (eye_dist < eye_dist_threshold):\n                continue\n\n            if self.template_3points:\n                landmark = np.array([[bbox[i], bbox[i + 1]] for i in range(5, 11, 2)])\n            else:\n                landmark = np.array([[bbox[i], bbox[i + 1]] for i in range(5, 15, 2)])\n            self.all_landmarks_5.append(landmark)\n            self.det_faces.append(bbox[0:5])\n        if len(self.det_faces) == 0:\n            return 0\n        if only_keep_largest:\n            h, w, _ = self.input_img.shape\n            self.det_faces, largest_idx = get_largest_face(self.det_faces, h, w)\n            self.all_landmarks_5 = [self.all_landmarks_5[largest_idx]]\n        elif only_center_face:\n            h, w, _ = self.input_img.shape\n            self.det_faces, center_idx = get_center_face(self.det_faces, h, w)\n            self.all_landmarks_5 = [self.all_landmarks_5[center_idx]]\n\n        # pad blurry images\n        if self.pad_blur:\n            self.pad_input_imgs = []\n            for landmarks in self.all_landmarks_5:\n                # get landmarks\n                eye_left = landmarks[0, :]\n                eye_right = landmarks[1, :]\n                eye_avg = (eye_left + eye_right) * 0.5\n                mouth_avg = (landmarks[3, :] + landmarks[4, :]) * 0.5\n                eye_to_eye = eye_right - eye_left\n                eye_to_mouth = mouth_avg - eye_avg\n\n                # Get the oriented crop rectangle\n                # x: half width of the oriented crop rectangle\n                x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]\n                #  - np.flipud(eye_to_mouth) * [-1, 1]: rotate 90 clockwise\n                # norm with the hypotenuse: get the direction\n                x /= np.hypot(*x)  # get the hypotenuse of a right triangle\n                rect_scale = 1.5\n                x *= max(np.hypot(*eye_to_eye) * 2.0 * rect_scale, np.hypot(*eye_to_mouth) * 1.8 * rect_scale)\n                # y: half height of the oriented crop rectangle\n                y = np.flipud(x) * [-1, 1]\n\n                # c: center\n                c = eye_avg + eye_to_mouth * 0.1\n                # quad: (left_top, left_bottom, right_bottom, right_top)\n                quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])\n                # qsize: side length of the square\n                qsize = np.hypot(*x) * 2\n                border = max(int(np.rint(qsize * 0.1)), 3)\n\n                # get pad\n                # pad: (width_left, height_top, width_right, height_bottom)\n                pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),\n                       int(np.ceil(max(quad[:, 1]))))\n                pad = [\n                    max(-pad[0] + border, 1),\n                    max(-pad[1] + border, 1),\n                    max(pad[2] - self.input_img.shape[0] + border, 1),\n                    max(pad[3] - self.input_img.shape[1] + border, 1)\n                ]\n\n                if max(pad) > 1:\n                    # pad image\n                    pad_img = np.pad(self.input_img, ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')\n                    # modify landmark coords\n                    landmarks[:, 0] += pad[0]\n                    landmarks[:, 1] += pad[1]\n                    # blur pad images\n                    h, w, _ = pad_img.shape\n                    y, x, _ = np.ogrid[:h, :w, :1]\n                    mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0],\n                                                       np.float32(w - 1 - x) / pad[2]),\n                                      1.0 - np.minimum(np.float32(y) / pad[1],\n                                                       np.float32(h - 1 - y) / pad[3]))\n                    blur = int(qsize * blur_ratio)\n                    if blur % 2 == 0:\n                        blur += 1\n                    blur_img = cv2.boxFilter(pad_img, 0, ksize=(blur, blur))\n                    # blur_img = cv2.GaussianBlur(pad_img, (blur, blur), 0)\n\n                    pad_img = pad_img.astype('float32')\n                    pad_img += (blur_img - pad_img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)\n                    pad_img += (np.median(pad_img, axis=(0, 1)) - pad_img) * np.clip(mask, 0.0, 1.0)\n                    pad_img = np.clip(pad_img, 0, 255)  # float32, [0, 255]\n                    self.pad_input_imgs.append(pad_img)\n                else:\n                    self.pad_input_imgs.append(np.copy(self.input_img))\n\n        return len(self.all_landmarks_5)\n\n    def align_warp_face(self, save_cropped_path=None, border_mode='constant'):\n        \"\"\"Align and warp faces with face template.\n        \"\"\"\n        if self.pad_blur:\n            assert len(self.pad_input_imgs) == len(\n                self.all_landmarks_5), f'Mismatched samples: {len(self.pad_input_imgs)} and {len(self.all_landmarks_5)}'\n        for idx, landmark in enumerate(self.all_landmarks_5):\n            # use 5 landmarks to get affine matrix\n            # use cv2.LMEDS method for the equivalence to skimage transform\n            # ref: https://blog.csdn.net/yichxi/article/details/115827338\n            affine_matrix = cv2.estimateAffinePartial2D(landmark, self.face_template, method=cv2.LMEDS)[0]\n            self.affine_matrices.append(affine_matrix)\n            # warp and crop faces\n            if border_mode == 'constant':\n                border_mode = cv2.BORDER_CONSTANT\n            elif border_mode == 'reflect101':\n                border_mode = cv2.BORDER_REFLECT101\n            elif border_mode == 'reflect':\n                border_mode = cv2.BORDER_REFLECT\n            if self.pad_blur:\n                input_img = self.pad_input_imgs[idx]\n            else:\n                input_img = self.input_img\n            cropped_face = cv2.warpAffine(\n                input_img, affine_matrix, self.face_size, borderMode=border_mode, borderValue=(135, 133, 132))  # gray\n            self.cropped_faces.append(cropped_face)\n            # save the cropped face\n            if save_cropped_path is not None:\n                path = os.path.splitext(save_cropped_path)[0]\n                save_path = f'{path}_{idx:02d}.{self.save_ext}'\n                imwrite(cropped_face, save_path)\n\n    def get_inverse_affine(self, save_inverse_affine_path=None):\n        \"\"\"Get inverse affine matrix.\"\"\"\n        for idx, affine_matrix in enumerate(self.affine_matrices):\n            inverse_affine = cv2.invertAffineTransform(affine_matrix)\n            inverse_affine *= self.upscale_factor\n            self.inverse_affine_matrices.append(inverse_affine)\n            # save inverse affine matrices\n            if save_inverse_affine_path is not None:\n                path, _ = os.path.splitext(save_inverse_affine_path)\n                save_path = f'{path}_{idx:02d}.pth'\n                torch.save(inverse_affine, save_path)\n\n    def add_restored_face(self, face):\n        self.restored_faces.append(face)\n\n    def paste_faces_to_input_image(self, save_path=None, upsample_img=None):\n        h, w, _ = self.input_img.shape\n        h_up, w_up = int(h * self.upscale_factor), int(w * self.upscale_factor)\n\n        if upsample_img is None:\n            # simply resize the background\n            upsample_img = cv2.resize(self.input_img, (w_up, h_up), interpolation=cv2.INTER_LANCZOS4)\n        else:\n            upsample_img = cv2.resize(upsample_img, (w_up, h_up), interpolation=cv2.INTER_LANCZOS4)\n\n        assert len(self.restored_faces) == len(\n            self.inverse_affine_matrices), ('length of restored_faces and affine_matrices are different.')\n        for restored_face, inverse_affine in zip(self.restored_faces, self.inverse_affine_matrices):\n            # Add an offset to inverse affine matrix, for more precise back alignment\n            if self.upscale_factor > 1:\n                extra_offset = 0.5 * self.upscale_factor\n            else:\n                extra_offset = 0\n            inverse_affine[:, 2] += extra_offset\n            inv_restored = cv2.warpAffine(restored_face, inverse_affine, (w_up, h_up))\n\n            if self.use_parse:\n                # inference\n                face_input = cv2.resize(restored_face, (512, 512), interpolation=cv2.INTER_LINEAR)\n                face_input = img2tensor(face_input.astype('float32') / 255., bgr2rgb=True, float32=True)\n                normalize(face_input, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)\n                face_input = torch.unsqueeze(face_input, 0).to(self.device)\n                with torch.no_grad():\n                    out = self.face_parse(face_input)[0]\n                out = out.argmax(dim=1).squeeze().cpu().numpy()\n\n                mask = np.zeros(out.shape)\n                MASK_COLORMAP = [0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 255, 0, 0, 0]\n                for idx, color in enumerate(MASK_COLORMAP):\n                    mask[out == idx] = color\n                #  blur the mask\n                mask = cv2.GaussianBlur(mask, (101, 101), 11)\n                mask = cv2.GaussianBlur(mask, (101, 101), 11)\n                # remove the black borders\n                thres = 10\n                mask[:thres, :] = 0\n                mask[-thres:, :] = 0\n                mask[:, :thres] = 0\n                mask[:, -thres:] = 0\n                mask = mask / 255.\n\n                mask = cv2.resize(mask, restored_face.shape[:2])\n                mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up), flags=3)\n                inv_soft_mask = mask[:, :, None]\n                pasted_face = inv_restored\n\n            else:  # use square parse maps\n                mask = np.ones(self.face_size, dtype=np.float32)\n                inv_mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up))\n                # remove the black borders\n                inv_mask_erosion = cv2.erode(\n                    inv_mask, np.ones((int(2 * self.upscale_factor), int(2 * self.upscale_factor)), np.uint8))\n                pasted_face = inv_mask_erosion[:, :, None] * inv_restored\n                total_face_area = np.sum(inv_mask_erosion)  # // 3\n                # compute the fusion edge based on the area of face\n                w_edge = int(total_face_area**0.5) // 20\n                erosion_radius = w_edge * 2\n                inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8))\n                blur_size = w_edge * 2\n                inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0)\n                if len(upsample_img.shape) == 2:  # upsample_img is gray image\n                    upsample_img = upsample_img[:, :, None]\n                inv_soft_mask = inv_soft_mask[:, :, None]\n\n            if len(upsample_img.shape) == 3 and upsample_img.shape[2] == 4:  # alpha channel\n                alpha = upsample_img[:, :, 3:]\n                upsample_img = inv_soft_mask * pasted_face + (1 - inv_soft_mask) * upsample_img[:, :, 0:3]\n                upsample_img = np.concatenate((upsample_img, alpha), axis=2)\n            else:\n                upsample_img = inv_soft_mask * pasted_face + (1 - inv_soft_mask) * upsample_img\n\n        if np.max(upsample_img) > 256:  # 16-bit image\n            upsample_img = upsample_img.astype(np.uint16)\n        else:\n            upsample_img = upsample_img.astype(np.uint8)\n        if save_path is not None:\n            path = os.path.splitext(save_path)[0]\n            save_path = f'{path}.{self.save_ext}'\n            imwrite(upsample_img, save_path)\n        return upsample_img\n\n    def clean_all(self):\n        self.all_landmarks_5 = []\n        self.restored_faces = []\n        self.affine_matrices = []\n        self.cropped_faces = []\n        self.inverse_affine_matrices = []\n        self.det_faces = []\n        self.pad_input_imgs = []\n"
  },
  {
    "path": "extras/facexlib/utils/face_utils.py",
    "content": "import cv2\nimport numpy as np\nimport torch\n\n\ndef compute_increased_bbox(bbox, increase_area, preserve_aspect=True):\n    left, top, right, bot = bbox\n    width = right - left\n    height = bot - top\n\n    if preserve_aspect:\n        width_increase = max(increase_area, ((1 + 2 * increase_area) * height - width) / (2 * width))\n        height_increase = max(increase_area, ((1 + 2 * increase_area) * width - height) / (2 * height))\n    else:\n        width_increase = height_increase = increase_area\n    left = int(left - width_increase * width)\n    top = int(top - height_increase * height)\n    right = int(right + width_increase * width)\n    bot = int(bot + height_increase * height)\n    return (left, top, right, bot)\n\n\ndef get_valid_bboxes(bboxes, h, w):\n    left = max(bboxes[0], 0)\n    top = max(bboxes[1], 0)\n    right = min(bboxes[2], w)\n    bottom = min(bboxes[3], h)\n    return (left, top, right, bottom)\n\n\ndef align_crop_face_landmarks(img,\n                              landmarks,\n                              output_size,\n                              transform_size=None,\n                              enable_padding=True,\n                              return_inverse_affine=False,\n                              shrink_ratio=(1, 1)):\n    \"\"\"Align and crop face with landmarks.\n\n    The output_size and transform_size are based on width. The height is\n    adjusted based on shrink_ratio_h/shring_ration_w.\n\n    Modified from:\n    https://github.com/NVlabs/ffhq-dataset/blob/master/download_ffhq.py\n\n    Args:\n        img (Numpy array): Input image.\n        landmarks (Numpy array): 5 or 68 or 98 landmarks.\n        output_size (int): Output face size.\n        transform_size (ing): Transform size. Usually the four time of\n            output_size.\n        enable_padding (float): Default: True.\n        shrink_ratio (float | tuple[float] | list[float]): Shring the whole\n            face for height and width (crop larger area). Default: (1, 1).\n\n    Returns:\n        (Numpy array): Cropped face.\n    \"\"\"\n    lm_type = 'retinaface_5'  # Options: dlib_5, retinaface_5\n\n    if isinstance(shrink_ratio, (float, int)):\n        shrink_ratio = (shrink_ratio, shrink_ratio)\n    if transform_size is None:\n        transform_size = output_size * 4\n\n    # Parse landmarks\n    lm = np.array(landmarks)\n    if lm.shape[0] == 5 and lm_type == 'retinaface_5':\n        eye_left = lm[0]\n        eye_right = lm[1]\n        mouth_avg = (lm[3] + lm[4]) * 0.5\n    elif lm.shape[0] == 5 and lm_type == 'dlib_5':\n        lm_eye_left = lm[2:4]\n        lm_eye_right = lm[0:2]\n        eye_left = np.mean(lm_eye_left, axis=0)\n        eye_right = np.mean(lm_eye_right, axis=0)\n        mouth_avg = lm[4]\n    elif lm.shape[0] == 68:\n        lm_eye_left = lm[36:42]\n        lm_eye_right = lm[42:48]\n        eye_left = np.mean(lm_eye_left, axis=0)\n        eye_right = np.mean(lm_eye_right, axis=0)\n        mouth_avg = (lm[48] + lm[54]) * 0.5\n    elif lm.shape[0] == 98:\n        lm_eye_left = lm[60:68]\n        lm_eye_right = lm[68:76]\n        eye_left = np.mean(lm_eye_left, axis=0)\n        eye_right = np.mean(lm_eye_right, axis=0)\n        mouth_avg = (lm[76] + lm[82]) * 0.5\n\n    eye_avg = (eye_left + eye_right) * 0.5\n    eye_to_eye = eye_right - eye_left\n    eye_to_mouth = mouth_avg - eye_avg\n\n    # Get the oriented crop rectangle\n    # x: half width of the oriented crop rectangle\n    x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]\n    #  - np.flipud(eye_to_mouth) * [-1, 1]: rotate 90 clockwise\n    # norm with the hypotenuse: get the direction\n    x /= np.hypot(*x)  # get the hypotenuse of a right triangle\n    rect_scale = 1  # TODO: you can edit it to get larger rect\n    x *= max(np.hypot(*eye_to_eye) * 2.0 * rect_scale, np.hypot(*eye_to_mouth) * 1.8 * rect_scale)\n    # y: half height of the oriented crop rectangle\n    y = np.flipud(x) * [-1, 1]\n\n    x *= shrink_ratio[1]  # width\n    y *= shrink_ratio[0]  # height\n\n    # c: center\n    c = eye_avg + eye_to_mouth * 0.1\n    # quad: (left_top, left_bottom, right_bottom, right_top)\n    quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])\n    # qsize: side length of the square\n    qsize = np.hypot(*x) * 2\n\n    quad_ori = np.copy(quad)\n    # Shrink, for large face\n    # TODO: do we really need shrink\n    shrink = int(np.floor(qsize / output_size * 0.5))\n    if shrink > 1:\n        h, w = img.shape[0:2]\n        rsize = (int(np.rint(float(w) / shrink)), int(np.rint(float(h) / shrink)))\n        img = cv2.resize(img, rsize, interpolation=cv2.INTER_AREA)\n        quad /= shrink\n        qsize /= shrink\n\n    # Crop\n    h, w = img.shape[0:2]\n    border = max(int(np.rint(qsize * 0.1)), 3)\n    crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),\n            int(np.ceil(max(quad[:, 1]))))\n    crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, w), min(crop[3] + border, h))\n    if crop[2] - crop[0] < w or crop[3] - crop[1] < h:\n        img = img[crop[1]:crop[3], crop[0]:crop[2], :]\n        quad -= crop[0:2]\n\n    # Pad\n    # pad: (width_left, height_top, width_right, height_bottom)\n    h, w = img.shape[0:2]\n    pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),\n           int(np.ceil(max(quad[:, 1]))))\n    pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - w + border, 0), max(pad[3] - h + border, 0))\n    if enable_padding and max(pad) > border - 4:\n        pad = np.maximum(pad, int(np.rint(qsize * 0.3)))\n        img = np.pad(img, ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')\n        h, w = img.shape[0:2]\n        y, x, _ = np.ogrid[:h, :w, :1]\n        mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0],\n                                           np.float32(w - 1 - x) / pad[2]),\n                          1.0 - np.minimum(np.float32(y) / pad[1],\n                                           np.float32(h - 1 - y) / pad[3]))\n        blur = int(qsize * 0.02)\n        if blur % 2 == 0:\n            blur += 1\n        blur_img = cv2.boxFilter(img, 0, ksize=(blur, blur))\n\n        img = img.astype('float32')\n        img += (blur_img - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)\n        img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)\n        img = np.clip(img, 0, 255)  # float32, [0, 255]\n        quad += pad[:2]\n\n    # Transform use cv2\n    h_ratio = shrink_ratio[0] / shrink_ratio[1]\n    dst_h, dst_w = int(transform_size * h_ratio), transform_size\n    template = np.array([[0, 0], [0, dst_h], [dst_w, dst_h], [dst_w, 0]])\n    # use cv2.LMEDS method for the equivalence to skimage transform\n    # ref: https://blog.csdn.net/yichxi/article/details/115827338\n    affine_matrix = cv2.estimateAffinePartial2D(quad, template, method=cv2.LMEDS)[0]\n    cropped_face = cv2.warpAffine(\n        img, affine_matrix, (dst_w, dst_h), borderMode=cv2.BORDER_CONSTANT, borderValue=(135, 133, 132))  # gray\n\n    if output_size < transform_size:\n        cropped_face = cv2.resize(\n            cropped_face, (output_size, int(output_size * h_ratio)), interpolation=cv2.INTER_LINEAR)\n\n    if return_inverse_affine:\n        dst_h, dst_w = int(output_size * h_ratio), output_size\n        template = np.array([[0, 0], [0, dst_h], [dst_w, dst_h], [dst_w, 0]])\n        # use cv2.LMEDS method for the equivalence to skimage transform\n        # ref: https://blog.csdn.net/yichxi/article/details/115827338\n        affine_matrix = cv2.estimateAffinePartial2D(\n            quad_ori, np.array([[0, 0], [0, output_size], [dst_w, dst_h], [dst_w, 0]]), method=cv2.LMEDS)[0]\n        inverse_affine = cv2.invertAffineTransform(affine_matrix)\n    else:\n        inverse_affine = None\n    return cropped_face, inverse_affine\n\n\ndef paste_face_back(img, face, inverse_affine):\n    h, w = img.shape[0:2]\n    face_h, face_w = face.shape[0:2]\n    inv_restored = cv2.warpAffine(face, inverse_affine, (w, h))\n    mask = np.ones((face_h, face_w, 3), dtype=np.float32)\n    inv_mask = cv2.warpAffine(mask, inverse_affine, (w, h))\n    # remove the black borders\n    inv_mask_erosion = cv2.erode(inv_mask, np.ones((2, 2), np.uint8))\n    inv_restored_remove_border = inv_mask_erosion * inv_restored\n    total_face_area = np.sum(inv_mask_erosion) // 3\n    # compute the fusion edge based on the area of face\n    w_edge = int(total_face_area**0.5) // 20\n    erosion_radius = w_edge * 2\n    inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8))\n    blur_size = w_edge * 2\n    inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0)\n    img = inv_soft_mask * inv_restored_remove_border + (1 - inv_soft_mask) * img\n    # float32, [0, 255]\n    return img\n\n\nif __name__ == '__main__':\n    import os\n\n    from extras.facexlib.detection import init_detection_model\n    from extras.facexlib.utils.face_restoration_helper import get_largest_face\n    from extras.facexlib.visualization import visualize_detection\n\n    img_path = '/home/wxt/datasets/ffhq/ffhq_wild/00009.png'\n    img_name = os.splitext(os.path.basename(img_path))[0]\n\n    # initialize model\n    det_net = init_detection_model('retinaface_resnet50', half=False)\n    img_ori = cv2.imread(img_path)\n    h, w = img_ori.shape[0:2]\n    # if larger than 800, scale it\n    scale = max(h / 800, w / 800)\n    if scale > 1:\n        img = cv2.resize(img_ori, (int(w / scale), int(h / scale)), interpolation=cv2.INTER_LINEAR)\n\n    with torch.no_grad():\n        bboxes = det_net.detect_faces(img, 0.97)\n    if scale > 1:\n        bboxes *= scale  # the score is incorrect\n    bboxes = get_largest_face(bboxes, h, w)[0]\n    visualize_detection(img_ori, [bboxes], f'tmp/{img_name}_det.png')\n\n    landmarks = np.array([[bboxes[i], bboxes[i + 1]] for i in range(5, 15, 2)])\n\n    cropped_face, inverse_affine = align_crop_face_landmarks(\n        img_ori,\n        landmarks,\n        output_size=512,\n        transform_size=None,\n        enable_padding=True,\n        return_inverse_affine=True,\n        shrink_ratio=(1, 1))\n\n    cv2.imwrite(f'tmp/{img_name}_cropeed_face.png', cropped_face)\n    img = paste_face_back(img_ori, cropped_face, inverse_affine)\n    cv2.imwrite(f'tmp/{img_name}_back.png', img)\n"
  },
  {
    "path": "extras/facexlib/utils/misc.py",
    "content": "import cv2\nimport os\nimport os.path as osp\nimport torch\nfrom torch.hub import download_url_to_file, get_dir\nfrom urllib.parse import urlparse\n\nROOT_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\n\n\ndef imwrite(img, file_path, params=None, auto_mkdir=True):\n    \"\"\"Write image to file.\n\n    Args:\n        img (ndarray): Image array to be written.\n        file_path (str): Image file path.\n        params (None or list): Same as opencv's :func:`imwrite` interface.\n        auto_mkdir (bool): If the parent folder of `file_path` does not exist,\n            whether to create it automatically.\n\n    Returns:\n        bool: Successful or not.\n    \"\"\"\n    if auto_mkdir:\n        dir_name = os.path.abspath(os.path.dirname(file_path))\n        os.makedirs(dir_name, exist_ok=True)\n    return cv2.imwrite(file_path, img, params)\n\n\ndef img2tensor(imgs, bgr2rgb=True, float32=True):\n    \"\"\"Numpy array to tensor.\n\n    Args:\n        imgs (list[ndarray] | ndarray): Input images.\n        bgr2rgb (bool): Whether to change bgr to rgb.\n        float32 (bool): Whether to change to float32.\n\n    Returns:\n        list[tensor] | tensor: Tensor images. If returned results only have\n            one element, just return tensor.\n    \"\"\"\n\n    def _totensor(img, bgr2rgb, float32):\n        if img.shape[2] == 3 and bgr2rgb:\n            if img.dtype == 'float64':\n                img = img.astype('float32')\n            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n        img = torch.from_numpy(img.transpose(2, 0, 1))\n        if float32:\n            img = img.float()\n        return img\n\n    if isinstance(imgs, list):\n        return [_totensor(img, bgr2rgb, float32) for img in imgs]\n    else:\n        return _totensor(imgs, bgr2rgb, float32)\n\n\ndef load_file_from_url(url, model_dir=None, progress=True, file_name=None, save_dir=None):\n    \"\"\"Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py\n    \"\"\"\n    if model_dir is None:\n        hub_dir = get_dir()\n        model_dir = os.path.join(hub_dir, 'checkpoints')\n\n    if save_dir is None:\n        save_dir = os.path.join(ROOT_DIR, model_dir)\n    os.makedirs(save_dir, exist_ok=True)\n\n    parts = urlparse(url)\n    filename = os.path.basename(parts.path)\n    if file_name is not None:\n        filename = file_name\n    cached_file = os.path.abspath(os.path.join(save_dir, filename))\n    if not os.path.exists(cached_file):\n        print(f'Downloading: \"{url}\" to {cached_file}\\n')\n        download_url_to_file(url, cached_file, hash_prefix=None, progress=progress)\n    return cached_file\n\n\ndef scandir(dir_path, suffix=None, recursive=False, full_path=False):\n    \"\"\"Scan a directory to find the interested files.\n    Args:\n        dir_path (str): Path of the directory.\n        suffix (str | tuple(str), optional): File suffix that we are\n            interested in. Default: None.\n        recursive (bool, optional): If set to True, recursively scan the\n            directory. Default: False.\n        full_path (bool, optional): If set to True, include the dir_path.\n            Default: False.\n    Returns:\n        A generator for all the interested files with relative paths.\n    \"\"\"\n\n    if (suffix is not None) and not isinstance(suffix, (str, tuple)):\n        raise TypeError('\"suffix\" must be a string or tuple of strings')\n\n    root = dir_path\n\n    def _scandir(dir_path, suffix, recursive):\n        for entry in os.scandir(dir_path):\n            if not entry.name.startswith('.') and entry.is_file():\n                if full_path:\n                    return_path = entry.path\n                else:\n                    return_path = osp.relpath(entry.path, root)\n\n                if suffix is None:\n                    yield return_path\n                elif return_path.endswith(suffix):\n                    yield return_path\n            else:\n                if recursive:\n                    yield from _scandir(entry.path, suffix=suffix, recursive=recursive)\n                else:\n                    continue\n\n    return _scandir(dir_path, suffix=suffix, recursive=recursive)\n"
  },
  {
    "path": "extras/inpaint_mask.py",
    "content": "import sys\n\nimport modules.config\nimport numpy as np\nimport torch\nfrom extras.GroundingDINO.util.inference import default_groundingdino\nfrom extras.sam.predictor import SamPredictor\nfrom rembg import remove, new_session\nfrom segment_anything import sam_model_registry\nfrom segment_anything.utils.amg import remove_small_regions\n\n\nclass SAMOptions:\n    def __init__(self,\n                 # GroundingDINO\n                 dino_prompt: str = '',\n                 dino_box_threshold=0.3,\n                 dino_text_threshold=0.25,\n                 dino_erode_or_dilate=0,\n                 dino_debug=False,\n\n                 # SAM\n                 max_detections=2,\n                 model_type='vit_b'\n                 ):\n        self.dino_prompt = dino_prompt\n        self.dino_box_threshold = dino_box_threshold\n        self.dino_text_threshold = dino_text_threshold\n        self.dino_erode_or_dilate = dino_erode_or_dilate\n        self.dino_debug = dino_debug\n        self.max_detections = max_detections\n        self.model_type = model_type\n\n\ndef optimize_masks(masks: torch.Tensor) -> torch.Tensor:\n    \"\"\"\n    removes small disconnected regions and holes\n    \"\"\"\n    fine_masks = []\n    for mask in masks.to('cpu').numpy():  # masks: [num_masks, 1, h, w]\n        fine_masks.append(remove_small_regions(mask[0], 400, mode=\"holes\")[0])\n    masks = np.stack(fine_masks, axis=0)[:, np.newaxis]\n    return torch.from_numpy(masks)\n\n\ndef generate_mask_from_image(image: np.ndarray, mask_model: str = 'sam', extras=None,\n                             sam_options: SAMOptions | None = SAMOptions) -> tuple[np.ndarray | None, int | None, int | None, int | None]:\n    dino_detection_count = 0\n    sam_detection_count = 0\n    sam_detection_on_mask_count = 0\n\n    if image is None:\n        return None, dino_detection_count, sam_detection_count, sam_detection_on_mask_count\n\n    if extras is None:\n        extras = {}\n\n    if 'image' in image:\n        image = image['image']\n\n    if mask_model != 'sam' or sam_options is None:\n        result = remove(\n            image,\n            session=new_session(mask_model, **extras),\n            only_mask=True,\n            **extras\n        )\n\n        return result, dino_detection_count, sam_detection_count, sam_detection_on_mask_count\n\n    detections, boxes, logits, phrases = default_groundingdino(\n        image=image,\n        caption=sam_options.dino_prompt,\n        box_threshold=sam_options.dino_box_threshold,\n        text_threshold=sam_options.dino_text_threshold\n    )\n\n    H, W = image.shape[0], image.shape[1]\n    boxes = boxes * torch.Tensor([W, H, W, H])\n    boxes[:, :2] = boxes[:, :2] - boxes[:, 2:] / 2\n    boxes[:, 2:] = boxes[:, 2:] + boxes[:, :2]\n\n    sam_checkpoint = modules.config.download_sam_model(sam_options.model_type)\n    sam = sam_model_registry[sam_options.model_type](checkpoint=sam_checkpoint)\n\n    sam_predictor = SamPredictor(sam)\n    final_mask_tensor = torch.zeros((image.shape[0], image.shape[1]))\n    dino_detection_count = boxes.size(0)\n\n    if dino_detection_count > 0:\n        sam_predictor.set_image(image)\n\n        if sam_options.dino_erode_or_dilate != 0:\n            for index in range(boxes.size(0)):\n                assert boxes.size(1) == 4\n                boxes[index][0] -= sam_options.dino_erode_or_dilate\n                boxes[index][1] -= sam_options.dino_erode_or_dilate\n                boxes[index][2] += sam_options.dino_erode_or_dilate\n                boxes[index][3] += sam_options.dino_erode_or_dilate\n\n        if sam_options.dino_debug:\n            from PIL import ImageDraw, Image\n            debug_dino_image = Image.new(\"RGB\", (image.shape[1], image.shape[0]), color=\"black\")\n            draw = ImageDraw.Draw(debug_dino_image)\n            for box in boxes.numpy():\n                draw.rectangle(box.tolist(), fill=\"white\")\n            return np.array(debug_dino_image), dino_detection_count, sam_detection_count, sam_detection_on_mask_count\n\n        transformed_boxes = sam_predictor.transform.apply_boxes_torch(boxes, image.shape[:2])\n        masks, _, _ = sam_predictor.predict_torch(\n            point_coords=None,\n            point_labels=None,\n            boxes=transformed_boxes,\n            multimask_output=False,\n        )\n\n        masks = optimize_masks(masks)\n        sam_detection_count = len(masks)\n        if sam_options.max_detections == 0:\n            sam_options.max_detections = sys.maxsize\n        sam_objects = min(len(logits), sam_options.max_detections)\n        for obj_ind in range(sam_objects):\n            mask_tensor = masks[obj_ind][0]\n            final_mask_tensor += mask_tensor\n            sam_detection_on_mask_count += 1\n\n    final_mask_tensor = (final_mask_tensor > 0).to('cpu').numpy()\n    mask_image = np.dstack((final_mask_tensor, final_mask_tensor, final_mask_tensor)) * 255\n    mask_image = np.array(mask_image, dtype=np.uint8)\n    return mask_image, dino_detection_count, sam_detection_count, sam_detection_on_mask_count\n"
  },
  {
    "path": "extras/interrogate.py",
    "content": "import os\nimport torch\nimport ldm_patched.modules.model_management as model_management\n\nfrom torchvision import transforms\nfrom torchvision.transforms.functional import InterpolationMode\nfrom modules.model_loader import load_file_from_url\nfrom modules.config import path_clip_vision\nfrom ldm_patched.modules.model_patcher import ModelPatcher\nfrom extras.BLIP.models.blip import blip_decoder\n\n\nblip_image_eval_size = 384\nblip_repo_root = os.path.join(os.path.dirname(__file__), 'BLIP')\n\n\nclass Interrogator:\n    def __init__(self):\n        self.blip_model = None\n        self.load_device = torch.device('cpu')\n        self.offload_device = torch.device('cpu')\n        self.dtype = torch.float32\n\n    @torch.no_grad()\n    @torch.inference_mode()\n    def interrogate(self, img_rgb):\n        if self.blip_model is None:\n            filename = load_file_from_url(\n                url='https://huggingface.co/lllyasviel/misc/resolve/main/model_base_caption_capfilt_large.pth',\n                model_dir=path_clip_vision,\n                file_name='model_base_caption_capfilt_large.pth',\n            )\n\n            model = blip_decoder(pretrained=filename, image_size=blip_image_eval_size, vit='base',\n                                 med_config=os.path.join(blip_repo_root, \"configs\", \"med_config.json\"))\n            model.eval()\n\n            self.load_device = model_management.text_encoder_device()\n            self.offload_device = model_management.text_encoder_offload_device()\n            self.dtype = torch.float32\n\n            model.to(self.offload_device)\n\n            if model_management.should_use_fp16(device=self.load_device):\n                model.half()\n                self.dtype = torch.float16\n\n            self.blip_model = ModelPatcher(model, load_device=self.load_device, offload_device=self.offload_device)\n\n        model_management.load_model_gpu(self.blip_model)\n\n        gpu_image = transforms.Compose([\n            transforms.ToTensor(),\n            transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC),\n            transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))\n        ])(img_rgb).unsqueeze(0).to(device=self.load_device, dtype=self.dtype)\n\n        caption = self.blip_model.model.generate(gpu_image, sample=True, num_beams=1, max_length=75)[0]\n\n        return caption\n\n\ndefault_interrogator = Interrogator().interrogate\n"
  },
  {
    "path": "extras/ip_adapter.py",
    "content": "import torch\nimport ldm_patched.modules.clip_vision\nimport safetensors.torch as sf\nimport ldm_patched.modules.model_management as model_management\nimport ldm_patched.ldm.modules.attention as attention\n\nfrom extras.resampler import Resampler\nfrom ldm_patched.modules.model_patcher import ModelPatcher\nfrom modules.core import numpy_to_pytorch\nfrom modules.ops import use_patched_ops\nfrom ldm_patched.modules.ops import manual_cast\n\n\nSD_V12_CHANNELS = [320] * 4 + [640] * 4 + [1280] * 4 + [1280] * 6 + [640] * 6 + [320] * 6 + [1280] * 2\nSD_XL_CHANNELS = [640] * 8 + [1280] * 40 + [1280] * 60 + [640] * 12 + [1280] * 20\n\n\ndef sdp(q, k, v, extra_options):\n    return attention.optimized_attention(q, k, v, heads=extra_options[\"n_heads\"], mask=None)\n\n\nclass ImageProjModel(torch.nn.Module):\n    def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4):\n        super().__init__()\n\n        self.cross_attention_dim = cross_attention_dim\n        self.clip_extra_context_tokens = clip_extra_context_tokens\n        self.proj = torch.nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim)\n        self.norm = torch.nn.LayerNorm(cross_attention_dim)\n\n    def forward(self, image_embeds):\n        embeds = image_embeds\n        clip_extra_context_tokens = self.proj(embeds).reshape(-1, self.clip_extra_context_tokens,\n                                                              self.cross_attention_dim)\n        clip_extra_context_tokens = self.norm(clip_extra_context_tokens)\n        return clip_extra_context_tokens\n\n\nclass To_KV(torch.nn.Module):\n    def __init__(self, cross_attention_dim):\n        super().__init__()\n\n        channels = SD_XL_CHANNELS if cross_attention_dim == 2048 else SD_V12_CHANNELS\n        self.to_kvs = torch.nn.ModuleList(\n            [torch.nn.Linear(cross_attention_dim, channel, bias=False) for channel in channels])\n\n    def load_state_dict_ordered(self, sd):\n        state_dict = []\n        for i in range(4096):\n            for k in ['k', 'v']:\n                key = f'{i}.to_{k}_ip.weight'\n                if key in sd:\n                    state_dict.append(sd[key])\n        for i, v in enumerate(state_dict):\n            self.to_kvs[i].weight = torch.nn.Parameter(v, requires_grad=False)\n\n\nclass IPAdapterModel(torch.nn.Module):\n    def __init__(self, state_dict, plus, cross_attention_dim=768, clip_embeddings_dim=1024, clip_extra_context_tokens=4,\n                 sdxl_plus=False):\n        super().__init__()\n        self.plus = plus\n        if self.plus:\n            self.image_proj_model = Resampler(\n                dim=1280 if sdxl_plus else cross_attention_dim,\n                depth=4,\n                dim_head=64,\n                heads=20 if sdxl_plus else 12,\n                num_queries=clip_extra_context_tokens,\n                embedding_dim=clip_embeddings_dim,\n                output_dim=cross_attention_dim,\n                ff_mult=4\n            )\n        else:\n            self.image_proj_model = ImageProjModel(\n                cross_attention_dim=cross_attention_dim,\n                clip_embeddings_dim=clip_embeddings_dim,\n                clip_extra_context_tokens=clip_extra_context_tokens\n            )\n\n        self.image_proj_model.load_state_dict(state_dict[\"image_proj\"])\n        self.ip_layers = To_KV(cross_attention_dim)\n        self.ip_layers.load_state_dict_ordered(state_dict[\"ip_adapter\"])\n\n\nclip_vision: ldm_patched.modules.clip_vision.ClipVisionModel = None\nip_negative: torch.Tensor = None\nip_adapters: dict = {}\n\n\ndef load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_path):\n    global clip_vision, ip_negative, ip_adapters\n\n    if clip_vision is None and isinstance(clip_vision_path, str):\n        clip_vision = ldm_patched.modules.clip_vision.load(clip_vision_path)\n\n    if ip_negative is None and isinstance(ip_negative_path, str):\n        ip_negative = sf.load_file(ip_negative_path)['data']\n\n    if not isinstance(ip_adapter_path, str) or ip_adapter_path in ip_adapters:\n        return\n\n    load_device = model_management.get_torch_device()\n    offload_device = torch.device('cpu')\n\n    use_fp16 = model_management.should_use_fp16(device=load_device)\n    ip_state_dict = torch.load(ip_adapter_path, map_location=\"cpu\", weights_only=True)\n    plus = \"latents\" in ip_state_dict[\"image_proj\"]\n    cross_attention_dim = ip_state_dict[\"ip_adapter\"][\"1.to_k_ip.weight\"].shape[1]\n    sdxl = cross_attention_dim == 2048\n    sdxl_plus = sdxl and plus\n\n    if plus:\n        clip_extra_context_tokens = ip_state_dict[\"image_proj\"][\"latents\"].shape[1]\n        clip_embeddings_dim = ip_state_dict[\"image_proj\"][\"latents\"].shape[2]\n    else:\n        clip_extra_context_tokens = ip_state_dict[\"image_proj\"][\"proj.weight\"].shape[0] // cross_attention_dim\n        clip_embeddings_dim = None\n\n    with use_patched_ops(manual_cast):\n        ip_adapter = IPAdapterModel(\n            ip_state_dict,\n            plus=plus,\n            cross_attention_dim=cross_attention_dim,\n            clip_embeddings_dim=clip_embeddings_dim,\n            clip_extra_context_tokens=clip_extra_context_tokens,\n            sdxl_plus=sdxl_plus\n        )\n\n    ip_adapter.sdxl = sdxl\n    ip_adapter.load_device = load_device\n    ip_adapter.offload_device = offload_device\n    ip_adapter.dtype = torch.float16 if use_fp16 else torch.float32\n    ip_adapter.to(offload_device, dtype=ip_adapter.dtype)\n\n    image_proj_model = ModelPatcher(model=ip_adapter.image_proj_model, load_device=load_device,\n                                    offload_device=offload_device)\n    ip_layers = ModelPatcher(model=ip_adapter.ip_layers, load_device=load_device,\n                             offload_device=offload_device)\n\n    ip_adapters[ip_adapter_path] = dict(\n        ip_adapter=ip_adapter,\n        image_proj_model=image_proj_model,\n        ip_layers=ip_layers,\n        ip_unconds=None\n    )\n\n    return\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef clip_preprocess(image):\n    mean = torch.tensor([0.48145466, 0.4578275, 0.40821073], device=image.device, dtype=image.dtype).view([1, 3, 1, 1])\n    std = torch.tensor([0.26862954, 0.26130258, 0.27577711], device=image.device, dtype=image.dtype).view([1, 3, 1, 1])\n    image = image.movedim(-1, 1)\n\n    # https://github.com/tencent-ailab/IP-Adapter/blob/d580c50a291566bbf9fc7ac0f760506607297e6d/README.md?plain=1#L75\n    B, C, H, W = image.shape\n    assert H == 224 and W == 224\n\n    return (image - mean) / std\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef preprocess(img, ip_adapter_path):\n    global ip_adapters\n    entry = ip_adapters[ip_adapter_path]\n\n    ldm_patched.modules.model_management.load_model_gpu(clip_vision.patcher)\n    pixel_values = clip_preprocess(numpy_to_pytorch(img).to(clip_vision.load_device))\n    outputs = clip_vision.model(pixel_values=pixel_values, output_hidden_states=True)\n\n    ip_adapter = entry['ip_adapter']\n    ip_layers = entry['ip_layers']\n    image_proj_model = entry['image_proj_model']\n    ip_unconds = entry['ip_unconds']\n\n    if ip_adapter.plus:\n        cond = outputs.hidden_states[-2]\n    else:\n        cond = outputs.image_embeds\n\n    cond = cond.to(device=ip_adapter.load_device, dtype=ip_adapter.dtype)\n\n    ldm_patched.modules.model_management.load_model_gpu(image_proj_model)\n    cond = image_proj_model.model(cond).to(device=ip_adapter.load_device, dtype=ip_adapter.dtype)\n\n    ldm_patched.modules.model_management.load_model_gpu(ip_layers)\n\n    if ip_unconds is None:\n        uncond = ip_negative.to(device=ip_adapter.load_device, dtype=ip_adapter.dtype)\n        ip_unconds = [m(uncond).cpu() for m in ip_layers.model.to_kvs]\n        entry['ip_unconds'] = ip_unconds\n\n    ip_conds = [m(cond).cpu() for m in ip_layers.model.to_kvs]\n\n    return ip_conds, ip_unconds\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef patch_model(model, tasks):\n    new_model = model.clone()\n\n    def make_attn_patcher(ip_index):\n        def patcher(n, context_attn2, value_attn2, extra_options):\n            org_dtype = n.dtype\n            current_step = float(model.model.diffusion_model.current_step.detach().cpu().numpy()[0])\n            cond_or_uncond = extra_options['cond_or_uncond']\n\n            q = n\n            k = [context_attn2]\n            v = [value_attn2]\n            b, _, _ = q.shape\n\n            for (cs, ucs), cn_stop, cn_weight in tasks:\n                if current_step < cn_stop:\n                    ip_k_c = cs[ip_index * 2].to(q)\n                    ip_v_c = cs[ip_index * 2 + 1].to(q)\n                    ip_k_uc = ucs[ip_index * 2].to(q)\n                    ip_v_uc = ucs[ip_index * 2 + 1].to(q)\n\n                    ip_k = torch.cat([(ip_k_c, ip_k_uc)[i] for i in cond_or_uncond], dim=0)\n                    ip_v = torch.cat([(ip_v_c, ip_v_uc)[i] for i in cond_or_uncond], dim=0)\n\n                    # Midjourney's attention formulation of image prompt (non-official reimplementation)\n                    # Written by Lvmin Zhang at Stanford University, 2023 Dec\n                    # For non-commercial use only - if you use this in commercial project then\n                    # probably it has some intellectual property issues.\n                    # Contact lvminzhang@acm.org if you are not sure.\n\n                    # Below is the sensitive part with potential intellectual property issues.\n\n                    ip_v_mean = torch.mean(ip_v, dim=1, keepdim=True)\n                    ip_v_offset = ip_v - ip_v_mean\n\n                    B, F, C = ip_k.shape\n                    channel_penalty = float(C) / 1280.0\n                    weight = cn_weight * channel_penalty\n\n                    ip_k = ip_k * weight\n                    ip_v = ip_v_offset + ip_v_mean * weight\n\n                    k.append(ip_k)\n                    v.append(ip_v)\n\n            k = torch.cat(k, dim=1)\n            v = torch.cat(v, dim=1)\n            out = sdp(q, k, v, extra_options)\n\n\n            return out.to(dtype=org_dtype)\n        return patcher\n\n    def set_model_patch_replace(model, number, key):\n        to = model.model_options[\"transformer_options\"]\n        if \"patches_replace\" not in to:\n            to[\"patches_replace\"] = {}\n        if \"attn2\" not in to[\"patches_replace\"]:\n            to[\"patches_replace\"][\"attn2\"] = {}\n        if key not in to[\"patches_replace\"][\"attn2\"]:\n            to[\"patches_replace\"][\"attn2\"][key] = make_attn_patcher(number)\n\n    number = 0\n\n    for id in [4, 5, 7, 8]:\n        block_indices = range(2) if id in [4, 5] else range(10)\n        for index in block_indices:\n            set_model_patch_replace(new_model, number, (\"input\", id, index))\n            number += 1\n\n    for id in range(6):\n        block_indices = range(2) if id in [3, 4, 5] else range(10)\n        for index in block_indices:\n            set_model_patch_replace(new_model, number, (\"output\", id, index))\n            number += 1\n\n    for index in range(10):\n        set_model_patch_replace(new_model, number, (\"middle\", 0, index))\n        number += 1\n\n    return new_model\n"
  },
  {
    "path": "extras/preprocessors.py",
    "content": "import cv2\nimport numpy as np\n\n\ndef centered_canny(x: np.ndarray, canny_low_threshold, canny_high_threshold):\n    assert isinstance(x, np.ndarray)\n    assert x.ndim == 2 and x.dtype == np.uint8\n\n    y = cv2.Canny(x, int(canny_low_threshold), int(canny_high_threshold))\n    y = y.astype(np.float32) / 255.0\n    return y\n\n\ndef centered_canny_color(x: np.ndarray, canny_low_threshold, canny_high_threshold):\n    assert isinstance(x, np.ndarray)\n    assert x.ndim == 3 and x.shape[2] == 3\n\n    result = [centered_canny(x[..., i], canny_low_threshold, canny_high_threshold) for i in range(3)]\n    result = np.stack(result, axis=2)\n    return result\n\n\ndef pyramid_canny_color(x: np.ndarray, canny_low_threshold, canny_high_threshold):\n    assert isinstance(x, np.ndarray)\n    assert x.ndim == 3 and x.shape[2] == 3\n\n    H, W, C = x.shape\n    acc_edge = None\n\n    for k in [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]:\n        Hs, Ws = int(H * k), int(W * k)\n        small = cv2.resize(x, (Ws, Hs), interpolation=cv2.INTER_AREA)\n        edge = centered_canny_color(small, canny_low_threshold, canny_high_threshold)\n        if acc_edge is None:\n            acc_edge = edge\n        else:\n            acc_edge = cv2.resize(acc_edge, (edge.shape[1], edge.shape[0]), interpolation=cv2.INTER_LINEAR)\n            acc_edge = acc_edge * 0.75 + edge * 0.25\n\n    return acc_edge\n\n\ndef norm255(x, low=4, high=96):\n    assert isinstance(x, np.ndarray)\n    assert x.ndim == 2 and x.dtype == np.float32\n\n    v_min = np.percentile(x, low)\n    v_max = np.percentile(x, high)\n\n    x -= v_min\n    x /= v_max - v_min\n\n    return x * 255.0\n\n\ndef canny_pyramid(x, canny_low_threshold, canny_high_threshold):\n    # For some reasons, SAI's Control-lora Canny seems to be trained on canny maps with non-standard resolutions.\n    # Then we use pyramid to use all resolutions to avoid missing any structure in specific resolutions.\n\n    color_canny = pyramid_canny_color(x, canny_low_threshold, canny_high_threshold)\n    result = np.sum(color_canny, axis=2)\n\n    return norm255(result, low=1, high=99).clip(0, 255).astype(np.uint8)\n\n\ndef cpds(x):\n    # cv2.decolor is not \"decolor\", it is Cewu Lu's method\n    # See http://www.cse.cuhk.edu.hk/leojia/projects/color2gray/index.html\n    # See https://docs.opencv.org/3.0-beta/modules/photo/doc/decolor.html\n\n    raw = cv2.GaussianBlur(x, (0, 0), 0.8)\n    density, boost = cv2.decolor(raw)\n\n    raw = raw.astype(np.float32)\n    density = density.astype(np.float32)\n    boost = boost.astype(np.float32)\n\n    offset = np.sum((raw - boost) ** 2.0, axis=2) ** 0.5\n    result = density + offset\n\n    return norm255(result, low=4, high=96).clip(0, 255).astype(np.uint8)\n"
  },
  {
    "path": "extras/resampler.py",
    "content": "# modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py\nimport math\n\nimport torch\nimport torch.nn as nn\n\n\n# FFN\ndef FeedForward(dim, mult=4):\n    inner_dim = int(dim * mult)\n    return nn.Sequential(\n        nn.LayerNorm(dim),\n        nn.Linear(dim, inner_dim, bias=False),\n        nn.GELU(),\n        nn.Linear(inner_dim, dim, bias=False),\n    )\n    \n    \ndef reshape_tensor(x, heads):\n    bs, length, width = x.shape\n    #(bs, length, width) --> (bs, length, n_heads, dim_per_head)\n    x = x.view(bs, length, heads, -1)\n    # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)\n    x = x.transpose(1, 2)\n    # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)\n    x = x.reshape(bs, heads, length, -1)\n    return x\n\n\nclass PerceiverAttention(nn.Module):\n    def __init__(self, *, dim, dim_head=64, heads=8):\n        super().__init__()\n        self.scale = dim_head**-0.5\n        self.dim_head = dim_head\n        self.heads = heads\n        inner_dim = dim_head * heads\n\n        self.norm1 = nn.LayerNorm(dim)\n        self.norm2 = nn.LayerNorm(dim)\n\n        self.to_q = nn.Linear(dim, inner_dim, bias=False)\n        self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)\n        self.to_out = nn.Linear(inner_dim, dim, bias=False)\n\n\n    def forward(self, x, latents):\n        \"\"\"\n        Args:\n            x (torch.Tensor): image features\n                shape (b, n1, D)\n            latent (torch.Tensor): latent features\n                shape (b, n2, D)\n        \"\"\"\n        x = self.norm1(x)\n        latents = self.norm2(latents)\n        \n        b, l, _ = latents.shape\n\n        q = self.to_q(latents)\n        kv_input = torch.cat((x, latents), dim=-2)\n        k, v = self.to_kv(kv_input).chunk(2, dim=-1)\n        \n        q = reshape_tensor(q, self.heads)\n        k = reshape_tensor(k, self.heads)\n        v = reshape_tensor(v, self.heads)\n\n        # attention\n        scale = 1 / math.sqrt(math.sqrt(self.dim_head))\n        weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards\n        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)\n        out = weight @ v\n        \n        out = out.permute(0, 2, 1, 3).reshape(b, l, -1)\n\n        return self.to_out(out)\n\n\nclass Resampler(nn.Module):\n    def __init__(\n        self,\n        dim=1024,\n        depth=8,\n        dim_head=64,\n        heads=16,\n        num_queries=8,\n        embedding_dim=768,\n        output_dim=1024,\n        ff_mult=4,\n    ):\n        super().__init__()\n        \n        self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)\n        \n        self.proj_in = nn.Linear(embedding_dim, dim)\n\n        self.proj_out = nn.Linear(dim, output_dim)\n        self.norm_out = nn.LayerNorm(output_dim)\n        \n        self.layers = nn.ModuleList([])\n        for _ in range(depth):\n            self.layers.append(\n                nn.ModuleList(\n                    [\n                        PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),\n                        FeedForward(dim=dim, mult=ff_mult),\n                    ]\n                )\n            )\n\n    def forward(self, x):\n        latents = self.latents.repeat(x.size(0), 1, 1).to(x)\n        \n        x = self.proj_in(x)\n        \n        for attn, ff in self.layers:\n            latents = attn(x, latents) + latents\n            latents = ff(latents) + latents\n            \n        latents = self.proj_out(latents)\n        return self.norm_out(latents)\n"
  },
  {
    "path": "extras/safety_checker/configs/config.json",
    "content": "{\n  \"_name_or_path\": \"clip-vit-large-patch14/\",\n  \"architectures\": [\n    \"SafetyChecker\"\n  ],\n  \"initializer_factor\": 1.0,\n  \"logit_scale_init_value\": 2.6592,\n  \"model_type\": \"clip\",\n  \"projection_dim\": 768,\n  \"text_config\": {\n    \"_name_or_path\": \"\",\n    \"add_cross_attention\": false,\n    \"architectures\": null,\n    \"attention_dropout\": 0.0,\n    \"bad_words_ids\": null,\n    \"bos_token_id\": 0,\n    \"chunk_size_feed_forward\": 0,\n    \"cross_attention_hidden_size\": null,\n    \"decoder_start_token_id\": null,\n    \"diversity_penalty\": 0.0,\n    \"do_sample\": false,\n    \"dropout\": 0.0,\n    \"early_stopping\": false,\n    \"encoder_no_repeat_ngram_size\": 0,\n    \"eos_token_id\": 2,\n    \"exponential_decay_length_penalty\": null,\n    \"finetuning_task\": null,\n    \"forced_bos_token_id\": null,\n    \"forced_eos_token_id\": null,\n    \"hidden_act\": \"quick_gelu\",\n    \"hidden_size\": 768,\n    \"id2label\": {\n      \"0\": \"LABEL_0\",\n      \"1\": \"LABEL_1\"\n    },\n    \"initializer_factor\": 1.0,\n    \"initializer_range\": 0.02,\n    \"intermediate_size\": 3072,\n    \"is_decoder\": false,\n    \"is_encoder_decoder\": false,\n    \"label2id\": {\n      \"LABEL_0\": 0,\n      \"LABEL_1\": 1\n    },\n    \"layer_norm_eps\": 1e-05,\n    \"length_penalty\": 1.0,\n    \"max_length\": 20,\n    \"max_position_embeddings\": 77,\n    \"min_length\": 0,\n    \"model_type\": \"clip_text_model\",\n    \"no_repeat_ngram_size\": 0,\n    \"num_attention_heads\": 12,\n    \"num_beam_groups\": 1,\n    \"num_beams\": 1,\n    \"num_hidden_layers\": 12,\n    \"num_return_sequences\": 1,\n    \"output_attentions\": false,\n    \"output_hidden_states\": false,\n    \"output_scores\": false,\n    \"pad_token_id\": 1,\n    \"prefix\": null,\n    \"problem_type\": null,\n    \"pruned_heads\": {},\n    \"remove_invalid_values\": false,\n    \"repetition_penalty\": 1.0,\n    \"return_dict\": true,\n    \"return_dict_in_generate\": false,\n    \"sep_token_id\": null,\n    \"task_specific_params\": null,\n    \"temperature\": 1.0,\n    \"tie_encoder_decoder\": false,\n    \"tie_word_embeddings\": true,\n    \"tokenizer_class\": null,\n    \"top_k\": 50,\n    \"top_p\": 1.0,\n    \"torch_dtype\": null,\n    \"torchscript\": false,\n    \"transformers_version\": \"4.21.0.dev0\",\n    \"typical_p\": 1.0,\n    \"use_bfloat16\": false,\n    \"vocab_size\": 49408\n  },\n  \"text_config_dict\": {\n    \"hidden_size\": 768,\n    \"intermediate_size\": 3072,\n    \"num_attention_heads\": 12,\n    \"num_hidden_layers\": 12\n  },\n  \"torch_dtype\": \"float32\",\n  \"transformers_version\": null,\n  \"vision_config\": {\n    \"_name_or_path\": \"\",\n    \"add_cross_attention\": false,\n    \"architectures\": null,\n    \"attention_dropout\": 0.0,\n    \"bad_words_ids\": null,\n    \"bos_token_id\": null,\n    \"chunk_size_feed_forward\": 0,\n    \"cross_attention_hidden_size\": null,\n    \"decoder_start_token_id\": null,\n    \"diversity_penalty\": 0.0,\n    \"do_sample\": false,\n    \"dropout\": 0.0,\n    \"early_stopping\": false,\n    \"encoder_no_repeat_ngram_size\": 0,\n    \"eos_token_id\": null,\n    \"exponential_decay_length_penalty\": null,\n    \"finetuning_task\": null,\n    \"forced_bos_token_id\": null,\n    \"forced_eos_token_id\": null,\n    \"hidden_act\": \"quick_gelu\",\n    \"hidden_size\": 1024,\n    \"id2label\": {\n      \"0\": \"LABEL_0\",\n      \"1\": \"LABEL_1\"\n    },\n    \"image_size\": 224,\n    \"initializer_factor\": 1.0,\n    \"initializer_range\": 0.02,\n    \"intermediate_size\": 4096,\n    \"is_decoder\": false,\n    \"is_encoder_decoder\": false,\n    \"label2id\": {\n      \"LABEL_0\": 0,\n      \"LABEL_1\": 1\n    },\n    \"layer_norm_eps\": 1e-05,\n    \"length_penalty\": 1.0,\n    \"max_length\": 20,\n    \"min_length\": 0,\n    \"model_type\": \"clip_vision_model\",\n    \"no_repeat_ngram_size\": 0,\n    \"num_attention_heads\": 16,\n    \"num_beam_groups\": 1,\n    \"num_beams\": 1,\n    \"num_hidden_layers\": 24,\n    \"num_return_sequences\": 1,\n    \"output_attentions\": false,\n    \"output_hidden_states\": false,\n    \"output_scores\": false,\n    \"pad_token_id\": null,\n    \"patch_size\": 14,\n    \"prefix\": null,\n    \"problem_type\": null,\n    \"pruned_heads\": {},\n    \"remove_invalid_values\": false,\n    \"repetition_penalty\": 1.0,\n    \"return_dict\": true,\n    \"return_dict_in_generate\": false,\n    \"sep_token_id\": null,\n    \"task_specific_params\": null,\n    \"temperature\": 1.0,\n    \"tie_encoder_decoder\": false,\n    \"tie_word_embeddings\": true,\n    \"tokenizer_class\": null,\n    \"top_k\": 50,\n    \"top_p\": 1.0,\n    \"torch_dtype\": null,\n    \"torchscript\": false,\n    \"transformers_version\": \"4.21.0.dev0\",\n    \"typical_p\": 1.0,\n    \"use_bfloat16\": false\n  },\n  \"vision_config_dict\": {\n    \"hidden_size\": 1024,\n    \"intermediate_size\": 4096,\n    \"num_attention_heads\": 16,\n    \"num_hidden_layers\": 24,\n    \"patch_size\": 14\n  }\n}\n"
  },
  {
    "path": "extras/safety_checker/configs/preprocessor_config.json",
    "content": "{\n  \"crop_size\": 224,\n  \"do_center_crop\": true,\n  \"do_convert_rgb\": true,\n  \"do_normalize\": true,\n  \"do_resize\": true,\n  \"feature_extractor_type\": \"CLIPFeatureExtractor\",\n  \"image_mean\": [\n    0.48145466,\n    0.4578275,\n    0.40821073\n  ],\n  \"image_std\": [\n    0.26862954,\n    0.26130258,\n    0.27577711\n  ],\n  \"resample\": 3,\n  \"size\": 224\n}\n"
  },
  {
    "path": "extras/safety_checker/models/safety_checker.py",
    "content": "# from https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py\n\n# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel\nfrom transformers.utils import logging\n\nlogger = logging.get_logger(__name__)\n\n\ndef cosine_distance(image_embeds, text_embeds):\n    normalized_image_embeds = nn.functional.normalize(image_embeds)\n    normalized_text_embeds = nn.functional.normalize(text_embeds)\n    return torch.mm(normalized_image_embeds, normalized_text_embeds.t())\n\n\nclass StableDiffusionSafetyChecker(PreTrainedModel):\n    config_class = CLIPConfig\n    main_input_name = \"clip_input\"\n\n    _no_split_modules = [\"CLIPEncoderLayer\"]\n\n    def __init__(self, config: CLIPConfig):\n        super().__init__(config)\n\n        self.vision_model = CLIPVisionModel(config.vision_config)\n        self.visual_projection = nn.Linear(config.vision_config.hidden_size, config.projection_dim, bias=False)\n\n        self.concept_embeds = nn.Parameter(torch.ones(17, config.projection_dim), requires_grad=False)\n        self.special_care_embeds = nn.Parameter(torch.ones(3, config.projection_dim), requires_grad=False)\n\n        self.concept_embeds_weights = nn.Parameter(torch.ones(17), requires_grad=False)\n        self.special_care_embeds_weights = nn.Parameter(torch.ones(3), requires_grad=False)\n\n    @torch.no_grad()\n    def forward(self, clip_input, images):\n        pooled_output = self.vision_model(clip_input)[1]  # pooled_output\n        image_embeds = self.visual_projection(pooled_output)\n\n        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n        special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds).cpu().float().numpy()\n        cos_dist = cosine_distance(image_embeds, self.concept_embeds).cpu().float().numpy()\n\n        result = []\n        batch_size = image_embeds.shape[0]\n        for i in range(batch_size):\n            result_img = {\"special_scores\": {}, \"special_care\": [], \"concept_scores\": {}, \"bad_concepts\": []}\n\n            # increase this value to create a stronger `nfsw` filter\n            # at the cost of increasing the possibility of filtering benign images\n            adjustment = 0.0\n\n            for concept_idx in range(len(special_cos_dist[0])):\n                concept_cos = special_cos_dist[i][concept_idx]\n                concept_threshold = self.special_care_embeds_weights[concept_idx].item()\n                result_img[\"special_scores\"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3)\n                if result_img[\"special_scores\"][concept_idx] > 0:\n                    result_img[\"special_care\"].append({concept_idx, result_img[\"special_scores\"][concept_idx]})\n                    adjustment = 0.01\n\n            for concept_idx in range(len(cos_dist[0])):\n                concept_cos = cos_dist[i][concept_idx]\n                concept_threshold = self.concept_embeds_weights[concept_idx].item()\n                result_img[\"concept_scores\"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3)\n                if result_img[\"concept_scores\"][concept_idx] > 0:\n                    result_img[\"bad_concepts\"].append(concept_idx)\n\n            result.append(result_img)\n\n        has_nsfw_concepts = [len(res[\"bad_concepts\"]) > 0 for res in result]\n\n        for idx, has_nsfw_concept in enumerate(has_nsfw_concepts):\n            if has_nsfw_concept:\n                if torch.is_tensor(images) or torch.is_tensor(images[0]):\n                    images[idx] = torch.zeros_like(images[idx])  # black image\n                else:\n                    images[idx] = np.zeros(images[idx].shape)  # black image\n\n        if any(has_nsfw_concepts):\n            logger.warning(\n                \"Potential NSFW content was detected in one or more images. A black image will be returned instead.\"\n                \" Try again with a different prompt and/or seed.\"\n            )\n\n        return images, has_nsfw_concepts\n\n    @torch.no_grad()\n    def forward_onnx(self, clip_input: torch.Tensor, images: torch.Tensor):\n        pooled_output = self.vision_model(clip_input)[1]  # pooled_output\n        image_embeds = self.visual_projection(pooled_output)\n\n        special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds)\n        cos_dist = cosine_distance(image_embeds, self.concept_embeds)\n\n        # increase this value to create a stronger `nsfw` filter\n        # at the cost of increasing the possibility of filtering benign images\n        adjustment = 0.0\n\n        special_scores = special_cos_dist - self.special_care_embeds_weights + adjustment\n        # special_scores = special_scores.round(decimals=3)\n        special_care = torch.any(special_scores > 0, dim=1)\n        special_adjustment = special_care * 0.01\n        special_adjustment = special_adjustment.unsqueeze(1).expand(-1, cos_dist.shape[1])\n\n        concept_scores = (cos_dist - self.concept_embeds_weights) + special_adjustment\n        # concept_scores = concept_scores.round(decimals=3)\n        has_nsfw_concepts = torch.any(concept_scores > 0, dim=1)\n\n        images[has_nsfw_concepts] = 0.0  # black image\n\n        return images, has_nsfw_concepts\n"
  },
  {
    "path": "extras/sam/predictor.py",
    "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport numpy as np\nimport torch\nfrom ldm_patched.modules import model_management\nfrom ldm_patched.modules.model_patcher import ModelPatcher\n\nfrom segment_anything.modeling import Sam\n\nfrom typing import Optional, Tuple\n\nfrom segment_anything.utils.transforms import ResizeLongestSide\n\n\nclass SamPredictor:\n    def __init__(\n        self,\n        model: Sam,\n        load_device=model_management.text_encoder_device(),\n        offload_device=model_management.text_encoder_offload_device()\n    ) -> None:\n        \"\"\"\n        Uses SAM to calculate the image embedding for an image, and then\n        allow repeated, efficient mask prediction given prompts.\n\n        Arguments:\n          model (Sam): The model to use for mask prediction.\n        \"\"\"\n        super().__init__()\n\n        self.load_device = load_device\n        self.offload_device = offload_device\n        # can't use model.half() here as slow_conv2d_cpu is not implemented for half\n        model.to(self.offload_device)\n\n        self.patcher = ModelPatcher(model, load_device=self.load_device, offload_device=self.offload_device)\n\n        self.transform = ResizeLongestSide(model.image_encoder.img_size)\n        self.reset_image()\n\n    def set_image(\n        self,\n        image: np.ndarray,\n        image_format: str = \"RGB\",\n    ) -> None:\n        \"\"\"\n        Calculates the image embeddings for the provided image, allowing\n        masks to be predicted with the 'predict' method.\n\n        Arguments:\n          image (np.ndarray): The image for calculating masks. Expects an\n            image in HWC uint8 format, with pixel values in [0, 255].\n          image_format (str): The color format of the image, in ['RGB', 'BGR'].\n        \"\"\"\n        assert image_format in [\n            \"RGB\",\n            \"BGR\",\n        ], f\"image_format must be in ['RGB', 'BGR'], is {image_format}.\"\n        if image_format != self.patcher.model.image_format:\n            image = image[..., ::-1]\n\n        # Transform the image to the form expected by the model\n        input_image = self.transform.apply_image(image)\n        input_image_torch = torch.as_tensor(input_image, device=self.load_device)\n        input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[None, :, :, :]\n\n        self.set_torch_image(input_image_torch, image.shape[:2])\n\n    @torch.no_grad()\n    def set_torch_image(\n        self,\n        transformed_image: torch.Tensor,\n        original_image_size: Tuple[int, ...],\n    ) -> None:\n        \"\"\"\n        Calculates the image embeddings for the provided image, allowing\n        masks to be predicted with the 'predict' method. Expects the input\n        image to be already transformed to the format expected by the model.\n\n        Arguments:\n          transformed_image (torch.Tensor): The input image, with shape\n            1x3xHxW, which has been transformed with ResizeLongestSide.\n          original_image_size (tuple(int, int)): The size of the image\n            before transformation, in (H, W) format.\n        \"\"\"\n        assert (\n            len(transformed_image.shape) == 4\n            and transformed_image.shape[1] == 3\n            and max(*transformed_image.shape[2:]) == self.patcher.model.image_encoder.img_size\n        ), f\"set_torch_image input must be BCHW with long side {self.patcher.model.image_encoder.img_size}.\"\n        self.reset_image()\n\n        self.original_size = original_image_size\n        self.input_size = tuple(transformed_image.shape[-2:])\n        model_management.load_model_gpu(self.patcher)\n        input_image = self.patcher.model.preprocess(transformed_image.to(self.load_device))\n        self.features = self.patcher.model.image_encoder(input_image)\n        self.is_image_set = True\n\n    def predict(\n        self,\n        point_coords: Optional[np.ndarray] = None,\n        point_labels: Optional[np.ndarray] = None,\n        box: Optional[np.ndarray] = None,\n        mask_input: Optional[np.ndarray] = None,\n        multimask_output: bool = True,\n        return_logits: bool = False,\n    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n        \"\"\"\n        Predict masks for the given input prompts, using the currently set image.\n\n        Arguments:\n          point_coords (np.ndarray or None): A Nx2 array of point prompts to the\n            model. Each point is in (X,Y) in pixels.\n          point_labels (np.ndarray or None): A length N array of labels for the\n            point prompts. 1 indicates a foreground point and 0 indicates a\n            background point.\n          box (np.ndarray or None): A length 4 array given a box prompt to the\n            model, in XYXY format.\n          mask_input (np.ndarray): A low resolution mask input to the model, typically\n            coming from a previous prediction iteration. Has form 1xHxW, where\n            for SAM, H=W=256.\n          multimask_output (bool): If true, the model will return three masks.\n            For ambiguous input prompts (such as a single click), this will often\n            produce better masks than a single prediction. If only a single\n            mask is needed, the model's predicted quality score can be used\n            to select the best mask. For non-ambiguous prompts, such as multiple\n            input prompts, multimask_output=False can give better results.\n          return_logits (bool): If true, returns un-thresholded masks logits\n            instead of a binary mask.\n\n        Returns:\n          (np.ndarray): The output masks in CxHxW format, where C is the\n            number of masks, and (H, W) is the original image size.\n          (np.ndarray): An array of length C containing the model's\n            predictions for the quality of each mask.\n          (np.ndarray): An array of shape CxHxW, where C is the number\n            of masks and H=W=256. These low resolution logits can be passed to\n            a subsequent iteration as mask input.\n        \"\"\"\n        if not self.is_image_set:\n            raise RuntimeError(\"An image must be set with .set_image(...) before mask prediction.\")\n\n        # Transform input prompts\n        coords_torch, labels_torch, box_torch, mask_input_torch = None, None, None, None\n        if point_coords is not None:\n            assert (\n                point_labels is not None\n            ), \"point_labels must be supplied if point_coords is supplied.\"\n            point_coords = self.transform.apply_coords(point_coords, self.original_size)\n            coords_torch = torch.as_tensor(point_coords, dtype=torch.float, device=self.load_device)\n            labels_torch = torch.as_tensor(point_labels, dtype=torch.int, device=self.load_device)\n            coords_torch, labels_torch = coords_torch[None, :, :], labels_torch[None, :]\n        if box is not None:\n            box = self.transform.apply_boxes(box, self.original_size)\n            box_torch = torch.as_tensor(box, dtype=torch.float, device=self.load_device)\n            box_torch = box_torch[None, :]\n        if mask_input is not None:\n            mask_input_torch = torch.as_tensor(mask_input, dtype=torch.float, device=self.load_device)\n            mask_input_torch = mask_input_torch[None, :, :, :]\n\n        masks, iou_predictions, low_res_masks = self.predict_torch(\n            coords_torch,\n            labels_torch,\n            box_torch,\n            mask_input_torch,\n            multimask_output,\n            return_logits=return_logits,\n        )\n\n        masks = masks[0].detach().cpu().numpy()\n        iou_predictions = iou_predictions[0].detach().cpu().numpy()\n        low_res_masks = low_res_masks[0].detach().cpu().numpy()\n        return masks, iou_predictions, low_res_masks\n\n    @torch.no_grad()\n    def predict_torch(\n        self,\n        point_coords: Optional[torch.Tensor],\n        point_labels: Optional[torch.Tensor],\n        boxes: Optional[torch.Tensor] = None,\n        mask_input: Optional[torch.Tensor] = None,\n        multimask_output: bool = True,\n        return_logits: bool = False,\n    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n        \"\"\"\n        Predict masks for the given input prompts, using the currently set image.\n        Input prompts are batched torch tensors and are expected to already be\n        transformed to the input frame using ResizeLongestSide.\n\n        Arguments:\n          point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the\n            model. Each point is in (X,Y) in pixels.\n          point_labels (torch.Tensor or None): A BxN array of labels for the\n            point prompts. 1 indicates a foreground point and 0 indicates a\n            background point.\n          box (np.ndarray or None): A Bx4 array given a box prompt to the\n            model, in XYXY format.\n          mask_input (np.ndarray): A low resolution mask input to the model, typically\n            coming from a previous prediction iteration. Has form Bx1xHxW, where\n            for SAM, H=W=256. Masks returned by a previous iteration of the\n            predict method do not need further transformation.\n          multimask_output (bool): If true, the model will return three masks.\n            For ambiguous input prompts (such as a single click), this will often\n            produce better masks than a single prediction. If only a single\n            mask is needed, the model's predicted quality score can be used\n            to select the best mask. For non-ambiguous prompts, such as multiple\n            input prompts, multimask_output=False can give better results.\n          return_logits (bool): If true, returns un-thresholded masks logits\n            instead of a binary mask.\n\n        Returns:\n          (torch.Tensor): The output masks in BxCxHxW format, where C is the\n            number of masks, and (H, W) is the original image size.\n          (torch.Tensor): An array of shape BxC containing the model's\n            predictions for the quality of each mask.\n          (torch.Tensor): An array of shape BxCxHxW, where C is the number\n            of masks and H=W=256. These low res logits can be passed to\n            a subsequent iteration as mask input.\n        \"\"\"\n        if not self.is_image_set:\n            raise RuntimeError(\"An image must be set with .set_image(...) before mask prediction.\")\n\n        if point_coords is not None:\n            points = (point_coords.to(self.load_device), point_labels.to(self.load_device))\n        else:\n            points = None\n\n        # load\n        if boxes is not None:\n            boxes = boxes.to(self.load_device)\n        if mask_input is not None:\n            mask_input = mask_input.to(self.load_device)\n        model_management.load_model_gpu(self.patcher)\n\n        # Embed prompts\n        sparse_embeddings, dense_embeddings = self.patcher.model.prompt_encoder(\n            points=points,\n            boxes=boxes,\n            masks=mask_input,\n        )\n\n        # Predict masks\n        low_res_masks, iou_predictions = self.patcher.model.mask_decoder(\n            image_embeddings=self.features,\n            image_pe=self.patcher.model.prompt_encoder.get_dense_pe(),\n            sparse_prompt_embeddings=sparse_embeddings,\n            dense_prompt_embeddings=dense_embeddings,\n            multimask_output=multimask_output,\n        )\n\n        # Upscale the masks to the original image resolution\n        masks = self.patcher.model.postprocess_masks(low_res_masks, self.input_size, self.original_size)\n\n        if not return_logits:\n            masks = masks > self.patcher.model.mask_threshold\n\n        return masks, iou_predictions, low_res_masks\n\n    def get_image_embedding(self) -> torch.Tensor:\n        \"\"\"\n        Returns the image embeddings for the currently set image, with\n        shape 1xCxHxW, where C is the embedding dimension and (H,W) are\n        the embedding spatial dimension of SAM (typically C=256, H=W=64).\n        \"\"\"\n        if not self.is_image_set:\n            raise RuntimeError(\n                \"An image must be set with .set_image(...) to generate an embedding.\"\n            )\n        assert self.features is not None, \"Features must exist if an image has been set.\"\n        return self.features\n\n    @property\n    def device(self) -> torch.device:\n        return self.patcher.model.device\n\n    def reset_image(self) -> None:\n        \"\"\"Resets the currently set image.\"\"\"\n        self.is_image_set = False\n        self.features = None\n        self.orig_h = None\n        self.orig_w = None\n        self.input_h = None\n        self.input_w = None\n"
  },
  {
    "path": "extras/vae_interpose.py",
    "content": "# https://github.com/city96/SD-Latent-Interposer/blob/main/interposer.py\n\nimport os\n\nimport safetensors.torch as sf\nimport torch\nimport torch.nn as nn\n\nimport ldm_patched.modules.model_management\nfrom ldm_patched.modules.model_patcher import ModelPatcher\nfrom modules.config import path_vae_approx\n\n\nclass ResBlock(nn.Module):\n    \"\"\"Block with residuals\"\"\"\n\n    def __init__(self, ch):\n        super().__init__()\n        self.join = nn.ReLU()\n        self.norm = nn.BatchNorm2d(ch)\n        self.long = nn.Sequential(\n            nn.Conv2d(ch, ch, kernel_size=3, stride=1, padding=1),\n            nn.SiLU(),\n            nn.Conv2d(ch, ch, kernel_size=3, stride=1, padding=1),\n            nn.SiLU(),\n            nn.Conv2d(ch, ch, kernel_size=3, stride=1, padding=1),\n            nn.Dropout(0.1)\n        )\n\n    def forward(self, x):\n        x = self.norm(x)\n        return self.join(self.long(x) + x)\n\n\nclass ExtractBlock(nn.Module):\n    \"\"\"Increase no. of channels by [out/in]\"\"\"\n\n    def __init__(self, ch_in, ch_out):\n        super().__init__()\n        self.join = nn.ReLU()\n        self.short = nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=1, padding=1)\n        self.long = nn.Sequential(\n            nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=1, padding=1),\n            nn.SiLU(),\n            nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1),\n            nn.SiLU(),\n            nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1),\n            nn.Dropout(0.1)\n        )\n\n    def forward(self, x):\n        return self.join(self.long(x) + self.short(x))\n\n\nclass InterposerModel(nn.Module):\n    \"\"\"Main neural network\"\"\"\n\n    def __init__(self, ch_in=4, ch_out=4, ch_mid=64, scale=1.0, blocks=12):\n        super().__init__()\n        self.ch_in = ch_in\n        self.ch_out = ch_out\n        self.ch_mid = ch_mid\n        self.blocks = blocks\n        self.scale = scale\n\n        self.head = ExtractBlock(self.ch_in, self.ch_mid)\n        self.core = nn.Sequential(\n            nn.Upsample(scale_factor=self.scale, mode=\"nearest\"),\n            *[ResBlock(self.ch_mid) for _ in range(blocks)],\n            nn.BatchNorm2d(self.ch_mid),\n            nn.SiLU(),\n        )\n        self.tail = nn.Conv2d(self.ch_mid, self.ch_out, kernel_size=3, stride=1, padding=1)\n\n    def forward(self, x):\n        y = self.head(x)\n        z = self.core(y)\n        return self.tail(z)\n\n\nvae_approx_model = None\nvae_approx_filename = os.path.join(path_vae_approx, 'xl-to-v1_interposer-v4.0.safetensors')\n\n\ndef parse(x):\n    global vae_approx_model\n\n    x_origin = x.clone()\n\n    if vae_approx_model is None:\n        model = InterposerModel()\n        model.eval()\n        sd = sf.load_file(vae_approx_filename)\n        model.load_state_dict(sd)\n        fp16 = ldm_patched.modules.model_management.should_use_fp16()\n        if fp16:\n            model = model.half()\n        vae_approx_model = ModelPatcher(\n            model=model,\n            load_device=ldm_patched.modules.model_management.get_torch_device(),\n            offload_device=torch.device('cpu')\n        )\n        vae_approx_model.dtype = torch.float16 if fp16 else torch.float32\n\n    ldm_patched.modules.model_management.load_model_gpu(vae_approx_model)\n\n    x = x_origin.to(device=vae_approx_model.load_device, dtype=vae_approx_model.dtype)\n    x = vae_approx_model.model(x).to(x_origin)\n    return x\n"
  },
  {
    "path": "extras/wd14tagger.py",
    "content": "# https://huggingface.co/spaces/SmilingWolf/wd-v1-4-tags\n# https://github.com/pythongosssss/ComfyUI-WD14-Tagger/blob/main/wd14tagger.py\n\n# {\n#     \"wd-v1-4-moat-tagger-v2\": \"https://huggingface.co/SmilingWolf/wd-v1-4-moat-tagger-v2\",\n#     \"wd-v1-4-convnextv2-tagger-v2\": \"https://huggingface.co/SmilingWolf/wd-v1-4-convnextv2-tagger-v2\",\n#     \"wd-v1-4-convnext-tagger-v2\": \"https://huggingface.co/SmilingWolf/wd-v1-4-convnext-tagger-v2\",\n#     \"wd-v1-4-convnext-tagger\": \"https://huggingface.co/SmilingWolf/wd-v1-4-convnext-tagger\",\n#     \"wd-v1-4-vit-tagger-v2\": \"https://huggingface.co/SmilingWolf/wd-v1-4-vit-tagger-v2\"\n# }\n\n\nimport numpy as np\nimport csv\nimport onnxruntime as ort\n\nfrom PIL import Image\nfrom onnxruntime import InferenceSession\nfrom modules.config import path_clip_vision\nfrom modules.model_loader import load_file_from_url\n\n\nglobal_model = None\nglobal_csv = None\n\n\ndef default_interrogator(image_rgb, threshold=0.35, character_threshold=0.85, exclude_tags=\"\"):\n    global global_model, global_csv\n\n    model_name = \"wd-v1-4-moat-tagger-v2\"\n\n    model_onnx_filename = load_file_from_url(\n        url=f'https://huggingface.co/lllyasviel/misc/resolve/main/{model_name}.onnx',\n        model_dir=path_clip_vision,\n        file_name=f'{model_name}.onnx',\n    )\n\n    model_csv_filename = load_file_from_url(\n        url=f'https://huggingface.co/lllyasviel/misc/resolve/main/{model_name}.csv',\n        model_dir=path_clip_vision,\n        file_name=f'{model_name}.csv',\n    )\n\n    if global_model is not None:\n        model = global_model\n    else:\n        model = InferenceSession(model_onnx_filename, providers=ort.get_available_providers())\n        global_model = model\n\n    input = model.get_inputs()[0]\n    height = input.shape[1]\n\n    image = Image.fromarray(image_rgb)  # RGB\n    ratio = float(height)/max(image.size)\n    new_size = tuple([int(x*ratio) for x in image.size])\n    image = image.resize(new_size, Image.LANCZOS)\n    square = Image.new(\"RGB\", (height, height), (255, 255, 255))\n    square.paste(image, ((height-new_size[0])//2, (height-new_size[1])//2))\n\n    image = np.array(square).astype(np.float32)\n    image = image[:, :, ::-1]  # RGB -> BGR\n    image = np.expand_dims(image, 0)\n\n    if global_csv is not None:\n        csv_lines = global_csv\n    else:\n        csv_lines = []\n        with open(model_csv_filename) as f:\n            reader = csv.reader(f)\n            next(reader)\n            for row in reader:\n                csv_lines.append(row)\n        global_csv = csv_lines\n\n    tags = []\n    general_index = None\n    character_index = None\n    for line_num, row in enumerate(csv_lines):\n        if general_index is None and row[2] == \"0\":\n            general_index = line_num\n        elif character_index is None and row[2] == \"4\":\n            character_index = line_num\n        tags.append(row[1])\n\n    label_name = model.get_outputs()[0].name\n    probs = model.run([label_name], {input.name: image})[0]\n\n    result = list(zip(tags, probs[0]))\n\n    general = [item for item in result[general_index:character_index] if item[1] > threshold]\n    character = [item for item in result[character_index:] if item[1] > character_threshold]\n\n    all = character + general\n    remove = [s.strip() for s in exclude_tags.lower().split(\",\")]\n    all = [tag for tag in all if tag[0] not in remove]\n\n    res = \", \".join((item[0].replace(\"(\", \"\\\\(\").replace(\")\", \"\\\\)\") for item in all)).replace('_', ' ')\n    return res\n"
  },
  {
    "path": "fooocus_colab.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"id\": \"VjYy0F2gZIPR\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!pip install pygit2==1.15.1\\n\",\n    \"%cd /content\\n\",\n    \"!git clone https://github.com/lllyasviel/Fooocus.git\\n\",\n    \"%cd /content/Fooocus\\n\",\n    \"!python entry_with_update.py --share --always-high-vram\\n\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"accelerator\": \"GPU\",\n  \"colab\": {\n   \"gpuType\": \"T4\",\n   \"provenance\": []\n  },\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"name\": \"python\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "fooocus_version.py",
    "content": "version = '2.5.5'"
  },
  {
    "path": "javascript/contextMenus.js",
    "content": "// based on https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/v1.6.0/javascript/contextMenus.js\n\nvar contextMenuInit = function() {\n    let eventListenerApplied = false;\n    let menuSpecs = new Map();\n\n    const uid = function() {\n        return Date.now().toString(36) + Math.random().toString(36).substring(2);\n    };\n\n    function showContextMenu(event, element, menuEntries) {\n        let posx = event.clientX + document.body.scrollLeft + document.documentElement.scrollLeft;\n        let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop;\n\n        let oldMenu = gradioApp().querySelector('#context-menu');\n        if (oldMenu) {\n            oldMenu.remove();\n        }\n\n        let baseStyle = window.getComputedStyle(gradioApp().querySelector('button.selected'));\n\n        const contextMenu = document.createElement('nav');\n        contextMenu.id = \"context-menu\";\n        contextMenu.style.background = baseStyle.background;\n        contextMenu.style.color = baseStyle.color;\n        contextMenu.style.fontFamily = baseStyle.fontFamily;\n        contextMenu.style.top = posy + 'px';\n        contextMenu.style.left = posx + 'px';\n\n        const contextMenuList = document.createElement('ul');\n        contextMenuList.className = 'context-menu-items';\n        contextMenu.append(contextMenuList);\n\n        menuEntries.forEach(function(entry) {\n            let contextMenuEntry = document.createElement('a');\n            contextMenuEntry.innerHTML = entry['name'];\n            contextMenuEntry.addEventListener(\"click\", function() {\n                entry['func']();\n            });\n            contextMenuList.append(contextMenuEntry);\n\n        });\n\n        gradioApp().appendChild(contextMenu);\n\n        let menuWidth = contextMenu.offsetWidth + 4;\n        let menuHeight = contextMenu.offsetHeight + 4;\n\n        let windowWidth = window.innerWidth;\n        let windowHeight = window.innerHeight;\n\n        if ((windowWidth - posx) < menuWidth) {\n            contextMenu.style.left = windowWidth - menuWidth + \"px\";\n        }\n\n        if ((windowHeight - posy) < menuHeight) {\n            contextMenu.style.top = windowHeight - menuHeight + \"px\";\n        }\n\n    }\n\n    function appendContextMenuOption(targetElementSelector, entryName, entryFunction) {\n\n        var currentItems = menuSpecs.get(targetElementSelector);\n\n        if (!currentItems) {\n            currentItems = [];\n            menuSpecs.set(targetElementSelector, currentItems);\n        }\n        let newItem = {\n            id: targetElementSelector + '_' + uid(),\n            name: entryName,\n            func: entryFunction,\n            isNew: true\n        };\n\n        currentItems.push(newItem);\n        return newItem['id'];\n    }\n\n    function removeContextMenuOption(uid) {\n        menuSpecs.forEach(function(v) {\n            let index = -1;\n            v.forEach(function(e, ei) {\n                if (e['id'] == uid) {\n                    index = ei;\n                }\n            });\n            if (index >= 0) {\n                v.splice(index, 1);\n            }\n        });\n    }\n\n    function addContextMenuEventListener() {\n        if (eventListenerApplied) {\n            return;\n        }\n        gradioApp().addEventListener(\"click\", function(e) {\n            if (!e.isTrusted) {\n                return;\n            }\n\n            let oldMenu = gradioApp().querySelector('#context-menu');\n            if (oldMenu) {\n                oldMenu.remove();\n            }\n        });\n        gradioApp().addEventListener(\"contextmenu\", function(e) {\n            let oldMenu = gradioApp().querySelector('#context-menu');\n            if (oldMenu) {\n                oldMenu.remove();\n            }\n            menuSpecs.forEach(function(v, k) {\n                if (e.composedPath()[0].matches(k)) {\n                    showContextMenu(e, e.composedPath()[0], v);\n                    e.preventDefault();\n                }\n            });\n        });\n        eventListenerApplied = true;\n\n    }\n\n    return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener];\n};\n\nvar initResponse = contextMenuInit();\nvar appendContextMenuOption = initResponse[0];\nvar removeContextMenuOption = initResponse[1];\nvar addContextMenuEventListener = initResponse[2];\n\nlet cancelGenerateForever = function() {\n    clearInterval(window.generateOnRepeatInterval);\n};\n\n(function() {\n    //Start example Context Menu Items\n    let generateOnRepeat = function(genbuttonid, interruptbuttonid) {\n        let genbutton = gradioApp().querySelector(genbuttonid);\n        let interruptbutton = gradioApp().querySelector(interruptbuttonid);\n        if (!interruptbutton.offsetParent) {\n            genbutton.click();\n        }\n        clearInterval(window.generateOnRepeatInterval);\n        window.generateOnRepeatInterval = setInterval(function() {\n            if (!interruptbutton.offsetParent) {\n                genbutton.click();\n            }\n        },\n        500);\n    };\n\n    let generateOnRepeatForButtons = function() {\n        generateOnRepeat('#generate_button', '#stop_button');\n    };\n    appendContextMenuOption('#generate_button', 'Generate forever', generateOnRepeatForButtons);\n\n})();\n//End example Context Menu Items\n\ndocument.onreadystatechange = function () {\n    if (document.readyState == \"complete\") {\n        addContextMenuEventListener();\n    }\n};\n"
  },
  {
    "path": "javascript/edit-attention.js",
    "content": "function updateInput(target) {\n    let e = new Event(\"input\", {bubbles: true});\n    Object.defineProperty(e, \"target\", {value: target});\n    target.dispatchEvent(e);\n}\n\nfunction keyupEditAttention(event) {\n    let target = event.originalTarget || event.composedPath()[0];\n    if (!target.matches(\"*:is([id*='_prompt'], .prompt) textarea\")) return;\n    if (!(event.metaKey || event.ctrlKey)) return;\n\n    let isPlus = event.key == \"ArrowUp\";\n    let isMinus = event.key == \"ArrowDown\";\n    if (!isPlus && !isMinus) return;\n\n    let selectionStart = target.selectionStart;\n    let selectionEnd = target.selectionEnd;\n    let text = target.value;\n\n    function selectCurrentParenthesisBlock(OPEN, CLOSE) {\n        if (selectionStart !== selectionEnd) return false;\n\n        // Find opening parenthesis around current cursor\n        const before = text.substring(0, selectionStart);\n        let beforeParen = before.lastIndexOf(OPEN);\n        if (beforeParen == -1) return false;\n        let beforeParenClose = before.lastIndexOf(CLOSE);\n        while (beforeParenClose !== -1 && beforeParenClose > beforeParen) {\n            beforeParen = before.lastIndexOf(OPEN, beforeParen - 1);\n            beforeParenClose = before.lastIndexOf(CLOSE, beforeParenClose - 1);\n        }\n\n        // Find closing parenthesis around current cursor\n        const after = text.substring(selectionStart);\n        let afterParen = after.indexOf(CLOSE);\n        if (afterParen == -1) return false;\n        let afterParenOpen = after.indexOf(OPEN);\n        while (afterParenOpen !== -1 && afterParen > afterParenOpen) {\n            afterParen = after.indexOf(CLOSE, afterParen + 1);\n            afterParenOpen = after.indexOf(OPEN, afterParenOpen + 1);\n        }\n        if (beforeParen === -1 || afterParen === -1) return false;\n\n        // Set the selection to the text between the parenthesis\n        const parenContent = text.substring(beforeParen + 1, selectionStart + afterParen);\n        const lastColon = parenContent.lastIndexOf(\":\");\n        selectionStart = beforeParen + 1;\n        selectionEnd = selectionStart + lastColon;\n        target.setSelectionRange(selectionStart, selectionEnd);\n        return true;\n    }\n\n    function selectCurrentWord() {\n        if (selectionStart !== selectionEnd) return false;\n        const delimiters = \".,\\\\/!?%^*;:{}=`~() \\r\\n\\t\";\n\n        // seek backward until to find beggining\n        while (!delimiters.includes(text[selectionStart - 1]) && selectionStart > 0) {\n            selectionStart--;\n        }\n\n        // seek forward to find end\n        while (!delimiters.includes(text[selectionEnd]) && selectionEnd < text.length) {\n            selectionEnd++;\n        }\n\n        target.setSelectionRange(selectionStart, selectionEnd);\n        return true;\n    }\n\n    // If the user hasn't selected anything, let's select their current parenthesis block or word\n    if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')')) {\n        selectCurrentWord();\n    }\n\n    event.preventDefault();\n\n    var closeCharacter = ')';\n    var delta = 0.1;\n\n    if (selectionStart > 0 && text[selectionStart - 1] == '<') {\n        closeCharacter = '>';\n        delta = 0.05;\n    } else if (selectionStart == 0 || text[selectionStart - 1] != \"(\") {\n\n        // do not include spaces at the end\n        while (selectionEnd > selectionStart && text[selectionEnd - 1] == ' ') {\n            selectionEnd -= 1;\n        }\n        if (selectionStart == selectionEnd) {\n            return;\n        }\n\n        text = text.slice(0, selectionStart) + \"(\" + text.slice(selectionStart, selectionEnd) + \":1.0)\" + text.slice(selectionEnd);\n\n        selectionStart += 1;\n        selectionEnd += 1;\n    }\n\n    var end = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1;\n    var weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + 1 + end));\n    if (isNaN(weight)) return;\n\n    weight += isPlus ? delta : -delta;\n    weight = parseFloat(weight.toPrecision(12));\n    if (String(weight).length == 1) weight += \".0\";\n\n    if (closeCharacter == ')' && weight == 1) {\n        var endParenPos = text.substring(selectionEnd).indexOf(')');\n        text = text.slice(0, selectionStart - 1) + text.slice(selectionStart, selectionEnd) + text.slice(selectionEnd + endParenPos + 1);\n        selectionStart--;\n        selectionEnd--;\n    } else {\n        text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + end);\n    }\n\n    target.focus();\n    target.value = text;\n    target.selectionStart = selectionStart;\n    target.selectionEnd = selectionEnd;\n\n    updateInput(target);\n\n}\n\naddEventListener('keydown', (event) => {\n    keyupEditAttention(event);\n});\n"
  },
  {
    "path": "javascript/imageviewer.js",
    "content": "// From A1111\n\nfunction closeModal() {\n    gradioApp().getElementById(\"lightboxModal\").style.display = \"none\";\n}\n\nfunction showModal(event) {\n    const source = event.target || event.srcElement;\n    const modalImage = gradioApp().getElementById(\"modalImage\");\n    const lb = gradioApp().getElementById(\"lightboxModal\");\n    modalImage.src = source.src;\n    if (modalImage.style.display === 'none') {\n        lb.style.setProperty('background-image', 'url(' + source.src + ')');\n    }\n    lb.style.display = \"flex\";\n    lb.focus();\n\n    event.stopPropagation();\n}\n\nfunction negmod(n, m) {\n    return ((n % m) + m) % m;\n}\n\nfunction updateOnBackgroundChange() {\n    const modalImage = gradioApp().getElementById(\"modalImage\");\n    if (modalImage && modalImage.offsetParent) {\n        let currentButton = selected_gallery_button();\n\n        if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) {\n            modalImage.src = currentButton.children[0].src;\n            if (modalImage.style.display === 'none') {\n                const modal = gradioApp().getElementById(\"lightboxModal\");\n                modal.style.setProperty('background-image', `url(${modalImage.src})`);\n            }\n        }\n    }\n}\n\nfunction all_gallery_buttons() {\n    var allGalleryButtons = gradioApp().querySelectorAll('.image_gallery .thumbnails > .thumbnail-item.thumbnail-small');\n    var visibleGalleryButtons = [];\n    allGalleryButtons.forEach(function(elem) {\n        if (elem.parentElement.offsetParent) {\n            visibleGalleryButtons.push(elem);\n        }\n    });\n    return visibleGalleryButtons;\n}\n\nfunction selected_gallery_button() {\n    return all_gallery_buttons().find(elem => elem.classList.contains('selected')) ?? null;\n}\n\nfunction selected_gallery_index() {\n    return all_gallery_buttons().findIndex(elem => elem.classList.contains('selected'));\n}\n\nfunction modalImageSwitch(offset) {\n    var galleryButtons = all_gallery_buttons();\n\n    if (galleryButtons.length > 1) {\n        var currentButton = selected_gallery_button();\n\n        var result = -1;\n        galleryButtons.forEach(function(v, i) {\n            if (v == currentButton) {\n                result = i;\n            }\n        });\n\n        if (result != -1) {\n            var nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)];\n            nextButton.click();\n            const modalImage = gradioApp().getElementById(\"modalImage\");\n            const modal = gradioApp().getElementById(\"lightboxModal\");\n            modalImage.src = nextButton.children[0].src;\n            if (modalImage.style.display === 'none') {\n                modal.style.setProperty('background-image', `url(${modalImage.src})`);\n            }\n            setTimeout(function() {\n                modal.focus();\n            }, 10);\n        }\n    }\n}\n\nfunction saveImage() {\n\n}\n\nfunction modalSaveImage(event) {\n    event.stopPropagation();\n}\n\nfunction modalNextImage(event) {\n    modalImageSwitch(1);\n    event.stopPropagation();\n}\n\nfunction modalPrevImage(event) {\n    modalImageSwitch(-1);\n    event.stopPropagation();\n}\n\nfunction modalKeyHandler(event) {\n    switch (event.key) {\n    case \"s\":\n        saveImage();\n        break;\n    case \"ArrowLeft\":\n        modalPrevImage(event);\n        break;\n    case \"ArrowRight\":\n        modalNextImage(event);\n        break;\n    case \"Escape\":\n        closeModal();\n        break;\n    }\n}\n\nfunction setupImageForLightbox(e) {\n    if (e.dataset.modded) {\n        return;\n    }\n\n    e.dataset.modded = true;\n    e.style.cursor = 'pointer';\n    e.style.userSelect = 'none';\n\n    var isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') > -1;\n\n    // For Firefox, listening on click first switched to next image then shows the lightbox.\n    // If you know how to fix this without switching to mousedown event, please.\n    // For other browsers the event is click to make it possiblr to drag picture.\n    var event = isFirefox ? 'mousedown' : 'click';\n\n    e.addEventListener(event, function(evt) {\n        if (evt.button == 1) {\n            open(evt.target.src);\n            evt.preventDefault();\n            return;\n        }\n        if (evt.button != 0) return;\n\n        modalZoomSet(gradioApp().getElementById('modalImage'), true);\n        evt.preventDefault();\n        showModal(evt);\n    }, true);\n\n}\n\nfunction modalZoomSet(modalImage, enable) {\n    if (modalImage) modalImage.classList.toggle('modalImageFullscreen', !!enable);\n}\n\nfunction modalZoomToggle(event) {\n    var modalImage = gradioApp().getElementById(\"modalImage\");\n    modalZoomSet(modalImage, !modalImage.classList.contains('modalImageFullscreen'));\n    event.stopPropagation();\n}\n\nfunction modalTileImageToggle(event) {\n    const modalImage = gradioApp().getElementById(\"modalImage\");\n    const modal = gradioApp().getElementById(\"lightboxModal\");\n    const isTiling = modalImage.style.display === 'none';\n    if (isTiling) {\n        modalImage.style.display = 'block';\n        modal.style.setProperty('background-image', 'none');\n    } else {\n        modalImage.style.display = 'none';\n        modal.style.setProperty('background-image', `url(${modalImage.src})`);\n    }\n\n    event.stopPropagation();\n}\n\nonAfterUiUpdate(function() {\n    var fullImg_preview = gradioApp().querySelectorAll('.image_gallery > div > img');\n    if (fullImg_preview != null) {\n        fullImg_preview.forEach(setupImageForLightbox);\n    }\n    updateOnBackgroundChange();\n});\n\ndocument.addEventListener(\"DOMContentLoaded\", function() {\n    //const modalFragment = document.createDocumentFragment();\n    const modal = document.createElement('div');\n    modal.onclick = closeModal;\n    modal.id = \"lightboxModal\";\n    modal.tabIndex = 0;\n    modal.addEventListener('keydown', modalKeyHandler, true);\n\n    const modalControls = document.createElement('div');\n    modalControls.className = 'modalControls gradio-container';\n    modal.append(modalControls);\n\n    const modalZoom = document.createElement('span');\n    modalZoom.className = 'modalZoom cursor';\n    modalZoom.innerHTML = '&#10529;';\n    modalZoom.addEventListener('click', modalZoomToggle, true);\n    modalZoom.title = \"Toggle zoomed view\";\n    modalControls.appendChild(modalZoom);\n\n    // const modalTileImage = document.createElement('span');\n    // modalTileImage.className = 'modalTileImage cursor';\n    // modalTileImage.innerHTML = '&#8862;';\n    // modalTileImage.addEventListener('click', modalTileImageToggle, true);\n    // modalTileImage.title = \"Preview tiling\";\n    // modalControls.appendChild(modalTileImage);\n    //\n    // const modalSave = document.createElement(\"span\");\n    // modalSave.className = \"modalSave cursor\";\n    // modalSave.id = \"modal_save\";\n    // modalSave.innerHTML = \"&#x1F5AB;\";\n    // modalSave.addEventListener(\"click\", modalSaveImage, true);\n    // modalSave.title = \"Save Image(s)\";\n    // modalControls.appendChild(modalSave);\n\n    const modalClose = document.createElement('span');\n    modalClose.className = 'modalClose cursor';\n    modalClose.innerHTML = '&times;';\n    modalClose.onclick = closeModal;\n    modalClose.title = \"Close image viewer\";\n    modalControls.appendChild(modalClose);\n\n    const modalImage = document.createElement('img');\n    modalImage.id = 'modalImage';\n    modalImage.onclick = closeModal;\n    modalImage.tabIndex = 0;\n    modalImage.addEventListener('keydown', modalKeyHandler, true);\n    modal.appendChild(modalImage);\n\n    const modalPrev = document.createElement('a');\n    modalPrev.className = 'modalPrev';\n    modalPrev.innerHTML = '&#10094;';\n    modalPrev.tabIndex = 0;\n    modalPrev.addEventListener('click', modalPrevImage, true);\n    modalPrev.addEventListener('keydown', modalKeyHandler, true);\n    modal.appendChild(modalPrev);\n\n    const modalNext = document.createElement('a');\n    modalNext.className = 'modalNext';\n    modalNext.innerHTML = '&#10095;';\n    modalNext.tabIndex = 0;\n    modalNext.addEventListener('click', modalNextImage, true);\n    modalNext.addEventListener('keydown', modalKeyHandler, true);\n\n    modal.appendChild(modalNext);\n\n    try {\n        gradioApp().appendChild(modal);\n    } catch (e) {\n        gradioApp().body.appendChild(modal);\n    }\n\n    document.body.appendChild(modal);\n\n});\n"
  },
  {
    "path": "javascript/localization.js",
    "content": "var re_num = /^[.\\d]+$/;\n\nvar original_lines = {};\nvar translated_lines = {};\n\nfunction hasLocalization() {\n    return window.localization && Object.keys(window.localization).length > 0;\n}\n\nfunction textNodesUnder(el) {\n    var n, a = [], walk = document.createTreeWalker(el, NodeFilter.SHOW_TEXT, null, false);\n    while ((n = walk.nextNode())) a.push(n);\n    return a;\n}\n\nfunction canBeTranslated(node, text) {\n    if (!text) return false;\n    if (!node.parentElement) return false;\n    var parentType = node.parentElement.nodeName;\n    if (parentType == 'SCRIPT' || parentType == 'STYLE' || parentType == 'TEXTAREA') return false;\n    if (re_num.test(text)) return false;\n    return true;\n}\n\nfunction getTranslation(text) {\n    if (!text) return undefined;\n\n    if (translated_lines[text] === undefined) {\n        original_lines[text] = 1;\n    }\n\n    var tl = localization[text];\n    if (tl !== undefined) {\n        translated_lines[tl] = 1;\n    }\n\n    return tl;\n}\n\nfunction processTextNode(node) {\n    var text = node.textContent.trim();\n\n    if (!canBeTranslated(node, text)) return;\n\n    var tl = getTranslation(text);\n    if (tl !== undefined) {\n        node.textContent = tl;\n        if (text && node.parentElement) {\n          node.parentElement.setAttribute(\"data-original-text\", text);\n        }\n    }\n}\n\nfunction processNode(node) {\n    if (node.nodeType == 3) {\n        processTextNode(node);\n        return;\n    }\n\n    if (node.title) {\n        let tl = getTranslation(node.title);\n        if (tl !== undefined) {\n            node.title = tl;\n        }\n    }\n\n    if (node.placeholder) {\n        let tl = getTranslation(node.placeholder);\n        if (tl !== undefined) {\n            node.placeholder = tl;\n        }\n    }\n\n    textNodesUnder(node).forEach(function(node) {\n        processTextNode(node);\n    });\n}\n\nfunction refresh_style_localization() {\n    processNode(document.querySelector('.style_selections'));\n}\n\nfunction refresh_aspect_ratios_label(value) {\n    label = document.querySelector('#aspect_ratios_accordion div span');\n    translation = getTranslation(\"Aspect Ratios\");\n    if (typeof translation == \"undefined\") {\n        translation = \"Aspect Ratios\";\n    }\n    label.textContent = translation + \" \" + htmlDecode(value);\n}\n\nfunction localizeWholePage() {\n    processNode(gradioApp());\n\n    function elem(comp) {\n        var elem_id = comp.props.elem_id ? comp.props.elem_id : \"component-\" + comp.id;\n        return gradioApp().getElementById(elem_id);\n    }\n\n    for (var comp of window.gradio_config.components) {\n        if (comp.props.webui_tooltip) {\n            let e = elem(comp);\n\n            let tl = e ? getTranslation(e.title) : undefined;\n            if (tl !== undefined) {\n                e.title = tl;\n            }\n        }\n        if (comp.props.placeholder) {\n            let e = elem(comp);\n            let textbox = e ? e.querySelector('[placeholder]') : null;\n\n            let tl = textbox ? getTranslation(textbox.placeholder) : undefined;\n            if (tl !== undefined) {\n                textbox.placeholder = tl;\n            }\n        }\n    }\n}\n\ndocument.addEventListener(\"DOMContentLoaded\", function() {\n    if (!hasLocalization()) {\n        return;\n    }\n\n    onUiUpdate(function(m) {\n        m.forEach(function(mutation) {\n            mutation.addedNodes.forEach(function(node) {\n                processNode(node);\n            });\n        });\n    });\n\n    localizeWholePage();\n\n    if (localization.rtl) { // if the language is from right to left,\n        (new MutationObserver((mutations, observer) => { // wait for the style to load\n            mutations.forEach(mutation => {\n                mutation.addedNodes.forEach(node => {\n                    if (node.tagName === 'STYLE') {\n                        observer.disconnect();\n\n                        for (const x of node.sheet.rules) { // find all rtl media rules\n                            if (Array.from(x.media || []).includes('rtl')) {\n                                x.media.appendMedium('all'); // enable them\n                            }\n                        }\n                    }\n                });\n            });\n        })).observe(gradioApp(), {childList: true});\n    }\n});\n"
  },
  {
    "path": "javascript/script.js",
    "content": "// based on https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/v1.6.0/script.js\nfunction gradioApp() {\n    const elems = document.getElementsByTagName('gradio-app');\n    const elem = elems.length == 0 ? document : elems[0];\n\n    if (elem !== document) {\n        elem.getElementById = function(id) {\n            return document.getElementById(id);\n        };\n    }\n    return elem.shadowRoot ? elem.shadowRoot : elem;\n}\n\n/**\n * Get the currently selected top-level UI tab button (e.g. the button that says \"Extras\").\n */\nfunction get_uiCurrentTab() {\n    return gradioApp().querySelector('#tabs > .tab-nav > button.selected');\n}\n\n/**\n * Get the first currently visible top-level UI tab content (e.g. the div hosting the \"txt2img\" UI).\n */\nfunction get_uiCurrentTabContent() {\n    return gradioApp().querySelector('#tabs > .tabitem[id^=tab_]:not([style*=\"display: none\"])');\n}\n\nvar uiUpdateCallbacks = [];\nvar uiAfterUpdateCallbacks = [];\nvar uiLoadedCallbacks = [];\nvar uiTabChangeCallbacks = [];\nvar optionsChangedCallbacks = [];\nvar uiAfterUpdateTimeout = null;\nvar uiCurrentTab = null;\n\n/**\n * Register callback to be called at each UI update.\n * The callback receives an array of MutationRecords as an argument.\n */\nfunction onUiUpdate(callback) {\n    uiUpdateCallbacks.push(callback);\n}\n\n/**\n * Register callback to be called soon after UI updates.\n * The callback receives no arguments.\n *\n * This is preferred over `onUiUpdate` if you don't need\n * access to the MutationRecords, as your function will\n * not be called quite as often.\n */\nfunction onAfterUiUpdate(callback) {\n    uiAfterUpdateCallbacks.push(callback);\n}\n\n/**\n * Register callback to be called when the UI is loaded.\n * The callback receives no arguments.\n */\nfunction onUiLoaded(callback) {\n    uiLoadedCallbacks.push(callback);\n}\n\n/**\n * Register callback to be called when the UI tab is changed.\n * The callback receives no arguments.\n */\nfunction onUiTabChange(callback) {\n    uiTabChangeCallbacks.push(callback);\n}\n\n/**\n * Register callback to be called when the options are changed.\n * The callback receives no arguments.\n * @param callback\n */\nfunction onOptionsChanged(callback) {\n    optionsChangedCallbacks.push(callback);\n}\n\nfunction executeCallbacks(queue, arg) {\n    for (const callback of queue) {\n        try {\n            callback(arg);\n        } catch (e) {\n            console.error(\"error running callback\", callback, \":\", e);\n        }\n    }\n}\n\n/**\n * Schedule the execution of the callbacks registered with onAfterUiUpdate.\n * The callbacks are executed after a short while, unless another call to this function\n * is made before that time. IOW, the callbacks are executed only once, even\n * when there are multiple mutations observed.\n */\nfunction scheduleAfterUiUpdateCallbacks() {\n    clearTimeout(uiAfterUpdateTimeout);\n    uiAfterUpdateTimeout = setTimeout(function() {\n        executeCallbacks(uiAfterUpdateCallbacks);\n    }, 200);\n}\n\nvar executedOnLoaded = false;\n\ndocument.addEventListener(\"DOMContentLoaded\", function() {\n    var mutationObserver = new MutationObserver(function(m) {\n        if (!executedOnLoaded && gradioApp().querySelector('#generate_button')) {\n            executedOnLoaded = true;\n            executeCallbacks(uiLoadedCallbacks);\n        }\n\n        executeCallbacks(uiUpdateCallbacks, m);\n        scheduleAfterUiUpdateCallbacks();\n        const newTab = get_uiCurrentTab();\n        if (newTab && (newTab !== uiCurrentTab)) {\n            uiCurrentTab = newTab;\n            executeCallbacks(uiTabChangeCallbacks);\n        }\n    });\n    mutationObserver.observe(gradioApp(), {childList: true, subtree: true});\n    initStylePreviewOverlay();\n});\n\nvar onAppend = function(elem, f) {\n    var observer = new MutationObserver(function(mutations) {\n        mutations.forEach(function(m) {\n            if (m.addedNodes.length) {\n                f(m.addedNodes);\n            }\n        });\n    });\n    observer.observe(elem, {childList: true});\n}\n\nfunction addObserverIfDesiredNodeAvailable(querySelector, callback) {\n    var elem = document.querySelector(querySelector);\n    if (!elem) {\n        window.setTimeout(() => addObserverIfDesiredNodeAvailable(querySelector, callback), 1000);\n        return;\n    }\n\n    onAppend(elem, callback);\n}\n\n/**\n * Show reset button on toast \"Connection errored out.\"\n */\naddObserverIfDesiredNodeAvailable(\".toast-wrap\", function(added) {\n    added.forEach(function(element) {\n         if (element.innerText.includes(\"Connection errored out.\")) {\n             window.setTimeout(function() {\n                document.getElementById(\"reset_button\").classList.remove(\"hidden\");\n                document.getElementById(\"generate_button\").classList.add(\"hidden\");\n                document.getElementById(\"skip_button\").classList.add(\"hidden\");\n                document.getElementById(\"stop_button\").classList.add(\"hidden\");\n            });\n         }\n    });\n});\n\n/**\n * Add a ctrl+enter as a shortcut to start a generation\n */\ndocument.addEventListener('keydown', function(e) {\n    const isModifierKey = (e.metaKey || e.ctrlKey || e.altKey);\n    const isEnterKey = (e.key == \"Enter\" || e.keyCode == 13);\n\n    if(isModifierKey && isEnterKey) {\n        const generateButton = gradioApp().querySelector('button:not(.hidden)[id=generate_button]');\n        if (generateButton) {\n            generateButton.click();\n            e.preventDefault();\n            return;\n        }\n\n        const stopButton = gradioApp().querySelector('button:not(.hidden)[id=stop_button]')\n        if(stopButton) {\n            stopButton.click();\n            e.preventDefault();\n            return;\n        }\n    }\n});\n\nfunction initStylePreviewOverlay() {\n    let overlayVisible = false;\n    const samplesPath = document.querySelector(\"meta[name='samples-path']\").getAttribute(\"content\")\n    const overlay = document.createElement('div');\n    const tooltip = document.createElement('div');\n    tooltip.className = 'preview-tooltip';\n    overlay.appendChild(tooltip);\n    overlay.id = 'stylePreviewOverlay';\n    document.body.appendChild(overlay);\n    document.addEventListener('mouseover', function (e) {\n        const label = e.target.closest('.style_selections label');\n        if (!label) return;\n        label.removeEventListener(\"mouseout\", onMouseLeave);\n        label.addEventListener(\"mouseout\", onMouseLeave);\n        overlayVisible = true;\n        overlay.style.opacity = \"1\";\n        const originalText = label.querySelector(\"span\").getAttribute(\"data-original-text\");\n        const name = originalText || label.querySelector(\"span\").textContent;\n        overlay.style.backgroundImage = `url(\"${samplesPath.replace(\n            \"fooocus_v2\",\n            name.toLowerCase().replaceAll(\" \", \"_\")\n        ).replaceAll(\"\\\\\", \"\\\\\\\\\")}\")`;\n\n        tooltip.textContent = name;\n\n        function onMouseLeave() {\n            overlayVisible = false;\n            overlay.style.opacity = \"0\";\n            overlay.style.backgroundImage = \"\";\n            label.removeEventListener(\"mouseout\", onMouseLeave);\n        }\n    });\n    document.addEventListener('mousemove', function (e) {\n        if (!overlayVisible) return;\n        overlay.style.left = `${e.clientX}px`;\n        overlay.style.top = `${e.clientY}px`;\n        overlay.className = e.clientY > window.innerHeight / 2 ? \"lower-half\" : \"upper-half\";\n    });\n}\n\n/**\n * checks that a UI element is not in another hidden element or tab content\n */\nfunction uiElementIsVisible(el) {\n    if (el === document) {\n        return true;\n    }\n\n    const computedStyle = getComputedStyle(el);\n    const isVisible = computedStyle.display !== 'none';\n\n    if (!isVisible) return false;\n    return uiElementIsVisible(el.parentNode);\n}\n\nfunction uiElementInSight(el) {\n    const clRect = el.getBoundingClientRect();\n    const windowHeight = window.innerHeight;\n    const isOnScreen = clRect.bottom > 0 && clRect.top < windowHeight;\n\n    return isOnScreen;\n}\n\nfunction playNotification() {\n    gradioApp().querySelector('#audio_notification audio')?.play();\n}\n\nfunction set_theme(theme) {\n    var gradioURL = window.location.href;\n    if (!gradioURL.includes('?__theme=')) {\n        window.location.replace(gradioURL + '?__theme=' + theme);\n    }\n}\n\nfunction htmlDecode(input) {\n  var doc = new DOMParser().parseFromString(input, \"text/html\");\n  return doc.documentElement.textContent;\n}"
  },
  {
    "path": "javascript/viewer.js",
    "content": "window.main_viewer_height = 512;\n\nfunction refresh_grid() {\n    let gridContainer = document.querySelector('#final_gallery .grid-container');\n    let final_gallery = document.getElementById('final_gallery');\n\n    if (gridContainer) if (final_gallery) {\n        let rect = final_gallery.getBoundingClientRect();\n        let cols = Math.ceil((rect.width - 16.0) / rect.height);\n        if (cols < 2) cols = 2;\n        gridContainer.style.setProperty('--grid-cols', cols);\n    }\n}\n\nfunction refresh_grid_delayed() {\n    refresh_grid();\n    setTimeout(refresh_grid, 100);\n    setTimeout(refresh_grid, 500);\n    setTimeout(refresh_grid, 1000);\n}\n\nfunction resized() {\n    let windowHeight = window.innerHeight - 260;\n    let elements = document.getElementsByClassName('main_view');\n\n    if (windowHeight > 745) windowHeight = 745;\n\n    for (let i = 0; i < elements.length; i++) {\n        elements[i].style.height = windowHeight + 'px';\n    }\n\n    window.main_viewer_height = windowHeight;\n\n    refresh_grid();\n}\n\nfunction viewer_to_top(delay = 100) {\n    setTimeout(() => window.scrollTo({top: 0, behavior: 'smooth'}), delay);\n}\n\nfunction viewer_to_bottom(delay = 100) {\n    let element = document.getElementById('positive_prompt');\n    let yPos = window.main_viewer_height;\n\n    if (element) {\n        yPos = element.getBoundingClientRect().top + window.scrollY;\n    }\n\n    setTimeout(() => window.scrollTo({top: yPos - 8, behavior: 'smooth'}), delay);\n}\n\nwindow.addEventListener('resize', (e) => {\n    resized();\n});\n\nonUiLoaded(async () => {\n    resized();\n});\n\nfunction on_style_selection_blur() {\n    let target = document.querySelector(\"#gradio_receiver_style_selections textarea\");\n    target.value = \"on_style_selection_blur \" + Math.random();\n    let e = new Event(\"input\", {bubbles: true})\n    Object.defineProperty(e, \"target\", {value: target})\n    target.dispatchEvent(e);\n}\n\nonUiLoaded(async () => {\n    let spans = document.querySelectorAll('.aspect_ratios span');\n\n    spans.forEach(function (span) {\n        span.innerHTML = span.innerHTML.replace(/&lt;/g, '<').replace(/&gt;/g, '>');\n    });\n\n    document.querySelector('.style_selections').addEventListener('focusout', function (event) {\n        setTimeout(() => {\n            if (!this.contains(document.activeElement)) {\n                on_style_selection_blur();\n            }\n        }, 200);\n    });\n\n    let inputs = document.querySelectorAll('.lora_weight input[type=\"range\"]');\n\n    inputs.forEach(function (input) {\n        input.style.marginTop = '12px';\n    });\n});\n"
  },
  {
    "path": "javascript/zoom.js",
    "content": "onUiLoaded(async() => {\n    // Helper functions\n\n    // Detect whether the element has a horizontal scroll bar\n    function hasHorizontalScrollbar(element) {\n        return element.scrollWidth > element.clientWidth;\n    }\n\n    // Function for defining the \"Ctrl\", \"Shift\" and \"Alt\" keys\n    function isModifierKey(event, key) {\n        switch (key) {\n        case \"Ctrl\":\n            return event.ctrlKey;\n        case \"Shift\":\n            return event.shiftKey;\n        case \"Alt\":\n            return event.altKey;\n        default:\n            return false;\n        }\n    }\n\n    // Create hotkey configuration with the provided options\n    function createHotkeyConfig(defaultHotkeysConfig) {\n        const result = {}; // Resulting hotkey configuration\n        for (const key in defaultHotkeysConfig) {\n            result[key] = defaultHotkeysConfig[key];\n        }\n        return result;\n    }\n\n    // Default config\n    const defaultHotkeysConfig = {\n        canvas_hotkey_zoom: \"Shift\",\n        canvas_hotkey_adjust: \"Ctrl\",\n        canvas_zoom_undo_extra_key: \"Ctrl\",\n        canvas_zoom_hotkey_undo: \"KeyZ\",\n        canvas_hotkey_reset: \"KeyR\",\n        canvas_hotkey_fullscreen: \"KeyS\",\n        canvas_hotkey_move: \"KeyF\",\n        canvas_show_tooltip: true,\n        canvas_auto_expand: true,\n        canvas_blur_prompt: true,\n    };\n\n    // Loading the configuration from opts\n    const hotkeysConfig = createHotkeyConfig(\n        defaultHotkeysConfig\n    );\n\n    let isMoving = false;\n    let activeElement;\n\n    const elemData = {};\n\n    function applyZoomAndPan(elemId) {\n        const targetElement = gradioApp().querySelector(elemId);\n\n        if (!targetElement) {\n            console.log(\"Element not found\");\n            return;\n        }\n\n        targetElement.style.transformOrigin = \"0 0\";\n\n        elemData[elemId] = {\n            zoom: 1,\n            panX: 0,\n            panY: 0\n        };\n\n        let fullScreenMode = false;\n\n        // Create tooltip\n        function createTooltip() {\n            const toolTipElemnt =\n                targetElement.querySelector(\".image-container\");\n            const tooltip = document.createElement(\"div\");\n            tooltip.className = \"canvas-tooltip\";\n\n            // Creating an item of information\n            const info = document.createElement(\"i\");\n            info.className = \"canvas-tooltip-info\";\n            info.textContent = \"\";\n\n            // Create a container for the contents of the tooltip\n            const tooltipContent = document.createElement(\"div\");\n            tooltipContent.className = \"canvas-tooltip-content\";\n\n            // Define an array with hotkey information and their actions\n            const hotkeysInfo = [\n                {\n                    configKey: \"canvas_hotkey_zoom\",\n                    action: \"Zoom canvas\",\n                    keySuffix: \" + wheel\"\n                },\n                {\n                    configKey: \"canvas_hotkey_adjust\",\n                    action: \"Adjust brush size\",\n                    keySuffix: \" + wheel\"\n                },\n                {configKey: \"canvas_zoom_hotkey_undo\", action: \"Undo last action\", keyPrefix: `${hotkeysConfig.canvas_zoom_undo_extra_key} + ` },\n                {configKey: \"canvas_hotkey_reset\", action: \"Reset zoom\"},\n                {\n                    configKey: \"canvas_hotkey_fullscreen\",\n                    action: \"Fullscreen mode\"\n                },\n                {configKey: \"canvas_hotkey_move\", action: \"Move canvas\"}\n            ];\n\n            // Create hotkeys array based on the config values\n            const hotkeys = hotkeysInfo.map((info) => {\n                const configValue = hotkeysConfig[info.configKey];\n        \n                let key = configValue.slice(-1);\n        \n                if (info.keySuffix) {\n                  key = `${configValue}${info.keySuffix}`;\n                }\n        \n                if (info.keyPrefix && info.keyPrefix !== \"None + \") {\n                  key = `${info.keyPrefix}${configValue[3]}`;\n                }\n        \n                return {\n                  key,\n                  action: info.action,\n                };\n              });\n        \n              hotkeys\n                .forEach(hotkey => {\n                  const p = document.createElement(\"p\");\n                  p.innerHTML = `<b>${hotkey.key}</b> - ${hotkey.action}`;\n                  tooltipContent.appendChild(p);\n                });\n        \n              tooltip.append(info, tooltipContent);\n\n              // Add a hint element to the target element\n              toolTipElemnt.appendChild(tooltip);\n        }\n\n        //Show tool tip if setting enable\n        if (hotkeysConfig.canvas_show_tooltip) {\n            createTooltip();\n        }\n\n        // Reset the zoom level and pan position of the target element to their initial values\n        function resetZoom() {\n            elemData[elemId] = {\n                zoomLevel: 1,\n                panX: 0,\n                panY: 0\n            };\n\n            targetElement.style.overflow = \"hidden\";\n\n            targetElement.isZoomed = false;\n\n            targetElement.style.transform = `scale(${elemData[elemId].zoomLevel}) translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px)`;\n\n            const canvas = gradioApp().querySelector(\n                `${elemId} canvas[key=\"interface\"]`\n            );\n\n            toggleOverlap(\"off\");\n            fullScreenMode = false;\n\n            const closeBtn = targetElement.querySelector(\"button[aria-label='Remove Image']\");\n            if (closeBtn) {\n                closeBtn.addEventListener(\"click\", resetZoom);\n            }\n\n            if (canvas) {\n                const parentElement = targetElement.closest('[id^=\"component-\"]');\n                if (\n                    canvas &&\n                    parseFloat(canvas.style.width) > parentElement.offsetWidth &&\n                    parseFloat(targetElement.style.width) > parentElement.offsetWidth\n                ) {\n                    fitToElement();\n                    return;\n                }\n\n            }\n\n            targetElement.style.width = \"\";\n        }\n\n        // Toggle the zIndex of the target element between two values, allowing it to overlap or be overlapped by other elements\n        function toggleOverlap(forced = \"\") {\n            const zIndex1 = \"0\";\n            const zIndex2 = \"998\";\n\n            targetElement.style.zIndex =\n                targetElement.style.zIndex !== zIndex2 ? zIndex2 : zIndex1;\n\n            if (forced === \"off\") {\n                targetElement.style.zIndex = zIndex1;\n            } else if (forced === \"on\") {\n                targetElement.style.zIndex = zIndex2;\n            }\n        }\n\n        // Adjust the brush size based on the deltaY value from a mouse wheel event\n        function adjustBrushSize(\n            elemId,\n            deltaY,\n            withoutValue = false,\n            percentage = 5\n        ) {\n            const input =\n                gradioApp().querySelector(\n                    `${elemId} input[aria-label='Brush radius']`\n                ) ||\n                gradioApp().querySelector(\n                    `${elemId} button[aria-label=\"Use brush\"]`\n                );\n\n            if (input) {\n                input.click();\n                if (!withoutValue) {\n                    const maxValue =\n                        parseFloat(input.getAttribute(\"max\")) || 100;\n                    const changeAmount = maxValue * (percentage / 100);\n                    const newValue =\n                        parseFloat(input.value) +\n                        (deltaY > 0 ? -changeAmount : changeAmount);\n                    input.value = Math.min(Math.max(newValue, 0), maxValue);\n                    input.dispatchEvent(new Event(\"change\"));\n                }\n            }\n        }\n\n        // Reset zoom when uploading a new image\n        const fileInput = gradioApp().querySelector(\n            `${elemId} input[type=\"file\"][accept=\"image/*\"].svelte-116rqfv`\n        );\n        fileInput.addEventListener(\"click\", resetZoom);\n\n        // Update the zoom level and pan position of the target element based on the values of the zoomLevel, panX and panY variables\n        function updateZoom(newZoomLevel, mouseX, mouseY) {\n            newZoomLevel = Math.max(0.1, Math.min(newZoomLevel, 15));\n\n            elemData[elemId].panX +=\n                mouseX - (mouseX * newZoomLevel) / elemData[elemId].zoomLevel;\n            elemData[elemId].panY +=\n                mouseY - (mouseY * newZoomLevel) / elemData[elemId].zoomLevel;\n\n            targetElement.style.transformOrigin = \"0 0\";\n            targetElement.style.transform = `translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px) scale(${newZoomLevel})`;\n            targetElement.style.overflow = \"visible\";\n\n            toggleOverlap(\"on\");\n \n            return newZoomLevel;\n        }\n\n        // Change the zoom level based on user interaction\n        function changeZoomLevel(operation, e) {\n            if (isModifierKey(e, hotkeysConfig.canvas_hotkey_zoom)) {\n                e.preventDefault();\n\n                let zoomPosX, zoomPosY;\n                let delta = 0.2;\n\n                if (elemData[elemId].zoomLevel > 7) {\n                    delta = 0.9;\n                } else if (elemData[elemId].zoomLevel > 2) {\n                    delta = 0.6;\n                }\n\n                zoomPosX = e.clientX;\n                zoomPosY = e.clientY;\n\n                fullScreenMode = false;\n                elemData[elemId].zoomLevel = updateZoom(\n                    elemData[elemId].zoomLevel +\n                    (operation === \"+\" ? delta : -delta),\n                    zoomPosX - targetElement.getBoundingClientRect().left,\n                    zoomPosY - targetElement.getBoundingClientRect().top\n                );\n\n                targetElement.isZoomed = true;\n            }\n        }\n\n        /**\n         * This function fits the target element to the screen by calculating\n         * the required scale and offsets. It also updates the global variables\n         * zoomLevel, panX, and panY to reflect the new state.\n         */\n\n        function fitToElement() {\n            //Reset Zoom\n            targetElement.style.transform = `translate(${0}px, ${0}px) scale(${1})`;\n\n            let parentElement;\n\n            parentElement = targetElement.closest('[id^=\"component-\"]');\n\n            // Get element and screen dimensions\n            const elementWidth = targetElement.offsetWidth;\n            const elementHeight = targetElement.offsetHeight;\n\n            const screenWidth = parentElement.clientWidth - 24;\n            const screenHeight = parentElement.clientHeight;\n\n            // Calculate scale and offsets\n            const scaleX = screenWidth / elementWidth;\n            const scaleY = screenHeight / elementHeight;\n            const scale = Math.min(scaleX, scaleY);\n\n            const offsetX =0;\n            const offsetY =0;\n\n            // Apply scale and offsets to the element\n            targetElement.style.transform = `translate(${offsetX}px, ${offsetY}px) scale(${scale})`;\n\n            // Update global variables\n            elemData[elemId].zoomLevel = scale;\n            elemData[elemId].panX = offsetX;\n            elemData[elemId].panY = offsetY;\n\n            fullScreenMode = false;\n            toggleOverlap(\"off\");\n        }\n\n        // Undo last action\n        function undoLastAction(e) {\n            let isCtrlPressed = isModifierKey(e, hotkeysConfig.canvas_zoom_undo_extra_key)\n            const isAuxButton = e.button >= 3;\n            \n            if (isAuxButton) {\n              isCtrlPressed = true\n            } else {\n              if (!isModifierKey(e, hotkeysConfig.canvas_zoom_undo_extra_key)) return;\n            }\n\n            // Move undoBtn query outside the if statement to avoid unnecessary queries\n            const undoBtn = document.querySelector(`${activeElement} button[aria-label=\"Undo\"]`);\n        \n            if ((isCtrlPressed) && undoBtn ) {\n                e.preventDefault();\n                undoBtn.click();\n            }\n        }\n\n        /**\n         * This function fits the target element to the screen by calculating\n         * the required scale and offsets. It also updates the global variables\n         * zoomLevel, panX, and panY to reflect the new state.\n         */\n\n        // Fullscreen mode\n        function fitToScreen() {\n            const canvas = gradioApp().querySelector(\n                `${elemId} canvas[key=\"interface\"]`\n            );\n\n            if (!canvas) return;\n\n            targetElement.style.width = (canvas.offsetWidth + 2) + \"px\";\n            targetElement.style.overflow = \"visible\";\n\n            if (fullScreenMode) {\n                resetZoom();\n                fullScreenMode = false;\n                return;\n            }\n\n            //Reset Zoom\n            targetElement.style.transform = `translate(${0}px, ${0}px) scale(${1})`;\n\n            // Get scrollbar width to right-align the image\n            const scrollbarWidth =\n                window.innerWidth - document.documentElement.clientWidth;\n\n            // Get element and screen dimensions\n            const elementWidth = targetElement.offsetWidth;\n            const elementHeight = targetElement.offsetHeight;\n            const screenWidth = window.innerWidth - scrollbarWidth;\n            const screenHeight = window.innerHeight;\n\n            // Get element's coordinates relative to the page\n            const elementRect = targetElement.getBoundingClientRect();\n            const elementY = elementRect.y;\n            const elementX = elementRect.x;\n\n            // Calculate scale and offsets\n            const scaleX = screenWidth / elementWidth;\n            const scaleY = screenHeight / elementHeight;\n            const scale = Math.min(scaleX, scaleY);\n\n            // Get the current transformOrigin\n            const computedStyle = window.getComputedStyle(targetElement);\n            const transformOrigin = computedStyle.transformOrigin;\n            const [originX, originY] = transformOrigin.split(\" \");\n            const originXValue = parseFloat(originX);\n            const originYValue = parseFloat(originY);\n\n            // Calculate offsets with respect to the transformOrigin\n            const offsetX =\n                (screenWidth - elementWidth * scale) / 2 -\n                elementX -\n                originXValue * (1 - scale);\n            const offsetY =\n                (screenHeight - elementHeight * scale) / 2 -\n                elementY -\n                originYValue * (1 - scale);\n\n            // Apply scale and offsets to the element\n            targetElement.style.transform = `translate(${offsetX}px, ${offsetY}px) scale(${scale})`;\n\n            // Update global variables\n            elemData[elemId].zoomLevel = scale;\n            elemData[elemId].panX = offsetX;\n            elemData[elemId].panY = offsetY;\n\n            fullScreenMode = true;\n            toggleOverlap(\"on\");\n        }\n\n        // Handle keydown events\n        function handleKeyDown(event) {\n            // Disable key locks to make pasting from the buffer work correctly\n            if ((event.ctrlKey && event.code === 'KeyV') || (event.ctrlKey && event.code === 'KeyC') || event.code === \"F5\") {\n                return;\n            }\n\n            // before activating shortcut, ensure user is not actively typing in an input field\n            if (!hotkeysConfig.canvas_blur_prompt) {\n                if (event.target.nodeName === 'TEXTAREA' || event.target.nodeName === 'INPUT') {\n                    return;\n                }\n            }\n\n            const hotkeyActions = {\n                [hotkeysConfig.canvas_hotkey_reset]: resetZoom,\n                [hotkeysConfig.canvas_hotkey_overlap]: toggleOverlap,\n                [hotkeysConfig.canvas_hotkey_fullscreen]: fitToScreen,\n                [hotkeysConfig.canvas_zoom_hotkey_undo]: undoLastAction,\n            };\n\n            const action = hotkeyActions[event.code];\n            if (action) {\n                event.preventDefault();\n                action(event);\n            }\n\n            if (\n                isModifierKey(event, hotkeysConfig.canvas_hotkey_zoom) ||\n                isModifierKey(event, hotkeysConfig.canvas_hotkey_adjust)\n            ) {\n                event.preventDefault();\n            }\n        }\n\n        // Get Mouse position\n        function getMousePosition(e) {\n            mouseX = e.offsetX;\n            mouseY = e.offsetY;\n        }\n\n        // Simulation of the function to put a long image into the screen.\n        // We detect if an image has a scroll bar or not, make a fullscreen to reveal the image, then reduce it to fit into the element.\n        // We hide the image and show it to the user when it is ready.\n\n        targetElement.isExpanded = false;\n        function autoExpand() {\n            const canvas = document.querySelector(`${elemId} canvas[key=\"interface\"]`);\n            if (canvas) {\n                if (hasHorizontalScrollbar(targetElement) && targetElement.isExpanded === false) {\n                    targetElement.style.visibility = \"hidden\";\n                    setTimeout(() => {\n                        fitToScreen();\n                        resetZoom();\n                        targetElement.style.visibility = \"visible\";\n                        targetElement.isExpanded = true;\n                    }, 10);\n                }\n            }\n        }\n\n        targetElement.addEventListener(\"mousemove\", getMousePosition);\n        targetElement.addEventListener(\"auxclick\", undoLastAction);\n\n        //observers\n        // Creating an observer with a callback function to handle DOM changes\n        const observer = new MutationObserver((mutationsList, observer) => {\n            for (let mutation of mutationsList) {\n              // If the style attribute of the canvas has changed, by observation it happens only when the picture changes\n              if (mutation.type === 'attributes' && mutation.attributeName === 'style' &&\n                mutation.target.tagName.toLowerCase() === 'canvas') {\n                targetElement.isExpanded = false;\n                setTimeout(resetZoom, 10);\n              }\n            }\n          });\n      \n          // Apply auto expand if enabled\n          if (hotkeysConfig.canvas_auto_expand) {\n            targetElement.addEventListener(\"mousemove\", autoExpand);\n            // Set up an observer to track attribute changes\n            observer.observe(targetElement, { attributes: true, childList: true, subtree: true });\n          }\n\n        // Handle events only inside the targetElement\n        let isKeyDownHandlerAttached = false;\n\n        function handleMouseMove() {\n            if (!isKeyDownHandlerAttached) {\n                document.addEventListener(\"keydown\", handleKeyDown);\n                isKeyDownHandlerAttached = true;\n\n                activeElement = elemId;\n            }\n        }\n\n        function handleMouseLeave() {\n            if (isKeyDownHandlerAttached) {\n                document.removeEventListener(\"keydown\", handleKeyDown);\n                isKeyDownHandlerAttached = false;\n\n                activeElement = null;\n            }\n        }\n\n        // Add mouse event handlers\n        targetElement.addEventListener(\"mousemove\", handleMouseMove);\n        targetElement.addEventListener(\"mouseleave\", handleMouseLeave);\n\n        targetElement.addEventListener(\"wheel\", e => {\n            // change zoom level\n            const operation = e.deltaY > 0 ? \"-\" : \"+\";\n            changeZoomLevel(operation, e);\n\n            // Handle brush size adjustment with ctrl key pressed\n            if (isModifierKey(e, hotkeysConfig.canvas_hotkey_adjust)) {\n                e.preventDefault();\n\n                // Increase or decrease brush size based on scroll direction\n                adjustBrushSize(elemId, e.deltaY);\n            }\n        });\n\n        // Handle the move event for pan functionality. Updates the panX and panY variables and applies the new transform to the target element.\n        function handleMoveKeyDown(e) {\n\n            // Disable key locks to make pasting from the buffer work correctly\n            if ((e.ctrlKey && e.code === 'KeyV') || (e.ctrlKey && e.code === 'KeyC') || e.code === \"F5\") {\n                return;\n            }\n\n            // before activating shortcut, ensure user is not actively typing in an input field\n            if (!hotkeysConfig.canvas_blur_prompt) {\n                if (e.target.nodeName === 'TEXTAREA' || e.target.nodeName === 'INPUT') {\n                    return;\n                }\n            }\n\n\n            if (e.code === hotkeysConfig.canvas_hotkey_move) {\n                if (!e.ctrlKey && !e.metaKey && isKeyDownHandlerAttached) {\n                    e.preventDefault();\n                    document.activeElement.blur();\n                    isMoving = true;\n                }\n            }\n        }\n\n        function handleMoveKeyUp(e) {\n            if (e.code === hotkeysConfig.canvas_hotkey_move) {\n                isMoving = false;\n            }\n        }\n\n        document.addEventListener(\"keydown\", handleMoveKeyDown);\n        document.addEventListener(\"keyup\", handleMoveKeyUp);\n\n        // Detect zoom level and update the pan speed.\n        function updatePanPosition(movementX, movementY) {\n            let panSpeed = 2;\n\n            if (elemData[elemId].zoomLevel > 8) {\n                panSpeed = 3.5;\n            }\n\n            elemData[elemId].panX += movementX * panSpeed;\n            elemData[elemId].panY += movementY * panSpeed;\n\n            // Delayed redraw of an element\n            requestAnimationFrame(() => {\n                targetElement.style.transform = `translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px) scale(${elemData[elemId].zoomLevel})`;\n                toggleOverlap(\"on\");\n            });\n        }\n\n        function handleMoveByKey(e) {\n            if (isMoving && elemId === activeElement) {\n                updatePanPosition(e.movementX, e.movementY);\n                targetElement.style.pointerEvents = \"none\";\n                targetElement.style.overflow = \"visible\";\n            } else {\n                targetElement.style.pointerEvents = \"auto\";\n            }\n        }\n\n        // Prevents sticking to the mouse\n        window.onblur = function() {\n            isMoving = false;\n        };\n\n        // Checks for extension\n        function checkForOutBox() {\n            const parentElement = targetElement.closest('[id^=\"component-\"]');\n            if (parentElement.offsetWidth < targetElement.offsetWidth && !targetElement.isExpanded) {\n                resetZoom();\n                targetElement.isExpanded = true;\n            }\n\n            if (parentElement.offsetWidth < targetElement.offsetWidth && elemData[elemId].zoomLevel == 1) {\n                resetZoom();\n            }\n\n            if (parentElement.offsetWidth < targetElement.offsetWidth && targetElement.offsetWidth * elemData[elemId].zoomLevel > parentElement.offsetWidth && elemData[elemId].zoomLevel < 1 && !targetElement.isZoomed) {\n                resetZoom();\n            }\n        }\n\n        targetElement.addEventListener(\"mousemove\", checkForOutBox);\n\n        window.addEventListener('resize', (e) => {\n            resetZoom();\n\n            targetElement.isExpanded = false;\n            targetElement.isZoomed = false;\n        });\n\n        gradioApp().addEventListener(\"mousemove\", handleMoveByKey);\n    }\n\n    applyZoomAndPan(\"#inpaint_canvas\");\n    applyZoomAndPan(\"#inpaint_mask_canvas\");\n});\n"
  },
  {
    "path": "language/en.json",
    "content": "{\n    \"Preview\": \"Preview\",\n    \"Gallery\": \"Gallery\",\n    \"Generate\": \"Generate\",\n    \"Skip\": \"Skip\",\n    \"Stop\": \"Stop\",\n    \"Reconnect\": \"Reconnect\",\n    \"Input Image\": \"Input Image\",\n    \"Advanced\": \"Advanced\",\n    \"Upscale or Variation\": \"Upscale or Variation\",\n    \"Image Prompt\": \"Image Prompt\",\n    \"Inpaint or Outpaint\": \"Inpaint or Outpaint\",\n    \"Outpaint Direction\": \"Outpaint Direction\",\n    \"Enable Advanced Masking Features\": \"Enable Advanced Masking Features\",\n    \"Method\": \"Method\",\n    \"Describe\": \"Describe\",\n    \"Content Type\": \"Content Type\",\n    \"Photograph\": \"Photograph\",\n    \"Art/Anime\": \"Art/Anime\",\n    \"Apply Styles\": \"Apply Styles\",\n    \"Describe this Image into Prompt\": \"Describe this Image into Prompt\",\n    \"Image Size and Recommended Size\": \"Image Size and Recommended Size\",\n    \"Upscale or Variation:\": \"Upscale or Variation:\",\n    \"Disabled\": \"Disabled\",\n    \"Vary (Subtle)\": \"Vary (Subtle)\",\n    \"Vary (Strong)\": \"Vary (Strong)\",\n    \"Upscale (1.5x)\": \"Upscale (1.5x)\",\n    \"Upscale (2x)\": \"Upscale (2x)\",\n    \"Upscale (Fast 2x)\": \"Upscale (Fast 2x)\",\n    \"\\ud83d\\udcd4 Documentation\": \"\\uD83D\\uDCD4 Documentation\",\n    \"Image\": \"Image\",\n    \"Stop At\": \"Stop At\",\n    \"Weight\": \"Weight\",\n    \"Type\": \"Type\",\n    \"PyraCanny\": \"PyraCanny\",\n    \"CPDS\": \"CPDS\",\n    \"* \\\"Image Prompt\\\" is powered by Fooocus Image Mixture Engine (v1.0.1).\": \"* \\\"Image Prompt\\\" is powered by Fooocus Image Mixture Engine (v1.0.1).\",\n    \"The scaler multiplied to positive ADM (use 1.0 to disable).\": \"The scaler multiplied to positive ADM (use 1.0 to disable).\",\n    \"The scaler multiplied to negative ADM (use 1.0 to disable).\": \"The scaler multiplied to negative ADM (use 1.0 to disable).\",\n    \"When to end the guidance from positive/negative ADM.\": \"When to end the guidance from positive/negative ADM.\",\n    \"Similar to the Control Mode in A1111 (use 0.0 to disable).\": \"Similar to the Control Mode in A1111 (use 0.0 to disable).\",\n    \"Outpaint Expansion (\": \"Outpaint Expansion (\",\n    \"Outpaint\": \"Outpaint\",\n    \"Left\": \"Left\",\n    \"Right\": \"Right\",\n    \"Top\": \"Top\",\n    \"Bottom\": \"Bottom\",\n    \"* \\\"Inpaint or Outpaint\\\" is powered by the sampler \\\"DPMPP Fooocus Seamless 2M SDE Karras Inpaint Sampler\\\" (beta)\": \"* \\\"Inpaint or Outpaint\\\" is powered by the sampler \\\"DPMPP Fooocus Seamless 2M SDE Karras Inpaint Sampler\\\" (beta)\",\n    \"Advanced options\": \"Advanced options\",\n    \"Generate mask from image\": \"Generate mask from image\",\n    \"Settings\": \"Settings\",\n    \"Style\": \"Style\",\n    \"Styles\": \"Styles\",\n    \"Preset\": \"Preset\",\n    \"Performance\": \"Performance\",\n    \"Speed\": \"Speed\",\n    \"Quality\": \"Quality\",\n    \"Extreme Speed\": \"Extreme Speed\",\n    \"Lightning\": \"Lightning\",\n    \"Aspect Ratios\": \"Aspect Ratios\",\n    \"width \\u00d7 height\": \"width \\u00d7 height\",\n    \"Image Number\": \"Image Number\",\n    \"Negative Prompt\": \"Negative Prompt\",\n    \"Describing what you do not want to see.\": \"Describing what you do not want to see.\",\n    \"Random\": \"Random\",\n    \"Seed\": \"Seed\",\n    \"Disable seed increment\": \"Disable seed increment\",\n    \"Disable automatic seed increment when image number is > 1.\": \"Disable automatic seed increment when image number is > 1.\",\n    \"Read wildcards in order\": \"Read wildcards in order\",\n    \"Black Out NSFW\": \"Black Out NSFW\",\n    \"Use black image if NSFW is detected.\": \"Use black image if NSFW is detected.\",\n    \"Save only final enhanced image\": \"Save only final enhanced image\",\n    \"Save Metadata to Images\": \"Save Metadata to Images\",\n    \"Adds parameters to generated images allowing manual regeneration.\": \"Adds parameters to generated images allowing manual regeneration.\",\n    \"\\ud83d\\udcda History Log\": \"\\uD83D\\uDCDA History Log\",\n    \"Image Style\": \"Image Style\",\n    \"Fooocus V2\": \"Fooocus V2\",\n    \"Random Style\": \"Random Style\",\n    \"Default (Slightly Cinematic)\": \"Default (Slightly Cinematic)\",\n    \"Fooocus Masterpiece\": \"Fooocus Masterpiece\",\n    \"Fooocus Photograph\": \"Fooocus Photograph\",\n    \"Fooocus Negative\": \"Fooocus Negative\",\n    \"SAI 3D Model\": \"SAI 3D Model\",\n    \"SAI Analog Film\": \"SAI Analog Film\",\n    \"SAI Anime\": \"SAI Anime\",\n    \"SAI Cinematic\": \"SAI Cinematic\",\n    \"SAI Comic Book\": \"SAI Comic Book\",\n    \"SAI Craft Clay\": \"SAI Craft Clay\",\n    \"SAI Digital Art\": \"SAI Digital Art\",\n    \"SAI Enhance\": \"SAI Enhance\",\n    \"SAI Fantasy Art\": \"SAI Fantasy Art\",\n    \"SAI Isometric\": \"SAI Isometric\",\n    \"SAI Line Art\": \"SAI Line Art\",\n    \"SAI Lowpoly\": \"SAI Lowpoly\",\n    \"SAI Neonpunk\": \"SAI Neonpunk\",\n    \"SAI Origami\": \"SAI Origami\",\n    \"SAI Photographic\": \"SAI Photographic\",\n    \"SAI Pixel Art\": \"SAI Pixel Art\",\n    \"SAI Texture\": \"SAI Texture\",\n    \"MRE Cinematic Dynamic\": \"MRE Cinematic Dynamic\",\n    \"MRE Spontaneous Picture\": \"MRE Spontaneous Picture\",\n    \"MRE Artistic Vision\": \"MRE Artistic Vision\",\n    \"MRE Dark Dream\": \"MRE Dark Dream\",\n    \"MRE Gloomy Art\": \"MRE Gloomy Art\",\n    \"MRE Bad Dream\": \"MRE Bad Dream\",\n    \"MRE Underground\": \"MRE Underground\",\n    \"MRE Surreal Painting\": \"MRE Surreal Painting\",\n    \"MRE Dynamic Illustration\": \"MRE Dynamic Illustration\",\n    \"MRE Undead Art\": \"MRE Undead Art\",\n    \"MRE Elemental Art\": \"MRE Elemental Art\",\n    \"MRE Space Art\": \"MRE Space Art\",\n    \"MRE Ancient Illustration\": \"MRE Ancient Illustration\",\n    \"MRE Brave Art\": \"MRE Brave Art\",\n    \"MRE Heroic Fantasy\": \"MRE Heroic Fantasy\",\n    \"MRE Dark Cyberpunk\": \"MRE Dark Cyberpunk\",\n    \"MRE Lyrical Geometry\": \"MRE Lyrical Geometry\",\n    \"MRE Sumi E Symbolic\": \"MRE Sumi E Symbolic\",\n    \"MRE Sumi E Detailed\": \"MRE Sumi E Detailed\",\n    \"MRE Manga\": \"MRE Manga\",\n    \"MRE Anime\": \"MRE Anime\",\n    \"MRE Comic\": \"MRE Comic\",\n    \"Ads Advertising\": \"Ads Advertising\",\n    \"Ads Automotive\": \"Ads Automotive\",\n    \"Ads Corporate\": \"Ads Corporate\",\n    \"Ads Fashion Editorial\": \"Ads Fashion Editorial\",\n    \"Ads Food Photography\": \"Ads Food Photography\",\n    \"Ads Gourmet Food Photography\": \"Ads Gourmet Food Photography\",\n    \"Ads Luxury\": \"Ads Luxury\",\n    \"Ads Real Estate\": \"Ads Real Estate\",\n    \"Ads Retail\": \"Ads Retail\",\n    \"Artstyle Abstract\": \"Artstyle Abstract\",\n    \"Artstyle Abstract Expressionism\": \"Artstyle Abstract Expressionism\",\n    \"Artstyle Art Deco\": \"Artstyle Art Deco\",\n    \"Artstyle Art Nouveau\": \"Artstyle Art Nouveau\",\n    \"Artstyle Constructivist\": \"Artstyle Constructivist\",\n    \"Artstyle Cubist\": \"Artstyle Cubist\",\n    \"Artstyle Expressionist\": \"Artstyle Expressionist\",\n    \"Artstyle Graffiti\": \"Artstyle Graffiti\",\n    \"Artstyle Hyperrealism\": \"Artstyle Hyperrealism\",\n    \"Artstyle Impressionist\": \"Artstyle Impressionist\",\n    \"Artstyle Pointillism\": \"Artstyle Pointillism\",\n    \"Artstyle Pop Art\": \"Artstyle Pop Art\",\n    \"Artstyle Psychedelic\": \"Artstyle Psychedelic\",\n    \"Artstyle Renaissance\": \"Artstyle Renaissance\",\n    \"Artstyle Steampunk\": \"Artstyle Steampunk\",\n    \"Artstyle Surrealist\": \"Artstyle Surrealist\",\n    \"Artstyle Typography\": \"Artstyle Typography\",\n    \"Artstyle Watercolor\": \"Artstyle Watercolor\",\n    \"Futuristic Biomechanical\": \"Futuristic Biomechanical\",\n    \"Futuristic Biomechanical Cyberpunk\": \"Futuristic Biomechanical Cyberpunk\",\n    \"Futuristic Cybernetic\": \"Futuristic Cybernetic\",\n    \"Futuristic Cybernetic Robot\": \"Futuristic Cybernetic Robot\",\n    \"Futuristic Cyberpunk Cityscape\": \"Futuristic Cyberpunk Cityscape\",\n    \"Futuristic Futuristic\": \"Futuristic Futuristic\",\n    \"Futuristic Retro Cyberpunk\": \"Futuristic Retro Cyberpunk\",\n    \"Futuristic Retro Futurism\": \"Futuristic Retro Futurism\",\n    \"Futuristic Sci Fi\": \"Futuristic Sci Fi\",\n    \"Futuristic Vaporwave\": \"Futuristic Vaporwave\",\n    \"Game Bubble Bobble\": \"Game Bubble Bobble\",\n    \"Game Cyberpunk Game\": \"Game Cyberpunk Game\",\n    \"Game Fighting Game\": \"Game Fighting Game\",\n    \"Game Gta\": \"Game Gta\",\n    \"Game Mario\": \"Game Mario\",\n    \"Game Minecraft\": \"Game Minecraft\",\n    \"Game Pokemon\": \"Game Pokemon\",\n    \"Game Retro Arcade\": \"Game Retro Arcade\",\n    \"Game Retro Game\": \"Game Retro Game\",\n    \"Game Rpg Fantasy Game\": \"Game Rpg Fantasy Game\",\n    \"Game Strategy Game\": \"Game Strategy Game\",\n    \"Game Streetfighter\": \"Game Streetfighter\",\n    \"Game Zelda\": \"Game Zelda\",\n    \"Misc Architectural\": \"Misc Architectural\",\n    \"Misc Disco\": \"Misc Disco\",\n    \"Misc Dreamscape\": \"Misc Dreamscape\",\n    \"Misc Dystopian\": \"Misc Dystopian\",\n    \"Misc Fairy Tale\": \"Misc Fairy Tale\",\n    \"Misc Gothic\": \"Misc Gothic\",\n    \"Misc Grunge\": \"Misc Grunge\",\n    \"Misc Horror\": \"Misc Horror\",\n    \"Misc Kawaii\": \"Misc Kawaii\",\n    \"Misc Lovecraftian\": \"Misc Lovecraftian\",\n    \"Misc Macabre\": \"Misc Macabre\",\n    \"Misc Manga\": \"Misc Manga\",\n    \"Misc Metropolis\": \"Misc Metropolis\",\n    \"Misc Minimalist\": \"Misc Minimalist\",\n    \"Misc Monochrome\": \"Misc Monochrome\",\n    \"Misc Nautical\": \"Misc Nautical\",\n    \"Misc Space\": \"Misc Space\",\n    \"Misc Stained Glass\": \"Misc Stained Glass\",\n    \"Misc Techwear Fashion\": \"Misc Techwear Fashion\",\n    \"Misc Tribal\": \"Misc Tribal\",\n    \"Misc Zentangle\": \"Misc Zentangle\",\n    \"Papercraft Collage\": \"Papercraft Collage\",\n    \"Papercraft Flat Papercut\": \"Papercraft Flat Papercut\",\n    \"Papercraft Kirigami\": \"Papercraft Kirigami\",\n    \"Papercraft Paper Mache\": \"Papercraft Paper Mache\",\n    \"Papercraft Paper Quilling\": \"Papercraft Paper Quilling\",\n    \"Papercraft Papercut Collage\": \"Papercraft Papercut Collage\",\n    \"Papercraft Papercut Shadow Box\": \"Papercraft Papercut Shadow Box\",\n    \"Papercraft Stacked Papercut\": \"Papercraft Stacked Papercut\",\n    \"Papercraft Thick Layered Papercut\": \"Papercraft Thick Layered Papercut\",\n    \"Photo Alien\": \"Photo Alien\",\n    \"Photo Film Noir\": \"Photo Film Noir\",\n    \"Photo Glamour\": \"Photo Glamour\",\n    \"Photo Hdr\": \"Photo Hdr\",\n    \"Photo Iphone Photographic\": \"Photo Iphone Photographic\",\n    \"Photo Long Exposure\": \"Photo Long Exposure\",\n    \"Photo Neon Noir\": \"Photo Neon Noir\",\n    \"Photo Silhouette\": \"Photo Silhouette\",\n    \"Photo Tilt Shift\": \"Photo Tilt Shift\",\n    \"Cinematic Diva\": \"Cinematic Diva\",\n    \"Abstract Expressionism\": \"Abstract Expressionism\",\n    \"Academia\": \"Academia\",\n    \"Action Figure\": \"Action Figure\",\n    \"Adorable 3D Character\": \"Adorable 3D Character\",\n    \"Adorable Kawaii\": \"Adorable Kawaii\",\n    \"Art Deco\": \"Art Deco\",\n    \"Art Nouveau\": \"Art Nouveau\",\n    \"Astral Aura\": \"Astral Aura\",\n    \"Avant Garde\": \"Avant Garde\",\n    \"Baroque\": \"Baroque\",\n    \"Bauhaus Style Poster\": \"Bauhaus Style Poster\",\n    \"Blueprint Schematic Drawing\": \"Blueprint Schematic Drawing\",\n    \"Caricature\": \"Caricature\",\n    \"Cel Shaded Art\": \"Cel Shaded Art\",\n    \"Character Design Sheet\": \"Character Design Sheet\",\n    \"Classicism Art\": \"Classicism Art\",\n    \"Color Field Painting\": \"Color Field Painting\",\n    \"Colored Pencil Art\": \"Colored Pencil Art\",\n    \"Conceptual Art\": \"Conceptual Art\",\n    \"Constructivism\": \"Constructivism\",\n    \"Cubism\": \"Cubism\",\n    \"Dadaism\": \"Dadaism\",\n    \"Dark Fantasy\": \"Dark Fantasy\",\n    \"Dark Moody Atmosphere\": \"Dark Moody Atmosphere\",\n    \"Dmt Art Style\": \"Dmt Art Style\",\n    \"Doodle Art\": \"Doodle Art\",\n    \"Double Exposure\": \"Double Exposure\",\n    \"Dripping Paint Splatter Art\": \"Dripping Paint Splatter Art\",\n    \"Expressionism\": \"Expressionism\",\n    \"Faded Polaroid Photo\": \"Faded Polaroid Photo\",\n    \"Fauvism\": \"Fauvism\",\n    \"Flat 2d Art\": \"Flat 2d Art\",\n    \"Fortnite Art Style\": \"Fortnite Art Style\",\n    \"Futurism\": \"Futurism\",\n    \"Glitchcore\": \"Glitchcore\",\n    \"Glo Fi\": \"Glo Fi\",\n    \"Googie Art Style\": \"Googie Art Style\",\n    \"Graffiti Art\": \"Graffiti Art\",\n    \"Harlem Renaissance Art\": \"Harlem Renaissance Art\",\n    \"High Fashion\": \"High Fashion\",\n    \"Idyllic\": \"Idyllic\",\n    \"Impressionism\": \"Impressionism\",\n    \"Infographic Drawing\": \"Infographic Drawing\",\n    \"Ink Dripping Drawing\": \"Ink Dripping Drawing\",\n    \"Japanese Ink Drawing\": \"Japanese Ink Drawing\",\n    \"Knolling Photography\": \"Knolling Photography\",\n    \"Light Cheery Atmosphere\": \"Light Cheery Atmosphere\",\n    \"Logo Design\": \"Logo Design\",\n    \"Luxurious Elegance\": \"Luxurious Elegance\",\n    \"Macro Photography\": \"Macro Photography\",\n    \"Mandola Art\": \"Mandola Art\",\n    \"Marker Drawing\": \"Marker Drawing\",\n    \"Medievalism\": \"Medievalism\",\n    \"Minimalism\": \"Minimalism\",\n    \"Neo Baroque\": \"Neo Baroque\",\n    \"Neo Byzantine\": \"Neo Byzantine\",\n    \"Neo Futurism\": \"Neo Futurism\",\n    \"Neo Impressionism\": \"Neo Impressionism\",\n    \"Neo Rococo\": \"Neo Rococo\",\n    \"Neoclassicism\": \"Neoclassicism\",\n    \"Op Art\": \"Op Art\",\n    \"Ornate And Intricate\": \"Ornate And Intricate\",\n    \"Pencil Sketch Drawing\": \"Pencil Sketch Drawing\",\n    \"Pop Art 2\": \"Pop Art 2\",\n    \"Rococo\": \"Rococo\",\n    \"Silhouette Art\": \"Silhouette Art\",\n    \"Simple Vector Art\": \"Simple Vector Art\",\n    \"Sketchup\": \"Sketchup\",\n    \"Steampunk 2\": \"Steampunk 2\",\n    \"Surrealism\": \"Surrealism\",\n    \"Suprematism\": \"Suprematism\",\n    \"Terragen\": \"Terragen\",\n    \"Tranquil Relaxing Atmosphere\": \"Tranquil Relaxing Atmosphere\",\n    \"Sticker Designs\": \"Sticker Designs\",\n    \"Vibrant Rim Light\": \"Vibrant Rim Light\",\n    \"Volumetric Lighting\": \"Volumetric Lighting\",\n    \"Watercolor 2\": \"Watercolor 2\",\n    \"Whimsical And Playful\": \"Whimsical And Playful\",\n    \"Models\": \"Models\",\n    \"Base Model (SDXL only)\": \"Base Model (SDXL only)\",\n    \"sd_xl_base_1.0_0.9vae.safetensors\": \"sd_xl_base_1.0_0.9vae.safetensors\",\n    \"bluePencilXL_v009.safetensors\": \"bluePencilXL_v009.safetensors\",\n    \"bluePencilXL_v050.safetensors\": \"bluePencilXL_v050.safetensors\",\n    \"DreamShaper_8_pruned.safetensors\": \"DreamShaper_8_pruned.safetensors\",\n    \"realisticStockPhoto_v10.safetensors\": \"realisticStockPhoto_v10.safetensors\",\n    \"realisticVisionV51_v51VAE.safetensors\": \"realisticVisionV51_v51VAE.safetensors\",\n    \"sd_xl_refiner_1.0_0.9vae.safetensors\": \"sd_xl_refiner_1.0_0.9vae.safetensors\",\n    \"Refiner (SDXL or SD 1.5)\": \"Refiner (SDXL or SD 1.5)\",\n    \"None\": \"None\",\n    \"LoRAs\": \"LoRAs\",\n    \"SDXL LoRA 1\": \"SDXL LoRA 1\",\n    \"sd_xl_offset_example-lora_1.0.safetensors\": \"sd_xl_offset_example-lora_1.0.safetensors\",\n    \"3d_render_style_xl.safetensors\": \"3d_render_style_xl.safetensors\",\n    \"Bloodstained-XL-V1.safetensors\": \"Bloodstained-XL-V1.safetensors\",\n    \"SDXL_FILM_PHOTOGRAPHY_STYLE_BetaV0.4.safetensors\": \"SDXL_FILM_PHOTOGRAPHY_STYLE_BetaV0.4.safetensors\",\n    \"SDXL LoRA 2\": \"SDXL LoRA 2\",\n    \"SDXL LoRA 3\": \"SDXL LoRA 3\",\n    \"SDXL LoRA 4\": \"SDXL LoRA 4\",\n    \"SDXL LoRA 5\": \"SDXL LoRA 5\",\n    \"Refresh\": \"Refresh\",\n    \"\\ud83d\\udd04 Refresh All Files\": \"\\ud83d\\udd04 Refresh All Files\",\n    \"Sampling Sharpness\": \"Sampling Sharpness\",\n    \"Higher value means image and texture are sharper.\": \"Higher value means image and texture are sharper.\",\n    \"Guidance Scale\": \"Guidance Scale\",\n    \"Higher value means style is cleaner, vivider, and more artistic.\": \"Higher value means style is cleaner, vivider, and more artistic.\",\n    \"Developer Debug Mode\": \"Developer Debug Mode\",\n    \"Developer Debug Tools\": \"Developer Debug Tools\",\n    \"Positive ADM Guidance Scaler\": \"Positive ADM Guidance Scaler\",\n    \"The scaler multiplied to positive ADM (use 1.0 to disable). \": \"The scaler multiplied to positive ADM (use 1.0 to disable). \",\n    \"Negative ADM Guidance Scaler\": \"Negative ADM Guidance Scaler\",\n    \"The scaler multiplied to negative ADM (use 1.0 to disable). \": \"The scaler multiplied to negative ADM (use 1.0 to disable). \",\n    \"ADM Guidance End At Step\": \"ADM Guidance End At Step\",\n    \"When to end the guidance from positive/negative ADM. \": \"When to end the guidance from positive/negative ADM. \",\n    \"Refiner swap method\": \"Refiner swap method\",\n    \"joint\": \"joint\",\n    \"separate\": \"separate\",\n    \"vae\": \"vae\",\n    \"CFG Mimicking from TSNR\": \"CFG Mimicking from TSNR\",\n    \"Enabling Fooocus's implementation of CFG mimicking for TSNR (effective when real CFG > mimicked CFG).\": \"Enabling Fooocus's implementation of CFG mimicking for TSNR (effective when real CFG > mimicked CFG).\",\n    \"CLIP Skip\": \"CLIP Skip\",\n    \"Bypass CLIP layers to avoid overfitting (use 1 to not skip any layers, 2 is recommended).\": \"Bypass CLIP layers to avoid overfitting (use 1 to not skip any layers, 2 is recommended).\",\n    \"Sampler\": \"Sampler\",\n    \"dpmpp_2m_sde_gpu\": \"dpmpp_2m_sde_gpu\",\n    \"Only effective in non-inpaint mode.\": \"Only effective in non-inpaint mode.\",\n    \"euler\": \"euler\",\n    \"euler_ancestral\": \"euler_ancestral\",\n    \"heun\": \"heun\",\n    \"dpm_2\": \"dpm_2\",\n    \"dpm_2_ancestral\": \"dpm_2_ancestral\",\n    \"lms\": \"lms\",\n    \"dpm_fast\": \"dpm_fast\",\n    \"dpm_adaptive\": \"dpm_adaptive\",\n    \"dpmpp_2s_ancestral\": \"dpmpp_2s_ancestral\",\n    \"dpmpp_sde\": \"dpmpp_sde\",\n    \"dpmpp_sde_gpu\": \"dpmpp_sde_gpu\",\n    \"dpmpp_2m\": \"dpmpp_2m\",\n    \"dpmpp_2m_sde\": \"dpmpp_2m_sde\",\n    \"dpmpp_3m_sde\": \"dpmpp_3m_sde\",\n    \"dpmpp_3m_sde_gpu\": \"dpmpp_3m_sde_gpu\",\n    \"ddpm\": \"ddpm\",\n    \"ddim\": \"ddim\",\n    \"uni_pc\": \"uni_pc\",\n    \"uni_pc_bh2\": \"uni_pc_bh2\",\n    \"Scheduler\": \"Scheduler\",\n    \"karras\": \"karras\",\n    \"Scheduler of Sampler.\": \"Scheduler of Sampler.\",\n    \"normal\": \"normal\",\n    \"exponential\": \"exponential\",\n    \"sgm_uniform\": \"sgm_uniform\",\n    \"simple\": \"simple\",\n    \"ddim_uniform\": \"ddim_uniform\",\n    \"VAE\": \"VAE\",\n    \"Default (model)\": \"Default (model)\",\n    \"Forced Overwrite of Sampling Step\": \"Forced Overwrite of Sampling Step\",\n    \"Set as -1 to disable. For developer debugging.\": \"Set as -1 to disable. For developer debugging.\",\n    \"Forced Overwrite of Refiner Switch Step\": \"Forced Overwrite of Refiner Switch Step\",\n    \"Forced Overwrite of Generating Width\": \"Forced Overwrite of Generating Width\",\n    \"Set as -1 to disable. For developer debugging. Results will be worse for non-standard numbers that SDXL is not trained on.\": \"Set as -1 to disable. For developer debugging. Results will be worse for non-standard numbers that SDXL is not trained on.\",\n    \"Forced Overwrite of Generating Height\": \"Forced Overwrite of Generating Height\",\n    \"Forced Overwrite of Denoising Strength of \\\"Vary\\\"\": \"Forced Overwrite of Denoising Strength of \\\"Vary\\\"\",\n    \"Set as negative number to disable. For developer debugging.\": \"Set as negative number to disable. For developer debugging.\",\n    \"Forced Overwrite of Denoising Strength of \\\"Upscale\\\"\": \"Forced Overwrite of Denoising Strength of \\\"Upscale\\\"\",\n    \"Disable Preview\": \"Disable Preview\",\n    \"Disable preview during generation.\": \"Disable preview during generation.\",\n    \"Disable Intermediate Results\": \"Disable Intermediate Results\",\n    \"Disable intermediate results during generation, only show final gallery.\": \"Disable intermediate results during generation, only show final gallery.\",\n    \"Debug Inpaint Preprocessing\": \"Debug Inpaint Preprocessing\",\n    \"Debug GroundingDINO\": \"Debug GroundingDINO\",\n    \"Used for SAM object detection and box generation\": \"Used for SAM object detection and box generation\",\n    \"GroundingDINO Box Erode or Dilate\": \"GroundingDINO Box Erode or Dilate\",\n    \"Inpaint Engine\": \"Inpaint Engine\",\n    \"v1\": \"v1\",\n    \"v2.5\": \"v2.5\",\n    \"v2.6\": \"v2.6\",\n    \"Control Debug\": \"Control Debug\",\n    \"Debug Preprocessors\": \"Debug Preprocessors\",\n    \"Mixing Image Prompt and Vary/Upscale\": \"Mixing Image Prompt and Vary/Upscale\",\n    \"Mixing Image Prompt and Inpaint\": \"Mixing Image Prompt and Inpaint\",\n    \"Softness of ControlNet\": \"Softness of ControlNet\",\n    \"Similar to the Control Mode in A1111 (use 0.0 to disable). \": \"Similar to the Control Mode in A1111 (use 0.0 to disable). \",\n    \"Canny\": \"Canny\",\n    \"Canny Low Threshold\": \"Canny Low Threshold\",\n    \"Canny High Threshold\": \"Canny High Threshold\",\n    \"FreeU\": \"FreeU\",\n    \"Enabled\": \"Enabled\",\n    \"B1\": \"B1\",\n    \"B2\": \"B2\",\n    \"S1\": \"S1\",\n    \"S2\": \"S2\",\n    \"\\uD83D\\uDD0E Type here to search styles ...\": \"\\uD83D\\uDD0E Type here to search styles ...\",\n    \"Type prompt here.\": \"Type prompt here.\",\n    \"Outpaint Expansion Direction:\": \"Outpaint Expansion Direction:\",\n    \"* Powered by Fooocus Inpaint Engine (beta)\": \"* Powered by Fooocus Inpaint Engine (beta)\",\n    \"Fooocus Enhance\": \"Fooocus Enhance\",\n    \"Fooocus Cinematic\": \"Fooocus Cinematic\",\n    \"Fooocus Sharp\": \"Fooocus Sharp\",\n    \"For images created by Fooocus\": \"For images created by Fooocus\",\n    \"Metadata\": \"Metadata\",\n    \"Apply Metadata\": \"Apply Metadata\",\n    \"Metadata Scheme\": \"Metadata Scheme\",\n    \"Image Prompt parameters are not included. Use png and a1111 for compatibility with Civitai.\": \"Image Prompt parameters are not included. Use png and a1111 for compatibility with Civitai.\",\n    \"fooocus (json)\": \"fooocus (json)\",\n    \"a1111 (plain text)\": \"a1111 (plain text)\",\n    \"Unsupported image type in input\": \"Unsupported image type in input\",\n    \"Enhance\": \"Enhance\",\n    \"Detection prompt\": \"Detection prompt\",\n    \"Detection Prompt Quick List\": \"Detection Prompt Quick List\",\n    \"Maximum number of detections\": \"Maximum number of detections\",\n    \"Use with Enhance, skips image generation\": \"Use with Enhance, skips image generation\",\n    \"Order of Processing\": \"Order of Processing\",\n    \"Use before to enhance small details and after to enhance large areas.\": \"Use before to enhance small details and after to enhance large areas.\",\n    \"Before First Enhancement\": \"Before First Enhancement\",\n    \"After Last Enhancement\": \"After Last Enhancement\",\n    \"Prompt Type\": \"Prompt Type\",\n    \"Choose which prompt to use for Upscale or Variation.\": \"Choose which prompt to use for Upscale or Variation.\",\n    \"Original Prompts\": \"Original Prompts\",\n    \"Last Filled Enhancement Prompts\": \"Last Filled Enhancement Prompts\",\n    \"Enable\": \"Enable\",\n    \"Describe what you want to detect.\": \"Describe what you want to detect.\",\n    \"Enhancement positive prompt\": \"Enhancement positive prompt\",\n    \"Uses original prompt instead if empty.\": \"Uses original prompt instead if empty.\",\n    \"Enhancement negative prompt\": \"Enhancement negative prompt\",\n    \"Uses original negative prompt instead if empty.\": \"Uses original negative prompt instead if empty.\",\n    \"Detection\": \"Detection\",\n    \"u2net\": \"u2net\",\n    \"u2netp\": \"u2netp\",\n    \"u2net_human_seg\": \"u2net_human_seg\",\n    \"u2net_cloth_seg\": \"u2net_cloth_seg\",\n    \"silueta\": \"silueta\",\n    \"isnet-general-use\": \"isnet-general-use\",\n    \"isnet-anime\": \"isnet-anime\",\n    \"sam\": \"sam\",\n    \"Mask generation model\": \"Mask generation model\",\n    \"Cloth category\": \"Cloth category\",\n    \"Use singular whenever possible\": \"Use singular whenever possible\",\n    \"full\": \"full\",\n    \"upper\": \"upper\",\n    \"lower\": \"lower\",\n    \"SAM Options\": \"SAM Options\",\n    \"SAM model\": \"SAM model\",\n    \"vit_b\": \"vit_b\",\n    \"vit_l\": \"vit_l\",\n    \"vit_h\": \"vit_h\",\n    \"Box Threshold\": \"Box Threshold\",\n    \"Text Threshold\": \"Text Threshold\",\n    \"Set to 0 to detect all\": \"Set to 0 to detect all\",\n    \"Inpaint\": \"Inpaint\",\n    \"Inpaint or Outpaint (default)\": \"Inpaint or Outpaint (default)\",\n    \"Improve Detail (face, hand, eyes, etc.)\": \"Improve Detail (face, hand, eyes, etc.)\",\n    \"Modify Content (add objects, change background, etc.)\": \"Modify Content (add objects, change background, etc.)\",\n    \"Disable initial latent in inpaint\": \"Disable initial latent in inpaint\",\n    \"Version of Fooocus inpaint model. If set, use performance Quality or Speed (no performance LoRAs) for best results.\": \"Version of Fooocus inpaint model. If set, use performance Quality or Speed (no performance LoRAs) for best results.\",\n    \"Inpaint Denoising Strength\": \"Inpaint Denoising Strength\",\n    \"Same as the denoising strength in A1111 inpaint. Only used in inpaint, not used in outpaint. (Outpaint always use 1.0)\": \"Same as the denoising strength in A1111 inpaint. Only used in inpaint, not used in outpaint. (Outpaint always use 1.0)\",\n    \"Inpaint Respective Field\": \"Inpaint Respective Field\",\n    \"The area to inpaint. Value 0 is same as \\\"Only Masked\\\" in A1111. Value 1 is same as \\\"Whole Image\\\" in A1111. Only used in inpaint, not used in outpaint. (Outpaint always use 1.0)\": \"The area to inpaint. Value 0 is same as \\\"Only Masked\\\" in A1111. Value 1 is same as \\\"Whole Image\\\" in A1111. Only used in inpaint, not used in outpaint. (Outpaint always use 1.0)\",\n    \"Mask Erode or Dilate\": \"Mask Erode or Dilate\",\n    \"Positive value will make white area in the mask larger, negative value will make white area smaller. (default is 0, always processed before any mask invert)\": \"Positive value will make white area in the mask larger, negative value will make white area smaller. (default is 0, always processed before any mask invert)\",\n    \"Invert Mask When Generating\": \"Invert Mask When Generating\",\n    \"Debug Enhance Masks\": \"Debug Enhance Masks\",\n    \"Show enhance masks in preview and final results\": \"Show enhance masks in preview and final results\",\n    \"Use GroundingDINO boxes instead of more detailed SAM masks\": \"Use GroundingDINO boxes instead of more detailed SAM masks\",\n    \"highly detailed face\": \"highly detailed face\",\n    \"detailed girl face\": \"detailed girl face\",\n    \"detailed man face\": \"detailed man face\",\n    \"detailed hand\": \"detailed hand\",\n    \"beautiful eyes\": \"beautiful eyes\",\n    \"face\": \"face\",\n    \"eye\": \"eye\",\n    \"mouth\": \"mouth\",\n    \"hair\": \"hair\",\n    \"hand\": \"hand\",\n    \"body\": \"body\"\n}"
  },
  {
    "path": "language/example.json",
    "content": "{\n  \"Generate\": \"生成\",\n  \"Input Image\": \"入力画像\",\n  \"Advanced\": \"고급\",\n  \"SAI 3D Model\": \"SAI 3D Modèle\"\n}\n"
  },
  {
    "path": "launch.py",
    "content": "import os\nimport ssl\nimport sys\n\nprint('[System ARGV] ' + str(sys.argv))\n\nroot = os.path.dirname(os.path.abspath(__file__))\nsys.path.append(root)\nos.chdir(root)\n\nos.environ[\"PYTORCH_ENABLE_MPS_FALLBACK\"] = \"1\"\nos.environ[\"PYTORCH_MPS_HIGH_WATERMARK_RATIO\"] = \"0.0\"\nif \"GRADIO_SERVER_PORT\" not in os.environ:\n    os.environ[\"GRADIO_SERVER_PORT\"] = \"7865\"\n\nssl._create_default_https_context = ssl._create_unverified_context\n\nimport platform\nimport fooocus_version\n\nfrom build_launcher import build_launcher\nfrom modules.launch_util import is_installed, run, python, run_pip, requirements_met, delete_folder_content\nfrom modules.model_loader import load_file_from_url\n\nREINSTALL_ALL = False\nTRY_INSTALL_XFORMERS = False\n\n\ndef prepare_environment():\n    torch_index_url = os.environ.get('TORCH_INDEX_URL', \"https://download.pytorch.org/whl/cu121\")\n    torch_command = os.environ.get('TORCH_COMMAND',\n                                   f\"pip install torch==2.1.0 torchvision==0.16.0 --extra-index-url {torch_index_url}\")\n    requirements_file = os.environ.get('REQS_FILE', \"requirements_versions.txt\")\n\n    print(f\"Python {sys.version}\")\n    print(f\"Fooocus version: {fooocus_version.version}\")\n\n    if REINSTALL_ALL or not is_installed(\"torch\") or not is_installed(\"torchvision\"):\n        run(f'\"{python}\" -m {torch_command}', \"Installing torch and torchvision\", \"Couldn't install torch\", live=True)\n\n    if TRY_INSTALL_XFORMERS:\n        if REINSTALL_ALL or not is_installed(\"xformers\"):\n            xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.23')\n            if platform.system() == \"Windows\":\n                if platform.python_version().startswith(\"3.10\"):\n                    run_pip(f\"install -U -I --no-deps {xformers_package}\", \"xformers\", live=True)\n                else:\n                    print(\"Installation of xformers is not supported in this version of Python.\")\n                    print(\n                        \"You can also check this and build manually: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers#building-xformers-on-windows-by-duckness\")\n                    if not is_installed(\"xformers\"):\n                        exit(0)\n            elif platform.system() == \"Linux\":\n                run_pip(f\"install -U -I --no-deps {xformers_package}\", \"xformers\")\n\n    if REINSTALL_ALL or not requirements_met(requirements_file):\n        run_pip(f\"install -r \\\"{requirements_file}\\\"\", \"requirements\")\n\n    return\n\n\nvae_approx_filenames = [\n    ('xlvaeapp.pth', 'https://huggingface.co/lllyasviel/misc/resolve/main/xlvaeapp.pth'),\n    ('vaeapp_sd15.pth', 'https://huggingface.co/lllyasviel/misc/resolve/main/vaeapp_sd15.pt'),\n    ('xl-to-v1_interposer-v4.0.safetensors',\n     'https://huggingface.co/mashb1t/misc/resolve/main/xl-to-v1_interposer-v4.0.safetensors')\n]\n\n\ndef ini_args():\n    from args_manager import args\n    return args\n\n\nprepare_environment()\nbuild_launcher()\nargs = ini_args()\n\nif args.gpu_device_id is not None:\n    os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu_device_id)\n    print(\"Set device to:\", args.gpu_device_id)\n\nif args.hf_mirror is not None:\n    os.environ['HF_MIRROR'] = str(args.hf_mirror)\n    print(\"Set hf_mirror to:\", args.hf_mirror)\n\nfrom modules import config\nfrom modules.hash_cache import init_cache\n\nos.environ[\"U2NET_HOME\"] = config.path_inpaint\n\nos.environ['GRADIO_TEMP_DIR'] = config.temp_path\n\nif config.temp_path_cleanup_on_launch:\n    print(f'[Cleanup] Attempting to delete content of temp dir {config.temp_path}')\n    result = delete_folder_content(config.temp_path, '[Cleanup] ')\n    if result:\n        print(\"[Cleanup] Cleanup successful\")\n    else:\n        print(f\"[Cleanup] Failed to delete content of temp dir.\")\n\n\ndef download_models(default_model, previous_default_models, checkpoint_downloads, embeddings_downloads, lora_downloads, vae_downloads):\n    from modules.util import get_file_from_folder_list\n\n    for file_name, url in vae_approx_filenames:\n        load_file_from_url(url=url, model_dir=config.path_vae_approx, file_name=file_name)\n\n    load_file_from_url(\n        url='https://huggingface.co/lllyasviel/misc/resolve/main/fooocus_expansion.bin',\n        model_dir=config.path_fooocus_expansion,\n        file_name='pytorch_model.bin'\n    )\n\n    if args.disable_preset_download:\n        print('Skipped model download.')\n        return default_model, checkpoint_downloads\n\n    if not args.always_download_new_model:\n        if not os.path.isfile(get_file_from_folder_list(default_model, config.paths_checkpoints)):\n            for alternative_model_name in previous_default_models:\n                if os.path.isfile(get_file_from_folder_list(alternative_model_name, config.paths_checkpoints)):\n                    print(f'You do not have [{default_model}] but you have [{alternative_model_name}].')\n                    print(f'Fooocus will use [{alternative_model_name}] to avoid downloading new models, '\n                          f'but you are not using the latest models.')\n                    print('Use --always-download-new-model to avoid fallback and always get new models.')\n                    checkpoint_downloads = {}\n                    default_model = alternative_model_name\n                    break\n\n    for file_name, url in checkpoint_downloads.items():\n        model_dir = os.path.dirname(get_file_from_folder_list(file_name, config.paths_checkpoints))\n        load_file_from_url(url=url, model_dir=model_dir, file_name=file_name)\n    for file_name, url in embeddings_downloads.items():\n        load_file_from_url(url=url, model_dir=config.path_embeddings, file_name=file_name)\n    for file_name, url in lora_downloads.items():\n        model_dir = os.path.dirname(get_file_from_folder_list(file_name, config.paths_loras))\n        load_file_from_url(url=url, model_dir=model_dir, file_name=file_name)\n    for file_name, url in vae_downloads.items():\n        load_file_from_url(url=url, model_dir=config.path_vae, file_name=file_name)\n\n    return default_model, checkpoint_downloads\n\n\nconfig.default_base_model_name, config.checkpoint_downloads = download_models(\n    config.default_base_model_name, config.previous_default_models, config.checkpoint_downloads,\n    config.embeddings_downloads, config.lora_downloads, config.vae_downloads)\n\nconfig.update_files()\ninit_cache(config.model_filenames, config.paths_checkpoints, config.lora_filenames, config.paths_loras)\n\nfrom webui import *\n"
  },
  {
    "path": "ldm_patched/contrib/external.py",
    "content": "# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py \n\nimport torch\n\nimport os\nimport sys\nimport json\nimport hashlib\nimport traceback\nimport math\nimport time\nimport random\n\nfrom PIL import Image, ImageOps, ImageSequence\nfrom PIL.PngImagePlugin import PngInfo\nimport numpy as np\nimport safetensors.torch\n\npass  # sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), \"ldm_patched\"))\n\n\nimport ldm_patched.modules.diffusers_load\nimport ldm_patched.modules.samplers\nimport ldm_patched.modules.sample\nimport ldm_patched.modules.sd\nimport ldm_patched.modules.utils\nimport ldm_patched.modules.controlnet\n\nimport ldm_patched.modules.clip_vision\n\nimport ldm_patched.modules.model_management\nfrom ldm_patched.modules.args_parser import args\n\nimport importlib\n\nimport ldm_patched.utils.path_utils\nimport ldm_patched.utils.latent_visualization\n\ndef before_node_execution():\n    ldm_patched.modules.model_management.throw_exception_if_processing_interrupted()\n\ndef interrupt_processing(value=True):\n    ldm_patched.modules.model_management.interrupt_current_processing(value)\n\nMAX_RESOLUTION=8192\n\nclass CLIPTextEncode:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": {\"text\": (\"STRING\", {\"multiline\": True}), \"clip\": (\"CLIP\", )}}\n    RETURN_TYPES = (\"CONDITIONING\",)\n    FUNCTION = \"encode\"\n\n    CATEGORY = \"conditioning\"\n\n    def encode(self, clip, text):\n        tokens = clip.tokenize(text)\n        cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)\n        return ([[cond, {\"pooled_output\": pooled}]], )\n\nclass ConditioningCombine:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": {\"conditioning_1\": (\"CONDITIONING\", ), \"conditioning_2\": (\"CONDITIONING\", )}}\n    RETURN_TYPES = (\"CONDITIONING\",)\n    FUNCTION = \"combine\"\n\n    CATEGORY = \"conditioning\"\n\n    def combine(self, conditioning_1, conditioning_2):\n        return (conditioning_1 + conditioning_2, )\n\nclass ConditioningAverage :\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": {\"conditioning_to\": (\"CONDITIONING\", ), \"conditioning_from\": (\"CONDITIONING\", ),\n                              \"conditioning_to_strength\": (\"FLOAT\", {\"default\": 1.0, \"min\": 0.0, \"max\": 1.0, \"step\": 0.01})\n                             }}\n    RETURN_TYPES = (\"CONDITIONING\",)\n    FUNCTION = \"addWeighted\"\n\n    CATEGORY = \"conditioning\"\n\n    def addWeighted(self, conditioning_to, conditioning_from, conditioning_to_strength):\n        out = []\n\n        if len(conditioning_from) > 1:\n            print(\"Warning: ConditioningAverage conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.\")\n\n        cond_from = conditioning_from[0][0]\n        pooled_output_from = conditioning_from[0][1].get(\"pooled_output\", None)\n\n        for i in range(len(conditioning_to)):\n            t1 = conditioning_to[i][0]\n            pooled_output_to = conditioning_to[i][1].get(\"pooled_output\", pooled_output_from)\n            t0 = cond_from[:,:t1.shape[1]]\n            if t0.shape[1] < t1.shape[1]:\n                t0 = torch.cat([t0] + [torch.zeros((1, (t1.shape[1] - t0.shape[1]), t1.shape[2]))], dim=1)\n\n            tw = torch.mul(t1, conditioning_to_strength) + torch.mul(t0, (1.0 - conditioning_to_strength))\n            t_to = conditioning_to[i][1].copy()\n            if pooled_output_from is not None and pooled_output_to is not None:\n                t_to[\"pooled_output\"] = torch.mul(pooled_output_to, conditioning_to_strength) + torch.mul(pooled_output_from, (1.0 - conditioning_to_strength))\n            elif pooled_output_from is not None:\n                t_to[\"pooled_output\"] = pooled_output_from\n\n            n = [tw, t_to]\n            out.append(n)\n        return (out, )\n\nclass ConditioningConcat:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": {\n            \"conditioning_to\": (\"CONDITIONING\",),\n            \"conditioning_from\": (\"CONDITIONING\",),\n            }}\n    RETURN_TYPES = (\"CONDITIONING\",)\n    FUNCTION = \"concat\"\n\n    CATEGORY = \"conditioning\"\n\n    def concat(self, conditioning_to, conditioning_from):\n        out = []\n\n        if len(conditioning_from) > 1:\n            print(\"Warning: ConditioningConcat conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.\")\n\n        cond_from = conditioning_from[0][0]\n\n        for i in range(len(conditioning_to)):\n            t1 = conditioning_to[i][0]\n            tw = torch.cat((t1, cond_from),1)\n            n = [tw, conditioning_to[i][1].copy()]\n            out.append(n)\n\n        return (out, )\n\nclass ConditioningSetArea:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": {\"conditioning\": (\"CONDITIONING\", ),\n                              \"width\": (\"INT\", {\"default\": 64, \"min\": 64, \"max\": MAX_RESOLUTION, \"step\": 8}),\n                              \"height\": (\"INT\", {\"default\": 64, \"min\": 64, \"max\": MAX_RESOLUTION, \"step\": 8}),\n                              \"x\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 8}),\n                              \"y\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 8}),\n                              \"strength\": (\"FLOAT\", {\"default\": 1.0, \"min\": 0.0, \"max\": 10.0, \"step\": 0.01}),\n                             }}\n    RETURN_TYPES = (\"CONDITIONING\",)\n    FUNCTION = \"append\"\n\n    CATEGORY = \"conditioning\"\n\n    def append(self, conditioning, width, height, x, y, strength):\n        c = []\n        for t in conditioning:\n            n = [t[0], t[1].copy()]\n            n[1]['area'] = (height // 8, width // 8, y // 8, x // 8)\n            n[1]['strength'] = strength\n            n[1]['set_area_to_bounds'] = False\n            c.append(n)\n        return (c, )\n\nclass ConditioningSetAreaPercentage:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": {\"conditioning\": (\"CONDITIONING\", ),\n                              \"width\": (\"FLOAT\", {\"default\": 1.0, \"min\": 0, \"max\": 1.0, \"step\": 0.01}),\n                              \"height\": (\"FLOAT\", {\"default\": 1.0, \"min\": 0, \"max\": 1.0, \"step\": 0.01}),\n                              \"x\": (\"FLOAT\", {\"default\": 0, \"min\": 0, \"max\": 1.0, \"step\": 0.01}),\n                              \"y\": (\"FLOAT\", {\"default\": 0, \"min\": 0, \"max\": 1.0, \"step\": 0.01}),\n                              \"strength\": (\"FLOAT\", {\"default\": 1.0, \"min\": 0.0, \"max\": 10.0, \"step\": 0.01}),\n                             }}\n    RETURN_TYPES = (\"CONDITIONING\",)\n    FUNCTION = \"append\"\n\n    CATEGORY = \"conditioning\"\n\n    def append(self, conditioning, width, height, x, y, strength):\n        c = []\n        for t in conditioning:\n            n = [t[0], t[1].copy()]\n            n[1]['area'] = (\"percentage\", height, width, y, x)\n            n[1]['strength'] = strength\n            n[1]['set_area_to_bounds'] = False\n            c.append(n)\n        return (c, )\n\nclass ConditioningSetMask:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": {\"conditioning\": (\"CONDITIONING\", ),\n                              \"mask\": (\"MASK\", ),\n                              \"strength\": (\"FLOAT\", {\"default\": 1.0, \"min\": 0.0, \"max\": 10.0, \"step\": 0.01}),\n                              \"set_cond_area\": ([\"default\", \"mask bounds\"],),\n                             }}\n    RETURN_TYPES = (\"CONDITIONING\",)\n    FUNCTION = \"append\"\n\n    CATEGORY = \"conditioning\"\n\n    def append(self, conditioning, mask, set_cond_area, strength):\n        c = []\n        set_area_to_bounds = False\n        if set_cond_area != \"default\":\n            set_area_to_bounds = True\n        if len(mask.shape) < 3:\n            mask = mask.unsqueeze(0)\n        for t in conditioning:\n            n = [t[0], t[1].copy()]\n            _, h, w = mask.shape\n            n[1]['mask'] = mask\n            n[1]['set_area_to_bounds'] = set_area_to_bounds\n            n[1]['mask_strength'] = strength\n            c.append(n)\n        return (c, )\n\nclass ConditioningZeroOut:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": {\"conditioning\": (\"CONDITIONING\", )}}\n    RETURN_TYPES = (\"CONDITIONING\",)\n    FUNCTION = \"zero_out\"\n\n    CATEGORY = \"advanced/conditioning\"\n\n    def zero_out(self, conditioning):\n        c = []\n        for t in conditioning:\n            d = t[1].copy()\n            if \"pooled_output\" in d:\n                d[\"pooled_output\"] = torch.zeros_like(d[\"pooled_output\"])\n            n = [torch.zeros_like(t[0]), d]\n            c.append(n)\n        return (c, )\n\nclass ConditioningSetTimestepRange:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": {\"conditioning\": (\"CONDITIONING\", ),\n                             \"start\": (\"FLOAT\", {\"default\": 0.0, \"min\": 0.0, \"max\": 1.0, \"step\": 0.001}),\n                             \"end\": (\"FLOAT\", {\"default\": 1.0, \"min\": 0.0, \"max\": 1.0, \"step\": 0.001})\n                             }}\n    RETURN_TYPES = (\"CONDITIONING\",)\n    FUNCTION = \"set_range\"\n\n    CATEGORY = \"advanced/conditioning\"\n\n    def set_range(self, conditioning, start, end):\n        c = []\n        for t in conditioning:\n            d = t[1].copy()\n            d['start_percent'] = start\n            d['end_percent'] = end\n            n = [t[0], d]\n            c.append(n)\n        return (c, )\n\nclass VAEDecode:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"samples\": (\"LATENT\", ), \"vae\": (\"VAE\", )}}\n    RETURN_TYPES = (\"IMAGE\",)\n    FUNCTION = \"decode\"\n\n    CATEGORY = \"latent\"\n\n    def decode(self, vae, samples):\n        return (vae.decode(samples[\"samples\"]), )\n\nclass VAEDecodeTiled:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": {\"samples\": (\"LATENT\", ), \"vae\": (\"VAE\", ),\n                             \"tile_size\": (\"INT\", {\"default\": 512, \"min\": 320, \"max\": 4096, \"step\": 64})\n                            }}\n    RETURN_TYPES = (\"IMAGE\",)\n    FUNCTION = \"decode\"\n\n    CATEGORY = \"_for_testing\"\n\n    def decode(self, vae, samples, tile_size):\n        return (vae.decode_tiled(samples[\"samples\"], tile_x=tile_size // 8, tile_y=tile_size // 8, ), )\n\nclass VAEEncode:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"pixels\": (\"IMAGE\", ), \"vae\": (\"VAE\", )}}\n    RETURN_TYPES = (\"LATENT\",)\n    FUNCTION = \"encode\"\n\n    CATEGORY = \"latent\"\n\n    @staticmethod\n    def vae_encode_crop_pixels(pixels):\n        x = (pixels.shape[1] // 8) * 8\n        y = (pixels.shape[2] // 8) * 8\n        if pixels.shape[1] != x or pixels.shape[2] != y:\n            x_offset = (pixels.shape[1] % 8) // 2\n            y_offset = (pixels.shape[2] % 8) // 2\n            pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :]\n        return pixels\n\n    def encode(self, vae, pixels):\n        pixels = self.vae_encode_crop_pixels(pixels)\n        t = vae.encode(pixels[:,:,:,:3])\n        return ({\"samples\":t}, )\n\nclass VAEEncodeTiled:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": {\"pixels\": (\"IMAGE\", ), \"vae\": (\"VAE\", ),\n                             \"tile_size\": (\"INT\", {\"default\": 512, \"min\": 320, \"max\": 4096, \"step\": 64})\n                            }}\n    RETURN_TYPES = (\"LATENT\",)\n    FUNCTION = \"encode\"\n\n    CATEGORY = \"_for_testing\"\n\n    def encode(self, vae, pixels, tile_size):\n        pixels = VAEEncode.vae_encode_crop_pixels(pixels)\n        t = vae.encode_tiled(pixels[:,:,:,:3], tile_x=tile_size, tile_y=tile_size, )\n        return ({\"samples\":t}, )\n\nclass VAEEncodeForInpaint:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"pixels\": (\"IMAGE\", ), \"vae\": (\"VAE\", ), \"mask\": (\"MASK\", ), \"grow_mask_by\": (\"INT\", {\"default\": 6, \"min\": 0, \"max\": 64, \"step\": 1}),}}\n    RETURN_TYPES = (\"LATENT\",)\n    FUNCTION = \"encode\"\n\n    CATEGORY = \"latent/inpaint\"\n\n    def encode(self, vae, pixels, mask, grow_mask_by=6):\n        x = (pixels.shape[1] // 8) * 8\n        y = (pixels.shape[2] // 8) * 8\n        mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode=\"bilinear\")\n\n        pixels = pixels.clone()\n        if pixels.shape[1] != x or pixels.shape[2] != y:\n            x_offset = (pixels.shape[1] % 8) // 2\n            y_offset = (pixels.shape[2] % 8) // 2\n            pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:]\n            mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset]\n\n        #grow mask by a few pixels to keep things seamless in latent space\n        if grow_mask_by == 0:\n            mask_erosion = mask\n        else:\n            kernel_tensor = torch.ones((1, 1, grow_mask_by, grow_mask_by))\n            padding = math.ceil((grow_mask_by - 1) / 2)\n\n            mask_erosion = torch.clamp(torch.nn.functional.conv2d(mask.round(), kernel_tensor, padding=padding), 0, 1)\n\n        m = (1.0 - mask.round()).squeeze(1)\n        for i in range(3):\n            pixels[:,:,:,i] -= 0.5\n            pixels[:,:,:,i] *= m\n            pixels[:,:,:,i] += 0.5\n        t = vae.encode(pixels)\n\n        return ({\"samples\":t, \"noise_mask\": (mask_erosion[:,:,:x,:y].round())}, )\n\n\nclass InpaintModelConditioning:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": {\"positive\": (\"CONDITIONING\", ),\n                             \"negative\": (\"CONDITIONING\", ),\n                             \"vae\": (\"VAE\", ),\n                             \"pixels\": (\"IMAGE\", ),\n                             \"mask\": (\"MASK\", ),\n                             }}\n\n    RETURN_TYPES = (\"CONDITIONING\",\"CONDITIONING\",\"LATENT\")\n    RETURN_NAMES = (\"positive\", \"negative\", \"latent\")\n    FUNCTION = \"encode\"\n\n    CATEGORY = \"conditioning/inpaint\"\n\n    def encode(self, positive, negative, pixels, vae, mask):\n        x = (pixels.shape[1] // 8) * 8\n        y = (pixels.shape[2] // 8) * 8\n        mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode=\"bilinear\")\n\n        orig_pixels = pixels\n        pixels = orig_pixels.clone()\n        if pixels.shape[1] != x or pixels.shape[2] != y:\n            x_offset = (pixels.shape[1] % 8) // 2\n            y_offset = (pixels.shape[2] % 8) // 2\n            pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:]\n            mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset]\n\n        m = (1.0 - mask.round()).squeeze(1)\n        for i in range(3):\n            pixels[:,:,:,i] -= 0.5\n            pixels[:,:,:,i] *= m\n            pixels[:,:,:,i] += 0.5\n        concat_latent = vae.encode(pixels)\n        orig_latent = vae.encode(orig_pixels)\n\n        out_latent = {}\n\n        out_latent[\"samples\"] = orig_latent\n        out_latent[\"noise_mask\"] = mask\n\n        out = []\n        for conditioning in [positive, negative]:\n            c = []\n            for t in conditioning:\n                d = t[1].copy()\n                d[\"concat_latent_image\"] = concat_latent\n                d[\"concat_mask\"] = mask\n                n = [t[0], d]\n                c.append(n)\n            out.append(c)\n        return (out[0], out[1], out_latent)\n\n\nclass SaveLatent:\n    def __init__(self):\n        self.output_dir = ldm_patched.utils.path_utils.get_output_directory()\n\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"samples\": (\"LATENT\", ),\n                              \"filename_prefix\": (\"STRING\", {\"default\": \"latents/ldm_patched\"})},\n                \"hidden\": {\"prompt\": \"PROMPT\", \"extra_pnginfo\": \"EXTRA_PNGINFO\"},\n                }\n    RETURN_TYPES = ()\n    FUNCTION = \"save\"\n\n    OUTPUT_NODE = True\n\n    CATEGORY = \"_for_testing\"\n\n    def save(self, samples, filename_prefix=\"ldm_patched\", prompt=None, extra_pnginfo=None):\n        full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, self.output_dir)\n\n        # support save metadata for latent sharing\n        prompt_info = \"\"\n        if prompt is not None:\n            prompt_info = json.dumps(prompt)\n\n        metadata = None\n        if not args.disable_server_info:\n            metadata = {\"prompt\": prompt_info}\n            if extra_pnginfo is not None:\n                for x in extra_pnginfo:\n                    metadata[x] = json.dumps(extra_pnginfo[x])\n\n        file = f\"{filename}_{counter:05}_.latent\"\n\n        results = list()\n        results.append({\n            \"filename\": file,\n            \"subfolder\": subfolder,\n            \"type\": \"output\"\n        })\n\n        file = os.path.join(full_output_folder, file)\n\n        output = {}\n        output[\"latent_tensor\"] = samples[\"samples\"]\n        output[\"latent_format_version_0\"] = torch.tensor([])\n\n        ldm_patched.modules.utils.save_torch_file(output, file, metadata=metadata)\n        return { \"ui\": { \"latents\": results } }\n\n\nclass LoadLatent:\n    @classmethod\n    def INPUT_TYPES(s):\n        input_dir = ldm_patched.utils.path_utils.get_input_directory()\n        files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f)) and f.endswith(\".latent\")]\n        return {\"required\": {\"latent\": [sorted(files), ]}, }\n\n    CATEGORY = \"_for_testing\"\n\n    RETURN_TYPES = (\"LATENT\", )\n    FUNCTION = \"load\"\n\n    def load(self, latent):\n        latent_path = ldm_patched.utils.path_utils.get_annotated_filepath(latent)\n        latent = safetensors.torch.load_file(latent_path, device=\"cpu\")\n        multiplier = 1.0\n        if \"latent_format_version_0\" not in latent:\n            multiplier = 1.0 / 0.18215\n        samples = {\"samples\": latent[\"latent_tensor\"].float() * multiplier}\n        return (samples, )\n\n    @classmethod\n    def IS_CHANGED(s, latent):\n        image_path = ldm_patched.utils.path_utils.get_annotated_filepath(latent)\n        m = hashlib.sha256()\n        with open(image_path, 'rb') as f:\n            m.update(f.read())\n        return m.digest().hex()\n\n    @classmethod\n    def VALIDATE_INPUTS(s, latent):\n        if not ldm_patched.utils.path_utils.exists_annotated_filepath(latent):\n            return \"Invalid latent file: {}\".format(latent)\n        return True\n\n\nclass CheckpointLoader:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"config_name\": (ldm_patched.utils.path_utils.get_filename_list(\"configs\"), ),\n                              \"ckpt_name\": (ldm_patched.utils.path_utils.get_filename_list(\"checkpoints\"), )}}\n    RETURN_TYPES = (\"MODEL\", \"CLIP\", \"VAE\")\n    FUNCTION = \"load_checkpoint\"\n\n    CATEGORY = \"advanced/loaders\"\n\n    def load_checkpoint(self, config_name, ckpt_name, output_vae=True, output_clip=True):\n        config_path = ldm_patched.utils.path_utils.get_full_path(\"configs\", config_name)\n        ckpt_path = ldm_patched.utils.path_utils.get_full_path(\"checkpoints\", ckpt_name)\n        return ldm_patched.modules.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=ldm_patched.utils.path_utils.get_folder_paths(\"embeddings\"))\n\nclass CheckpointLoaderSimple:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"ckpt_name\": (ldm_patched.utils.path_utils.get_filename_list(\"checkpoints\"), ),\n                             }}\n    RETURN_TYPES = (\"MODEL\", \"CLIP\", \"VAE\")\n    FUNCTION = \"load_checkpoint\"\n\n    CATEGORY = \"loaders\"\n\n    def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):\n        ckpt_path = ldm_patched.utils.path_utils.get_full_path(\"checkpoints\", ckpt_name)\n        out = ldm_patched.modules.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=ldm_patched.utils.path_utils.get_folder_paths(\"embeddings\"))\n        return out[:3]\n\nclass DiffusersLoader:\n    @classmethod\n    def INPUT_TYPES(cls):\n        paths = []\n        for search_path in ldm_patched.utils.path_utils.get_folder_paths(\"diffusers\"):\n            if os.path.exists(search_path):\n                for root, subdir, files in os.walk(search_path, followlinks=True):\n                    if \"model_index.json\" in files:\n                        paths.append(os.path.relpath(root, start=search_path))\n\n        return {\"required\": {\"model_path\": (paths,), }}\n    RETURN_TYPES = (\"MODEL\", \"CLIP\", \"VAE\")\n    FUNCTION = \"load_checkpoint\"\n\n    CATEGORY = \"advanced/loaders/deprecated\"\n\n    def load_checkpoint(self, model_path, output_vae=True, output_clip=True):\n        for search_path in ldm_patched.utils.path_utils.get_folder_paths(\"diffusers\"):\n            if os.path.exists(search_path):\n                path = os.path.join(search_path, model_path)\n                if os.path.exists(path):\n                    model_path = path\n                    break\n\n        return ldm_patched.modules.diffusers_load.load_diffusers(model_path, output_vae=output_vae, output_clip=output_clip, embedding_directory=ldm_patched.utils.path_utils.get_folder_paths(\"embeddings\"))\n\n\nclass unCLIPCheckpointLoader:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"ckpt_name\": (ldm_patched.utils.path_utils.get_filename_list(\"checkpoints\"), ),\n                             }}\n    RETURN_TYPES = (\"MODEL\", \"CLIP\", \"VAE\", \"CLIP_VISION\")\n    FUNCTION = \"load_checkpoint\"\n\n    CATEGORY = \"loaders\"\n\n    def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):\n        ckpt_path = ldm_patched.utils.path_utils.get_full_path(\"checkpoints\", ckpt_name)\n        out = ldm_patched.modules.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=True, embedding_directory=ldm_patched.utils.path_utils.get_folder_paths(\"embeddings\"))\n        return out\n\nclass CLIPSetLastLayer:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"clip\": (\"CLIP\", ),\n                              \"stop_at_clip_layer\": (\"INT\", {\"default\": -1, \"min\": -24, \"max\": -1, \"step\": 1}),\n                              }}\n    RETURN_TYPES = (\"CLIP\",)\n    FUNCTION = \"set_last_layer\"\n\n    CATEGORY = \"conditioning\"\n\n    def set_last_layer(self, clip, stop_at_clip_layer):\n        clip = clip.clone()\n        clip.clip_layer(stop_at_clip_layer)\n        return (clip,)\n\nclass LoraLoader:\n    def __init__(self):\n        self.loaded_lora = None\n\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"model\": (\"MODEL\",),\n                              \"clip\": (\"CLIP\", ),\n                              \"lora_name\": (ldm_patched.utils.path_utils.get_filename_list(\"loras\"), ),\n                              \"strength_model\": (\"FLOAT\", {\"default\": 1.0, \"min\": -20.0, \"max\": 20.0, \"step\": 0.01}),\n                              \"strength_clip\": (\"FLOAT\", {\"default\": 1.0, \"min\": -20.0, \"max\": 20.0, \"step\": 0.01}),\n                              }}\n    RETURN_TYPES = (\"MODEL\", \"CLIP\")\n    FUNCTION = \"load_lora\"\n\n    CATEGORY = \"loaders\"\n\n    def load_lora(self, model, clip, lora_name, strength_model, strength_clip):\n        if strength_model == 0 and strength_clip == 0:\n            return (model, clip)\n\n        lora_path = ldm_patched.utils.path_utils.get_full_path(\"loras\", lora_name)\n        lora = None\n        if self.loaded_lora is not None:\n            if self.loaded_lora[0] == lora_path:\n                lora = self.loaded_lora[1]\n            else:\n                temp = self.loaded_lora\n                self.loaded_lora = None\n                del temp\n\n        if lora is None:\n            lora = ldm_patched.modules.utils.load_torch_file(lora_path, safe_load=True)\n            self.loaded_lora = (lora_path, lora)\n\n        model_lora, clip_lora = ldm_patched.modules.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip)\n        return (model_lora, clip_lora)\n\nclass LoraLoaderModelOnly(LoraLoader):\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"model\": (\"MODEL\",),\n                              \"lora_name\": (ldm_patched.utils.path_utils.get_filename_list(\"loras\"), ),\n                              \"strength_model\": (\"FLOAT\", {\"default\": 1.0, \"min\": -20.0, \"max\": 20.0, \"step\": 0.01}),\n                              }}\n    RETURN_TYPES = (\"MODEL\",)\n    FUNCTION = \"load_lora_model_only\"\n\n    def load_lora_model_only(self, model, lora_name, strength_model):\n        return (self.load_lora(model, None, lora_name, strength_model, 0)[0],)\n\nclass VAELoader:\n    @staticmethod\n    def vae_list():\n        vaes = ldm_patched.utils.path_utils.get_filename_list(\"vae\")\n        approx_vaes = ldm_patched.utils.path_utils.get_filename_list(\"vae_approx\")\n        sdxl_taesd_enc = False\n        sdxl_taesd_dec = False\n        sd1_taesd_enc = False\n        sd1_taesd_dec = False\n\n        for v in approx_vaes:\n            if v.startswith(\"taesd_decoder.\"):\n                sd1_taesd_dec = True\n            elif v.startswith(\"taesd_encoder.\"):\n                sd1_taesd_enc = True\n            elif v.startswith(\"taesdxl_decoder.\"):\n                sdxl_taesd_dec = True\n            elif v.startswith(\"taesdxl_encoder.\"):\n                sdxl_taesd_enc = True\n        if sd1_taesd_dec and sd1_taesd_enc:\n            vaes.append(\"taesd\")\n        if sdxl_taesd_dec and sdxl_taesd_enc:\n            vaes.append(\"taesdxl\")\n        return vaes\n\n    @staticmethod\n    def load_taesd(name):\n        sd = {}\n        approx_vaes = ldm_patched.utils.path_utils.get_filename_list(\"vae_approx\")\n\n        encoder = next(filter(lambda a: a.startswith(\"{}_encoder.\".format(name)), approx_vaes))\n        decoder = next(filter(lambda a: a.startswith(\"{}_decoder.\".format(name)), approx_vaes))\n\n        enc = ldm_patched.modules.utils.load_torch_file(ldm_patched.utils.path_utils.get_full_path(\"vae_approx\", encoder))\n        for k in enc:\n            sd[\"taesd_encoder.{}\".format(k)] = enc[k]\n\n        dec = ldm_patched.modules.utils.load_torch_file(ldm_patched.utils.path_utils.get_full_path(\"vae_approx\", decoder))\n        for k in dec:\n            sd[\"taesd_decoder.{}\".format(k)] = dec[k]\n\n        if name == \"taesd\":\n            sd[\"vae_scale\"] = torch.tensor(0.18215)\n        elif name == \"taesdxl\":\n            sd[\"vae_scale\"] = torch.tensor(0.13025)\n        return sd\n\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"vae_name\": (s.vae_list(), )}}\n    RETURN_TYPES = (\"VAE\",)\n    FUNCTION = \"load_vae\"\n\n    CATEGORY = \"loaders\"\n\n    #TODO: scale factor?\n    def load_vae(self, vae_name):\n        if vae_name in [\"taesd\", \"taesdxl\"]:\n            sd = self.load_taesd(vae_name)\n        else:\n            vae_path = ldm_patched.utils.path_utils.get_full_path(\"vae\", vae_name)\n            sd = ldm_patched.modules.utils.load_torch_file(vae_path)\n        vae = ldm_patched.modules.sd.VAE(sd=sd)\n        return (vae,)\n\nclass ControlNetLoader:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"control_net_name\": (ldm_patched.utils.path_utils.get_filename_list(\"controlnet\"), )}}\n\n    RETURN_TYPES = (\"CONTROL_NET\",)\n    FUNCTION = \"load_controlnet\"\n\n    CATEGORY = \"loaders\"\n\n    def load_controlnet(self, control_net_name):\n        controlnet_path = ldm_patched.utils.path_utils.get_full_path(\"controlnet\", control_net_name)\n        controlnet = ldm_patched.modules.controlnet.load_controlnet(controlnet_path)\n        return (controlnet,)\n\nclass DiffControlNetLoader:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"model\": (\"MODEL\",),\n                              \"control_net_name\": (ldm_patched.utils.path_utils.get_filename_list(\"controlnet\"), )}}\n\n    RETURN_TYPES = (\"CONTROL_NET\",)\n    FUNCTION = \"load_controlnet\"\n\n    CATEGORY = \"loaders\"\n\n    def load_controlnet(self, model, control_net_name):\n        controlnet_path = ldm_patched.utils.path_utils.get_full_path(\"controlnet\", control_net_name)\n        controlnet = ldm_patched.modules.controlnet.load_controlnet(controlnet_path, model)\n        return (controlnet,)\n\n\nclass ControlNetApply:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": {\"conditioning\": (\"CONDITIONING\", ),\n                             \"control_net\": (\"CONTROL_NET\", ),\n                             \"image\": (\"IMAGE\", ),\n                             \"strength\": (\"FLOAT\", {\"default\": 1.0, \"min\": 0.0, \"max\": 10.0, \"step\": 0.01})\n                             }}\n    RETURN_TYPES = (\"CONDITIONING\",)\n    FUNCTION = \"apply_controlnet\"\n\n    CATEGORY = \"conditioning\"\n\n    def apply_controlnet(self, conditioning, control_net, image, strength):\n        if strength == 0:\n            return (conditioning, )\n\n        c = []\n        control_hint = image.movedim(-1,1)\n        for t in conditioning:\n            n = [t[0], t[1].copy()]\n            c_net = control_net.copy().set_cond_hint(control_hint, strength)\n            if 'control' in t[1]:\n                c_net.set_previous_controlnet(t[1]['control'])\n            n[1]['control'] = c_net\n            n[1]['control_apply_to_uncond'] = True\n            c.append(n)\n        return (c, )\n\n\nclass ControlNetApplyAdvanced:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": {\"positive\": (\"CONDITIONING\", ),\n                             \"negative\": (\"CONDITIONING\", ),\n                             \"control_net\": (\"CONTROL_NET\", ),\n                             \"image\": (\"IMAGE\", ),\n                             \"strength\": (\"FLOAT\", {\"default\": 1.0, \"min\": 0.0, \"max\": 10.0, \"step\": 0.01}),\n                             \"start_percent\": (\"FLOAT\", {\"default\": 0.0, \"min\": 0.0, \"max\": 1.0, \"step\": 0.001}),\n                             \"end_percent\": (\"FLOAT\", {\"default\": 1.0, \"min\": 0.0, \"max\": 1.0, \"step\": 0.001})\n                             }}\n\n    RETURN_TYPES = (\"CONDITIONING\",\"CONDITIONING\")\n    RETURN_NAMES = (\"positive\", \"negative\")\n    FUNCTION = \"apply_controlnet\"\n\n    CATEGORY = \"conditioning\"\n\n    def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent):\n        if strength == 0:\n            return (positive, negative)\n\n        control_hint = image.movedim(-1,1)\n        cnets = {}\n\n        out = []\n        for conditioning in [positive, negative]:\n            c = []\n            for t in conditioning:\n                d = t[1].copy()\n\n                prev_cnet = d.get('control', None)\n                if prev_cnet in cnets:\n                    c_net = cnets[prev_cnet]\n                else:\n                    c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent))\n                    c_net.set_previous_controlnet(prev_cnet)\n                    cnets[prev_cnet] = c_net\n\n                d['control'] = c_net\n                d['control_apply_to_uncond'] = False\n                n = [t[0], d]\n                c.append(n)\n            out.append(c)\n        return (out[0], out[1])\n\n\nclass UNETLoader:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"unet_name\": (ldm_patched.utils.path_utils.get_filename_list(\"unet\"), ),\n                             }}\n    RETURN_TYPES = (\"MODEL\",)\n    FUNCTION = \"load_unet\"\n\n    CATEGORY = \"advanced/loaders\"\n\n    def load_unet(self, unet_name):\n        unet_path = ldm_patched.utils.path_utils.get_full_path(\"unet\", unet_name)\n        model = ldm_patched.modules.sd.load_unet(unet_path)\n        return (model,)\n\nclass CLIPLoader:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"clip_name\": (ldm_patched.utils.path_utils.get_filename_list(\"clip\"), ),\n                             }}\n    RETURN_TYPES = (\"CLIP\",)\n    FUNCTION = \"load_clip\"\n\n    CATEGORY = \"advanced/loaders\"\n\n    def load_clip(self, clip_name):\n        clip_path = ldm_patched.utils.path_utils.get_full_path(\"clip\", clip_name)\n        clip = ldm_patched.modules.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=ldm_patched.utils.path_utils.get_folder_paths(\"embeddings\"))\n        return (clip,)\n\nclass DualCLIPLoader:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"clip_name1\": (ldm_patched.utils.path_utils.get_filename_list(\"clip\"), ), \"clip_name2\": (ldm_patched.utils.path_utils.get_filename_list(\"clip\"), ),\n                             }}\n    RETURN_TYPES = (\"CLIP\",)\n    FUNCTION = \"load_clip\"\n\n    CATEGORY = \"advanced/loaders\"\n\n    def load_clip(self, clip_name1, clip_name2):\n        clip_path1 = ldm_patched.utils.path_utils.get_full_path(\"clip\", clip_name1)\n        clip_path2 = ldm_patched.utils.path_utils.get_full_path(\"clip\", clip_name2)\n        clip = ldm_patched.modules.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=ldm_patched.utils.path_utils.get_folder_paths(\"embeddings\"))\n        return (clip,)\n\nclass CLIPVisionLoader:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"clip_name\": (ldm_patched.utils.path_utils.get_filename_list(\"clip_vision\"), ),\n                             }}\n    RETURN_TYPES = (\"CLIP_VISION\",)\n    FUNCTION = \"load_clip\"\n\n    CATEGORY = \"loaders\"\n\n    def load_clip(self, clip_name):\n        clip_path = ldm_patched.utils.path_utils.get_full_path(\"clip_vision\", clip_name)\n        clip_vision = ldm_patched.modules.clip_vision.load(clip_path)\n        return (clip_vision,)\n\nclass CLIPVisionEncode:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"clip_vision\": (\"CLIP_VISION\",),\n                              \"image\": (\"IMAGE\",)\n                             }}\n    RETURN_TYPES = (\"CLIP_VISION_OUTPUT\",)\n    FUNCTION = \"encode\"\n\n    CATEGORY = \"conditioning\"\n\n    def encode(self, clip_vision, image):\n        output = clip_vision.encode_image(image)\n        return (output,)\n\nclass StyleModelLoader:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"style_model_name\": (ldm_patched.utils.path_utils.get_filename_list(\"style_models\"), )}}\n\n    RETURN_TYPES = (\"STYLE_MODEL\",)\n    FUNCTION = \"load_style_model\"\n\n    CATEGORY = \"loaders\"\n\n    def load_style_model(self, style_model_name):\n        style_model_path = ldm_patched.utils.path_utils.get_full_path(\"style_models\", style_model_name)\n        style_model = ldm_patched.modules.sd.load_style_model(style_model_path)\n        return (style_model,)\n\n\nclass StyleModelApply:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": {\"conditioning\": (\"CONDITIONING\", ),\n                             \"style_model\": (\"STYLE_MODEL\", ),\n                             \"clip_vision_output\": (\"CLIP_VISION_OUTPUT\", ),\n                             }}\n    RETURN_TYPES = (\"CONDITIONING\",)\n    FUNCTION = \"apply_stylemodel\"\n\n    CATEGORY = \"conditioning/style_model\"\n\n    def apply_stylemodel(self, clip_vision_output, style_model, conditioning):\n        cond = style_model.get_cond(clip_vision_output).flatten(start_dim=0, end_dim=1).unsqueeze(dim=0)\n        c = []\n        for t in conditioning:\n            n = [torch.cat((t[0], cond), dim=1), t[1].copy()]\n            c.append(n)\n        return (c, )\n\nclass unCLIPConditioning:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": {\"conditioning\": (\"CONDITIONING\", ),\n                             \"clip_vision_output\": (\"CLIP_VISION_OUTPUT\", ),\n                             \"strength\": (\"FLOAT\", {\"default\": 1.0, \"min\": -10.0, \"max\": 10.0, \"step\": 0.01}),\n                             \"noise_augmentation\": (\"FLOAT\", {\"default\": 0.0, \"min\": 0.0, \"max\": 1.0, \"step\": 0.01}),\n                             }}\n    RETURN_TYPES = (\"CONDITIONING\",)\n    FUNCTION = \"apply_adm\"\n\n    CATEGORY = \"conditioning\"\n\n    def apply_adm(self, conditioning, clip_vision_output, strength, noise_augmentation):\n        if strength == 0:\n            return (conditioning, )\n\n        c = []\n        for t in conditioning:\n            o = t[1].copy()\n            x = {\"clip_vision_output\": clip_vision_output, \"strength\": strength, \"noise_augmentation\": noise_augmentation}\n            if \"unclip_conditioning\" in o:\n                o[\"unclip_conditioning\"] = o[\"unclip_conditioning\"][:] + [x]\n            else:\n                o[\"unclip_conditioning\"] = [x]\n            n = [t[0], o]\n            c.append(n)\n        return (c, )\n\nclass GLIGENLoader:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"gligen_name\": (ldm_patched.utils.path_utils.get_filename_list(\"gligen\"), )}}\n\n    RETURN_TYPES = (\"GLIGEN\",)\n    FUNCTION = \"load_gligen\"\n\n    CATEGORY = \"loaders\"\n\n    def load_gligen(self, gligen_name):\n        gligen_path = ldm_patched.utils.path_utils.get_full_path(\"gligen\", gligen_name)\n        gligen = ldm_patched.modules.sd.load_gligen(gligen_path)\n        return (gligen,)\n\nclass GLIGENTextBoxApply:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": {\"conditioning_to\": (\"CONDITIONING\", ),\n                              \"clip\": (\"CLIP\", ),\n                              \"gligen_textbox_model\": (\"GLIGEN\", ),\n                              \"text\": (\"STRING\", {\"multiline\": True}),\n                              \"width\": (\"INT\", {\"default\": 64, \"min\": 8, \"max\": MAX_RESOLUTION, \"step\": 8}),\n                              \"height\": (\"INT\", {\"default\": 64, \"min\": 8, \"max\": MAX_RESOLUTION, \"step\": 8}),\n                              \"x\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 8}),\n                              \"y\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 8}),\n                             }}\n    RETURN_TYPES = (\"CONDITIONING\",)\n    FUNCTION = \"append\"\n\n    CATEGORY = \"conditioning/gligen\"\n\n    def append(self, conditioning_to, clip, gligen_textbox_model, text, width, height, x, y):\n        c = []\n        cond, cond_pooled = clip.encode_from_tokens(clip.tokenize(text), return_pooled=True)\n        for t in conditioning_to:\n            n = [t[0], t[1].copy()]\n            position_params = [(cond_pooled, height // 8, width // 8, y // 8, x // 8)]\n            prev = []\n            if \"gligen\" in n[1]:\n                prev = n[1]['gligen'][2]\n\n            n[1]['gligen'] = (\"position\", gligen_textbox_model, prev + position_params)\n            c.append(n)\n        return (c, )\n\nclass EmptyLatentImage:\n    def __init__(self):\n        self.device = ldm_patched.modules.model_management.intermediate_device()\n\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"width\": (\"INT\", {\"default\": 512, \"min\": 16, \"max\": MAX_RESOLUTION, \"step\": 8}),\n                              \"height\": (\"INT\", {\"default\": 512, \"min\": 16, \"max\": MAX_RESOLUTION, \"step\": 8}),\n                              \"batch_size\": (\"INT\", {\"default\": 1, \"min\": 1, \"max\": 4096})}}\n    RETURN_TYPES = (\"LATENT\",)\n    FUNCTION = \"generate\"\n\n    CATEGORY = \"latent\"\n\n    def generate(self, width, height, batch_size=1):\n        latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=self.device)\n        return ({\"samples\":latent}, )\n\n\nclass LatentFromBatch:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"samples\": (\"LATENT\",),\n                              \"batch_index\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": 63}),\n                              \"length\": (\"INT\", {\"default\": 1, \"min\": 1, \"max\": 64}),\n                              }}\n    RETURN_TYPES = (\"LATENT\",)\n    FUNCTION = \"frombatch\"\n\n    CATEGORY = \"latent/batch\"\n\n    def frombatch(self, samples, batch_index, length):\n        s = samples.copy()\n        s_in = samples[\"samples\"]\n        batch_index = min(s_in.shape[0] - 1, batch_index)\n        length = min(s_in.shape[0] - batch_index, length)\n        s[\"samples\"] = s_in[batch_index:batch_index + length].clone()\n        if \"noise_mask\" in samples:\n            masks = samples[\"noise_mask\"]\n            if masks.shape[0] == 1:\n                s[\"noise_mask\"] = masks.clone()\n            else:\n                if masks.shape[0] < s_in.shape[0]:\n                    masks = masks.repeat(math.ceil(s_in.shape[0] / masks.shape[0]), 1, 1, 1)[:s_in.shape[0]]\n                s[\"noise_mask\"] = masks[batch_index:batch_index + length].clone()\n        if \"batch_index\" not in s:\n            s[\"batch_index\"] = [x for x in range(batch_index, batch_index+length)]\n        else:\n            s[\"batch_index\"] = samples[\"batch_index\"][batch_index:batch_index + length]\n        return (s,)\n    \nclass RepeatLatentBatch:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"samples\": (\"LATENT\",),\n                              \"amount\": (\"INT\", {\"default\": 1, \"min\": 1, \"max\": 64}),\n                              }}\n    RETURN_TYPES = (\"LATENT\",)\n    FUNCTION = \"repeat\"\n\n    CATEGORY = \"latent/batch\"\n\n    def repeat(self, samples, amount):\n        s = samples.copy()\n        s_in = samples[\"samples\"]\n        \n        s[\"samples\"] = s_in.repeat((amount, 1,1,1))\n        if \"noise_mask\" in samples and samples[\"noise_mask\"].shape[0] > 1:\n            masks = samples[\"noise_mask\"]\n            if masks.shape[0] < s_in.shape[0]:\n                masks = masks.repeat(math.ceil(s_in.shape[0] / masks.shape[0]), 1, 1, 1)[:s_in.shape[0]]\n            s[\"noise_mask\"] = samples[\"noise_mask\"].repeat((amount, 1,1,1))\n        if \"batch_index\" in s:\n            offset = max(s[\"batch_index\"]) - min(s[\"batch_index\"]) + 1\n            s[\"batch_index\"] = s[\"batch_index\"] + [x + (i * offset) for i in range(1, amount) for x in s[\"batch_index\"]]\n        return (s,)\n\nclass LatentUpscale:\n    upscale_methods = [\"nearest-exact\", \"bilinear\", \"area\", \"bicubic\", \"bislerp\"]\n    crop_methods = [\"disabled\", \"center\"]\n\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"samples\": (\"LATENT\",), \"upscale_method\": (s.upscale_methods,),\n                              \"width\": (\"INT\", {\"default\": 512, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 8}),\n                              \"height\": (\"INT\", {\"default\": 512, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 8}),\n                              \"crop\": (s.crop_methods,)}}\n    RETURN_TYPES = (\"LATENT\",)\n    FUNCTION = \"upscale\"\n\n    CATEGORY = \"latent\"\n\n    def upscale(self, samples, upscale_method, width, height, crop):\n        if width == 0 and height == 0:\n            s = samples\n        else:\n            s = samples.copy()\n\n            if width == 0:\n                height = max(64, height)\n                width = max(64, round(samples[\"samples\"].shape[3] * height / samples[\"samples\"].shape[2]))\n            elif height == 0:\n                width = max(64, width)\n                height = max(64, round(samples[\"samples\"].shape[2] * width / samples[\"samples\"].shape[3]))\n            else:\n                width = max(64, width)\n                height = max(64, height)\n\n            s[\"samples\"] = ldm_patched.modules.utils.common_upscale(samples[\"samples\"], width // 8, height // 8, upscale_method, crop)\n        return (s,)\n\nclass LatentUpscaleBy:\n    upscale_methods = [\"nearest-exact\", \"bilinear\", \"area\", \"bicubic\", \"bislerp\"]\n\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"samples\": (\"LATENT\",), \"upscale_method\": (s.upscale_methods,),\n                              \"scale_by\": (\"FLOAT\", {\"default\": 1.5, \"min\": 0.01, \"max\": 8.0, \"step\": 0.01}),}}\n    RETURN_TYPES = (\"LATENT\",)\n    FUNCTION = \"upscale\"\n\n    CATEGORY = \"latent\"\n\n    def upscale(self, samples, upscale_method, scale_by):\n        s = samples.copy()\n        width = round(samples[\"samples\"].shape[3] * scale_by)\n        height = round(samples[\"samples\"].shape[2] * scale_by)\n        s[\"samples\"] = ldm_patched.modules.utils.common_upscale(samples[\"samples\"], width, height, upscale_method, \"disabled\")\n        return (s,)\n\nclass LatentRotate:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"samples\": (\"LATENT\",),\n                              \"rotation\": ([\"none\", \"90 degrees\", \"180 degrees\", \"270 degrees\"],),\n                              }}\n    RETURN_TYPES = (\"LATENT\",)\n    FUNCTION = \"rotate\"\n\n    CATEGORY = \"latent/transform\"\n\n    def rotate(self, samples, rotation):\n        s = samples.copy()\n        rotate_by = 0\n        if rotation.startswith(\"90\"):\n            rotate_by = 1\n        elif rotation.startswith(\"180\"):\n            rotate_by = 2\n        elif rotation.startswith(\"270\"):\n            rotate_by = 3\n\n        s[\"samples\"] = torch.rot90(samples[\"samples\"], k=rotate_by, dims=[3, 2])\n        return (s,)\n\nclass LatentFlip:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"samples\": (\"LATENT\",),\n                              \"flip_method\": ([\"x-axis: vertically\", \"y-axis: horizontally\"],),\n                              }}\n    RETURN_TYPES = (\"LATENT\",)\n    FUNCTION = \"flip\"\n\n    CATEGORY = \"latent/transform\"\n\n    def flip(self, samples, flip_method):\n        s = samples.copy()\n        if flip_method.startswith(\"x\"):\n            s[\"samples\"] = torch.flip(samples[\"samples\"], dims=[2])\n        elif flip_method.startswith(\"y\"):\n            s[\"samples\"] = torch.flip(samples[\"samples\"], dims=[3])\n\n        return (s,)\n\nclass LatentComposite:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"samples_to\": (\"LATENT\",),\n                              \"samples_from\": (\"LATENT\",),\n                              \"x\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 8}),\n                              \"y\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 8}),\n                              \"feather\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 8}),\n                              }}\n    RETURN_TYPES = (\"LATENT\",)\n    FUNCTION = \"composite\"\n\n    CATEGORY = \"latent\"\n\n    def composite(self, samples_to, samples_from, x, y, composite_method=\"normal\", feather=0):\n        x =  x // 8\n        y = y // 8\n        feather = feather // 8\n        samples_out = samples_to.copy()\n        s = samples_to[\"samples\"].clone()\n        samples_to = samples_to[\"samples\"]\n        samples_from = samples_from[\"samples\"]\n        if feather == 0:\n            s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x]\n        else:\n            samples_from = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x]\n            mask = torch.ones_like(samples_from)\n            for t in range(feather):\n                if y != 0:\n                    mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1))\n\n                if y + samples_from.shape[2] < samples_to.shape[2]:\n                    mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1))\n                if x != 0:\n                    mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1))\n                if x + samples_from.shape[3] < samples_to.shape[3]:\n                    mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1))\n            rev_mask = torch.ones_like(mask) - mask\n            s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x] * mask + s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] * rev_mask\n        samples_out[\"samples\"] = s\n        return (samples_out,)\n\nclass LatentBlend:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": {\n            \"samples1\": (\"LATENT\",),\n            \"samples2\": (\"LATENT\",),\n            \"blend_factor\": (\"FLOAT\", {\n                \"default\": 0.5,\n                \"min\": 0,\n                \"max\": 1,\n                \"step\": 0.01\n            }),\n        }}\n\n    RETURN_TYPES = (\"LATENT\",)\n    FUNCTION = \"blend\"\n\n    CATEGORY = \"_for_testing\"\n\n    def blend(self, samples1, samples2, blend_factor:float, blend_mode: str=\"normal\"):\n\n        samples_out = samples1.copy()\n        samples1 = samples1[\"samples\"]\n        samples2 = samples2[\"samples\"]\n\n        if samples1.shape != samples2.shape:\n            samples2.permute(0, 3, 1, 2)\n            samples2 = ldm_patched.modules.utils.common_upscale(samples2, samples1.shape[3], samples1.shape[2], 'bicubic', crop='center')\n            samples2.permute(0, 2, 3, 1)\n\n        samples_blended = self.blend_mode(samples1, samples2, blend_mode)\n        samples_blended = samples1 * blend_factor + samples_blended * (1 - blend_factor)\n        samples_out[\"samples\"] = samples_blended\n        return (samples_out,)\n\n    def blend_mode(self, img1, img2, mode):\n        if mode == \"normal\":\n            return img2\n        else:\n            raise ValueError(f\"Unsupported blend mode: {mode}\")\n\nclass LatentCrop:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"samples\": (\"LATENT\",),\n                              \"width\": (\"INT\", {\"default\": 512, \"min\": 64, \"max\": MAX_RESOLUTION, \"step\": 8}),\n                              \"height\": (\"INT\", {\"default\": 512, \"min\": 64, \"max\": MAX_RESOLUTION, \"step\": 8}),\n                              \"x\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 8}),\n                              \"y\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 8}),\n                              }}\n    RETURN_TYPES = (\"LATENT\",)\n    FUNCTION = \"crop\"\n\n    CATEGORY = \"latent/transform\"\n\n    def crop(self, samples, width, height, x, y):\n        s = samples.copy()\n        samples = samples['samples']\n        x =  x // 8\n        y = y // 8\n\n        #enfonce minimum size of 64\n        if x > (samples.shape[3] - 8):\n            x = samples.shape[3] - 8\n        if y > (samples.shape[2] - 8):\n            y = samples.shape[2] - 8\n\n        new_height = height // 8\n        new_width = width // 8\n        to_x = new_width + x\n        to_y = new_height + y\n        s['samples'] = samples[:,:,y:to_y, x:to_x]\n        return (s,)\n\nclass SetLatentNoiseMask:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"samples\": (\"LATENT\",),\n                              \"mask\": (\"MASK\",),\n                              }}\n    RETURN_TYPES = (\"LATENT\",)\n    FUNCTION = \"set_mask\"\n\n    CATEGORY = \"latent/inpaint\"\n\n    def set_mask(self, samples, mask):\n        s = samples.copy()\n        s[\"noise_mask\"] = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1]))\n        return (s,)\n\ndef common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False):\n    latent_image = latent[\"samples\"]\n    if disable_noise:\n        noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device=\"cpu\")\n    else:\n        batch_inds = latent[\"batch_index\"] if \"batch_index\" in latent else None\n        noise = ldm_patched.modules.sample.prepare_noise(latent_image, seed, batch_inds)\n\n    noise_mask = None\n    if \"noise_mask\" in latent:\n        noise_mask = latent[\"noise_mask\"]\n\n    callback = ldm_patched.utils.latent_visualization.prepare_callback(model, steps)\n    disable_pbar = not ldm_patched.modules.utils.PROGRESS_BAR_ENABLED\n    samples = ldm_patched.modules.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,\n                                  denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step,\n                                  force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)\n    out = latent.copy()\n    out[\"samples\"] = samples\n    return (out, )\n\nclass KSampler:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\":\n                    {\"model\": (\"MODEL\",),\n                    \"seed\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": 0xffffffffffffffff}),\n                    \"steps\": (\"INT\", {\"default\": 20, \"min\": 1, \"max\": 10000}),\n                    \"cfg\": (\"FLOAT\", {\"default\": 8.0, \"min\": 0.0, \"max\": 100.0, \"step\":0.1, \"round\": 0.01}),\n                    \"sampler_name\": (ldm_patched.modules.samplers.KSampler.SAMPLERS, ),\n                    \"scheduler\": (ldm_patched.modules.samplers.KSampler.SCHEDULERS, ),\n                    \"positive\": (\"CONDITIONING\", ),\n                    \"negative\": (\"CONDITIONING\", ),\n                    \"latent_image\": (\"LATENT\", ),\n                    \"denoise\": (\"FLOAT\", {\"default\": 1.0, \"min\": 0.0, \"max\": 1.0, \"step\": 0.01}),\n                     }\n                }\n\n    RETURN_TYPES = (\"LATENT\",)\n    FUNCTION = \"sample\"\n\n    CATEGORY = \"sampling\"\n\n    def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0):\n        return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)\n\nclass KSamplerAdvanced:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\":\n                    {\"model\": (\"MODEL\",),\n                    \"add_noise\": ([\"enable\", \"disable\"], ),\n                    \"noise_seed\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": 0xffffffffffffffff}),\n                    \"steps\": (\"INT\", {\"default\": 20, \"min\": 1, \"max\": 10000}),\n                    \"cfg\": (\"FLOAT\", {\"default\": 8.0, \"min\": 0.0, \"max\": 100.0, \"step\":0.1, \"round\": 0.01}),\n                    \"sampler_name\": (ldm_patched.modules.samplers.KSampler.SAMPLERS, ),\n                    \"scheduler\": (ldm_patched.modules.samplers.KSampler.SCHEDULERS, ),\n                    \"positive\": (\"CONDITIONING\", ),\n                    \"negative\": (\"CONDITIONING\", ),\n                    \"latent_image\": (\"LATENT\", ),\n                    \"start_at_step\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": 10000}),\n                    \"end_at_step\": (\"INT\", {\"default\": 10000, \"min\": 0, \"max\": 10000}),\n                    \"return_with_leftover_noise\": ([\"disable\", \"enable\"], ),\n                     }\n                }\n\n    RETURN_TYPES = (\"LATENT\",)\n    FUNCTION = \"sample\"\n\n    CATEGORY = \"sampling\"\n\n    def sample(self, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0):\n        force_full_denoise = True\n        if return_with_leftover_noise == \"enable\":\n            force_full_denoise = False\n        disable_noise = False\n        if add_noise == \"disable\":\n            disable_noise = True\n        return common_ksampler(model, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise, disable_noise=disable_noise, start_step=start_at_step, last_step=end_at_step, force_full_denoise=force_full_denoise)\n\nclass SaveImage:\n    def __init__(self):\n        self.output_dir = ldm_patched.utils.path_utils.get_output_directory()\n        self.type = \"output\"\n        self.prefix_append = \"\"\n        self.compress_level = 4\n\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": \n                    {\"images\": (\"IMAGE\", ),\n                     \"filename_prefix\": (\"STRING\", {\"default\": \"ldm_patched\"})},\n                \"hidden\": {\"prompt\": \"PROMPT\", \"extra_pnginfo\": \"EXTRA_PNGINFO\"},\n                }\n\n    RETURN_TYPES = ()\n    FUNCTION = \"save_images\"\n\n    OUTPUT_NODE = True\n\n    CATEGORY = \"image\"\n\n    def save_images(self, images, filename_prefix=\"ldm_patched\", prompt=None, extra_pnginfo=None):\n        filename_prefix += self.prefix_append\n        full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])\n        results = list()\n        for image in images:\n            i = 255. * image.cpu().numpy()\n            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))\n            metadata = None\n            if not args.disable_server_info:\n                metadata = PngInfo()\n                if prompt is not None:\n                    metadata.add_text(\"prompt\", json.dumps(prompt))\n                if extra_pnginfo is not None:\n                    for x in extra_pnginfo:\n                        metadata.add_text(x, json.dumps(extra_pnginfo[x]))\n\n            file = f\"{filename}_{counter:05}_.png\"\n            img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level)\n            results.append({\n                \"filename\": file,\n                \"subfolder\": subfolder,\n                \"type\": self.type\n            })\n            counter += 1\n\n        return { \"ui\": { \"images\": results } }\n\nclass PreviewImage(SaveImage):\n    def __init__(self):\n        self.output_dir = ldm_patched.utils.path_utils.get_temp_directory()\n        self.type = \"temp\"\n        self.prefix_append = \"_temp_\" + ''.join(random.choice(\"abcdefghijklmnopqrstupvxyz\") for x in range(5))\n        self.compress_level = 1\n\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\":\n                    {\"images\": (\"IMAGE\", ), },\n                \"hidden\": {\"prompt\": \"PROMPT\", \"extra_pnginfo\": \"EXTRA_PNGINFO\"},\n                }\n\nclass LoadImage:\n    @classmethod\n    def INPUT_TYPES(s):\n        input_dir = ldm_patched.utils.path_utils.get_input_directory()\n        files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]\n        return {\"required\":\n                    {\"image\": (sorted(files), {\"image_upload\": True})},\n                }\n\n    CATEGORY = \"image\"\n\n    RETURN_TYPES = (\"IMAGE\", \"MASK\")\n    FUNCTION = \"load_image\"\n    def load_image(self, image):\n        image_path = ldm_patched.utils.path_utils.get_annotated_filepath(image)\n        img = Image.open(image_path)\n        output_images = []\n        output_masks = []\n        for i in ImageSequence.Iterator(img):\n            i = ImageOps.exif_transpose(i)\n            if i.mode == 'I':\n                i = i.point(lambda i: i * (1 / 255))\n            image = i.convert(\"RGB\")\n            image = np.array(image).astype(np.float32) / 255.0\n            image = torch.from_numpy(image)[None,]\n            if 'A' in i.getbands():\n                mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0\n                mask = 1. - torch.from_numpy(mask)\n            else:\n                mask = torch.zeros((64,64), dtype=torch.float32, device=\"cpu\")\n            output_images.append(image)\n            output_masks.append(mask.unsqueeze(0))\n\n        if len(output_images) > 1:\n            output_image = torch.cat(output_images, dim=0)\n            output_mask = torch.cat(output_masks, dim=0)\n        else:\n            output_image = output_images[0]\n            output_mask = output_masks[0]\n\n        return (output_image, output_mask)\n\n    @classmethod\n    def IS_CHANGED(s, image):\n        image_path = ldm_patched.utils.path_utils.get_annotated_filepath(image)\n        m = hashlib.sha256()\n        with open(image_path, 'rb') as f:\n            m.update(f.read())\n        return m.digest().hex()\n\n    @classmethod\n    def VALIDATE_INPUTS(s, image):\n        if not ldm_patched.utils.path_utils.exists_annotated_filepath(image):\n            return \"Invalid image file: {}\".format(image)\n\n        return True\n\nclass LoadImageMask:\n    _color_channels = [\"alpha\", \"red\", \"green\", \"blue\"]\n    @classmethod\n    def INPUT_TYPES(s):\n        input_dir = ldm_patched.utils.path_utils.get_input_directory()\n        files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]\n        return {\"required\":\n                    {\"image\": (sorted(files), {\"image_upload\": True}),\n                     \"channel\": (s._color_channels, ), }\n                }\n\n    CATEGORY = \"mask\"\n\n    RETURN_TYPES = (\"MASK\",)\n    FUNCTION = \"load_image\"\n    def load_image(self, image, channel):\n        image_path = ldm_patched.utils.path_utils.get_annotated_filepath(image)\n        i = Image.open(image_path)\n        i = ImageOps.exif_transpose(i)\n        if i.getbands() != (\"R\", \"G\", \"B\", \"A\"):\n            if i.mode == 'I':\n                i = i.point(lambda i: i * (1 / 255))\n            i = i.convert(\"RGBA\")\n        mask = None\n        c = channel[0].upper()\n        if c in i.getbands():\n            mask = np.array(i.getchannel(c)).astype(np.float32) / 255.0\n            mask = torch.from_numpy(mask)\n            if c == 'A':\n                mask = 1. - mask\n        else:\n            mask = torch.zeros((64,64), dtype=torch.float32, device=\"cpu\")\n        return (mask.unsqueeze(0),)\n\n    @classmethod\n    def IS_CHANGED(s, image, channel):\n        image_path = ldm_patched.utils.path_utils.get_annotated_filepath(image)\n        m = hashlib.sha256()\n        with open(image_path, 'rb') as f:\n            m.update(f.read())\n        return m.digest().hex()\n\n    @classmethod\n    def VALIDATE_INPUTS(s, image):\n        if not ldm_patched.utils.path_utils.exists_annotated_filepath(image):\n            return \"Invalid image file: {}\".format(image)\n\n        return True\n\nclass ImageScale:\n    upscale_methods = [\"nearest-exact\", \"bilinear\", \"area\", \"bicubic\", \"lanczos\"]\n    crop_methods = [\"disabled\", \"center\"]\n\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"image\": (\"IMAGE\",), \"upscale_method\": (s.upscale_methods,),\n                              \"width\": (\"INT\", {\"default\": 512, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 1}),\n                              \"height\": (\"INT\", {\"default\": 512, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 1}),\n                              \"crop\": (s.crop_methods,)}}\n    RETURN_TYPES = (\"IMAGE\",)\n    FUNCTION = \"upscale\"\n\n    CATEGORY = \"image/upscaling\"\n\n    def upscale(self, image, upscale_method, width, height, crop):\n        if width == 0 and height == 0:\n            s = image\n        else:\n            samples = image.movedim(-1,1)\n\n            if width == 0:\n                width = max(1, round(samples.shape[3] * height / samples.shape[2]))\n            elif height == 0:\n                height = max(1, round(samples.shape[2] * width / samples.shape[3]))\n\n            s = ldm_patched.modules.utils.common_upscale(samples, width, height, upscale_method, crop)\n            s = s.movedim(1,-1)\n        return (s,)\n\nclass ImageScaleBy:\n    upscale_methods = [\"nearest-exact\", \"bilinear\", \"area\", \"bicubic\", \"lanczos\"]\n\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"image\": (\"IMAGE\",), \"upscale_method\": (s.upscale_methods,),\n                              \"scale_by\": (\"FLOAT\", {\"default\": 1.0, \"min\": 0.01, \"max\": 8.0, \"step\": 0.01}),}}\n    RETURN_TYPES = (\"IMAGE\",)\n    FUNCTION = \"upscale\"\n\n    CATEGORY = \"image/upscaling\"\n\n    def upscale(self, image, upscale_method, scale_by):\n        samples = image.movedim(-1,1)\n        width = round(samples.shape[3] * scale_by)\n        height = round(samples.shape[2] * scale_by)\n        s = ldm_patched.modules.utils.common_upscale(samples, width, height, upscale_method, \"disabled\")\n        s = s.movedim(1,-1)\n        return (s,)\n\nclass ImageInvert:\n\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"image\": (\"IMAGE\",)}}\n\n    RETURN_TYPES = (\"IMAGE\",)\n    FUNCTION = \"invert\"\n\n    CATEGORY = \"image\"\n\n    def invert(self, image):\n        s = 1.0 - image\n        return (s,)\n\nclass ImageBatch:\n\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"image1\": (\"IMAGE\",), \"image2\": (\"IMAGE\",)}}\n\n    RETURN_TYPES = (\"IMAGE\",)\n    FUNCTION = \"batch\"\n\n    CATEGORY = \"image\"\n\n    def batch(self, image1, image2):\n        if image1.shape[1:] != image2.shape[1:]:\n            image2 = ldm_patched.modules.utils.common_upscale(image2.movedim(-1,1), image1.shape[2], image1.shape[1], \"bilinear\", \"center\").movedim(1,-1)\n        s = torch.cat((image1, image2), dim=0)\n        return (s,)\n\nclass EmptyImage:\n    def __init__(self, device=\"cpu\"):\n        self.device = device\n\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"width\": (\"INT\", {\"default\": 512, \"min\": 1, \"max\": MAX_RESOLUTION, \"step\": 1}),\n                              \"height\": (\"INT\", {\"default\": 512, \"min\": 1, \"max\": MAX_RESOLUTION, \"step\": 1}),\n                              \"batch_size\": (\"INT\", {\"default\": 1, \"min\": 1, \"max\": 4096}),\n                              \"color\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": 0xFFFFFF, \"step\": 1, \"display\": \"color\"}),\n                              }}\n    RETURN_TYPES = (\"IMAGE\",)\n    FUNCTION = \"generate\"\n\n    CATEGORY = \"image\"\n\n    def generate(self, width, height, batch_size=1, color=0):\n        r = torch.full([batch_size, height, width, 1], ((color >> 16) & 0xFF) / 0xFF)\n        g = torch.full([batch_size, height, width, 1], ((color >> 8) & 0xFF) / 0xFF)\n        b = torch.full([batch_size, height, width, 1], ((color) & 0xFF) / 0xFF)\n        return (torch.cat((r, g, b), dim=-1), )\n\nclass ImagePadForOutpaint:\n\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\n            \"required\": {\n                \"image\": (\"IMAGE\",),\n                \"left\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 8}),\n                \"top\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 8}),\n                \"right\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 8}),\n                \"bottom\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 8}),\n                \"feathering\": (\"INT\", {\"default\": 40, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 1}),\n            }\n        }\n\n    RETURN_TYPES = (\"IMAGE\", \"MASK\")\n    FUNCTION = \"expand_image\"\n\n    CATEGORY = \"image\"\n\n    def expand_image(self, image, left, top, right, bottom, feathering):\n        d1, d2, d3, d4 = image.size()\n\n        new_image = torch.ones(\n            (d1, d2 + top + bottom, d3 + left + right, d4),\n            dtype=torch.float32,\n        ) * 0.5\n\n        new_image[:, top:top + d2, left:left + d3, :] = image\n\n        mask = torch.ones(\n            (d2 + top + bottom, d3 + left + right),\n            dtype=torch.float32,\n        )\n\n        t = torch.zeros(\n            (d2, d3),\n            dtype=torch.float32\n        )\n\n        if feathering > 0 and feathering * 2 < d2 and feathering * 2 < d3:\n\n            for i in range(d2):\n                for j in range(d3):\n                    dt = i if top != 0 else d2\n                    db = d2 - i if bottom != 0 else d2\n\n                    dl = j if left != 0 else d3\n                    dr = d3 - j if right != 0 else d3\n\n                    d = min(dt, db, dl, dr)\n\n                    if d >= feathering:\n                        continue\n\n                    v = (feathering - d) / feathering\n\n                    t[i, j] = v * v\n\n        mask[top:top + d2, left:left + d3] = t\n\n        return (new_image, mask)\n\n\nNODE_CLASS_MAPPINGS = {\n    \"KSampler\": KSampler,\n    \"CheckpointLoaderSimple\": CheckpointLoaderSimple,\n    \"CLIPTextEncode\": CLIPTextEncode,\n    \"CLIPSetLastLayer\": CLIPSetLastLayer,\n    \"VAEDecode\": VAEDecode,\n    \"VAEEncode\": VAEEncode,\n    \"VAEEncodeForInpaint\": VAEEncodeForInpaint,\n    \"VAELoader\": VAELoader,\n    \"EmptyLatentImage\": EmptyLatentImage,\n    \"LatentUpscale\": LatentUpscale,\n    \"LatentUpscaleBy\": LatentUpscaleBy,\n    \"LatentFromBatch\": LatentFromBatch,\n    \"RepeatLatentBatch\": RepeatLatentBatch,\n    \"SaveImage\": SaveImage,\n    \"PreviewImage\": PreviewImage,\n    \"LoadImage\": LoadImage,\n    \"LoadImageMask\": LoadImageMask,\n    \"ImageScale\": ImageScale,\n    \"ImageScaleBy\": ImageScaleBy,\n    \"ImageInvert\": ImageInvert,\n    \"ImageBatch\": ImageBatch,\n    \"ImagePadForOutpaint\": ImagePadForOutpaint,\n    \"EmptyImage\": EmptyImage,\n    \"ConditioningAverage\": ConditioningAverage ,\n    \"ConditioningCombine\": ConditioningCombine,\n    \"ConditioningConcat\": ConditioningConcat,\n    \"ConditioningSetArea\": ConditioningSetArea,\n    \"ConditioningSetAreaPercentage\": ConditioningSetAreaPercentage,\n    \"ConditioningSetMask\": ConditioningSetMask,\n    \"KSamplerAdvanced\": KSamplerAdvanced,\n    \"SetLatentNoiseMask\": SetLatentNoiseMask,\n    \"LatentComposite\": LatentComposite,\n    \"LatentBlend\": LatentBlend,\n    \"LatentRotate\": LatentRotate,\n    \"LatentFlip\": LatentFlip,\n    \"LatentCrop\": LatentCrop,\n    \"LoraLoader\": LoraLoader,\n    \"CLIPLoader\": CLIPLoader,\n    \"UNETLoader\": UNETLoader,\n    \"DualCLIPLoader\": DualCLIPLoader,\n    \"CLIPVisionEncode\": CLIPVisionEncode,\n    \"StyleModelApply\": StyleModelApply,\n    \"unCLIPConditioning\": unCLIPConditioning,\n    \"ControlNetApply\": ControlNetApply,\n    \"ControlNetApplyAdvanced\": ControlNetApplyAdvanced,\n    \"ControlNetLoader\": ControlNetLoader,\n    \"DiffControlNetLoader\": DiffControlNetLoader,\n    \"StyleModelLoader\": StyleModelLoader,\n    \"CLIPVisionLoader\": CLIPVisionLoader,\n    \"VAEDecodeTiled\": VAEDecodeTiled,\n    \"VAEEncodeTiled\": VAEEncodeTiled,\n    \"unCLIPCheckpointLoader\": unCLIPCheckpointLoader,\n    \"GLIGENLoader\": GLIGENLoader,\n    \"GLIGENTextBoxApply\": GLIGENTextBoxApply,\n    \"InpaintModelConditioning\": InpaintModelConditioning,\n\n    \"CheckpointLoader\": CheckpointLoader,\n    \"DiffusersLoader\": DiffusersLoader,\n\n    \"LoadLatent\": LoadLatent,\n    \"SaveLatent\": SaveLatent,\n\n    \"ConditioningZeroOut\": ConditioningZeroOut,\n    \"ConditioningSetTimestepRange\": ConditioningSetTimestepRange,\n    \"LoraLoaderModelOnly\": LoraLoaderModelOnly,\n}\n\nNODE_DISPLAY_NAME_MAPPINGS = {\n    # Sampling\n    \"KSampler\": \"KSampler\",\n    \"KSamplerAdvanced\": \"KSampler (Advanced)\",\n    # Loaders\n    \"CheckpointLoader\": \"Load Checkpoint With Config (DEPRECATED)\",\n    \"CheckpointLoaderSimple\": \"Load Checkpoint\",\n    \"VAELoader\": \"Load VAE\",\n    \"LoraLoader\": \"Load LoRA\",\n    \"CLIPLoader\": \"Load CLIP\",\n    \"ControlNetLoader\": \"Load ControlNet Model\",\n    \"DiffControlNetLoader\": \"Load ControlNet Model (diff)\",\n    \"StyleModelLoader\": \"Load Style Model\",\n    \"CLIPVisionLoader\": \"Load CLIP Vision\",\n    \"UpscaleModelLoader\": \"Load Upscale Model\",\n    # Conditioning\n    \"CLIPVisionEncode\": \"CLIP Vision Encode\",\n    \"StyleModelApply\": \"Apply Style Model\",\n    \"CLIPTextEncode\": \"CLIP Text Encode (Prompt)\",\n    \"CLIPSetLastLayer\": \"CLIP Set Last Layer\",\n    \"ConditioningCombine\": \"Conditioning (Combine)\",\n    \"ConditioningAverage \": \"Conditioning (Average)\",\n    \"ConditioningConcat\": \"Conditioning (Concat)\",\n    \"ConditioningSetArea\": \"Conditioning (Set Area)\",\n    \"ConditioningSetAreaPercentage\": \"Conditioning (Set Area with Percentage)\",\n    \"ConditioningSetMask\": \"Conditioning (Set Mask)\",\n    \"ControlNetApply\": \"Apply ControlNet\",\n    \"ControlNetApplyAdvanced\": \"Apply ControlNet (Advanced)\",\n    # Latent\n    \"VAEEncodeForInpaint\": \"VAE Encode (for Inpainting)\",\n    \"SetLatentNoiseMask\": \"Set Latent Noise Mask\",\n    \"VAEDecode\": \"VAE Decode\",\n    \"VAEEncode\": \"VAE Encode\",\n    \"LatentRotate\": \"Rotate Latent\",\n    \"LatentFlip\": \"Flip Latent\",\n    \"LatentCrop\": \"Crop Latent\",\n    \"EmptyLatentImage\": \"Empty Latent Image\",\n    \"LatentUpscale\": \"Upscale Latent\",\n    \"LatentUpscaleBy\": \"Upscale Latent By\",\n    \"LatentComposite\": \"Latent Composite\",\n    \"LatentBlend\": \"Latent Blend\",\n    \"LatentFromBatch\" : \"Latent From Batch\",\n    \"RepeatLatentBatch\": \"Repeat Latent Batch\",\n    # Image\n    \"SaveImage\": \"Save Image\",\n    \"PreviewImage\": \"Preview Image\",\n    \"LoadImage\": \"Load Image\",\n    \"LoadImageMask\": \"Load Image (as Mask)\",\n    \"ImageScale\": \"Upscale Image\",\n    \"ImageScaleBy\": \"Upscale Image By\",\n    \"ImageUpscaleWithModel\": \"Upscale Image (using Model)\",\n    \"ImageInvert\": \"Invert Image\",\n    \"ImagePadForOutpaint\": \"Pad Image for Outpainting\",\n    \"ImageBatch\": \"Batch Images\",\n    # _for_testing\n    \"VAEDecodeTiled\": \"VAE Decode (Tiled)\",\n    \"VAEEncodeTiled\": \"VAE Encode (Tiled)\",\n}\n\nEXTENSION_WEB_DIRS = {}\n\ndef load_custom_node(module_path, ignore=set()):\n    module_name = os.path.basename(module_path)\n    if os.path.isfile(module_path):\n        sp = os.path.splitext(module_path)\n        module_name = sp[0]\n    try:\n        if os.path.isfile(module_path):\n            module_spec = importlib.util.spec_from_file_location(module_name, module_path)\n            module_dir = os.path.split(module_path)[0]\n        else:\n            module_spec = importlib.util.spec_from_file_location(module_name, os.path.join(module_path, \"__init__.py\"))\n            module_dir = module_path\n\n        module = importlib.util.module_from_spec(module_spec)\n        sys.modules[module_name] = module\n        module_spec.loader.exec_module(module)\n\n        if hasattr(module, \"WEB_DIRECTORY\") and getattr(module, \"WEB_DIRECTORY\") is not None:\n            web_dir = os.path.abspath(os.path.join(module_dir, getattr(module, \"WEB_DIRECTORY\")))\n            if os.path.isdir(web_dir):\n                EXTENSION_WEB_DIRS[module_name] = web_dir\n\n        if hasattr(module, \"NODE_CLASS_MAPPINGS\") and getattr(module, \"NODE_CLASS_MAPPINGS\") is not None:\n            for name in module.NODE_CLASS_MAPPINGS:\n                if name not in ignore:\n                    NODE_CLASS_MAPPINGS[name] = module.NODE_CLASS_MAPPINGS[name]\n            if hasattr(module, \"NODE_DISPLAY_NAME_MAPPINGS\") and getattr(module, \"NODE_DISPLAY_NAME_MAPPINGS\") is not None:\n                NODE_DISPLAY_NAME_MAPPINGS.update(module.NODE_DISPLAY_NAME_MAPPINGS)\n            return True\n        else:\n            print(f\"Skip {module_path} module for custom nodes due to the lack of NODE_CLASS_MAPPINGS.\")\n            return False\n    except Exception as e:\n        print(traceback.format_exc())\n        print(f\"Cannot import {module_path} module for custom nodes:\", e)\n        return False\n\ndef load_custom_nodes():\n    base_node_names = set(NODE_CLASS_MAPPINGS.keys())\n    node_paths = ldm_patched.utils.path_utils.get_folder_paths(\"custom_nodes\")\n    node_import_times = []\n    for custom_node_path in node_paths:\n        possible_modules = os.listdir(os.path.realpath(custom_node_path))\n        if \"__pycache__\" in possible_modules:\n            possible_modules.remove(\"__pycache__\")\n\n        for possible_module in possible_modules:\n            module_path = os.path.join(custom_node_path, possible_module)\n            if os.path.isfile(module_path) and os.path.splitext(module_path)[1] != \".py\": continue\n            if module_path.endswith(\".disabled\"): continue\n            time_before = time.perf_counter()\n            success = load_custom_node(module_path, base_node_names)\n            node_import_times.append((time.perf_counter() - time_before, module_path, success))\n\n    if len(node_import_times) > 0:\n        print(\"\\nImport times for custom nodes:\")\n        for n in sorted(node_import_times):\n            if n[2]:\n                import_message = \"\"\n            else:\n                import_message = \" (IMPORT FAILED)\"\n            print(\"{:6.1f} seconds{}:\".format(n[0], import_message), n[1])\n        print()\n\ndef init_custom_nodes():\n    extras_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"ldm_patched_extras\")\n    extras_files = [\n        \"nodes_latent.py\",\n        \"nodes_hypernetwork.py\",\n        \"nodes_upscale_model.py\",\n        \"nodes_post_processing.py\",\n        \"nodes_mask.py\",\n        \"nodes_compositing.py\",\n        \"nodes_rebatch.py\",\n        \"nodes_model_merging.py\",\n        \"nodes_tomesd.py\",\n        \"nodes_clip_sdxl.py\",\n        \"nodes_canny.py\",\n        \"nodes_freelunch.py\",\n        \"nodes_custom_sampler.py\",\n        \"nodes_hypertile.py\",\n        \"nodes_model_advanced.py\",\n        \"nodes_model_downscale.py\",\n        \"nodes_images.py\",\n        \"nodes_video_model.py\",\n        \"nodes_sag.py\",\n        \"nodes_perpneg.py\",\n        \"nodes_stable3d.py\",\n        \"nodes_sdupscale.py\",\n        \"nodes_photomaker.py\",\n    ]\n\n    for node_file in extras_files:\n        load_custom_node(os.path.join(extras_dir, node_file))\n\n    load_custom_nodes()\n"
  },
  {
    "path": "ldm_patched/contrib/external_align_your_steps.py",
    "content": "# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py\n\n#from: https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/howto.html\nimport numpy as np\nimport torch\n\ndef loglinear_interp(t_steps, num_steps):\n    \"\"\"\n    Performs log-linear interpolation of a given array of decreasing numbers.\n    \"\"\"\n    xs = np.linspace(0, 1, len(t_steps))\n    ys = np.log(t_steps[::-1])\n\n    new_xs = np.linspace(0, 1, num_steps)\n    new_ys = np.interp(new_xs, xs, ys)\n\n    interped_ys = np.exp(new_ys)[::-1].copy()\n    return interped_ys\n\nNOISE_LEVELS = {\"SD1\": [14.6146412293, 6.4745760956,  3.8636745985,  2.6946151520, 1.8841921177,  1.3943805092,  0.9642583904,  0.6523686016, 0.3977456272,  0.1515232662,  0.0291671582],\n                \"SDXL\":[14.6146412293, 6.3184485287,  3.7681790315,  2.1811480769, 1.3405244945,  0.8620721141,  0.5550693289,  0.3798540708, 0.2332364134,  0.1114188177,  0.0291671582],\n                \"SVD\": [700.00, 54.5, 15.886, 7.977, 4.248, 1.789, 0.981, 0.403, 0.173, 0.034, 0.002]}\n\nclass AlignYourStepsScheduler:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\":\n                    {\"model_type\": ([\"SD1\", \"SDXL\", \"SVD\"], ),\n                     \"steps\": (\"INT\", {\"default\": 10, \"min\": 10, \"max\": 10000}),\n                     \"denoise\": (\"FLOAT\", {\"default\": 1.0, \"min\": 0.0, \"max\": 1.0, \"step\": 0.01}),\n                      }\n               }\n    RETURN_TYPES = (\"SIGMAS\",)\n    CATEGORY = \"sampling/custom_sampling/schedulers\"\n\n    FUNCTION = \"get_sigmas\"\n\n    def get_sigmas(self, model_type, steps, denoise):\n        total_steps = steps\n        if denoise < 1.0:\n            if denoise <= 0.0:\n                return (torch.FloatTensor([]),)\n            total_steps = round(steps * denoise)\n\n        sigmas = NOISE_LEVELS[model_type][:]\n        if (steps + 1) != len(sigmas):\n            sigmas = loglinear_interp(sigmas, steps + 1)\n\n        sigmas = sigmas[-(total_steps + 1):]\n        sigmas[-1] = 0\n        return (torch.FloatTensor(sigmas), )\n\nNODE_CLASS_MAPPINGS = {\n    \"AlignYourStepsScheduler\": AlignYourStepsScheduler,\n}"
  },
  {
    "path": "ldm_patched/contrib/external_canny.py",
    "content": "# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py \n\n#From https://github.com/kornia/kornia\nimport math\n\nimport torch\nimport torch.nn.functional as F\nimport ldm_patched.modules.model_management\n\ndef get_canny_nms_kernel(device=None, dtype=None):\n    \"\"\"Utility function that returns 3x3 kernels for the Canny Non-maximal suppression.\"\"\"\n    return torch.tensor(\n        [\n            [[[0.0, 0.0, 0.0], [0.0, 1.0, -1.0], [0.0, 0.0, 0.0]]],\n            [[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, -1.0]]],\n            [[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, -1.0, 0.0]]],\n            [[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [-1.0, 0.0, 0.0]]],\n            [[[0.0, 0.0, 0.0], [-1.0, 1.0, 0.0], [0.0, 0.0, 0.0]]],\n            [[[-1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]]],\n            [[[0.0, -1.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]]],\n            [[[0.0, 0.0, -1.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]]],\n        ],\n        device=device,\n        dtype=dtype,\n    )\n\n\ndef get_hysteresis_kernel(device=None, dtype=None):\n    \"\"\"Utility function that returns the 3x3 kernels for the Canny hysteresis.\"\"\"\n    return torch.tensor(\n        [\n            [[[0.0, 0.0, 0.0], [0.0, 0.0, 1.0], [0.0, 0.0, 0.0]]],\n            [[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 1.0]]],\n            [[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0]]],\n            [[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [1.0, 0.0, 0.0]]],\n            [[[0.0, 0.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 0.0]]],\n            [[[1.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]],\n            [[[0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]],\n            [[[0.0, 0.0, 1.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]],\n        ],\n        device=device,\n        dtype=dtype,\n    )\n\ndef gaussian_blur_2d(img, kernel_size, sigma):\n    ksize_half = (kernel_size - 1) * 0.5\n\n    x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size)\n\n    pdf = torch.exp(-0.5 * (x / sigma).pow(2))\n\n    x_kernel = pdf / pdf.sum()\n    x_kernel = x_kernel.to(device=img.device, dtype=img.dtype)\n\n    kernel2d = torch.mm(x_kernel[:, None], x_kernel[None, :])\n    kernel2d = kernel2d.expand(img.shape[-3], 1, kernel2d.shape[0], kernel2d.shape[1])\n\n    padding = [kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2]\n\n    img = torch.nn.functional.pad(img, padding, mode=\"reflect\")\n    img = torch.nn.functional.conv2d(img, kernel2d, groups=img.shape[-3])\n\n    return img\n\ndef get_sobel_kernel2d(device=None, dtype=None):\n    kernel_x = torch.tensor([[-1.0, 0.0, 1.0], [-2.0, 0.0, 2.0], [-1.0, 0.0, 1.0]], device=device, dtype=dtype)\n    kernel_y = kernel_x.transpose(0, 1)\n    return torch.stack([kernel_x, kernel_y])\n\ndef spatial_gradient(input, normalized: bool = True):\n    r\"\"\"Compute the first order image derivative in both x and y using a Sobel operator.\n    .. image:: _static/img/spatial_gradient.png\n    Args:\n        input: input image tensor with shape :math:`(B, C, H, W)`.\n        mode: derivatives modality, can be: `sobel` or `diff`.\n        order: the order of the derivatives.\n        normalized: whether the output is normalized.\n    Return:\n        the derivatives of the input feature map. with shape :math:`(B, C, 2, H, W)`.\n    .. note::\n       See a working example `here <https://kornia.readthedocs.io/en/latest/\n       filtering_edges.html>`__.\n    Examples:\n        >>> input = torch.rand(1, 3, 4, 4)\n        >>> output = spatial_gradient(input)  # 1x3x2x4x4\n        >>> output.shape\n        torch.Size([1, 3, 2, 4, 4])\n    \"\"\"\n    # KORNIA_CHECK_IS_TENSOR(input)\n    # KORNIA_CHECK_SHAPE(input, ['B', 'C', 'H', 'W'])\n\n    # allocate kernel\n    kernel = get_sobel_kernel2d(device=input.device, dtype=input.dtype)\n    if normalized:\n        kernel = normalize_kernel2d(kernel)\n\n    # prepare kernel\n    b, c, h, w = input.shape\n    tmp_kernel = kernel[:, None, ...]\n\n    # Pad with \"replicate for spatial dims, but with zeros for channel\n    spatial_pad = [kernel.size(1) // 2, kernel.size(1) // 2, kernel.size(2) // 2, kernel.size(2) // 2]\n    out_channels: int = 2\n    padded_inp = torch.nn.functional.pad(input.reshape(b * c, 1, h, w), spatial_pad, 'replicate')\n    out = F.conv2d(padded_inp, tmp_kernel, groups=1, padding=0, stride=1)\n    return out.reshape(b, c, out_channels, h, w)\n\ndef rgb_to_grayscale(image, rgb_weights = None):\n    r\"\"\"Convert a RGB image to grayscale version of image.\n\n    .. image:: _static/img/rgb_to_grayscale.png\n\n    The image data is assumed to be in the range of (0, 1).\n\n    Args:\n        image: RGB image to be converted to grayscale with shape :math:`(*,3,H,W)`.\n        rgb_weights: Weights that will be applied on each channel (RGB).\n            The sum of the weights should add up to one.\n    Returns:\n        grayscale version of the image with shape :math:`(*,1,H,W)`.\n\n    .. note::\n       See a working example `here <https://kornia.readthedocs.io/en/latest/\n       color_conversions.html>`__.\n\n    Example:\n        >>> input = torch.rand(2, 3, 4, 5)\n        >>> gray = rgb_to_grayscale(input) # 2x1x4x5\n    \"\"\"\n\n    if len(image.shape) < 3 or image.shape[-3] != 3:\n        raise ValueError(f\"Input size must have a shape of (*, 3, H, W). Got {image.shape}\")\n\n    if rgb_weights is None:\n        # 8 bit images\n        if image.dtype == torch.uint8:\n            rgb_weights = torch.tensor([76, 150, 29], device=image.device, dtype=torch.uint8)\n        # floating point images\n        elif image.dtype in (torch.float16, torch.float32, torch.float64):\n            rgb_weights = torch.tensor([0.299, 0.587, 0.114], device=image.device, dtype=image.dtype)\n        else:\n            raise TypeError(f\"Unknown data type: {image.dtype}\")\n    else:\n        # is tensor that we make sure is in the same device/dtype\n        rgb_weights = rgb_weights.to(image)\n\n    # unpack the color image channels with RGB order\n    r: Tensor = image[..., 0:1, :, :]\n    g: Tensor = image[..., 1:2, :, :]\n    b: Tensor = image[..., 2:3, :, :]\n\n    w_r, w_g, w_b = rgb_weights.unbind()\n    return w_r * r + w_g * g + w_b * b\n\ndef canny(\n    input,\n    low_threshold = 0.1,\n    high_threshold = 0.2,\n    kernel_size  = 5,\n    sigma = 1,\n    hysteresis = True,\n    eps = 1e-6,\n):\n    r\"\"\"Find edges of the input image and filters them using the Canny algorithm.\n    .. image:: _static/img/canny.png\n    Args:\n        input: input image tensor with shape :math:`(B,C,H,W)`.\n        low_threshold: lower threshold for the hysteresis procedure.\n        high_threshold: upper threshold for the hysteresis procedure.\n        kernel_size: the size of the kernel for the gaussian blur.\n        sigma: the standard deviation of the kernel for the gaussian blur.\n        hysteresis: if True, applies the hysteresis edge tracking.\n            Otherwise, the edges are divided between weak (0.5) and strong (1) edges.\n        eps: regularization number to avoid NaN during backprop.\n    Returns:\n        - the canny edge magnitudes map, shape of :math:`(B,1,H,W)`.\n        - the canny edge detection filtered by thresholds and hysteresis, shape of :math:`(B,1,H,W)`.\n    .. note::\n       See a working example `here <https://kornia.readthedocs.io/en/latest/\n       canny.html>`__.\n    Example:\n        >>> input = torch.rand(5, 3, 4, 4)\n        >>> magnitude, edges = canny(input)  # 5x3x4x4\n        >>> magnitude.shape\n        torch.Size([5, 1, 4, 4])\n        >>> edges.shape\n        torch.Size([5, 1, 4, 4])\n    \"\"\"\n    # KORNIA_CHECK_IS_TENSOR(input)\n    # KORNIA_CHECK_SHAPE(input, ['B', 'C', 'H', 'W'])\n    # KORNIA_CHECK(\n    #     low_threshold <= high_threshold,\n    #     \"Invalid input thresholds. low_threshold should be smaller than the high_threshold. Got: \"\n    #     f\"{low_threshold}>{high_threshold}\",\n    # )\n    # KORNIA_CHECK(0 < low_threshold < 1, f'Invalid low threshold. Should be in range (0, 1). Got: {low_threshold}')\n    # KORNIA_CHECK(0 < high_threshold < 1, f'Invalid high threshold. Should be in range (0, 1). Got: {high_threshold}')\n\n    device = input.device\n    dtype = input.dtype\n\n    # To Grayscale\n    if input.shape[1] == 3:\n        input = rgb_to_grayscale(input)\n\n    # Gaussian filter\n    blurred: Tensor = gaussian_blur_2d(input, kernel_size, sigma)\n\n    # Compute the gradients\n    gradients: Tensor = spatial_gradient(blurred, normalized=False)\n\n    # Unpack the edges\n    gx: Tensor = gradients[:, :, 0]\n    gy: Tensor = gradients[:, :, 1]\n\n    # Compute gradient magnitude and angle\n    magnitude: Tensor = torch.sqrt(gx * gx + gy * gy + eps)\n    angle: Tensor = torch.atan2(gy, gx)\n\n    # Radians to Degrees\n    angle = 180.0 * angle / math.pi\n\n    # Round angle to the nearest 45 degree\n    angle = torch.round(angle / 45) * 45\n\n    # Non-maximal suppression\n    nms_kernels: Tensor = get_canny_nms_kernel(device, dtype)\n    nms_magnitude: Tensor = F.conv2d(magnitude, nms_kernels, padding=nms_kernels.shape[-1] // 2)\n\n    # Get the indices for both directions\n    positive_idx: Tensor = (angle / 45) % 8\n    positive_idx = positive_idx.long()\n\n    negative_idx: Tensor = ((angle / 45) + 4) % 8\n    negative_idx = negative_idx.long()\n\n    # Apply the non-maximum suppression to the different directions\n    channel_select_filtered_positive: Tensor = torch.gather(nms_magnitude, 1, positive_idx)\n    channel_select_filtered_negative: Tensor = torch.gather(nms_magnitude, 1, negative_idx)\n\n    channel_select_filtered: Tensor = torch.stack(\n        [channel_select_filtered_positive, channel_select_filtered_negative], 1\n    )\n\n    is_max: Tensor = channel_select_filtered.min(dim=1)[0] > 0.0\n\n    magnitude = magnitude * is_max\n\n    # Threshold\n    edges: Tensor = F.threshold(magnitude, low_threshold, 0.0)\n\n    low: Tensor = magnitude > low_threshold\n    high: Tensor = magnitude > high_threshold\n\n    edges = low * 0.5 + high * 0.5\n    edges = edges.to(dtype)\n\n    # Hysteresis\n    if hysteresis:\n        edges_old: Tensor = -torch.ones(edges.shape, device=edges.device, dtype=dtype)\n        hysteresis_kernels: Tensor = get_hysteresis_kernel(device, dtype)\n\n        while ((edges_old - edges).abs() != 0).any():\n            weak: Tensor = (edges == 0.5).float()\n            strong: Tensor = (edges == 1).float()\n\n            hysteresis_magnitude: Tensor = F.conv2d(\n                edges, hysteresis_kernels, padding=hysteresis_kernels.shape[-1] // 2\n            )\n            hysteresis_magnitude = (hysteresis_magnitude == 1).any(1, keepdim=True).to(dtype)\n            hysteresis_magnitude = hysteresis_magnitude * weak + strong\n\n            edges_old = edges.clone()\n            edges = hysteresis_magnitude + (hysteresis_magnitude == 0) * weak * 0.5\n\n        edges = hysteresis_magnitude\n\n    return magnitude, edges\n\n\nclass Canny:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": {\"image\": (\"IMAGE\",),\n                                \"low_threshold\": (\"FLOAT\", {\"default\": 0.4, \"min\": 0.01, \"max\": 0.99, \"step\": 0.01}),\n                                \"high_threshold\": (\"FLOAT\", {\"default\": 0.8, \"min\": 0.01, \"max\": 0.99, \"step\": 0.01})\n                                }}\n\n    RETURN_TYPES = (\"IMAGE\",)\n    FUNCTION = \"detect_edge\"\n\n    CATEGORY = \"image/preprocessors\"\n\n    def detect_edge(self, image, low_threshold, high_threshold):\n        output = canny(image.to(ldm_patched.modules.model_management.get_torch_device()).movedim(-1, 1), low_threshold, high_threshold)\n        img_out = output[1].to(ldm_patched.modules.model_management.intermediate_device()).repeat(1, 3, 1, 1).movedim(1, -1)\n        return (img_out,)\n\nNODE_CLASS_MAPPINGS = {\n    \"Canny\": Canny,\n}\n"
  },
  {
    "path": "ldm_patched/contrib/external_clip_sdxl.py",
    "content": "# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py \n\nimport torch\nfrom ldm_patched.contrib.external import MAX_RESOLUTION\n\nclass CLIPTextEncodeSDXLRefiner:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": {\n            \"ascore\": (\"FLOAT\", {\"default\": 6.0, \"min\": 0.0, \"max\": 1000.0, \"step\": 0.01}),\n            \"width\": (\"INT\", {\"default\": 1024.0, \"min\": 0, \"max\": MAX_RESOLUTION}),\n            \"height\": (\"INT\", {\"default\": 1024.0, \"min\": 0, \"max\": MAX_RESOLUTION}),\n            \"text\": (\"STRING\", {\"multiline\": True}), \"clip\": (\"CLIP\", ),\n            }}\n    RETURN_TYPES = (\"CONDITIONING\",)\n    FUNCTION = \"encode\"\n\n    CATEGORY = \"advanced/conditioning\"\n\n    def encode(self, clip, ascore, width, height, text):\n        tokens = clip.tokenize(text)\n        cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)\n        return ([[cond, {\"pooled_output\": pooled, \"aesthetic_score\": ascore, \"width\": width,\"height\": height}]], )\n\nclass CLIPTextEncodeSDXL:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": {\n            \"width\": (\"INT\", {\"default\": 1024.0, \"min\": 0, \"max\": MAX_RESOLUTION}),\n            \"height\": (\"INT\", {\"default\": 1024.0, \"min\": 0, \"max\": MAX_RESOLUTION}),\n            \"crop_w\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": MAX_RESOLUTION}),\n            \"crop_h\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": MAX_RESOLUTION}),\n            \"target_width\": (\"INT\", {\"default\": 1024.0, \"min\": 0, \"max\": MAX_RESOLUTION}),\n            \"target_height\": (\"INT\", {\"default\": 1024.0, \"min\": 0, \"max\": MAX_RESOLUTION}),\n            \"text_g\": (\"STRING\", {\"multiline\": True, \"default\": \"CLIP_G\"}), \"clip\": (\"CLIP\", ),\n            \"text_l\": (\"STRING\", {\"multiline\": True, \"default\": \"CLIP_L\"}), \"clip\": (\"CLIP\", ),\n            }}\n    RETURN_TYPES = (\"CONDITIONING\",)\n    FUNCTION = \"encode\"\n\n    CATEGORY = \"advanced/conditioning\"\n\n    def encode(self, clip, width, height, crop_w, crop_h, target_width, target_height, text_g, text_l):\n        tokens = clip.tokenize(text_g)\n        tokens[\"l\"] = clip.tokenize(text_l)[\"l\"]\n        if len(tokens[\"l\"]) != len(tokens[\"g\"]):\n            empty = clip.tokenize(\"\")\n            while len(tokens[\"l\"]) < len(tokens[\"g\"]):\n                tokens[\"l\"] += empty[\"l\"]\n            while len(tokens[\"l\"]) > len(tokens[\"g\"]):\n                tokens[\"g\"] += empty[\"g\"]\n        cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)\n        return ([[cond, {\"pooled_output\": pooled, \"width\": width, \"height\": height, \"crop_w\": crop_w, \"crop_h\": crop_h, \"target_width\": target_width, \"target_height\": target_height}]], )\n\nNODE_CLASS_MAPPINGS = {\n    \"CLIPTextEncodeSDXLRefiner\": CLIPTextEncodeSDXLRefiner,\n    \"CLIPTextEncodeSDXL\": CLIPTextEncodeSDXL,\n}\n"
  },
  {
    "path": "ldm_patched/contrib/external_compositing.py",
    "content": "# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py \n\nimport numpy as np\nimport torch\nimport ldm_patched.modules.utils\nfrom enum import Enum\n\ndef resize_mask(mask, shape):\n    return torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[0], shape[1]), mode=\"bilinear\").squeeze(1)\n\nclass PorterDuffMode(Enum):\n    ADD = 0\n    CLEAR = 1\n    DARKEN = 2\n    DST = 3\n    DST_ATOP = 4\n    DST_IN = 5\n    DST_OUT = 6\n    DST_OVER = 7\n    LIGHTEN = 8\n    MULTIPLY = 9\n    OVERLAY = 10\n    SCREEN = 11\n    SRC = 12\n    SRC_ATOP = 13\n    SRC_IN = 14\n    SRC_OUT = 15\n    SRC_OVER = 16\n    XOR = 17\n\n\ndef porter_duff_composite(src_image: torch.Tensor, src_alpha: torch.Tensor, dst_image: torch.Tensor, dst_alpha: torch.Tensor, mode: PorterDuffMode):\n    if mode == PorterDuffMode.ADD:\n        out_alpha = torch.clamp(src_alpha + dst_alpha, 0, 1)\n        out_image = torch.clamp(src_image + dst_image, 0, 1)\n    elif mode == PorterDuffMode.CLEAR:\n        out_alpha = torch.zeros_like(dst_alpha)\n        out_image = torch.zeros_like(dst_image)\n    elif mode == PorterDuffMode.DARKEN:\n        out_alpha = src_alpha + dst_alpha  - src_alpha * dst_alpha\n        out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + torch.min(src_image, dst_image)\n    elif mode == PorterDuffMode.DST:\n        out_alpha = dst_alpha\n        out_image = dst_image\n    elif mode == PorterDuffMode.DST_ATOP:\n        out_alpha = src_alpha\n        out_image = src_alpha * dst_image + (1 - dst_alpha) * src_image\n    elif mode == PorterDuffMode.DST_IN:\n        out_alpha = src_alpha * dst_alpha\n        out_image = dst_image * src_alpha\n    elif mode == PorterDuffMode.DST_OUT:\n        out_alpha = (1 - src_alpha) * dst_alpha\n        out_image = (1 - src_alpha) * dst_image\n    elif mode == PorterDuffMode.DST_OVER:\n        out_alpha = dst_alpha + (1 - dst_alpha) * src_alpha\n        out_image = dst_image + (1 - dst_alpha) * src_image\n    elif mode == PorterDuffMode.LIGHTEN:\n        out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha\n        out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + torch.max(src_image, dst_image)\n    elif mode == PorterDuffMode.MULTIPLY:\n        out_alpha = src_alpha * dst_alpha\n        out_image = src_image * dst_image\n    elif mode == PorterDuffMode.OVERLAY:\n        out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha\n        out_image = torch.where(2 * dst_image < dst_alpha, 2 * src_image * dst_image,\n            src_alpha * dst_alpha - 2 * (dst_alpha - src_image) * (src_alpha - dst_image))\n    elif mode == PorterDuffMode.SCREEN:\n        out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha\n        out_image = src_image + dst_image - src_image * dst_image\n    elif mode == PorterDuffMode.SRC:\n        out_alpha = src_alpha\n        out_image = src_image\n    elif mode == PorterDuffMode.SRC_ATOP:\n        out_alpha = dst_alpha\n        out_image = dst_alpha * src_image + (1 - src_alpha) * dst_image\n    elif mode == PorterDuffMode.SRC_IN:\n        out_alpha = src_alpha * dst_alpha\n        out_image = src_image * dst_alpha\n    elif mode == PorterDuffMode.SRC_OUT:\n        out_alpha = (1 - dst_alpha) * src_alpha\n        out_image = (1 - dst_alpha) * src_image\n    elif mode == PorterDuffMode.SRC_OVER:\n        out_alpha = src_alpha + (1 - src_alpha) * dst_alpha\n        out_image = src_image + (1 - src_alpha) * dst_image\n    elif mode == PorterDuffMode.XOR:\n        out_alpha = (1 - dst_alpha) * src_alpha + (1 - src_alpha) * dst_alpha\n        out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image\n    else:\n        out_alpha = None\n        out_image = None\n    return out_image, out_alpha\n\n\nclass PorterDuffImageComposite:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\n            \"required\": {\n                \"source\": (\"IMAGE\",),\n                \"source_alpha\": (\"MASK\",),\n                \"destination\": (\"IMAGE\",),\n                \"destination_alpha\": (\"MASK\",),\n                \"mode\": ([mode.name for mode in PorterDuffMode], {\"default\": PorterDuffMode.DST.name}),\n            },\n        }\n\n    RETURN_TYPES = (\"IMAGE\", \"MASK\")\n    FUNCTION = \"composite\"\n    CATEGORY = \"mask/compositing\"\n\n    def composite(self, source: torch.Tensor, source_alpha: torch.Tensor, destination: torch.Tensor, destination_alpha: torch.Tensor, mode):\n        batch_size = min(len(source), len(source_alpha), len(destination), len(destination_alpha))\n        out_images = []\n        out_alphas = []\n\n        for i in range(batch_size):\n            src_image = source[i]\n            dst_image = destination[i]\n\n            assert src_image.shape[2] == dst_image.shape[2] # inputs need to have same number of channels\n\n            src_alpha = source_alpha[i].unsqueeze(2)\n            dst_alpha = destination_alpha[i].unsqueeze(2)\n\n            if dst_alpha.shape[:2] != dst_image.shape[:2]:\n                upscale_input = dst_alpha.unsqueeze(0).permute(0, 3, 1, 2)\n                upscale_output = ldm_patched.modules.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center')\n                dst_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0)\n            if src_image.shape != dst_image.shape:\n                upscale_input = src_image.unsqueeze(0).permute(0, 3, 1, 2)\n                upscale_output = ldm_patched.modules.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center')\n                src_image = upscale_output.permute(0, 2, 3, 1).squeeze(0)\n            if src_alpha.shape != dst_alpha.shape:\n                upscale_input = src_alpha.unsqueeze(0).permute(0, 3, 1, 2)\n                upscale_output = ldm_patched.modules.utils.common_upscale(upscale_input, dst_alpha.shape[1], dst_alpha.shape[0], upscale_method='bicubic', crop='center')\n                src_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0)\n\n            out_image, out_alpha = porter_duff_composite(src_image, src_alpha, dst_image, dst_alpha, PorterDuffMode[mode])\n\n            out_images.append(out_image)\n            out_alphas.append(out_alpha.squeeze(2))\n\n        result = (torch.stack(out_images), torch.stack(out_alphas))\n        return result\n\n\nclass SplitImageWithAlpha:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\n                \"required\": {\n                    \"image\": (\"IMAGE\",),\n                }\n        }\n\n    CATEGORY = \"mask/compositing\"\n    RETURN_TYPES = (\"IMAGE\", \"MASK\")\n    FUNCTION = \"split_image_with_alpha\"\n\n    def split_image_with_alpha(self, image: torch.Tensor):\n        out_images = [i[:,:,:3] for i in image]\n        out_alphas = [i[:,:,3] if i.shape[2] > 3 else torch.ones_like(i[:,:,0]) for i in image]\n        result = (torch.stack(out_images), 1.0 - torch.stack(out_alphas))\n        return result\n\n\nclass JoinImageWithAlpha:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\n                \"required\": {\n                    \"image\": (\"IMAGE\",),\n                    \"alpha\": (\"MASK\",),\n                }\n        }\n\n    CATEGORY = \"mask/compositing\"\n    RETURN_TYPES = (\"IMAGE\",)\n    FUNCTION = \"join_image_with_alpha\"\n\n    def join_image_with_alpha(self, image: torch.Tensor, alpha: torch.Tensor):\n        batch_size = min(len(image), len(alpha))\n        out_images = []\n\n        alpha = 1.0 - resize_mask(alpha, image.shape[1:])\n        for i in range(batch_size):\n           out_images.append(torch.cat((image[i][:,:,:3], alpha[i].unsqueeze(2)), dim=2))\n\n        result = (torch.stack(out_images),)\n        return result\n\n\nNODE_CLASS_MAPPINGS = {\n    \"PorterDuffImageComposite\": PorterDuffImageComposite,\n    \"SplitImageWithAlpha\": SplitImageWithAlpha,\n    \"JoinImageWithAlpha\": JoinImageWithAlpha,\n}\n\n\nNODE_DISPLAY_NAME_MAPPINGS = {\n    \"PorterDuffImageComposite\": \"Porter-Duff Image Composite\",\n    \"SplitImageWithAlpha\": \"Split Image with Alpha\",\n    \"JoinImageWithAlpha\": \"Join Image with Alpha\",\n}\n"
  },
  {
    "path": "ldm_patched/contrib/external_custom_sampler.py",
    "content": "# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py \n\nimport ldm_patched.modules.samplers\nimport ldm_patched.modules.sample\nfrom ldm_patched.k_diffusion import sampling as k_diffusion_sampling\nimport ldm_patched.utils.latent_visualization\nimport torch\nimport ldm_patched.modules.utils\n\n\nclass BasicScheduler:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\":\n                    {\"model\": (\"MODEL\",),\n                     \"scheduler\": (ldm_patched.modules.samplers.SCHEDULER_NAMES, ),\n                     \"steps\": (\"INT\", {\"default\": 20, \"min\": 1, \"max\": 10000}),\n                     \"denoise\": (\"FLOAT\", {\"default\": 1.0, \"min\": 0.0, \"max\": 1.0, \"step\": 0.01}),\n                      }\n               }\n    RETURN_TYPES = (\"SIGMAS\",)\n    CATEGORY = \"sampling/custom_sampling/schedulers\"\n\n    FUNCTION = \"get_sigmas\"\n\n    def get_sigmas(self, model, scheduler, steps, denoise):\n        total_steps = steps\n        if denoise < 1.0:\n            total_steps = int(steps/denoise)\n\n        ldm_patched.modules.model_management.load_models_gpu([model])\n        sigmas = ldm_patched.modules.samplers.calculate_sigmas_scheduler(model.model, scheduler, total_steps).cpu()\n        sigmas = sigmas[-(steps + 1):]\n        return (sigmas, )\n\n\nclass KarrasScheduler:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\":\n                    {\"steps\": (\"INT\", {\"default\": 20, \"min\": 1, \"max\": 10000}),\n                     \"sigma_max\": (\"FLOAT\", {\"default\": 14.614642, \"min\": 0.0, \"max\": 1000.0, \"step\":0.01, \"round\": False}),\n                     \"sigma_min\": (\"FLOAT\", {\"default\": 0.0291675, \"min\": 0.0, \"max\": 1000.0, \"step\":0.01, \"round\": False}),\n                     \"rho\": (\"FLOAT\", {\"default\": 7.0, \"min\": 0.0, \"max\": 100.0, \"step\":0.01, \"round\": False}),\n                    }\n               }\n    RETURN_TYPES = (\"SIGMAS\",)\n    CATEGORY = \"sampling/custom_sampling/schedulers\"\n\n    FUNCTION = \"get_sigmas\"\n\n    def get_sigmas(self, steps, sigma_max, sigma_min, rho):\n        sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho)\n        return (sigmas, )\n\nclass ExponentialScheduler:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\":\n                    {\"steps\": (\"INT\", {\"default\": 20, \"min\": 1, \"max\": 10000}),\n                     \"sigma_max\": (\"FLOAT\", {\"default\": 14.614642, \"min\": 0.0, \"max\": 1000.0, \"step\":0.01, \"round\": False}),\n                     \"sigma_min\": (\"FLOAT\", {\"default\": 0.0291675, \"min\": 0.0, \"max\": 1000.0, \"step\":0.01, \"round\": False}),\n                    }\n               }\n    RETURN_TYPES = (\"SIGMAS\",)\n    CATEGORY = \"sampling/custom_sampling/schedulers\"\n\n    FUNCTION = \"get_sigmas\"\n\n    def get_sigmas(self, steps, sigma_max, sigma_min):\n        sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max)\n        return (sigmas, )\n\nclass PolyexponentialScheduler:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\":\n                    {\"steps\": (\"INT\", {\"default\": 20, \"min\": 1, \"max\": 10000}),\n                     \"sigma_max\": (\"FLOAT\", {\"default\": 14.614642, \"min\": 0.0, \"max\": 1000.0, \"step\":0.01, \"round\": False}),\n                     \"sigma_min\": (\"FLOAT\", {\"default\": 0.0291675, \"min\": 0.0, \"max\": 1000.0, \"step\":0.01, \"round\": False}),\n                     \"rho\": (\"FLOAT\", {\"default\": 1.0, \"min\": 0.0, \"max\": 100.0, \"step\":0.01, \"round\": False}),\n                    }\n               }\n    RETURN_TYPES = (\"SIGMAS\",)\n    CATEGORY = \"sampling/custom_sampling/schedulers\"\n\n    FUNCTION = \"get_sigmas\"\n\n    def get_sigmas(self, steps, sigma_max, sigma_min, rho):\n        sigmas = k_diffusion_sampling.get_sigmas_polyexponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho)\n        return (sigmas, )\n\nclass SDTurboScheduler:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\":\n                    {\"model\": (\"MODEL\",),\n                     \"steps\": (\"INT\", {\"default\": 1, \"min\": 1, \"max\": 10}),\n                     \"denoise\": (\"FLOAT\", {\"default\": 1.0, \"min\": 0, \"max\": 1.0, \"step\": 0.01}),\n                      }\n               }\n    RETURN_TYPES = (\"SIGMAS\",)\n    CATEGORY = \"sampling/custom_sampling/schedulers\"\n\n    FUNCTION = \"get_sigmas\"\n\n    def get_sigmas(self, model, steps, denoise):\n        start_step = 10 - int(10 * denoise)\n        timesteps = torch.flip(torch.arange(1, 11) * 100 - 1, (0,))[start_step:start_step + steps]\n        sigmas = model.model_sampling.sigma(timesteps)\n        sigmas = torch.cat([sigmas, sigmas.new_zeros([1])])\n        return (sigmas, )\n\nclass VPScheduler:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\":\n                    {\"steps\": (\"INT\", {\"default\": 20, \"min\": 1, \"max\": 10000}),\n                     \"beta_d\": (\"FLOAT\", {\"default\": 19.9, \"min\": 0.0, \"max\": 1000.0, \"step\":0.01, \"round\": False}), #TODO: fix default values\n                     \"beta_min\": (\"FLOAT\", {\"default\": 0.1, \"min\": 0.0, \"max\": 1000.0, \"step\":0.01, \"round\": False}),\n                     \"eps_s\": (\"FLOAT\", {\"default\": 0.001, \"min\": 0.0, \"max\": 1.0, \"step\":0.0001, \"round\": False}),\n                    }\n               }\n    RETURN_TYPES = (\"SIGMAS\",)\n    CATEGORY = \"sampling/custom_sampling/schedulers\"\n\n    FUNCTION = \"get_sigmas\"\n\n    def get_sigmas(self, steps, beta_d, beta_min, eps_s):\n        sigmas = k_diffusion_sampling.get_sigmas_vp(n=steps, beta_d=beta_d, beta_min=beta_min, eps_s=eps_s)\n        return (sigmas, )\n\nclass SplitSigmas:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\":\n                    {\"sigmas\": (\"SIGMAS\", ),\n                    \"step\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": 10000}),\n                     }\n                }\n    RETURN_TYPES = (\"SIGMAS\",\"SIGMAS\")\n    CATEGORY = \"sampling/custom_sampling/sigmas\"\n\n    FUNCTION = \"get_sigmas\"\n\n    def get_sigmas(self, sigmas, step):\n        sigmas1 = sigmas[:step + 1]\n        sigmas2 = sigmas[step:]\n        return (sigmas1, sigmas2)\n\nclass FlipSigmas:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\":\n                    {\"sigmas\": (\"SIGMAS\", ),\n                     }\n                }\n    RETURN_TYPES = (\"SIGMAS\",)\n    CATEGORY = \"sampling/custom_sampling/sigmas\"\n\n    FUNCTION = \"get_sigmas\"\n\n    def get_sigmas(self, sigmas):\n        sigmas = sigmas.flip(0)\n        if sigmas[0] == 0:\n            sigmas[0] = 0.0001\n        return (sigmas,)\n\nclass KSamplerSelect:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\":\n                    {\"sampler_name\": (ldm_patched.modules.samplers.SAMPLER_NAMES, ),\n                      }\n               }\n    RETURN_TYPES = (\"SAMPLER\",)\n    CATEGORY = \"sampling/custom_sampling/samplers\"\n\n    FUNCTION = \"get_sampler\"\n\n    def get_sampler(self, sampler_name):\n        sampler = ldm_patched.modules.samplers.sampler_object(sampler_name)\n        return (sampler, )\n\nclass SamplerDPMPP_2M_SDE:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\":\n                    {\"solver_type\": (['midpoint', 'heun'], ),\n                     \"eta\": (\"FLOAT\", {\"default\": 1.0, \"min\": 0.0, \"max\": 100.0, \"step\":0.01, \"round\": False}),\n                     \"s_noise\": (\"FLOAT\", {\"default\": 1.0, \"min\": 0.0, \"max\": 100.0, \"step\":0.01, \"round\": False}),\n                     \"noise_device\": (['gpu', 'cpu'], ),\n                      }\n               }\n    RETURN_TYPES = (\"SAMPLER\",)\n    CATEGORY = \"sampling/custom_sampling/samplers\"\n\n    FUNCTION = \"get_sampler\"\n\n    def get_sampler(self, solver_type, eta, s_noise, noise_device):\n        if noise_device == 'cpu':\n            sampler_name = \"dpmpp_2m_sde\"\n        else:\n            sampler_name = \"dpmpp_2m_sde_gpu\"\n        sampler = ldm_patched.modules.samplers.ksampler(sampler_name, {\"eta\": eta, \"s_noise\": s_noise, \"solver_type\": solver_type})\n        return (sampler, )\n\n\nclass SamplerDPMPP_SDE:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\":\n                    {\"eta\": (\"FLOAT\", {\"default\": 1.0, \"min\": 0.0, \"max\": 100.0, \"step\":0.01, \"round\": False}),\n                     \"s_noise\": (\"FLOAT\", {\"default\": 1.0, \"min\": 0.0, \"max\": 100.0, \"step\":0.01, \"round\": False}),\n                     \"r\": (\"FLOAT\", {\"default\": 0.5, \"min\": 0.0, \"max\": 100.0, \"step\":0.01, \"round\": False}),\n                     \"noise_device\": (['gpu', 'cpu'], ),\n                      }\n               }\n    RETURN_TYPES = (\"SAMPLER\",)\n    CATEGORY = \"sampling/custom_sampling/samplers\"\n\n    FUNCTION = \"get_sampler\"\n\n    def get_sampler(self, eta, s_noise, r, noise_device):\n        if noise_device == 'cpu':\n            sampler_name = \"dpmpp_sde\"\n        else:\n            sampler_name = \"dpmpp_sde_gpu\"\n        sampler = ldm_patched.modules.samplers.ksampler(sampler_name, {\"eta\": eta, \"s_noise\": s_noise, \"r\": r})\n        return (sampler, )\n\n\nclass SamplerTCD:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\n            \"required\": {\n                \"eta\": (\"FLOAT\", {\"default\": 0.3, \"min\": 0.0, \"max\": 1.0, \"step\": 0.01}),\n            }\n        }\n    RETURN_TYPES = (\"SAMPLER\",)\n    CATEGORY = \"sampling/custom_sampling/samplers\"\n\n    FUNCTION = \"get_sampler\"\n\n    def get_sampler(self, eta=0.3):\n        sampler = ldm_patched.modules.samplers.ksampler(\"tcd\", {\"eta\": eta})\n        return (sampler, )\n\n\nclass SamplerCustom:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\":\n                    {\"model\": (\"MODEL\",),\n                    \"add_noise\": (\"BOOLEAN\", {\"default\": True}),\n                    \"noise_seed\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": 0xffffffffffffffff}),\n                    \"cfg\": (\"FLOAT\", {\"default\": 8.0, \"min\": 0.0, \"max\": 100.0, \"step\":0.1, \"round\": 0.01}),\n                    \"positive\": (\"CONDITIONING\", ),\n                    \"negative\": (\"CONDITIONING\", ),\n                    \"sampler\": (\"SAMPLER\", ),\n                    \"sigmas\": (\"SIGMAS\", ),\n                    \"latent_image\": (\"LATENT\", ),\n                     }\n                }\n\n    RETURN_TYPES = (\"LATENT\",\"LATENT\")\n    RETURN_NAMES = (\"output\", \"denoised_output\")\n\n    FUNCTION = \"sample\"\n\n    CATEGORY = \"sampling/custom_sampling\"\n\n    def sample(self, model, add_noise, noise_seed, cfg, positive, negative, sampler, sigmas, latent_image):\n        latent = latent_image\n        latent_image = latent[\"samples\"]\n        if not add_noise:\n            noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device=\"cpu\")\n        else:\n            batch_inds = latent[\"batch_index\"] if \"batch_index\" in latent else None\n            noise = ldm_patched.modules.sample.prepare_noise(latent_image, noise_seed, batch_inds)\n\n        noise_mask = None\n        if \"noise_mask\" in latent:\n            noise_mask = latent[\"noise_mask\"]\n\n        x0_output = {}\n        callback = ldm_patched.utils.latent_visualization.prepare_callback(model, sigmas.shape[-1] - 1, x0_output)\n\n        disable_pbar = not ldm_patched.modules.utils.PROGRESS_BAR_ENABLED\n        samples = ldm_patched.modules.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed)\n\n        out = latent.copy()\n        out[\"samples\"] = samples\n        if \"x0\" in x0_output:\n            out_denoised = latent.copy()\n            out_denoised[\"samples\"] = model.model.process_latent_out(x0_output[\"x0\"].cpu())\n        else:\n            out_denoised = out\n        return (out, out_denoised)\n\nNODE_CLASS_MAPPINGS = {\n    \"SamplerCustom\": SamplerCustom,\n    \"BasicScheduler\": BasicScheduler,\n    \"KarrasScheduler\": KarrasScheduler,\n    \"ExponentialScheduler\": ExponentialScheduler,\n    \"PolyexponentialScheduler\": PolyexponentialScheduler,\n    \"VPScheduler\": VPScheduler,\n    \"SDTurboScheduler\": SDTurboScheduler,\n    \"KSamplerSelect\": KSamplerSelect,\n    \"SamplerDPMPP_2M_SDE\": SamplerDPMPP_2M_SDE,\n    \"SamplerDPMPP_SDE\": SamplerDPMPP_SDE,\n    \"SamplerTCD\": SamplerTCD,\n    \"SplitSigmas\": SplitSigmas,\n    \"FlipSigmas\": FlipSigmas,\n}\n"
  },
  {
    "path": "ldm_patched/contrib/external_freelunch.py",
    "content": "# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py \n\n#code originally taken from: https://github.com/ChenyangSi/FreeU (under MIT License)\n\nimport torch\n\n\ndef Fourier_filter(x, threshold, scale):\n    # FFT\n    x_freq = torch.fft.fftn(x.float(), dim=(-2, -1))\n    x_freq = torch.fft.fftshift(x_freq, dim=(-2, -1))\n\n    B, C, H, W = x_freq.shape\n    mask = torch.ones((B, C, H, W), device=x.device)\n\n    crow, ccol = H // 2, W //2\n    mask[..., crow - threshold:crow + threshold, ccol - threshold:ccol + threshold] = scale\n    x_freq = x_freq * mask\n\n    # IFFT\n    x_freq = torch.fft.ifftshift(x_freq, dim=(-2, -1))\n    x_filtered = torch.fft.ifftn(x_freq, dim=(-2, -1)).real\n\n    return x_filtered.to(x.dtype)\n\n\nclass FreeU:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"model\": (\"MODEL\",),\n                             \"b1\": (\"FLOAT\", {\"default\": 1.1, \"min\": 0.0, \"max\": 10.0, \"step\": 0.01}),\n                             \"b2\": (\"FLOAT\", {\"default\": 1.2, \"min\": 0.0, \"max\": 10.0, \"step\": 0.01}),\n                             \"s1\": (\"FLOAT\", {\"default\": 0.9, \"min\": 0.0, \"max\": 10.0, \"step\": 0.01}),\n                             \"s2\": (\"FLOAT\", {\"default\": 0.2, \"min\": 0.0, \"max\": 10.0, \"step\": 0.01}),\n                              }}\n    RETURN_TYPES = (\"MODEL\",)\n    FUNCTION = \"patch\"\n\n    CATEGORY = \"model_patches\"\n\n    def patch(self, model, b1, b2, s1, s2):\n        model_channels = model.model.model_config.unet_config[\"model_channels\"]\n        scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)}\n        on_cpu_devices = {}\n\n        def output_block_patch(h, hsp, transformer_options):\n            scale = scale_dict.get(h.shape[1], None)\n            if scale is not None:\n                h[:,:h.shape[1] // 2] = h[:,:h.shape[1] // 2] * scale[0]\n                if hsp.device not in on_cpu_devices:\n                    try:\n                        hsp = Fourier_filter(hsp, threshold=1, scale=scale[1])\n                    except:\n                        print(\"Device\", hsp.device, \"does not support the torch.fft functions used in the FreeU node, switching to CPU.\")\n                        on_cpu_devices[hsp.device] = True\n                        hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)\n                else:\n                    hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)\n\n            return h, hsp\n\n        m = model.clone()\n        m.set_model_output_block_patch(output_block_patch)\n        return (m, )\n\nclass FreeU_V2:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"model\": (\"MODEL\",),\n                             \"b1\": (\"FLOAT\", {\"default\": 1.3, \"min\": 0.0, \"max\": 10.0, \"step\": 0.01}),\n                             \"b2\": (\"FLOAT\", {\"default\": 1.4, \"min\": 0.0, \"max\": 10.0, \"step\": 0.01}),\n                             \"s1\": (\"FLOAT\", {\"default\": 0.9, \"min\": 0.0, \"max\": 10.0, \"step\": 0.01}),\n                             \"s2\": (\"FLOAT\", {\"default\": 0.2, \"min\": 0.0, \"max\": 10.0, \"step\": 0.01}),\n                              }}\n    RETURN_TYPES = (\"MODEL\",)\n    FUNCTION = \"patch\"\n\n    CATEGORY = \"model_patches\"\n\n    def patch(self, model, b1, b2, s1, s2):\n        model_channels = model.model.model_config.unet_config[\"model_channels\"]\n        scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)}\n        on_cpu_devices = {}\n\n        def output_block_patch(h, hsp, transformer_options):\n            scale = scale_dict.get(h.shape[1], None)\n            if scale is not None:\n                hidden_mean = h.mean(1).unsqueeze(1)\n                B = hidden_mean.shape[0]\n                hidden_max, _ = torch.max(hidden_mean.view(B, -1), dim=-1, keepdim=True)\n                hidden_min, _ = torch.min(hidden_mean.view(B, -1), dim=-1, keepdim=True)\n                hidden_mean = (hidden_mean - hidden_min.unsqueeze(2).unsqueeze(3)) / (hidden_max - hidden_min).unsqueeze(2).unsqueeze(3)\n\n                h[:,:h.shape[1] // 2] = h[:,:h.shape[1] // 2] * ((scale[0] - 1 ) * hidden_mean + 1)\n\n                if hsp.device not in on_cpu_devices:\n                    try:\n                        hsp = Fourier_filter(hsp, threshold=1, scale=scale[1])\n                    except:\n                        print(\"Device\", hsp.device, \"does not support the torch.fft functions used in the FreeU node, switching to CPU.\")\n                        on_cpu_devices[hsp.device] = True\n                        hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)\n                else:\n                    hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)\n\n            return h, hsp\n\n        m = model.clone()\n        m.set_model_output_block_patch(output_block_patch)\n        return (m, )\n\nNODE_CLASS_MAPPINGS = {\n    \"FreeU\": FreeU,\n    \"FreeU_V2\": FreeU_V2,\n}\n"
  },
  {
    "path": "ldm_patched/contrib/external_hypernetwork.py",
    "content": "# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py \n\nimport ldm_patched.modules.utils\nimport ldm_patched.utils.path_utils\nimport torch\n\ndef load_hypernetwork_patch(path, strength):\n    sd = ldm_patched.modules.utils.load_torch_file(path, safe_load=True)\n    activation_func = sd.get('activation_func', 'linear')\n    is_layer_norm = sd.get('is_layer_norm', False)\n    use_dropout = sd.get('use_dropout', False)\n    activate_output = sd.get('activate_output', False)\n    last_layer_dropout = sd.get('last_layer_dropout', False)\n\n    valid_activation = {\n        \"linear\": torch.nn.Identity,\n        \"relu\": torch.nn.ReLU,\n        \"leakyrelu\": torch.nn.LeakyReLU,\n        \"elu\": torch.nn.ELU,\n        \"swish\": torch.nn.Hardswish,\n        \"tanh\": torch.nn.Tanh,\n        \"sigmoid\": torch.nn.Sigmoid,\n        \"softsign\": torch.nn.Softsign,\n        \"mish\": torch.nn.Mish,\n    }\n\n    if activation_func not in valid_activation:\n        print(\"Unsupported Hypernetwork format, if you report it I might implement it.\", path, \" \", activation_func, is_layer_norm, use_dropout, activate_output, last_layer_dropout)\n        return None\n\n    out = {}\n\n    for d in sd:\n        try:\n            dim = int(d)\n        except:\n            continue\n\n        output = []\n        for index in [0, 1]:\n            attn_weights = sd[dim][index]\n            keys = attn_weights.keys()\n\n            linears = filter(lambda a: a.endswith(\".weight\"), keys)\n            linears = list(map(lambda a: a[:-len(\".weight\")], linears))\n            layers = []\n\n            i = 0\n            while i < len(linears):\n                lin_name = linears[i]\n                last_layer = (i == (len(linears) - 1))\n                penultimate_layer = (i == (len(linears) - 2))\n\n                lin_weight = attn_weights['{}.weight'.format(lin_name)]\n                lin_bias = attn_weights['{}.bias'.format(lin_name)]\n                layer = torch.nn.Linear(lin_weight.shape[1], lin_weight.shape[0])\n                layer.load_state_dict({\"weight\": lin_weight, \"bias\": lin_bias})\n                layers.append(layer)\n                if activation_func != \"linear\":\n                    if (not last_layer) or (activate_output):\n                        layers.append(valid_activation[activation_func]())\n                if is_layer_norm:\n                    i += 1\n                    ln_name = linears[i]\n                    ln_weight = attn_weights['{}.weight'.format(ln_name)]\n                    ln_bias = attn_weights['{}.bias'.format(ln_name)]\n                    ln = torch.nn.LayerNorm(ln_weight.shape[0])\n                    ln.load_state_dict({\"weight\": ln_weight, \"bias\": ln_bias})\n                    layers.append(ln)\n                if use_dropout:\n                    if (not last_layer) and (not penultimate_layer or last_layer_dropout):\n                        layers.append(torch.nn.Dropout(p=0.3))\n                i += 1\n\n            output.append(torch.nn.Sequential(*layers))\n        out[dim] = torch.nn.ModuleList(output)\n\n    class hypernetwork_patch:\n        def __init__(self, hypernet, strength):\n            self.hypernet = hypernet\n            self.strength = strength\n        def __call__(self, q, k, v, extra_options):\n            dim = k.shape[-1]\n            if dim in self.hypernet:\n                hn = self.hypernet[dim]\n                k = k + hn[0](k) * self.strength\n                v = v + hn[1](v) * self.strength\n\n            return q, k, v\n\n        def to(self, device):\n            for d in self.hypernet.keys():\n                self.hypernet[d] = self.hypernet[d].to(device)\n            return self\n\n    return hypernetwork_patch(out, strength)\n\nclass HypernetworkLoader:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"model\": (\"MODEL\",),\n                              \"hypernetwork_name\": (ldm_patched.utils.path_utils.get_filename_list(\"hypernetworks\"), ),\n                              \"strength\": (\"FLOAT\", {\"default\": 1.0, \"min\": -10.0, \"max\": 10.0, \"step\": 0.01}),\n                              }}\n    RETURN_TYPES = (\"MODEL\",)\n    FUNCTION = \"load_hypernetwork\"\n\n    CATEGORY = \"loaders\"\n\n    def load_hypernetwork(self, model, hypernetwork_name, strength):\n        hypernetwork_path = ldm_patched.utils.path_utils.get_full_path(\"hypernetworks\", hypernetwork_name)\n        model_hypernetwork = model.clone()\n        patch = load_hypernetwork_patch(hypernetwork_path, strength)\n        if patch is not None:\n            model_hypernetwork.set_model_attn1_patch(patch)\n            model_hypernetwork.set_model_attn2_patch(patch)\n        return (model_hypernetwork,)\n\nNODE_CLASS_MAPPINGS = {\n    \"HypernetworkLoader\": HypernetworkLoader\n}\n"
  },
  {
    "path": "ldm_patched/contrib/external_hypertile.py",
    "content": "# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py \n\n#Taken from: https://github.com/tfernd/HyperTile/\n\nimport math\nfrom einops import rearrange\n# Use torch rng for consistency across generations\nfrom torch import randint\n\ndef random_divisor(value: int, min_value: int, /, max_options: int = 1) -> int:\n    min_value = min(min_value, value)\n\n    # All big divisors of value (inclusive)\n    divisors = [i for i in range(min_value, value + 1) if value % i == 0]\n\n    ns = [value // i for i in divisors[:max_options]]  # has at least 1 element\n\n    if len(ns) - 1 > 0:\n        idx = randint(low=0, high=len(ns) - 1, size=(1,)).item()\n    else:\n        idx = 0\n\n    return ns[idx]\n\nclass HyperTile:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"model\": (\"MODEL\",),\n                             \"tile_size\": (\"INT\", {\"default\": 256, \"min\": 1, \"max\": 2048}),\n                             \"swap_size\": (\"INT\", {\"default\": 2, \"min\": 1, \"max\": 128}),\n                             \"max_depth\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": 10}),\n                             \"scale_depth\": (\"BOOLEAN\", {\"default\": False}),\n                              }}\n    RETURN_TYPES = (\"MODEL\",)\n    FUNCTION = \"patch\"\n\n    CATEGORY = \"model_patches\"\n\n    def patch(self, model, tile_size, swap_size, max_depth, scale_depth):\n        model_channels = model.model.model_config.unet_config[\"model_channels\"]\n\n        latent_tile_size = max(32, tile_size) // 8\n        self.temp = None\n\n        def hypertile_in(q, k, v, extra_options):\n            model_chans = q.shape[-2]\n            orig_shape = extra_options['original_shape']\n            apply_to = []\n            for i in range(max_depth + 1):\n                apply_to.append((orig_shape[-2] / (2 ** i)) * (orig_shape[-1] / (2 ** i)))\n\n            if model_chans in apply_to:\n                shape = extra_options[\"original_shape\"]\n                aspect_ratio = shape[-1] / shape[-2]\n\n                hw = q.size(1)\n                h, w = round(math.sqrt(hw * aspect_ratio)), round(math.sqrt(hw / aspect_ratio))\n\n                factor = (2 ** apply_to.index(model_chans)) if scale_depth else 1\n                nh = random_divisor(h, latent_tile_size * factor, swap_size)\n                nw = random_divisor(w, latent_tile_size * factor, swap_size)\n\n                if nh * nw > 1:\n                    q = rearrange(q, \"b (nh h nw w) c -> (b nh nw) (h w) c\", h=h // nh, w=w // nw, nh=nh, nw=nw)\n                    self.temp = (nh, nw, h, w)\n                return q, k, v\n\n            return q, k, v\n        def hypertile_out(out, extra_options):\n            if self.temp is not None:\n                nh, nw, h, w = self.temp\n                self.temp = None\n                out = rearrange(out, \"(b nh nw) hw c -> b nh nw hw c\", nh=nh, nw=nw)\n                out = rearrange(out, \"b nh nw (h w) c -> b (nh h nw w) c\", h=h // nh, w=w // nw)\n            return out\n\n\n        m = model.clone()\n        m.set_model_attn1_patch(hypertile_in)\n        m.set_model_attn1_output_patch(hypertile_out)\n        return (m, )\n\nNODE_CLASS_MAPPINGS = {\n    \"HyperTile\": HyperTile,\n}\n"
  },
  {
    "path": "ldm_patched/contrib/external_images.py",
    "content": "# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py \n\nimport ldm_patched.contrib.external\nimport ldm_patched.utils.path_utils\nfrom ldm_patched.modules.args_parser import args\n\nfrom PIL import Image\nfrom PIL.PngImagePlugin import PngInfo\n\nimport numpy as np\nimport json\nimport os\n\nMAX_RESOLUTION = ldm_patched.contrib.external.MAX_RESOLUTION\n\nclass ImageCrop:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"image\": (\"IMAGE\",),\n                              \"width\": (\"INT\", {\"default\": 512, \"min\": 1, \"max\": MAX_RESOLUTION, \"step\": 1}),\n                              \"height\": (\"INT\", {\"default\": 512, \"min\": 1, \"max\": MAX_RESOLUTION, \"step\": 1}),\n                              \"x\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 1}),\n                              \"y\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 1}),\n                              }}\n    RETURN_TYPES = (\"IMAGE\",)\n    FUNCTION = \"crop\"\n\n    CATEGORY = \"image/transform\"\n\n    def crop(self, image, width, height, x, y):\n        x = min(x, image.shape[2] - 1)\n        y = min(y, image.shape[1] - 1)\n        to_x = width + x\n        to_y = height + y\n        img = image[:,y:to_y, x:to_x, :]\n        return (img,)\n\nclass RepeatImageBatch:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"image\": (\"IMAGE\",),\n                              \"amount\": (\"INT\", {\"default\": 1, \"min\": 1, \"max\": 64}),\n                              }}\n    RETURN_TYPES = (\"IMAGE\",)\n    FUNCTION = \"repeat\"\n\n    CATEGORY = \"image/batch\"\n\n    def repeat(self, image, amount):\n        s = image.repeat((amount, 1,1,1))\n        return (s,)\n\nclass SaveAnimatedWEBP:\n    def __init__(self):\n        self.output_dir = ldm_patched.utils.path_utils.get_output_directory()\n        self.type = \"output\"\n        self.prefix_append = \"\"\n\n    methods = {\"default\": 4, \"fastest\": 0, \"slowest\": 6}\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\":\n                    {\"images\": (\"IMAGE\", ),\n                     \"filename_prefix\": (\"STRING\", {\"default\": \"ldm_patched\"}),\n                     \"fps\": (\"FLOAT\", {\"default\": 6.0, \"min\": 0.01, \"max\": 1000.0, \"step\": 0.01}),\n                     \"lossless\": (\"BOOLEAN\", {\"default\": True}),\n                     \"quality\": (\"INT\", {\"default\": 80, \"min\": 0, \"max\": 100}),\n                     \"method\": (list(s.methods.keys()),),\n                     # \"num_frames\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": 8192}),\n                     },\n                \"hidden\": {\"prompt\": \"PROMPT\", \"extra_pnginfo\": \"EXTRA_PNGINFO\"},\n                }\n\n    RETURN_TYPES = ()\n    FUNCTION = \"save_images\"\n\n    OUTPUT_NODE = True\n\n    CATEGORY = \"image/animation\"\n\n    def save_images(self, images, fps, filename_prefix, lossless, quality, method, num_frames=0, prompt=None, extra_pnginfo=None):\n        method = self.methods.get(method)\n        filename_prefix += self.prefix_append\n        full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])\n        results = list()\n        pil_images = []\n        for image in images:\n            i = 255. * image.cpu().numpy()\n            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))\n            pil_images.append(img)\n\n        metadata = pil_images[0].getexif()\n        if not args.disable_server_info:\n            if prompt is not None:\n                metadata[0x0110] = \"prompt:{}\".format(json.dumps(prompt))\n            if extra_pnginfo is not None:\n                inital_exif = 0x010f\n                for x in extra_pnginfo:\n                    metadata[inital_exif] = \"{}:{}\".format(x, json.dumps(extra_pnginfo[x]))\n                    inital_exif -= 1\n\n        if num_frames == 0:\n            num_frames = len(pil_images)\n\n        c = len(pil_images)\n        for i in range(0, c, num_frames):\n            file = f\"{filename}_{counter:05}_.webp\"\n            pil_images[i].save(os.path.join(full_output_folder, file), save_all=True, duration=int(1000.0/fps), append_images=pil_images[i + 1:i + num_frames], exif=metadata, lossless=lossless, quality=quality, method=method)\n            results.append({\n                \"filename\": file,\n                \"subfolder\": subfolder,\n                \"type\": self.type\n            })\n            counter += 1\n\n        animated = num_frames != 1\n        return { \"ui\": { \"images\": results, \"animated\": (animated,) } }\n\nclass SaveAnimatedPNG:\n    def __init__(self):\n        self.output_dir = ldm_patched.utils.path_utils.get_output_directory()\n        self.type = \"output\"\n        self.prefix_append = \"\"\n\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\":\n                    {\"images\": (\"IMAGE\", ),\n                     \"filename_prefix\": (\"STRING\", {\"default\": \"ldm_patched\"}),\n                     \"fps\": (\"FLOAT\", {\"default\": 6.0, \"min\": 0.01, \"max\": 1000.0, \"step\": 0.01}),\n                     \"compress_level\": (\"INT\", {\"default\": 4, \"min\": 0, \"max\": 9})\n                     },\n                \"hidden\": {\"prompt\": \"PROMPT\", \"extra_pnginfo\": \"EXTRA_PNGINFO\"},\n                }\n\n    RETURN_TYPES = ()\n    FUNCTION = \"save_images\"\n\n    OUTPUT_NODE = True\n\n    CATEGORY = \"image/animation\"\n\n    def save_images(self, images, fps, compress_level, filename_prefix=\"ldm_patched\", prompt=None, extra_pnginfo=None):\n        filename_prefix += self.prefix_append\n        full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])\n        results = list()\n        pil_images = []\n        for image in images:\n            i = 255. * image.cpu().numpy()\n            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))\n            pil_images.append(img)\n\n        metadata = None\n        if not args.disable_server_info:\n            metadata = PngInfo()\n            if prompt is not None:\n                metadata.add(b\"ldm_patched\", \"prompt\".encode(\"latin-1\", \"strict\") + b\"\\0\" + json.dumps(prompt).encode(\"latin-1\", \"strict\"), after_idat=True)\n            if extra_pnginfo is not None:\n                for x in extra_pnginfo:\n                    metadata.add(b\"ldm_patched\", x.encode(\"latin-1\", \"strict\") + b\"\\0\" + json.dumps(extra_pnginfo[x]).encode(\"latin-1\", \"strict\"), after_idat=True)\n\n        file = f\"{filename}_{counter:05}_.png\"\n        pil_images[0].save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=compress_level, save_all=True, duration=int(1000.0/fps), append_images=pil_images[1:])\n        results.append({\n            \"filename\": file,\n            \"subfolder\": subfolder,\n            \"type\": self.type\n        })\n\n        return { \"ui\": { \"images\": results, \"animated\": (True,)} }\n\nNODE_CLASS_MAPPINGS = {\n    \"ImageCrop\": ImageCrop,\n    \"RepeatImageBatch\": RepeatImageBatch,\n    \"SaveAnimatedWEBP\": SaveAnimatedWEBP,\n    \"SaveAnimatedPNG\": SaveAnimatedPNG,\n}\n"
  },
  {
    "path": "ldm_patched/contrib/external_latent.py",
    "content": "# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py \n\nimport ldm_patched.modules.utils\nimport torch\n\ndef reshape_latent_to(target_shape, latent):\n    if latent.shape[1:] != target_shape[1:]:\n        latent = ldm_patched.modules.utils.common_upscale(latent, target_shape[3], target_shape[2], \"bilinear\", \"center\")\n    return ldm_patched.modules.utils.repeat_to_batch_size(latent, target_shape[0])\n\n\nclass LatentAdd:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"samples1\": (\"LATENT\",), \"samples2\": (\"LATENT\",)}}\n\n    RETURN_TYPES = (\"LATENT\",)\n    FUNCTION = \"op\"\n\n    CATEGORY = \"latent/advanced\"\n\n    def op(self, samples1, samples2):\n        samples_out = samples1.copy()\n\n        s1 = samples1[\"samples\"]\n        s2 = samples2[\"samples\"]\n\n        s2 = reshape_latent_to(s1.shape, s2)\n        samples_out[\"samples\"] = s1 + s2\n        return (samples_out,)\n\nclass LatentSubtract:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"samples1\": (\"LATENT\",), \"samples2\": (\"LATENT\",)}}\n\n    RETURN_TYPES = (\"LATENT\",)\n    FUNCTION = \"op\"\n\n    CATEGORY = \"latent/advanced\"\n\n    def op(self, samples1, samples2):\n        samples_out = samples1.copy()\n\n        s1 = samples1[\"samples\"]\n        s2 = samples2[\"samples\"]\n\n        s2 = reshape_latent_to(s1.shape, s2)\n        samples_out[\"samples\"] = s1 - s2\n        return (samples_out,)\n\nclass LatentMultiply:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"samples\": (\"LATENT\",),\n                              \"multiplier\": (\"FLOAT\", {\"default\": 1.0, \"min\": -10.0, \"max\": 10.0, \"step\": 0.01}),\n                             }}\n\n    RETURN_TYPES = (\"LATENT\",)\n    FUNCTION = \"op\"\n\n    CATEGORY = \"latent/advanced\"\n\n    def op(self, samples, multiplier):\n        samples_out = samples.copy()\n\n        s1 = samples[\"samples\"]\n        samples_out[\"samples\"] = s1 * multiplier\n        return (samples_out,)\n\nclass LatentInterpolate:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"samples1\": (\"LATENT\",),\n                              \"samples2\": (\"LATENT\",),\n                              \"ratio\": (\"FLOAT\", {\"default\": 1.0, \"min\": 0.0, \"max\": 1.0, \"step\": 0.01}),\n                              }}\n\n    RETURN_TYPES = (\"LATENT\",)\n    FUNCTION = \"op\"\n\n    CATEGORY = \"latent/advanced\"\n\n    def op(self, samples1, samples2, ratio):\n        samples_out = samples1.copy()\n\n        s1 = samples1[\"samples\"]\n        s2 = samples2[\"samples\"]\n\n        s2 = reshape_latent_to(s1.shape, s2)\n\n        m1 = torch.linalg.vector_norm(s1, dim=(1))\n        m2 = torch.linalg.vector_norm(s2, dim=(1))\n\n        s1 = torch.nan_to_num(s1 / m1)\n        s2 = torch.nan_to_num(s2 / m2)\n\n        t = (s1 * ratio + s2 * (1.0 - ratio))\n        mt = torch.linalg.vector_norm(t, dim=(1))\n        st = torch.nan_to_num(t / mt)\n\n        samples_out[\"samples\"] = st * (m1 * ratio + m2 * (1.0 - ratio))\n        return (samples_out,)\n\nclass LatentBatch:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"samples1\": (\"LATENT\",), \"samples2\": (\"LATENT\",)}}\n\n    RETURN_TYPES = (\"LATENT\",)\n    FUNCTION = \"batch\"\n\n    CATEGORY = \"latent/batch\"\n\n    def batch(self, samples1, samples2):\n        samples_out = samples1.copy()\n        s1 = samples1[\"samples\"]\n        s2 = samples2[\"samples\"]\n\n        if s1.shape[1:] != s2.shape[1:]:\n            s2 = ldm_patched.modules.utils.common_upscale(s2, s1.shape[3], s1.shape[2], \"bilinear\", \"center\")\n        s = torch.cat((s1, s2), dim=0)\n        samples_out[\"samples\"] = s\n        samples_out[\"batch_index\"] = samples1.get(\"batch_index\", [x for x in range(0, s1.shape[0])]) + samples2.get(\"batch_index\", [x for x in range(0, s2.shape[0])])\n        return (samples_out,)\n\nclass LatentBatchSeedBehavior:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"samples\": (\"LATENT\",),\n                              \"seed_behavior\": ([\"random\", \"fixed\"],),}}\n\n    RETURN_TYPES = (\"LATENT\",)\n    FUNCTION = \"op\"\n\n    CATEGORY = \"latent/advanced\"\n\n    def op(self, samples, seed_behavior):\n        samples_out = samples.copy()\n        latent = samples[\"samples\"]\n        if seed_behavior == \"random\":\n            if 'batch_index' in samples_out:\n                samples_out.pop('batch_index')\n        elif seed_behavior == \"fixed\":\n            batch_number = samples_out.get(\"batch_index\", [0])[0]\n            samples_out[\"batch_index\"] = [batch_number] * latent.shape[0]\n\n        return (samples_out,)\n\nNODE_CLASS_MAPPINGS = {\n    \"LatentAdd\": LatentAdd,\n    \"LatentSubtract\": LatentSubtract,\n    \"LatentMultiply\": LatentMultiply,\n    \"LatentInterpolate\": LatentInterpolate,\n    \"LatentBatch\": LatentBatch,\n    \"LatentBatchSeedBehavior\": LatentBatchSeedBehavior,\n}\n"
  },
  {
    "path": "ldm_patched/contrib/external_mask.py",
    "content": "# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py \n\nimport numpy as np\nimport scipy.ndimage\nimport torch\nimport ldm_patched.modules.utils\n\nfrom ldm_patched.contrib.external import MAX_RESOLUTION\n\ndef composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False):\n    source = source.to(destination.device)\n    if resize_source:\n        source = torch.nn.functional.interpolate(source, size=(destination.shape[2], destination.shape[3]), mode=\"bilinear\")\n\n    source = ldm_patched.modules.utils.repeat_to_batch_size(source, destination.shape[0])\n\n    x = max(-source.shape[3] * multiplier, min(x, destination.shape[3] * multiplier))\n    y = max(-source.shape[2] * multiplier, min(y, destination.shape[2] * multiplier))\n\n    left, top = (x // multiplier, y // multiplier)\n    right, bottom = (left + source.shape[3], top + source.shape[2],)\n\n    if mask is None:\n        mask = torch.ones_like(source)\n    else:\n        mask = mask.to(destination.device, copy=True)\n        mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(source.shape[2], source.shape[3]), mode=\"bilinear\")\n        mask = ldm_patched.modules.utils.repeat_to_batch_size(mask, source.shape[0])\n\n    # calculate the bounds of the source that will be overlapping the destination\n    # this prevents the source trying to overwrite latent pixels that are out of bounds\n    # of the destination\n    visible_width, visible_height = (destination.shape[3] - left + min(0, x), destination.shape[2] - top + min(0, y),)\n\n    mask = mask[:, :, :visible_height, :visible_width]\n    inverse_mask = torch.ones_like(mask) - mask\n\n    source_portion = mask * source[:, :, :visible_height, :visible_width]\n    destination_portion = inverse_mask  * destination[:, :, top:bottom, left:right]\n\n    destination[:, :, top:bottom, left:right] = source_portion + destination_portion\n    return destination\n\nclass LatentCompositeMasked:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\n            \"required\": {\n                \"destination\": (\"LATENT\",),\n                \"source\": (\"LATENT\",),\n                \"x\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 8}),\n                \"y\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 8}),\n                \"resize_source\": (\"BOOLEAN\", {\"default\": False}),\n            },\n            \"optional\": {\n                \"mask\": (\"MASK\",),\n            }\n        }\n    RETURN_TYPES = (\"LATENT\",)\n    FUNCTION = \"composite\"\n\n    CATEGORY = \"latent\"\n\n    def composite(self, destination, source, x, y, resize_source, mask = None):\n        output = destination.copy()\n        destination = destination[\"samples\"].clone()\n        source = source[\"samples\"]\n        output[\"samples\"] = composite(destination, source, x, y, mask, 8, resize_source)\n        return (output,)\n\nclass ImageCompositeMasked:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\n            \"required\": {\n                \"destination\": (\"IMAGE\",),\n                \"source\": (\"IMAGE\",),\n                \"x\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 1}),\n                \"y\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 1}),\n                \"resize_source\": (\"BOOLEAN\", {\"default\": False}),\n            },\n            \"optional\": {\n                \"mask\": (\"MASK\",),\n            }\n        }\n    RETURN_TYPES = (\"IMAGE\",)\n    FUNCTION = \"composite\"\n\n    CATEGORY = \"image\"\n\n    def composite(self, destination, source, x, y, resize_source, mask = None):\n        destination = destination.clone().movedim(-1, 1)\n        output = composite(destination, source.movedim(-1, 1), x, y, mask, 1, resize_source).movedim(1, -1)\n        return (output,)\n\nclass MaskToImage:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\n                \"required\": {\n                    \"mask\": (\"MASK\",),\n                }\n        }\n\n    CATEGORY = \"mask\"\n\n    RETURN_TYPES = (\"IMAGE\",)\n    FUNCTION = \"mask_to_image\"\n\n    def mask_to_image(self, mask):\n        result = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3)\n        return (result,)\n\nclass ImageToMask:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\n                \"required\": {\n                    \"image\": (\"IMAGE\",),\n                    \"channel\": ([\"red\", \"green\", \"blue\", \"alpha\"],),\n                }\n        }\n\n    CATEGORY = \"mask\"\n\n    RETURN_TYPES = (\"MASK\",)\n    FUNCTION = \"image_to_mask\"\n\n    def image_to_mask(self, image, channel):\n        channels = [\"red\", \"green\", \"blue\", \"alpha\"]\n        mask = image[:, :, :, channels.index(channel)]\n        return (mask,)\n\nclass ImageColorToMask:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\n                \"required\": {\n                    \"image\": (\"IMAGE\",),\n                    \"color\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": 0xFFFFFF, \"step\": 1, \"display\": \"color\"}),\n                }\n        }\n\n    CATEGORY = \"mask\"\n\n    RETURN_TYPES = (\"MASK\",)\n    FUNCTION = \"image_to_mask\"\n\n    def image_to_mask(self, image, color):\n        temp = (torch.clamp(image, 0, 1.0) * 255.0).round().to(torch.int)\n        temp = torch.bitwise_left_shift(temp[:,:,:,0], 16) + torch.bitwise_left_shift(temp[:,:,:,1], 8) + temp[:,:,:,2]\n        mask = torch.where(temp == color, 255, 0).float()\n        return (mask,)\n\nclass SolidMask:\n    @classmethod\n    def INPUT_TYPES(cls):\n        return {\n            \"required\": {\n                \"value\": (\"FLOAT\", {\"default\": 1.0, \"min\": 0.0, \"max\": 1.0, \"step\": 0.01}),\n                \"width\": (\"INT\", {\"default\": 512, \"min\": 1, \"max\": MAX_RESOLUTION, \"step\": 1}),\n                \"height\": (\"INT\", {\"default\": 512, \"min\": 1, \"max\": MAX_RESOLUTION, \"step\": 1}),\n            }\n        }\n\n    CATEGORY = \"mask\"\n\n    RETURN_TYPES = (\"MASK\",)\n\n    FUNCTION = \"solid\"\n\n    def solid(self, value, width, height):\n        out = torch.full((1, height, width), value, dtype=torch.float32, device=\"cpu\")\n        return (out,)\n\nclass InvertMask:\n    @classmethod\n    def INPUT_TYPES(cls):\n        return {\n            \"required\": {\n                \"mask\": (\"MASK\",),\n            }\n        }\n\n    CATEGORY = \"mask\"\n\n    RETURN_TYPES = (\"MASK\",)\n\n    FUNCTION = \"invert\"\n\n    def invert(self, mask):\n        out = 1.0 - mask\n        return (out,)\n\nclass CropMask:\n    @classmethod\n    def INPUT_TYPES(cls):\n        return {\n            \"required\": {\n                \"mask\": (\"MASK\",),\n                \"x\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 1}),\n                \"y\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 1}),\n                \"width\": (\"INT\", {\"default\": 512, \"min\": 1, \"max\": MAX_RESOLUTION, \"step\": 1}),\n                \"height\": (\"INT\", {\"default\": 512, \"min\": 1, \"max\": MAX_RESOLUTION, \"step\": 1}),\n            }\n        }\n\n    CATEGORY = \"mask\"\n\n    RETURN_TYPES = (\"MASK\",)\n\n    FUNCTION = \"crop\"\n\n    def crop(self, mask, x, y, width, height):\n        mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1]))\n        out = mask[:, y:y + height, x:x + width]\n        return (out,)\n\nclass MaskComposite:\n    @classmethod\n    def INPUT_TYPES(cls):\n        return {\n            \"required\": {\n                \"destination\": (\"MASK\",),\n                \"source\": (\"MASK\",),\n                \"x\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 1}),\n                \"y\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 1}),\n                \"operation\": ([\"multiply\", \"add\", \"subtract\", \"and\", \"or\", \"xor\"],),\n            }\n        }\n\n    CATEGORY = \"mask\"\n\n    RETURN_TYPES = (\"MASK\",)\n\n    FUNCTION = \"combine\"\n\n    def combine(self, destination, source, x, y, operation):\n        output = destination.reshape((-1, destination.shape[-2], destination.shape[-1])).clone()\n        source = source.reshape((-1, source.shape[-2], source.shape[-1]))\n\n        left, top = (x, y,)\n        right, bottom = (min(left + source.shape[-1], destination.shape[-1]), min(top + source.shape[-2], destination.shape[-2]))\n        visible_width, visible_height = (right - left, bottom - top,)\n\n        source_portion = source[:, :visible_height, :visible_width]\n        destination_portion = destination[:, top:bottom, left:right]\n\n        if operation == \"multiply\":\n            output[:, top:bottom, left:right] = destination_portion * source_portion\n        elif operation == \"add\":\n            output[:, top:bottom, left:right] = destination_portion + source_portion\n        elif operation == \"subtract\":\n            output[:, top:bottom, left:right] = destination_portion - source_portion\n        elif operation == \"and\":\n            output[:, top:bottom, left:right] = torch.bitwise_and(destination_portion.round().bool(), source_portion.round().bool()).float()\n        elif operation == \"or\":\n            output[:, top:bottom, left:right] = torch.bitwise_or(destination_portion.round().bool(), source_portion.round().bool()).float()\n        elif operation == \"xor\":\n            output[:, top:bottom, left:right] = torch.bitwise_xor(destination_portion.round().bool(), source_portion.round().bool()).float()\n\n        output = torch.clamp(output, 0.0, 1.0)\n\n        return (output,)\n\nclass FeatherMask:\n    @classmethod\n    def INPUT_TYPES(cls):\n        return {\n            \"required\": {\n                \"mask\": (\"MASK\",),\n                \"left\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 1}),\n                \"top\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 1}),\n                \"right\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 1}),\n                \"bottom\": (\"INT\", {\"default\": 0, \"min\": 0, \"max\": MAX_RESOLUTION, \"step\": 1}),\n            }\n        }\n\n    CATEGORY = \"mask\"\n\n    RETURN_TYPES = (\"MASK\",)\n\n    FUNCTION = \"feather\"\n\n    def feather(self, mask, left, top, right, bottom):\n        output = mask.reshape((-1, mask.shape[-2], mask.shape[-1])).clone()\n\n        left = min(left, output.shape[-1])\n        right = min(right, output.shape[-1])\n        top = min(top, output.shape[-2])\n        bottom = min(bottom, output.shape[-2])\n\n        for x in range(left):\n            feather_rate = (x + 1.0) / left\n            output[:, :, x] *= feather_rate\n\n        for x in range(right):\n            feather_rate = (x + 1) / right\n            output[:, :, -x] *= feather_rate\n\n        for y in range(top):\n            feather_rate = (y + 1) / top\n            output[:, y, :] *= feather_rate\n\n        for y in range(bottom):\n            feather_rate = (y + 1) / bottom\n            output[:, -y, :] *= feather_rate\n\n        return (output,)\n    \nclass GrowMask:\n    @classmethod\n    def INPUT_TYPES(cls):\n        return {\n            \"required\": {\n                \"mask\": (\"MASK\",),\n                \"expand\": (\"INT\", {\"default\": 0, \"min\": -MAX_RESOLUTION, \"max\": MAX_RESOLUTION, \"step\": 1}),\n                \"tapered_corners\": (\"BOOLEAN\", {\"default\": True}),\n            },\n        }\n    \n    CATEGORY = \"mask\"\n\n    RETURN_TYPES = (\"MASK\",)\n\n    FUNCTION = \"expand_mask\"\n\n    def expand_mask(self, mask, expand, tapered_corners):\n        c = 0 if tapered_corners else 1\n        kernel = np.array([[c, 1, c],\n                           [1, 1, 1],\n                           [c, 1, c]])\n        mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1]))\n        out = []\n        for m in mask:\n            output = m.numpy()\n            for _ in range(abs(expand)):\n                if expand < 0:\n                    output = scipy.ndimage.grey_erosion(output, footprint=kernel)\n                else:\n                    output = scipy.ndimage.grey_dilation(output, footprint=kernel)\n            output = torch.from_numpy(output)\n            out.append(output)\n        return (torch.stack(out, dim=0),)\n\n\n\nNODE_CLASS_MAPPINGS = {\n    \"LatentCompositeMasked\": LatentCompositeMasked,\n    \"ImageCompositeMasked\": ImageCompositeMasked,\n    \"MaskToImage\": MaskToImage,\n    \"ImageToMask\": ImageToMask,\n    \"ImageColorToMask\": ImageColorToMask,\n    \"SolidMask\": SolidMask,\n    \"InvertMask\": InvertMask,\n    \"CropMask\": CropMask,\n    \"MaskComposite\": MaskComposite,\n    \"FeatherMask\": FeatherMask,\n    \"GrowMask\": GrowMask,\n}\n\nNODE_DISPLAY_NAME_MAPPINGS = {\n    \"ImageToMask\": \"Convert Image to Mask\",\n    \"MaskToImage\": \"Convert Mask to Image\",\n}\n"
  },
  {
    "path": "ldm_patched/contrib/external_model_advanced.py",
    "content": "# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py \n\nimport ldm_patched.utils.path_utils\nimport ldm_patched.modules.sd\nimport ldm_patched.modules.model_sampling\nimport torch\n\nclass LCM(ldm_patched.modules.model_sampling.EPS):\n    def calculate_denoised(self, sigma, model_output, model_input):\n        timestep = self.timestep(sigma).view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))\n        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))\n        x0 = model_input - model_output * sigma\n\n        sigma_data = 0.5\n        scaled_timestep = timestep * 10.0 #timestep_scaling\n\n        c_skip = sigma_data**2 / (scaled_timestep**2 + sigma_data**2)\n        c_out = scaled_timestep / (scaled_timestep**2 + sigma_data**2) ** 0.5\n\n        return c_out * x0 + c_skip * model_input\n\nclass ModelSamplingDiscreteDistilled(ldm_patched.modules.model_sampling.ModelSamplingDiscrete):\n    original_timesteps = 50\n\n    def __init__(self, model_config=None):\n        super().__init__(model_config)\n\n        self.skip_steps = self.num_timesteps // self.original_timesteps\n\n        sigmas_valid = torch.zeros((self.original_timesteps), dtype=torch.float32)\n        for x in range(self.original_timesteps):\n            sigmas_valid[self.original_timesteps - 1 - x] = self.sigmas[self.num_timesteps - 1 - x * self.skip_steps]\n\n        self.set_sigmas(sigmas_valid)\n\n    def timestep(self, sigma):\n        log_sigma = sigma.log()\n        dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None]\n        return (dists.abs().argmin(dim=0).view(sigma.shape) * self.skip_steps + (self.skip_steps - 1)).to(sigma.device)\n\n    def sigma(self, timestep):\n        t = torch.clamp(((timestep.float().to(self.log_sigmas.device) - (self.skip_steps - 1)) / self.skip_steps).float(), min=0, max=(len(self.sigmas) - 1))\n        low_idx = t.floor().long()\n        high_idx = t.ceil().long()\n        w = t.frac()\n        log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx]\n        return log_sigma.exp().to(timestep.device)\n\n\ndef rescale_zero_terminal_snr_sigmas(sigmas):\n    alphas_cumprod = 1 / ((sigmas * sigmas) + 1)\n    alphas_bar_sqrt = alphas_cumprod.sqrt()\n\n    # Store old values.\n    alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()\n    alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()\n\n    # Shift so the last timestep is zero.\n    alphas_bar_sqrt -= (alphas_bar_sqrt_T)\n\n    # Scale so the first timestep is back to the old value.\n    alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)\n\n    # Convert alphas_bar_sqrt to betas\n    alphas_bar = alphas_bar_sqrt**2  # Revert sqrt\n    alphas_bar[-1] = 4.8973451890853435e-08\n    return ((1 - alphas_bar) / alphas_bar) ** 0.5\n\nclass ModelSamplingDiscrete:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"model\": (\"MODEL\",),\n                              \"sampling\": ([\"eps\", \"v_prediction\", \"lcm\", \"tcd\"]),\n                              \"zsnr\": (\"BOOLEAN\", {\"default\": False}),\n                              }}\n\n    RETURN_TYPES = (\"MODEL\",)\n    FUNCTION = \"patch\"\n\n    CATEGORY = \"advanced/model\"\n\n    def patch(self, model, sampling, zsnr):\n        m = model.clone()\n\n        sampling_base = ldm_patched.modules.model_sampling.ModelSamplingDiscrete\n        if sampling == \"eps\":\n            sampling_type = ldm_patched.modules.model_sampling.EPS\n        elif sampling == \"v_prediction\":\n            sampling_type = ldm_patched.modules.model_sampling.V_PREDICTION\n        elif sampling == \"lcm\":\n            sampling_type = LCM\n            sampling_base = ModelSamplingDiscreteDistilled\n        elif sampling == \"tcd\":\n            sampling_type = ldm_patched.modules.model_sampling.EPS\n            sampling_base = ModelSamplingDiscreteDistilled\n\n        class ModelSamplingAdvanced(sampling_base, sampling_type):\n            pass\n\n        model_sampling = ModelSamplingAdvanced(model.model.model_config)\n        if zsnr:\n            model_sampling.set_sigmas(rescale_zero_terminal_snr_sigmas(model_sampling.sigmas))\n\n        m.add_object_patch(\"model_sampling\", model_sampling)\n        return (m, )\n\nclass ModelSamplingContinuousEDM:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"model\": (\"MODEL\",),\n                              \"sampling\": ([\"v_prediction\", \"edm_playground_v2.5\", \"eps\"],),\n                              \"sigma_max\": (\"FLOAT\", {\"default\": 120.0, \"min\": 0.0, \"max\": 1000.0, \"step\":0.001, \"round\": False}),\n                              \"sigma_min\": (\"FLOAT\", {\"default\": 0.002, \"min\": 0.0, \"max\": 1000.0, \"step\":0.001, \"round\": False}),\n                              }}\n\n    RETURN_TYPES = (\"MODEL\",)\n    FUNCTION = \"patch\"\n\n    CATEGORY = \"advanced/model\"\n\n    def patch(self, model, sampling, sigma_max, sigma_min):\n        m = model.clone()\n\n        latent_format = None\n        sigma_data = 1.0\n        if sampling == \"eps\":\n            sampling_type = ldm_patched.modules.model_sampling.EPS\n        elif sampling == \"v_prediction\":\n            sampling_type = ldm_patched.modules.model_sampling.V_PREDICTION\n        elif sampling == \"edm_playground_v2.5\":\n            sampling_type = ldm_patched.modules.model_sampling.EDM\n            sigma_data = 0.5\n            latent_format = ldm_patched.modules.latent_formats.SDXL_Playground_2_5()\n\n        class ModelSamplingAdvanced(ldm_patched.modules.model_sampling.ModelSamplingContinuousEDM, sampling_type):\n            pass\n\n        model_sampling = ModelSamplingAdvanced(model.model.model_config)\n        model_sampling.set_parameters(sigma_min, sigma_max, sigma_data)\n        m.add_object_patch(\"model_sampling\", model_sampling)\n        if latent_format is not None:\n            m.add_object_patch(\"latent_format\", latent_format)\n        return (m, )\n\nclass RescaleCFG:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"model\": (\"MODEL\",),\n                              \"multiplier\": (\"FLOAT\", {\"default\": 0.7, \"min\": 0.0, \"max\": 1.0, \"step\": 0.01}),\n                              }}\n    RETURN_TYPES = (\"MODEL\",)\n    FUNCTION = \"patch\"\n\n    CATEGORY = \"advanced/model\"\n\n    def patch(self, model, multiplier):\n        def rescale_cfg(args):\n            cond = args[\"cond\"]\n            uncond = args[\"uncond\"]\n            cond_scale = args[\"cond_scale\"]\n            sigma = args[\"sigma\"]\n            sigma = sigma.view(sigma.shape[:1] + (1,) * (cond.ndim - 1))\n            x_orig = args[\"input\"]\n\n            #rescale cfg has to be done on v-pred model output\n            x = x_orig / (sigma * sigma + 1.0)\n            cond = ((x - (x_orig - cond)) * (sigma ** 2 + 1.0) ** 0.5) / (sigma)\n            uncond = ((x - (x_orig - uncond)) * (sigma ** 2 + 1.0) ** 0.5) / (sigma)\n\n            #rescalecfg\n            x_cfg = uncond + cond_scale * (cond - uncond)\n            ro_pos = torch.std(cond, dim=(1,2,3), keepdim=True)\n            ro_cfg = torch.std(x_cfg, dim=(1,2,3), keepdim=True)\n\n            x_rescaled = x_cfg * (ro_pos / ro_cfg)\n            x_final = multiplier * x_rescaled + (1.0 - multiplier) * x_cfg\n\n            return x_orig - (x - x_final * sigma / (sigma * sigma + 1.0) ** 0.5)\n\n        m = model.clone()\n        m.set_model_sampler_cfg_function(rescale_cfg)\n        return (m, )\n\nNODE_CLASS_MAPPINGS = {\n    \"ModelSamplingDiscrete\": ModelSamplingDiscrete,\n    \"ModelSamplingContinuousEDM\": ModelSamplingContinuousEDM,\n    \"RescaleCFG\": RescaleCFG,\n}\n"
  },
  {
    "path": "ldm_patched/contrib/external_model_downscale.py",
    "content": "# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py \n\nimport torch\nimport ldm_patched.modules.utils\n\nclass PatchModelAddDownscale:\n    upscale_methods = [\"bicubic\", \"nearest-exact\", \"bilinear\", \"area\", \"bislerp\"]\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"model\": (\"MODEL\",),\n                              \"block_number\": (\"INT\", {\"default\": 3, \"min\": 1, \"max\": 32, \"step\": 1}),\n                              \"downscale_factor\": (\"FLOAT\", {\"default\": 2.0, \"min\": 0.1, \"max\": 9.0, \"step\": 0.001}),\n                              \"start_percent\": (\"FLOAT\", {\"default\": 0.0, \"min\": 0.0, \"max\": 1.0, \"step\": 0.001}),\n                              \"end_percent\": (\"FLOAT\", {\"default\": 0.35, \"min\": 0.0, \"max\": 1.0, \"step\": 0.001}),\n                              \"downscale_after_skip\": (\"BOOLEAN\", {\"default\": True}),\n                              \"downscale_method\": (s.upscale_methods,),\n                              \"upscale_method\": (s.upscale_methods,),\n                              }}\n    RETURN_TYPES = (\"MODEL\",)\n    FUNCTION = \"patch\"\n\n    CATEGORY = \"_for_testing\"\n\n    def patch(self, model, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip, downscale_method, upscale_method):\n        sigma_start = model.model.model_sampling.percent_to_sigma(start_percent)\n        sigma_end = model.model.model_sampling.percent_to_sigma(end_percent)\n\n        def input_block_patch(h, transformer_options):\n            if transformer_options[\"block\"][1] == block_number:\n                sigma = transformer_options[\"sigmas\"][0].item()\n                if sigma <= sigma_start and sigma >= sigma_end:\n                    h = ldm_patched.modules.utils.common_upscale(h, round(h.shape[-1] * (1.0 / downscale_factor)), round(h.shape[-2] * (1.0 / downscale_factor)), downscale_method, \"disabled\")\n            return h\n\n        def output_block_patch(h, hsp, transformer_options):\n            if h.shape[2] != hsp.shape[2]:\n                h = ldm_patched.modules.utils.common_upscale(h, hsp.shape[-1], hsp.shape[-2], upscale_method, \"disabled\")\n            return h, hsp\n\n        m = model.clone()\n        if downscale_after_skip:\n            m.set_model_input_block_patch_after_skip(input_block_patch)\n        else:\n            m.set_model_input_block_patch(input_block_patch)\n        m.set_model_output_block_patch(output_block_patch)\n        return (m, )\n\nNODE_CLASS_MAPPINGS = {\n    \"PatchModelAddDownscale\": PatchModelAddDownscale,\n}\n\nNODE_DISPLAY_NAME_MAPPINGS = {\n    # Sampling\n    \"PatchModelAddDownscale\": \"PatchModelAddDownscale (Kohya Deep Shrink)\",\n}\n"
  },
  {
    "path": "ldm_patched/contrib/external_model_merging.py",
    "content": "# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py \n\nimport ldm_patched.modules.sd\nimport ldm_patched.modules.utils\nimport ldm_patched.modules.model_base\nimport ldm_patched.modules.model_management\n\nimport ldm_patched.utils.path_utils\nimport json\nimport os\n\nfrom ldm_patched.modules.args_parser import args\n\nclass ModelMergeSimple:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"model1\": (\"MODEL\",),\n                              \"model2\": (\"MODEL\",),\n                              \"ratio\": (\"FLOAT\", {\"default\": 1.0, \"min\": 0.0, \"max\": 1.0, \"step\": 0.01}),\n                              }}\n    RETURN_TYPES = (\"MODEL\",)\n    FUNCTION = \"merge\"\n\n    CATEGORY = \"advanced/model_merging\"\n\n    def merge(self, model1, model2, ratio):\n        m = model1.clone()\n        kp = model2.get_key_patches(\"diffusion_model.\")\n        for k in kp:\n            m.add_patches({k: kp[k]}, 1.0 - ratio, ratio)\n        return (m, )\n\nclass ModelSubtract:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"model1\": (\"MODEL\",),\n                              \"model2\": (\"MODEL\",),\n                              \"multiplier\": (\"FLOAT\", {\"default\": 1.0, \"min\": -10.0, \"max\": 10.0, \"step\": 0.01}),\n                              }}\n    RETURN_TYPES = (\"MODEL\",)\n    FUNCTION = \"merge\"\n\n    CATEGORY = \"advanced/model_merging\"\n\n    def merge(self, model1, model2, multiplier):\n        m = model1.clone()\n        kp = model2.get_key_patches(\"diffusion_model.\")\n        for k in kp:\n            m.add_patches({k: kp[k]}, - multiplier, multiplier)\n        return (m, )\n\nclass ModelAdd:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"model1\": (\"MODEL\",),\n                              \"model2\": (\"MODEL\",),\n                              }}\n    RETURN_TYPES = (\"MODEL\",)\n    FUNCTION = \"merge\"\n\n    CATEGORY = \"advanced/model_merging\"\n\n    def merge(self, model1, model2):\n        m = model1.clone()\n        kp = model2.get_key_patches(\"diffusion_model.\")\n        for k in kp:\n            m.add_patches({k: kp[k]}, 1.0, 1.0)\n        return (m, )\n\n\nclass CLIPMergeSimple:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"clip1\": (\"CLIP\",),\n                              \"clip2\": (\"CLIP\",),\n                              \"ratio\": (\"FLOAT\", {\"default\": 1.0, \"min\": 0.0, \"max\": 1.0, \"step\": 0.01}),\n                              }}\n    RETURN_TYPES = (\"CLIP\",)\n    FUNCTION = \"merge\"\n\n    CATEGORY = \"advanced/model_merging\"\n\n    def merge(self, clip1, clip2, ratio):\n        m = clip1.clone()\n        kp = clip2.get_key_patches()\n        for k in kp:\n            if k.endswith(\".position_ids\") or k.endswith(\".logit_scale\"):\n                continue\n            m.add_patches({k: kp[k]}, 1.0 - ratio, ratio)\n        return (m, )\n\nclass ModelMergeBlocks:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"model1\": (\"MODEL\",),\n                              \"model2\": (\"MODEL\",),\n                              \"input\": (\"FLOAT\", {\"default\": 1.0, \"min\": 0.0, \"max\": 1.0, \"step\": 0.01}),\n                              \"middle\": (\"FLOAT\", {\"default\": 1.0, \"min\": 0.0, \"max\": 1.0, \"step\": 0.01}),\n                              \"out\": (\"FLOAT\", {\"default\": 1.0, \"min\": 0.0, \"max\": 1.0, \"step\": 0.01})\n                              }}\n    RETURN_TYPES = (\"MODEL\",)\n    FUNCTION = \"merge\"\n\n    CATEGORY = \"advanced/model_merging\"\n\n    def merge(self, model1, model2, **kwargs):\n        m = model1.clone()\n        kp = model2.get_key_patches(\"diffusion_model.\")\n        default_ratio = next(iter(kwargs.values()))\n\n        for k in kp:\n            ratio = default_ratio\n            k_unet = k[len(\"diffusion_model.\"):]\n\n            last_arg_size = 0\n            for arg in kwargs:\n                if k_unet.startswith(arg) and last_arg_size < len(arg):\n                    ratio = kwargs[arg]\n                    last_arg_size = len(arg)\n\n            m.add_patches({k: kp[k]}, 1.0 - ratio, ratio)\n        return (m, )\n\ndef save_checkpoint(model, clip=None, vae=None, clip_vision=None, filename_prefix=None, output_dir=None, prompt=None, extra_pnginfo=None):\n    full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, output_dir)\n    prompt_info = \"\"\n    if prompt is not None:\n        prompt_info = json.dumps(prompt)\n\n    metadata = {}\n\n    enable_modelspec = True\n    if isinstance(model.model, ldm_patched.modules.model_base.SDXL):\n        metadata[\"modelspec.architecture\"] = \"stable-diffusion-xl-v1-base\"\n    elif isinstance(model.model, ldm_patched.modules.model_base.SDXLRefiner):\n        metadata[\"modelspec.architecture\"] = \"stable-diffusion-xl-v1-refiner\"\n    else:\n        enable_modelspec = False\n\n    if enable_modelspec:\n        metadata[\"modelspec.sai_model_spec\"] = \"1.0.0\"\n        metadata[\"modelspec.implementation\"] = \"sgm\"\n        metadata[\"modelspec.title\"] = \"{} {}\".format(filename, counter)\n\n    #TODO:\n    # \"stable-diffusion-v1\", \"stable-diffusion-v1-inpainting\", \"stable-diffusion-v2-512\",\n    # \"stable-diffusion-v2-768-v\", \"stable-diffusion-v2-unclip-l\", \"stable-diffusion-v2-unclip-h\",\n    # \"v2-inpainting\"\n\n    if model.model.model_type == ldm_patched.modules.model_base.ModelType.EPS:\n        metadata[\"modelspec.predict_key\"] = \"epsilon\"\n    elif model.model.model_type == ldm_patched.modules.model_base.ModelType.V_PREDICTION:\n        metadata[\"modelspec.predict_key\"] = \"v\"\n\n    if not args.disable_server_info:\n        metadata[\"prompt\"] = prompt_info\n        if extra_pnginfo is not None:\n            for x in extra_pnginfo:\n                metadata[x] = json.dumps(extra_pnginfo[x])\n\n    output_checkpoint = f\"{filename}_{counter:05}_.safetensors\"\n    output_checkpoint = os.path.join(full_output_folder, output_checkpoint)\n\n    ldm_patched.modules.sd.save_checkpoint(output_checkpoint, model, clip, vae, clip_vision, metadata=metadata)\n\nclass CheckpointSave:\n    def __init__(self):\n        self.output_dir = ldm_patched.utils.path_utils.get_output_directory()\n\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"model\": (\"MODEL\",),\n                              \"clip\": (\"CLIP\",),\n                              \"vae\": (\"VAE\",),\n                              \"filename_prefix\": (\"STRING\", {\"default\": \"checkpoints/ldm_patched\"}),},\n                \"hidden\": {\"prompt\": \"PROMPT\", \"extra_pnginfo\": \"EXTRA_PNGINFO\"},}\n    RETURN_TYPES = ()\n    FUNCTION = \"save\"\n    OUTPUT_NODE = True\n\n    CATEGORY = \"advanced/model_merging\"\n\n    def save(self, model, clip, vae, filename_prefix, prompt=None, extra_pnginfo=None):\n        save_checkpoint(model, clip=clip, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo)\n        return {}\n\nclass CLIPSave:\n    def __init__(self):\n        self.output_dir = ldm_patched.utils.path_utils.get_output_directory()\n\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"clip\": (\"CLIP\",),\n                              \"filename_prefix\": (\"STRING\", {\"default\": \"clip/ldm_patched\"}),},\n                \"hidden\": {\"prompt\": \"PROMPT\", \"extra_pnginfo\": \"EXTRA_PNGINFO\"},}\n    RETURN_TYPES = ()\n    FUNCTION = \"save\"\n    OUTPUT_NODE = True\n\n    CATEGORY = \"advanced/model_merging\"\n\n    def save(self, clip, filename_prefix, prompt=None, extra_pnginfo=None):\n        prompt_info = \"\"\n        if prompt is not None:\n            prompt_info = json.dumps(prompt)\n\n        metadata = {}\n        if not args.disable_server_info:\n            metadata[\"prompt\"] = prompt_info\n            if extra_pnginfo is not None:\n                for x in extra_pnginfo:\n                    metadata[x] = json.dumps(extra_pnginfo[x])\n\n        ldm_patched.modules.model_management.load_models_gpu([clip.load_model()])\n        clip_sd = clip.get_sd()\n\n        for prefix in [\"clip_l.\", \"clip_g.\", \"\"]:\n            k = list(filter(lambda a: a.startswith(prefix), clip_sd.keys()))\n            current_clip_sd = {}\n            for x in k:\n                current_clip_sd[x] = clip_sd.pop(x)\n            if len(current_clip_sd) == 0:\n                continue\n\n            p = prefix[:-1]\n            replace_prefix = {}\n            filename_prefix_ = filename_prefix\n            if len(p) > 0:\n                filename_prefix_ = \"{}_{}\".format(filename_prefix_, p)\n                replace_prefix[prefix] = \"\"\n            replace_prefix[\"transformer.\"] = \"\"\n\n            full_output_folder, filename, counter, subfolder, filename_prefix_ = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix_, self.output_dir)\n\n            output_checkpoint = f\"{filename}_{counter:05}_.safetensors\"\n            output_checkpoint = os.path.join(full_output_folder, output_checkpoint)\n\n            current_clip_sd = ldm_patched.modules.utils.state_dict_prefix_replace(current_clip_sd, replace_prefix)\n\n            ldm_patched.modules.utils.save_torch_file(current_clip_sd, output_checkpoint, metadata=metadata)\n        return {}\n\nclass VAESave:\n    def __init__(self):\n        self.output_dir = ldm_patched.utils.path_utils.get_output_directory()\n\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"vae\": (\"VAE\",),\n                              \"filename_prefix\": (\"STRING\", {\"default\": \"vae/ldm_patched_vae\"}),},\n                \"hidden\": {\"prompt\": \"PROMPT\", \"extra_pnginfo\": \"EXTRA_PNGINFO\"},}\n    RETURN_TYPES = ()\n    FUNCTION = \"save\"\n    OUTPUT_NODE = True\n\n    CATEGORY = \"advanced/model_merging\"\n\n    def save(self, vae, filename_prefix, prompt=None, extra_pnginfo=None):\n        full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, self.output_dir)\n        prompt_info = \"\"\n        if prompt is not None:\n            prompt_info = json.dumps(prompt)\n\n        metadata = {}\n        if not args.disable_server_info:\n            metadata[\"prompt\"] = prompt_info\n            if extra_pnginfo is not None:\n                for x in extra_pnginfo:\n                    metadata[x] = json.dumps(extra_pnginfo[x])\n\n        output_checkpoint = f\"{filename}_{counter:05}_.safetensors\"\n        output_checkpoint = os.path.join(full_output_folder, output_checkpoint)\n\n        ldm_patched.modules.utils.save_torch_file(vae.get_sd(), output_checkpoint, metadata=metadata)\n        return {}\n\nNODE_CLASS_MAPPINGS = {\n    \"ModelMergeSimple\": ModelMergeSimple,\n    \"ModelMergeBlocks\": ModelMergeBlocks,\n    \"ModelMergeSubtract\": ModelSubtract,\n    \"ModelMergeAdd\": ModelAdd,\n    \"CheckpointSave\": CheckpointSave,\n    \"CLIPMergeSimple\": CLIPMergeSimple,\n    \"CLIPSave\": CLIPSave,\n    \"VAESave\": VAESave,\n}\n"
  },
  {
    "path": "ldm_patched/contrib/external_perpneg.py",
    "content": "# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py \n\nimport torch\nimport ldm_patched.modules.model_management\nimport ldm_patched.modules.sample\nimport ldm_patched.modules.samplers\nimport ldm_patched.modules.utils\n\n\nclass PerpNeg:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": {\"model\": (\"MODEL\", ),\n                             \"empty_conditioning\": (\"CONDITIONING\", ),\n                             \"neg_scale\": (\"FLOAT\", {\"default\": 1.0, \"min\": 0.0, \"max\": 100.0}),\n                            }}\n    RETURN_TYPES = (\"MODEL\",)\n    FUNCTION = \"patch\"\n\n    CATEGORY = \"_for_testing\"\n\n    def patch(self, model, empty_conditioning, neg_scale):\n        m = model.clone()\n        nocond = ldm_patched.modules.sample.convert_cond(empty_conditioning)\n\n        def cfg_function(args):\n            model = args[\"model\"]\n            noise_pred_pos = args[\"cond_denoised\"]\n            noise_pred_neg = args[\"uncond_denoised\"]\n            cond_scale = args[\"cond_scale\"]\n            x = args[\"input\"]\n            sigma = args[\"sigma\"]\n            model_options = args[\"model_options\"]\n            nocond_processed = ldm_patched.modules.samplers.encode_model_conds(model.extra_conds, nocond, x, x.device, \"negative\")\n\n            (noise_pred_nocond, _) = ldm_patched.modules.samplers.calc_cond_uncond_batch(model, nocond_processed, None, x, sigma, model_options)\n\n            pos = noise_pred_pos - noise_pred_nocond\n            neg = noise_pred_neg - noise_pred_nocond\n            perp = ((torch.mul(pos, neg).sum())/(torch.norm(neg)**2)) * neg\n            perp_neg = perp * neg_scale\n            cfg_result = noise_pred_nocond + cond_scale*(pos - perp_neg)\n            cfg_result = x - cfg_result\n            return cfg_result\n\n        m.set_model_sampler_cfg_function(cfg_function)\n\n        return (m, )\n\n\nNODE_CLASS_MAPPINGS = {\n    \"PerpNeg\": PerpNeg,\n}\n\nNODE_DISPLAY_NAME_MAPPINGS = {\n    \"PerpNeg\": \"Perp-Neg\",\n}\n"
  },
  {
    "path": "ldm_patched/contrib/external_photomaker.py",
    "content": "# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py \n\nimport torch\nimport torch.nn as nn\nimport ldm_patched.utils.path_utils\nimport ldm_patched.modules.clip_model\nimport ldm_patched.modules.clip_vision\nimport ldm_patched.modules.ops\n\n# code for model from: https://github.com/TencentARC/PhotoMaker/blob/main/photomaker/model.py under Apache License Version 2.0\nVISION_CONFIG_DICT = {\n    \"hidden_size\": 1024,\n    \"image_size\": 224,\n    \"intermediate_size\": 4096,\n    \"num_attention_heads\": 16,\n    \"num_channels\": 3,\n    \"num_hidden_layers\": 24,\n    \"patch_size\": 14,\n    \"projection_dim\": 768,\n    \"hidden_act\": \"quick_gelu\",\n}\n\nclass MLP(nn.Module):\n    def __init__(self, in_dim, out_dim, hidden_dim, use_residual=True, operations=ldm_patched.modules.ops):\n        super().__init__()\n        if use_residual:\n            assert in_dim == out_dim\n        self.layernorm = operations.LayerNorm(in_dim)\n        self.fc1 = operations.Linear(in_dim, hidden_dim)\n        self.fc2 = operations.Linear(hidden_dim, out_dim)\n        self.use_residual = use_residual\n        self.act_fn = nn.GELU()\n\n    def forward(self, x):\n        residual = x\n        x = self.layernorm(x)\n        x = self.fc1(x)\n        x = self.act_fn(x)\n        x = self.fc2(x)\n        if self.use_residual:\n            x = x + residual\n        return x\n\n\nclass FuseModule(nn.Module):\n    def __init__(self, embed_dim, operations):\n        super().__init__()\n        self.mlp1 = MLP(embed_dim * 2, embed_dim, embed_dim, use_residual=False, operations=operations)\n        self.mlp2 = MLP(embed_dim, embed_dim, embed_dim, use_residual=True, operations=operations)\n        self.layer_norm = operations.LayerNorm(embed_dim)\n\n    def fuse_fn(self, prompt_embeds, id_embeds):\n        stacked_id_embeds = torch.cat([prompt_embeds, id_embeds], dim=-1)\n        stacked_id_embeds = self.mlp1(stacked_id_embeds) + prompt_embeds\n        stacked_id_embeds = self.mlp2(stacked_id_embeds)\n        stacked_id_embeds = self.layer_norm(stacked_id_embeds)\n        return stacked_id_embeds\n\n    def forward(\n        self,\n        prompt_embeds,\n        id_embeds,\n        class_tokens_mask,\n    ) -> torch.Tensor:\n        # id_embeds shape: [b, max_num_inputs, 1, 2048]\n        id_embeds = id_embeds.to(prompt_embeds.dtype)\n        num_inputs = class_tokens_mask.sum().unsqueeze(0) # TODO: check for training case\n        batch_size, max_num_inputs = id_embeds.shape[:2]\n        # seq_length: 77\n        seq_length = prompt_embeds.shape[1]\n        # flat_id_embeds shape: [b*max_num_inputs, 1, 2048]\n        flat_id_embeds = id_embeds.view(\n            -1, id_embeds.shape[-2], id_embeds.shape[-1]\n        )\n        # valid_id_mask [b*max_num_inputs]\n        valid_id_mask = (\n            torch.arange(max_num_inputs, device=flat_id_embeds.device)[None, :]\n            < num_inputs[:, None]\n        )\n        valid_id_embeds = flat_id_embeds[valid_id_mask.flatten()]\n\n        prompt_embeds = prompt_embeds.view(-1, prompt_embeds.shape[-1])\n        class_tokens_mask = class_tokens_mask.view(-1)\n        valid_id_embeds = valid_id_embeds.view(-1, valid_id_embeds.shape[-1])\n        # slice out the image token embeddings\n        image_token_embeds = prompt_embeds[class_tokens_mask]\n        stacked_id_embeds = self.fuse_fn(image_token_embeds, valid_id_embeds)\n        assert class_tokens_mask.sum() == stacked_id_embeds.shape[0], f\"{class_tokens_mask.sum()} != {stacked_id_embeds.shape[0]}\"\n        prompt_embeds.masked_scatter_(class_tokens_mask[:, None], stacked_id_embeds.to(prompt_embeds.dtype))\n        updated_prompt_embeds = prompt_embeds.view(batch_size, seq_length, -1)\n        return updated_prompt_embeds\n\nclass PhotoMakerIDEncoder(ldm_patched.modules.clip_model.CLIPVisionModelProjection):\n    def __init__(self):\n        self.load_device = ldm_patched.modules.model_management.text_encoder_device()\n        offload_device = ldm_patched.modules.model_management.text_encoder_offload_device()\n        dtype = ldm_patched.modules.model_management.text_encoder_dtype(self.load_device)\n\n        super().__init__(VISION_CONFIG_DICT, dtype, offload_device, ldm_patched.modules.ops.manual_cast)\n        self.visual_projection_2 = ldm_patched.modules.ops.manual_cast.Linear(1024, 1280, bias=False)\n        self.fuse_module = FuseModule(2048, ldm_patched.modules.ops.manual_cast)\n\n    def forward(self, id_pixel_values, prompt_embeds, class_tokens_mask):\n        b, num_inputs, c, h, w = id_pixel_values.shape\n        id_pixel_values = id_pixel_values.view(b * num_inputs, c, h, w)\n\n        shared_id_embeds = self.vision_model(id_pixel_values)[2]\n        id_embeds = self.visual_projection(shared_id_embeds)\n        id_embeds_2 = self.visual_projection_2(shared_id_embeds)\n\n        id_embeds = id_embeds.view(b, num_inputs, 1, -1)\n        id_embeds_2 = id_embeds_2.view(b, num_inputs, 1, -1)\n\n        id_embeds = torch.cat((id_embeds, id_embeds_2), dim=-1)\n        updated_prompt_embeds = self.fuse_module(prompt_embeds, id_embeds, class_tokens_mask)\n\n        return updated_prompt_embeds\n\n\nclass PhotoMakerLoader:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"photomaker_model_name\": (ldm_patched.utils.path_utils.get_filename_list(\"photomaker\"), )}}\n\n    RETURN_TYPES = (\"PHOTOMAKER\",)\n    FUNCTION = \"load_photomaker_model\"\n\n    CATEGORY = \"_for_testing/photomaker\"\n\n    def load_photomaker_model(self, photomaker_model_name):\n        photomaker_model_path = ldm_patched.utils.path_utils.get_full_path(\"photomaker\", photomaker_model_name)\n        photomaker_model = PhotoMakerIDEncoder()\n        data = ldm_patched.modules.utils.load_torch_file(photomaker_model_path, safe_load=True)\n        if \"id_encoder\" in data:\n            data = data[\"id_encoder\"]\n        photomaker_model.load_state_dict(data)\n        return (photomaker_model,)\n\n\nclass PhotoMakerEncode:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"photomaker\": (\"PHOTOMAKER\",),\n                              \"image\": (\"IMAGE\",),\n                              \"clip\": (\"CLIP\", ),\n                              \"text\": (\"STRING\", {\"multiline\": True, \"default\": \"photograph of photomaker\"}),\n                             }}\n\n    RETURN_TYPES = (\"CONDITIONING\",)\n    FUNCTION = \"apply_photomaker\"\n\n    CATEGORY = \"_for_testing/photomaker\"\n\n    def apply_photomaker(self, photomaker, image, clip, text):\n        special_token = \"photomaker\"\n        pixel_values = ldm_patched.modules.clip_vision.clip_preprocess(image.to(photomaker.load_device)).float()\n        try:\n            index = text.split(\" \").index(special_token) + 1\n        except ValueError:\n            index = -1\n        tokens = clip.tokenize(text, return_word_ids=True)\n        out_tokens = {}\n        for k in tokens:\n            out_tokens[k] = []\n            for t in tokens[k]:\n                f = list(filter(lambda x: x[2] != index, t))\n                while len(f) < len(t):\n                    f.append(t[-1])\n                out_tokens[k].append(f)\n\n        cond, pooled = clip.encode_from_tokens(out_tokens, return_pooled=True)\n\n        if index > 0:\n            token_index = index - 1\n            num_id_images = 1\n            class_tokens_mask = [True if token_index <= i < token_index+num_id_images else False for i in range(77)]\n            out = photomaker(id_pixel_values=pixel_values.unsqueeze(0), prompt_embeds=cond.to(photomaker.load_device),\n                            class_tokens_mask=torch.tensor(class_tokens_mask, dtype=torch.bool, device=photomaker.load_device).unsqueeze(0))\n        else:\n            out = cond\n\n        return ([[out, {\"pooled_output\": pooled}]], )\n\n\nNODE_CLASS_MAPPINGS = {\n    \"PhotoMakerLoader\": PhotoMakerLoader,\n    \"PhotoMakerEncode\": PhotoMakerEncode,\n}\n\n"
  },
  {
    "path": "ldm_patched/contrib/external_post_processing.py",
    "content": "# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py \n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom PIL import Image\nimport math\n\nimport ldm_patched.modules.utils\n\n\nclass Blend:\n    def __init__(self):\n        pass\n\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\n            \"required\": {\n                \"image1\": (\"IMAGE\",),\n                \"image2\": (\"IMAGE\",),\n                \"blend_factor\": (\"FLOAT\", {\n                    \"default\": 0.5,\n                    \"min\": 0.0,\n                    \"max\": 1.0,\n                    \"step\": 0.01\n                }),\n                \"blend_mode\": ([\"normal\", \"multiply\", \"screen\", \"overlay\", \"soft_light\", \"difference\"],),\n            },\n        }\n\n    RETURN_TYPES = (\"IMAGE\",)\n    FUNCTION = \"blend_images\"\n\n    CATEGORY = \"image/postprocessing\"\n\n    def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str):\n        image2 = image2.to(image1.device)\n        if image1.shape != image2.shape:\n            image2 = image2.permute(0, 3, 1, 2)\n            image2 = ldm_patched.modules.utils.common_upscale(image2, image1.shape[2], image1.shape[1], upscale_method='bicubic', crop='center')\n            image2 = image2.permute(0, 2, 3, 1)\n\n        blended_image = self.blend_mode(image1, image2, blend_mode)\n        blended_image = image1 * (1 - blend_factor) + blended_image * blend_factor\n        blended_image = torch.clamp(blended_image, 0, 1)\n        return (blended_image,)\n\n    def blend_mode(self, img1, img2, mode):\n        if mode == \"normal\":\n            return img2\n        elif mode == \"multiply\":\n            return img1 * img2\n        elif mode == \"screen\":\n            return 1 - (1 - img1) * (1 - img2)\n        elif mode == \"overlay\":\n            return torch.where(img1 <= 0.5, 2 * img1 * img2, 1 - 2 * (1 - img1) * (1 - img2))\n        elif mode == \"soft_light\":\n            return torch.where(img2 <= 0.5, img1 - (1 - 2 * img2) * img1 * (1 - img1), img1 + (2 * img2 - 1) * (self.g(img1) - img1))\n        elif mode == \"difference\":\n            return img1 - img2\n        else:\n            raise ValueError(f\"Unsupported blend mode: {mode}\")\n\n    def g(self, x):\n        return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x))\n\ndef gaussian_kernel(kernel_size: int, sigma: float, device=None):\n    x, y = torch.meshgrid(torch.linspace(-1, 1, kernel_size, device=device), torch.linspace(-1, 1, kernel_size, device=device), indexing=\"ij\")\n    d = torch.sqrt(x * x + y * y)\n    g = torch.exp(-(d * d) / (2.0 * sigma * sigma))\n    return g / g.sum()\n\nclass Blur:\n    def __init__(self):\n        pass\n\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\n            \"required\": {\n                \"image\": (\"IMAGE\",),\n                \"blur_radius\": (\"INT\", {\n                    \"default\": 1,\n                    \"min\": 1,\n                    \"max\": 31,\n                    \"step\": 1\n                }),\n                \"sigma\": (\"FLOAT\", {\n                    \"default\": 1.0,\n                    \"min\": 0.1,\n                    \"max\": 10.0,\n                    \"step\": 0.1\n                }),\n            },\n        }\n\n    RETURN_TYPES = (\"IMAGE\",)\n    FUNCTION = \"blur\"\n\n    CATEGORY = \"image/postprocessing\"\n\n    def blur(self, image: torch.Tensor, blur_radius: int, sigma: float):\n        if blur_radius == 0:\n            return (image,)\n\n        batch_size, height, width, channels = image.shape\n\n        kernel_size = blur_radius * 2 + 1\n        kernel = gaussian_kernel(kernel_size, sigma, device=image.device).repeat(channels, 1, 1).unsqueeze(1)\n\n        image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C)\n        padded_image = F.pad(image, (blur_radius,blur_radius,blur_radius,blur_radius), 'reflect')\n        blurred = F.conv2d(padded_image, kernel, padding=kernel_size // 2, groups=channels)[:,:,blur_radius:-blur_radius, blur_radius:-blur_radius]\n        blurred = blurred.permute(0, 2, 3, 1)\n\n        return (blurred,)\n\nclass Quantize:\n    def __init__(self):\n        pass\n\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\n            \"required\": {\n                \"image\": (\"IMAGE\",),\n                \"colors\": (\"INT\", {\n                    \"default\": 256,\n                    \"min\": 1,\n                    \"max\": 256,\n                    \"step\": 1\n                }),\n                \"dither\": ([\"none\", \"floyd-steinberg\", \"bayer-2\", \"bayer-4\", \"bayer-8\", \"bayer-16\"],),\n            },\n        }\n\n    RETURN_TYPES = (\"IMAGE\",)\n    FUNCTION = \"quantize\"\n\n    CATEGORY = \"image/postprocessing\"\n\n    def bayer(im, pal_im, order):\n        def normalized_bayer_matrix(n):\n            if n == 0:\n                return np.zeros((1,1), \"float32\")\n            else:\n                q = 4 ** n\n                m = q * normalized_bayer_matrix(n - 1)\n                return np.bmat(((m-1.5, m+0.5), (m+1.5, m-0.5))) / q\n\n        num_colors = len(pal_im.getpalette()) // 3\n        spread = 2 * 256 / num_colors\n        bayer_n = int(math.log2(order))\n        bayer_matrix = torch.from_numpy(spread * normalized_bayer_matrix(bayer_n) + 0.5)\n\n        result = torch.from_numpy(np.array(im).astype(np.float32))\n        tw = math.ceil(result.shape[0] / bayer_matrix.shape[0])\n        th = math.ceil(result.shape[1] / bayer_matrix.shape[1])\n        tiled_matrix = bayer_matrix.tile(tw, th).unsqueeze(-1)\n        result.add_(tiled_matrix[:result.shape[0],:result.shape[1]]).clamp_(0, 255)\n        result = result.to(dtype=torch.uint8)\n\n        im = Image.fromarray(result.cpu().numpy())\n        im = im.quantize(palette=pal_im, dither=Image.Dither.NONE)\n        return im\n\n    def quantize(self, image: torch.Tensor, colors: int, dither: str):\n        batch_size, height, width, _ = image.shape\n        result = torch.zeros_like(image)\n\n        for b in range(batch_size):\n            im = Image.fromarray((image[b] * 255).to(torch.uint8).numpy(), mode='RGB')\n\n            pal_im = im.quantize(colors=colors) # Required as described in https://github.com/python-pillow/Pillow/issues/5836\n\n            if dither == \"none\":\n                quantized_image = im.quantize(palette=pal_im, dither=Image.Dither.NONE)\n            elif dither == \"floyd-steinberg\":\n                quantized_image = im.quantize(palette=pal_im, dither=Image.Dither.FLOYDSTEINBERG)\n            elif dither.startswith(\"bayer\"):\n                order = int(dither.split('-')[-1])\n                quantized_image = Quantize.bayer(im, pal_im, order)\n\n            quantized_array = torch.tensor(np.array(quantized_image.convert(\"RGB\"))).float() / 255\n            result[b] = quantized_array\n\n        return (result,)\n\nclass Sharpen:\n    def __init__(self):\n        pass\n\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\n            \"required\": {\n                \"image\": (\"IMAGE\",),\n                \"sharpen_radius\": (\"INT\", {\n                    \"default\": 1,\n                    \"min\": 1,\n                    \"max\": 31,\n                    \"step\": 1\n                }),\n                \"sigma\": (\"FLOAT\", {\n                    \"default\": 1.0,\n                    \"min\": 0.1,\n                    \"max\": 10.0,\n                    \"step\": 0.1\n                }),\n                \"alpha\": (\"FLOAT\", {\n                    \"default\": 1.0,\n                    \"min\": 0.0,\n                    \"max\": 5.0,\n                    \"step\": 0.1\n                }),\n            },\n        }\n\n    RETURN_TYPES = (\"IMAGE\",)\n    FUNCTION = \"sharpen\"\n\n    CATEGORY = \"image/postprocessing\"\n\n    def sharpen(self, image: torch.Tensor, sharpen_radius: int, sigma:float, alpha: float):\n        if sharpen_radius == 0:\n            return (image,)\n\n        batch_size, height, width, channels = image.shape\n\n        kernel_size = sharpen_radius * 2 + 1\n        kernel = gaussian_kernel(kernel_size, sigma, device=image.device) * -(alpha*10)\n        center = kernel_size // 2\n        kernel[center, center] = kernel[center, center] - kernel.sum() + 1.0\n        kernel = kernel.repeat(channels, 1, 1).unsqueeze(1)\n\n        tensor_image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C)\n        tensor_image = F.pad(tensor_image, (sharpen_radius,sharpen_radius,sharpen_radius,sharpen_radius), 'reflect')\n        sharpened = F.conv2d(tensor_image, kernel, padding=center, groups=channels)[:,:,sharpen_radius:-sharpen_radius, sharpen_radius:-sharpen_radius]\n        sharpened = sharpened.permute(0, 2, 3, 1)\n\n        result = torch.clamp(sharpened, 0, 1)\n\n        return (result,)\n\nclass ImageScaleToTotalPixels:\n    upscale_methods = [\"nearest-exact\", \"bilinear\", \"area\", \"bicubic\", \"lanczos\"]\n    crop_methods = [\"disabled\", \"center\"]\n\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"image\": (\"IMAGE\",), \"upscale_method\": (s.upscale_methods,),\n                              \"megapixels\": (\"FLOAT\", {\"default\": 1.0, \"min\": 0.01, \"max\": 16.0, \"step\": 0.01}),\n                            }}\n    RETURN_TYPES = (\"IMAGE\",)\n    FUNCTION = \"upscale\"\n\n    CATEGORY = \"image/upscaling\"\n\n    def upscale(self, image, upscale_method, megapixels):\n        samples = image.movedim(-1,1)\n        total = int(megapixels * 1024 * 1024)\n\n        scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))\n        width = round(samples.shape[3] * scale_by)\n        height = round(samples.shape[2] * scale_by)\n\n        s = ldm_patched.modules.utils.common_upscale(samples, width, height, upscale_method, \"disabled\")\n        s = s.movedim(1,-1)\n        return (s,)\n\nNODE_CLASS_MAPPINGS = {\n    \"ImageBlend\": Blend,\n    \"ImageBlur\": Blur,\n    \"ImageQuantize\": Quantize,\n    \"ImageSharpen\": Sharpen,\n    \"ImageScaleToTotalPixels\": ImageScaleToTotalPixels,\n}\n"
  },
  {
    "path": "ldm_patched/contrib/external_rebatch.py",
    "content": "# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py \n\nimport torch\n\nclass LatentRebatch:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"latents\": (\"LATENT\",),\n                              \"batch_size\": (\"INT\", {\"default\": 1, \"min\": 1, \"max\": 4096}),\n                              }}\n    RETURN_TYPES = (\"LATENT\",)\n    INPUT_IS_LIST = True\n    OUTPUT_IS_LIST = (True, )\n\n    FUNCTION = \"rebatch\"\n\n    CATEGORY = \"latent/batch\"\n\n    @staticmethod\n    def get_batch(latents, list_ind, offset):\n        '''prepare a batch out of the list of latents'''\n        samples = latents[list_ind]['samples']\n        shape = samples.shape\n        mask = latents[list_ind]['noise_mask'] if 'noise_mask' in latents[list_ind] else torch.ones((shape[0], 1, shape[2]*8, shape[3]*8), device='cpu')\n        if mask.shape[-1] != shape[-1] * 8 or mask.shape[-2] != shape[-2]:\n            torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[-2]*8, shape[-1]*8), mode=\"bilinear\")\n        if mask.shape[0] < samples.shape[0]:\n            mask = mask.repeat((shape[0] - 1) // mask.shape[0] + 1, 1, 1, 1)[:shape[0]]\n        if 'batch_index' in latents[list_ind]:\n            batch_inds = latents[list_ind]['batch_index']\n        else:\n            batch_inds = [x+offset for x in range(shape[0])]\n        return samples, mask, batch_inds\n\n    @staticmethod\n    def get_slices(indexable, num, batch_size):\n        '''divides an indexable object into num slices of length batch_size, and a remainder'''\n        slices = []\n        for i in range(num):\n            slices.append(indexable[i*batch_size:(i+1)*batch_size])\n        if num * batch_size < len(indexable):\n            return slices, indexable[num * batch_size:]\n        else:\n            return slices, None\n    \n    @staticmethod\n    def slice_batch(batch, num, batch_size):\n        result = [LatentRebatch.get_slices(x, num, batch_size) for x in batch]\n        return list(zip(*result))\n\n    @staticmethod\n    def cat_batch(batch1, batch2):\n        if batch1[0] is None:\n            return batch2\n        result = [torch.cat((b1, b2)) if torch.is_tensor(b1) else b1 + b2 for b1, b2 in zip(batch1, batch2)]\n        return result\n\n    def rebatch(self, latents, batch_size):\n        batch_size = batch_size[0]\n\n        output_list = []\n        current_batch = (None, None, None)\n        processed = 0\n\n        for i in range(len(latents)):\n            # fetch new entry of list\n            #samples, masks, indices = self.get_batch(latents, i)\n            next_batch = self.get_batch(latents, i, processed)\n            processed += len(next_batch[2])\n            # set to current if current is None\n            if current_batch[0] is None:\n                current_batch = next_batch\n            # add previous to list if dimensions do not match\n            elif next_batch[0].shape[-1] != current_batch[0].shape[-1] or next_batch[0].shape[-2] != current_batch[0].shape[-2]:\n                sliced, _ = self.slice_batch(current_batch, 1, batch_size)\n                output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]})\n                current_batch = next_batch\n            # cat if everything checks out\n            else:\n                current_batch = self.cat_batch(current_batch, next_batch)\n\n            # add to list if dimensions gone above target batch size\n            if current_batch[0].shape[0] > batch_size:\n                num = current_batch[0].shape[0] // batch_size\n                sliced, remainder = self.slice_batch(current_batch, num, batch_size)\n                \n                for i in range(num):\n                    output_list.append({'samples': sliced[0][i], 'noise_mask': sliced[1][i], 'batch_index': sliced[2][i]})\n\n                current_batch = remainder\n\n        #add remainder\n        if current_batch[0] is not None:\n            sliced, _ = self.slice_batch(current_batch, 1, batch_size)\n            output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]})\n\n        #get rid of empty masks\n        for s in output_list:\n            if s['noise_mask'].mean() == 1.0:\n                del s['noise_mask']\n\n        return (output_list,)\n\nclass ImageRebatch:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"images\": (\"IMAGE\",),\n                              \"batch_size\": (\"INT\", {\"default\": 1, \"min\": 1, \"max\": 4096}),\n                              }}\n    RETURN_TYPES = (\"IMAGE\",)\n    INPUT_IS_LIST = True\n    OUTPUT_IS_LIST = (True, )\n\n    FUNCTION = \"rebatch\"\n\n    CATEGORY = \"image/batch\"\n\n    def rebatch(self, images, batch_size):\n        batch_size = batch_size[0]\n\n        output_list = []\n        all_images = []\n        for img in images:\n            for i in range(img.shape[0]):\n                all_images.append(img[i:i+1])\n\n        for i in range(0, len(all_images), batch_size):\n            output_list.append(torch.cat(all_images[i:i+batch_size], dim=0))\n\n        return (output_list,)\n\nNODE_CLASS_MAPPINGS = {\n    \"RebatchLatents\": LatentRebatch,\n    \"RebatchImages\": ImageRebatch,\n}\n\nNODE_DISPLAY_NAME_MAPPINGS = {\n    \"RebatchLatents\": \"Rebatch Latents\",\n    \"RebatchImages\": \"Rebatch Images\",\n}\n"
  },
  {
    "path": "ldm_patched/contrib/external_sag.py",
    "content": "# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py \n\nimport torch\nfrom torch import einsum\nimport torch.nn.functional as F\nimport math\n\nfrom einops import rearrange, repeat\nimport os\nfrom ldm_patched.ldm.modules.attention import optimized_attention, _ATTN_PRECISION\nimport ldm_patched.modules.samplers\n\n# from ldm_patched.modules/ldm/modules/attention.py\n# but modified to return attention scores as well as output\ndef attention_basic_with_sim(q, k, v, heads, mask=None):\n    b, _, dim_head = q.shape\n    dim_head //= heads\n    scale = dim_head ** -0.5\n\n    h = heads\n    q, k, v = map(\n        lambda t: t.unsqueeze(3)\n        .reshape(b, -1, heads, dim_head)\n        .permute(0, 2, 1, 3)\n        .reshape(b * heads, -1, dim_head)\n        .contiguous(),\n        (q, k, v),\n    )\n\n    # force cast to fp32 to avoid overflowing\n    if _ATTN_PRECISION ==\"fp32\":\n        sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale\n    else:\n        sim = einsum('b i d, b j d -> b i j', q, k) * scale\n\n    del q, k\n\n    if mask is not None:\n        mask = rearrange(mask, 'b ... -> b (...)')\n        max_neg_value = -torch.finfo(sim.dtype).max\n        mask = repeat(mask, 'b j -> (b h) () j', h=h)\n        sim.masked_fill_(~mask, max_neg_value)\n\n    # attention, what we cannot get enough of\n    sim = sim.softmax(dim=-1)\n\n    out = einsum('b i j, b j d -> b i d', sim.to(v.dtype), v)\n    out = (\n        out.unsqueeze(0)\n        .reshape(b, heads, -1, dim_head)\n        .permute(0, 2, 1, 3)\n        .reshape(b, -1, heads * dim_head)\n    )\n    return (out, sim)\n\ndef create_blur_map(x0, attn, sigma=3.0, threshold=1.0):\n    # reshape and GAP the attention map\n    _, hw1, hw2 = attn.shape\n    b, _, lh, lw = x0.shape\n    attn = attn.reshape(b, -1, hw1, hw2)\n    # Global Average Pool\n    mask = attn.mean(1, keepdim=False).sum(1, keepdim=False) > threshold\n    ratio = 2**(math.ceil(math.sqrt(lh * lw / hw1)) - 1).bit_length()\n    mid_shape = [math.ceil(lh / ratio), math.ceil(lw / ratio)]\n\n    # Reshape\n    mask = (\n        mask.reshape(b, *mid_shape)\n        .unsqueeze(1)\n        .type(attn.dtype)\n    )\n    # Upsample\n    mask = F.interpolate(mask, (lh, lw))\n\n    blurred = gaussian_blur_2d(x0, kernel_size=9, sigma=sigma)\n    blurred = blurred * mask + x0 * (1 - mask)\n    return blurred\n\ndef gaussian_blur_2d(img, kernel_size, sigma):\n    ksize_half = (kernel_size - 1) * 0.5\n\n    x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size)\n\n    pdf = torch.exp(-0.5 * (x / sigma).pow(2))\n\n    x_kernel = pdf / pdf.sum()\n    x_kernel = x_kernel.to(device=img.device, dtype=img.dtype)\n\n    kernel2d = torch.mm(x_kernel[:, None], x_kernel[None, :])\n    kernel2d = kernel2d.expand(img.shape[-3], 1, kernel2d.shape[0], kernel2d.shape[1])\n\n    padding = [kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2]\n\n    img = F.pad(img, padding, mode=\"reflect\")\n    img = F.conv2d(img, kernel2d, groups=img.shape[-3])\n    return img\n\nclass SelfAttentionGuidance:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"model\": (\"MODEL\",),\n                             \"scale\": (\"FLOAT\", {\"default\": 0.5, \"min\": -2.0, \"max\": 5.0, \"step\": 0.1}),\n                             \"blur_sigma\": (\"FLOAT\", {\"default\": 2.0, \"min\": 0.0, \"max\": 10.0, \"step\": 0.1}),\n                              }}\n    RETURN_TYPES = (\"MODEL\",)\n    FUNCTION = \"patch\"\n\n    CATEGORY = \"_for_testing\"\n\n    def patch(self, model, scale, blur_sigma):\n        m = model.clone()\n\n        attn_scores = None\n\n        # TODO: make this work properly with chunked batches\n        #       currently, we can only save the attn from one UNet call\n        def attn_and_record(q, k, v, extra_options):\n            nonlocal attn_scores\n            # if uncond, save the attention scores\n            heads = extra_options[\"n_heads\"]\n            cond_or_uncond = extra_options[\"cond_or_uncond\"]\n            b = q.shape[0] // len(cond_or_uncond)\n            if 1 in cond_or_uncond:\n                uncond_index = cond_or_uncond.index(1)\n                # do the entire attention operation, but save the attention scores to attn_scores\n                (out, sim) = attention_basic_with_sim(q, k, v, heads=heads)\n                # when using a higher batch size, I BELIEVE the result batch dimension is [uc1, ... ucn, c1, ... cn]\n                n_slices = heads * b\n                attn_scores = sim[n_slices * uncond_index:n_slices * (uncond_index+1)]\n                return out\n            else:\n                return optimized_attention(q, k, v, heads=heads)\n\n        def post_cfg_function(args):\n            nonlocal attn_scores\n            uncond_attn = attn_scores\n\n            sag_scale = scale\n            sag_sigma = blur_sigma\n            sag_threshold = 1.0\n            model = args[\"model\"]\n            uncond_pred = args[\"uncond_denoised\"]\n            uncond = args[\"uncond\"]\n            cfg_result = args[\"denoised\"]\n            sigma = args[\"sigma\"]\n            model_options = args[\"model_options\"]\n            x = args[\"input\"]\n            if min(cfg_result.shape[2:]) <= 4: #skip when too small to add padding\n                return cfg_result\n\n            # create the adversarially blurred image\n            degraded = create_blur_map(uncond_pred, uncond_attn, sag_sigma, sag_threshold)\n            degraded_noised = degraded + x - uncond_pred\n            # call into the UNet\n            (sag, _) = ldm_patched.modules.samplers.calc_cond_uncond_batch(model, uncond, None, degraded_noised, sigma, model_options)\n            return cfg_result + (degraded - sag) * sag_scale\n\n        m.set_model_sampler_post_cfg_function(post_cfg_function, disable_cfg1_optimization=True)\n\n        # from diffusers:\n        # unet.mid_block.attentions[0].transformer_blocks[0].attn1.patch\n        m.set_model_attn1_replace(attn_and_record, \"middle\", 0, 0)\n\n        return (m, )\n\nNODE_CLASS_MAPPINGS = {\n    \"SelfAttentionGuidance\": SelfAttentionGuidance,\n}\n\nNODE_DISPLAY_NAME_MAPPINGS = {\n    \"SelfAttentionGuidance\": \"Self-Attention Guidance\",\n}\n"
  },
  {
    "path": "ldm_patched/contrib/external_sdupscale.py",
    "content": "# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py \n\nimport torch\nimport ldm_patched.contrib.external\nimport ldm_patched.modules.utils\n\nclass SD_4XUpscale_Conditioning:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"images\": (\"IMAGE\",),\n                              \"positive\": (\"CONDITIONING\",),\n                              \"negative\": (\"CONDITIONING\",),\n                              \"scale_ratio\": (\"FLOAT\", {\"default\": 4.0, \"min\": 0.0, \"max\": 10.0, \"step\": 0.01}),\n                              \"noise_augmentation\": (\"FLOAT\", {\"default\": 0.0, \"min\": 0.0, \"max\": 1.0, \"step\": 0.001}),\n                             }}\n    RETURN_TYPES = (\"CONDITIONING\", \"CONDITIONING\", \"LATENT\")\n    RETURN_NAMES = (\"positive\", \"negative\", \"latent\")\n\n    FUNCTION = \"encode\"\n\n    CATEGORY = \"conditioning/upscale_diffusion\"\n\n    def encode(self, images, positive, negative, scale_ratio, noise_augmentation):\n        width = max(1, round(images.shape[-2] * scale_ratio))\n        height = max(1, round(images.shape[-3] * scale_ratio))\n\n        pixels = ldm_patched.modules.utils.common_upscale((images.movedim(-1,1) * 2.0) - 1.0, width // 4, height // 4, \"bilinear\", \"center\")\n\n        out_cp = []\n        out_cn = []\n\n        for t in positive:\n            n = [t[0], t[1].copy()]\n            n[1]['concat_image'] = pixels\n            n[1]['noise_augmentation'] = noise_augmentation\n            out_cp.append(n)\n\n        for t in negative:\n            n = [t[0], t[1].copy()]\n            n[1]['concat_image'] = pixels\n            n[1]['noise_augmentation'] = noise_augmentation\n            out_cn.append(n)\n\n        latent = torch.zeros([images.shape[0], 4, height // 4, width // 4])\n        return (out_cp, out_cn, {\"samples\":latent})\n\nNODE_CLASS_MAPPINGS = {\n    \"SD_4XUpscale_Conditioning\": SD_4XUpscale_Conditioning,\n}\n"
  },
  {
    "path": "ldm_patched/contrib/external_stable3d.py",
    "content": "# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py \n\nimport torch\nimport ldm_patched.contrib.external\nimport ldm_patched.modules.utils\n\ndef camera_embeddings(elevation, azimuth):\n    elevation = torch.as_tensor([elevation])\n    azimuth = torch.as_tensor([azimuth])\n    embeddings = torch.stack(\n        [\n                torch.deg2rad(\n                    (90 - elevation) - (90)\n                ),  # Zero123 polar is 90-elevation\n                torch.sin(torch.deg2rad(azimuth)),\n                torch.cos(torch.deg2rad(azimuth)),\n                torch.deg2rad(\n                    90 - torch.full_like(elevation, 0)\n                ),\n        ], dim=-1).unsqueeze(1)\n\n    return embeddings\n\n\nclass StableZero123_Conditioning:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"clip_vision\": (\"CLIP_VISION\",),\n                              \"init_image\": (\"IMAGE\",),\n                              \"vae\": (\"VAE\",),\n                              \"width\": (\"INT\", {\"default\": 256, \"min\": 16, \"max\": ldm_patched.contrib.external.MAX_RESOLUTION, \"step\": 8}),\n                              \"height\": (\"INT\", {\"default\": 256, \"min\": 16, \"max\": ldm_patched.contrib.external.MAX_RESOLUTION, \"step\": 8}),\n                              \"batch_size\": (\"INT\", {\"default\": 1, \"min\": 1, \"max\": 4096}),\n                              \"elevation\": (\"FLOAT\", {\"default\": 0.0, \"min\": -180.0, \"max\": 180.0}),\n                              \"azimuth\": (\"FLOAT\", {\"default\": 0.0, \"min\": -180.0, \"max\": 180.0}),\n                             }}\n    RETURN_TYPES = (\"CONDITIONING\", \"CONDITIONING\", \"LATENT\")\n    RETURN_NAMES = (\"positive\", \"negative\", \"latent\")\n\n    FUNCTION = \"encode\"\n\n    CATEGORY = \"conditioning/3d_models\"\n\n    def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth):\n        output = clip_vision.encode_image(init_image)\n        pooled = output.image_embeds.unsqueeze(0)\n        pixels = ldm_patched.modules.utils.common_upscale(init_image.movedim(-1,1), width, height, \"bilinear\", \"center\").movedim(1,-1)\n        encode_pixels = pixels[:,:,:,:3]\n        t = vae.encode(encode_pixels)\n        cam_embeds = camera_embeddings(elevation, azimuth)\n        cond = torch.cat([pooled, cam_embeds.to(pooled.device).repeat((pooled.shape[0], 1, 1))], dim=-1)\n\n        positive = [[cond, {\"concat_latent_image\": t}]]\n        negative = [[torch.zeros_like(pooled), {\"concat_latent_image\": torch.zeros_like(t)}]]\n        latent = torch.zeros([batch_size, 4, height // 8, width // 8])\n        return (positive, negative, {\"samples\":latent})\n\nclass StableZero123_Conditioning_Batched:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"clip_vision\": (\"CLIP_VISION\",),\n                              \"init_image\": (\"IMAGE\",),\n                              \"vae\": (\"VAE\",),\n                              \"width\": (\"INT\", {\"default\": 256, \"min\": 16, \"max\": ldm_patched.contrib.external.MAX_RESOLUTION, \"step\": 8}),\n                              \"height\": (\"INT\", {\"default\": 256, \"min\": 16, \"max\": ldm_patched.contrib.external.MAX_RESOLUTION, \"step\": 8}),\n                              \"batch_size\": (\"INT\", {\"default\": 1, \"min\": 1, \"max\": 4096}),\n                              \"elevation\": (\"FLOAT\", {\"default\": 0.0, \"min\": -180.0, \"max\": 180.0}),\n                              \"azimuth\": (\"FLOAT\", {\"default\": 0.0, \"min\": -180.0, \"max\": 180.0}),\n                              \"elevation_batch_increment\": (\"FLOAT\", {\"default\": 0.0, \"min\": -180.0, \"max\": 180.0}),\n                              \"azimuth_batch_increment\": (\"FLOAT\", {\"default\": 0.0, \"min\": -180.0, \"max\": 180.0}),\n                             }}\n    RETURN_TYPES = (\"CONDITIONING\", \"CONDITIONING\", \"LATENT\")\n    RETURN_NAMES = (\"positive\", \"negative\", \"latent\")\n\n    FUNCTION = \"encode\"\n\n    CATEGORY = \"conditioning/3d_models\"\n\n    def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth, elevation_batch_increment, azimuth_batch_increment):\n        output = clip_vision.encode_image(init_image)\n        pooled = output.image_embeds.unsqueeze(0)\n        pixels = ldm_patched.modules.utils.common_upscale(init_image.movedim(-1,1), width, height, \"bilinear\", \"center\").movedim(1,-1)\n        encode_pixels = pixels[:,:,:,:3]\n        t = vae.encode(encode_pixels)\n\n        cam_embeds = []\n        for i in range(batch_size):\n            cam_embeds.append(camera_embeddings(elevation, azimuth))\n            elevation += elevation_batch_increment\n            azimuth += azimuth_batch_increment\n\n        cam_embeds = torch.cat(cam_embeds, dim=0)\n        cond = torch.cat([ldm_patched.modules.utils.repeat_to_batch_size(pooled, batch_size), cam_embeds], dim=-1)\n\n        positive = [[cond, {\"concat_latent_image\": t}]]\n        negative = [[torch.zeros_like(pooled), {\"concat_latent_image\": torch.zeros_like(t)}]]\n        latent = torch.zeros([batch_size, 4, height // 8, width // 8])\n        return (positive, negative, {\"samples\":latent, \"batch_index\": [0] * batch_size})\n\n\nNODE_CLASS_MAPPINGS = {\n    \"StableZero123_Conditioning\": StableZero123_Conditioning,\n    \"StableZero123_Conditioning_Batched\": StableZero123_Conditioning_Batched,\n}\n"
  },
  {
    "path": "ldm_patched/contrib/external_tomesd.py",
    "content": "# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py \n\n#Taken from: https://github.com/dbolya/tomesd\n\nimport torch\nfrom typing import Tuple, Callable\nimport math\n\ndef do_nothing(x: torch.Tensor, mode:str=None):\n    return x\n\n\ndef mps_gather_workaround(input, dim, index):\n    if input.shape[-1] == 1:\n        return torch.gather(\n            input.unsqueeze(-1),\n            dim - 1 if dim < 0 else dim,\n            index.unsqueeze(-1)\n        ).squeeze(-1)\n    else:\n        return torch.gather(input, dim, index)\n\n\ndef bipartite_soft_matching_random2d(metric: torch.Tensor,\n                                     w: int, h: int, sx: int, sy: int, r: int,\n                                     no_rand: bool = False) -> Tuple[Callable, Callable]:\n    \"\"\"\n    Partitions the tokens into src and dst and merges r tokens from src to dst.\n    Dst tokens are partitioned by choosing one randomy in each (sx, sy) region.\n    Args:\n     - metric [B, N, C]: metric to use for similarity\n     - w: image width in tokens\n     - h: image height in tokens\n     - sx: stride in the x dimension for dst, must divide w\n     - sy: stride in the y dimension for dst, must divide h\n     - r: number of tokens to remove (by merging)\n     - no_rand: if true, disable randomness (use top left corner only)\n    \"\"\"\n    B, N, _ = metric.shape\n\n    if r <= 0 or w == 1 or h == 1:\n        return do_nothing, do_nothing\n\n    gather = mps_gather_workaround if metric.device.type == \"mps\" else torch.gather\n    \n    with torch.no_grad():\n        \n        hsy, wsx = h // sy, w // sx\n\n        # For each sy by sx kernel, randomly assign one token to be dst and the rest src\n        if no_rand:\n            rand_idx = torch.zeros(hsy, wsx, 1, device=metric.device, dtype=torch.int64)\n        else:\n            rand_idx = torch.randint(sy*sx, size=(hsy, wsx, 1), device=metric.device)\n        \n        # The image might not divide sx and sy, so we need to work on a view of the top left if the idx buffer instead\n        idx_buffer_view = torch.zeros(hsy, wsx, sy*sx, device=metric.device, dtype=torch.int64)\n        idx_buffer_view.scatter_(dim=2, index=rand_idx, src=-torch.ones_like(rand_idx, dtype=rand_idx.dtype))\n        idx_buffer_view = idx_buffer_view.view(hsy, wsx, sy, sx).transpose(1, 2).reshape(hsy * sy, wsx * sx)\n\n        # Image is not divisible by sx or sy so we need to move it into a new buffer\n        if (hsy * sy) < h or (wsx * sx) < w:\n            idx_buffer = torch.zeros(h, w, device=metric.device, dtype=torch.int64)\n            idx_buffer[:(hsy * sy), :(wsx * sx)] = idx_buffer_view\n        else:\n            idx_buffer = idx_buffer_view\n\n        # We set dst tokens to be -1 and src to be 0, so an argsort gives us dst|src indices\n        rand_idx = idx_buffer.reshape(1, -1, 1).argsort(dim=1)\n\n        # We're finished with these\n        del idx_buffer, idx_buffer_view\n\n        # rand_idx is currently dst|src, so split them\n        num_dst = hsy * wsx\n        a_idx = rand_idx[:, num_dst:, :] # src\n        b_idx = rand_idx[:, :num_dst, :] # dst\n\n        def split(x):\n            C = x.shape[-1]\n            src = gather(x, dim=1, index=a_idx.expand(B, N - num_dst, C))\n            dst = gather(x, dim=1, index=b_idx.expand(B, num_dst, C))\n            return src, dst\n\n        # Cosine similarity between A and B\n        metric = metric / metric.norm(dim=-1, keepdim=True)\n        a, b = split(metric)\n        scores = a @ b.transpose(-1, -2)\n\n        # Can't reduce more than the # tokens in src\n        r = min(a.shape[1], r)\n\n        # Find the most similar greedily\n        node_max, node_idx = scores.max(dim=-1)\n        edge_idx = node_max.argsort(dim=-1, descending=True)[..., None]\n\n        unm_idx = edge_idx[..., r:, :]  # Unmerged Tokens\n        src_idx = edge_idx[..., :r, :]  # Merged Tokens\n        dst_idx = gather(node_idx[..., None], dim=-2, index=src_idx)\n\n    def merge(x: torch.Tensor, mode=\"mean\") -> torch.Tensor:\n        src, dst = split(x)\n        n, t1, c = src.shape\n        \n        unm = gather(src, dim=-2, index=unm_idx.expand(n, t1 - r, c))\n        src = gather(src, dim=-2, index=src_idx.expand(n, r, c))\n        dst = dst.scatter_reduce(-2, dst_idx.expand(n, r, c), src, reduce=mode)\n\n        return torch.cat([unm, dst], dim=1)\n\n    def unmerge(x: torch.Tensor) -> torch.Tensor:\n        unm_len = unm_idx.shape[1]\n        unm, dst = x[..., :unm_len, :], x[..., unm_len:, :]\n        _, _, c = unm.shape\n\n        src = gather(dst, dim=-2, index=dst_idx.expand(B, r, c))\n\n        # Combine back to the original shape\n        out = torch.zeros(B, N, c, device=x.device, dtype=x.dtype)\n        out.scatter_(dim=-2, index=b_idx.expand(B, num_dst, c), src=dst)\n        out.scatter_(dim=-2, index=gather(a_idx.expand(B, a_idx.shape[1], 1), dim=1, index=unm_idx).expand(B, unm_len, c), src=unm)\n        out.scatter_(dim=-2, index=gather(a_idx.expand(B, a_idx.shape[1], 1), dim=1, index=src_idx).expand(B, r, c), src=src)\n\n        return out\n\n    return merge, unmerge\n\n\ndef get_functions(x, ratio, original_shape):\n    b, c, original_h, original_w = original_shape\n    original_tokens = original_h * original_w\n    downsample = int(math.ceil(math.sqrt(original_tokens // x.shape[1])))\n    stride_x = 2\n    stride_y = 2\n    max_downsample = 1\n\n    if downsample <= max_downsample:\n        w = int(math.ceil(original_w / downsample))\n        h = int(math.ceil(original_h / downsample))\n        r = int(x.shape[1] * ratio)\n        no_rand = False\n        m, u = bipartite_soft_matching_random2d(x, w, h, stride_x, stride_y, r, no_rand)\n        return m, u\n\n    nothing = lambda y: y\n    return nothing, nothing\n\n\n\nclass TomePatchModel:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"model\": (\"MODEL\",),\n                              \"ratio\": (\"FLOAT\", {\"default\": 0.3, \"min\": 0.0, \"max\": 1.0, \"step\": 0.01}),\n                              }}\n    RETURN_TYPES = (\"MODEL\",)\n    FUNCTION = \"patch\"\n\n    CATEGORY = \"_for_testing\"\n\n    def patch(self, model, ratio):\n        self.u = None\n        def tomesd_m(q, k, v, extra_options):\n            #NOTE: In the reference code get_functions takes x (input of the transformer block) as the argument instead of q\n            #however from my basic testing it seems that using q instead gives better results\n            m, self.u = get_functions(q, ratio, extra_options[\"original_shape\"])\n            return m(q), k, v\n        def tomesd_u(n, extra_options):\n            return self.u(n)\n\n        m = model.clone()\n        m.set_model_attn1_patch(tomesd_m)\n        m.set_model_attn1_output_patch(tomesd_u)\n        return (m, )\n\n\nNODE_CLASS_MAPPINGS = {\n    \"TomePatchModel\": TomePatchModel,\n}\n"
  },
  {
    "path": "ldm_patched/contrib/external_upscale_model.py",
    "content": "# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py \n\nimport os\nfrom ldm_patched.pfn import model_loading\nfrom ldm_patched.modules import model_management\nimport torch\nimport ldm_patched.modules.utils\nimport ldm_patched.utils.path_utils\n\nclass UpscaleModelLoader:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"model_name\": (ldm_patched.utils.path_utils.get_filename_list(\"upscale_models\"), ),\n                             }}\n    RETURN_TYPES = (\"UPSCALE_MODEL\",)\n    FUNCTION = \"load_model\"\n\n    CATEGORY = \"loaders\"\n\n    def load_model(self, model_name):\n        model_path = ldm_patched.utils.path_utils.get_full_path(\"upscale_models\", model_name)\n        sd = ldm_patched.modules.utils.load_torch_file(model_path, safe_load=True)\n        if \"module.layers.0.residual_group.blocks.0.norm1.weight\" in sd:\n            sd = ldm_patched.modules.utils.state_dict_prefix_replace(sd, {\"module.\":\"\"})\n        out = model_loading.load_state_dict(sd).eval()\n        return (out, )\n\n\nclass ImageUpscaleWithModel:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"upscale_model\": (\"UPSCALE_MODEL\",),\n                              \"image\": (\"IMAGE\",),\n                              }}\n    RETURN_TYPES = (\"IMAGE\",)\n    FUNCTION = \"upscale\"\n\n    CATEGORY = \"image/upscaling\"\n\n    def upscale(self, upscale_model, image):\n        device = model_management.get_torch_device()\n        upscale_model.to(device)\n        in_img = image.movedim(-1,-3).to(device)\n        free_memory = model_management.get_free_memory(device)\n\n        tile = 512\n        overlap = 32\n\n        oom = True\n        while oom:\n            try:\n                steps = in_img.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(in_img.shape[3], in_img.shape[2], tile_x=tile, tile_y=tile, overlap=overlap)\n                pbar = ldm_patched.modules.utils.ProgressBar(steps)\n                s = ldm_patched.modules.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar)\n                oom = False\n            except model_management.OOM_EXCEPTION as e:\n                tile //= 2\n                if tile < 128:\n                    raise e\n\n        upscale_model.cpu()\n        s = torch.clamp(s.movedim(-3,-1), min=0, max=1.0)\n        return (s,)\n\nNODE_CLASS_MAPPINGS = {\n    \"UpscaleModelLoader\": UpscaleModelLoader,\n    \"ImageUpscaleWithModel\": ImageUpscaleWithModel\n}\n"
  },
  {
    "path": "ldm_patched/contrib/external_video_model.py",
    "content": "# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py \n\nimport ldm_patched.contrib.external\nimport torch\nimport ldm_patched.modules.utils\nimport ldm_patched.modules.sd\nimport ldm_patched.utils.path_utils\nimport ldm_patched.contrib.external_model_merging\n\n\nclass ImageOnlyCheckpointLoader:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"ckpt_name\": (ldm_patched.utils.path_utils.get_filename_list(\"checkpoints\"), ),\n                             }}\n    RETURN_TYPES = (\"MODEL\", \"CLIP_VISION\", \"VAE\")\n    FUNCTION = \"load_checkpoint\"\n\n    CATEGORY = \"loaders/video_models\"\n\n    def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):\n        ckpt_path = ldm_patched.utils.path_utils.get_full_path(\"checkpoints\", ckpt_name)\n        out = ldm_patched.modules.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=False, output_clipvision=True, embedding_directory=ldm_patched.utils.path_utils.get_folder_paths(\"embeddings\"))\n        return (out[0], out[3], out[2])\n\n\nclass SVD_img2vid_Conditioning:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"clip_vision\": (\"CLIP_VISION\",),\n                              \"init_image\": (\"IMAGE\",),\n                              \"vae\": (\"VAE\",),\n                              \"width\": (\"INT\", {\"default\": 1024, \"min\": 16, \"max\": ldm_patched.contrib.external.MAX_RESOLUTION, \"step\": 8}),\n                              \"height\": (\"INT\", {\"default\": 576, \"min\": 16, \"max\": ldm_patched.contrib.external.MAX_RESOLUTION, \"step\": 8}),\n                              \"video_frames\": (\"INT\", {\"default\": 14, \"min\": 1, \"max\": 4096}),\n                              \"motion_bucket_id\": (\"INT\", {\"default\": 127, \"min\": 1, \"max\": 1023}),\n                              \"fps\": (\"INT\", {\"default\": 6, \"min\": 1, \"max\": 1024}),\n                              \"augmentation_level\": (\"FLOAT\", {\"default\": 0.0, \"min\": 0.0, \"max\": 10.0, \"step\": 0.01})\n                             }}\n    RETURN_TYPES = (\"CONDITIONING\", \"CONDITIONING\", \"LATENT\")\n    RETURN_NAMES = (\"positive\", \"negative\", \"latent\")\n\n    FUNCTION = \"encode\"\n\n    CATEGORY = \"conditioning/video_models\"\n\n    def encode(self, clip_vision, init_image, vae, width, height, video_frames, motion_bucket_id, fps, augmentation_level):\n        output = clip_vision.encode_image(init_image)\n        pooled = output.image_embeds.unsqueeze(0)\n        pixels = ldm_patched.modules.utils.common_upscale(init_image.movedim(-1,1), width, height, \"bilinear\", \"center\").movedim(1,-1)\n        encode_pixels = pixels[:,:,:,:3]\n        if augmentation_level > 0:\n            encode_pixels += torch.randn_like(pixels) * augmentation_level\n        t = vae.encode(encode_pixels)\n        positive = [[pooled, {\"motion_bucket_id\": motion_bucket_id, \"fps\": fps, \"augmentation_level\": augmentation_level, \"concat_latent_image\": t}]]\n        negative = [[torch.zeros_like(pooled), {\"motion_bucket_id\": motion_bucket_id, \"fps\": fps, \"augmentation_level\": augmentation_level, \"concat_latent_image\": torch.zeros_like(t)}]]\n        latent = torch.zeros([video_frames, 4, height // 8, width // 8])\n        return (positive, negative, {\"samples\":latent})\n\nclass VideoLinearCFGGuidance:\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"model\": (\"MODEL\",),\n                              \"min_cfg\": (\"FLOAT\", {\"default\": 1.0, \"min\": 0.0, \"max\": 100.0, \"step\":0.5, \"round\": 0.01}),\n                              }}\n    RETURN_TYPES = (\"MODEL\",)\n    FUNCTION = \"patch\"\n\n    CATEGORY = \"sampling/video_models\"\n\n    def patch(self, model, min_cfg):\n        def linear_cfg(args):\n            cond = args[\"cond\"]\n            uncond = args[\"uncond\"]\n            cond_scale = args[\"cond_scale\"]\n\n            scale = torch.linspace(min_cfg, cond_scale, cond.shape[0], device=cond.device).reshape((cond.shape[0], 1, 1, 1))\n            return uncond + scale * (cond - uncond)\n\n        m = model.clone()\n        m.set_model_sampler_cfg_function(linear_cfg)\n        return (m, )\n\nclass ImageOnlyCheckpointSave(ldm_patched.contrib.external_model_merging.CheckpointSave):\n    CATEGORY = \"_for_testing\"\n\n    @classmethod\n    def INPUT_TYPES(s):\n        return {\"required\": { \"model\": (\"MODEL\",),\n                              \"clip_vision\": (\"CLIP_VISION\",),\n                              \"vae\": (\"VAE\",),\n                              \"filename_prefix\": (\"STRING\", {\"default\": \"checkpoints/ldm_patched\"}),},\n                \"hidden\": {\"prompt\": \"PROMPT\", \"extra_pnginfo\": \"EXTRA_PNGINFO\"},}\n\n    def save(self, model, clip_vision, vae, filename_prefix, prompt=None, extra_pnginfo=None):\n        ldm_patched.contrib.external_model_merging.save_checkpoint(model, clip_vision=clip_vision, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo)\n        return {}\n\nNODE_CLASS_MAPPINGS = {\n    \"ImageOnlyCheckpointLoader\": ImageOnlyCheckpointLoader,\n    \"SVD_img2vid_Conditioning\": SVD_img2vid_Conditioning,\n    \"VideoLinearCFGGuidance\": VideoLinearCFGGuidance,\n    \"ImageOnlyCheckpointSave\": ImageOnlyCheckpointSave,\n}\n\nNODE_DISPLAY_NAME_MAPPINGS = {\n    \"ImageOnlyCheckpointLoader\": \"Image Only Checkpoint Loader (img2vid model)\",\n}\n"
  },
  {
    "path": "ldm_patched/controlnet/cldm.py",
    "content": "#taken from: https://github.com/lllyasviel/ControlNet\n#and modified\n\nimport torch\nimport torch as th\nimport torch.nn as nn\n\nfrom ldm_patched.ldm.modules.diffusionmodules.util import (\n    zero_module,\n    timestep_embedding,\n)\n\nfrom ldm_patched.ldm.modules.attention import SpatialTransformer\nfrom ldm_patched.ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample\nfrom ldm_patched.ldm.util import exists\nimport ldm_patched.modules.ops\n\nclass ControlledUnetModel(UNetModel):\n    #implemented in the ldm unet\n    pass\n\nclass ControlNet(nn.Module):\n    def __init__(\n        self,\n        image_size,\n        in_channels,\n        model_channels,\n        hint_channels,\n        num_res_blocks,\n        dropout=0,\n        channel_mult=(1, 2, 4, 8),\n        conv_resample=True,\n        dims=2,\n        num_classes=None,\n        use_checkpoint=False,\n        dtype=torch.float32,\n        num_heads=-1,\n        num_head_channels=-1,\n        num_heads_upsample=-1,\n        use_scale_shift_norm=False,\n        resblock_updown=False,\n        use_new_attention_order=False,\n        use_spatial_transformer=False,    # custom transformer support\n        transformer_depth=1,              # custom transformer support\n        context_dim=None,                 # custom transformer support\n        n_embed=None,                     # custom support for prediction of discrete ids into codebook of first stage vq model\n        legacy=True,\n        disable_self_attentions=None,\n        num_attention_blocks=None,\n        disable_middle_self_attn=False,\n        use_linear_in_transformer=False,\n        adm_in_channels=None,\n        transformer_depth_middle=None,\n        transformer_depth_output=None,\n        device=None,\n        operations=ldm_patched.modules.ops.disable_weight_init,\n        **kwargs,\n    ):\n        super().__init__()\n        assert use_spatial_transformer == True, \"use_spatial_transformer has to be true\"\n        if use_spatial_transformer:\n            assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'\n\n        if context_dim is not None:\n            assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'\n            # from omegaconf.listconfig import ListConfig\n            # if type(context_dim) == ListConfig:\n            #     context_dim = list(context_dim)\n\n        if num_heads_upsample == -1:\n            num_heads_upsample = num_heads\n\n        if num_heads == -1:\n            assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'\n\n        if num_head_channels == -1:\n            assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'\n\n        self.dims = dims\n        self.image_size = image_size\n        self.in_channels = in_channels\n        self.model_channels = model_channels\n\n        if isinstance(num_res_blocks, int):\n            self.num_res_blocks = len(channel_mult) * [num_res_blocks]\n        else:\n            if len(num_res_blocks) != len(channel_mult):\n                raise ValueError(\"provide num_res_blocks either as an int (globally constant) or \"\n                                 \"as a list/tuple (per-level) with the same length as channel_mult\")\n            self.num_res_blocks = num_res_blocks\n\n        if disable_self_attentions is not None:\n            # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not\n            assert len(disable_self_attentions) == len(channel_mult)\n        if num_attention_blocks is not None:\n            assert len(num_attention_blocks) == len(self.num_res_blocks)\n            assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))\n\n        transformer_depth = transformer_depth[:]\n\n        self.dropout = dropout\n        self.channel_mult = channel_mult\n        self.conv_resample = conv_resample\n        self.num_classes = num_classes\n        self.use_checkpoint = use_checkpoint\n        self.dtype = dtype\n        self.num_heads = num_heads\n        self.num_head_channels = num_head_channels\n        self.num_heads_upsample = num_heads_upsample\n        self.predict_codebook_ids = n_embed is not None\n\n        time_embed_dim = model_channels * 4\n        self.time_embed = nn.Sequential(\n            operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device),\n            nn.SiLU(),\n            operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),\n        )\n\n        if self.num_classes is not None:\n            if isinstance(self.num_classes, int):\n                self.label_emb = nn.Embedding(num_classes, time_embed_dim)\n            elif self.num_classes == \"continuous\":\n                print(\"setting up linear c_adm embedding layer\")\n                self.label_emb = nn.Linear(1, time_embed_dim)\n            elif self.num_classes == \"sequential\":\n                assert adm_in_channels is not None\n                self.label_emb = nn.Sequential(\n                    nn.Sequential(\n                        operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device),\n                        nn.SiLU(),\n                        operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),\n                    )\n                )\n            else:\n                raise ValueError()\n\n        self.input_blocks = nn.ModuleList(\n            [\n                TimestepEmbedSequential(\n                    operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device)\n                )\n            ]\n        )\n        self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels, operations=operations, dtype=self.dtype, device=device)])\n\n        self.input_hint_block = TimestepEmbedSequential(\n                    operations.conv_nd(dims, hint_channels, 16, 3, padding=1, dtype=self.dtype, device=device),\n                    nn.SiLU(),\n                    operations.conv_nd(dims, 16, 16, 3, padding=1, dtype=self.dtype, device=device),\n                    nn.SiLU(),\n                    operations.conv_nd(dims, 16, 32, 3, padding=1, stride=2, dtype=self.dtype, device=device),\n                    nn.SiLU(),\n                    operations.conv_nd(dims, 32, 32, 3, padding=1, dtype=self.dtype, device=device),\n                    nn.SiLU(),\n                    operations.conv_nd(dims, 32, 96, 3, padding=1, stride=2, dtype=self.dtype, device=device),\n                    nn.SiLU(),\n                    operations.conv_nd(dims, 96, 96, 3, padding=1, dtype=self.dtype, device=device),\n                    nn.SiLU(),\n                    operations.conv_nd(dims, 96, 256, 3, padding=1, stride=2, dtype=self.dtype, device=device),\n                    nn.SiLU(),\n                    operations.conv_nd(dims, 256, model_channels, 3, padding=1, dtype=self.dtype, device=device)\n        )\n\n        self._feature_size = model_channels\n        input_block_chans = [model_channels]\n        ch = model_channels\n        ds = 1\n        for level, mult in enumerate(channel_mult):\n            for nr in range(self.num_res_blocks[level]):\n                layers = [\n                    ResBlock(\n                        ch,\n                        time_embed_dim,\n                        dropout,\n                        out_channels=mult * model_channels,\n                        dims=dims,\n                        use_checkpoint=use_checkpoint,\n                        use_scale_shift_norm=use_scale_shift_norm,\n                        dtype=self.dtype,\n                        device=device,\n                        operations=operations,\n                    )\n                ]\n                ch = mult * model_channels\n                num_transformers = transformer_depth.pop(0)\n                if num_transformers > 0:\n                    if num_head_channels == -1:\n                        dim_head = ch // num_heads\n                    else:\n                        num_heads = ch // num_head_channels\n                        dim_head = num_head_channels\n                    if legacy:\n                        #num_heads = 1\n                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels\n                    if exists(disable_self_attentions):\n                        disabled_sa = disable_self_attentions[level]\n                    else:\n                        disabled_sa = False\n\n                    if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:\n                        layers.append(\n                            SpatialTransformer(\n                                ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim,\n                                disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,\n                                use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations\n                            )\n                        )\n                self.input_blocks.append(TimestepEmbedSequential(*layers))\n                self.zero_convs.append(self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device))\n                self._feature_size += ch\n                input_block_chans.append(ch)\n            if level != len(channel_mult) - 1:\n                out_ch = ch\n                self.input_blocks.append(\n                    TimestepEmbedSequential(\n                        ResBlock(\n                            ch,\n                            time_embed_dim,\n                            dropout,\n                            out_channels=out_ch,\n                            dims=dims,\n                            use_checkpoint=use_checkpoint,\n                            use_scale_shift_norm=use_scale_shift_norm,\n                            down=True,\n                            dtype=self.dtype,\n                            device=device,\n                            operations=operations\n                        )\n                        if resblock_updown\n                        else Downsample(\n                            ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations\n                        )\n                    )\n                )\n                ch = out_ch\n                input_block_chans.append(ch)\n                self.zero_convs.append(self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device))\n                ds *= 2\n                self._feature_size += ch\n\n        if num_head_channels == -1:\n            dim_head = ch // num_heads\n        else:\n            num_heads = ch // num_head_channels\n            dim_head = num_head_channels\n        if legacy:\n            #num_heads = 1\n            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels\n        mid_block = [\n            ResBlock(\n                ch,\n                time_embed_dim,\n                dropout,\n                dims=dims,\n                use_checkpoint=use_checkpoint,\n                use_scale_shift_norm=use_scale_shift_norm,\n                dtype=self.dtype,\n                device=device,\n                operations=operations\n            )]\n        if transformer_depth_middle >= 0:\n            mid_block += [SpatialTransformer(  # always uses a self-attn\n                            ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim,\n                            disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,\n                            use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations\n                        ),\n            ResBlock(\n                ch,\n                time_embed_dim,\n                dropout,\n                dims=dims,\n                use_checkpoint=use_checkpoint,\n                use_scale_shift_norm=use_scale_shift_norm,\n                dtype=self.dtype,\n                device=device,\n                operations=operations\n            )]\n        self.middle_block = TimestepEmbedSequential(*mid_block)\n        self.middle_block_out = self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device)\n        self._feature_size += ch\n\n    def make_zero_conv(self, channels, operations=None, dtype=None, device=None):\n        return TimestepEmbedSequential(operations.conv_nd(self.dims, channels, channels, 1, padding=0, dtype=dtype, device=device))\n\n    def forward(self, x, hint, timesteps, context, y=None, **kwargs):\n        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)\n        emb = self.time_embed(t_emb)\n\n        guided_hint = self.input_hint_block(hint, emb, context)\n\n        outs = []\n\n        hs = []\n        if self.num_classes is not None:\n            assert y.shape[0] == x.shape[0]\n            emb = emb + self.label_emb(y)\n\n        h = x\n        for module, zero_conv in zip(self.input_blocks, self.zero_convs):\n            if guided_hint is not None:\n                h = module(h, emb, context)\n                h += guided_hint\n                guided_hint = None\n            else:\n                h = module(h, emb, context)\n            outs.append(zero_conv(h, emb, context))\n\n        h = self.middle_block(h, emb, context)\n        outs.append(self.middle_block_out(h, emb, context))\n\n        return outs\n\n"
  },
  {
    "path": "ldm_patched/k_diffusion/sampling.py",
    "content": "import math\n\nfrom scipy import integrate\nimport torch\nfrom torch import nn\nimport torchsde\nfrom tqdm.auto import trange, tqdm\n\nfrom . import utils\n\n\ndef append_zero(x):\n    return torch.cat([x, x.new_zeros([1])])\n\n\ndef get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'):\n    \"\"\"Constructs the noise schedule of Karras et al. (2022).\"\"\"\n    ramp = torch.linspace(0, 1, n, device=device)\n    min_inv_rho = sigma_min ** (1 / rho)\n    max_inv_rho = sigma_max ** (1 / rho)\n    sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho\n    return append_zero(sigmas).to(device)\n\n\ndef get_sigmas_exponential(n, sigma_min, sigma_max, device='cpu'):\n    \"\"\"Constructs an exponential noise schedule.\"\"\"\n    sigmas = torch.linspace(math.log(sigma_max), math.log(sigma_min), n, device=device).exp()\n    return append_zero(sigmas)\n\n\ndef get_sigmas_polyexponential(n, sigma_min, sigma_max, rho=1., device='cpu'):\n    \"\"\"Constructs an polynomial in log sigma noise schedule.\"\"\"\n    ramp = torch.linspace(1, 0, n, device=device) ** rho\n    sigmas = torch.exp(ramp * (math.log(sigma_max) - math.log(sigma_min)) + math.log(sigma_min))\n    return append_zero(sigmas)\n\n\ndef get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'):\n    \"\"\"Constructs a continuous VP noise schedule.\"\"\"\n    t = torch.linspace(1, eps_s, n, device=device)\n    sigmas = torch.sqrt(torch.exp(beta_d * t ** 2 / 2 + beta_min * t) - 1)\n    return append_zero(sigmas)\n\n\ndef to_d(x, sigma, denoised):\n    \"\"\"Converts a denoiser output to a Karras ODE derivative.\"\"\"\n    return (x - denoised) / utils.append_dims(sigma, x.ndim)\n\n\ndef get_ancestral_step(sigma_from, sigma_to, eta=1.):\n    \"\"\"Calculates the noise level (sigma_down) to step down to and the amount\n    of noise to add (sigma_up) when doing an ancestral sampling step.\"\"\"\n    if not eta:\n        return sigma_to, 0.\n    sigma_up = min(sigma_to, eta * (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5)\n    sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5\n    return sigma_down, sigma_up\n\n\ndef default_noise_sampler(x):\n    return lambda sigma, sigma_next: torch.randn_like(x)\n\n\nclass BatchedBrownianTree:\n    \"\"\"A wrapper around torchsde.BrownianTree that enables batches of entropy.\"\"\"\n\n    def __init__(self, x, t0, t1, seed=None, **kwargs):\n        self.cpu_tree = True\n        if \"cpu\" in kwargs:\n            self.cpu_tree = kwargs.pop(\"cpu\")\n        t0, t1, self.sign = self.sort(t0, t1)\n        w0 = kwargs.get('w0', torch.zeros_like(x))\n        if seed is None:\n            seed = torch.randint(0, 2 ** 63 - 1, []).item()\n        self.batched = True\n        try:\n            assert len(seed) == x.shape[0]\n            w0 = w0[0]\n        except TypeError:\n            seed = [seed]\n            self.batched = False\n        if self.cpu_tree:\n            self.trees = [torchsde.BrownianTree(t0.cpu(), w0.cpu(), t1.cpu(), entropy=s, **kwargs) for s in seed]\n        else:\n            self.trees = [torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed]\n\n    @staticmethod\n    def sort(a, b):\n        return (a, b, 1) if a < b else (b, a, -1)\n\n    def __call__(self, t0, t1):\n        t0, t1, sign = self.sort(t0, t1)\n        if self.cpu_tree:\n            w = torch.stack([tree(t0.cpu().float(), t1.cpu().float()).to(t0.dtype).to(t0.device) for tree in self.trees]) * (self.sign * sign)\n        else:\n            w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign)\n\n        return w if self.batched else w[0]\n\n\nclass BrownianTreeNoiseSampler:\n    \"\"\"A noise sampler backed by a torchsde.BrownianTree.\n\n    Args:\n        x (Tensor): The tensor whose shape, device and dtype to use to generate\n            random samples.\n        sigma_min (float): The low end of the valid interval.\n        sigma_max (float): The high end of the valid interval.\n        seed (int or List[int]): The random seed. If a list of seeds is\n            supplied instead of a single integer, then the noise sampler will\n            use one BrownianTree per batch item, each with its own seed.\n        transform (callable): A function that maps sigma to the sampler's\n            internal timestep.\n    \"\"\"\n\n    def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x, cpu=False):\n        self.transform = transform\n        t0, t1 = self.transform(torch.as_tensor(sigma_min)), self.transform(torch.as_tensor(sigma_max))\n        self.tree = BatchedBrownianTree(x, t0, t1, seed, cpu=cpu)\n\n    def __call__(self, sigma, sigma_next):\n        t0, t1 = self.transform(torch.as_tensor(sigma)), self.transform(torch.as_tensor(sigma_next))\n        return self.tree(t0, t1) / (t1 - t0).abs().sqrt()\n\n\n@torch.no_grad()\ndef sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):\n    \"\"\"Implements Algorithm 2 (Euler steps) from Karras et al. (2022).\"\"\"\n    extra_args = {} if extra_args is None else extra_args\n    s_in = x.new_ones([x.shape[0]])\n    for i in trange(len(sigmas) - 1, disable=disable):\n        gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.\n        sigma_hat = sigmas[i] * (gamma + 1)\n        if gamma > 0:\n            eps = torch.randn_like(x) * s_noise\n            x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5\n        denoised = model(x, sigma_hat * s_in, **extra_args)\n        d = to_d(x, sigma_hat, denoised)\n        if callback is not None:\n            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})\n        dt = sigmas[i + 1] - sigma_hat\n        # Euler method\n        x = x + d * dt\n    return x\n\n\n@torch.no_grad()\ndef sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):\n    \"\"\"Ancestral sampling with Euler method steps.\"\"\"\n    extra_args = {} if extra_args is None else extra_args\n    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler\n    s_in = x.new_ones([x.shape[0]])\n    for i in trange(len(sigmas) - 1, disable=disable):\n        denoised = model(x, sigmas[i] * s_in, **extra_args)\n        sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)\n        if callback is not None:\n            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})\n        d = to_d(x, sigmas[i], denoised)\n        # Euler method\n        dt = sigma_down - sigmas[i]\n        x = x + d * dt\n        if sigmas[i + 1] > 0:\n            x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up\n    return x\n\n\n@torch.no_grad()\ndef sample_heun(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):\n    \"\"\"Implements Algorithm 2 (Heun steps) from Karras et al. (2022).\"\"\"\n    extra_args = {} if extra_args is None else extra_args\n    s_in = x.new_ones([x.shape[0]])\n    for i in trange(len(sigmas) - 1, disable=disable):\n        gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.\n        sigma_hat = sigmas[i] * (gamma + 1)\n        if gamma > 0:\n            eps = torch.randn_like(x) * s_noise\n            x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5\n        denoised = model(x, sigma_hat * s_in, **extra_args)\n        d = to_d(x, sigma_hat, denoised)\n        if callback is not None:\n            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})\n        dt = sigmas[i + 1] - sigma_hat\n        if sigmas[i + 1] == 0:\n            # Euler method\n            x = x + d * dt\n        else:\n            # Heun's method\n            x_2 = x + d * dt\n            denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args)\n            d_2 = to_d(x_2, sigmas[i + 1], denoised_2)\n            d_prime = (d + d_2) / 2\n            x = x + d_prime * dt\n    return x\n\n\n@torch.no_grad()\ndef sample_dpm_2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):\n    \"\"\"A sampler inspired by DPM-Solver-2 and Algorithm 2 from Karras et al. (2022).\"\"\"\n    extra_args = {} if extra_args is None else extra_args\n    s_in = x.new_ones([x.shape[0]])\n    for i in trange(len(sigmas) - 1, disable=disable):\n        gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.\n        sigma_hat = sigmas[i] * (gamma + 1)\n        if gamma > 0:\n            eps = torch.randn_like(x) * s_noise\n            x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5\n        denoised = model(x, sigma_hat * s_in, **extra_args)\n        d = to_d(x, sigma_hat, denoised)\n        if callback is not None:\n            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})\n        if sigmas[i + 1] == 0:\n            # Euler method\n            dt = sigmas[i + 1] - sigma_hat\n            x = x + d * dt\n        else:\n            # DPM-Solver-2\n            sigma_mid = sigma_hat.log().lerp(sigmas[i + 1].log(), 0.5).exp()\n            dt_1 = sigma_mid - sigma_hat\n            dt_2 = sigmas[i + 1] - sigma_hat\n            x_2 = x + d * dt_1\n            denoised_2 = model(x_2, sigma_mid * s_in, **extra_args)\n            d_2 = to_d(x_2, sigma_mid, denoised_2)\n            x = x + d_2 * dt_2\n    return x\n\n\n@torch.no_grad()\ndef sample_dpm_2_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):\n    \"\"\"Ancestral sampling with DPM-Solver second-order steps.\"\"\"\n    extra_args = {} if extra_args is None else extra_args\n    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler\n    s_in = x.new_ones([x.shape[0]])\n    for i in trange(len(sigmas) - 1, disable=disable):\n        denoised = model(x, sigmas[i] * s_in, **extra_args)\n        sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)\n        if callback is not None:\n            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})\n        d = to_d(x, sigmas[i], denoised)\n        if sigma_down == 0:\n            # Euler method\n            dt = sigma_down - sigmas[i]\n            x = x + d * dt\n        else:\n            # DPM-Solver-2\n            sigma_mid = sigmas[i].log().lerp(sigma_down.log(), 0.5).exp()\n            dt_1 = sigma_mid - sigmas[i]\n            dt_2 = sigma_down - sigmas[i]\n            x_2 = x + d * dt_1\n            denoised_2 = model(x_2, sigma_mid * s_in, **extra_args)\n            d_2 = to_d(x_2, sigma_mid, denoised_2)\n            x = x + d_2 * dt_2\n            x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up\n    return x\n\n\ndef linear_multistep_coeff(order, t, i, j):\n    if order - 1 > i:\n        raise ValueError(f'Order {order} too high for step {i}')\n    def fn(tau):\n        prod = 1.\n        for k in range(order):\n            if j == k:\n                continue\n            prod *= (tau - t[i - k]) / (t[i - j] - t[i - k])\n        return prod\n    return integrate.quad(fn, t[i], t[i + 1], epsrel=1e-4)[0]\n\n\n@torch.no_grad()\ndef sample_lms(model, x, sigmas, extra_args=None, callback=None, disable=None, order=4):\n    extra_args = {} if extra_args is None else extra_args\n    s_in = x.new_ones([x.shape[0]])\n    sigmas_cpu = sigmas.detach().cpu().numpy()\n    ds = []\n    for i in trange(len(sigmas) - 1, disable=disable):\n        denoised = model(x, sigmas[i] * s_in, **extra_args)\n        d = to_d(x, sigmas[i], denoised)\n        ds.append(d)\n        if len(ds) > order:\n            ds.pop(0)\n        if callback is not None:\n            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})\n        cur_order = min(i + 1, order)\n        coeffs = [linear_multistep_coeff(cur_order, sigmas_cpu, i, j) for j in range(cur_order)]\n        x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds)))\n    return x\n\n\nclass PIDStepSizeController:\n    \"\"\"A PID controller for ODE adaptive step size control.\"\"\"\n    def __init__(self, h, pcoeff, icoeff, dcoeff, order=1, accept_safety=0.81, eps=1e-8):\n        self.h = h\n        self.b1 = (pcoeff + icoeff + dcoeff) / order\n        self.b2 = -(pcoeff + 2 * dcoeff) / order\n        self.b3 = dcoeff / order\n        self.accept_safety = accept_safety\n        self.eps = eps\n        self.errs = []\n\n    def limiter(self, x):\n        return 1 + math.atan(x - 1)\n\n    def propose_step(self, error):\n        inv_error = 1 / (float(error) + self.eps)\n        if not self.errs:\n            self.errs = [inv_error, inv_error, inv_error]\n        self.errs[0] = inv_error\n        factor = self.errs[0] ** self.b1 * self.errs[1] ** self.b2 * self.errs[2] ** self.b3\n        factor = self.limiter(factor)\n        accept = factor >= self.accept_safety\n        if accept:\n            self.errs[2] = self.errs[1]\n            self.errs[1] = self.errs[0]\n        self.h *= factor\n        return accept\n\n\nclass DPMSolver(nn.Module):\n    \"\"\"DPM-Solver. See https://arxiv.org/abs/2206.00927.\"\"\"\n\n    def __init__(self, model, extra_args=None, eps_callback=None, info_callback=None):\n        super().__init__()\n        self.model = model\n        self.extra_args = {} if extra_args is None else extra_args\n        self.eps_callback = eps_callback\n        self.info_callback = info_callback\n\n    def t(self, sigma):\n        return -sigma.log()\n\n    def sigma(self, t):\n        return t.neg().exp()\n\n    def eps(self, eps_cache, key, x, t, *args, **kwargs):\n        if key in eps_cache:\n            return eps_cache[key], eps_cache\n        sigma = self.sigma(t) * x.new_ones([x.shape[0]])\n        eps = (x - self.model(x, sigma, *args, **self.extra_args, **kwargs)) / self.sigma(t)\n        if self.eps_callback is not None:\n            self.eps_callback()\n        return eps, {key: eps, **eps_cache}\n\n    def dpm_solver_1_step(self, x, t, t_next, eps_cache=None):\n        eps_cache = {} if eps_cache is None else eps_cache\n        h = t_next - t\n        eps, eps_cache = self.eps(eps_cache, 'eps', x, t)\n        x_1 = x - self.sigma(t_next) * h.expm1() * eps\n        return x_1, eps_cache\n\n    def dpm_solver_2_step(self, x, t, t_next, r1=1 / 2, eps_cache=None):\n        eps_cache = {} if eps_cache is None else eps_cache\n        h = t_next - t\n        eps, eps_cache = self.eps(eps_cache, 'eps', x, t)\n        s1 = t + r1 * h\n        u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps\n        eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1)\n        x_2 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / (2 * r1) * h.expm1() * (eps_r1 - eps)\n        return x_2, eps_cache\n\n    def dpm_solver_3_step(self, x, t, t_next, r1=1 / 3, r2=2 / 3, eps_cache=None):\n        eps_cache = {} if eps_cache is None else eps_cache\n        h = t_next - t\n        eps, eps_cache = self.eps(eps_cache, 'eps', x, t)\n        s1 = t + r1 * h\n        s2 = t + r2 * h\n        u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps\n        eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1)\n        u2 = x - self.sigma(s2) * (r2 * h).expm1() * eps - self.sigma(s2) * (r2 / r1) * ((r2 * h).expm1() / (r2 * h) - 1) * (eps_r1 - eps)\n        eps_r2, eps_cache = self.eps(eps_cache, 'eps_r2', u2, s2)\n        x_3 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / r2 * (h.expm1() / h - 1) * (eps_r2 - eps)\n        return x_3, eps_cache\n\n    def dpm_solver_fast(self, x, t_start, t_end, nfe, eta=0., s_noise=1., noise_sampler=None):\n        noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler\n        if not t_end > t_start and eta:\n            raise ValueError('eta must be 0 for reverse sampling')\n\n        m = math.floor(nfe / 3) + 1\n        ts = torch.linspace(t_start, t_end, m + 1, device=x.device)\n\n        if nfe % 3 == 0:\n            orders = [3] * (m - 2) + [2, 1]\n        else:\n            orders = [3] * (m - 1) + [nfe % 3]\n\n        for i in range(len(orders)):\n            eps_cache = {}\n            t, t_next = ts[i], ts[i + 1]\n            if eta:\n                sd, su = get_ancestral_step(self.sigma(t), self.sigma(t_next), eta)\n                t_next_ = torch.minimum(t_end, self.t(sd))\n                su = (self.sigma(t_next) ** 2 - self.sigma(t_next_) ** 2) ** 0.5\n            else:\n                t_next_, su = t_next, 0.\n\n            eps, eps_cache = self.eps(eps_cache, 'eps', x, t)\n            denoised = x - self.sigma(t) * eps\n            if self.info_callback is not None:\n                self.info_callback({'x': x, 'i': i, 't': ts[i], 't_up': t, 'denoised': denoised})\n\n            if orders[i] == 1:\n                x, eps_cache = self.dpm_solver_1_step(x, t, t_next_, eps_cache=eps_cache)\n            elif orders[i] == 2:\n                x, eps_cache = self.dpm_solver_2_step(x, t, t_next_, eps_cache=eps_cache)\n            else:\n                x, eps_cache = self.dpm_solver_3_step(x, t, t_next_, eps_cache=eps_cache)\n\n            x = x + su * s_noise * noise_sampler(self.sigma(t), self.sigma(t_next))\n\n        return x\n\n    def dpm_solver_adaptive(self, x, t_start, t_end, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None):\n        noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler\n        if order not in {2, 3}:\n            raise ValueError('order should be 2 or 3')\n        forward = t_end > t_start\n        if not forward and eta:\n            raise ValueError('eta must be 0 for reverse sampling')\n        h_init = abs(h_init) * (1 if forward else -1)\n        atol = torch.tensor(atol)\n        rtol = torch.tensor(rtol)\n        s = t_start\n        x_prev = x\n        accept = True\n        pid = PIDStepSizeController(h_init, pcoeff, icoeff, dcoeff, 1.5 if eta else order, accept_safety)\n        info = {'steps': 0, 'nfe': 0, 'n_accept': 0, 'n_reject': 0}\n\n        while s < t_end - 1e-5 if forward else s > t_end + 1e-5:\n            eps_cache = {}\n            t = torch.minimum(t_end, s + pid.h) if forward else torch.maximum(t_end, s + pid.h)\n            if eta:\n                sd, su = get_ancestral_step(self.sigma(s), self.sigma(t), eta)\n                t_ = torch.minimum(t_end, self.t(sd))\n                su = (self.sigma(t) ** 2 - self.sigma(t_) ** 2) ** 0.5\n            else:\n                t_, su = t, 0.\n\n            eps, eps_cache = self.eps(eps_cache, 'eps', x, s)\n            denoised = x - self.sigma(s) * eps\n\n            if order == 2:\n                x_low, eps_cache = self.dpm_solver_1_step(x, s, t_, eps_cache=eps_cache)\n                x_high, eps_cache = self.dpm_solver_2_step(x, s, t_, eps_cache=eps_cache)\n            else:\n                x_low, eps_cache = self.dpm_solver_2_step(x, s, t_, r1=1 / 3, eps_cache=eps_cache)\n                x_high, eps_cache = self.dpm_solver_3_step(x, s, t_, eps_cache=eps_cache)\n            delta = torch.maximum(atol, rtol * torch.maximum(x_low.abs(), x_prev.abs()))\n            error = torch.linalg.norm((x_low - x_high) / delta) / x.numel() ** 0.5\n            accept = pid.propose_step(error)\n            if accept:\n                x_prev = x_low\n                x = x_high + su * s_noise * noise_sampler(self.sigma(s), self.sigma(t))\n                s = t\n                info['n_accept'] += 1\n            else:\n                info['n_reject'] += 1\n            info['nfe'] += order\n            info['steps'] += 1\n\n            if self.info_callback is not None:\n                self.info_callback({'x': x, 'i': info['steps'] - 1, 't': s, 't_up': s, 'denoised': denoised, 'error': error, 'h': pid.h, **info})\n\n        return x, info\n\n\n@torch.no_grad()\ndef sample_dpm_fast(model, x, sigma_min, sigma_max, n, extra_args=None, callback=None, disable=None, eta=0., s_noise=1., noise_sampler=None):\n    \"\"\"DPM-Solver-Fast (fixed step size). See https://arxiv.org/abs/2206.00927.\"\"\"\n    if sigma_min <= 0 or sigma_max <= 0:\n        raise ValueError('sigma_min and sigma_max must not be 0')\n    with tqdm(total=n, disable=disable) as pbar:\n        dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update)\n        if callback is not None:\n            dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info})\n        return dpm_solver.dpm_solver_fast(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), n, eta, s_noise, noise_sampler)\n\n\n@torch.no_grad()\ndef sample_dpm_adaptive(model, x, sigma_min, sigma_max, extra_args=None, callback=None, disable=None, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None, return_info=False):\n    \"\"\"DPM-Solver-12 and 23 (adaptive step size). See https://arxiv.org/abs/2206.00927.\"\"\"\n    if sigma_min <= 0 or sigma_max <= 0:\n        raise ValueError('sigma_min and sigma_max must not be 0')\n    with tqdm(disable=disable) as pbar:\n        dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update)\n        if callback is not None:\n            dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info})\n        x, info = dpm_solver.dpm_solver_adaptive(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), order, rtol, atol, h_init, pcoeff, icoeff, dcoeff, accept_safety, eta, s_noise, noise_sampler)\n    if return_info:\n        return x, info\n    return x\n\n\n@torch.no_grad()\ndef sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):\n    \"\"\"Ancestral sampling with DPM-Solver++(2S) second-order steps.\"\"\"\n    extra_args = {} if extra_args is None else extra_args\n    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler\n    s_in = x.new_ones([x.shape[0]])\n    sigma_fn = lambda t: t.neg().exp()\n    t_fn = lambda sigma: sigma.log().neg()\n\n    for i in trange(len(sigmas) - 1, disable=disable):\n        denoised = model(x, sigmas[i] * s_in, **extra_args)\n        sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)\n        if callback is not None:\n            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})\n        if sigma_down == 0:\n            # Euler method\n            d = to_d(x, sigmas[i], denoised)\n            dt = sigma_down - sigmas[i]\n            x = x + d * dt\n        else:\n            # DPM-Solver++(2S)\n            t, t_next = t_fn(sigmas[i]), t_fn(sigma_down)\n            r = 1 / 2\n            h = t_next - t\n            s = t + r * h\n            x_2 = (sigma_fn(s) / sigma_fn(t)) * x - (-h * r).expm1() * denoised\n            denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args)\n            x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_2\n        # Noise addition\n        if sigmas[i + 1] > 0:\n            x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up\n    return x\n\n\n@torch.no_grad()\ndef sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):\n    \"\"\"DPM-Solver++ (stochastic).\"\"\"\n    sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()\n    seed = extra_args.get(\"seed\", None)\n    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler\n    extra_args = {} if extra_args is None else extra_args\n    s_in = x.new_ones([x.shape[0]])\n    sigma_fn = lambda t: t.neg().exp()\n    t_fn = lambda sigma: sigma.log().neg()\n\n    for i in trange(len(sigmas) - 1, disable=disable):\n        denoised = model(x, sigmas[i] * s_in, **extra_args)\n        if callback is not None:\n            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})\n        if sigmas[i + 1] == 0:\n            # Euler method\n            d = to_d(x, sigmas[i], denoised)\n            dt = sigmas[i + 1] - sigmas[i]\n            x = x + d * dt\n        else:\n            # DPM-Solver++\n            t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])\n            h = t_next - t\n            s = t + h * r\n            fac = 1 / (2 * r)\n\n            # Step 1\n            sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(s), eta)\n            s_ = t_fn(sd)\n            x_2 = (sigma_fn(s_) / sigma_fn(t)) * x - (t - s_).expm1() * denoised\n            x_2 = x_2 + noise_sampler(sigma_fn(t), sigma_fn(s)) * s_noise * su\n            denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args)\n\n            # Step 2\n            sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(t_next), eta)\n            t_next_ = t_fn(sd)\n            denoised_d = (1 - fac) * denoised + fac * denoised_2\n            x = (sigma_fn(t_next_) / sigma_fn(t)) * x - (t - t_next_).expm1() * denoised_d\n            x = x + noise_sampler(sigma_fn(t), sigma_fn(t_next)) * s_noise * su\n    return x\n\n\n@torch.no_grad()\ndef sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=None):\n    \"\"\"DPM-Solver++(2M).\"\"\"\n    extra_args = {} if extra_args is None else extra_args\n    s_in = x.new_ones([x.shape[0]])\n    sigma_fn = lambda t: t.neg().exp()\n    t_fn = lambda sigma: sigma.log().neg()\n    old_denoised = None\n\n    for i in trange(len(sigmas) - 1, disable=disable):\n        denoised = model(x, sigmas[i] * s_in, **extra_args)\n        if callback is not None:\n            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})\n        t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])\n        h = t_next - t\n        if old_denoised is None or sigmas[i + 1] == 0:\n            x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised\n        else:\n            h_last = t - t_fn(sigmas[i - 1])\n            r = h_last / h\n            denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised\n            x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d\n        old_denoised = denoised\n    return x\n\n@torch.no_grad()\ndef sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):\n    \"\"\"DPM-Solver++(2M) SDE.\"\"\"\n\n    if solver_type not in {'heun', 'midpoint'}:\n        raise ValueError('solver_type must be \\'heun\\' or \\'midpoint\\'')\n\n    seed = extra_args.get(\"seed\", None)\n    sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()\n    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler\n    extra_args = {} if extra_args is None else extra_args\n    s_in = x.new_ones([x.shape[0]])\n\n    old_denoised = None\n    h_last = None\n    h = None\n\n    for i in trange(len(sigmas) - 1, disable=disable):\n        denoised = model(x, sigmas[i] * s_in, **extra_args)\n        if callback is not None:\n            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})\n        if sigmas[i + 1] == 0:\n            # Denoising step\n            x = denoised\n        else:\n            # DPM-Solver++(2M) SDE\n            t, s = -sigmas[i].log(), -sigmas[i + 1].log()\n            h = s - t\n            eta_h = eta * h\n\n            x = sigmas[i + 1] / sigmas[i] * (-eta_h).exp() * x + (-h - eta_h).expm1().neg() * denoised\n\n            if old_denoised is not None:\n                r = h_last / h\n                if solver_type == 'heun':\n                    x = x + ((-h - eta_h).expm1().neg() / (-h - eta_h) + 1) * (1 / r) * (denoised - old_denoised)\n                elif solver_type == 'midpoint':\n                    x = x + 0.5 * (-h - eta_h).expm1().neg() * (1 / r) * (denoised - old_denoised)\n\n            if eta:\n                x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * eta_h).expm1().neg().sqrt() * s_noise\n\n        old_denoised = denoised\n        h_last = h\n    return x\n\n@torch.no_grad()\ndef sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):\n    \"\"\"DPM-Solver++(3M) SDE.\"\"\"\n\n    seed = extra_args.get(\"seed\", None)\n    sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()\n    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler\n    extra_args = {} if extra_args is None else extra_args\n    s_in = x.new_ones([x.shape[0]])\n\n    denoised_1, denoised_2 = None, None\n    h, h_1, h_2 = None, None, None\n\n    for i in trange(len(sigmas) - 1, disable=disable):\n        denoised = model(x, sigmas[i] * s_in, **extra_args)\n        if callback is not None:\n            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})\n        if sigmas[i + 1] == 0:\n            # Denoising step\n            x = denoised\n        else:\n            t, s = -sigmas[i].log(), -sigmas[i + 1].log()\n            h = s - t\n            h_eta = h * (eta + 1)\n\n            x = torch.exp(-h_eta) * x + (-h_eta).expm1().neg() * denoised\n\n            if h_2 is not None:\n                r0 = h_1 / h\n                r1 = h_2 / h\n                d1_0 = (denoised - denoised_1) / r0\n                d1_1 = (denoised_1 - denoised_2) / r1\n                d1 = d1_0 + (d1_0 - d1_1) * r0 / (r0 + r1)\n                d2 = (d1_0 - d1_1) / (r0 + r1)\n                phi_2 = h_eta.neg().expm1() / h_eta + 1\n                phi_3 = phi_2 / h_eta - 0.5\n                x = x + phi_2 * d1 - phi_3 * d2\n            elif h_1 is not None:\n                r = h_1 / h\n                d = (denoised - denoised_1) / r\n                phi_2 = h_eta.neg().expm1() / h_eta + 1\n                x = x + phi_2 * d\n\n            if eta:\n                x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise\n\n        denoised_1, denoised_2 = denoised, denoised_1\n        h_1, h_2 = h, h_1\n    return x\n\n@torch.no_grad()\ndef sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):\n    sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()\n    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get(\"seed\", None), cpu=False) if noise_sampler is None else noise_sampler\n    return sample_dpmpp_3m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler)\n\n@torch.no_grad()\ndef sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):\n    sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()\n    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get(\"seed\", None), cpu=False) if noise_sampler is None else noise_sampler\n    return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type)\n\n@torch.no_grad()\ndef sample_dpmpp_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):\n    sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()\n    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get(\"seed\", None), cpu=False) if noise_sampler is None else noise_sampler\n    return sample_dpmpp_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, r=r)\n\n\ndef DDPMSampler_step(x, sigma, sigma_prev, noise, noise_sampler):\n    alpha_cumprod = 1 / ((sigma * sigma) + 1)\n    alpha_cumprod_prev = 1 / ((sigma_prev * sigma_prev) + 1)\n    alpha = (alpha_cumprod / alpha_cumprod_prev)\n\n    mu = (1.0 / alpha).sqrt() * (x - (1 - alpha) * noise / (1 - alpha_cumprod).sqrt())\n    if sigma_prev > 0:\n        mu += ((1 - alpha) * (1. - alpha_cumprod_prev) / (1. - alpha_cumprod)).sqrt() * noise_sampler(sigma, sigma_prev)\n    return mu\n\ndef generic_step_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, step_function=None):\n    extra_args = {} if extra_args is None else extra_args\n    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler\n    s_in = x.new_ones([x.shape[0]])\n\n    for i in trange(len(sigmas) - 1, disable=disable):\n        denoised = model(x, sigmas[i] * s_in, **extra_args)\n        if callback is not None:\n            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})\n        x = step_function(x / torch.sqrt(1.0 + sigmas[i] ** 2.0), sigmas[i], sigmas[i + 1], (x - denoised) / sigmas[i], noise_sampler)\n        if sigmas[i + 1] != 0:\n            x *= torch.sqrt(1.0 + sigmas[i + 1] ** 2.0)\n    return x\n\n\n@torch.no_grad()\ndef sample_ddpm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None):\n    return generic_step_sampler(model, x, sigmas, extra_args, callback, disable, noise_sampler, DDPMSampler_step)\n\n@torch.no_grad()\ndef sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None):\n    extra_args = {} if extra_args is None else extra_args\n    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler\n    s_in = x.new_ones([x.shape[0]])\n    for i in trange(len(sigmas) - 1, disable=disable):\n        denoised = model(x, sigmas[i] * s_in, **extra_args)\n        if callback is not None:\n            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})\n\n        x = denoised\n        if sigmas[i + 1] > 0:\n            x += sigmas[i + 1] * noise_sampler(sigmas[i], sigmas[i + 1])\n    return x\n\n\n@torch.no_grad()\ndef sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):\n    # From MIT licensed: https://github.com/Carzit/sd-webui-samplers-scheduler/\n    extra_args = {} if extra_args is None else extra_args\n    s_in = x.new_ones([x.shape[0]])\n    s_end = sigmas[-1]\n    for i in trange(len(sigmas) - 1, disable=disable):\n        gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.\n        eps = torch.randn_like(x) * s_noise\n        sigma_hat = sigmas[i] * (gamma + 1)\n        if gamma > 0:\n            x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5\n        denoised = model(x, sigma_hat * s_in, **extra_args)\n        d = to_d(x, sigma_hat, denoised)\n        if callback is not None:\n            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})\n        dt = sigmas[i + 1] - sigma_hat\n        if sigmas[i + 1] == s_end:\n            # Euler method\n            x = x + d * dt\n        elif sigmas[i + 2] == s_end:\n\n            # Heun's method\n            x_2 = x + d * dt\n            denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args)\n            d_2 = to_d(x_2, sigmas[i + 1], denoised_2)\n\n            w = 2 * sigmas[0]\n            w2 = sigmas[i+1]/w\n            w1 = 1 - w2\n\n            d_prime = d * w1 + d_2 * w2\n\n\n            x = x + d_prime * dt\n\n        else:\n            # Heun++\n            x_2 = x + d * dt\n            denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args)\n            d_2 = to_d(x_2, sigmas[i + 1], denoised_2)\n            dt_2 = sigmas[i + 2] - sigmas[i + 1]\n\n            x_3 = x_2 + d_2 * dt_2\n            denoised_3 = model(x_3, sigmas[i + 2] * s_in, **extra_args)\n            d_3 = to_d(x_3, sigmas[i + 2], denoised_3)\n\n            w = 3 * sigmas[0]\n            w2 = sigmas[i + 1] / w\n            w3 = sigmas[i + 2] / w\n            w1 = 1 - w2 - w3\n\n            d_prime = w1 * d + w2 * d_2 + w3 * d_3\n            x = x + d_prime * dt\n    return x\n\n\n@torch.no_grad()\ndef sample_tcd(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, eta=0.3):\n    extra_args = {} if extra_args is None else extra_args\n    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler\n    s_in = x.new_ones([x.shape[0]])\n\n    model_sampling = model.inner_model.inner_model.model_sampling\n    timesteps_s = torch.floor((1 - eta) * model_sampling.timestep(sigmas)).to(dtype=torch.long).detach().cpu()\n    timesteps_s[-1] = 0\n    alpha_prod_s = model_sampling.alphas_cumprod[timesteps_s]\n    beta_prod_s = 1 - alpha_prod_s\n    for i in trange(len(sigmas) - 1, disable=disable):\n        denoised = model(x, sigmas[i] * s_in, **extra_args)  # predicted_original_sample\n        eps = (x - denoised) / sigmas[i]\n        denoised = alpha_prod_s[i + 1].sqrt() * denoised + beta_prod_s[i + 1].sqrt() * eps\n\n        if callback is not None:\n            callback({\"x\": x, \"i\": i, \"sigma\": sigmas[i], \"sigma_hat\": sigmas[i], \"denoised\": denoised})\n\n        x = denoised\n        if eta > 0 and sigmas[i + 1] > 0:\n            noise = noise_sampler(sigmas[i], sigmas[i + 1])\n            x = x / alpha_prod_s[i+1].sqrt() + noise * (sigmas[i+1]**2 + 1 - 1/alpha_prod_s[i+1]).sqrt()\n        else:\n            x *= torch.sqrt(1.0 + sigmas[i + 1] ** 2)\n\n    return x\n\n\n@torch.no_grad()\ndef sample_restart(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list=None):\n    \"\"\"Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)\n    Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}\n    If restart_list is None: will choose restart_list automatically, otherwise will use the given restart_list\n    \"\"\"\n    extra_args = {} if extra_args is None else extra_args\n    s_in = x.new_ones([x.shape[0]])\n    step_id = 0\n\n    def heun_step(x, old_sigma, new_sigma, second_order=True):\n        nonlocal step_id\n        denoised = model(x, old_sigma * s_in, **extra_args)\n        d = to_d(x, old_sigma, denoised)\n        if callback is not None:\n            callback({'x': x, 'i': step_id, 'sigma': new_sigma, 'sigma_hat': old_sigma, 'denoised': denoised})\n        dt = new_sigma - old_sigma\n        if new_sigma == 0 or not second_order:\n            # Euler method\n            x = x + d * dt\n        else:\n            # Heun's method\n            x_2 = x + d * dt\n            denoised_2 = model(x_2, new_sigma * s_in, **extra_args)\n            d_2 = to_d(x_2, new_sigma, denoised_2)\n            d_prime = (d + d_2) / 2\n            x = x + d_prime * dt\n        step_id += 1\n        return x\n\n    steps = sigmas.shape[0] - 1\n    if restart_list is None:\n        if steps >= 20:\n            restart_steps = 9\n            restart_times = 1\n            if steps >= 36:\n                restart_steps = steps // 4\n                restart_times = 2\n            sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2].item(), sigmas[0].item(), device=sigmas.device)\n            restart_list = {0.1: [restart_steps + 1, restart_times, 2]}\n        else:\n            restart_list = {}\n\n    restart_list = {int(torch.argmin(abs(sigmas - key), dim=0)): value for key, value in restart_list.items()}\n\n    step_list = []\n    for i in range(len(sigmas) - 1):\n        step_list.append((sigmas[i], sigmas[i + 1]))\n        if i + 1 in restart_list:\n            restart_steps, restart_times, restart_max = restart_list[i + 1]\n            min_idx = i + 1\n            max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0))\n            if max_idx < min_idx:\n                sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1]\n                while restart_times > 0:\n                    restart_times -= 1\n                    step_list.extend(zip(sigma_restart[:-1], sigma_restart[1:]))\n\n    last_sigma = None\n    for old_sigma, new_sigma in tqdm(step_list, disable=disable):\n        if last_sigma is None:\n            last_sigma = old_sigma\n        elif last_sigma < old_sigma:\n            x = x + torch.randn_like(x) * s_noise * (old_sigma ** 2 - last_sigma ** 2) ** 0.5\n        x = heun_step(x, old_sigma, new_sigma)\n        last_sigma = new_sigma\n\n    return x"
  },
  {
    "path": "ldm_patched/k_diffusion/utils.py",
    "content": "from contextlib import contextmanager\nimport hashlib\nimport math\nfrom pathlib import Path\nimport shutil\nimport urllib\nimport warnings\n\nfrom PIL import Image\nimport torch\nfrom torch import nn, optim\nfrom torch.utils import data\n\n\ndef hf_datasets_augs_helper(examples, transform, image_key, mode='RGB'):\n    \"\"\"Apply passed in transforms for HuggingFace Datasets.\"\"\"\n    images = [transform(image.convert(mode)) for image in examples[image_key]]\n    return {image_key: images}\n\n\ndef append_dims(x, target_dims):\n    \"\"\"Appends dimensions to the end of a tensor until it has target_dims dimensions.\"\"\"\n    dims_to_append = target_dims - x.ndim\n    if dims_to_append < 0:\n        raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')\n    expanded = x[(...,) + (None,) * dims_to_append]\n    # MPS will get inf values if it tries to index into the new axes, but detaching fixes this.\n    # https://github.com/pytorch/pytorch/issues/84364\n    return expanded.detach().clone() if expanded.device.type == 'mps' else expanded\n\n\ndef n_params(module):\n    \"\"\"Returns the number of trainable parameters in a module.\"\"\"\n    return sum(p.numel() for p in module.parameters())\n\n\ndef download_file(path, url, digest=None):\n    \"\"\"Downloads a file if it does not exist, optionally checking its SHA-256 hash.\"\"\"\n    path = Path(path)\n    path.parent.mkdir(parents=True, exist_ok=True)\n    if not path.exists():\n        with urllib.request.urlopen(url) as response, open(path, 'wb') as f:\n            shutil.copyfileobj(response, f)\n    if digest is not None:\n        file_digest = hashlib.sha256(open(path, 'rb').read()).hexdigest()\n        if digest != file_digest:\n            raise OSError(f'hash of {path} (url: {url}) failed to validate')\n    return path\n\n\n@contextmanager\ndef train_mode(model, mode=True):\n    \"\"\"A context manager that places a model into training mode and restores\n    the previous mode on exit.\"\"\"\n    modes = [module.training for module in model.modules()]\n    try:\n        yield model.train(mode)\n    finally:\n        for i, module in enumerate(model.modules()):\n            module.training = modes[i]\n\n\ndef eval_mode(model):\n    \"\"\"A context manager that places a model into evaluation mode and restores\n    the previous mode on exit.\"\"\"\n    return train_mode(model, False)\n\n\n@torch.no_grad()\ndef ema_update(model, averaged_model, decay):\n    \"\"\"Incorporates updated model parameters into an exponential moving averaged\n    version of a model. It should be called after each optimizer step.\"\"\"\n    model_params = dict(model.named_parameters())\n    averaged_params = dict(averaged_model.named_parameters())\n    assert model_params.keys() == averaged_params.keys()\n\n    for name, param in model_params.items():\n        averaged_params[name].mul_(decay).add_(param, alpha=1 - decay)\n\n    model_buffers = dict(model.named_buffers())\n    averaged_buffers = dict(averaged_model.named_buffers())\n    assert model_buffers.keys() == averaged_buffers.keys()\n\n    for name, buf in model_buffers.items():\n        averaged_buffers[name].copy_(buf)\n\n\nclass EMAWarmup:\n    \"\"\"Implements an EMA warmup using an inverse decay schedule.\n    If inv_gamma=1 and power=1, implements a simple average. inv_gamma=1, power=2/3 are\n    good values for models you plan to train for a million or more steps (reaches decay\n    factor 0.999 at 31.6K steps, 0.9999 at 1M steps), inv_gamma=1, power=3/4 for models\n    you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999 at\n    215.4k steps).\n    Args:\n        inv_gamma (float): Inverse multiplicative factor of EMA warmup. Default: 1.\n        power (float): Exponential factor of EMA warmup. Default: 1.\n        min_value (float): The minimum EMA decay rate. Default: 0.\n        max_value (float): The maximum EMA decay rate. Default: 1.\n        start_at (int): The epoch to start averaging at. Default: 0.\n        last_epoch (int): The index of last epoch. Default: 0.\n    \"\"\"\n\n    def __init__(self, inv_gamma=1., power=1., min_value=0., max_value=1., start_at=0,\n                 last_epoch=0):\n        self.inv_gamma = inv_gamma\n        self.power = power\n        self.min_value = min_value\n        self.max_value = max_value\n        self.start_at = start_at\n        self.last_epoch = last_epoch\n\n    def state_dict(self):\n        \"\"\"Returns the state of the class as a :class:`dict`.\"\"\"\n        return dict(self.__dict__.items())\n\n    def load_state_dict(self, state_dict):\n        \"\"\"Loads the class's state.\n        Args:\n            state_dict (dict): scaler state. Should be an object returned\n                from a call to :meth:`state_dict`.\n        \"\"\"\n        self.__dict__.update(state_dict)\n\n    def get_value(self):\n        \"\"\"Gets the current EMA decay rate.\"\"\"\n        epoch = max(0, self.last_epoch - self.start_at)\n        value = 1 - (1 + epoch / self.inv_gamma) ** -self.power\n        return 0. if epoch < 0 else min(self.max_value, max(self.min_value, value))\n\n    def step(self):\n        \"\"\"Updates the step count.\"\"\"\n        self.last_epoch += 1\n\n\nclass InverseLR(optim.lr_scheduler._LRScheduler):\n    \"\"\"Implements an inverse decay learning rate schedule with an optional exponential\n    warmup. When last_epoch=-1, sets initial lr as lr.\n    inv_gamma is the number of steps/epochs required for the learning rate to decay to\n    (1 / 2)**power of its original value.\n    Args:\n        optimizer (Optimizer): Wrapped optimizer.\n        inv_gamma (float): Inverse multiplicative factor of learning rate decay. Default: 1.\n        power (float): Exponential factor of learning rate decay. Default: 1.\n        warmup (float): Exponential warmup factor (0 <= warmup < 1, 0 to disable)\n            Default: 0.\n        min_lr (float): The minimum learning rate. Default: 0.\n        last_epoch (int): The index of last epoch. Default: -1.\n        verbose (bool): If ``True``, prints a message to stdout for\n            each update. Default: ``False``.\n    \"\"\"\n\n    def __init__(self, optimizer, inv_gamma=1., power=1., warmup=0., min_lr=0.,\n                 last_epoch=-1, verbose=False):\n        self.inv_gamma = inv_gamma\n        self.power = power\n        if not 0. <= warmup < 1:\n            raise ValueError('Invalid value for warmup')\n        self.warmup = warmup\n        self.min_lr = min_lr\n        super().__init__(optimizer, last_epoch, verbose)\n\n    def get_lr(self):\n        if not self._get_lr_called_within_step:\n            warnings.warn(\"To get the last learning rate computed by the scheduler, \"\n                          \"please use `get_last_lr()`.\")\n\n        return self._get_closed_form_lr()\n\n    def _get_closed_form_lr(self):\n        warmup = 1 - self.warmup ** (self.last_epoch + 1)\n        lr_mult = (1 + self.last_epoch / self.inv_gamma) ** -self.power\n        return [warmup * max(self.min_lr, base_lr * lr_mult)\n                for base_lr in self.base_lrs]\n\n\nclass ExponentialLR(optim.lr_scheduler._LRScheduler):\n    \"\"\"Implements an exponential learning rate schedule with an optional exponential\n    warmup. When last_epoch=-1, sets initial lr as lr. Decays the learning rate\n    continuously by decay (default 0.5) every num_steps steps.\n    Args:\n        optimizer (Optimizer): Wrapped optimizer.\n        num_steps (float): The number of steps to decay the learning rate by decay in.\n        decay (float): The factor by which to decay the learning rate every num_steps\n            steps. Default: 0.5.\n        warmup (float): Exponential warmup factor (0 <= warmup < 1, 0 to disable)\n            Default: 0.\n        min_lr (float): The minimum learning rate. Default: 0.\n        last_epoch (int): The index of last epoch. Default: -1.\n        verbose (bool): If ``True``, prints a message to stdout for\n            each update. Default: ``False``.\n    \"\"\"\n\n    def __init__(self, optimizer, num_steps, decay=0.5, warmup=0., min_lr=0.,\n                 last_epoch=-1, verbose=False):\n        self.num_steps = num_steps\n        self.decay = decay\n        if not 0. <= warmup < 1:\n            raise ValueError('Invalid value for warmup')\n        self.warmup = warmup\n        self.min_lr = min_lr\n        super().__init__(optimizer, last_epoch, verbose)\n\n    def get_lr(self):\n        if not self._get_lr_called_within_step:\n            warnings.warn(\"To get the last learning rate computed by the scheduler, \"\n                          \"please use `get_last_lr()`.\")\n\n        return self._get_closed_form_lr()\n\n    def _get_closed_form_lr(self):\n        warmup = 1 - self.warmup ** (self.last_epoch + 1)\n        lr_mult = (self.decay ** (1 / self.num_steps)) ** self.last_epoch\n        return [warmup * max(self.min_lr, base_lr * lr_mult)\n                for base_lr in self.base_lrs]\n\n\ndef rand_log_normal(shape, loc=0., scale=1., device='cpu', dtype=torch.float32):\n    \"\"\"Draws samples from an lognormal distribution.\"\"\"\n    return (torch.randn(shape, device=device, dtype=dtype) * scale + loc).exp()\n\n\ndef rand_log_logistic(shape, loc=0., scale=1., min_value=0., max_value=float('inf'), device='cpu', dtype=torch.float32):\n    \"\"\"Draws samples from an optionally truncated log-logistic distribution.\"\"\"\n    min_value = torch.as_tensor(min_value, device=device, dtype=torch.float64)\n    max_value = torch.as_tensor(max_value, device=device, dtype=torch.float64)\n    min_cdf = min_value.log().sub(loc).div(scale).sigmoid()\n    max_cdf = max_value.log().sub(loc).div(scale).sigmoid()\n    u = torch.rand(shape, device=device, dtype=torch.float64) * (max_cdf - min_cdf) + min_cdf\n    return u.logit().mul(scale).add(loc).exp().to(dtype)\n\n\ndef rand_log_uniform(shape, min_value, max_value, device='cpu', dtype=torch.float32):\n    \"\"\"Draws samples from an log-uniform distribution.\"\"\"\n    min_value = math.log(min_value)\n    max_value = math.log(max_value)\n    return (torch.rand(shape, device=device, dtype=dtype) * (max_value - min_value) + min_value).exp()\n\n\ndef rand_v_diffusion(shape, sigma_data=1., min_value=0., max_value=float('inf'), device='cpu', dtype=torch.float32):\n    \"\"\"Draws samples from a truncated v-diffusion training timestep distribution.\"\"\"\n    min_cdf = math.atan(min_value / sigma_data) * 2 / math.pi\n    max_cdf = math.atan(max_value / sigma_data) * 2 / math.pi\n    u = torch.rand(shape, device=device, dtype=dtype) * (max_cdf - min_cdf) + min_cdf\n    return torch.tan(u * math.pi / 2) * sigma_data\n\n\ndef rand_split_log_normal(shape, loc, scale_1, scale_2, device='cpu', dtype=torch.float32):\n    \"\"\"Draws samples from a split lognormal distribution.\"\"\"\n    n = torch.randn(shape, device=device, dtype=dtype).abs()\n    u = torch.rand(shape, device=device, dtype=dtype)\n    n_left = n * -scale_1 + loc\n    n_right = n * scale_2 + loc\n    ratio = scale_1 / (scale_1 + scale_2)\n    return torch.where(u < ratio, n_left, n_right).exp()\n\n\nclass FolderOfImages(data.Dataset):\n    \"\"\"Recursively finds all images in a directory. It does not support\n    classes/targets.\"\"\"\n\n    IMG_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp'}\n\n    def __init__(self, root, transform=None):\n        super().__init__()\n        self.root = Path(root)\n        self.transform = nn.Identity() if transform is None else transform\n        self.paths = sorted(path for path in self.root.rglob('*') if path.suffix.lower() in self.IMG_EXTENSIONS)\n\n    def __repr__(self):\n        return f'FolderOfImages(root=\"{self.root}\", len: {len(self)})'\n\n    def __len__(self):\n        return len(self.paths)\n\n    def __getitem__(self, key):\n        path = self.paths[key]\n        with open(path, 'rb') as f:\n            image = Image.open(f).convert('RGB')\n        image = self.transform(image)\n        return image,\n\n\nclass CSVLogger:\n    def __init__(self, filename, columns):\n        self.filename = Path(filename)\n        self.columns = columns\n        if self.filename.exists():\n            self.file = open(self.filename, 'a')\n        else:\n            self.file = open(self.filename, 'w')\n            self.write(*self.columns)\n\n    def write(self, *args):\n        print(*args, sep=',', file=self.file, flush=True)\n\n\n@contextmanager\ndef tf32_mode(cudnn=None, matmul=None):\n    \"\"\"A context manager that sets whether TF32 is allowed on cuDNN or matmul.\"\"\"\n    cudnn_old = torch.backends.cudnn.allow_tf32\n    matmul_old = torch.backends.cuda.matmul.allow_tf32\n    try:\n        if cudnn is not None:\n            torch.backends.cudnn.allow_tf32 = cudnn\n        if matmul is not None:\n            torch.backends.cuda.matmul.allow_tf32 = matmul\n        yield\n    finally:\n        if cudnn is not None:\n            torch.backends.cudnn.allow_tf32 = cudnn_old\n        if matmul is not None:\n            torch.backends.cuda.matmul.allow_tf32 = matmul_old\n"
  },
  {
    "path": "ldm_patched/ldm/models/autoencoder.py",
    "content": "import torch\n# import pytorch_lightning as pl\nimport torch.nn.functional as F\nfrom contextlib import contextmanager\nfrom typing import Any, Dict, List, Optional, Tuple, Union\n\nfrom ldm_patched.ldm.modules.distributions.distributions import DiagonalGaussianDistribution\n\nfrom ldm_patched.ldm.util import instantiate_from_config\nfrom ldm_patched.ldm.modules.ema import LitEma\nimport ldm_patched.modules.ops\n\nclass DiagonalGaussianRegularizer(torch.nn.Module):\n    def __init__(self, sample: bool = True):\n        super().__init__()\n        self.sample = sample\n\n    def get_trainable_parameters(self) -> Any:\n        yield from ()\n\n    def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]:\n        log = dict()\n        posterior = DiagonalGaussianDistribution(z)\n        if self.sample:\n            z = posterior.sample()\n        else:\n            z = posterior.mode()\n        kl_loss = posterior.kl()\n        kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]\n        log[\"kl_loss\"] = kl_loss\n        return z, log\n\n\nclass AbstractAutoencoder(torch.nn.Module):\n    \"\"\"\n    This is the base class for all autoencoders, including image autoencoders, image autoencoders with discriminators,\n    unCLIP models, etc. Hence, it is fairly general, and specific features\n    (e.g. discriminator training, encoding, decoding) must be implemented in subclasses.\n    \"\"\"\n\n    def __init__(\n        self,\n        ema_decay: Union[None, float] = None,\n        monitor: Union[None, str] = None,\n        input_key: str = \"jpg\",\n        **kwargs,\n    ):\n        super().__init__()\n\n        self.input_key = input_key\n        self.use_ema = ema_decay is not None\n        if monitor is not None:\n            self.monitor = monitor\n\n        if self.use_ema:\n            self.model_ema = LitEma(self, decay=ema_decay)\n            logpy.info(f\"Keeping EMAs of {len(list(self.model_ema.buffers()))}.\")\n\n    def get_input(self, batch) -> Any:\n        raise NotImplementedError()\n\n    def on_train_batch_end(self, *args, **kwargs):\n        # for EMA computation\n        if self.use_ema:\n            self.model_ema(self)\n\n    @contextmanager\n    def ema_scope(self, context=None):\n        if self.use_ema:\n            self.model_ema.store(self.parameters())\n            self.model_ema.copy_to(self)\n            if context is not None:\n                logpy.info(f\"{context}: Switched to EMA weights\")\n        try:\n            yield None\n        finally:\n            if self.use_ema:\n                self.model_ema.restore(self.parameters())\n                if context is not None:\n                    logpy.info(f\"{context}: Restored training weights\")\n\n    def encode(self, *args, **kwargs) -> torch.Tensor:\n        raise NotImplementedError(\"encode()-method of abstract base class called\")\n\n    def decode(self, *args, **kwargs) -> torch.Tensor:\n        raise NotImplementedError(\"decode()-method of abstract base class called\")\n\n    def instantiate_optimizer_from_config(self, params, lr, cfg):\n        logpy.info(f\"loading >>> {cfg['target']} <<< optimizer from config\")\n        return get_obj_from_str(cfg[\"target\"])(\n            params, lr=lr, **cfg.get(\"params\", dict())\n        )\n\n    def configure_optimizers(self) -> Any:\n        raise NotImplementedError()\n\n\nclass AutoencodingEngine(AbstractAutoencoder):\n    \"\"\"\n    Base class for all image autoencoders that we train, like VQGAN or AutoencoderKL\n    (we also restore them explicitly as special cases for legacy reasons).\n    Regularizations such as KL or VQ are moved to the regularizer class.\n    \"\"\"\n\n    def __init__(\n        self,\n        *args,\n        encoder_config: Dict,\n        decoder_config: Dict,\n        regularizer_config: Dict,\n        **kwargs,\n    ):\n        super().__init__(*args, **kwargs)\n\n        self.encoder: torch.nn.Module = instantiate_from_config(encoder_config)\n        self.decoder: torch.nn.Module = instantiate_from_config(decoder_config)\n        self.regularization: AbstractRegularizer = instantiate_from_config(\n            regularizer_config\n        )\n\n    def get_last_layer(self):\n        return self.decoder.get_last_layer()\n\n    def encode(\n        self,\n        x: torch.Tensor,\n        return_reg_log: bool = False,\n        unregularized: bool = False,\n    ) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]:\n        z = self.encoder(x)\n        if unregularized:\n            return z, dict()\n        z, reg_log = self.regularization(z)\n        if return_reg_log:\n            return z, reg_log\n        return z\n\n    def decode(self, z: torch.Tensor, **kwargs) -> torch.Tensor:\n        x = self.decoder(z, **kwargs)\n        return x\n\n    def forward(\n        self, x: torch.Tensor, **additional_decode_kwargs\n    ) -> Tuple[torch.Tensor, torch.Tensor, dict]:\n        z, reg_log = self.encode(x, return_reg_log=True)\n        dec = self.decode(z, **additional_decode_kwargs)\n        return z, dec, reg_log\n\n\nclass AutoencodingEngineLegacy(AutoencodingEngine):\n    def __init__(self, embed_dim: int, **kwargs):\n        self.max_batch_size = kwargs.pop(\"max_batch_size\", None)\n        ddconfig = kwargs.pop(\"ddconfig\")\n        super().__init__(\n            encoder_config={\n                \"target\": \"ldm_patched.ldm.modules.diffusionmodules.model.Encoder\",\n                \"params\": ddconfig,\n            },\n            decoder_config={\n                \"target\": \"ldm_patched.ldm.modules.diffusionmodules.model.Decoder\",\n                \"params\": ddconfig,\n            },\n            **kwargs,\n        )\n        self.quant_conv = ldm_patched.modules.ops.disable_weight_init.Conv2d(\n            (1 + ddconfig[\"double_z\"]) * ddconfig[\"z_channels\"],\n            (1 + ddconfig[\"double_z\"]) * embed_dim,\n            1,\n        )\n        self.post_quant_conv = ldm_patched.modules.ops.disable_weight_init.Conv2d(embed_dim, ddconfig[\"z_channels\"], 1)\n        self.embed_dim = embed_dim\n\n    def get_autoencoder_params(self) -> list:\n        params = super().get_autoencoder_params()\n        return params\n\n    def encode(\n        self, x: torch.Tensor, return_reg_log: bool = False\n    ) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]:\n        if self.max_batch_size is None:\n            z = self.encoder(x)\n            z = self.quant_conv(z)\n        else:\n            N = x.shape[0]\n            bs = self.max_batch_size\n            n_batches = int(math.ceil(N / bs))\n            z = list()\n            for i_batch in range(n_batches):\n                z_batch = self.encoder(x[i_batch * bs : (i_batch + 1) * bs])\n                z_batch = self.quant_conv(z_batch)\n                z.append(z_batch)\n            z = torch.cat(z, 0)\n\n        z, reg_log = self.regularization(z)\n        if return_reg_log:\n            return z, reg_log\n        return z\n\n    def decode(self, z: torch.Tensor, **decoder_kwargs) -> torch.Tensor:\n        if self.max_batch_size is None:\n            dec = self.post_quant_conv(z)\n            dec = self.decoder(dec, **decoder_kwargs)\n        else:\n            N = z.shape[0]\n            bs = self.max_batch_size\n            n_batches = int(math.ceil(N / bs))\n            dec = list()\n            for i_batch in range(n_batches):\n                dec_batch = self.post_quant_conv(z[i_batch * bs : (i_batch + 1) * bs])\n                dec_batch = self.decoder(dec_batch, **decoder_kwargs)\n                dec.append(dec_batch)\n            dec = torch.cat(dec, 0)\n\n        return dec\n\n\nclass AutoencoderKL(AutoencodingEngineLegacy):\n    def __init__(self, **kwargs):\n        if \"lossconfig\" in kwargs:\n            kwargs[\"loss_config\"] = kwargs.pop(\"lossconfig\")\n        super().__init__(\n            regularizer_config={\n                \"target\": (\n                    \"ldm_patched.ldm.models.autoencoder.DiagonalGaussianRegularizer\"\n                )\n            },\n            **kwargs,\n        )\n"
  },
  {
    "path": "ldm_patched/ldm/modules/attention.py",
    "content": "import math\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn, einsum\nfrom einops import rearrange, repeat\nfrom typing import Optional, Any\n\nfrom .diffusionmodules.util import checkpoint, AlphaBlender, timestep_embedding\nfrom .sub_quadratic_attention import efficient_dot_product_attention\n\nfrom ldm_patched.modules import model_management\n\nif model_management.xformers_enabled():\n    import xformers\n    import xformers.ops\n\nfrom ldm_patched.modules.args_parser import args\nimport ldm_patched.modules.ops\nops = ldm_patched.modules.ops.disable_weight_init\n\n# CrossAttn precision handling\nif args.disable_attention_upcast:\n    print(\"disabling upcasting of attention\")\n    _ATTN_PRECISION = \"fp16\"\nelse:\n    _ATTN_PRECISION = \"fp32\"\n\n\ndef exists(val):\n    return val is not None\n\n\ndef uniq(arr):\n    return{el: True for el in arr}.keys()\n\n\ndef default(val, d):\n    if exists(val):\n        return val\n    return d\n\n\ndef max_neg_value(t):\n    return -torch.finfo(t.dtype).max\n\n\ndef init_(tensor):\n    dim = tensor.shape[-1]\n    std = 1 / math.sqrt(dim)\n    tensor.uniform_(-std, std)\n    return tensor\n\n\n# feedforward\nclass GEGLU(nn.Module):\n    def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=ops):\n        super().__init__()\n        self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device)\n\n    def forward(self, x):\n        x, gate = self.proj(x).chunk(2, dim=-1)\n        return x * F.gelu(gate)\n\n\nclass FeedForward(nn.Module):\n    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None, device=None, operations=ops):\n        super().__init__()\n        inner_dim = int(dim * mult)\n        dim_out = default(dim_out, dim)\n        project_in = nn.Sequential(\n            operations.Linear(dim, inner_dim, dtype=dtype, device=device),\n            nn.GELU()\n        ) if not glu else GEGLU(dim, inner_dim, dtype=dtype, device=device, operations=operations)\n\n        self.net = nn.Sequential(\n            project_in,\n            nn.Dropout(dropout),\n            operations.Linear(inner_dim, dim_out, dtype=dtype, device=device)\n        )\n\n    def forward(self, x):\n        return self.net(x)\n\ndef Normalize(in_channels, dtype=None, device=None):\n    return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device)\n\ndef attention_basic(q, k, v, heads, mask=None):\n    b, _, dim_head = q.shape\n    dim_head //= heads\n    scale = dim_head ** -0.5\n\n    h = heads\n    q, k, v = map(\n        lambda t: t.unsqueeze(3)\n        .reshape(b, -1, heads, dim_head)\n        .permute(0, 2, 1, 3)\n        .reshape(b * heads, -1, dim_head)\n        .contiguous(),\n        (q, k, v),\n    )\n\n    # force cast to fp32 to avoid overflowing\n    if _ATTN_PRECISION ==\"fp32\":\n        sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale\n    else:\n        sim = einsum('b i d, b j d -> b i j', q, k) * scale\n\n    del q, k\n\n    if exists(mask):\n        if mask.dtype == torch.bool:\n            mask = rearrange(mask, 'b ... -> b (...)') #TODO: check if this bool part matches pytorch attention\n            max_neg_value = -torch.finfo(sim.dtype).max\n            mask = repeat(mask, 'b j -> (b h) () j', h=h)\n            sim.masked_fill_(~mask, max_neg_value)\n        else:\n            sim += mask\n\n    # attention, what we cannot get enough of\n    sim = sim.softmax(dim=-1)\n\n    out = einsum('b i j, b j d -> b i d', sim.to(v.dtype), v)\n    out = (\n        out.unsqueeze(0)\n        .reshape(b, heads, -1, dim_head)\n        .permute(0, 2, 1, 3)\n        .reshape(b, -1, heads * dim_head)\n    )\n    return out\n\n\ndef attention_sub_quad(query, key, value, heads, mask=None):\n    b, _, dim_head = query.shape\n    dim_head //= heads\n\n    scale = dim_head ** -0.5\n    query = query.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head)\n    value = value.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head)\n\n    key = key.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 3, 1).reshape(b * heads, dim_head, -1)\n\n    dtype = query.dtype\n    upcast_attention = _ATTN_PRECISION ==\"fp32\" and query.dtype != torch.float32\n    if upcast_attention:\n        bytes_per_token = torch.finfo(torch.float32).bits//8\n    else:\n        bytes_per_token = torch.finfo(query.dtype).bits//8\n    batch_x_heads, q_tokens, _ = query.shape\n    _, _, k_tokens = key.shape\n    qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens\n\n    mem_free_total, mem_free_torch = model_management.get_free_memory(query.device, True)\n\n    kv_chunk_size_min = None\n    kv_chunk_size = None\n    query_chunk_size = None\n\n    for x in [4096, 2048, 1024, 512, 256]:\n        count = mem_free_total / (batch_x_heads * bytes_per_token * x * 4.0)\n        if count >= k_tokens:\n            kv_chunk_size = k_tokens\n            query_chunk_size = x\n            break\n\n    if query_chunk_size is None:\n        query_chunk_size = 512\n\n    hidden_states = efficient_dot_product_attention(\n        query,\n        key,\n        value,\n        query_chunk_size=query_chunk_size,\n        kv_chunk_size=kv_chunk_size,\n        kv_chunk_size_min=kv_chunk_size_min,\n        use_checkpoint=False,\n        upcast_attention=upcast_attention,\n        mask=mask,\n    )\n\n    hidden_states = hidden_states.to(dtype)\n\n    hidden_states = hidden_states.unflatten(0, (-1, heads)).transpose(1,2).flatten(start_dim=2)\n    return hidden_states\n\ndef attention_split(q, k, v, heads, mask=None):\n    b, _, dim_head = q.shape\n    dim_head //= heads\n    scale = dim_head ** -0.5\n\n    h = heads\n    q, k, v = map(\n        lambda t: t.unsqueeze(3)\n        .reshape(b, -1, heads, dim_head)\n        .permute(0, 2, 1, 3)\n        .reshape(b * heads, -1, dim_head)\n        .contiguous(),\n        (q, k, v),\n    )\n\n    r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)\n\n    mem_free_total = model_management.get_free_memory(q.device)\n\n    if _ATTN_PRECISION ==\"fp32\":\n        element_size = 4\n    else:\n        element_size = q.element_size()\n\n    gb = 1024 ** 3\n    tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * element_size\n    modifier = 3\n    mem_required = tensor_size * modifier\n    steps = 1\n\n\n    if mem_required > mem_free_total:\n        steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))\n        # print(f\"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB \"\n        #      f\"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}\")\n\n    if steps > 64:\n        max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64\n        raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '\n                            f'Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free')\n\n    # print(\"steps\", steps, mem_required, mem_free_total, modifier, q.element_size(), tensor_size)\n    first_op_done = False\n    cleared_cache = False\n    while True:\n        try:\n            slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]\n            for i in range(0, q.shape[1], slice_size):\n                end = i + slice_size\n                if _ATTN_PRECISION ==\"fp32\":\n                    with torch.autocast(enabled=False, device_type = 'cuda'):\n                        s1 = einsum('b i d, b j d -> b i j', q[:, i:end].float(), k.float()) * scale\n                else:\n                    s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * scale\n\n                if mask is not None:\n                    if len(mask.shape) == 2:\n                        s1 += mask[i:end]\n                    else:\n                        s1 += mask[:, i:end]\n\n                s2 = s1.softmax(dim=-1).to(v.dtype)\n                del s1\n                first_op_done = True\n\n                r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)\n                del s2\n            break\n        except model_management.OOM_EXCEPTION as e:\n            if first_op_done == False:\n                model_management.soft_empty_cache(True)\n                if cleared_cache == False:\n                    cleared_cache = True\n                    print(\"out of memory error, emptying cache and trying again\")\n                    continue\n                steps *= 2\n                if steps > 64:\n                    raise e\n                print(\"out of memory error, increasing steps and trying again\", steps)\n            else:\n                raise e\n\n    del q, k, v\n\n    r1 = (\n        r1.unsqueeze(0)\n        .reshape(b, heads, -1, dim_head)\n        .permute(0, 2, 1, 3)\n        .reshape(b, -1, heads * dim_head)\n    )\n    return r1\n\nBROKEN_XFORMERS = False\ntry:\n    x_vers = xformers.__version__\n    #I think 0.0.23 is also broken (q with bs bigger than 65535 gives CUDA error)\n    BROKEN_XFORMERS = x_vers.startswith(\"0.0.21\") or x_vers.startswith(\"0.0.22\") or x_vers.startswith(\"0.0.23\")\nexcept:\n    pass\n\ndef attention_xformers(q, k, v, heads, mask=None):\n    b, _, dim_head = q.shape\n    dim_head //= heads\n    if BROKEN_XFORMERS:\n        if b * heads > 65535:\n            return attention_pytorch(q, k, v, heads, mask)\n\n    q, k, v = map(\n        lambda t: t.unsqueeze(3)\n        .reshape(b, -1, heads, dim_head)\n        .permute(0, 2, 1, 3)\n        .reshape(b * heads, -1, dim_head)\n        .contiguous(),\n        (q, k, v),\n    )\n\n    if mask is not None:\n        pad = 8 - q.shape[1] % 8\n        mask_out = torch.empty([q.shape[0], q.shape[1], q.shape[1] + pad], dtype=q.dtype, device=q.device)\n        mask_out[:, :, :mask.shape[-1]] = mask\n        mask = mask_out[:, :, :mask.shape[-1]]\n\n    out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask)\n\n    out = (\n        out.unsqueeze(0)\n        .reshape(b, heads, -1, dim_head)\n        .permute(0, 2, 1, 3)\n        .reshape(b, -1, heads * dim_head)\n    )\n    return out\n\ndef attention_pytorch(q, k, v, heads, mask=None):\n    b, _, dim_head = q.shape\n    dim_head //= heads\n    q, k, v = map(\n        lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2),\n        (q, k, v),\n    )\n\n    out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)\n    out = (\n        out.transpose(1, 2).reshape(b, -1, heads * dim_head)\n    )\n    return out\n\n\noptimized_attention = attention_basic\n\nif model_management.xformers_enabled():\n    print(\"Using xformers cross attention\")\n    optimized_attention = attention_xformers\nelif model_management.pytorch_attention_enabled():\n    print(\"Using pytorch cross attention\")\n    optimized_attention = attention_pytorch\nelse:\n    if args.attention_split:\n        print(\"Using split optimization for cross attention\")\n        optimized_attention = attention_split\n    else:\n        print(\"Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --attention-split\")\n        optimized_attention = attention_sub_quad\n\noptimized_attention_masked = optimized_attention\n\ndef optimized_attention_for_device(device, mask=False, small_input=False):\n    if small_input:\n        if model_management.pytorch_attention_enabled():\n            return attention_pytorch #TODO: need to confirm but this is probably slightly faster for small inputs in all cases\n        else:\n            return attention_basic\n\n    if device == torch.device(\"cpu\"):\n        return attention_sub_quad\n\n    if mask:\n        return optimized_attention_masked\n\n    return optimized_attention\n\n\nclass CrossAttention(nn.Module):\n    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=ops):\n        super().__init__()\n        inner_dim = dim_head * heads\n        context_dim = default(context_dim, query_dim)\n\n        self.heads = heads\n        self.dim_head = dim_head\n\n        self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)\n        self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)\n        self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)\n\n        self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))\n\n    def forward(self, x, context=None, value=None, mask=None):\n        q = self.to_q(x)\n        context = default(context, x)\n        k = self.to_k(context)\n        if value is not None:\n            v = self.to_v(value)\n            del value\n        else:\n            v = self.to_v(context)\n\n        if mask is None:\n            out = optimized_attention(q, k, v, self.heads)\n        else:\n            out = optimized_attention_masked(q, k, v, self.heads, mask)\n        return self.to_out(out)\n\n\nclass BasicTransformerBlock(nn.Module):\n    def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, ff_in=False, inner_dim=None,\n                 disable_self_attn=False, disable_temporal_crossattention=False, switch_temporal_ca_to_sa=False, dtype=None, device=None, operations=ops):\n        super().__init__()\n\n        self.ff_in = ff_in or inner_dim is not None\n        if inner_dim is None:\n            inner_dim = dim\n\n        self.is_res = inner_dim == dim\n\n        if self.ff_in:\n            self.norm_in = operations.LayerNorm(dim, dtype=dtype, device=device)\n            self.ff_in = FeedForward(dim, dim_out=inner_dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations)\n\n        self.disable_self_attn = disable_self_attn\n        self.attn1 = CrossAttention(query_dim=inner_dim, heads=n_heads, dim_head=d_head, dropout=dropout,\n                              context_dim=context_dim if self.disable_self_attn else None, dtype=dtype, device=device, operations=operations)  # is a self-attention if not self.disable_self_attn\n        self.ff = FeedForward(inner_dim, dim_out=dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations)\n\n        if disable_temporal_crossattention:\n            if switch_temporal_ca_to_sa:\n                raise ValueError\n            else:\n                self.attn2 = None\n        else:\n            context_dim_attn2 = None\n            if not switch_temporal_ca_to_sa:\n                context_dim_attn2 = context_dim\n\n            self.attn2 = CrossAttention(query_dim=inner_dim, context_dim=context_dim_attn2,\n                                heads=n_heads, dim_head=d_head, dropout=dropout, dtype=dtype, device=device, operations=operations)  # is self-attn if context is none\n            self.norm2 = operations.LayerNorm(inner_dim, dtype=dtype, device=device)\n\n        self.norm1 = operations.LayerNorm(inner_dim, dtype=dtype, device=device)\n        self.norm3 = operations.LayerNorm(inner_dim, dtype=dtype, device=device)\n        self.checkpoint = checkpoint\n        self.n_heads = n_heads\n        self.d_head = d_head\n        self.switch_temporal_ca_to_sa = switch_temporal_ca_to_sa\n\n    def forward(self, x, context=None, transformer_options={}):\n        return checkpoint(self._forward, (x, context, transformer_options), self.parameters(), self.checkpoint)\n\n    def _forward(self, x, context=None, transformer_options={}):\n        extra_options = {}\n        block = transformer_options.get(\"block\", None)\n        block_index = transformer_options.get(\"block_index\", 0)\n        transformer_patches = {}\n        transformer_patches_replace = {}\n\n        for k in transformer_options:\n            if k == \"patches\":\n                transformer_patches = transformer_options[k]\n            elif k == \"patches_replace\":\n                transformer_patches_replace = transformer_options[k]\n            else:\n                extra_options[k] = transformer_options[k]\n\n        extra_options[\"n_heads\"] = self.n_heads\n        extra_options[\"dim_head\"] = self.d_head\n\n        if self.ff_in:\n            x_skip = x\n            x = self.ff_in(self.norm_in(x))\n            if self.is_res:\n                x += x_skip\n\n        n = self.norm1(x)\n        if self.disable_self_attn:\n            context_attn1 = context\n        else:\n            context_attn1 = None\n        value_attn1 = None\n\n        if \"attn1_patch\" in transformer_patches:\n            patch = transformer_patches[\"attn1_patch\"]\n            if context_attn1 is None:\n                context_attn1 = n\n            value_attn1 = context_attn1\n            for p in patch:\n                n, context_attn1, value_attn1 = p(n, context_attn1, value_attn1, extra_options)\n\n        if block is not None:\n            transformer_block = (block[0], block[1], block_index)\n        else:\n            transformer_block = None\n        attn1_replace_patch = transformer_patches_replace.get(\"attn1\", {})\n        block_attn1 = transformer_block\n        if block_attn1 not in attn1_replace_patch:\n            block_attn1 = block\n\n        if block_attn1 in attn1_replace_patch:\n            if context_attn1 is None:\n                context_attn1 = n\n                value_attn1 = n\n            n = self.attn1.to_q(n)\n            context_attn1 = self.attn1.to_k(context_attn1)\n            value_attn1 = self.attn1.to_v(value_attn1)\n            n = attn1_replace_patch[block_attn1](n, context_attn1, value_attn1, extra_options)\n            n = self.attn1.to_out(n)\n        else:\n            n = self.attn1(n, context=context_attn1, value=value_attn1)\n\n        if \"attn1_output_patch\" in transformer_patches:\n            patch = transformer_patches[\"attn1_output_patch\"]\n            for p in patch:\n                n = p(n, extra_options)\n\n        x += n\n        if \"middle_patch\" in transformer_patches:\n            patch = transformer_patches[\"middle_patch\"]\n            for p in patch:\n                x = p(x, extra_options)\n\n        if self.attn2 is not None:\n            n = self.norm2(x)\n            if self.switch_temporal_ca_to_sa:\n                context_attn2 = n\n            else:\n                context_attn2 = context\n            value_attn2 = None\n            if \"attn2_patch\" in transformer_patches:\n                patch = transformer_patches[\"attn2_patch\"]\n                value_attn2 = context_attn2\n                for p in patch:\n                    n, context_attn2, value_attn2 = p(n, context_attn2, value_attn2, extra_options)\n\n            attn2_replace_patch = transformer_patches_replace.get(\"attn2\", {})\n            block_attn2 = transformer_block\n            if block_attn2 not in attn2_replace_patch:\n                block_attn2 = block\n\n            if block_attn2 in attn2_replace_patch:\n                if value_attn2 is None:\n                    value_attn2 = context_attn2\n                n = self.attn2.to_q(n)\n                context_attn2 = self.attn2.to_k(context_attn2)\n                value_attn2 = self.attn2.to_v(value_attn2)\n                n = attn2_replace_patch[block_attn2](n, context_attn2, value_attn2, extra_options)\n                n = self.attn2.to_out(n)\n            else:\n                n = self.attn2(n, context=context_attn2, value=value_attn2)\n\n        if \"attn2_output_patch\" in transformer_patches:\n            patch = transformer_patches[\"attn2_output_patch\"]\n            for p in patch:\n                n = p(n, extra_options)\n\n        x += n\n        if self.is_res:\n            x_skip = x\n        x = self.ff(self.norm3(x))\n        if self.is_res:\n            x += x_skip\n\n        return x\n\n\nclass SpatialTransformer(nn.Module):\n    \"\"\"\n    Transformer block for image-like data.\n    First, project the input (aka embedding)\n    and reshape to b, t, d.\n    Then apply standard transformer action.\n    Finally, reshape to image\n    NEW: use_linear for more efficiency instead of the 1x1 convs\n    \"\"\"\n    def __init__(self, in_channels, n_heads, d_head,\n                 depth=1, dropout=0., context_dim=None,\n                 disable_self_attn=False, use_linear=False,\n                 use_checkpoint=True, dtype=None, device=None, operations=ops):\n        super().__init__()\n        if exists(context_dim) and not isinstance(context_dim, list):\n            context_dim = [context_dim] * depth\n        self.in_channels = in_channels\n        inner_dim = n_heads * d_head\n        self.norm = operations.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device)\n        if not use_linear:\n            self.proj_in = operations.Conv2d(in_channels,\n                                     inner_dim,\n                                     kernel_size=1,\n                                     stride=1,\n                                     padding=0, dtype=dtype, device=device)\n        else:\n            self.proj_in = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device)\n\n        self.transformer_blocks = nn.ModuleList(\n            [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d],\n                                   disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, dtype=dtype, device=device, operations=operations)\n                for d in range(depth)]\n        )\n        if not use_linear:\n            self.proj_out = operations.Conv2d(inner_dim,in_channels,\n                                                  kernel_size=1,\n                                                  stride=1,\n                                                  padding=0, dtype=dtype, device=device)\n        else:\n            self.proj_out = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device)\n        self.use_linear = use_linear\n\n    def forward(self, x, context=None, transformer_options={}):\n        # note: if no context is given, cross-attention defaults to self-attention\n        if not isinstance(context, list):\n            context = [context] * len(self.transformer_blocks)\n        b, c, h, w = x.shape\n        x_in = x\n        x = self.norm(x)\n        if not self.use_linear:\n            x = self.proj_in(x)\n        x = rearrange(x, 'b c h w -> b (h w) c').contiguous()\n        if self.use_linear:\n            x = self.proj_in(x)\n        for i, block in enumerate(self.transformer_blocks):\n            transformer_options[\"block_index\"] = i\n            x = block(x, context=context[i], transformer_options=transformer_options)\n        if self.use_linear:\n            x = self.proj_out(x)\n        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()\n        if not self.use_linear:\n            x = self.proj_out(x)\n        return x + x_in\n\n\nclass SpatialVideoTransformer(SpatialTransformer):\n    def __init__(\n        self,\n        in_channels,\n        n_heads,\n        d_head,\n        depth=1,\n        dropout=0.0,\n        use_linear=False,\n        context_dim=None,\n        use_spatial_context=False,\n        timesteps=None,\n        merge_strategy: str = \"fixed\",\n        merge_factor: float = 0.5,\n        time_context_dim=None,\n        ff_in=False,\n        checkpoint=False,\n        time_depth=1,\n        disable_self_attn=False,\n        disable_temporal_crossattention=False,\n        max_time_embed_period: int = 10000,\n        dtype=None, device=None, operations=ops\n    ):\n        super().__init__(\n            in_channels,\n            n_heads,\n            d_head,\n            depth=depth,\n            dropout=dropout,\n            use_checkpoint=checkpoint,\n            context_dim=context_dim,\n            use_linear=use_linear,\n            disable_self_attn=disable_self_attn,\n            dtype=dtype, device=device, operations=operations\n        )\n        self.time_depth = time_depth\n        self.depth = depth\n        self.max_time_embed_period = max_time_embed_period\n\n        time_mix_d_head = d_head\n        n_time_mix_heads = n_heads\n\n        time_mix_inner_dim = int(time_mix_d_head * n_time_mix_heads)\n\n        inner_dim = n_heads * d_head\n        if use_spatial_context:\n            time_context_dim = context_dim\n\n        self.time_stack = nn.ModuleList(\n            [\n                BasicTransformerBlock(\n                    inner_dim,\n                    n_time_mix_heads,\n                    time_mix_d_head,\n                    dropout=dropout,\n                    context_dim=time_context_dim,\n                    # timesteps=timesteps,\n                    checkpoint=checkpoint,\n                    ff_in=ff_in,\n                    inner_dim=time_mix_inner_dim,\n                    disable_self_attn=disable_self_attn,\n                    disable_temporal_crossattention=disable_temporal_crossattention,\n                    dtype=dtype, device=device, operations=operations\n                )\n                for _ in range(self.depth)\n            ]\n        )\n\n        assert len(self.time_stack) == len(self.transformer_blocks)\n\n        self.use_spatial_context = use_spatial_context\n        self.in_channels = in_channels\n\n        time_embed_dim = self.in_channels * 4\n        self.time_pos_embed = nn.Sequential(\n            operations.Linear(self.in_channels, time_embed_dim, dtype=dtype, device=device),\n            nn.SiLU(),\n            operations.Linear(time_embed_dim, self.in_channels, dtype=dtype, device=device),\n        )\n\n        self.time_mixer = AlphaBlender(\n            alpha=merge_factor, merge_strategy=merge_strategy\n        )\n\n    def forward(\n        self,\n        x: torch.Tensor,\n        context: Optional[torch.Tensor] = None,\n        time_context: Optional[torch.Tensor] = None,\n        timesteps: Optional[int] = None,\n        image_only_indicator: Optional[torch.Tensor] = None,\n        transformer_options={}\n    ) -> torch.Tensor:\n        _, _, h, w = x.shape\n        x_in = x\n        spatial_context = None\n        if exists(context):\n            spatial_context = context\n\n        if self.use_spatial_context:\n            assert (\n                context.ndim == 3\n            ), f\"n dims of spatial context should be 3 but are {context.ndim}\"\n\n            if time_context is None:\n                time_context = context\n            time_context_first_timestep = time_context[::timesteps]\n            time_context = repeat(\n                time_context_first_timestep, \"b ... -> (b n) ...\", n=h * w\n            )\n        elif time_context is not None and not self.use_spatial_context:\n            time_context = repeat(time_context, \"b ... -> (b n) ...\", n=h * w)\n            if time_context.ndim == 2:\n                time_context = rearrange(time_context, \"b c -> b 1 c\")\n\n        x = self.norm(x)\n        if not self.use_linear:\n            x = self.proj_in(x)\n        x = rearrange(x, \"b c h w -> b (h w) c\")\n        if self.use_linear:\n            x = self.proj_in(x)\n\n        num_frames = torch.arange(timesteps, device=x.device)\n        num_frames = repeat(num_frames, \"t -> b t\", b=x.shape[0] // timesteps)\n        num_frames = rearrange(num_frames, \"b t -> (b t)\")\n        t_emb = timestep_embedding(num_frames, self.in_channels, repeat_only=False, max_period=self.max_time_embed_period).to(x.dtype)\n        emb = self.time_pos_embed(t_emb)\n        emb = emb[:, None, :]\n\n        for it_, (block, mix_block) in enumerate(\n            zip(self.transformer_blocks, self.time_stack)\n        ):\n            transformer_options[\"block_index\"] = it_\n            x = block(\n                x,\n                context=spatial_context,\n                transformer_options=transformer_options,\n            )\n\n            x_mix = x\n            x_mix = x_mix + emb\n\n            B, S, C = x_mix.shape\n            x_mix = rearrange(x_mix, \"(b t) s c -> (b s) t c\", t=timesteps)\n            x_mix = mix_block(x_mix, context=time_context) #TODO: transformer_options\n            x_mix = rearrange(\n                x_mix, \"(b s) t c -> (b t) s c\", s=S, b=B // timesteps, c=C, t=timesteps\n            )\n\n            x = self.time_mixer(x_spatial=x, x_temporal=x_mix, image_only_indicator=image_only_indicator)\n\n        if self.use_linear:\n            x = self.proj_out(x)\n        x = rearrange(x, \"b (h w) c -> b c h w\", h=h, w=w)\n        if not self.use_linear:\n            x = self.proj_out(x)\n        out = x + x_in\n        return out\n\n\n"
  },
  {
    "path": "ldm_patched/ldm/modules/diffusionmodules/__init__.py",
    "content": ""
  },
  {
    "path": "ldm_patched/ldm/modules/diffusionmodules/model.py",
    "content": "# pytorch_diffusion + derived encoder decoder\nimport math\nimport torch\nimport torch.nn as nn\nimport numpy as np\nfrom einops import rearrange\nfrom typing import Optional, Any\n\nfrom ldm_patched.modules import model_management\nimport ldm_patched.modules.ops\nops = ldm_patched.modules.ops.disable_weight_init\n\nif model_management.xformers_enabled_vae():\n    import xformers\n    import xformers.ops\n\ndef get_timestep_embedding(timesteps, embedding_dim):\n    \"\"\"\n    This matches the implementation in Denoising Diffusion Probabilistic Models:\n    From Fairseq.\n    Build sinusoidal embeddings.\n    This matches the implementation in tensor2tensor, but differs slightly\n    from the description in Section 3.5 of \"Attention Is All You Need\".\n    \"\"\"\n    assert len(timesteps.shape) == 1\n\n    half_dim = embedding_dim // 2\n    emb = math.log(10000) / (half_dim - 1)\n    emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)\n    emb = emb.to(device=timesteps.device)\n    emb = timesteps.float()[:, None] * emb[None, :]\n    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n    if embedding_dim % 2 == 1:  # zero pad\n        emb = torch.nn.functional.pad(emb, (0,1,0,0))\n    return emb\n\n\ndef nonlinearity(x):\n    # swish\n    return x*torch.sigmoid(x)\n\n\ndef Normalize(in_channels, num_groups=32):\n    return ops.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)\n\n\nclass Upsample(nn.Module):\n    def __init__(self, in_channels, with_conv):\n        super().__init__()\n        self.with_conv = with_conv\n        if self.with_conv:\n            self.conv = ops.Conv2d(in_channels,\n                                        in_channels,\n                                        kernel_size=3,\n                                        stride=1,\n                                        padding=1)\n\n    def forward(self, x):\n        try:\n            x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode=\"nearest\")\n        except: #operation not implemented for bf16\n            b, c, h, w = x.shape\n            out = torch.empty((b, c, h*2, w*2), dtype=x.dtype, layout=x.layout, device=x.device)\n            split = 8\n            l = out.shape[1] // split\n            for i in range(0, out.shape[1], l):\n                out[:,i:i+l] = torch.nn.functional.interpolate(x[:,i:i+l].to(torch.float32), scale_factor=2.0, mode=\"nearest\").to(x.dtype)\n            del x\n            x = out\n\n        if self.with_conv:\n            x = self.conv(x)\n        return x\n\n\nclass Downsample(nn.Module):\n    def __init__(self, in_channels, with_conv):\n        super().__init__()\n        self.with_conv = with_conv\n        if self.with_conv:\n            # no asymmetric padding in torch conv, must do it ourselves\n            self.conv = ops.Conv2d(in_channels,\n                                        in_channels,\n                                        kernel_size=3,\n                                        stride=2,\n                                        padding=0)\n\n    def forward(self, x):\n        if self.with_conv:\n            pad = (0,1,0,1)\n            x = torch.nn.functional.pad(x, pad, mode=\"constant\", value=0)\n            x = self.conv(x)\n        else:\n            x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)\n        return x\n\n\nclass ResnetBlock(nn.Module):\n    def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,\n                 dropout, temb_channels=512):\n        super().__init__()\n        self.in_channels = in_channels\n        out_channels = in_channels if out_channels is None else out_channels\n        self.out_channels = out_channels\n        self.use_conv_shortcut = conv_shortcut\n\n        self.swish = torch.nn.SiLU(inplace=True)\n        self.norm1 = Normalize(in_channels)\n        self.conv1 = ops.Conv2d(in_channels,\n                                     out_channels,\n                                     kernel_size=3,\n                                     stride=1,\n                                     padding=1)\n        if temb_channels > 0:\n            self.temb_proj = ops.Linear(temb_channels,\n                                             out_channels)\n        self.norm2 = Normalize(out_channels)\n        self.dropout = torch.nn.Dropout(dropout, inplace=True)\n        self.conv2 = ops.Conv2d(out_channels,\n                                     out_channels,\n                                     kernel_size=3,\n                                     stride=1,\n                                     padding=1)\n        if self.in_channels != self.out_channels:\n            if self.use_conv_shortcut:\n                self.conv_shortcut = ops.Conv2d(in_channels,\n                                                     out_channels,\n                                                     kernel_size=3,\n                                                     stride=1,\n                                                     padding=1)\n            else:\n                self.nin_shortcut = ops.Conv2d(in_channels,\n                                                    out_channels,\n                                                    kernel_size=1,\n                                                    stride=1,\n                                                    padding=0)\n\n    def forward(self, x, temb):\n        h = x\n        h = self.norm1(h)\n        h = self.swish(h)\n        h = self.conv1(h)\n\n        if temb is not None:\n            h = h + self.temb_proj(self.swish(temb))[:,:,None,None]\n\n        h = self.norm2(h)\n        h = self.swish(h)\n        h = self.dropout(h)\n        h = self.conv2(h)\n\n        if self.in_channels != self.out_channels:\n            if self.use_conv_shortcut:\n                x = self.conv_shortcut(x)\n            else:\n                x = self.nin_shortcut(x)\n\n        return x+h\n\ndef slice_attention(q, k, v):\n    r1 = torch.zeros_like(k, device=q.device)\n    scale = (int(q.shape[-1])**(-0.5))\n\n    mem_free_total = model_management.get_free_memory(q.device)\n\n    gb = 1024 ** 3\n    tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()\n    modifier = 3 if q.element_size() == 2 else 2.5\n    mem_required = tensor_size * modifier\n    steps = 1\n\n    if mem_required > mem_free_total:\n        steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))\n\n    while True:\n        try:\n            slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]\n            for i in range(0, q.shape[1], slice_size):\n                end = i + slice_size\n                s1 = torch.bmm(q[:, i:end], k) * scale\n\n                s2 = torch.nn.functional.softmax(s1, dim=2).permute(0,2,1)\n                del s1\n\n                r1[:, :, i:end] = torch.bmm(v, s2)\n                del s2\n            break\n        except model_management.OOM_EXCEPTION as e:\n            model_management.soft_empty_cache(True)\n            steps *= 2\n            if steps > 128:\n                raise e\n            print(\"out of memory error, increasing steps and trying again\", steps)\n\n    return r1\n\ndef normal_attention(q, k, v):\n    # compute attention\n    b,c,h,w = q.shape\n\n    q = q.reshape(b,c,h*w)\n    q = q.permute(0,2,1)   # b,hw,c\n    k = k.reshape(b,c,h*w) # b,c,hw\n    v = v.reshape(b,c,h*w)\n\n    r1 = slice_attention(q, k, v)\n    h_ = r1.reshape(b,c,h,w)\n    del r1\n    return h_\n\ndef xformers_attention(q, k, v):\n    # compute attention\n    B, C, H, W = q.shape\n    q, k, v = map(\n        lambda t: t.view(B, C, -1).transpose(1, 2).contiguous(),\n        (q, k, v),\n    )\n\n    try:\n        out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None)\n        out = out.transpose(1, 2).reshape(B, C, H, W)\n    except NotImplementedError as e:\n        out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W)\n    return out\n\ndef pytorch_attention(q, k, v):\n    # compute attention\n    B, C, H, W = q.shape\n    q, k, v = map(\n        lambda t: t.view(B, 1, C, -1).transpose(2, 3).contiguous(),\n        (q, k, v),\n    )\n\n    try:\n        out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)\n        out = out.transpose(2, 3).reshape(B, C, H, W)\n    except model_management.OOM_EXCEPTION as e:\n        print(\"scaled_dot_product_attention OOMed: switched to slice attention\")\n        out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W)\n    return out\n\n\nclass AttnBlock(nn.Module):\n    def __init__(self, in_channels):\n        super().__init__()\n        self.in_channels = in_channels\n\n        self.norm = Normalize(in_channels)\n        self.q = ops.Conv2d(in_channels,\n                                 in_channels,\n                                 kernel_size=1,\n                                 stride=1,\n                                 padding=0)\n        self.k = ops.Conv2d(in_channels,\n                                 in_channels,\n                                 kernel_size=1,\n                                 stride=1,\n                                 padding=0)\n        self.v = ops.Conv2d(in_channels,\n                                 in_channels,\n                                 kernel_size=1,\n                                 stride=1,\n                                 padding=0)\n        self.proj_out = ops.Conv2d(in_channels,\n                                        in_channels,\n                                        kernel_size=1,\n                                        stride=1,\n                                        padding=0)\n\n        if model_management.xformers_enabled_vae():\n            print(\"Using xformers attention in VAE\")\n            self.optimized_attention = xformers_attention\n        elif model_management.pytorch_attention_enabled():\n            print(\"Using pytorch attention in VAE\")\n            self.optimized_attention = pytorch_attention\n        else:\n            print(\"Using split attention in VAE\")\n            self.optimized_attention = normal_attention\n\n    def forward(self, x):\n        h_ = x\n        h_ = self.norm(h_)\n        q = self.q(h_)\n        k = self.k(h_)\n        v = self.v(h_)\n\n        h_ = self.optimized_attention(q, k, v)\n\n        h_ = self.proj_out(h_)\n\n        return x+h_\n\n\ndef make_attn(in_channels, attn_type=\"vanilla\", attn_kwargs=None):\n    return AttnBlock(in_channels)\n\n\nclass Model(nn.Module):\n    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,\n                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,\n                 resolution, use_timestep=True, use_linear_attn=False, attn_type=\"vanilla\"):\n        super().__init__()\n        if use_linear_attn: attn_type = \"linear\"\n        self.ch = ch\n        self.temb_ch = self.ch*4\n        self.num_resolutions = len(ch_mult)\n        self.num_res_blocks = num_res_blocks\n        self.resolution = resolution\n        self.in_channels = in_channels\n\n        self.use_timestep = use_timestep\n        if self.use_timestep:\n            # timestep embedding\n            self.temb = nn.Module()\n            self.temb.dense = nn.ModuleList([\n                ops.Linear(self.ch,\n                                self.temb_ch),\n                ops.Linear(self.temb_ch,\n                                self.temb_ch),\n            ])\n\n        # downsampling\n        self.conv_in = ops.Conv2d(in_channels,\n                                       self.ch,\n                                       kernel_size=3,\n                                       stride=1,\n                                       padding=1)\n\n        curr_res = resolution\n        in_ch_mult = (1,)+tuple(ch_mult)\n        self.down = nn.ModuleList()\n        for i_level in range(self.num_resolutions):\n            block = nn.ModuleList()\n            attn = nn.ModuleList()\n            block_in = ch*in_ch_mult[i_level]\n            block_out = ch*ch_mult[i_level]\n            for i_block in range(self.num_res_blocks):\n                block.append(ResnetBlock(in_channels=block_in,\n                                         out_channels=block_out,\n                                         temb_channels=self.temb_ch,\n                                         dropout=dropout))\n                block_in = block_out\n                if curr_res in attn_resolutions:\n                    attn.append(make_attn(block_in, attn_type=attn_type))\n            down = nn.Module()\n            down.block = block\n            down.attn = attn\n            if i_level != self.num_resolutions-1:\n                down.downsample = Downsample(block_in, resamp_with_conv)\n                curr_res = curr_res // 2\n            self.down.append(down)\n\n        # middle\n        self.mid = nn.Module()\n        self.mid.block_1 = ResnetBlock(in_channels=block_in,\n                                       out_channels=block_in,\n                                       temb_channels=self.temb_ch,\n                                       dropout=dropout)\n        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)\n        self.mid.block_2 = ResnetBlock(in_channels=block_in,\n                                       out_channels=block_in,\n                                       temb_channels=self.temb_ch,\n                                       dropout=dropout)\n\n        # upsampling\n        self.up = nn.ModuleList()\n        for i_level in reversed(range(self.num_resolutions)):\n            block = nn.ModuleList()\n            attn = nn.ModuleList()\n            block_out = ch*ch_mult[i_level]\n            skip_in = ch*ch_mult[i_level]\n            for i_block in range(self.num_res_blocks+1):\n                if i_block == self.num_res_blocks:\n                    skip_in = ch*in_ch_mult[i_level]\n                block.append(ResnetBlock(in_channels=block_in+skip_in,\n                                         out_channels=block_out,\n                                         temb_channels=self.temb_ch,\n                                         dropout=dropout))\n                block_in = block_out\n                if curr_res in attn_resolutions:\n                    attn.append(make_attn(block_in, attn_type=attn_type))\n            up = nn.Module()\n            up.block = block\n            up.attn = attn\n            if i_level != 0:\n                up.upsample = Upsample(block_in, resamp_with_conv)\n                curr_res = curr_res * 2\n            self.up.insert(0, up) # prepend to get consistent order\n\n        # end\n        self.norm_out = Normalize(block_in)\n        self.conv_out = ops.Conv2d(block_in,\n                                        out_ch,\n                                        kernel_size=3,\n                                        stride=1,\n                                        padding=1)\n\n    def forward(self, x, t=None, context=None):\n        #assert x.shape[2] == x.shape[3] == self.resolution\n        if context is not None:\n            # assume aligned context, cat along channel axis\n            x = torch.cat((x, context), dim=1)\n        if self.use_timestep:\n            # timestep embedding\n            assert t is not None\n            temb = get_timestep_embedding(t, self.ch)\n            temb = self.temb.dense[0](temb)\n            temb = nonlinearity(temb)\n            temb = self.temb.dense[1](temb)\n        else:\n            temb = None\n\n        # downsampling\n        hs = [self.conv_in(x)]\n        for i_level in range(self.num_resolutions):\n            for i_block in range(self.num_res_blocks):\n                h = self.down[i_level].block[i_block](hs[-1], temb)\n                if len(self.down[i_level].attn) > 0:\n                    h = self.down[i_level].attn[i_block](h)\n                hs.append(h)\n            if i_level != self.num_resolutions-1:\n                hs.append(self.down[i_level].downsample(hs[-1]))\n\n        # middle\n        h = hs[-1]\n        h = self.mid.block_1(h, temb)\n        h = self.mid.attn_1(h)\n        h = self.mid.block_2(h, temb)\n\n        # upsampling\n        for i_level in reversed(range(self.num_resolutions)):\n            for i_block in range(self.num_res_blocks+1):\n                h = self.up[i_level].block[i_block](\n                    torch.cat([h, hs.pop()], dim=1), temb)\n                if len(self.up[i_level].attn) > 0:\n                    h = self.up[i_level].attn[i_block](h)\n            if i_level != 0:\n                h = self.up[i_level].upsample(h)\n\n        # end\n        h = self.norm_out(h)\n        h = nonlinearity(h)\n        h = self.conv_out(h)\n        return h\n\n    def get_last_layer(self):\n        return self.conv_out.weight\n\n\nclass Encoder(nn.Module):\n    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,\n                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,\n                 resolution, z_channels, double_z=True, use_linear_attn=False, attn_type=\"vanilla\",\n                 **ignore_kwargs):\n        super().__init__()\n        if use_linear_attn: attn_type = \"linear\"\n        self.ch = ch\n        self.temb_ch = 0\n        self.num_resolutions = len(ch_mult)\n        self.num_res_blocks = num_res_blocks\n        self.resolution = resolution\n        self.in_channels = in_channels\n\n        # downsampling\n        self.conv_in = ops.Conv2d(in_channels,\n                                       self.ch,\n                                       kernel_size=3,\n                                       stride=1,\n                                       padding=1)\n\n        curr_res = resolution\n        in_ch_mult = (1,)+tuple(ch_mult)\n        self.in_ch_mult = in_ch_mult\n        self.down = nn.ModuleList()\n        for i_level in range(self.num_resolutions):\n            block = nn.ModuleList()\n            attn = nn.ModuleList()\n            block_in = ch*in_ch_mult[i_level]\n            block_out = ch*ch_mult[i_level]\n            for i_block in range(self.num_res_blocks):\n                block.append(ResnetBlock(in_channels=block_in,\n                                         out_channels=block_out,\n                                         temb_channels=self.temb_ch,\n                                         dropout=dropout))\n                block_in = block_out\n                if curr_res in attn_resolutions:\n                    attn.append(make_attn(block_in, attn_type=attn_type))\n            down = nn.Module()\n            down.block = block\n            down.attn = attn\n            if i_level != self.num_resolutions-1:\n                down.downsample = Downsample(block_in, resamp_with_conv)\n                curr_res = curr_res // 2\n            self.down.append(down)\n\n        # middle\n        self.mid = nn.Module()\n        self.mid.block_1 = ResnetBlock(in_channels=block_in,\n                                       out_channels=block_in,\n                                       temb_channels=self.temb_ch,\n                                       dropout=dropout)\n        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)\n        self.mid.block_2 = ResnetBlock(in_channels=block_in,\n                                       out_channels=block_in,\n                                       temb_channels=self.temb_ch,\n                                       dropout=dropout)\n\n        # end\n        self.norm_out = Normalize(block_in)\n        self.conv_out = ops.Conv2d(block_in,\n                                        2*z_channels if double_z else z_channels,\n                                        kernel_size=3,\n                                        stride=1,\n                                        padding=1)\n\n    def forward(self, x):\n        # timestep embedding\n        temb = None\n        # downsampling\n        h = self.conv_in(x)\n        for i_level in range(self.num_resolutions):\n            for i_block in range(self.num_res_blocks):\n                h = self.down[i_level].block[i_block](h, temb)\n                if len(self.down[i_level].attn) > 0:\n                    h = self.down[i_level].attn[i_block](h)\n            if i_level != self.num_resolutions-1:\n                h = self.down[i_level].downsample(h)\n\n        # middle\n        h = self.mid.block_1(h, temb)\n        h = self.mid.attn_1(h)\n        h = self.mid.block_2(h, temb)\n\n        # end\n        h = self.norm_out(h)\n        h = nonlinearity(h)\n        h = self.conv_out(h)\n        return h\n\n\nclass Decoder(nn.Module):\n    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,\n                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,\n                 resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,\n                 conv_out_op=ops.Conv2d,\n                 resnet_op=ResnetBlock,\n                 attn_op=AttnBlock,\n                **ignorekwargs):\n        super().__init__()\n        if use_linear_attn: attn_type = \"linear\"\n        self.ch = ch\n        self.temb_ch = 0\n        self.num_resolutions = len(ch_mult)\n        self.num_res_blocks = num_res_blocks\n        self.resolution = resolution\n        self.in_channels = in_channels\n        self.give_pre_end = give_pre_end\n        self.tanh_out = tanh_out\n\n        # compute in_ch_mult, block_in and curr_res at lowest res\n        in_ch_mult = (1,)+tuple(ch_mult)\n        block_in = ch*ch_mult[self.num_resolutions-1]\n        curr_res = resolution // 2**(self.num_resolutions-1)\n        self.z_shape = (1,z_channels,curr_res,curr_res)\n        print(\"Working with z of shape {} = {} dimensions.\".format(\n            self.z_shape, np.prod(self.z_shape)))\n\n        # z to block_in\n        self.conv_in = ops.Conv2d(z_channels,\n                                       block_in,\n                                       kernel_size=3,\n                                       stride=1,\n                                       padding=1)\n\n        # middle\n        self.mid = nn.Module()\n        self.mid.block_1 = resnet_op(in_channels=block_in,\n                                       out_channels=block_in,\n                                       temb_channels=self.temb_ch,\n                                       dropout=dropout)\n        self.mid.attn_1 = attn_op(block_in)\n        self.mid.block_2 = resnet_op(in_channels=block_in,\n                                       out_channels=block_in,\n                                       temb_channels=self.temb_ch,\n                                       dropout=dropout)\n\n        # upsampling\n        self.up = nn.ModuleList()\n        for i_level in reversed(range(self.num_resolutions)):\n            block = nn.ModuleList()\n            attn = nn.ModuleList()\n            block_out = ch*ch_mult[i_level]\n            for i_block in range(self.num_res_blocks+1):\n                block.append(resnet_op(in_channels=block_in,\n                                         out_channels=block_out,\n                                         temb_channels=self.temb_ch,\n                                         dropout=dropout))\n                block_in = block_out\n                if curr_res in attn_resolutions:\n                    attn.append(attn_op(block_in))\n            up = nn.Module()\n            up.block = block\n            up.attn = attn\n            if i_level != 0:\n                up.upsample = Upsample(block_in, resamp_with_conv)\n                curr_res = curr_res * 2\n            self.up.insert(0, up) # prepend to get consistent order\n\n        # end\n        self.norm_out = Normalize(block_in)\n        self.conv_out = conv_out_op(block_in,\n                                        out_ch,\n                                        kernel_size=3,\n                                        stride=1,\n                                        padding=1)\n\n    def forward(self, z, **kwargs):\n        #assert z.shape[1:] == self.z_shape[1:]\n        self.last_z_shape = z.shape\n\n        # timestep embedding\n        temb = None\n\n        # z to block_in\n        h = self.conv_in(z)\n\n        # middle\n        h = self.mid.block_1(h, temb, **kwargs)\n        h = self.mid.attn_1(h, **kwargs)\n        h = self.mid.block_2(h, temb, **kwargs)\n\n        # upsampling\n        for i_level in reversed(range(self.num_resolutions)):\n            for i_block in range(self.num_res_blocks+1):\n                h = self.up[i_level].block[i_block](h, temb, **kwargs)\n                if len(self.up[i_level].attn) > 0:\n                    h = self.up[i_level].attn[i_block](h, **kwargs)\n            if i_level != 0:\n                h = self.up[i_level].upsample(h)\n\n        # end\n        if self.give_pre_end:\n            return h\n\n        h = self.norm_out(h)\n        h = nonlinearity(h)\n        h = self.conv_out(h, **kwargs)\n        if self.tanh_out:\n            h = torch.tanh(h)\n        return h\n"
  },
  {
    "path": "ldm_patched/ldm/modules/diffusionmodules/openaimodel.py",
    "content": "from abc import abstractmethod\n\nimport torch as th\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom einops import rearrange\n\nfrom .util import (\n    checkpoint,\n    avg_pool_nd,\n    zero_module,\n    timestep_embedding,\n    AlphaBlender,\n)\nfrom ..attention import SpatialTransformer, SpatialVideoTransformer, default\nfrom ldm_patched.ldm.util import exists\nimport ldm_patched.modules.ops\nops = ldm_patched.modules.ops.disable_weight_init\n\nclass TimestepBlock(nn.Module):\n    \"\"\"\n    Any module where forward() takes timestep embeddings as a second argument.\n    \"\"\"\n\n    @abstractmethod\n    def forward(self, x, emb):\n        \"\"\"\n        Apply the module to `x` given `emb` timestep embeddings.\n        \"\"\"\n\n#This is needed because accelerate makes a copy of transformer_options which breaks \"transformer_index\"\ndef forward_timestep_embed(ts, x, emb, context=None, transformer_options={}, output_shape=None, time_context=None, num_video_frames=None, image_only_indicator=None):\n    for layer in ts:\n        if isinstance(layer, VideoResBlock):\n            x = layer(x, emb, num_video_frames, image_only_indicator)\n        elif isinstance(layer, TimestepBlock):\n            x = layer(x, emb)\n        elif isinstance(layer, SpatialVideoTransformer):\n            x = layer(x, context, time_context, num_video_frames, image_only_indicator, transformer_options)\n            if \"transformer_index\" in transformer_options:\n                transformer_options[\"transformer_index\"] += 1\n        elif isinstance(layer, SpatialTransformer):\n            x = layer(x, context, transformer_options)\n            if \"transformer_index\" in transformer_options:\n                transformer_options[\"transformer_index\"] += 1\n        elif isinstance(layer, Upsample):\n            x = layer(x, output_shape=output_shape)\n        else:\n            x = layer(x)\n    return x\n\nclass TimestepEmbedSequential(nn.Sequential, TimestepBlock):\n    \"\"\"\n    A sequential module that passes timestep embeddings to the children that\n    support it as an extra input.\n    \"\"\"\n\n    def forward(self, *args, **kwargs):\n        return forward_timestep_embed(self, *args, **kwargs)\n\nclass Upsample(nn.Module):\n    \"\"\"\n    An upsampling layer with an optional convolution.\n    :param channels: channels in the inputs and outputs.\n    :param use_conv: a bool determining if a convolution is applied.\n    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then\n                 upsampling occurs in the inner-two dimensions.\n    \"\"\"\n\n    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=ops):\n        super().__init__()\n        self.channels = channels\n        self.out_channels = out_channels or channels\n        self.use_conv = use_conv\n        self.dims = dims\n        if use_conv:\n            self.conv = operations.conv_nd(dims, self.channels, self.out_channels, 3, padding=padding, dtype=dtype, device=device)\n\n    def forward(self, x, output_shape=None):\n        assert x.shape[1] == self.channels\n        if self.dims == 3:\n            shape = [x.shape[2], x.shape[3] * 2, x.shape[4] * 2]\n            if output_shape is not None:\n                shape[1] = output_shape[3]\n                shape[2] = output_shape[4]\n        else:\n            shape = [x.shape[2] * 2, x.shape[3] * 2]\n            if output_shape is not None:\n                shape[0] = output_shape[2]\n                shape[1] = output_shape[3]\n\n        x = F.interpolate(x, size=shape, mode=\"nearest\")\n        if self.use_conv:\n            x = self.conv(x)\n        return x\n\nclass Downsample(nn.Module):\n    \"\"\"\n    A downsampling layer with an optional convolution.\n    :param channels: channels in the inputs and outputs.\n    :param use_conv: a bool determining if a convolution is applied.\n    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then\n                 downsampling occurs in the inner-two dimensions.\n    \"\"\"\n\n    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=ops):\n        super().__init__()\n        self.channels = channels\n        self.out_channels = out_channels or channels\n        self.use_conv = use_conv\n        self.dims = dims\n        stride = 2 if dims != 3 else (1, 2, 2)\n        if use_conv:\n            self.op = operations.conv_nd(\n                dims, self.channels, self.out_channels, 3, stride=stride, padding=padding, dtype=dtype, device=device\n            )\n        else:\n            assert self.channels == self.out_channels\n            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)\n\n    def forward(self, x):\n        assert x.shape[1] == self.channels\n        return self.op(x)\n\n\nclass ResBlock(TimestepBlock):\n    \"\"\"\n    A residual block that can optionally change the number of channels.\n    :param channels: the number of input channels.\n    :param emb_channels: the number of timestep embedding channels.\n    :param dropout: the rate of dropout.\n    :param out_channels: if specified, the number of out channels.\n    :param use_conv: if True and out_channels is specified, use a spatial\n        convolution instead of a smaller 1x1 convolution to change the\n        channels in the skip connection.\n    :param dims: determines if the signal is 1D, 2D, or 3D.\n    :param use_checkpoint: if True, use gradient checkpointing on this module.\n    :param up: if True, use this block for upsampling.\n    :param down: if True, use this block for downsampling.\n    \"\"\"\n\n    def __init__(\n        self,\n        channels,\n        emb_channels,\n        dropout,\n        out_channels=None,\n        use_conv=False,\n        use_scale_shift_norm=False,\n        dims=2,\n        use_checkpoint=False,\n        up=False,\n        down=False,\n        kernel_size=3,\n        exchange_temb_dims=False,\n        skip_t_emb=False,\n        dtype=None,\n        device=None,\n        operations=ops\n    ):\n        super().__init__()\n        self.channels = channels\n        self.emb_channels = emb_channels\n        self.dropout = dropout\n        self.out_channels = out_channels or channels\n        self.use_conv = use_conv\n        self.use_checkpoint = use_checkpoint\n        self.use_scale_shift_norm = use_scale_shift_norm\n        self.exchange_temb_dims = exchange_temb_dims\n\n        if isinstance(kernel_size, list):\n            padding = [k // 2 for k in kernel_size]\n        else:\n            padding = kernel_size // 2\n\n        self.in_layers = nn.Sequential(\n            operations.GroupNorm(32, channels, dtype=dtype, device=device),\n            nn.SiLU(),\n            operations.conv_nd(dims, channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device),\n        )\n\n        self.updown = up or down\n\n        if up:\n            self.h_upd = Upsample(channels, False, dims, dtype=dtype, device=device)\n            self.x_upd = Upsample(channels, False, dims, dtype=dtype, device=device)\n        elif down:\n            self.h_upd = Downsample(channels, False, dims, dtype=dtype, device=device)\n            self.x_upd = Downsample(channels, False, dims, dtype=dtype, device=device)\n        else:\n            self.h_upd = self.x_upd = nn.Identity()\n\n        self.skip_t_emb = skip_t_emb\n        if self.skip_t_emb:\n            self.emb_layers = None\n            self.exchange_temb_dims = False\n        else:\n            self.emb_layers = nn.Sequential(\n                nn.SiLU(),\n                operations.Linear(\n                    emb_channels,\n                    2 * self.out_channels if use_scale_shift_norm else self.out_channels, dtype=dtype, device=device\n                ),\n            )\n        self.out_layers = nn.Sequential(\n            operations.GroupNorm(32, self.out_channels, dtype=dtype, device=device),\n            nn.SiLU(),\n            nn.Dropout(p=dropout),\n            operations.conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device)\n            ,\n        )\n\n        if self.out_channels == channels:\n            self.skip_connection = nn.Identity()\n        elif use_conv:\n            self.skip_connection = operations.conv_nd(\n                dims, channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device\n            )\n        else:\n            self.skip_connection = operations.conv_nd(dims, channels, self.out_channels, 1, dtype=dtype, device=device)\n\n    def forward(self, x, emb):\n        \"\"\"\n        Apply the block to a Tensor, conditioned on a timestep embedding.\n        :param x: an [N x C x ...] Tensor of features.\n        :param emb: an [N x emb_channels] Tensor of timestep embeddings.\n        :return: an [N x C x ...] Tensor of outputs.\n        \"\"\"\n        return checkpoint(\n            self._forward, (x, emb), self.parameters(), self.use_checkpoint\n        )\n\n\n    def _forward(self, x, emb):\n        if self.updown:\n            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]\n            h = in_rest(x)\n            h = self.h_upd(h)\n            x = self.x_upd(x)\n            h = in_conv(h)\n        else:\n            h = self.in_layers(x)\n\n        emb_out = None\n        if not self.skip_t_emb:\n            emb_out = self.emb_layers(emb).type(h.dtype)\n            while len(emb_out.shape) < len(h.shape):\n                emb_out = emb_out[..., None]\n        if self.use_scale_shift_norm:\n            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]\n            h = out_norm(h)\n            if emb_out is not None:\n                scale, shift = th.chunk(emb_out, 2, dim=1)\n                h *= (1 + scale)\n                h += shift\n            h = out_rest(h)\n        else:\n            if emb_out is not None:\n                if self.exchange_temb_dims:\n                    emb_out = rearrange(emb_out, \"b t c ... -> b c t ...\")\n                h = h + emb_out\n            h = self.out_layers(h)\n        return self.skip_connection(x) + h\n\n\nclass VideoResBlock(ResBlock):\n    def __init__(\n        self,\n        channels: int,\n        emb_channels: int,\n        dropout: float,\n        video_kernel_size=3,\n        merge_strategy: str = \"fixed\",\n        merge_factor: float = 0.5,\n        out_channels=None,\n        use_conv: bool = False,\n        use_scale_shift_norm: bool = False,\n        dims: int = 2,\n        use_checkpoint: bool = False,\n        up: bool = False,\n        down: bool = False,\n        dtype=None,\n        device=None,\n        operations=ops\n    ):\n        super().__init__(\n            channels,\n            emb_channels,\n            dropout,\n            out_channels=out_channels,\n            use_conv=use_conv,\n            use_scale_shift_norm=use_scale_shift_norm,\n            dims=dims,\n            use_checkpoint=use_checkpoint,\n            up=up,\n            down=down,\n            dtype=dtype,\n            device=device,\n            operations=operations\n        )\n\n        self.time_stack = ResBlock(\n            default(out_channels, channels),\n            emb_channels,\n            dropout=dropout,\n            dims=3,\n            out_channels=default(out_channels, channels),\n            use_scale_shift_norm=False,\n            use_conv=False,\n            up=False,\n            down=False,\n            kernel_size=video_kernel_size,\n            use_checkpoint=use_checkpoint,\n            exchange_temb_dims=True,\n            dtype=dtype,\n            device=device,\n            operations=operations\n        )\n        self.time_mixer = AlphaBlender(\n            alpha=merge_factor,\n            merge_strategy=merge_strategy,\n            rearrange_pattern=\"b t -> b 1 t 1 1\",\n        )\n\n    def forward(\n        self,\n        x: th.Tensor,\n        emb: th.Tensor,\n        num_video_frames: int,\n        image_only_indicator = None,\n    ) -> th.Tensor:\n        x = super().forward(x, emb)\n\n        x_mix = rearrange(x, \"(b t) c h w -> b c t h w\", t=num_video_frames)\n        x = rearrange(x, \"(b t) c h w -> b c t h w\", t=num_video_frames)\n\n        x = self.time_stack(\n            x, rearrange(emb, \"(b t) ... -> b t ...\", t=num_video_frames)\n        )\n        x = self.time_mixer(\n            x_spatial=x_mix, x_temporal=x, image_only_indicator=image_only_indicator\n        )\n        x = rearrange(x, \"b c t h w -> (b t) c h w\")\n        return x\n\n\nclass Timestep(nn.Module):\n    def __init__(self, dim):\n        super().__init__()\n        self.dim = dim\n\n    def forward(self, t):\n        return timestep_embedding(t, self.dim)\n\ndef apply_control(h, control, name):\n    if control is not None and name in control and len(control[name]) > 0:\n        ctrl = control[name].pop()\n        if ctrl is not None:\n            try:\n                h += ctrl\n            except:\n                print(\"warning control could not be applied\", h.shape, ctrl.shape)\n    return h\n\nclass UNetModel(nn.Module):\n    \"\"\"\n    The full UNet model with attention and timestep embedding.\n    :param in_channels: channels in the input Tensor.\n    :param model_channels: base channel count for the model.\n    :param out_channels: channels in the output Tensor.\n    :param num_res_blocks: number of residual blocks per downsample.\n    :param dropout: the dropout probability.\n    :param channel_mult: channel multiplier for each level of the UNet.\n    :param conv_resample: if True, use learned convolutions for upsampling and\n        downsampling.\n    :param dims: determines if the signal is 1D, 2D, or 3D.\n    :param num_classes: if specified (as an int), then this model will be\n        class-conditional with `num_classes` classes.\n    :param use_checkpoint: use gradient checkpointing to reduce memory usage.\n    :param num_heads: the number of attention heads in each attention layer.\n    :param num_heads_channels: if specified, ignore num_heads and instead use\n                               a fixed channel width per attention head.\n    :param num_heads_upsample: works with num_heads to set a different number\n                               of heads for upsampling. Deprecated.\n    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.\n    :param resblock_updown: use residual blocks for up/downsampling.\n    :param use_new_attention_order: use a different attention pattern for potentially\n                                    increased efficiency.\n    \"\"\"\n\n    def __init__(\n        self,\n        image_size,\n        in_channels,\n        model_channels,\n        out_channels,\n        num_res_blocks,\n        dropout=0,\n        channel_mult=(1, 2, 4, 8),\n        conv_resample=True,\n        dims=2,\n        num_classes=None,\n        use_checkpoint=False,\n        dtype=th.float32,\n        num_heads=-1,\n        num_head_channels=-1,\n        num_heads_upsample=-1,\n        use_scale_shift_norm=False,\n        resblock_updown=False,\n        use_new_attention_order=False,\n        use_spatial_transformer=False,    # custom transformer support\n        transformer_depth=1,              # custom transformer support\n        context_dim=None,                 # custom transformer support\n        n_embed=None,                     # custom support for prediction of discrete ids into codebook of first stage vq model\n        legacy=True,\n        disable_self_attentions=None,\n        num_attention_blocks=None,\n        disable_middle_self_attn=False,\n        use_linear_in_transformer=False,\n        adm_in_channels=None,\n        transformer_depth_middle=None,\n        transformer_depth_output=None,\n        use_temporal_resblock=False,\n        use_temporal_attention=False,\n        time_context_dim=None,\n        extra_ff_mix_layer=False,\n        use_spatial_context=False,\n        merge_strategy=None,\n        merge_factor=0.0,\n        video_kernel_size=None,\n        disable_temporal_crossattention=False,\n        max_ddpm_temb_period=10000,\n        device=None,\n        operations=ops,\n    ):\n        super().__init__()\n\n        if context_dim is not None:\n            assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'\n            # from omegaconf.listconfig import ListConfig\n            # if type(context_dim) == ListConfig:\n            #     context_dim = list(context_dim)\n\n        if num_heads_upsample == -1:\n            num_heads_upsample = num_heads\n\n        if num_heads == -1:\n            assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'\n\n        if num_head_channels == -1:\n            assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'\n\n        self.in_channels = in_channels\n        self.model_channels = model_channels\n        self.out_channels = out_channels\n\n        if isinstance(num_res_blocks, int):\n            self.num_res_blocks = len(channel_mult) * [num_res_blocks]\n        else:\n            if len(num_res_blocks) != len(channel_mult):\n                raise ValueError(\"provide num_res_blocks either as an int (globally constant) or \"\n                                 \"as a list/tuple (per-level) with the same length as channel_mult\")\n            self.num_res_blocks = num_res_blocks\n\n        if disable_self_attentions is not None:\n            # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not\n            assert len(disable_self_attentions) == len(channel_mult)\n        if num_attention_blocks is not None:\n            assert len(num_attention_blocks) == len(self.num_res_blocks)\n\n        transformer_depth = transformer_depth[:]\n        transformer_depth_output = transformer_depth_output[:]\n\n        self.dropout = dropout\n        self.channel_mult = channel_mult\n        self.conv_resample = conv_resample\n        self.num_classes = num_classes\n        self.use_checkpoint = use_checkpoint\n        self.dtype = dtype\n        self.num_heads = num_heads\n        self.num_head_channels = num_head_channels\n        self.num_heads_upsample = num_heads_upsample\n        self.use_temporal_resblocks = use_temporal_resblock\n        self.predict_codebook_ids = n_embed is not None\n\n        self.default_num_video_frames = None\n        self.default_image_only_indicator = None\n\n        time_embed_dim = model_channels * 4\n        self.time_embed = nn.Sequential(\n            operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device),\n            nn.SiLU(),\n            operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),\n        )\n\n        if self.num_classes is not None:\n            if isinstance(self.num_classes, int):\n                self.label_emb = nn.Embedding(num_classes, time_embed_dim, dtype=self.dtype, device=device)\n            elif self.num_classes == \"continuous\":\n                print(\"setting up linear c_adm embedding layer\")\n                self.label_emb = nn.Linear(1, time_embed_dim)\n            elif self.num_classes == \"sequential\":\n                assert adm_in_channels is not None\n                self.label_emb = nn.Sequential(\n                    nn.Sequential(\n                        operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device),\n                        nn.SiLU(),\n                        operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),\n                    )\n                )\n            else:\n                raise ValueError()\n\n        self.input_blocks = nn.ModuleList(\n            [\n                TimestepEmbedSequential(\n                    operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device)\n                )\n            ]\n        )\n        self._feature_size = model_channels\n        input_block_chans = [model_channels]\n        ch = model_channels\n        ds = 1\n\n        def get_attention_layer(\n            ch,\n            num_heads,\n            dim_head,\n            depth=1,\n            context_dim=None,\n            use_checkpoint=False,\n            disable_self_attn=False,\n        ):\n            if use_temporal_attention:\n                return SpatialVideoTransformer(\n                    ch,\n                    num_heads,\n                    dim_head,\n                    depth=depth,\n                    context_dim=context_dim,\n                    time_context_dim=time_context_dim,\n                    dropout=dropout,\n                    ff_in=extra_ff_mix_layer,\n                    use_spatial_context=use_spatial_context,\n                    merge_strategy=merge_strategy,\n                    merge_factor=merge_factor,\n                    checkpoint=use_checkpoint,\n                    use_linear=use_linear_in_transformer,\n                    disable_self_attn=disable_self_attn,\n                    disable_temporal_crossattention=disable_temporal_crossattention,\n                    max_time_embed_period=max_ddpm_temb_period,\n                    dtype=self.dtype, device=device, operations=operations\n                )\n            else:\n                return SpatialTransformer(\n                                ch, num_heads, dim_head, depth=depth, context_dim=context_dim,\n                                disable_self_attn=disable_self_attn, use_linear=use_linear_in_transformer,\n                                use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations\n                            )\n\n        def get_resblock(\n            merge_factor,\n            merge_strategy,\n            video_kernel_size,\n            ch,\n            time_embed_dim,\n            dropout,\n            out_channels,\n            dims,\n            use_checkpoint,\n            use_scale_shift_norm,\n            down=False,\n            up=False,\n            dtype=None,\n            device=None,\n            operations=ops\n        ):\n            if self.use_temporal_resblocks:\n                return VideoResBlock(\n                    merge_factor=merge_factor,\n                    merge_strategy=merge_strategy,\n                    video_kernel_size=video_kernel_size,\n                    channels=ch,\n                    emb_channels=time_embed_dim,\n                    dropout=dropout,\n                    out_channels=out_channels,\n                    dims=dims,\n                    use_checkpoint=use_checkpoint,\n                    use_scale_shift_norm=use_scale_shift_norm,\n                    down=down,\n                    up=up,\n                    dtype=dtype,\n                    device=device,\n                    operations=operations\n                )\n            else:\n                return ResBlock(\n                    channels=ch,\n                    emb_channels=time_embed_dim,\n                    dropout=dropout,\n                    out_channels=out_channels,\n                    use_checkpoint=use_checkpoint,\n                    dims=dims,\n                    use_scale_shift_norm=use_scale_shift_norm,\n                    down=down,\n                    up=up,\n                    dtype=dtype,\n                    device=device,\n                    operations=operations\n                )\n\n        for level, mult in enumerate(channel_mult):\n            for nr in range(self.num_res_blocks[level]):\n                layers = [\n                    get_resblock(\n                        merge_factor=merge_factor,\n                        merge_strategy=merge_strategy,\n                        video_kernel_size=video_kernel_size,\n                        ch=ch,\n                        time_embed_dim=time_embed_dim,\n                        dropout=dropout,\n                        out_channels=mult * model_channels,\n                        dims=dims,\n                        use_checkpoint=use_checkpoint,\n                        use_scale_shift_norm=use_scale_shift_norm,\n                        dtype=self.dtype,\n                        device=device,\n                        operations=operations,\n                    )\n                ]\n                ch = mult * model_channels\n                num_transformers = transformer_depth.pop(0)\n                if num_transformers > 0:\n                    if num_head_channels == -1:\n                        dim_head = ch // num_heads\n                    else:\n                        num_heads = ch // num_head_channels\n                        dim_head = num_head_channels\n                    if legacy:\n                        #num_heads = 1\n                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels\n                    if exists(disable_self_attentions):\n                        disabled_sa = disable_self_attentions[level]\n                    else:\n                        disabled_sa = False\n\n                    if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:\n                        layers.append(get_attention_layer(\n                                ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim,\n                                disable_self_attn=disabled_sa, use_checkpoint=use_checkpoint)\n                        )\n                self.input_blocks.append(TimestepEmbedSequential(*layers))\n                self._feature_size += ch\n                input_block_chans.append(ch)\n            if level != len(channel_mult) - 1:\n                out_ch = ch\n                self.input_blocks.append(\n                    TimestepEmbedSequential(\n                        get_resblock(\n                            merge_factor=merge_factor,\n                            merge_strategy=merge_strategy,\n                            video_kernel_size=video_kernel_size,\n                            ch=ch,\n                            time_embed_dim=time_embed_dim,\n                            dropout=dropout,\n                            out_channels=out_ch,\n                            dims=dims,\n                            use_checkpoint=use_checkpoint,\n                            use_scale_shift_norm=use_scale_shift_norm,\n                            down=True,\n                            dtype=self.dtype,\n                            device=device,\n                            operations=operations\n                        )\n                        if resblock_updown\n                        else Downsample(\n                            ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations\n                        )\n                    )\n                )\n                ch = out_ch\n                input_block_chans.append(ch)\n                ds *= 2\n                self._feature_size += ch\n\n        if num_head_channels == -1:\n            dim_head = ch // num_heads\n        else:\n            num_heads = ch // num_head_channels\n            dim_head = num_head_channels\n        if legacy:\n            #num_heads = 1\n            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels\n        mid_block = [\n            get_resblock(\n                merge_factor=merge_factor,\n                merge_strategy=merge_strategy,\n                video_kernel_size=video_kernel_size,\n                ch=ch,\n                time_embed_dim=time_embed_dim,\n                dropout=dropout,\n                out_channels=None,\n                dims=dims,\n                use_checkpoint=use_checkpoint,\n                use_scale_shift_norm=use_scale_shift_norm,\n                dtype=self.dtype,\n                device=device,\n                operations=operations\n            )]\n        if transformer_depth_middle >= 0:\n            mid_block += [get_attention_layer(  # always uses a self-attn\n                            ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim,\n                            disable_self_attn=disable_middle_self_attn, use_checkpoint=use_checkpoint\n                        ),\n            get_resblock(\n                merge_factor=merge_factor,\n                merge_strategy=merge_strategy,\n                video_kernel_size=video_kernel_size,\n                ch=ch,\n                time_embed_dim=time_embed_dim,\n                dropout=dropout,\n                out_channels=None,\n                dims=dims,\n                use_checkpoint=use_checkpoint,\n                use_scale_shift_norm=use_scale_shift_norm,\n                dtype=self.dtype,\n                device=device,\n                operations=operations\n            )]\n        self.middle_block = TimestepEmbedSequential(*mid_block)\n        self._feature_size += ch\n\n        self.output_blocks = nn.ModuleList([])\n        for level, mult in list(enumerate(channel_mult))[::-1]:\n            for i in range(self.num_res_blocks[level] + 1):\n                ich = input_block_chans.pop()\n                layers = [\n                    get_resblock(\n                        merge_factor=merge_factor,\n                        merge_strategy=merge_strategy,\n                        video_kernel_size=video_kernel_size,\n                        ch=ch + ich,\n                        time_embed_dim=time_embed_dim,\n                        dropout=dropout,\n                        out_channels=model_channels * mult,\n                        dims=dims,\n                        use_checkpoint=use_checkpoint,\n                        use_scale_shift_norm=use_scale_shift_norm,\n                        dtype=self.dtype,\n                        device=device,\n                        operations=operations\n                    )\n                ]\n                ch = model_channels * mult\n                num_transformers = transformer_depth_output.pop()\n                if num_transformers > 0:\n                    if num_head_channels == -1:\n                        dim_head = ch // num_heads\n                    else:\n                        num_heads = ch // num_head_channels\n                        dim_head = num_head_channels\n                    if legacy:\n                        #num_heads = 1\n                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels\n                    if exists(disable_self_attentions):\n                        disabled_sa = disable_self_attentions[level]\n                    else:\n                        disabled_sa = False\n\n                    if not exists(num_attention_blocks) or i < num_attention_blocks[level]:\n                        layers.append(\n                            get_attention_layer(\n                                ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim,\n                                disable_self_attn=disabled_sa, use_checkpoint=use_checkpoint\n                            )\n                        )\n                if level and i == self.num_res_blocks[level]:\n                    out_ch = ch\n                    layers.append(\n                        get_resblock(\n                            merge_factor=merge_factor,\n                            merge_strategy=merge_strategy,\n                            video_kernel_size=video_kernel_size,\n                            ch=ch,\n                            time_embed_dim=time_embed_dim,\n                            dropout=dropout,\n                            out_channels=out_ch,\n                            dims=dims,\n                            use_checkpoint=use_checkpoint,\n                            use_scale_shift_norm=use_scale_shift_norm,\n                            up=True,\n                            dtype=self.dtype,\n                            device=device,\n                            operations=operations\n                        )\n                        if resblock_updown\n                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations)\n                    )\n                    ds //= 2\n                self.output_blocks.append(TimestepEmbedSequential(*layers))\n                self._feature_size += ch\n\n        self.out = nn.Sequential(\n            operations.GroupNorm(32, ch, dtype=self.dtype, device=device),\n            nn.SiLU(),\n            zero_module(operations.conv_nd(dims, model_channels, out_channels, 3, padding=1, dtype=self.dtype, device=device)),\n        )\n        if self.predict_codebook_ids:\n            self.id_predictor = nn.Sequential(\n            operations.GroupNorm(32, ch, dtype=self.dtype, device=device),\n            operations.conv_nd(dims, model_channels, n_embed, 1, dtype=self.dtype, device=device),\n            #nn.LogSoftmax(dim=1)  # change to cross_entropy and produce non-normalized logits\n        )\n\n    def forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs):\n        \"\"\"\n        Apply the model to an input batch.\n        :param x: an [N x C x ...] Tensor of inputs.\n        :param timesteps: a 1-D batch of timesteps.\n        :param context: conditioning plugged in via crossattn\n        :param y: an [N] Tensor of labels, if class-conditional.\n        :return: an [N x C x ...] Tensor of outputs.\n        \"\"\"\n        transformer_options[\"original_shape\"] = list(x.shape)\n        transformer_options[\"transformer_index\"] = 0\n        transformer_patches = transformer_options.get(\"patches\", {})\n\n        num_video_frames = kwargs.get(\"num_video_frames\", self.default_num_video_frames)\n        image_only_indicator = kwargs.get(\"image_only_indicator\", self.default_image_only_indicator)\n        time_context = kwargs.get(\"time_context\", None)\n\n        assert (y is not None) == (\n            self.num_classes is not None\n        ), \"must specify y if and only if the model is class-conditional\"\n        hs = []\n        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)\n        emb = self.time_embed(t_emb)\n\n        if self.num_classes is not None:\n            assert y.shape[0] == x.shape[0]\n            emb = emb + self.label_emb(y)\n\n        h = x\n        for id, module in enumerate(self.input_blocks):\n            transformer_options[\"block\"] = (\"input\", id)\n            h = forward_timestep_embed(module, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator)\n            h = apply_control(h, control, 'input')\n            if \"input_block_patch\" in transformer_patches:\n                patch = transformer_patches[\"input_block_patch\"]\n                for p in patch:\n                    h = p(h, transformer_options)\n\n            hs.append(h)\n            if \"input_block_patch_after_skip\" in transformer_patches:\n                patch = transformer_patches[\"input_block_patch_after_skip\"]\n                for p in patch:\n                    h = p(h, transformer_options)\n\n        transformer_options[\"block\"] = (\"middle\", 0)\n        h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator)\n        h = apply_control(h, control, 'middle')\n\n\n        for id, module in enumerate(self.output_blocks):\n            transformer_options[\"block\"] = (\"output\", id)\n            hsp = hs.pop()\n            hsp = apply_control(hsp, control, 'output')\n\n            if \"output_block_patch\" in transformer_patches:\n                patch = transformer_patches[\"output_block_patch\"]\n                for p in patch:\n                    h, hsp = p(h, hsp, transformer_options)\n\n            h = th.cat([h, hsp], dim=1)\n            del hsp\n            if len(hs) > 0:\n                output_shape = hs[-1].shape\n            else:\n                output_shape = None\n            h = forward_timestep_embed(module, h, emb, context, transformer_options, output_shape, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator)\n        h = h.type(x.dtype)\n        if self.predict_codebook_ids:\n            return self.id_predictor(h)\n        else:\n            return self.out(h)\n"
  },
  {
    "path": "ldm_patched/ldm/modules/diffusionmodules/upscaling.py",
    "content": "import torch\nimport torch.nn as nn\nimport numpy as np\nfrom functools import partial\n\nfrom .util import extract_into_tensor, make_beta_schedule\nfrom ldm_patched.ldm.util import default\n\n\nclass AbstractLowScaleModel(nn.Module):\n    # for concatenating a downsampled image to the latent representation\n    def __init__(self, noise_schedule_config=None):\n        super(AbstractLowScaleModel, self).__init__()\n        if noise_schedule_config is not None:\n            self.register_schedule(**noise_schedule_config)\n\n    def register_schedule(self, beta_schedule=\"linear\", timesteps=1000,\n                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):\n        betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,\n                                   cosine_s=cosine_s)\n        alphas = 1. - betas\n        alphas_cumprod = np.cumprod(alphas, axis=0)\n        alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])\n\n        timesteps, = betas.shape\n        self.num_timesteps = int(timesteps)\n        self.linear_start = linear_start\n        self.linear_end = linear_end\n        assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'\n\n        to_torch = partial(torch.tensor, dtype=torch.float32)\n\n        self.register_buffer('betas', to_torch(betas))\n        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))\n        self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))\n\n        # calculations for diffusion q(x_t | x_{t-1}) and others\n        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))\n        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))\n        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))\n        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))\n        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))\n\n    def q_sample(self, x_start, t, noise=None, seed=None):\n        if noise is None:\n            if seed is None:\n                noise = torch.randn_like(x_start)\n            else:\n                noise = torch.randn(x_start.size(), dtype=x_start.dtype, layout=x_start.layout, generator=torch.manual_seed(seed)).to(x_start.device)\n        return (extract_into_tensor(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start +\n                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape) * noise)\n\n    def forward(self, x):\n        return x, None\n\n    def decode(self, x):\n        return x\n\n\nclass SimpleImageConcat(AbstractLowScaleModel):\n    # no noise level conditioning\n    def __init__(self):\n        super(SimpleImageConcat, self).__init__(noise_schedule_config=None)\n        self.max_noise_level = 0\n\n    def forward(self, x):\n        # fix to constant noise level\n        return x, torch.zeros(x.shape[0], device=x.device).long()\n\n\nclass ImageConcatWithNoiseAugmentation(AbstractLowScaleModel):\n    def __init__(self, noise_schedule_config, max_noise_level=1000, to_cuda=False):\n        super().__init__(noise_schedule_config=noise_schedule_config)\n        self.max_noise_level = max_noise_level\n\n    def forward(self, x, noise_level=None, seed=None):\n        if noise_level is None:\n            noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long()\n        else:\n            assert isinstance(noise_level, torch.Tensor)\n        z = self.q_sample(x, noise_level, seed=seed)\n        return z, noise_level\n\n\n\n"
  },
  {
    "path": "ldm_patched/ldm/modules/diffusionmodules/util.py",
    "content": "# adopted from\n# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py\n# and\n# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py\n# and\n# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py\n#\n# thanks!\n\n\nimport os\nimport math\nimport torch\nimport torch.nn as nn\nimport numpy as np\nfrom einops import repeat, rearrange\n\nfrom ldm_patched.ldm.util import instantiate_from_config\n\nclass AlphaBlender(nn.Module):\n    strategies = [\"learned\", \"fixed\", \"learned_with_images\"]\n\n    def __init__(\n        self,\n        alpha: float,\n        merge_strategy: str = \"learned_with_images\",\n        rearrange_pattern: str = \"b t -> (b t) 1 1\",\n    ):\n        super().__init__()\n        self.merge_strategy = merge_strategy\n        self.rearrange_pattern = rearrange_pattern\n\n        assert (\n            merge_strategy in self.strategies\n        ), f\"merge_strategy needs to be in {self.strategies}\"\n\n        if self.merge_strategy == \"fixed\":\n            self.register_buffer(\"mix_factor\", torch.Tensor([alpha]))\n        elif (\n            self.merge_strategy == \"learned\"\n            or self.merge_strategy == \"learned_with_images\"\n        ):\n            self.register_parameter(\n                \"mix_factor\", torch.nn.Parameter(torch.Tensor([alpha]))\n            )\n        else:\n            raise ValueError(f\"unknown merge strategy {self.merge_strategy}\")\n\n    def get_alpha(self, image_only_indicator: torch.Tensor) -> torch.Tensor:\n        # skip_time_mix = rearrange(repeat(skip_time_mix, 'b -> (b t) () () ()', t=t), '(b t) 1 ... -> b 1 t ...', t=t)\n        if self.merge_strategy == \"fixed\":\n            # make shape compatible\n            # alpha = repeat(self.mix_factor, '1 -> b () t  () ()', t=t, b=bs)\n            alpha = self.mix_factor.to(image_only_indicator.device)\n        elif self.merge_strategy == \"learned\":\n            alpha = torch.sigmoid(self.mix_factor.to(image_only_indicator.device))\n            # make shape compatible\n            # alpha = repeat(alpha, '1 -> s () ()', s = t * bs)\n        elif self.merge_strategy == \"learned_with_images\":\n            assert image_only_indicator is not None, \"need image_only_indicator ...\"\n            alpha = torch.where(\n                image_only_indicator.bool(),\n                torch.ones(1, 1, device=image_only_indicator.device),\n                rearrange(torch.sigmoid(self.mix_factor.to(image_only_indicator.device)), \"... -> ... 1\"),\n            )\n            alpha = rearrange(alpha, self.rearrange_pattern)\n            # make shape compatible\n            # alpha = repeat(alpha, '1 -> s () ()', s = t * bs)\n        else:\n            raise NotImplementedError()\n        return alpha\n\n    def forward(\n        self,\n        x_spatial,\n        x_temporal,\n        image_only_indicator=None,\n    ) -> torch.Tensor:\n        alpha = self.get_alpha(image_only_indicator)\n        x = (\n            alpha.to(x_spatial.dtype) * x_spatial\n            + (1.0 - alpha).to(x_spatial.dtype) * x_temporal\n        )\n        return x\n\n\ndef make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):\n    if schedule == \"linear\":\n        betas = (\n                torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2\n        )\n\n    elif schedule == \"cosine\":\n        timesteps = (\n                torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s\n        )\n        alphas = timesteps / (1 + cosine_s) * np.pi / 2\n        alphas = torch.cos(alphas).pow(2)\n        alphas = alphas / alphas[0]\n        betas = 1 - alphas[1:] / alphas[:-1]\n        betas = np.clip(betas, a_min=0, a_max=0.999)\n\n    elif schedule == \"squaredcos_cap_v2\":  # used for karlo prior\n        # return early\n        return betas_for_alpha_bar(\n            n_timestep,\n            lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2,\n        )\n\n    elif schedule == \"sqrt_linear\":\n        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)\n    elif schedule == \"sqrt\":\n        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5\n    else:\n        raise ValueError(f\"schedule '{schedule}' unknown.\")\n    return betas.numpy()\n\n\ndef make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):\n    if ddim_discr_method == 'uniform':\n        c = num_ddpm_timesteps // num_ddim_timesteps\n        ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))\n    elif ddim_discr_method == 'quad':\n        ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)\n    else:\n        raise NotImplementedError(f'There is no ddim discretization method called \"{ddim_discr_method}\"')\n\n    # assert ddim_timesteps.shape[0] == num_ddim_timesteps\n    # add one to get the final alpha values right (the ones from first scale to data during sampling)\n    steps_out = ddim_timesteps + 1\n    if verbose:\n        print(f'Selected timesteps for ddim sampler: {steps_out}')\n    return steps_out\n\n\ndef make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):\n    # select alphas for computing the variance schedule\n    alphas = alphacums[ddim_timesteps]\n    alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())\n\n    # according the the formula provided in https://arxiv.org/abs/2010.02502\n    sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))\n    if verbose:\n        print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')\n        print(f'For the chosen value of eta, which is {eta}, '\n              f'this results in the following sigma_t schedule for ddim sampler {sigmas}')\n    return sigmas, alphas, alphas_prev\n\n\ndef betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):\n    \"\"\"\n    Create a beta schedule that discretizes the given alpha_t_bar function,\n    which defines the cumulative product of (1-beta) over time from t = [0,1].\n    :param num_diffusion_timesteps: the number of betas to produce.\n    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and\n                      produces the cumulative product of (1-beta) up to that\n                      part of the diffusion process.\n    :param max_beta: the maximum beta to use; use values lower than 1 to\n                     prevent singularities.\n    \"\"\"\n    betas = []\n    for i in range(num_diffusion_timesteps):\n        t1 = i / num_diffusion_timesteps\n        t2 = (i + 1) / num_diffusion_timesteps\n        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))\n    return np.array(betas)\n\n\ndef extract_into_tensor(a, t, x_shape):\n    b, *_ = t.shape\n    out = a.gather(-1, t)\n    return out.reshape(b, *((1,) * (len(x_shape) - 1)))\n\n\ndef checkpoint(func, inputs, params, flag):\n    \"\"\"\n    Evaluate a function without caching intermediate activations, allowing for\n    reduced memory at the expense of extra compute in the backward pass.\n    :param func: the function to evaluate.\n    :param inputs: the argument sequence to pass to `func`.\n    :param params: a sequence of parameters `func` depends on but does not\n                   explicitly take as arguments.\n    :param flag: if False, disable gradient checkpointing.\n    \"\"\"\n    if flag:\n        args = tuple(inputs) + tuple(params)\n        return CheckpointFunction.apply(func, len(inputs), *args)\n    else:\n        return func(*inputs)\n\n\nclass CheckpointFunction(torch.autograd.Function):\n    @staticmethod\n    def forward(ctx, run_function, length, *args):\n        ctx.run_function = run_function\n        ctx.input_tensors = list(args[:length])\n        ctx.input_params = list(args[length:])\n        ctx.gpu_autocast_kwargs = {\"enabled\": torch.is_autocast_enabled(),\n                                   \"dtype\": torch.get_autocast_gpu_dtype(),\n                                   \"cache_enabled\": torch.is_autocast_cache_enabled()}\n        with torch.no_grad():\n            output_tensors = ctx.run_function(*ctx.input_tensors)\n        return output_tensors\n\n    @staticmethod\n    def backward(ctx, *output_grads):\n        ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]\n        with torch.enable_grad(), \\\n                torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs):\n            # Fixes a bug where the first op in run_function modifies the\n            # Tensor storage in place, which is not allowed for detach()'d\n            # Tensors.\n            shallow_copies = [x.view_as(x) for x in ctx.input_tensors]\n            output_tensors = ctx.run_function(*shallow_copies)\n        input_grads = torch.autograd.grad(\n            output_tensors,\n            ctx.input_tensors + ctx.input_params,\n            output_grads,\n            allow_unused=True,\n        )\n        del ctx.input_tensors\n        del ctx.input_params\n        del output_tensors\n        return (None, None) + input_grads\n\n\ndef timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):\n    \"\"\"\n    Create sinusoidal timestep embeddings.\n    :param timesteps: a 1-D Tensor of N indices, one per batch element.\n                      These may be fractional.\n    :param dim: the dimension of the output.\n    :param max_period: controls the minimum frequency of the embeddings.\n    :return: an [N x dim] Tensor of positional embeddings.\n    \"\"\"\n    if not repeat_only:\n        half = dim // 2\n        freqs = torch.exp(\n            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=timesteps.device) / half\n        )\n        args = timesteps[:, None].float() * freqs[None]\n        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)\n        if dim % 2:\n            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)\n    else:\n        embedding = repeat(timesteps, 'b -> b d', d=dim)\n    return embedding\n\n\ndef zero_module(module):\n    \"\"\"\n    Zero out the parameters of a module and return it.\n    \"\"\"\n    for p in module.parameters():\n        p.detach().zero_()\n    return module\n\n\ndef scale_module(module, scale):\n    \"\"\"\n    Scale the parameters of a module and return it.\n    \"\"\"\n    for p in module.parameters():\n        p.detach().mul_(scale)\n    return module\n\n\ndef mean_flat(tensor):\n    \"\"\"\n    Take the mean over all non-batch dimensions.\n    \"\"\"\n    return tensor.mean(dim=list(range(1, len(tensor.shape))))\n\n\ndef avg_pool_nd(dims, *args, **kwargs):\n    \"\"\"\n    Create a 1D, 2D, or 3D average pooling module.\n    \"\"\"\n    if dims == 1:\n        return nn.AvgPool1d(*args, **kwargs)\n    elif dims == 2:\n        return nn.AvgPool2d(*args, **kwargs)\n    elif dims == 3:\n        return nn.AvgPool3d(*args, **kwargs)\n    raise ValueError(f\"unsupported dimensions: {dims}\")\n\n\nclass HybridConditioner(nn.Module):\n\n    def __init__(self, c_concat_config, c_crossattn_config):\n        super().__init__()\n        self.concat_conditioner = instantiate_from_config(c_concat_config)\n        self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)\n\n    def forward(self, c_concat, c_crossattn):\n        c_concat = self.concat_conditioner(c_concat)\n        c_crossattn = self.crossattn_conditioner(c_crossattn)\n        return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}\n\n\ndef noise_like(shape, device, repeat=False):\n    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))\n    noise = lambda: torch.randn(shape, device=device)\n    return repeat_noise() if repeat else noise()\n"
  },
  {
    "path": "ldm_patched/ldm/modules/distributions/__init__.py",
    "content": ""
  },
  {
    "path": "ldm_patched/ldm/modules/distributions/distributions.py",
    "content": "import torch\nimport numpy as np\n\n\nclass AbstractDistribution:\n    def sample(self):\n        raise NotImplementedError()\n\n    def mode(self):\n        raise NotImplementedError()\n\n\nclass DiracDistribution(AbstractDistribution):\n    def __init__(self, value):\n        self.value = value\n\n    def sample(self):\n        return self.value\n\n    def mode(self):\n        return self.value\n\n\nclass DiagonalGaussianDistribution(object):\n    def __init__(self, parameters, deterministic=False):\n        self.parameters = parameters\n        self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)\n        self.logvar = torch.clamp(self.logvar, -30.0, 20.0)\n        self.deterministic = deterministic\n        self.std = torch.exp(0.5 * self.logvar)\n        self.var = torch.exp(self.logvar)\n        if self.deterministic:\n            self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)\n\n    def sample(self):\n        x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)\n        return x\n\n    def kl(self, other=None):\n        if self.deterministic:\n            return torch.Tensor([0.])\n        else:\n            if other is None:\n                return 0.5 * torch.sum(torch.pow(self.mean, 2)\n                                       + self.var - 1.0 - self.logvar,\n                                       dim=[1, 2, 3])\n            else:\n                return 0.5 * torch.sum(\n                    torch.pow(self.mean - other.mean, 2) / other.var\n                    + self.var / other.var - 1.0 - self.logvar + other.logvar,\n                    dim=[1, 2, 3])\n\n    def nll(self, sample, dims=[1,2,3]):\n        if self.deterministic:\n            return torch.Tensor([0.])\n        logtwopi = np.log(2.0 * np.pi)\n        return 0.5 * torch.sum(\n            logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,\n            dim=dims)\n\n    def mode(self):\n        return self.mean\n\n\ndef normal_kl(mean1, logvar1, mean2, logvar2):\n    \"\"\"\n    source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12\n    Compute the KL divergence between two gaussians.\n    Shapes are automatically broadcasted, so batches can be compared to\n    scalars, among other use cases.\n    \"\"\"\n    tensor = None\n    for obj in (mean1, logvar1, mean2, logvar2):\n        if isinstance(obj, torch.Tensor):\n            tensor = obj\n            break\n    assert tensor is not None, \"at least one argument must be a Tensor\"\n\n    # Force variances to be Tensors. Broadcasting helps convert scalars to\n    # Tensors, but it does not work for torch.exp().\n    logvar1, logvar2 = [\n        x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)\n        for x in (logvar1, logvar2)\n    ]\n\n    return 0.5 * (\n        -1.0\n        + logvar2\n        - logvar1\n        + torch.exp(logvar1 - logvar2)\n        + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)\n    )\n"
  },
  {
    "path": "ldm_patched/ldm/modules/ema.py",
    "content": "import torch\nfrom torch import nn\n\n\nclass LitEma(nn.Module):\n    def __init__(self, model, decay=0.9999, use_num_upates=True):\n        super().__init__()\n        if decay < 0.0 or decay > 1.0:\n            raise ValueError('Decay must be between 0 and 1')\n\n        self.m_name2s_name = {}\n        self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))\n        self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int) if use_num_upates\n        else torch.tensor(-1, dtype=torch.int))\n\n        for name, p in model.named_parameters():\n            if p.requires_grad:\n                # remove as '.'-character is not allowed in buffers\n                s_name = name.replace('.', '')\n                self.m_name2s_name.update({name: s_name})\n                self.register_buffer(s_name, p.clone().detach().data)\n\n        self.collected_params = []\n\n    def reset_num_updates(self):\n        del self.num_updates\n        self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int))\n\n    def forward(self, model):\n        decay = self.decay\n\n        if self.num_updates >= 0:\n            self.num_updates += 1\n            decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates))\n\n        one_minus_decay = 1.0 - decay\n\n        with torch.no_grad():\n            m_param = dict(model.named_parameters())\n            shadow_params = dict(self.named_buffers())\n\n            for key in m_param:\n                if m_param[key].requires_grad:\n                    sname = self.m_name2s_name[key]\n                    shadow_params[sname] = shadow_params[sname].type_as(m_param[key])\n                    shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))\n                else:\n                    assert not key in self.m_name2s_name\n\n    def copy_to(self, model):\n        m_param = dict(model.named_parameters())\n        shadow_params = dict(self.named_buffers())\n        for key in m_param:\n            if m_param[key].requires_grad:\n                m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)\n            else:\n                assert not key in self.m_name2s_name\n\n    def store(self, parameters):\n        \"\"\"\n        Save the current parameters for restoring later.\n        Args:\n          parameters: Iterable of `torch.nn.Parameter`; the parameters to be\n            temporarily stored.\n        \"\"\"\n        self.collected_params = [param.clone() for param in parameters]\n\n    def restore(self, parameters):\n        \"\"\"\n        Restore the parameters stored with the `store` method.\n        Useful to validate the model with EMA parameters without affecting the\n        original optimization process. Store the parameters before the\n        `copy_to` method. After validation (or model saving), use this to\n        restore the former parameters.\n        Args:\n          parameters: Iterable of `torch.nn.Parameter`; the parameters to be\n            updated with the stored parameters.\n        \"\"\"\n        for c_param, param in zip(self.collected_params, parameters):\n            param.data.copy_(c_param.data)\n"
  },
  {
    "path": "ldm_patched/ldm/modules/encoders/__init__.py",
    "content": ""
  },
  {
    "path": "ldm_patched/ldm/modules/encoders/noise_aug_modules.py",
    "content": "from ..diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation\nfrom ..diffusionmodules.openaimodel import Timestep\nimport torch\n\nclass CLIPEmbeddingNoiseAugmentation(ImageConcatWithNoiseAugmentation):\n    def __init__(self, *args, clip_stats_path=None, timestep_dim=256, **kwargs):\n        super().__init__(*args, **kwargs)\n        if clip_stats_path is None:\n            clip_mean, clip_std = torch.zeros(timestep_dim), torch.ones(timestep_dim)\n        else:\n            clip_mean, clip_std = torch.load(clip_stats_path, map_location=\"cpu\", weights_only=True)\n        self.register_buffer(\"data_mean\", clip_mean[None, :], persistent=False)\n        self.register_buffer(\"data_std\", clip_std[None, :], persistent=False)\n        self.time_embed = Timestep(timestep_dim)\n\n    def scale(self, x):\n        # re-normalize to centered mean and unit variance\n        x = (x - self.data_mean.to(x.device)) * 1. / self.data_std.to(x.device)\n        return x\n\n    def unscale(self, x):\n        # back to original data stats\n        x = (x * self.data_std.to(x.device)) + self.data_mean.to(x.device)\n        return x\n\n    def forward(self, x, noise_level=None, seed=None):\n        if noise_level is None:\n            noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long()\n        else:\n            assert isinstance(noise_level, torch.Tensor)\n        x = self.scale(x)\n        z = self.q_sample(x, noise_level, seed=seed)\n        z = self.unscale(z)\n        noise_level = self.time_embed(noise_level)\n        return z, noise_level\n"
  },
  {
    "path": "ldm_patched/ldm/modules/sub_quadratic_attention.py",
    "content": "# original source:\n#   https://github.com/AminRezaei0x443/memory-efficient-attention/blob/1bc0d9e6ac5f82ea43a375135c4e1d3896ee1694/memory_efficient_attention/attention_torch.py\n# license:\n#   MIT\n# credit:\n#   Amin Rezaei (original author)\n#   Alex Birch (optimized algorithm for 3D tensors, at the expense of removing bias, masking and callbacks)\n# implementation of:\n#   Self-attention Does Not Need O(n2) Memory\":\n#   https://arxiv.org/abs/2112.05682v2\n\nfrom functools import partial\nimport torch\nfrom torch import Tensor\nfrom torch.utils.checkpoint import checkpoint\nimport math\n\ntry:\n\tfrom typing import Optional, NamedTuple, List, Protocol\nexcept ImportError:\n\tfrom typing import Optional, NamedTuple, List\n\tfrom typing_extensions import Protocol\n\nfrom torch import Tensor\nfrom typing import List\n\nfrom ldm_patched.modules import model_management\n\ndef dynamic_slice(\n    x: Tensor,\n    starts: List[int],\n    sizes: List[int],\n) -> Tensor:\n    slicing = [slice(start, start + size) for start, size in zip(starts, sizes)]\n    return x[slicing]\n\nclass AttnChunk(NamedTuple):\n    exp_values: Tensor\n    exp_weights_sum: Tensor\n    max_score: Tensor\n\nclass SummarizeChunk(Protocol):\n    @staticmethod\n    def __call__(\n        query: Tensor,\n        key_t: Tensor,\n        value: Tensor,\n    ) -> AttnChunk: ...\n\nclass ComputeQueryChunkAttn(Protocol):\n    @staticmethod\n    def __call__(\n        query: Tensor,\n        key_t: Tensor,\n        value: Tensor,\n    ) -> Tensor: ...\n\ndef _summarize_chunk(\n    query: Tensor,\n    key_t: Tensor,\n    value: Tensor,\n    scale: float,\n    upcast_attention: bool,\n    mask,\n) -> AttnChunk:\n    if upcast_attention:\n        with torch.autocast(enabled=False, device_type = 'cuda'):\n            query = query.float()\n            key_t = key_t.float()\n            attn_weights = torch.baddbmm(\n                torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),\n                query,\n                key_t,\n                alpha=scale,\n                beta=0,\n            )\n    else:\n        attn_weights = torch.baddbmm(\n            torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),\n            query,\n            key_t,\n            alpha=scale,\n            beta=0,\n        )\n    max_score, _ = torch.max(attn_weights, -1, keepdim=True)\n    max_score = max_score.detach()\n    attn_weights -= max_score\n    if mask is not None:\n        attn_weights += mask\n    torch.exp(attn_weights, out=attn_weights)\n    exp_weights = attn_weights.to(value.dtype)\n    exp_values = torch.bmm(exp_weights, value)\n    max_score = max_score.squeeze(-1)\n    return AttnChunk(exp_values, exp_weights.sum(dim=-1), max_score)\n\ndef _query_chunk_attention(\n    query: Tensor,\n    key_t: Tensor,\n    value: Tensor,\n    summarize_chunk: SummarizeChunk,\n    kv_chunk_size: int,\n    mask,\n) -> Tensor:\n    batch_x_heads, k_channels_per_head, k_tokens = key_t.shape\n    _, _, v_channels_per_head = value.shape\n\n    def chunk_scanner(chunk_idx: int, mask) -> AttnChunk:\n        key_chunk = dynamic_slice(\n            key_t,\n            (0, 0, chunk_idx),\n            (batch_x_heads, k_channels_per_head, kv_chunk_size)\n        )\n        value_chunk = dynamic_slice(\n            value,\n            (0, chunk_idx, 0),\n            (batch_x_heads, kv_chunk_size, v_channels_per_head)\n        )\n        if mask is not None:\n            mask = mask[:,:,chunk_idx:chunk_idx + kv_chunk_size]\n\n        return summarize_chunk(query, key_chunk, value_chunk, mask=mask)\n\n    chunks: List[AttnChunk] = [\n        chunk_scanner(chunk, mask) for chunk in torch.arange(0, k_tokens, kv_chunk_size)\n    ]\n    acc_chunk = AttnChunk(*map(torch.stack, zip(*chunks)))\n    chunk_values, chunk_weights, chunk_max = acc_chunk\n\n    global_max, _ = torch.max(chunk_max, 0, keepdim=True)\n    max_diffs = torch.exp(chunk_max - global_max)\n    chunk_values *= torch.unsqueeze(max_diffs, -1)\n    chunk_weights *= max_diffs\n\n    all_values = chunk_values.sum(dim=0)\n    all_weights = torch.unsqueeze(chunk_weights, -1).sum(dim=0)\n    return all_values / all_weights\n\n# TODO: refactor CrossAttention#get_attention_scores to share code with this\ndef _get_attention_scores_no_kv_chunking(\n    query: Tensor,\n    key_t: Tensor,\n    value: Tensor,\n    scale: float,\n    upcast_attention: bool,\n    mask,\n) -> Tensor:\n    if upcast_attention:\n        with torch.autocast(enabled=False, device_type = 'cuda'):\n            query = query.float()\n            key_t = key_t.float()\n            attn_scores = torch.baddbmm(\n                torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),\n                query,\n                key_t,\n                alpha=scale,\n                beta=0,\n            )\n    else:\n        attn_scores = torch.baddbmm(\n            torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),\n            query,\n            key_t,\n            alpha=scale,\n            beta=0,\n        )\n\n    if mask is not None:\n        attn_scores += mask\n    try:\n        attn_probs = attn_scores.softmax(dim=-1)\n        del attn_scores\n    except model_management.OOM_EXCEPTION:\n        print(\"ran out of memory while running softmax in  _get_attention_scores_no_kv_chunking, trying slower in place softmax instead\")\n        attn_scores -= attn_scores.max(dim=-1, keepdim=True).values\n        torch.exp(attn_scores, out=attn_scores)\n        summed = torch.sum(attn_scores, dim=-1, keepdim=True)\n        attn_scores /= summed\n        attn_probs = attn_scores\n\n    hidden_states_slice = torch.bmm(attn_probs.to(value.dtype), value)\n    return hidden_states_slice\n\nclass ScannedChunk(NamedTuple):\n    chunk_idx: int\n    attn_chunk: AttnChunk\n\ndef efficient_dot_product_attention(\n    query: Tensor,\n    key_t: Tensor,\n    value: Tensor,\n    query_chunk_size=1024,\n    kv_chunk_size: Optional[int] = None,\n    kv_chunk_size_min: Optional[int] = None,\n    use_checkpoint=True,\n    upcast_attention=False,\n    mask = None,\n):\n    \"\"\"Computes efficient dot-product attention given query, transposed key, and value.\n      This is efficient version of attention presented in\n      https://arxiv.org/abs/2112.05682v2 which comes with O(sqrt(n)) memory requirements.\n      Args:\n        query: queries for calculating attention with shape of\n          `[batch * num_heads, tokens, channels_per_head]`.\n        key_t: keys for calculating attention with shape of\n          `[batch * num_heads, channels_per_head, tokens]`.\n        value: values to be used in attention with shape of\n          `[batch * num_heads, tokens, channels_per_head]`.\n        query_chunk_size: int: query chunks size\n        kv_chunk_size: Optional[int]: key/value chunks size. if None: defaults to sqrt(key_tokens)\n        kv_chunk_size_min: Optional[int]: key/value minimum chunk size. only considered when kv_chunk_size is None. changes `sqrt(key_tokens)` into `max(sqrt(key_tokens), kv_chunk_size_min)`, to ensure our chunk sizes don't get too small (smaller chunks = more chunks = less concurrent work done).\n        use_checkpoint: bool: whether to use checkpointing (recommended True for training, False for inference)\n      Returns:\n        Output of shape `[batch * num_heads, query_tokens, channels_per_head]`.\n      \"\"\"\n    batch_x_heads, q_tokens, q_channels_per_head = query.shape\n    _, _, k_tokens = key_t.shape\n    scale = q_channels_per_head ** -0.5\n\n    kv_chunk_size = min(kv_chunk_size or int(math.sqrt(k_tokens)), k_tokens)\n    if kv_chunk_size_min is not None:\n        kv_chunk_size = max(kv_chunk_size, kv_chunk_size_min)\n\n    if mask is not None and len(mask.shape) == 2:\n        mask = mask.unsqueeze(0)\n\n    def get_query_chunk(chunk_idx: int) -> Tensor:\n        return dynamic_slice(\n            query,\n            (0, chunk_idx, 0),\n            (batch_x_heads, min(query_chunk_size, q_tokens), q_channels_per_head)\n        )\n\n    def get_mask_chunk(chunk_idx: int) -> Tensor:\n        if mask is None:\n            return None\n        chunk = min(query_chunk_size, q_tokens)\n        return mask[:,chunk_idx:chunk_idx + chunk]\n\n    summarize_chunk: SummarizeChunk = partial(_summarize_chunk, scale=scale, upcast_attention=upcast_attention)\n    summarize_chunk: SummarizeChunk = partial(checkpoint, summarize_chunk) if use_checkpoint else summarize_chunk\n    compute_query_chunk_attn: ComputeQueryChunkAttn = partial(\n        _get_attention_scores_no_kv_chunking,\n        scale=scale,\n        upcast_attention=upcast_attention\n    ) if k_tokens <= kv_chunk_size else (\n        # fast-path for when there's just 1 key-value chunk per query chunk (this is just sliced attention btw)\n        partial(\n            _query_chunk_attention,\n            kv_chunk_size=kv_chunk_size,\n            summarize_chunk=summarize_chunk,\n        )\n    )\n\n    if q_tokens <= query_chunk_size:\n        # fast-path for when there's just 1 query chunk\n        return compute_query_chunk_attn(\n            query=query,\n            key_t=key_t,\n            value=value,\n            mask=mask,\n        )\n    \n    # TODO: maybe we should use torch.empty_like(query) to allocate storage in-advance,\n    # and pass slices to be mutated, instead of torch.cat()ing the returned slices\n    res = torch.cat([\n        compute_query_chunk_attn(\n            query=get_query_chunk(i * query_chunk_size),\n            key_t=key_t,\n            value=value,\n            mask=get_mask_chunk(i * query_chunk_size)\n        ) for i in range(math.ceil(q_tokens / query_chunk_size))\n    ], dim=1)\n    return res\n"
  },
  {
    "path": "ldm_patched/ldm/modules/temporal_ae.py",
    "content": "import functools\nfrom typing import Callable, Iterable, Union\n\nimport torch\nfrom einops import rearrange, repeat\n\nimport ldm_patched.modules.ops\nops = ldm_patched.modules.ops.disable_weight_init\n\nfrom .diffusionmodules.model import (\n    AttnBlock,\n    Decoder,\n    ResnetBlock,\n)\nfrom .diffusionmodules.openaimodel import ResBlock, timestep_embedding\nfrom .attention import BasicTransformerBlock\n\ndef partialclass(cls, *args, **kwargs):\n    class NewCls(cls):\n        __init__ = functools.partialmethod(cls.__init__, *args, **kwargs)\n\n    return NewCls\n\n\nclass VideoResBlock(ResnetBlock):\n    def __init__(\n        self,\n        out_channels,\n        *args,\n        dropout=0.0,\n        video_kernel_size=3,\n        alpha=0.0,\n        merge_strategy=\"learned\",\n        **kwargs,\n    ):\n        super().__init__(out_channels=out_channels, dropout=dropout, *args, **kwargs)\n        if video_kernel_size is None:\n            video_kernel_size = [3, 1, 1]\n        self.time_stack = ResBlock(\n            channels=out_channels,\n            emb_channels=0,\n            dropout=dropout,\n            dims=3,\n            use_scale_shift_norm=False,\n            use_conv=False,\n            up=False,\n            down=False,\n            kernel_size=video_kernel_size,\n            use_checkpoint=False,\n            skip_t_emb=True,\n        )\n\n        self.merge_strategy = merge_strategy\n        if self.merge_strategy == \"fixed\":\n            self.register_buffer(\"mix_factor\", torch.Tensor([alpha]))\n        elif self.merge_strategy == \"learned\":\n            self.register_parameter(\n                \"mix_factor\", torch.nn.Parameter(torch.Tensor([alpha]))\n            )\n        else:\n            raise ValueError(f\"unknown merge strategy {self.merge_strategy}\")\n\n    def get_alpha(self, bs):\n        if self.merge_strategy == \"fixed\":\n            return self.mix_factor\n        elif self.merge_strategy == \"learned\":\n            return torch.sigmoid(self.mix_factor)\n        else:\n            raise NotImplementedError()\n\n    def forward(self, x, temb, skip_video=False, timesteps=None):\n        b, c, h, w = x.shape\n        if timesteps is None:\n            timesteps = b\n\n        x = super().forward(x, temb)\n\n        if not skip_video:\n            x_mix = rearrange(x, \"(b t) c h w -> b c t h w\", t=timesteps)\n\n            x = rearrange(x, \"(b t) c h w -> b c t h w\", t=timesteps)\n\n            x = self.time_stack(x, temb)\n\n            alpha = self.get_alpha(bs=b // timesteps).to(x.device)\n            x = alpha * x + (1.0 - alpha) * x_mix\n\n            x = rearrange(x, \"b c t h w -> (b t) c h w\")\n        return x\n\n\nclass AE3DConv(ops.Conv2d):\n    def __init__(self, in_channels, out_channels, video_kernel_size=3, *args, **kwargs):\n        super().__init__(in_channels, out_channels, *args, **kwargs)\n        if isinstance(video_kernel_size, Iterable):\n            padding = [int(k // 2) for k in video_kernel_size]\n        else:\n            padding = int(video_kernel_size // 2)\n\n        self.time_mix_conv = ops.Conv3d(\n            in_channels=out_channels,\n            out_channels=out_channels,\n            kernel_size=video_kernel_size,\n            padding=padding,\n        )\n\n    def forward(self, input, timesteps=None, skip_video=False):\n        if timesteps is None:\n            timesteps = input.shape[0]\n        x = super().forward(input)\n        if skip_video:\n            return x\n        x = rearrange(x, \"(b t) c h w -> b c t h w\", t=timesteps)\n        x = self.time_mix_conv(x)\n        return rearrange(x, \"b c t h w -> (b t) c h w\")\n\n\nclass AttnVideoBlock(AttnBlock):\n    def __init__(\n        self, in_channels: int, alpha: float = 0, merge_strategy: str = \"learned\"\n    ):\n        super().__init__(in_channels)\n        # no context, single headed, as in base class\n        self.time_mix_block = BasicTransformerBlock(\n            dim=in_channels,\n            n_heads=1,\n            d_head=in_channels,\n            checkpoint=False,\n            ff_in=True,\n        )\n\n        time_embed_dim = self.in_channels * 4\n        self.video_time_embed = torch.nn.Sequential(\n            ops.Linear(self.in_channels, time_embed_dim),\n            torch.nn.SiLU(),\n            ops.Linear(time_embed_dim, self.in_channels),\n        )\n\n        self.merge_strategy = merge_strategy\n        if self.merge_strategy == \"fixed\":\n            self.register_buffer(\"mix_factor\", torch.Tensor([alpha]))\n        elif self.merge_strategy == \"learned\":\n            self.register_parameter(\n                \"mix_factor\", torch.nn.Parameter(torch.Tensor([alpha]))\n            )\n        else:\n            raise ValueError(f\"unknown merge strategy {self.merge_strategy}\")\n\n    def forward(self, x, timesteps=None, skip_time_block=False):\n        if skip_time_block:\n            return super().forward(x)\n\n        if timesteps is None:\n            timesteps = x.shape[0]\n\n        x_in = x\n        x = self.attention(x)\n        h, w = x.shape[2:]\n        x = rearrange(x, \"b c h w -> b (h w) c\")\n\n        x_mix = x\n        num_frames = torch.arange(timesteps, device=x.device)\n        num_frames = repeat(num_frames, \"t -> b t\", b=x.shape[0] // timesteps)\n        num_frames = rearrange(num_frames, \"b t -> (b t)\")\n        t_emb = timestep_embedding(num_frames, self.in_channels, repeat_only=False)\n        emb = self.video_time_embed(t_emb)  # b, n_channels\n        emb = emb[:, None, :]\n        x_mix = x_mix + emb\n\n        alpha = self.get_alpha().to(x.device)\n        x_mix = self.time_mix_block(x_mix, timesteps=timesteps)\n        x = alpha * x + (1.0 - alpha) * x_mix  # alpha merge\n\n        x = rearrange(x, \"b (h w) c -> b c h w\", h=h, w=w)\n        x = self.proj_out(x)\n\n        return x_in + x\n\n    def get_alpha(\n        self,\n    ):\n        if self.merge_strategy == \"fixed\":\n            return self.mix_factor\n        elif self.merge_strategy == \"learned\":\n            return torch.sigmoid(self.mix_factor)\n        else:\n            raise NotImplementedError(f\"unknown merge strategy {self.merge_strategy}\")\n\n\n\ndef make_time_attn(\n    in_channels,\n    attn_type=\"vanilla\",\n    attn_kwargs=None,\n    alpha: float = 0,\n    merge_strategy: str = \"learned\",\n):\n    return partialclass(\n        AttnVideoBlock, in_channels, alpha=alpha, merge_strategy=merge_strategy\n    )\n\n\nclass Conv2DWrapper(torch.nn.Conv2d):\n    def forward(self, input: torch.Tensor, **kwargs) -> torch.Tensor:\n        return super().forward(input)\n\n\nclass VideoDecoder(Decoder):\n    available_time_modes = [\"all\", \"conv-only\", \"attn-only\"]\n\n    def __init__(\n        self,\n        *args,\n        video_kernel_size: Union[int, list] = 3,\n        alpha: float = 0.0,\n        merge_strategy: str = \"learned\",\n        time_mode: str = \"conv-only\",\n        **kwargs,\n    ):\n        self.video_kernel_size = video_kernel_size\n        self.alpha = alpha\n        self.merge_strategy = merge_strategy\n        self.time_mode = time_mode\n        assert (\n            self.time_mode in self.available_time_modes\n        ), f\"time_mode parameter has to be in {self.available_time_modes}\"\n\n        if self.time_mode != \"attn-only\":\n            kwargs[\"conv_out_op\"] = partialclass(AE3DConv, video_kernel_size=self.video_kernel_size)\n        if self.time_mode not in [\"conv-only\", \"only-last-conv\"]:\n            kwargs[\"attn_op\"] = partialclass(make_time_attn, alpha=self.alpha, merge_strategy=self.merge_strategy)\n        if self.time_mode not in [\"attn-only\", \"only-last-conv\"]:\n            kwargs[\"resnet_op\"] = partialclass(VideoResBlock, video_kernel_size=self.video_kernel_size, alpha=self.alpha, merge_strategy=self.merge_strategy)\n\n        super().__init__(*args, **kwargs)\n\n    def get_last_layer(self, skip_time_mix=False, **kwargs):\n        if self.time_mode == \"attn-only\":\n            raise NotImplementedError(\"TODO\")\n        else:\n            return (\n                self.conv_out.time_mix_conv.weight\n                if not skip_time_mix\n                else self.conv_out.weight\n            )\n"
  },
  {
    "path": "ldm_patched/ldm/util.py",
    "content": "import importlib\n\nimport torch\nfrom torch import optim\nimport numpy as np\n\nfrom inspect import isfunction\nfrom PIL import Image, ImageDraw, ImageFont\n\n\ndef log_txt_as_img(wh, xc, size=10):\n    # wh a tuple of (width, height)\n    # xc a list of captions to plot\n    b = len(xc)\n    txts = list()\n    for bi in range(b):\n        txt = Image.new(\"RGB\", wh, color=\"white\")\n        draw = ImageDraw.Draw(txt)\n        font = ImageFont.truetype('data/DejaVuSans.ttf', size=size)\n        nc = int(40 * (wh[0] / 256))\n        lines = \"\\n\".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc))\n\n        try:\n            draw.text((0, 0), lines, fill=\"black\", font=font)\n        except UnicodeEncodeError:\n            print(\"Cant encode string for logging. Skipping.\")\n\n        txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0\n        txts.append(txt)\n    txts = np.stack(txts)\n    txts = torch.tensor(txts)\n    return txts\n\n\ndef ismap(x):\n    if not isinstance(x, torch.Tensor):\n        return False\n    return (len(x.shape) == 4) and (x.shape[1] > 3)\n\n\ndef isimage(x):\n    if not isinstance(x,torch.Tensor):\n        return False\n    return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)\n\n\ndef exists(x):\n    return x is not None\n\n\ndef default(val, d):\n    if exists(val):\n        return val\n    return d() if isfunction(d) else d\n\n\ndef mean_flat(tensor):\n    \"\"\"\n    https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86\n    Take the mean over all non-batch dimensions.\n    \"\"\"\n    return tensor.mean(dim=list(range(1, len(tensor.shape))))\n\n\ndef count_params(model, verbose=False):\n    total_params = sum(p.numel() for p in model.parameters())\n    if verbose:\n        print(f\"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.\")\n    return total_params\n\n\ndef instantiate_from_config(config):\n    if not \"target\" in config:\n        if config == '__is_first_stage__':\n            return None\n        elif config == \"__is_unconditional__\":\n            return None\n        raise KeyError(\"Expected key `target` to instantiate.\")\n    return get_obj_from_str(config[\"target\"])(**config.get(\"params\", dict()))\n\n\ndef get_obj_from_str(string, reload=False):\n    module, cls = string.rsplit(\".\", 1)\n    if reload:\n        module_imp = importlib.import_module(module)\n        importlib.reload(module_imp)\n    return getattr(importlib.import_module(module, package=None), cls)\n\n\nclass AdamWwithEMAandWings(optim.Optimizer):\n    # credit to https://gist.github.com/crowsonkb/65f7265353f403714fce3b2595e0b298\n    def __init__(self, params, lr=1.e-3, betas=(0.9, 0.999), eps=1.e-8,  # TODO: check hyperparameters before using\n                 weight_decay=1.e-2, amsgrad=False, ema_decay=0.9999,   # ema decay to match previous code\n                 ema_power=1., param_names=()):\n        \"\"\"AdamW that saves EMA versions of the parameters.\"\"\"\n        if not 0.0 <= lr:\n            raise ValueError(\"Invalid learning rate: {}\".format(lr))\n        if not 0.0 <= eps:\n            raise ValueError(\"Invalid epsilon value: {}\".format(eps))\n        if not 0.0 <= betas[0] < 1.0:\n            raise ValueError(\"Invalid beta parameter at index 0: {}\".format(betas[0]))\n        if not 0.0 <= betas[1] < 1.0:\n            raise ValueError(\"Invalid beta parameter at index 1: {}\".format(betas[1]))\n        if not 0.0 <= weight_decay:\n            raise ValueError(\"Invalid weight_decay value: {}\".format(weight_decay))\n        if not 0.0 <= ema_decay <= 1.0:\n            raise ValueError(\"Invalid ema_decay value: {}\".format(ema_decay))\n        defaults = dict(lr=lr, betas=betas, eps=eps,\n                        weight_decay=weight_decay, amsgrad=amsgrad, ema_decay=ema_decay,\n                        ema_power=ema_power, param_names=param_names)\n        super().__init__(params, defaults)\n\n    def __setstate__(self, state):\n        super().__setstate__(state)\n        for group in self.param_groups:\n            group.setdefault('amsgrad', False)\n\n    @torch.no_grad()\n    def step(self, closure=None):\n        \"\"\"Performs a single optimization step.\n        Args:\n            closure (callable, optional): A closure that reevaluates the model\n                and returns the loss.\n        \"\"\"\n        loss = None\n        if closure is not None:\n            with torch.enable_grad():\n                loss = closure()\n\n        for group in self.param_groups:\n            params_with_grad = []\n            grads = []\n            exp_avgs = []\n            exp_avg_sqs = []\n            ema_params_with_grad = []\n            state_sums = []\n            max_exp_avg_sqs = []\n            state_steps = []\n            amsgrad = group['amsgrad']\n            beta1, beta2 = group['betas']\n            ema_decay = group['ema_decay']\n            ema_power = group['ema_power']\n\n            for p in group['params']:\n                if p.grad is None:\n                    continue\n                params_with_grad.append(p)\n                if p.grad.is_sparse:\n                    raise RuntimeError('AdamW does not support sparse gradients')\n                grads.append(p.grad)\n\n                state = self.state[p]\n\n                # State initialization\n                if len(state) == 0:\n                    state['step'] = 0\n                    # Exponential moving average of gradient values\n                    state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)\n                    # Exponential moving average of squared gradient values\n                    state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)\n                    if amsgrad:\n                        # Maintains max of all exp. moving avg. of sq. grad. values\n                        state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)\n                    # Exponential moving average of parameter values\n                    state['param_exp_avg'] = p.detach().float().clone()\n\n                exp_avgs.append(state['exp_avg'])\n                exp_avg_sqs.append(state['exp_avg_sq'])\n                ema_params_with_grad.append(state['param_exp_avg'])\n\n                if amsgrad:\n                    max_exp_avg_sqs.append(state['max_exp_avg_sq'])\n\n                # update the steps for each param group update\n                state['step'] += 1\n                # record the step after step update\n                state_steps.append(state['step'])\n\n            optim._functional.adamw(params_with_grad,\n                    grads,\n                    exp_avgs,\n                    exp_avg_sqs,\n                    max_exp_avg_sqs,\n                    state_steps,\n                    amsgrad=amsgrad,\n                    beta1=beta1,\n                    beta2=beta2,\n                    lr=group['lr'],\n                    weight_decay=group['weight_decay'],\n                    eps=group['eps'],\n                    maximize=False)\n\n            cur_ema_decay = min(ema_decay, 1 - state['step'] ** -ema_power)\n            for param, ema_param in zip(params_with_grad, ema_params_with_grad):\n                ema_param.mul_(cur_ema_decay).add_(param.float(), alpha=1 - cur_ema_decay)\n\n        return loss"
  },
  {
    "path": "ldm_patched/licenses-3rd/chainer",
    "content": "Copyright (c) 2015 Preferred Infrastructure, Inc.\nCopyright (c) 2015 Preferred Networks, Inc.\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE."
  },
  {
    "path": "ldm_patched/licenses-3rd/comfyui",
    "content": "                    GNU GENERAL PUBLIC LICENSE\n                       Version 3, 29 June 2007\n\n Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>\n Everyone is permitted to copy and distribute verbatim copies\n of this license document, but changing it is not allowed.\n\n                            Preamble\n\n  The GNU General Public License is a free, copyleft license for\nsoftware and other kinds of works.\n\n  The licenses for most software and other practical works are designed\nto take away your freedom to share and change the works.  By contrast,\nthe GNU General Public License is intended to guarantee your freedom to\nshare and change all versions of a program--to make sure it remains free\nsoftware for all its users.  We, the Free Software Foundation, use the\nGNU General Public License for most of our software; it applies also to\nany other work released this way by its authors.  You can apply it to\nyour programs, too.\n\n  When we speak of free software, we are referring to freedom, not\nprice.  Our General Public Licenses are designed to make sure that you\nhave the freedom to distribute copies of free software (and charge for\nthem if you wish), that you receive source code or can get it if you\nwant it, that you can change the software or use pieces of it in new\nfree programs, and that you know you can do these things.\n\n  To protect your rights, we need to prevent others from denying you\nthese rights or asking you to surrender the rights.  Therefore, you have\ncertain responsibilities if you distribute copies of the software, or if\nyou modify it: responsibilities to respect the freedom of others.\n\n  For example, if you distribute copies of such a program, whether\ngratis or for a fee, you must pass on to the recipients the same\nfreedoms that you received.  You must make sure that they, too, receive\nor can get the source code.  And you must show them these terms so they\nknow their rights.\n\n  Developers that use the GNU GPL protect your rights with two steps:\n(1) assert copyright on the software, and (2) offer you this License\ngiving you legal permission to copy, distribute and/or modify it.\n\n  For the developers' and authors' protection, the GPL clearly explains\nthat there is no warranty for this free software.  For both users' and\nauthors' sake, the GPL requires that modified versions be marked as\nchanged, so that their problems will not be attributed erroneously to\nauthors of previous versions.\n\n  Some devices are designed to deny users access to install or run\nmodified versions of the software inside them, although the manufacturer\ncan do so.  This is fundamentally incompatible with the aim of\nprotecting users' freedom to change the software.  The systematic\npattern of such abuse occurs in the area of products for individuals to\nuse, which is precisely where it is most unacceptable.  Therefore, we\nhave designed this version of the GPL to prohibit the practice for those\nproducts.  If such problems arise substantially in other domains, we\nstand ready to extend this provision to those domains in future versions\nof the GPL, as needed to protect the freedom of users.\n\n  Finally, every program is threatened constantly by software patents.\nStates should not allow patents to restrict development and use of\nsoftware on general-purpose computers, but in those that do, we wish to\navoid the special danger that patents applied to a free program could\nmake it effectively proprietary.  To prevent this, the GPL assures that\npatents cannot be used to render the program non-free.\n\n  The precise terms and conditions for copying, distribution and\nmodification follow.\n\n                       TERMS AND CONDITIONS\n\n  0. Definitions.\n\n  \"This License\" refers to version 3 of the GNU General Public License.\n\n  \"Copyright\" also means copyright-like laws that apply to other kinds of\nworks, such as semiconductor masks.\n\n  \"The Program\" refers to any copyrightable work licensed under this\nLicense.  Each licensee is addressed as \"you\".  \"Licensees\" and\n\"recipients\" may be individuals or organizations.\n\n  To \"modify\" a work means to copy from or adapt all or part of the work\nin a fashion requiring copyright permission, other than the making of an\nexact copy.  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But this requirement does not apply\nif neither you nor any third party retains the ability to install\nmodified object code on the User Product (for example, the work has\nbeen installed in ROM).\n\n  The requirement to provide Installation Information does not include a\nrequirement to continue to provide support service, warranty, or updates\nfor a work that has been modified or installed by the recipient, or for\nthe User Product in which it has been modified or installed.  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If additional permissions\napply only to part of the Program, that part may be used separately\nunder those permissions, but the entire Program remains governed by\nthis License without regard to the additional permissions.\n\n  When you convey a copy of a covered work, you may at your option\nremove any additional permissions from that copy, or from any part of\nit.  (Additional permissions may be written to require their own\nremoval in certain cases when you modify the work.)  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If the Program as you\nreceived it, or any part of it, contains a notice stating that it is\ngoverned by this License along with a term that is a further\nrestriction, you may remove that term.  If a license document contains\na further restriction but permits relicensing or conveying under this\nLicense, you may add to a covered work material governed by the terms\nof that license document, provided that the further restriction does\nnot survive such relicensing or conveying.\n\n  If you add terms to a covered work in accord with this section, you\nmust place, in the relevant source files, a statement of the\nadditional terms that apply to those files, or a notice indicating\nwhere to find the applicable terms.\n\n  Additional terms, permissive or non-permissive, may be stated in the\nform of a separately written license, or stated as exceptions;\nthe above requirements apply either way.\n\n  8. Termination.\n\n  You may not propagate or modify a covered work except as expressly\nprovided under this License.  Any attempt otherwise to propagate or\nmodify it is void, and will automatically terminate your rights under\nthis License (including any patent licenses granted under the third\nparagraph of section 11).\n\n  However, if you cease all violation of this License, then your\nlicense from a particular copyright holder is reinstated (a)\nprovisionally, unless and until the copyright holder explicitly and\nfinally terminates your license, and (b) permanently, if the copyright\nholder fails to notify you of the violation by some reasonable means\nprior to 60 days after the cessation.\n\n  Moreover, your license from a particular copyright holder is\nreinstated permanently if the copyright holder notifies you of the\nviolation by some reasonable means, this is the first time you have\nreceived notice of violation of this License (for any work) from that\ncopyright holder, and you cure the violation prior to 30 days after\nyour receipt of the notice.\n\n  Termination of your rights under this section does not terminate the\nlicenses of parties who have received copies or rights from you under\nthis License.  If your rights have been terminated and not permanently\nreinstated, you do not qualify to receive new licenses for the same\nmaterial under section 10.\n\n  9. Acceptance Not Required for Having Copies.\n\n  You are not required to accept this License in order to receive or\nrun a copy of the Program.  Ancillary propagation of a covered work\noccurring solely as a consequence of using peer-to-peer transmission\nto receive a copy likewise does not require acceptance.  However,\nnothing other than this License grants you permission to propagate or\nmodify any covered work.  These actions infringe copyright if you do\nnot accept this License.  Therefore, by modifying or propagating a\ncovered work, you indicate your acceptance of this License to do so.\n\n  10. Automatic Licensing of Downstream Recipients.\n\n  Each time you convey a covered work, the recipient automatically\nreceives a license from the original licensors, to run, modify and\npropagate that work, subject to this License.  You are not responsible\nfor enforcing compliance by third parties with this License.\n\n  An \"entity transaction\" is a transaction transferring control of an\norganization, or substantially all assets of one, or subdividing an\norganization, or merging organizations.  If propagation of a covered\nwork results from an entity transaction, each party to that\ntransaction who receives a copy of the work also receives whatever\nlicenses to the work the party's predecessor in interest had or could\ngive under the previous paragraph, plus a right to possession of the\nCorresponding Source of the work from the predecessor in interest, if\nthe predecessor has it or can get it with reasonable efforts.\n\n  You may not impose any further restrictions on the exercise of the\nrights granted or affirmed under this License.  For example, you may\nnot impose a license fee, royalty, or other charge for exercise of\nrights granted under this License, and you may not initiate litigation\n(including a cross-claim or counterclaim in a lawsuit) alleging that\nany patent claim is infringed by making, using, selling, offering for\nsale, or importing the Program or any portion of it.\n\n  11. Patents.\n\n  A \"contributor\" is a copyright holder who authorizes use under this\nLicense of the Program or a work on which the Program is based.  The\nwork thus licensed is called the contributor's \"contributor version\".\n\n  A contributor's \"essential patent claims\" are all patent claims\nowned or controlled by the contributor, whether already acquired or\nhereafter acquired, that would be infringed by some manner, permitted\nby this License, of making, using, or selling its contributor version,\nbut do not include claims that would be infringed only as a\nconsequence of further modification of the contributor version.  For\npurposes of this definition, \"control\" includes the right to grant\npatent sublicenses in a manner consistent with the requirements of\nthis License.\n\n  Each contributor grants you a non-exclusive, worldwide, royalty-free\npatent license under the contributor's essential patent claims, to\nmake, use, sell, offer for sale, import and otherwise run, modify and\npropagate the contents of its contributor version.\n\n  In the following three paragraphs, a \"patent license\" is any express\nagreement or commitment, however denominated, not to enforce a patent\n(such as an express permission to practice a patent or covenant not to\nsue for patent infringement).  To \"grant\" such a patent license to a\nparty means to make such an agreement or commitment not to enforce a\npatent against the party.\n\n  If you convey a covered work, knowingly relying on a patent license,\nand the Corresponding Source of the work is not available for anyone\nto copy, free of charge and under the terms of this License, through a\npublicly available network server or other readily accessible means,\nthen you must either (1) cause the Corresponding Source to be so\navailable, or (2) arrange to deprive yourself of the benefit of the\npatent license for this particular work, or (3) arrange, in a manner\nconsistent with the requirements of this License, to extend the patent\nlicense to downstream recipients.  \"Knowingly relying\" means you have\nactual knowledge that, but for the patent license, your conveying the\ncovered work in a country, or your recipient's use of the covered work\nin a country, would infringe one or more identifiable patents in that\ncountry that you have reason to believe are valid.\n\n  If, pursuant to or in connection with a single transaction or\narrangement, you convey, or propagate by procuring conveyance of, a\ncovered work, and grant a patent license to some of the parties\nreceiving the covered work authorizing them to use, propagate, modify\nor convey a specific copy of the covered work, then the patent license\nyou grant is automatically extended to all recipients of the covered\nwork and works based on it.\n\n  A patent license is \"discriminatory\" if it does not include within\nthe scope of its coverage, prohibits the exercise of, or is\nconditioned on the non-exercise of one or more of the rights that are\nspecifically granted under this License.  You may not convey a covered\nwork if you are a party to an arrangement with a third party that is\nin the business of distributing software, under which you make payment\nto the third party based on the extent of your activity of conveying\nthe work, and under which the third party grants, to any of the\nparties who would receive the covered work from you, a discriminatory\npatent license (a) in connection with copies of the covered work\nconveyed by you (or copies made from those copies), or (b) primarily\nfor and in connection with specific products or compilations that\ncontain the covered work, unless you entered into that arrangement,\nor that patent license was granted, prior to 28 March 2007.\n\n  Nothing in this License shall be construed as excluding or limiting\nany implied license or other defenses to infringement that may\notherwise be available to you under applicable patent law.\n\n  12. No Surrender of Others' Freedom.\n\n  If conditions are imposed on you (whether by court order, agreement or\notherwise) that contradict the conditions of this License, they do not\nexcuse you from the conditions of this License.  If you cannot convey a\ncovered work so as to satisfy simultaneously your obligations under this\nLicense and any other pertinent obligations, then as a consequence you may\nnot convey it at all.  For example, if you agree to terms that obligate you\nto collect a royalty for further conveying from those to whom you convey\nthe Program, the only way you could satisfy both those terms and this\nLicense would be to refrain entirely from conveying the Program.\n\n  13. Use with the GNU Affero General Public License.\n\n  Notwithstanding any other provision of this License, you have\npermission to link or combine any covered work with a work licensed\nunder version 3 of the GNU Affero General Public License into a single\ncombined work, and to convey the resulting work.  The terms of this\nLicense will continue to apply to the part which is the covered work,\nbut the special requirements of the GNU Affero General Public License,\nsection 13, concerning interaction through a network will apply to the\ncombination as such.\n\n  14. Revised Versions of this License.\n\n  The Free Software Foundation may publish revised and/or new versions of\nthe GNU General Public License from time to time.  Such new versions will\nbe similar in spirit to the present version, but may differ in detail to\naddress new problems or concerns.\n\n  Each version is given a distinguishing version number.  If the\nProgram specifies that a certain numbered version of the GNU General\nPublic License \"or any later version\" applies to it, you have the\noption of following the terms and conditions either of that numbered\nversion or of any later version published by the Free Software\nFoundation.  If the Program does not specify a version number of the\nGNU General Public License, you may choose any version ever published\nby the Free Software Foundation.\n\n  If the Program specifies that a proxy can decide which future\nversions of the GNU General Public License can be used, that proxy's\npublic statement of acceptance of a version permanently authorizes you\nto choose that version for the Program.\n\n  Later license versions may give you additional or different\npermissions.  However, no additional obligations are imposed on any\nauthor or copyright holder as a result of your choosing to follow a\nlater version.\n\n  15. Disclaimer of Warranty.\n\n  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY\nAPPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT\nHOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM \"AS IS\" WITHOUT WARRANTY\nOF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,\nTHE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR\nPURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM\nIS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF\nALL NECESSARY SERVICING, REPAIR OR CORRECTION.\n\n  16. Limitation of Liability.\n\n  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING\nWILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS\nTHE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY\nGENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE\nUSE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF\nDATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD\nPARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),\nEVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF\nSUCH DAMAGES.\n\n  17. Interpretation of Sections 15 and 16.\n\n  If the disclaimer of warranty and limitation of liability provided\nabove cannot be given local legal effect according to their terms,\nreviewing courts shall apply local law that most closely approximates\nan absolute waiver of all civil liability in connection with the\nProgram, unless a warranty or assumption of liability accompanies a\ncopy of the Program in return for a fee.\n\n                     END OF TERMS AND CONDITIONS\n\n            How to Apply These Terms to Your New Programs\n\n  If you develop a new program, and you want it to be of the greatest\npossible use to the public, the best way to achieve this is to make it\nfree software which everyone can redistribute and change under these terms.\n\n  To do so, attach the following notices to the program.  It is safest\nto attach them to the start of each source file to most effectively\nstate the exclusion of warranty; and each file should have at least\nthe \"copyright\" line and a pointer to where the full notice is found.\n\n    <one line to give the program's name and a brief idea of what it does.>\n    Copyright (C) <year>  <name of author>\n\n    This program is free software: you can redistribute it and/or modify\n    it under the terms of the GNU General Public License as published by\n    the Free Software Foundation, either version 3 of the License, or\n    (at your option) any later version.\n\n    This program is distributed in the hope that it will be useful,\n    but WITHOUT ANY WARRANTY; without even the implied warranty of\n    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n    GNU General Public License for more details.\n\n    You should have received a copy of the GNU General Public License\n    along with this program.  If not, see <https://www.gnu.org/licenses/>.\n\nAlso add information on how to contact you by electronic and paper mail.\n\n  If the program does terminal interaction, make it output a short\nnotice like this when it starts in an interactive mode:\n\n    <program>  Copyright (C) <year>  <name of author>\n    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.\n    This is free software, and you are welcome to redistribute it\n    under certain conditions; type `show c' for details.\n\nThe hypothetical commands `show w' and `show c' should show the appropriate\nparts of the General Public License.  Of course, your program's commands\nmight be different; for a GUI interface, you would use an \"about box\".\n\n  You should also get your employer (if you work as a programmer) or school,\nif any, to sign a \"copyright disclaimer\" for the program, if necessary.\nFor more information on this, and how to apply and follow the GNU GPL, see\n<https://www.gnu.org/licenses/>.\n\n  The GNU General Public License does not permit incorporating your program\ninto proprietary programs.  If your program is a subroutine library, you\nmay consider it more useful to permit linking proprietary applications with\nthe library.  If this is what you want to do, use the GNU Lesser General\nPublic License instead of this License.  But first, please read\n<https://www.gnu.org/licenses/why-not-lgpl.html>."
  },
  {
    "path": "ldm_patched/licenses-3rd/diffusers",
    "content": "                                 Apache License\n                           Version 2.0, January 2004\n                        http://www.apache.org/licenses/\n\n   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n   1. Definitions.\n\n      \"License\" shall mean the terms and conditions for use, reproduction,\n      and distribution as defined by Sections 1 through 9 of this document.\n\n      \"Licensor\" shall mean the copyright owner or entity authorized by\n      the copyright owner that is granting the License.\n\n      \"Legal Entity\" shall mean the union of the acting entity and all\n      other entities that control, are controlled by, or are under common\n      control with that entity. For the purposes of this definition,\n      \"control\" means (i) the power, direct or indirect, to cause the\n      direction or management of such entity, whether by contract or\n      otherwise, or (ii) ownership of fifty percent (50%) or more of the\n      outstanding shares, or (iii) beneficial ownership of such entity.\n\n      \"You\" (or \"Your\") shall mean an individual or Legal Entity\n      exercising permissions granted by this License.\n\n      \"Source\" form shall mean the preferred form for making modifications,\n      including but not limited to software source code, documentation\n      source, and configuration files.\n\n      \"Object\" form shall mean any form resulting from mechanical\n      transformation or translation of a Source form, including but\n      not limited to compiled object code, generated documentation,\n      and conversions to other media types.\n\n      \"Work\" shall mean the work of authorship, whether in Source or\n      Object form, made available under the License, as indicated by a\n      copyright notice that is included in or attached to the work\n      (an example is provided in the Appendix below).\n\n      \"Derivative Works\" shall mean any work, whether in Source or Object\n      form, that is based on (or derived from) the Work and for which the\n      editorial revisions, annotations, elaborations, or other modifications\n      represent, as a whole, an original work of authorship. For the purposes\n      of this License, Derivative Works shall not include works that remain\n      separable from, or merely link (or bind by name) to the interfaces of,\n      the Work and Derivative Works thereof.\n\n      \"Contribution\" shall mean any work of authorship, including\n      the original version of the Work and any modifications or additions\n      to that Work or Derivative Works thereof, that is intentionally\n      submitted to Licensor for inclusion in the Work by the copyright owner\n      or by an individual or Legal Entity authorized to submit on behalf of\n      the copyright owner. For the purposes of this definition, \"submitted\"\n      means any form of electronic, verbal, or written communication sent\n      to the Licensor or its representatives, including but not limited to\n      communication on electronic mailing lists, source code control systems,\n      and issue tracking systems that are managed by, or on behalf of, the\n      Licensor for the purpose of discussing and improving the Work, but\n      excluding communication that is conspicuously marked or otherwise\n      designated in writing by the copyright owner as \"Not a Contribution.\"\n\n      \"Contributor\" shall mean Licensor and any individual or Legal Entity\n      on behalf of whom a Contribution has been received by Licensor and\n      subsequently incorporated within the Work.\n\n   2. Grant of Copyright License. Subject to the terms and conditions of\n      this License, each Contributor hereby grants to You a perpetual,\n      worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n      copyright license to reproduce, prepare Derivative Works of,\n      publicly display, publicly perform, sublicense, and distribute the\n      Work and such Derivative Works in Source or Object form.\n\n   3. Grant of Patent License. Subject to the terms and conditions of\n      this License, each Contributor hereby grants to You a perpetual,\n      worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n      (except as stated in this section) patent license to make, have made,\n      use, offer to sell, sell, import, and otherwise transfer the Work,\n      where such license applies only to those patent claims licensable\n      by such Contributor that are necessarily infringed by their\n      Contribution(s) alone or by combination of their Contribution(s)\n      with the Work to which such Contribution(s) was submitted. If You\n      institute patent litigation against any entity (including a\n      cross-claim or counterclaim in a lawsuit) alleging that the Work\n      or a Contribution incorporated within the Work constitutes direct\n      or contributory patent infringement, then any patent licenses\n      granted to You under this License for that Work shall terminate\n      as of the date such litigation is filed.\n\n   4. Redistribution. You may reproduce and distribute copies of the\n      Work or Derivative Works thereof in any medium, with or without\n      modifications, and in Source or Object form, provided that You\n      meet the following conditions:\n\n      (a) You must give any other recipients of the Work or\n          Derivative Works a copy of this License; and\n\n      (b) You must cause any modified files to carry prominent notices\n          stating that You changed the files; and\n\n      (c) You must retain, in the Source form of any Derivative Works\n          that You distribute, all copyright, patent, trademark, and\n          attribution notices from the Source form of the Work,\n          excluding those notices that do not pertain to any part of\n          the Derivative Works; and\n\n      (d) If the Work includes a \"NOTICE\" text file as part of its\n          distribution, then any Derivative Works that You distribute must\n          include a readable copy of the attribution notices contained\n          within such NOTICE file, excluding those notices that do not\n          pertain to any part of the Derivative Works, in at least one\n          of the following places: within a NOTICE text file distributed\n          as part of the Derivative Works; within the Source form or\n          documentation, if provided along with the Derivative Works; or,\n          within a display generated by the Derivative Works, if and\n          wherever such third-party notices normally appear. The contents\n          of the NOTICE file are for informational purposes only and\n          do not modify the License. You may add Your own attribution\n          notices within Derivative Works that You distribute, alongside\n          or as an addendum to the NOTICE text from the Work, provided\n          that such additional attribution notices cannot be construed\n          as modifying the License.\n\n      You may add Your own copyright statement to Your modifications and\n      may provide additional or different license terms and conditions\n      for use, reproduction, or distribution of Your modifications, or\n      for any such Derivative Works as a whole, provided Your use,\n      reproduction, and distribution of the Work otherwise complies with\n      the conditions stated in this License.\n\n   5. Submission of Contributions. Unless You explicitly state otherwise,\n      any Contribution intentionally submitted for inclusion in the Work\n      by You to the Licensor shall be under the terms and conditions of\n      this License, without any additional terms or conditions.\n      Notwithstanding the above, nothing herein shall supersede or modify\n      the terms of any separate license agreement you may have executed\n      with Licensor regarding such Contributions.\n\n   6. Trademarks. This License does not grant permission to use the trade\n      names, trademarks, service marks, or product names of the Licensor,\n      except as required for reasonable and customary use in describing the\n      origin of the Work and reproducing the content of the NOTICE file.\n\n   7. Disclaimer of Warranty. Unless required by applicable law or\n      agreed to in writing, Licensor provides the Work (and each\n      Contributor provides its Contributions) on an \"AS IS\" BASIS,\n      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n      implied, including, without limitation, any warranties or conditions\n      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A\n      PARTICULAR PURPOSE. You are solely responsible for determining the\n      appropriateness of using or redistributing the Work and assume any\n      risks associated with Your exercise of permissions under this License.\n\n   8. Limitation of Liability. In no event and under no legal theory,\n      whether in tort (including negligence), contract, or otherwise,\n      unless required by applicable law (such as deliberate and grossly\n      negligent acts) or agreed to in writing, shall any Contributor be\n      liable to You for damages, including any direct, indirect, special,\n      incidental, or consequential damages of any character arising as a\n      result of this License or out of the use or inability to use the\n      Work (including but not limited to damages for loss of goodwill,\n      work stoppage, computer failure or malfunction, or any and all\n      other commercial damages or losses), even if such Contributor\n      has been advised of the possibility of such damages.\n\n   9. Accepting Warranty or Additional Liability. While redistributing\n      the Work or Derivative Works thereof, You may choose to offer,\n      and charge a fee for, acceptance of support, warranty, indemnity,\n      or other liability obligations and/or rights consistent with this\n      License. However, in accepting such obligations, You may act only\n      on Your own behalf and on Your sole responsibility, not on behalf\n      of any other Contributor, and only if You agree to indemnify,\n      defend, and hold each Contributor harmless for any liability\n      incurred by, or claims asserted against, such Contributor by reason\n      of your accepting any such warranty or additional liability.\n\n   END OF TERMS AND CONDITIONS\n\n   APPENDIX: How to apply the Apache License to your work.\n\n      To apply the Apache License to your work, attach the following\n      boilerplate notice, with the fields enclosed by brackets \"[]\"\n      replaced with your own identifying information. (Don't include\n      the brackets!)  The text should be enclosed in the appropriate\n      comment syntax for the file format. We also recommend that a\n      file or class name and description of purpose be included on the\n      same \"printed page\" as the copyright notice for easier\n      identification within third-party archives.\n\n   Copyright [yyyy] [name of copyright owner]\n\n   Licensed under the Apache License, Version 2.0 (the \"License\");\n   you may not use this file except in compliance with the License.\n   You may obtain a copy of the License at\n\n       http://www.apache.org/licenses/LICENSE-2.0\n\n   Unless required by applicable law or agreed to in writing, software\n   distributed under the License is distributed on an \"AS IS\" BASIS,\n   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n   See the License for the specific language governing permissions and\n   limitations under the License."
  },
  {
    "path": "ldm_patched/licenses-3rd/kdiffusion",
    "content": "Copyright (c) 2022 Katherine Crowson\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE."
  },
  {
    "path": "ldm_patched/licenses-3rd/ldm",
    "content": "MIT License\n\nCopyright (c) 2022 Machine Vision and Learning Group, LMU Munich\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE."
  },
  {
    "path": "ldm_patched/licenses-3rd/taesd",
    "content": "MIT License\n\nCopyright (c) 2023 Ollin Boer Bohan\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE."
  },
  {
    "path": "ldm_patched/licenses-3rd/transformers",
    "content": "Copyright 2018- The Hugging Face team. All rights reserved.\n\n                                 Apache License\n                           Version 2.0, January 2004\n                        http://www.apache.org/licenses/\n\n   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n   1. Definitions.\n\n      \"License\" shall mean the terms and conditions for use, reproduction,\n      and distribution as defined by Sections 1 through 9 of this document.\n\n      \"Licensor\" shall mean the copyright owner or entity authorized by\n      the copyright owner that is granting the License.\n\n      \"Legal Entity\" shall mean the union of the acting entity and all\n      other entities that control, are controlled by, or are under common\n      control with that entity. For the purposes of this definition,\n      \"control\" means (i) the power, direct or indirect, to cause the\n      direction or management of such entity, whether by contract or\n      otherwise, or (ii) ownership of fifty percent (50%) or more of the\n      outstanding shares, or (iii) beneficial ownership of such entity.\n\n      \"You\" (or \"Your\") shall mean an individual or Legal Entity\n      exercising permissions granted by this License.\n\n      \"Source\" form shall mean the preferred form for making modifications,\n      including but not limited to software source code, documentation\n      source, and configuration files.\n\n      \"Object\" form shall mean any form resulting from mechanical\n      transformation or translation of a Source form, including but\n      not limited to compiled object code, generated documentation,\n      and conversions to other media types.\n\n      \"Work\" shall mean the work of authorship, whether in Source or\n      Object form, made available under the License, as indicated by a\n      copyright notice that is included in or attached to the work\n      (an example is provided in the Appendix below).\n\n      \"Derivative Works\" shall mean any work, whether in Source or Object\n      form, that is based on (or derived from) the Work and for which the\n      editorial revisions, annotations, elaborations, or other modifications\n      represent, as a whole, an original work of authorship. For the purposes\n      of this License, Derivative Works shall not include works that remain\n      separable from, or merely link (or bind by name) to the interfaces of,\n      the Work and Derivative Works thereof.\n\n      \"Contribution\" shall mean any work of authorship, including\n      the original version of the Work and any modifications or additions\n      to that Work or Derivative Works thereof, that is intentionally\n      submitted to Licensor for inclusion in the Work by the copyright owner\n      or by an individual or Legal Entity authorized to submit on behalf of\n      the copyright owner. For the purposes of this definition, \"submitted\"\n      means any form of electronic, verbal, or written communication sent\n      to the Licensor or its representatives, including but not limited to\n      communication on electronic mailing lists, source code control systems,\n      and issue tracking systems that are managed by, or on behalf of, the\n      Licensor for the purpose of discussing and improving the Work, but\n      excluding communication that is conspicuously marked or otherwise\n      designated in writing by the copyright owner as \"Not a Contribution.\"\n\n      \"Contributor\" shall mean Licensor and any individual or Legal Entity\n      on behalf of whom a Contribution has been received by Licensor and\n      subsequently incorporated within the Work.\n\n   2. Grant of Copyright License. Subject to the terms and conditions of\n      this License, each Contributor hereby grants to You a perpetual,\n      worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n      copyright license to reproduce, prepare Derivative Works of,\n      publicly display, publicly perform, sublicense, and distribute the\n      Work and such Derivative Works in Source or Object form.\n\n   3. Grant of Patent License. Subject to the terms and conditions of\n      this License, each Contributor hereby grants to You a perpetual,\n      worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n      (except as stated in this section) patent license to make, have made,\n      use, offer to sell, sell, import, and otherwise transfer the Work,\n      where such license applies only to those patent claims licensable\n      by such Contributor that are necessarily infringed by their\n      Contribution(s) alone or by combination of their Contribution(s)\n      with the Work to which such Contribution(s) was submitted. If You\n      institute patent litigation against any entity (including a\n      cross-claim or counterclaim in a lawsuit) alleging that the Work\n      or a Contribution incorporated within the Work constitutes direct\n      or contributory patent infringement, then any patent licenses\n      granted to You under this License for that Work shall terminate\n      as of the date such litigation is filed.\n\n   4. Redistribution. You may reproduce and distribute copies of the\n      Work or Derivative Works thereof in any medium, with or without\n      modifications, and in Source or Object form, provided that You\n      meet the following conditions:\n\n      (a) You must give any other recipients of the Work or\n          Derivative Works a copy of this License; and\n\n      (b) You must cause any modified files to carry prominent notices\n          stating that You changed the files; and\n\n      (c) You must retain, in the Source form of any Derivative Works\n          that You distribute, all copyright, patent, trademark, and\n          attribution notices from the Source form of the Work,\n          excluding those notices that do not pertain to any part of\n          the Derivative Works; and\n\n      (d) If the Work includes a \"NOTICE\" text file as part of its\n          distribution, then any Derivative Works that You distribute must\n          include a readable copy of the attribution notices contained\n          within such NOTICE file, excluding those notices that do not\n          pertain to any part of the Derivative Works, in at least one\n          of the following places: within a NOTICE text file distributed\n          as part of the Derivative Works; within the Source form or\n          documentation, if provided along with the Derivative Works; or,\n          within a display generated by the Derivative Works, if and\n          wherever such third-party notices normally appear. The contents\n          of the NOTICE file are for informational purposes only and\n          do not modify the License. You may add Your own attribution\n          notices within Derivative Works that You distribute, alongside\n          or as an addendum to the NOTICE text from the Work, provided\n          that such additional attribution notices cannot be construed\n          as modifying the License.\n\n      You may add Your own copyright statement to Your modifications and\n      may provide additional or different license terms and conditions\n      for use, reproduction, or distribution of Your modifications, or\n      for any such Derivative Works as a whole, provided Your use,\n      reproduction, and distribution of the Work otherwise complies with\n      the conditions stated in this License.\n\n   5. Submission of Contributions. Unless You explicitly state otherwise,\n      any Contribution intentionally submitted for inclusion in the Work\n      by You to the Licensor shall be under the terms and conditions of\n      this License, without any additional terms or conditions.\n      Notwithstanding the above, nothing herein shall supersede or modify\n      the terms of any separate license agreement you may have executed\n      with Licensor regarding such Contributions.\n\n   6. Trademarks. This License does not grant permission to use the trade\n      names, trademarks, service marks, or product names of the Licensor,\n      except as required for reasonable and customary use in describing the\n      origin of the Work and reproducing the content of the NOTICE file.\n\n   7. Disclaimer of Warranty. Unless required by applicable law or\n      agreed to in writing, Licensor provides the Work (and each\n      Contributor provides its Contributions) on an \"AS IS\" BASIS,\n      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n      implied, including, without limitation, any warranties or conditions\n      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A\n      PARTICULAR PURPOSE. You are solely responsible for determining the\n      appropriateness of using or redistributing the Work and assume any\n      risks associated with Your exercise of permissions under this License.\n\n   8. Limitation of Liability. In no event and under no legal theory,\n      whether in tort (including negligence), contract, or otherwise,\n      unless required by applicable law (such as deliberate and grossly\n      negligent acts) or agreed to in writing, shall any Contributor be\n      liable to You for damages, including any direct, indirect, special,\n      incidental, or consequential damages of any character arising as a\n      result of this License or out of the use or inability to use the\n      Work (including but not limited to damages for loss of goodwill,\n      work stoppage, computer failure or malfunction, or any and all\n      other commercial damages or losses), even if such Contributor\n      has been advised of the possibility of such damages.\n\n   9. Accepting Warranty or Additional Liability. While redistributing\n      the Work or Derivative Works thereof, You may choose to offer,\n      and charge a fee for, acceptance of support, warranty, indemnity,\n      or other liability obligations and/or rights consistent with this\n      License. However, in accepting such obligations, You may act only\n      on Your own behalf and on Your sole responsibility, not on behalf\n      of any other Contributor, and only if You agree to indemnify,\n      defend, and hold each Contributor harmless for any liability\n      incurred by, or claims asserted against, such Contributor by reason\n      of your accepting any such warranty or additional liability.\n\n   END OF TERMS AND CONDITIONS\n\n   APPENDIX: How to apply the Apache License to your work.\n\n      To apply the Apache License to your work, attach the following\n      boilerplate notice, with the fields enclosed by brackets \"[]\"\n      replaced with your own identifying information. (Don't include\n      the brackets!)  The text should be enclosed in the appropriate\n      comment syntax for the file format. We also recommend that a\n      file or class name and description of purpose be included on the\n      same \"printed page\" as the copyright notice for easier\n      identification within third-party archives.\n\n   Copyright [yyyy] [name of copyright owner]\n\n   Licensed under the Apache License, Version 2.0 (the \"License\");\n   you may not use this file except in compliance with the License.\n   You may obtain a copy of the License at\n\n       http://www.apache.org/licenses/LICENSE-2.0\n\n   Unless required by applicable law or agreed to in writing, software\n   distributed under the License is distributed on an \"AS IS\" BASIS,\n   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n   See the License for the specific language governing permissions and\n   limitations under the License."
  },
  {
    "path": "ldm_patched/modules/args_parser.py",
    "content": "import argparse\nimport enum\nimport ldm_patched.modules.options\n\nclass EnumAction(argparse.Action):\n    \"\"\"\n    Argparse action for handling Enums\n    \"\"\"\n    def __init__(self, **kwargs):\n        # Pop off the type value\n        enum_type = kwargs.pop(\"type\", None)\n\n        # Ensure an Enum subclass is provided\n        if enum_type is None:\n            raise ValueError(\"type must be assigned an Enum when using EnumAction\")\n        if not issubclass(enum_type, enum.Enum):\n            raise TypeError(\"type must be an Enum when using EnumAction\")\n\n        # Generate choices from the Enum\n        choices = tuple(e.value for e in enum_type)\n        kwargs.setdefault(\"choices\", choices)\n        kwargs.setdefault(\"metavar\", f\"[{','.join(list(choices))}]\")\n\n        super(EnumAction, self).__init__(**kwargs)\n\n        self._enum = enum_type\n\n    def __call__(self, parser, namespace, values, option_string=None):\n        # Convert value back into an Enum\n        value = self._enum(values)\n        setattr(namespace, self.dest, value)\n\n\nparser = argparse.ArgumentParser()\n\nparser.add_argument(\"--listen\", type=str, default=\"127.0.0.1\", metavar=\"IP\", nargs=\"?\", const=\"0.0.0.0\")\nparser.add_argument(\"--port\", type=int, default=8188)\nparser.add_argument(\"--disable-header-check\", type=str, default=None, metavar=\"ORIGIN\", nargs=\"?\", const=\"*\")\nparser.add_argument(\"--web-upload-size\", type=float, default=100)\nparser.add_argument(\"--hf-mirror\", type=str, default=None)\n\nparser.add_argument(\"--external-working-path\", type=str, default=None, metavar=\"PATH\", nargs='+', action='append')\nparser.add_argument(\"--output-path\", type=str, default=None)\nparser.add_argument(\"--temp-path\", type=str, default=None)\nparser.add_argument(\"--cache-path\", type=str, default=None)\nparser.add_argument(\"--in-browser\", action=\"store_true\")\nparser.add_argument(\"--disable-in-browser\", action=\"store_true\")\nparser.add_argument(\"--gpu-device-id\", type=int, default=None, metavar=\"DEVICE_ID\")\ncm_group = parser.add_mutually_exclusive_group()\ncm_group.add_argument(\"--async-cuda-allocation\", action=\"store_true\")\ncm_group.add_argument(\"--disable-async-cuda-allocation\", action=\"store_true\")\n\nparser.add_argument(\"--disable-attention-upcast\", action=\"store_true\")\n\nfp_group = parser.add_mutually_exclusive_group()\nfp_group.add_argument(\"--all-in-fp32\", action=\"store_true\")\nfp_group.add_argument(\"--all-in-fp16\", action=\"store_true\")\n\nfpunet_group = parser.add_mutually_exclusive_group()\nfpunet_group.add_argument(\"--unet-in-bf16\", action=\"store_true\")\nfpunet_group.add_argument(\"--unet-in-fp16\", action=\"store_true\")\nfpunet_group.add_argument(\"--unet-in-fp8-e4m3fn\", action=\"store_true\")\nfpunet_group.add_argument(\"--unet-in-fp8-e5m2\", action=\"store_true\")\n\nfpvae_group = parser.add_mutually_exclusive_group()\nfpvae_group.add_argument(\"--vae-in-fp16\", action=\"store_true\")\nfpvae_group.add_argument(\"--vae-in-fp32\", action=\"store_true\")\nfpvae_group.add_argument(\"--vae-in-bf16\", action=\"store_true\")\n\nparser.add_argument(\"--vae-in-cpu\", action=\"store_true\")\n\nfpte_group = parser.add_mutually_exclusive_group()\nfpte_group.add_argument(\"--clip-in-fp8-e4m3fn\", action=\"store_true\")\nfpte_group.add_argument(\"--clip-in-fp8-e5m2\", action=\"store_true\")\nfpte_group.add_argument(\"--clip-in-fp16\", action=\"store_true\")\nfpte_group.add_argument(\"--clip-in-fp32\", action=\"store_true\")\n\n\nparser.add_argument(\"--directml\", type=int, nargs=\"?\", metavar=\"DIRECTML_DEVICE\", const=-1)\n\nparser.add_argument(\"--disable-ipex-hijack\", action=\"store_true\")\n\nclass LatentPreviewMethod(enum.Enum):\n    NoPreviews = \"none\"\n    Auto = \"auto\"\n    Latent2RGB = \"fast\"\n    TAESD = \"taesd\"\n\nparser.add_argument(\"--preview-option\", type=LatentPreviewMethod, default=LatentPreviewMethod.NoPreviews, action=EnumAction)\n\nattn_group = parser.add_mutually_exclusive_group()\nattn_group.add_argument(\"--attention-split\", action=\"store_true\")\nattn_group.add_argument(\"--attention-quad\", action=\"store_true\")\nattn_group.add_argument(\"--attention-pytorch\", action=\"store_true\")\n\nparser.add_argument(\"--disable-xformers\", action=\"store_true\")\n\nvram_group = parser.add_mutually_exclusive_group()\nvram_group.add_argument(\"--always-gpu\", action=\"store_true\")\nvram_group.add_argument(\"--always-high-vram\", action=\"store_true\")\nvram_group.add_argument(\"--always-normal-vram\", action=\"store_true\")\nvram_group.add_argument(\"--always-low-vram\", action=\"store_true\")\nvram_group.add_argument(\"--always-no-vram\", action=\"store_true\")\nvram_group.add_argument(\"--always-cpu\", type=int, nargs=\"?\", metavar=\"CPU_NUM_THREADS\", const=-1)\n\nparser.add_argument(\"--always-offload-from-vram\", action=\"store_true\")\nparser.add_argument(\"--pytorch-deterministic\", action=\"store_true\")\n\nparser.add_argument(\"--disable-server-log\", action=\"store_true\")\nparser.add_argument(\"--debug-mode\", action=\"store_true\")\nparser.add_argument(\"--is-windows-embedded-python\", action=\"store_true\")\n\nparser.add_argument(\"--disable-server-info\", action=\"store_true\")\n\nparser.add_argument(\"--multi-user\", action=\"store_true\")\n\nif ldm_patched.modules.options.args_parsing:\n    args = parser.parse_args([])\nelse:\n    args = parser.parse_args([])\n\nif args.is_windows_embedded_python:\n    args.in_browser = True\n\nif args.disable_in_browser:\n    args.in_browser = False\n"
  },
  {
    "path": "ldm_patched/modules/checkpoint_pickle.py",
    "content": "import pickle\n\nload = pickle.load\n\nclass Empty:\n    pass\n\nclass Unpickler(pickle.Unpickler):\n    def find_class(self, module, name):\n        #TODO: safe unpickle\n        if module.startswith(\"pytorch_lightning\"):\n            return Empty\n        return super().find_class(module, name)\n"
  },
  {
    "path": "ldm_patched/modules/clip_config_bigg.json",
    "content": "{\n  \"architectures\": [\n    \"CLIPTextModel\"\n  ],\n  \"attention_dropout\": 0.0,\n  \"bos_token_id\": 0,\n  \"dropout\": 0.0,\n  \"eos_token_id\": 2,\n  \"hidden_act\": \"gelu\",\n  \"hidden_size\": 1280,\n  \"initializer_factor\": 1.0,\n  \"initializer_range\": 0.02,\n  \"intermediate_size\": 5120,\n  \"layer_norm_eps\": 1e-05,\n  \"max_position_embeddings\": 77,\n  \"model_type\": \"clip_text_model\",\n  \"num_attention_heads\": 20,\n  \"num_hidden_layers\": 32,\n  \"pad_token_id\": 1,\n  \"projection_dim\": 1280,\n  \"torch_dtype\": \"float32\",\n  \"vocab_size\": 49408\n}\n"
  },
  {
    "path": "ldm_patched/modules/clip_model.py",
    "content": "import torch\nfrom ldm_patched.ldm.modules.attention import optimized_attention_for_device\n\nclass CLIPAttention(torch.nn.Module):\n    def __init__(self, embed_dim, heads, dtype, device, operations):\n        super().__init__()\n\n        self.heads = heads\n        self.q_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)\n        self.k_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)\n        self.v_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)\n\n        self.out_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)\n\n    def forward(self, x, mask=None, optimized_attention=None):\n        q = self.q_proj(x)\n        k = self.k_proj(x)\n        v = self.v_proj(x)\n\n        out = optimized_attention(q, k, v, self.heads, mask)\n        return self.out_proj(out)\n\nACTIVATIONS = {\"quick_gelu\": lambda a: a * torch.sigmoid(1.702 * a),\n               \"gelu\": torch.nn.functional.gelu,\n}\n\nclass CLIPMLP(torch.nn.Module):\n    def __init__(self, embed_dim, intermediate_size, activation, dtype, device, operations):\n        super().__init__()\n        self.fc1 = operations.Linear(embed_dim, intermediate_size, bias=True, dtype=dtype, device=device)\n        self.activation = ACTIVATIONS[activation]\n        self.fc2 = operations.Linear(intermediate_size, embed_dim, bias=True, dtype=dtype, device=device)\n\n    def forward(self, x):\n        x = self.fc1(x)\n        x = self.activation(x)\n        x = self.fc2(x)\n        return x\n\nclass CLIPLayer(torch.nn.Module):\n    def __init__(self, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations):\n        super().__init__()\n        self.layer_norm1 = operations.LayerNorm(embed_dim, dtype=dtype, device=device)\n        self.self_attn = CLIPAttention(embed_dim, heads, dtype, device, operations)\n        self.layer_norm2 = operations.LayerNorm(embed_dim, dtype=dtype, device=device)\n        self.mlp = CLIPMLP(embed_dim, intermediate_size, intermediate_activation, dtype, device, operations)\n\n    def forward(self, x, mask=None, optimized_attention=None):\n        x += self.self_attn(self.layer_norm1(x), mask, optimized_attention)\n        x += self.mlp(self.layer_norm2(x))\n        return x\n\n\nclass CLIPEncoder(torch.nn.Module):\n    def __init__(self, num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations):\n        super().__init__()\n        self.layers = torch.nn.ModuleList([CLIPLayer(embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) for i in range(num_layers)])\n\n    def forward(self, x, mask=None, intermediate_output=None):\n        optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True)\n\n        if intermediate_output is not None:\n            if intermediate_output < 0:\n                intermediate_output = len(self.layers) + intermediate_output\n\n        intermediate = None\n        for i, l in enumerate(self.layers):\n            x = l(x, mask, optimized_attention)\n            if i == intermediate_output:\n                intermediate = x.clone()\n        return x, intermediate\n\nclass CLIPEmbeddings(torch.nn.Module):\n    def __init__(self, embed_dim, vocab_size=49408, num_positions=77, dtype=None, device=None):\n        super().__init__()\n        self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim, dtype=dtype, device=device)\n        self.position_embedding = torch.nn.Embedding(num_positions, embed_dim, dtype=dtype, device=device)\n\n    def forward(self, input_tokens):\n        return self.token_embedding(input_tokens) + self.position_embedding.weight\n\n\nclass CLIPTextModel_(torch.nn.Module):\n    def __init__(self, config_dict, dtype, device, operations):\n        num_layers = config_dict[\"num_hidden_layers\"]\n        embed_dim = config_dict[\"hidden_size\"]\n        heads = config_dict[\"num_attention_heads\"]\n        intermediate_size = config_dict[\"intermediate_size\"]\n        intermediate_activation = config_dict[\"hidden_act\"]\n\n        super().__init__()\n        self.embeddings = CLIPEmbeddings(embed_dim, dtype=torch.float32, device=device)\n        self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations)\n        self.final_layer_norm = operations.LayerNorm(embed_dim, dtype=dtype, device=device)\n\n    def forward(self, input_tokens, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True):\n        x = self.embeddings(input_tokens)\n        mask = None\n        if attention_mask is not None:\n            mask = 1.0 - attention_mask.to(x.dtype).unsqueeze(1).unsqueeze(1).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])\n            mask = mask.masked_fill(mask.to(torch.bool), float(\"-inf\"))\n\n        causal_mask = torch.empty(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device).fill_(float(\"-inf\")).triu_(1)\n        if mask is not None:\n            mask += causal_mask\n        else:\n            mask = causal_mask\n\n        x, i = self.encoder(x, mask=mask, intermediate_output=intermediate_output)\n        x = self.final_layer_norm(x)\n        if i is not None and final_layer_norm_intermediate:\n            i = self.final_layer_norm(i)\n\n        pooled_output = x[torch.arange(x.shape[0], device=x.device), input_tokens.to(dtype=torch.int, device=x.device).argmax(dim=-1),]\n        return x, i, pooled_output\n\nclass CLIPTextModel(torch.nn.Module):\n    def __init__(self, config_dict, dtype, device, operations):\n        super().__init__()\n        self.num_layers = config_dict[\"num_hidden_layers\"]\n        self.text_model = CLIPTextModel_(config_dict, dtype, device, operations)\n        self.dtype = dtype\n\n    def get_input_embeddings(self):\n        return self.text_model.embeddings.token_embedding\n\n    def set_input_embeddings(self, embeddings):\n        self.text_model.embeddings.token_embedding = embeddings\n\n    def forward(self, *args, **kwargs):\n        return self.text_model(*args, **kwargs)\n\nclass CLIPVisionEmbeddings(torch.nn.Module):\n    def __init__(self, embed_dim, num_channels=3, patch_size=14, image_size=224, dtype=None, device=None, operations=None):\n        super().__init__()\n        self.class_embedding = torch.nn.Parameter(torch.empty(embed_dim, dtype=dtype, device=device))\n\n        self.patch_embedding = operations.Conv2d(\n            in_channels=num_channels,\n            out_channels=embed_dim,\n            kernel_size=patch_size,\n            stride=patch_size,\n            bias=False,\n            dtype=dtype,\n            device=device\n        )\n\n        num_patches = (image_size // patch_size) ** 2\n        num_positions = num_patches + 1\n        self.position_embedding = torch.nn.Embedding(num_positions, embed_dim, dtype=dtype, device=device)\n\n    def forward(self, pixel_values):\n        embeds = self.patch_embedding(pixel_values).flatten(2).transpose(1, 2)\n        return torch.cat([self.class_embedding.to(embeds.device).expand(pixel_values.shape[0], 1, -1), embeds], dim=1) + self.position_embedding.weight.to(embeds.device)\n\n\nclass CLIPVision(torch.nn.Module):\n    def __init__(self, config_dict, dtype, device, operations):\n        super().__init__()\n        num_layers = config_dict[\"num_hidden_layers\"]\n        embed_dim = config_dict[\"hidden_size\"]\n        heads = config_dict[\"num_attention_heads\"]\n        intermediate_size = config_dict[\"intermediate_size\"]\n        intermediate_activation = config_dict[\"hidden_act\"]\n\n        self.embeddings = CLIPVisionEmbeddings(embed_dim, config_dict[\"num_channels\"], config_dict[\"patch_size\"], config_dict[\"image_size\"], dtype=torch.float32, device=device, operations=operations)\n        self.pre_layrnorm = operations.LayerNorm(embed_dim)\n        self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations)\n        self.post_layernorm = operations.LayerNorm(embed_dim)\n\n    def forward(self, pixel_values, attention_mask=None, intermediate_output=None):\n        x = self.embeddings(pixel_values)\n        x = self.pre_layrnorm(x)\n        #TODO: attention_mask?\n        x, i = self.encoder(x, mask=None, intermediate_output=intermediate_output)\n        pooled_output = self.post_layernorm(x[:, 0, :])\n        return x, i, pooled_output\n\nclass CLIPVisionModelProjection(torch.nn.Module):\n    def __init__(self, config_dict, dtype, device, operations):\n        super().__init__()\n        self.vision_model = CLIPVision(config_dict, dtype, device, operations)\n        self.visual_projection = operations.Linear(config_dict[\"hidden_size\"], config_dict[\"projection_dim\"], bias=False)\n\n    def forward(self, *args, **kwargs):\n        x = self.vision_model(*args, **kwargs)\n        out = self.visual_projection(x[2])\n        return (x[0], x[1], out)\n"
  },
  {
    "path": "ldm_patched/modules/clip_vision.py",
    "content": "from .utils import load_torch_file, transformers_convert, state_dict_prefix_replace\nimport os\nimport torch\nimport json\n\nimport ldm_patched.modules.ops\nimport ldm_patched.modules.model_patcher\nimport ldm_patched.modules.model_management\nimport ldm_patched.modules.utils\nimport ldm_patched.modules.clip_model\n\nclass Output:\n    def __getitem__(self, key):\n        return getattr(self, key)\n    def __setitem__(self, key, item):\n        setattr(self, key, item)\n\ndef clip_preprocess(image, size=224):\n    mean = torch.tensor([ 0.48145466,0.4578275,0.40821073], device=image.device, dtype=image.dtype)\n    std = torch.tensor([0.26862954,0.26130258,0.27577711], device=image.device, dtype=image.dtype)\n    image = image.movedim(-1, 1)\n    if not (image.shape[2] == size and image.shape[3] == size):\n        scale = (size / min(image.shape[2], image.shape[3]))\n        image = torch.nn.functional.interpolate(image, size=(round(scale * image.shape[2]), round(scale * image.shape[3])), mode=\"bicubic\", antialias=True)\n        h = (image.shape[2] - size)//2\n        w = (image.shape[3] - size)//2\n        image = image[:,:,h:h+size,w:w+size]\n    image = torch.clip((255. * image), 0, 255).round() / 255.0\n    return (image - mean.view([3,1,1])) / std.view([3,1,1])\n\nclass ClipVisionModel():\n    def __init__(self, json_config):\n        with open(json_config) as f:\n            config = json.load(f)\n\n        self.load_device = ldm_patched.modules.model_management.text_encoder_device()\n        offload_device = ldm_patched.modules.model_management.text_encoder_offload_device()\n        self.dtype = ldm_patched.modules.model_management.text_encoder_dtype(self.load_device)\n        self.model = ldm_patched.modules.clip_model.CLIPVisionModelProjection(config, self.dtype, offload_device, ldm_patched.modules.ops.manual_cast)\n        self.model.eval()\n\n        self.patcher = ldm_patched.modules.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)\n\n    def load_sd(self, sd):\n        return self.model.load_state_dict(sd, strict=False)\n\n    def get_sd(self):\n        return self.model.state_dict()\n\n    def encode_image(self, image):\n        ldm_patched.modules.model_management.load_model_gpu(self.patcher)\n        pixel_values = clip_preprocess(image.to(self.load_device)).float()\n        out = self.model(pixel_values=pixel_values, intermediate_output=-2)\n\n        outputs = Output()\n        outputs[\"last_hidden_state\"] = out[0].to(ldm_patched.modules.model_management.intermediate_device())\n        outputs[\"image_embeds\"] = out[2].to(ldm_patched.modules.model_management.intermediate_device())\n        outputs[\"penultimate_hidden_states\"] = out[1].to(ldm_patched.modules.model_management.intermediate_device())\n        return outputs\n\ndef convert_to_transformers(sd, prefix):\n    sd_k = sd.keys()\n    if \"{}transformer.resblocks.0.attn.in_proj_weight\".format(prefix) in sd_k:\n        keys_to_replace = {\n            \"{}class_embedding\".format(prefix): \"vision_model.embeddings.class_embedding\",\n            \"{}conv1.weight\".format(prefix): \"vision_model.embeddings.patch_embedding.weight\",\n            \"{}positional_embedding\".format(prefix): \"vision_model.embeddings.position_embedding.weight\",\n            \"{}ln_post.bias\".format(prefix): \"vision_model.post_layernorm.bias\",\n            \"{}ln_post.weight\".format(prefix): \"vision_model.post_layernorm.weight\",\n            \"{}ln_pre.bias\".format(prefix): \"vision_model.pre_layrnorm.bias\",\n            \"{}ln_pre.weight\".format(prefix): \"vision_model.pre_layrnorm.weight\",\n        }\n\n        for x in keys_to_replace:\n            if x in sd_k:\n                sd[keys_to_replace[x]] = sd.pop(x)\n\n        if \"{}proj\".format(prefix) in sd_k:\n            sd['visual_projection.weight'] = sd.pop(\"{}proj\".format(prefix)).transpose(0, 1)\n\n        sd = transformers_convert(sd, prefix, \"vision_model.\", 48)\n    else:\n        replace_prefix = {prefix: \"\"}\n        sd = state_dict_prefix_replace(sd, replace_prefix)\n    return sd\n\ndef load_clipvision_from_sd(sd, prefix=\"\", convert_keys=False):\n    if convert_keys:\n        sd = convert_to_transformers(sd, prefix)\n    if \"vision_model.encoder.layers.47.layer_norm1.weight\" in sd:\n        json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"clip_vision_config_g.json\")\n    elif \"vision_model.encoder.layers.30.layer_norm1.weight\" in sd:\n        json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"clip_vision_config_h.json\")\n    elif \"vision_model.encoder.layers.22.layer_norm1.weight\" in sd:\n        json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"clip_vision_config_vitl.json\")\n    else:\n        return None\n\n    clip = ClipVisionModel(json_config)\n    m, u = clip.load_sd(sd)\n    if len(m) > 0:\n        print(\"extra clip vision:\", m)\n    u = set(u)\n    keys = list(sd.keys())\n    for k in keys:\n        if k not in u:\n            t = sd.pop(k)\n            del t\n    return clip\n\ndef load(ckpt_path):\n    sd = load_torch_file(ckpt_path)\n    if \"visual.transformer.resblocks.0.attn.in_proj_weight\" in sd:\n        return load_clipvision_from_sd(sd, prefix=\"visual.\", convert_keys=True)\n    else:\n        return load_clipvision_from_sd(sd)\n"
  },
  {
    "path": "ldm_patched/modules/clip_vision_config_g.json",
    "content": "{\n  \"attention_dropout\": 0.0,\n  \"dropout\": 0.0,\n  \"hidden_act\": \"gelu\",\n  \"hidden_size\": 1664,\n  \"image_size\": 224,\n  \"initializer_factor\": 1.0,\n  \"initializer_range\": 0.02,\n  \"intermediate_size\": 8192,\n  \"layer_norm_eps\": 1e-05,\n  \"model_type\": \"clip_vision_model\",\n  \"num_attention_heads\": 16,\n  \"num_channels\": 3,\n  \"num_hidden_layers\": 48,\n  \"patch_size\": 14,\n  \"projection_dim\": 1280,\n  \"torch_dtype\": \"float32\"\n}\n"
  },
  {
    "path": "ldm_patched/modules/clip_vision_config_h.json",
    "content": "{\n  \"attention_dropout\": 0.0,\n  \"dropout\": 0.0,\n  \"hidden_act\": \"gelu\",\n  \"hidden_size\": 1280,\n  \"image_size\": 224,\n  \"initializer_factor\": 1.0,\n  \"initializer_range\": 0.02,\n  \"intermediate_size\": 5120,\n  \"layer_norm_eps\": 1e-05,\n  \"model_type\": \"clip_vision_model\",\n  \"num_attention_heads\": 16,\n  \"num_channels\": 3,\n  \"num_hidden_layers\": 32,\n  \"patch_size\": 14,\n  \"projection_dim\": 1024,\n  \"torch_dtype\": \"float32\"\n}\n"
  },
  {
    "path": "ldm_patched/modules/clip_vision_config_vitl.json",
    "content": "{\n  \"attention_dropout\": 0.0,\n  \"dropout\": 0.0,\n  \"hidden_act\": \"quick_gelu\",\n  \"hidden_size\": 1024,\n  \"image_size\": 224,\n  \"initializer_factor\": 1.0,\n  \"initializer_range\": 0.02,\n  \"intermediate_size\": 4096,\n  \"layer_norm_eps\": 1e-05,\n  \"model_type\": \"clip_vision_model\",\n  \"num_attention_heads\": 16,\n  \"num_channels\": 3,\n  \"num_hidden_layers\": 24,\n  \"patch_size\": 14,\n  \"projection_dim\": 768,\n  \"torch_dtype\": \"float32\"\n}\n"
  },
  {
    "path": "ldm_patched/modules/conds.py",
    "content": "import torch\nimport math\nimport ldm_patched.modules.utils\n\n\n\nclass CONDRegular:\n    def __init__(self, cond):\n        self.cond = cond\n\n    def _copy_with(self, cond):\n        return self.__class__(cond)\n\n    def process_cond(self, batch_size, device, **kwargs):\n        return self._copy_with(ldm_patched.modules.utils.repeat_to_batch_size(self.cond, batch_size).to(device))\n\n    def can_concat(self, other):\n        if self.cond.shape != other.cond.shape:\n            return False\n        return True\n\n    def concat(self, others):\n        conds = [self.cond]\n        for x in others:\n            conds.append(x.cond)\n        return torch.cat(conds)\n\nclass CONDNoiseShape(CONDRegular):\n    def process_cond(self, batch_size, device, area, **kwargs):\n        data = self.cond[:,:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]]\n        return self._copy_with(ldm_patched.modules.utils.repeat_to_batch_size(data, batch_size).to(device))\n\n\nclass CONDCrossAttn(CONDRegular):\n    def can_concat(self, other):\n        s1 = self.cond.shape\n        s2 = other.cond.shape\n        if s1 != s2:\n            if s1[0] != s2[0] or s1[2] != s2[2]: #these 2 cases should not happen\n                return False\n\n            mult_min = math.lcm(s1[1], s2[1])\n            diff = mult_min // min(s1[1], s2[1])\n            if diff > 4: #arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much\n                return False\n        return True\n\n    def concat(self, others):\n        conds = [self.cond]\n        crossattn_max_len = self.cond.shape[1]\n        for x in others:\n            c = x.cond\n            crossattn_max_len = math.lcm(crossattn_max_len, c.shape[1])\n            conds.append(c)\n\n        out = []\n        for c in conds:\n            if c.shape[1] < crossattn_max_len:\n                c = c.repeat(1, crossattn_max_len // c.shape[1], 1) #padding with repeat doesn't change result\n            out.append(c)\n        return torch.cat(out)\n\nclass CONDConstant(CONDRegular):\n    def __init__(self, cond):\n        self.cond = cond\n\n    def process_cond(self, batch_size, device, **kwargs):\n        return self._copy_with(self.cond)\n\n    def can_concat(self, other):\n        if self.cond != other.cond:\n            return False\n        return True\n\n    def concat(self, others):\n        return self.cond\n"
  },
  {
    "path": "ldm_patched/modules/controlnet.py",
    "content": "import torch\nimport math\nimport os\nimport ldm_patched.modules.utils\nimport ldm_patched.modules.model_management\nimport ldm_patched.modules.model_detection\nimport ldm_patched.modules.model_patcher\nimport ldm_patched.modules.ops\n\nimport ldm_patched.controlnet.cldm\nimport ldm_patched.t2ia.adapter\n\n\ndef broadcast_image_to(tensor, target_batch_size, batched_number):\n    current_batch_size = tensor.shape[0]\n    #print(current_batch_size, target_batch_size)\n    if current_batch_size == 1:\n        return tensor\n\n    per_batch = target_batch_size // batched_number\n    tensor = tensor[:per_batch]\n\n    if per_batch > tensor.shape[0]:\n        tensor = torch.cat([tensor] * (per_batch // tensor.shape[0]) + [tensor[:(per_batch % tensor.shape[0])]], dim=0)\n\n    current_batch_size = tensor.shape[0]\n    if current_batch_size == target_batch_size:\n        return tensor\n    else:\n        return torch.cat([tensor] * batched_number, dim=0)\n\nclass ControlBase:\n    def __init__(self, device=None):\n        self.cond_hint_original = None\n        self.cond_hint = None\n        self.strength = 1.0\n        self.timestep_percent_range = (0.0, 1.0)\n        self.global_average_pooling = False\n        self.timestep_range = None\n\n        if device is None:\n            device = ldm_patched.modules.model_management.get_torch_device()\n        self.device = device\n        self.previous_controlnet = None\n\n    def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0)):\n        self.cond_hint_original = cond_hint\n        self.strength = strength\n        self.timestep_percent_range = timestep_percent_range\n        return self\n\n    def pre_run(self, model, percent_to_timestep_function):\n        self.timestep_range = (percent_to_timestep_function(self.timestep_percent_range[0]), percent_to_timestep_function(self.timestep_percent_range[1]))\n        if self.previous_controlnet is not None:\n            self.previous_controlnet.pre_run(model, percent_to_timestep_function)\n\n    def set_previous_controlnet(self, controlnet):\n        self.previous_controlnet = controlnet\n        return self\n\n    def cleanup(self):\n        if self.previous_controlnet is not None:\n            self.previous_controlnet.cleanup()\n        if self.cond_hint is not None:\n            del self.cond_hint\n            self.cond_hint = None\n        self.timestep_range = None\n\n    def get_models(self):\n        out = []\n        if self.previous_controlnet is not None:\n            out += self.previous_controlnet.get_models()\n        return out\n\n    def copy_to(self, c):\n        c.cond_hint_original = self.cond_hint_original\n        c.strength = self.strength\n        c.timestep_percent_range = self.timestep_percent_range\n        c.global_average_pooling = self.global_average_pooling\n\n    def inference_memory_requirements(self, dtype):\n        if self.previous_controlnet is not None:\n            return self.previous_controlnet.inference_memory_requirements(dtype)\n        return 0\n\n    def control_merge(self, control_input, control_output, control_prev, output_dtype):\n        out = {'input':[], 'middle':[], 'output': []}\n\n        if control_input is not None:\n            for i in range(len(control_input)):\n                key = 'input'\n                x = control_input[i]\n                if x is not None:\n                    x *= self.strength\n                    if x.dtype != output_dtype:\n                        x = x.to(output_dtype)\n                out[key].insert(0, x)\n\n        if control_output is not None:\n            for i in range(len(control_output)):\n                if i == (len(control_output) - 1):\n                    key = 'middle'\n                    index = 0\n                else:\n                    key = 'output'\n                    index = i\n                x = control_output[i]\n                if x is not None:\n                    if self.global_average_pooling:\n                        x = torch.mean(x, dim=(2, 3), keepdim=True).repeat(1, 1, x.shape[2], x.shape[3])\n\n                    x *= self.strength\n                    if x.dtype != output_dtype:\n                        x = x.to(output_dtype)\n\n                out[key].append(x)\n        if control_prev is not None:\n            for x in ['input', 'middle', 'output']:\n                o = out[x]\n                for i in range(len(control_prev[x])):\n                    prev_val = control_prev[x][i]\n                    if i >= len(o):\n                        o.append(prev_val)\n                    elif prev_val is not None:\n                        if o[i] is None:\n                            o[i] = prev_val\n                        else:\n                            if o[i].shape[0] < prev_val.shape[0]:\n                                o[i] = prev_val + o[i]\n                            else:\n                                o[i] += prev_val\n        return out\n\nclass ControlNet(ControlBase):\n    def __init__(self, control_model, global_average_pooling=False, device=None, load_device=None, manual_cast_dtype=None):\n        super().__init__(device)\n        self.control_model = control_model\n        self.load_device = load_device\n        self.control_model_wrapped = ldm_patched.modules.model_patcher.ModelPatcher(self.control_model, load_device=load_device, offload_device=ldm_patched.modules.model_management.unet_offload_device())\n        self.global_average_pooling = global_average_pooling\n        self.model_sampling_current = None\n        self.manual_cast_dtype = manual_cast_dtype\n\n    def get_control(self, x_noisy, t, cond, batched_number):\n        control_prev = None\n        if self.previous_controlnet is not None:\n            control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number)\n\n        if self.timestep_range is not None:\n            if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]:\n                if control_prev is not None:\n                    return control_prev\n                else:\n                    return None\n\n        dtype = self.control_model.dtype\n        if self.manual_cast_dtype is not None:\n            dtype = self.manual_cast_dtype\n\n        output_dtype = x_noisy.dtype\n        if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]:\n            if self.cond_hint is not None:\n                del self.cond_hint\n            self.cond_hint = None\n            self.cond_hint = ldm_patched.modules.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', \"center\").to(dtype).to(self.device)\n        if x_noisy.shape[0] != self.cond_hint.shape[0]:\n            self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)\n\n        context = cond['c_crossattn']\n        y = cond.get('y', None)\n        if y is not None:\n            y = y.to(dtype)\n        timestep = self.model_sampling_current.timestep(t)\n        x_noisy = self.model_sampling_current.calculate_input(t, x_noisy)\n\n        control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.float(), context=context.to(dtype), y=y)\n        return self.control_merge(None, control, control_prev, output_dtype)\n\n    def copy(self):\n        c = ControlNet(self.control_model, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype)\n        self.copy_to(c)\n        return c\n\n    def get_models(self):\n        out = super().get_models()\n        out.append(self.control_model_wrapped)\n        return out\n\n    def pre_run(self, model, percent_to_timestep_function):\n        super().pre_run(model, percent_to_timestep_function)\n        self.model_sampling_current = model.model_sampling\n\n    def cleanup(self):\n        self.model_sampling_current = None\n        super().cleanup()\n\nclass ControlLoraOps:\n    class Linear(torch.nn.Module):\n        def __init__(self, in_features: int, out_features: int, bias: bool = True,\n                    device=None, dtype=None) -> None:\n            factory_kwargs = {'device': device, 'dtype': dtype}\n            super().__init__()\n            self.in_features = in_features\n            self.out_features = out_features\n            self.weight = None\n            self.up = None\n            self.down = None\n            self.bias = None\n\n        def forward(self, input):\n            weight, bias = ldm_patched.modules.ops.cast_bias_weight(self, input)\n            if self.up is not None:\n                return torch.nn.functional.linear(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias)\n            else:\n                return torch.nn.functional.linear(input, weight, bias)\n\n    class Conv2d(torch.nn.Module):\n        def __init__(\n            self,\n            in_channels,\n            out_channels,\n            kernel_size,\n            stride=1,\n            padding=0,\n            dilation=1,\n            groups=1,\n            bias=True,\n            padding_mode='zeros',\n            device=None,\n            dtype=None\n        ):\n            super().__init__()\n            self.in_channels = in_channels\n            self.out_channels = out_channels\n            self.kernel_size = kernel_size\n            self.stride = stride\n            self.padding = padding\n            self.dilation = dilation\n            self.transposed = False\n            self.output_padding = 0\n            self.groups = groups\n            self.padding_mode = padding_mode\n\n            self.weight = None\n            self.bias = None\n            self.up = None\n            self.down = None\n\n\n        def forward(self, input):\n            weight, bias = ldm_patched.modules.ops.cast_bias_weight(self, input)\n            if self.up is not None:\n                return torch.nn.functional.conv2d(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias, self.stride, self.padding, self.dilation, self.groups)\n            else:\n                return torch.nn.functional.conv2d(input, weight, bias, self.stride, self.padding, self.dilation, self.groups)\n\n\nclass ControlLora(ControlNet):\n    def __init__(self, control_weights, global_average_pooling=False, device=None):\n        ControlBase.__init__(self, device)\n        self.control_weights = control_weights\n        self.global_average_pooling = global_average_pooling\n\n    def pre_run(self, model, percent_to_timestep_function):\n        super().pre_run(model, percent_to_timestep_function)\n        controlnet_config = model.model_config.unet_config.copy()\n        controlnet_config.pop(\"out_channels\")\n        controlnet_config[\"hint_channels\"] = self.control_weights[\"input_hint_block.0.weight\"].shape[1]\n        self.manual_cast_dtype = model.manual_cast_dtype\n        dtype = model.get_dtype()\n        if self.manual_cast_dtype is None:\n            class control_lora_ops(ControlLoraOps, ldm_patched.modules.ops.disable_weight_init):\n                pass\n        else:\n            class control_lora_ops(ControlLoraOps, ldm_patched.modules.ops.manual_cast):\n                pass\n            dtype = self.manual_cast_dtype\n\n        controlnet_config[\"operations\"] = control_lora_ops\n        controlnet_config[\"dtype\"] = dtype\n        self.control_model = ldm_patched.controlnet.cldm.ControlNet(**controlnet_config)\n        self.control_model.to(ldm_patched.modules.model_management.get_torch_device())\n        diffusion_model = model.diffusion_model\n        sd = diffusion_model.state_dict()\n        cm = self.control_model.state_dict()\n\n        for k in sd:\n            weight = sd[k]\n            try:\n                ldm_patched.modules.utils.set_attr(self.control_model, k, weight)\n            except:\n                pass\n\n        for k in self.control_weights:\n            if k not in {\"lora_controlnet\"}:\n                ldm_patched.modules.utils.set_attr(self.control_model, k, self.control_weights[k].to(dtype).to(ldm_patched.modules.model_management.get_torch_device()))\n\n    def copy(self):\n        c = ControlLora(self.control_weights, global_average_pooling=self.global_average_pooling)\n        self.copy_to(c)\n        return c\n\n    def cleanup(self):\n        del self.control_model\n        self.control_model = None\n        super().cleanup()\n\n    def get_models(self):\n        out = ControlBase.get_models(self)\n        return out\n\n    def inference_memory_requirements(self, dtype):\n        return ldm_patched.modules.utils.calculate_parameters(self.control_weights) * ldm_patched.modules.model_management.dtype_size(dtype) + ControlBase.inference_memory_requirements(self, dtype)\n\ndef load_controlnet(ckpt_path, model=None):\n    controlnet_data = ldm_patched.modules.utils.load_torch_file(ckpt_path, safe_load=True)\n    if \"lora_controlnet\" in controlnet_data:\n        return ControlLora(controlnet_data)\n\n    controlnet_config = None\n    if \"controlnet_cond_embedding.conv_in.weight\" in controlnet_data: #diffusers format\n        unet_dtype = ldm_patched.modules.model_management.unet_dtype()\n        controlnet_config = ldm_patched.modules.model_detection.unet_config_from_diffusers_unet(controlnet_data, unet_dtype)\n        diffusers_keys = ldm_patched.modules.utils.unet_to_diffusers(controlnet_config)\n        diffusers_keys[\"controlnet_mid_block.weight\"] = \"middle_block_out.0.weight\"\n        diffusers_keys[\"controlnet_mid_block.bias\"] = \"middle_block_out.0.bias\"\n\n        count = 0\n        loop = True\n        while loop:\n            suffix = [\".weight\", \".bias\"]\n            for s in suffix:\n                k_in = \"controlnet_down_blocks.{}{}\".format(count, s)\n                k_out = \"zero_convs.{}.0{}\".format(count, s)\n                if k_in not in controlnet_data:\n                    loop = False\n                    break\n                diffusers_keys[k_in] = k_out\n            count += 1\n\n        count = 0\n        loop = True\n        while loop:\n            suffix = [\".weight\", \".bias\"]\n            for s in suffix:\n                if count == 0:\n                    k_in = \"controlnet_cond_embedding.conv_in{}\".format(s)\n                else:\n                    k_in = \"controlnet_cond_embedding.blocks.{}{}\".format(count - 1, s)\n                k_out = \"input_hint_block.{}{}\".format(count * 2, s)\n                if k_in not in controlnet_data:\n                    k_in = \"controlnet_cond_embedding.conv_out{}\".format(s)\n                    loop = False\n                diffusers_keys[k_in] = k_out\n            count += 1\n\n        new_sd = {}\n        for k in diffusers_keys:\n            if k in controlnet_data:\n                new_sd[diffusers_keys[k]] = controlnet_data.pop(k)\n\n        leftover_keys = controlnet_data.keys()\n        if len(leftover_keys) > 0:\n            print(\"leftover keys:\", leftover_keys)\n        controlnet_data = new_sd\n\n    pth_key = 'control_model.zero_convs.0.0.weight'\n    pth = False\n    key = 'zero_convs.0.0.weight'\n    if pth_key in controlnet_data:\n        pth = True\n        key = pth_key\n        prefix = \"control_model.\"\n    elif key in controlnet_data:\n        prefix = \"\"\n    else:\n        net = load_t2i_adapter(controlnet_data)\n        if net is None:\n            print(\"error checkpoint does not contain controlnet or t2i adapter data\", ckpt_path)\n        return net\n\n    if controlnet_config is None:\n        unet_dtype = ldm_patched.modules.model_management.unet_dtype()\n        controlnet_config = ldm_patched.modules.model_detection.model_config_from_unet(controlnet_data, prefix, unet_dtype, True).unet_config\n    load_device = ldm_patched.modules.model_management.get_torch_device()\n    manual_cast_dtype = ldm_patched.modules.model_management.unet_manual_cast(unet_dtype, load_device)\n    if manual_cast_dtype is not None:\n        controlnet_config[\"operations\"] = ldm_patched.modules.ops.manual_cast\n    controlnet_config.pop(\"out_channels\")\n    controlnet_config[\"hint_channels\"] = controlnet_data[\"{}input_hint_block.0.weight\".format(prefix)].shape[1]\n    control_model = ldm_patched.controlnet.cldm.ControlNet(**controlnet_config)\n\n    if pth:\n        if 'difference' in controlnet_data:\n            if model is not None:\n                ldm_patched.modules.model_management.load_models_gpu([model])\n                model_sd = model.model_state_dict()\n                for x in controlnet_data:\n                    c_m = \"control_model.\"\n                    if x.startswith(c_m):\n                        sd_key = \"diffusion_model.{}\".format(x[len(c_m):])\n                        if sd_key in model_sd:\n                            cd = controlnet_data[x]\n                            cd += model_sd[sd_key].type(cd.dtype).to(cd.device)\n            else:\n                print(\"WARNING: Loaded a diff controlnet without a model. It will very likely not work.\")\n\n        class WeightsLoader(torch.nn.Module):\n            pass\n        w = WeightsLoader()\n        w.control_model = control_model\n        missing, unexpected = w.load_state_dict(controlnet_data, strict=False)\n    else:\n        missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False)\n    print(missing, unexpected)\n\n    global_average_pooling = False\n    filename = os.path.splitext(ckpt_path)[0]\n    if filename.endswith(\"_shuffle\") or filename.endswith(\"_shuffle_fp16\"): #TODO: smarter way of enabling global_average_pooling\n        global_average_pooling = True\n\n    control = ControlNet(control_model, global_average_pooling=global_average_pooling, load_device=load_device, manual_cast_dtype=manual_cast_dtype)\n    return control\n\nclass T2IAdapter(ControlBase):\n    def __init__(self, t2i_model, channels_in, device=None):\n        super().__init__(device)\n        self.t2i_model = t2i_model\n        self.channels_in = channels_in\n        self.control_input = None\n\n    def scale_image_to(self, width, height):\n        unshuffle_amount = self.t2i_model.unshuffle_amount\n        width = math.ceil(width / unshuffle_amount) * unshuffle_amount\n        height = math.ceil(height / unshuffle_amount) * unshuffle_amount\n        return width, height\n\n    def get_control(self, x_noisy, t, cond, batched_number):\n        control_prev = None\n        if self.previous_controlnet is not None:\n            control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number)\n\n        if self.timestep_range is not None:\n            if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]:\n                if control_prev is not None:\n                    return control_prev\n                else:\n                    return None\n\n        if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]:\n            if self.cond_hint is not None:\n                del self.cond_hint\n            self.control_input = None\n            self.cond_hint = None\n            width, height = self.scale_image_to(x_noisy.shape[3] * 8, x_noisy.shape[2] * 8)\n            self.cond_hint = ldm_patched.modules.utils.common_upscale(self.cond_hint_original, width, height, 'nearest-exact', \"center\").float().to(self.device)\n            if self.channels_in == 1 and self.cond_hint.shape[1] > 1:\n                self.cond_hint = torch.mean(self.cond_hint, 1, keepdim=True)\n        if x_noisy.shape[0] != self.cond_hint.shape[0]:\n            self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)\n        if self.control_input is None:\n            self.t2i_model.to(x_noisy.dtype)\n            self.t2i_model.to(self.device)\n            self.control_input = self.t2i_model(self.cond_hint.to(x_noisy.dtype))\n            self.t2i_model.cpu()\n\n        control_input = list(map(lambda a: None if a is None else a.clone(), self.control_input))\n        mid = None\n        if self.t2i_model.xl == True:\n            mid = control_input[-1:]\n            control_input = control_input[:-1]\n        return self.control_merge(control_input, mid, control_prev, x_noisy.dtype)\n\n    def copy(self):\n        c = T2IAdapter(self.t2i_model, self.channels_in)\n        self.copy_to(c)\n        return c\n\ndef load_t2i_adapter(t2i_data):\n    if 'adapter' in t2i_data:\n        t2i_data = t2i_data['adapter']\n    if 'adapter.body.0.resnets.0.block1.weight' in t2i_data: #diffusers format\n        prefix_replace = {}\n        for i in range(4):\n            for j in range(2):\n                prefix_replace[\"adapter.body.{}.resnets.{}.\".format(i, j)] = \"body.{}.\".format(i * 2 + j)\n            prefix_replace[\"adapter.body.{}.\".format(i, j)] = \"body.{}.\".format(i * 2)\n        prefix_replace[\"adapter.\"] = \"\"\n        t2i_data = ldm_patched.modules.utils.state_dict_prefix_replace(t2i_data, prefix_replace)\n    keys = t2i_data.keys()\n\n    if \"body.0.in_conv.weight\" in keys:\n        cin = t2i_data['body.0.in_conv.weight'].shape[1]\n        model_ad = ldm_patched.t2ia.adapter.Adapter_light(cin=cin, channels=[320, 640, 1280, 1280], nums_rb=4)\n    elif 'conv_in.weight' in keys:\n        cin = t2i_data['conv_in.weight'].shape[1]\n        channel = t2i_data['conv_in.weight'].shape[0]\n        ksize = t2i_data['body.0.block2.weight'].shape[2]\n        use_conv = False\n        down_opts = list(filter(lambda a: a.endswith(\"down_opt.op.weight\"), keys))\n        if len(down_opts) > 0:\n            use_conv = True\n        xl = False\n        if cin == 256 or cin == 768:\n            xl = True\n        model_ad = ldm_patched.t2ia.adapter.Adapter(cin=cin, channels=[channel, channel*2, channel*4, channel*4][:4], nums_rb=2, ksize=ksize, sk=True, use_conv=use_conv, xl=xl)\n    else:\n        return None\n    missing, unexpected = model_ad.load_state_dict(t2i_data)\n    if len(missing) > 0:\n        print(\"t2i missing\", missing)\n\n    if len(unexpected) > 0:\n        print(\"t2i unexpected\", unexpected)\n\n    return T2IAdapter(model_ad, model_ad.input_channels)\n"
  },
  {
    "path": "ldm_patched/modules/diffusers_convert.py",
    "content": "import re\nimport torch\n\n# conversion code from https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_stable_diffusion.py\n\n# =================#\n# UNet Conversion #\n# =================#\n\nunet_conversion_map = [\n    # (stable-diffusion, HF Diffusers)\n    (\"time_embed.0.weight\", \"time_embedding.linear_1.weight\"),\n    (\"time_embed.0.bias\", \"time_embedding.linear_1.bias\"),\n    (\"time_embed.2.weight\", \"time_embedding.linear_2.weight\"),\n    (\"time_embed.2.bias\", \"time_embedding.linear_2.bias\"),\n    (\"input_blocks.0.0.weight\", \"conv_in.weight\"),\n    (\"input_blocks.0.0.bias\", \"conv_in.bias\"),\n    (\"out.0.weight\", \"conv_norm_out.weight\"),\n    (\"out.0.bias\", \"conv_norm_out.bias\"),\n    (\"out.2.weight\", \"conv_out.weight\"),\n    (\"out.2.bias\", \"conv_out.bias\"),\n]\n\nunet_conversion_map_resnet = [\n    # (stable-diffusion, HF Diffusers)\n    (\"in_layers.0\", \"norm1\"),\n    (\"in_layers.2\", \"conv1\"),\n    (\"out_layers.0\", \"norm2\"),\n    (\"out_layers.3\", \"conv2\"),\n    (\"emb_layers.1\", \"time_emb_proj\"),\n    (\"skip_connection\", \"conv_shortcut\"),\n]\n\nunet_conversion_map_layer = []\n# hardcoded number of downblocks and resnets/attentions...\n# would need smarter logic for other networks.\nfor i in range(4):\n    # loop over downblocks/upblocks\n\n    for j in range(2):\n        # loop over resnets/attentions for downblocks\n        hf_down_res_prefix = f\"down_blocks.{i}.resnets.{j}.\"\n        sd_down_res_prefix = f\"input_blocks.{3 * i + j + 1}.0.\"\n        unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))\n\n        if i < 3:\n            # no attention layers in down_blocks.3\n            hf_down_atn_prefix = f\"down_blocks.{i}.attentions.{j}.\"\n            sd_down_atn_prefix = f\"input_blocks.{3 * i + j + 1}.1.\"\n            unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))\n\n    for j in range(3):\n        # loop over resnets/attentions for upblocks\n        hf_up_res_prefix = f\"up_blocks.{i}.resnets.{j}.\"\n        sd_up_res_prefix = f\"output_blocks.{3 * i + j}.0.\"\n        unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))\n\n        if i > 0:\n            # no attention layers in up_blocks.0\n            hf_up_atn_prefix = f\"up_blocks.{i}.attentions.{j}.\"\n            sd_up_atn_prefix = f\"output_blocks.{3 * i + j}.1.\"\n            unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))\n\n    if i < 3:\n        # no downsample in down_blocks.3\n        hf_downsample_prefix = f\"down_blocks.{i}.downsamplers.0.conv.\"\n        sd_downsample_prefix = f\"input_blocks.{3 * (i + 1)}.0.op.\"\n        unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))\n\n        # no upsample in up_blocks.3\n        hf_upsample_prefix = f\"up_blocks.{i}.upsamplers.0.\"\n        sd_upsample_prefix = f\"output_blocks.{3 * i + 2}.{1 if i == 0 else 2}.\"\n        unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))\n\nhf_mid_atn_prefix = \"mid_block.attentions.0.\"\nsd_mid_atn_prefix = \"middle_block.1.\"\nunet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))\n\nfor j in range(2):\n    hf_mid_res_prefix = f\"mid_block.resnets.{j}.\"\n    sd_mid_res_prefix = f\"middle_block.{2 * j}.\"\n    unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))\n\n\ndef convert_unet_state_dict(unet_state_dict):\n    # buyer beware: this is a *brittle* function,\n    # and correct output requires that all of these pieces interact in\n    # the exact order in which I have arranged them.\n    mapping = {k: k for k in unet_state_dict.keys()}\n    for sd_name, hf_name in unet_conversion_map:\n        mapping[hf_name] = sd_name\n    for k, v in mapping.items():\n        if \"resnets\" in k:\n            for sd_part, hf_part in unet_conversion_map_resnet:\n                v = v.replace(hf_part, sd_part)\n            mapping[k] = v\n    for k, v in mapping.items():\n        for sd_part, hf_part in unet_conversion_map_layer:\n            v = v.replace(hf_part, sd_part)\n        mapping[k] = v\n    new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()}\n    return new_state_dict\n\n\n# ================#\n# VAE Conversion #\n# ================#\n\nvae_conversion_map = [\n    # (stable-diffusion, HF Diffusers)\n    (\"nin_shortcut\", \"conv_shortcut\"),\n    (\"norm_out\", \"conv_norm_out\"),\n    (\"mid.attn_1.\", \"mid_block.attentions.0.\"),\n]\n\nfor i in range(4):\n    # down_blocks have two resnets\n    for j in range(2):\n        hf_down_prefix = f\"encoder.down_blocks.{i}.resnets.{j}.\"\n        sd_down_prefix = f\"encoder.down.{i}.block.{j}.\"\n        vae_conversion_map.append((sd_down_prefix, hf_down_prefix))\n\n    if i < 3:\n        hf_downsample_prefix = f\"down_blocks.{i}.downsamplers.0.\"\n        sd_downsample_prefix = f\"down.{i}.downsample.\"\n        vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))\n\n        hf_upsample_prefix = f\"up_blocks.{i}.upsamplers.0.\"\n        sd_upsample_prefix = f\"up.{3 - i}.upsample.\"\n        vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))\n\n    # up_blocks have three resnets\n    # also, up blocks in hf are numbered in reverse from sd\n    for j in range(3):\n        hf_up_prefix = f\"decoder.up_blocks.{i}.resnets.{j}.\"\n        sd_up_prefix = f\"decoder.up.{3 - i}.block.{j}.\"\n        vae_conversion_map.append((sd_up_prefix, hf_up_prefix))\n\n# this part accounts for mid blocks in both the encoder and the decoder\nfor i in range(2):\n    hf_mid_res_prefix = f\"mid_block.resnets.{i}.\"\n    sd_mid_res_prefix = f\"mid.block_{i + 1}.\"\n    vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))\n\nvae_conversion_map_attn = [\n    # (stable-diffusion, HF Diffusers)\n    (\"norm.\", \"group_norm.\"),\n    (\"q.\", \"query.\"),\n    (\"k.\", \"key.\"),\n    (\"v.\", \"value.\"),\n    (\"q.\", \"to_q.\"),\n    (\"k.\", \"to_k.\"),\n    (\"v.\", \"to_v.\"),\n    (\"proj_out.\", \"to_out.0.\"),\n    (\"proj_out.\", \"proj_attn.\"),\n]\n\n\ndef reshape_weight_for_sd(w):\n    # convert HF linear weights to SD conv2d weights\n    return w.reshape(*w.shape, 1, 1)\n\n\ndef convert_vae_state_dict(vae_state_dict):\n    mapping = {k: k for k in vae_state_dict.keys()}\n    for k, v in mapping.items():\n        for sd_part, hf_part in vae_conversion_map:\n            v = v.replace(hf_part, sd_part)\n        mapping[k] = v\n    for k, v in mapping.items():\n        if \"attentions\" in k:\n            for sd_part, hf_part in vae_conversion_map_attn:\n                v = v.replace(hf_part, sd_part)\n            mapping[k] = v\n    new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()}\n    weights_to_convert = [\"q\", \"k\", \"v\", \"proj_out\"]\n    for k, v in new_state_dict.items():\n        for weight_name in weights_to_convert:\n            if f\"mid.attn_1.{weight_name}.weight\" in k:\n                print(f\"Reshaping {k} for SD format\")\n                new_state_dict[k] = reshape_weight_for_sd(v)\n    return new_state_dict\n\n\n# =========================#\n# Text Encoder Conversion #\n# =========================#\n\n\ntextenc_conversion_lst = [\n    # (stable-diffusion, HF Diffusers)\n    (\"resblocks.\", \"text_model.encoder.layers.\"),\n    (\"ln_1\", \"layer_norm1\"),\n    (\"ln_2\", \"layer_norm2\"),\n    (\".c_fc.\", \".fc1.\"),\n    (\".c_proj.\", \".fc2.\"),\n    (\".attn\", \".self_attn\"),\n    (\"ln_final.\", \"transformer.text_model.final_layer_norm.\"),\n    (\"token_embedding.weight\", \"transformer.text_model.embeddings.token_embedding.weight\"),\n    (\"positional_embedding\", \"transformer.text_model.embeddings.position_embedding.weight\"),\n]\nprotected = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}\ntextenc_pattern = re.compile(\"|\".join(protected.keys()))\n\n# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp\ncode2idx = {\"q\": 0, \"k\": 1, \"v\": 2}\n\n\ndef convert_text_enc_state_dict_v20(text_enc_dict, prefix=\"\"):\n    new_state_dict = {}\n    capture_qkv_weight = {}\n    capture_qkv_bias = {}\n    for k, v in text_enc_dict.items():\n        if not k.startswith(prefix):\n            continue\n        if (\n                k.endswith(\".self_attn.q_proj.weight\")\n                or k.endswith(\".self_attn.k_proj.weight\")\n                or k.endswith(\".self_attn.v_proj.weight\")\n        ):\n            k_pre = k[: -len(\".q_proj.weight\")]\n            k_code = k[-len(\"q_proj.weight\")]\n            if k_pre not in capture_qkv_weight:\n                capture_qkv_weight[k_pre] = [None, None, None]\n            capture_qkv_weight[k_pre][code2idx[k_code]] = v\n            continue\n\n        if (\n                k.endswith(\".self_attn.q_proj.bias\")\n                or k.endswith(\".self_attn.k_proj.bias\")\n                or k.endswith(\".self_attn.v_proj.bias\")\n        ):\n            k_pre = k[: -len(\".q_proj.bias\")]\n            k_code = k[-len(\"q_proj.bias\")]\n            if k_pre not in capture_qkv_bias:\n                capture_qkv_bias[k_pre] = [None, None, None]\n            capture_qkv_bias[k_pre][code2idx[k_code]] = v\n            continue\n\n        relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k)\n        new_state_dict[relabelled_key] = v\n\n    for k_pre, tensors in capture_qkv_weight.items():\n        if None in tensors:\n            raise Exception(\"CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing\")\n        relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre)\n        new_state_dict[relabelled_key + \".in_proj_weight\"] = torch.cat(tensors)\n\n    for k_pre, tensors in capture_qkv_bias.items():\n        if None in tensors:\n            raise Exception(\"CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing\")\n        relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre)\n        new_state_dict[relabelled_key + \".in_proj_bias\"] = torch.cat(tensors)\n\n    return new_state_dict\n\n\ndef convert_text_enc_state_dict(text_enc_dict):\n    return text_enc_dict\n\n\n"
  },
  {
    "path": "ldm_patched/modules/diffusers_load.py",
    "content": "import os\n\nimport ldm_patched.modules.sd\n\ndef first_file(path, filenames):\n    for f in filenames:\n        p = os.path.join(path, f)\n        if os.path.exists(p):\n            return p\n    return None\n\ndef load_diffusers(model_path, output_vae=True, output_clip=True, embedding_directory=None):\n    diffusion_model_names = [\"diffusion_pytorch_model.fp16.safetensors\", \"diffusion_pytorch_model.safetensors\", \"diffusion_pytorch_model.fp16.bin\", \"diffusion_pytorch_model.bin\"]\n    unet_path = first_file(os.path.join(model_path, \"unet\"), diffusion_model_names)\n    vae_path = first_file(os.path.join(model_path, \"vae\"), diffusion_model_names)\n\n    text_encoder_model_names = [\"model.fp16.safetensors\", \"model.safetensors\", \"pytorch_model.fp16.bin\", \"pytorch_model.bin\"]\n    text_encoder1_path = first_file(os.path.join(model_path, \"text_encoder\"), text_encoder_model_names)\n    text_encoder2_path = first_file(os.path.join(model_path, \"text_encoder_2\"), text_encoder_model_names)\n\n    text_encoder_paths = [text_encoder1_path]\n    if text_encoder2_path is not None:\n        text_encoder_paths.append(text_encoder2_path)\n\n    unet = ldm_patched.modules.sd.load_unet(unet_path)\n\n    clip = None\n    if output_clip:\n        clip = ldm_patched.modules.sd.load_clip(text_encoder_paths, embedding_directory=embedding_directory)\n\n    vae = None\n    if output_vae:\n        sd = ldm_patched.modules.utils.load_torch_file(vae_path)\n        vae = ldm_patched.modules.sd.VAE(sd=sd)\n\n    return (unet, clip, vae)\n"
  },
  {
    "path": "ldm_patched/modules/gligen.py",
    "content": "import torch\nfrom torch import nn\nfrom ldm_patched.ldm.modules.attention import CrossAttention\nfrom inspect import isfunction\n\n\ndef exists(val):\n    return val is not None\n\n\ndef uniq(arr):\n    return{el: True for el in arr}.keys()\n\n\ndef default(val, d):\n    if exists(val):\n        return val\n    return d() if isfunction(d) else d\n\n\n# feedforward\nclass GEGLU(nn.Module):\n    def __init__(self, dim_in, dim_out):\n        super().__init__()\n        self.proj = nn.Linear(dim_in, dim_out * 2)\n\n    def forward(self, x):\n        x, gate = self.proj(x).chunk(2, dim=-1)\n        return x * torch.nn.functional.gelu(gate)\n\n\nclass FeedForward(nn.Module):\n    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):\n        super().__init__()\n        inner_dim = int(dim * mult)\n        dim_out = default(dim_out, dim)\n        project_in = nn.Sequential(\n            nn.Linear(dim, inner_dim),\n            nn.GELU()\n        ) if not glu else GEGLU(dim, inner_dim)\n\n        self.net = nn.Sequential(\n            project_in,\n            nn.Dropout(dropout),\n            nn.Linear(inner_dim, dim_out)\n        )\n\n    def forward(self, x):\n        return self.net(x)\n\n\nclass GatedCrossAttentionDense(nn.Module):\n    def __init__(self, query_dim, context_dim, n_heads, d_head):\n        super().__init__()\n\n        self.attn = CrossAttention(\n            query_dim=query_dim,\n            context_dim=context_dim,\n            heads=n_heads,\n            dim_head=d_head)\n        self.ff = FeedForward(query_dim, glu=True)\n\n        self.norm1 = nn.LayerNorm(query_dim)\n        self.norm2 = nn.LayerNorm(query_dim)\n\n        self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.)))\n        self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.)))\n\n        # this can be useful: we can externally change magnitude of tanh(alpha)\n        # for example, when it is set to 0, then the entire model is same as\n        # original one\n        self.scale = 1\n\n    def forward(self, x, objs):\n\n        x = x + self.scale * \\\n            torch.tanh(self.alpha_attn) * self.attn(self.norm1(x), objs, objs)\n        x = x + self.scale * \\\n            torch.tanh(self.alpha_dense) * self.ff(self.norm2(x))\n\n        return x\n\n\nclass GatedSelfAttentionDense(nn.Module):\n    def __init__(self, query_dim, context_dim, n_heads, d_head):\n        super().__init__()\n\n        # we need a linear projection since we need cat visual feature and obj\n        # feature\n        self.linear = nn.Linear(context_dim, query_dim)\n\n        self.attn = CrossAttention(\n            query_dim=query_dim,\n            context_dim=query_dim,\n            heads=n_heads,\n            dim_head=d_head)\n        self.ff = FeedForward(query_dim, glu=True)\n\n        self.norm1 = nn.LayerNorm(query_dim)\n        self.norm2 = nn.LayerNorm(query_dim)\n\n        self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.)))\n        self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.)))\n\n        # this can be useful: we can externally change magnitude of tanh(alpha)\n        # for example, when it is set to 0, then the entire model is same as\n        # original one\n        self.scale = 1\n\n    def forward(self, x, objs):\n\n        N_visual = x.shape[1]\n        objs = self.linear(objs)\n\n        x = x + self.scale * torch.tanh(self.alpha_attn) * self.attn(\n            self.norm1(torch.cat([x, objs], dim=1)))[:, 0:N_visual, :]\n        x = x + self.scale * \\\n            torch.tanh(self.alpha_dense) * self.ff(self.norm2(x))\n\n        return x\n\n\nclass GatedSelfAttentionDense2(nn.Module):\n    def __init__(self, query_dim, context_dim, n_heads, d_head):\n        super().__init__()\n\n        # we need a linear projection since we need cat visual feature and obj\n        # feature\n        self.linear = nn.Linear(context_dim, query_dim)\n\n        self.attn = CrossAttention(\n            query_dim=query_dim, context_dim=query_dim, dim_head=d_head)\n        self.ff = FeedForward(query_dim, glu=True)\n\n        self.norm1 = nn.LayerNorm(query_dim)\n        self.norm2 = nn.LayerNorm(query_dim)\n\n        self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.)))\n        self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.)))\n\n        # this can be useful: we can externally change magnitude of tanh(alpha)\n        # for example, when it is set to 0, then the entire model is same as\n        # original one\n        self.scale = 1\n\n    def forward(self, x, objs):\n\n        B, N_visual, _ = x.shape\n        B, N_ground, _ = objs.shape\n\n        objs = self.linear(objs)\n\n        # sanity check\n        size_v = math.sqrt(N_visual)\n        size_g = math.sqrt(N_ground)\n        assert int(size_v) == size_v, \"Visual tokens must be square rootable\"\n        assert int(size_g) == size_g, \"Grounding tokens must be square rootable\"\n        size_v = int(size_v)\n        size_g = int(size_g)\n\n        # select grounding token and resize it to visual token size as residual\n        out = self.attn(self.norm1(torch.cat([x, objs], dim=1)))[\n            :, N_visual:, :]\n        out = out.permute(0, 2, 1).reshape(B, -1, size_g, size_g)\n        out = torch.nn.functional.interpolate(\n            out, (size_v, size_v), mode='bicubic')\n        residual = out.reshape(B, -1, N_visual).permute(0, 2, 1)\n\n        # add residual to visual feature\n        x = x + self.scale * torch.tanh(self.alpha_attn) * residual\n        x = x + self.scale * \\\n            torch.tanh(self.alpha_dense) * self.ff(self.norm2(x))\n\n        return x\n\n\nclass FourierEmbedder():\n    def __init__(self, num_freqs=64, temperature=100):\n\n        self.num_freqs = num_freqs\n        self.temperature = temperature\n        self.freq_bands = temperature ** (torch.arange(num_freqs) / num_freqs)\n\n    @torch.no_grad()\n    def __call__(self, x, cat_dim=-1):\n        \"x: arbitrary shape of tensor. dim: cat dim\"\n        out = []\n        for freq in self.freq_bands:\n            out.append(torch.sin(freq * x))\n            out.append(torch.cos(freq * x))\n        return torch.cat(out, cat_dim)\n\n\nclass PositionNet(nn.Module):\n    def __init__(self, in_dim, out_dim, fourier_freqs=8):\n        super().__init__()\n        self.in_dim = in_dim\n        self.out_dim = out_dim\n\n        self.fourier_embedder = FourierEmbedder(num_freqs=fourier_freqs)\n        self.position_dim = fourier_freqs * 2 * 4  # 2 is sin&cos, 4 is xyxy\n\n        self.linears = nn.Sequential(\n            nn.Linear(self.in_dim + self.position_dim, 512),\n            nn.SiLU(),\n            nn.Linear(512, 512),\n            nn.SiLU(),\n            nn.Linear(512, out_dim),\n        )\n\n        self.null_positive_feature = torch.nn.Parameter(\n            torch.zeros([self.in_dim]))\n        self.null_position_feature = torch.nn.Parameter(\n            torch.zeros([self.position_dim]))\n\n    def forward(self, boxes, masks, positive_embeddings):\n        B, N, _ = boxes.shape\n        dtype = self.linears[0].weight.dtype\n        masks = masks.unsqueeze(-1).to(dtype)\n        positive_embeddings = positive_embeddings.to(dtype)\n\n        # embedding position (it may includes padding as placeholder)\n        xyxy_embedding = self.fourier_embedder(boxes.to(dtype))  # B*N*4 --> B*N*C\n\n        # learnable null embedding\n        positive_null = self.null_positive_feature.view(1, 1, -1)\n        xyxy_null = self.null_position_feature.view(1, 1, -1)\n\n        # replace padding with learnable null embedding\n        positive_embeddings = positive_embeddings * \\\n            masks + (1 - masks) * positive_null\n        xyxy_embedding = xyxy_embedding * masks + (1 - masks) * xyxy_null\n\n        objs = self.linears(\n            torch.cat([positive_embeddings, xyxy_embedding], dim=-1))\n        assert objs.shape == torch.Size([B, N, self.out_dim])\n        return objs\n\n\nclass Gligen(nn.Module):\n    def __init__(self, modules, position_net, key_dim):\n        super().__init__()\n        self.module_list = nn.ModuleList(modules)\n        self.position_net = position_net\n        self.key_dim = key_dim\n        self.max_objs = 30\n        self.current_device = torch.device(\"cpu\")\n\n    def _set_position(self, boxes, masks, positive_embeddings):\n        objs = self.position_net(boxes, masks, positive_embeddings)\n        def func(x, extra_options):\n            key = extra_options[\"transformer_index\"]\n            module = self.module_list[key]\n            return module(x, objs)\n        return func\n\n    def set_position(self, latent_image_shape, position_params, device):\n        batch, c, h, w = latent_image_shape\n        masks = torch.zeros([self.max_objs], device=\"cpu\")\n        boxes = []\n        positive_embeddings = []\n        for p in position_params:\n            x1 = (p[4]) / w\n            y1 = (p[3]) / h\n            x2 = (p[4] + p[2]) / w\n            y2 = (p[3] + p[1]) / h\n            masks[len(boxes)] = 1.0\n            boxes += [torch.tensor((x1, y1, x2, y2)).unsqueeze(0)]\n            positive_embeddings += [p[0]]\n        append_boxes = []\n        append_conds = []\n        if len(boxes) < self.max_objs:\n            append_boxes = [torch.zeros(\n                [self.max_objs - len(boxes), 4], device=\"cpu\")]\n            append_conds = [torch.zeros(\n                [self.max_objs - len(boxes), self.key_dim], device=\"cpu\")]\n\n        box_out = torch.cat(\n            boxes + append_boxes).unsqueeze(0).repeat(batch, 1, 1)\n        masks = masks.unsqueeze(0).repeat(batch, 1)\n        conds = torch.cat(positive_embeddings +\n                          append_conds).unsqueeze(0).repeat(batch, 1, 1)\n        return self._set_position(\n            box_out.to(device),\n            masks.to(device),\n            conds.to(device))\n\n    def set_empty(self, latent_image_shape, device):\n        batch, c, h, w = latent_image_shape\n        masks = torch.zeros([self.max_objs], device=\"cpu\").repeat(batch, 1)\n        box_out = torch.zeros([self.max_objs, 4],\n                              device=\"cpu\").repeat(batch, 1, 1)\n        conds = torch.zeros([self.max_objs, self.key_dim],\n                            device=\"cpu\").repeat(batch, 1, 1)\n        return self._set_position(\n            box_out.to(device),\n            masks.to(device),\n            conds.to(device))\n\n\ndef load_gligen(sd):\n    sd_k = sd.keys()\n    output_list = []\n    key_dim = 768\n    for a in [\"input_blocks\", \"middle_block\", \"output_blocks\"]:\n        for b in range(20):\n            k_temp = filter(lambda k: \"{}.{}.\".format(a, b)\n                            in k and \".fuser.\" in k, sd_k)\n            k_temp = map(lambda k: (k, k.split(\".fuser.\")[-1]), k_temp)\n\n            n_sd = {}\n            for k in k_temp:\n                n_sd[k[1]] = sd[k[0]]\n            if len(n_sd) > 0:\n                query_dim = n_sd[\"linear.weight\"].shape[0]\n                key_dim = n_sd[\"linear.weight\"].shape[1]\n\n                if key_dim == 768:  # SD1.x\n                    n_heads = 8\n                    d_head = query_dim // n_heads\n                else:\n                    d_head = 64\n                    n_heads = query_dim // d_head\n\n                gated = GatedSelfAttentionDense(\n                    query_dim, key_dim, n_heads, d_head)\n                gated.load_state_dict(n_sd, strict=False)\n                output_list.append(gated)\n\n    if \"position_net.null_positive_feature\" in sd_k:\n        in_dim = sd[\"position_net.null_positive_feature\"].shape[0]\n        out_dim = sd[\"position_net.linears.4.weight\"].shape[0]\n\n        class WeightsLoader(torch.nn.Module):\n            pass\n        w = WeightsLoader()\n        w.position_net = PositionNet(in_dim, out_dim)\n        w.load_state_dict(sd, strict=False)\n\n    gligen = Gligen(output_list, w.position_net, key_dim)\n    return gligen\n"
  },
  {
    "path": "ldm_patched/modules/latent_formats.py",
    "content": "import torch\n\nclass LatentFormat:\n    scale_factor = 1.0\n    latent_rgb_factors = None\n    taesd_decoder_name = None\n\n    def process_in(self, latent):\n        return latent * self.scale_factor\n\n    def process_out(self, latent):\n        return latent / self.scale_factor\n\nclass SD15(LatentFormat):\n    def __init__(self, scale_factor=0.18215):\n        self.scale_factor = scale_factor\n        self.latent_rgb_factors = [\n                    #   R        G        B\n                    [ 0.3512,  0.2297,  0.3227],\n                    [ 0.3250,  0.4974,  0.2350],\n                    [-0.2829,  0.1762,  0.2721],\n                    [-0.2120, -0.2616, -0.7177]\n                ]\n        self.taesd_decoder_name = \"taesd_decoder\"\n\nclass SDXL(LatentFormat):\n    def __init__(self):\n        self.scale_factor = 0.13025\n        self.latent_rgb_factors = [\n                    #   R        G        B\n                    [ 0.3920,  0.4054,  0.4549],\n                    [-0.2634, -0.0196,  0.0653],\n                    [ 0.0568,  0.1687, -0.0755],\n                    [-0.3112, -0.2359, -0.2076]\n                ]\n        self.taesd_decoder_name = \"taesdxl_decoder\"\n\nclass SDXL_Playground_2_5(LatentFormat):\n    def __init__(self):\n        self.scale_factor = 0.5\n        self.latents_mean = torch.tensor([-1.6574, 1.886, -1.383, 2.5155]).view(1, 4, 1, 1)\n        self.latents_std = torch.tensor([8.4927, 5.9022, 6.5498, 5.2299]).view(1, 4, 1, 1)\n\n        self.latent_rgb_factors = [\n                    #   R        G        B\n                    [ 0.3920,  0.4054,  0.4549],\n                    [-0.2634, -0.0196,  0.0653],\n                    [ 0.0568,  0.1687, -0.0755],\n                    [-0.3112, -0.2359, -0.2076]\n                ]\n        self.taesd_decoder_name = \"taesdxl_decoder\"\n\n    def process_in(self, latent):\n        latents_mean = self.latents_mean.to(latent.device, latent.dtype)\n        latents_std = self.latents_std.to(latent.device, latent.dtype)\n        return (latent - latents_mean) * self.scale_factor / latents_std\n\n    def process_out(self, latent):\n        latents_mean = self.latents_mean.to(latent.device, latent.dtype)\n        latents_std = self.latents_std.to(latent.device, latent.dtype)\n        return latent * latents_std / self.scale_factor + latents_mean\n\n\nclass SD_X4(LatentFormat):\n    def __init__(self):\n        self.scale_factor = 0.08333\n        self.latent_rgb_factors = [\n            [-0.2340, -0.3863, -0.3257],\n            [ 0.0994,  0.0885, -0.0908],\n            [-0.2833, -0.2349, -0.3741],\n            [ 0.2523, -0.0055, -0.1651]\n        ]\n\nclass SC_Prior(LatentFormat):\n    def __init__(self):\n        self.scale_factor = 1.0\n        self.latent_rgb_factors = [\n            [-0.0326, -0.0204, -0.0127],\n            [-0.1592, -0.0427,  0.0216],\n            [ 0.0873,  0.0638, -0.0020],\n            [-0.0602,  0.0442,  0.1304],\n            [ 0.0800, -0.0313, -0.1796],\n            [-0.0810, -0.0638, -0.1581],\n            [ 0.1791,  0.1180,  0.0967],\n            [ 0.0740,  0.1416,  0.0432],\n            [-0.1745, -0.1888, -0.1373],\n            [ 0.2412,  0.1577,  0.0928],\n            [ 0.1908,  0.0998,  0.0682],\n            [ 0.0209,  0.0365, -0.0092],\n            [ 0.0448, -0.0650, -0.1728],\n            [-0.1658, -0.1045, -0.1308],\n            [ 0.0542,  0.1545,  0.1325],\n            [-0.0352, -0.1672, -0.2541]\n        ]\n\nclass SC_B(LatentFormat):\n    def __init__(self):\n        self.scale_factor = 1.0 / 0.43\n        self.latent_rgb_factors = [\n            [ 0.1121,  0.2006,  0.1023],\n            [-0.2093, -0.0222, -0.0195],\n            [-0.3087, -0.1535,  0.0366],\n            [ 0.0290, -0.1574, -0.4078]\n        ]"
  },
  {
    "path": "ldm_patched/modules/lora.py",
    "content": "import ldm_patched.modules.utils\n\nLORA_CLIP_MAP = {\n    \"mlp.fc1\": \"mlp_fc1\",\n    \"mlp.fc2\": \"mlp_fc2\",\n    \"self_attn.k_proj\": \"self_attn_k_proj\",\n    \"self_attn.q_proj\": \"self_attn_q_proj\",\n    \"self_attn.v_proj\": \"self_attn_v_proj\",\n    \"self_attn.out_proj\": \"self_attn_out_proj\",\n}\n\n\ndef load_lora(lora, to_load):\n    patch_dict = {}\n    loaded_keys = set()\n    for x in to_load:\n        alpha_name = \"{}.alpha\".format(x)\n        alpha = None\n        if alpha_name in lora.keys():\n            alpha = lora[alpha_name].item()\n            loaded_keys.add(alpha_name)\n\n        regular_lora = \"{}.lora_up.weight\".format(x)\n        diffusers_lora = \"{}_lora.up.weight\".format(x)\n        transformers_lora = \"{}.lora_linear_layer.up.weight\".format(x)\n        A_name = None\n\n        if regular_lora in lora.keys():\n            A_name = regular_lora\n            B_name = \"{}.lora_down.weight\".format(x)\n            mid_name = \"{}.lora_mid.weight\".format(x)\n        elif diffusers_lora in lora.keys():\n            A_name = diffusers_lora\n            B_name = \"{}_lora.down.weight\".format(x)\n            mid_name = None\n        elif transformers_lora in lora.keys():\n            A_name = transformers_lora\n            B_name =\"{}.lora_linear_layer.down.weight\".format(x)\n            mid_name = None\n\n        if A_name is not None:\n            mid = None\n            if mid_name is not None and mid_name in lora.keys():\n                mid = lora[mid_name]\n                loaded_keys.add(mid_name)\n            patch_dict[to_load[x]] = (\"lora\", (lora[A_name], lora[B_name], alpha, mid))\n            loaded_keys.add(A_name)\n            loaded_keys.add(B_name)\n\n\n        ######## loha\n        hada_w1_a_name = \"{}.hada_w1_a\".format(x)\n        hada_w1_b_name = \"{}.hada_w1_b\".format(x)\n        hada_w2_a_name = \"{}.hada_w2_a\".format(x)\n        hada_w2_b_name = \"{}.hada_w2_b\".format(x)\n        hada_t1_name = \"{}.hada_t1\".format(x)\n        hada_t2_name = \"{}.hada_t2\".format(x)\n        if hada_w1_a_name in lora.keys():\n            hada_t1 = None\n            hada_t2 = None\n            if hada_t1_name in lora.keys():\n                hada_t1 = lora[hada_t1_name]\n                hada_t2 = lora[hada_t2_name]\n                loaded_keys.add(hada_t1_name)\n                loaded_keys.add(hada_t2_name)\n\n            patch_dict[to_load[x]] = (\"loha\", (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2))\n            loaded_keys.add(hada_w1_a_name)\n            loaded_keys.add(hada_w1_b_name)\n            loaded_keys.add(hada_w2_a_name)\n            loaded_keys.add(hada_w2_b_name)\n\n\n        ######## lokr\n        lokr_w1_name = \"{}.lokr_w1\".format(x)\n        lokr_w2_name = \"{}.lokr_w2\".format(x)\n        lokr_w1_a_name = \"{}.lokr_w1_a\".format(x)\n        lokr_w1_b_name = \"{}.lokr_w1_b\".format(x)\n        lokr_t2_name = \"{}.lokr_t2\".format(x)\n        lokr_w2_a_name = \"{}.lokr_w2_a\".format(x)\n        lokr_w2_b_name = \"{}.lokr_w2_b\".format(x)\n\n        lokr_w1 = None\n        if lokr_w1_name in lora.keys():\n            lokr_w1 = lora[lokr_w1_name]\n            loaded_keys.add(lokr_w1_name)\n\n        lokr_w2 = None\n        if lokr_w2_name in lora.keys():\n            lokr_w2 = lora[lokr_w2_name]\n            loaded_keys.add(lokr_w2_name)\n\n        lokr_w1_a = None\n        if lokr_w1_a_name in lora.keys():\n            lokr_w1_a = lora[lokr_w1_a_name]\n            loaded_keys.add(lokr_w1_a_name)\n\n        lokr_w1_b = None\n        if lokr_w1_b_name in lora.keys():\n            lokr_w1_b = lora[lokr_w1_b_name]\n            loaded_keys.add(lokr_w1_b_name)\n\n        lokr_w2_a = None\n        if lokr_w2_a_name in lora.keys():\n            lokr_w2_a = lora[lokr_w2_a_name]\n            loaded_keys.add(lokr_w2_a_name)\n\n        lokr_w2_b = None\n        if lokr_w2_b_name in lora.keys():\n            lokr_w2_b = lora[lokr_w2_b_name]\n            loaded_keys.add(lokr_w2_b_name)\n\n        lokr_t2 = None\n        if lokr_t2_name in lora.keys():\n            lokr_t2 = lora[lokr_t2_name]\n            loaded_keys.add(lokr_t2_name)\n\n        if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None):\n            patch_dict[to_load[x]] = (\"lokr\", (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2))\n\n        #glora\n        a1_name = \"{}.a1.weight\".format(x)\n        a2_name = \"{}.a2.weight\".format(x)\n        b1_name = \"{}.b1.weight\".format(x)\n        b2_name = \"{}.b2.weight\".format(x)\n        if a1_name in lora:\n            patch_dict[to_load[x]] = (\"glora\", (lora[a1_name], lora[a2_name], lora[b1_name], lora[b2_name], alpha))\n            loaded_keys.add(a1_name)\n            loaded_keys.add(a2_name)\n            loaded_keys.add(b1_name)\n            loaded_keys.add(b2_name)\n\n        w_norm_name = \"{}.w_norm\".format(x)\n        b_norm_name = \"{}.b_norm\".format(x)\n        w_norm = lora.get(w_norm_name, None)\n        b_norm = lora.get(b_norm_name, None)\n\n        if w_norm is not None:\n            loaded_keys.add(w_norm_name)\n            patch_dict[to_load[x]] = (\"diff\", (w_norm,))\n            if b_norm is not None:\n                loaded_keys.add(b_norm_name)\n                patch_dict[\"{}.bias\".format(to_load[x][:-len(\".weight\")])] = (\"diff\", (b_norm,))\n\n        diff_name = \"{}.diff\".format(x)\n        diff_weight = lora.get(diff_name, None)\n        if diff_weight is not None:\n            patch_dict[to_load[x]] = (\"diff\", (diff_weight,))\n            loaded_keys.add(diff_name)\n\n        diff_bias_name = \"{}.diff_b\".format(x)\n        diff_bias = lora.get(diff_bias_name, None)\n        if diff_bias is not None:\n            patch_dict[\"{}.bias\".format(to_load[x][:-len(\".weight\")])] = (\"diff\", (diff_bias,))\n            loaded_keys.add(diff_bias_name)\n\n    for x in lora.keys():\n        if x not in loaded_keys:\n            print(\"lora key not loaded\", x)\n    return patch_dict\n\ndef model_lora_keys_clip(model, key_map={}):\n    sdk = model.state_dict().keys()\n\n    text_model_lora_key = \"lora_te_text_model_encoder_layers_{}_{}\"\n    clip_l_present = False\n    for b in range(32): #TODO: clean up\n        for c in LORA_CLIP_MAP:\n            k = \"clip_h.transformer.text_model.encoder.layers.{}.{}.weight\".format(b, c)\n            if k in sdk:\n                lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c])\n                key_map[lora_key] = k\n                lora_key = \"lora_te1_text_model_encoder_layers_{}_{}\".format(b, LORA_CLIP_MAP[c])\n                key_map[lora_key] = k\n                lora_key = \"text_encoder.text_model.encoder.layers.{}.{}\".format(b, c) #diffusers lora\n                key_map[lora_key] = k\n\n            k = \"clip_l.transformer.text_model.encoder.layers.{}.{}.weight\".format(b, c)\n            if k in sdk:\n                lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c])\n                key_map[lora_key] = k\n                lora_key = \"lora_te1_text_model_encoder_layers_{}_{}\".format(b, LORA_CLIP_MAP[c]) #SDXL base\n                key_map[lora_key] = k\n                clip_l_present = True\n                lora_key = \"text_encoder.text_model.encoder.layers.{}.{}\".format(b, c) #diffusers lora\n                key_map[lora_key] = k\n\n            k = \"clip_g.transformer.text_model.encoder.layers.{}.{}.weight\".format(b, c)\n            if k in sdk:\n                if clip_l_present:\n                    lora_key = \"lora_te2_text_model_encoder_layers_{}_{}\".format(b, LORA_CLIP_MAP[c]) #SDXL base\n                    key_map[lora_key] = k\n                    lora_key = \"text_encoder_2.text_model.encoder.layers.{}.{}\".format(b, c) #diffusers lora\n                    key_map[lora_key] = k\n                else:\n                    lora_key = \"lora_te_text_model_encoder_layers_{}_{}\".format(b, LORA_CLIP_MAP[c]) #TODO: test if this is correct for SDXL-Refiner\n                    key_map[lora_key] = k\n                    lora_key = \"text_encoder.text_model.encoder.layers.{}.{}\".format(b, c) #diffusers lora\n                    key_map[lora_key] = k\n\n    return key_map\n\ndef model_lora_keys_unet(model, key_map={}):\n    sdk = model.state_dict().keys()\n\n    for k in sdk:\n        if k.startswith(\"diffusion_model.\") and k.endswith(\".weight\"):\n            key_lora = k[len(\"diffusion_model.\"):-len(\".weight\")].replace(\".\", \"_\")\n            key_map[\"lora_unet_{}\".format(key_lora)] = k\n\n    diffusers_keys = ldm_patched.modules.utils.unet_to_diffusers(model.model_config.unet_config)\n    for k in diffusers_keys:\n        if k.endswith(\".weight\"):\n            unet_key = \"diffusion_model.{}\".format(diffusers_keys[k])\n            key_lora = k[:-len(\".weight\")].replace(\".\", \"_\")\n            key_map[\"lora_unet_{}\".format(key_lora)] = unet_key\n\n            diffusers_lora_prefix = [\"\", \"unet.\"]\n            for p in diffusers_lora_prefix:\n                diffusers_lora_key = \"{}{}\".format(p, k[:-len(\".weight\")].replace(\".to_\", \".processor.to_\"))\n                if diffusers_lora_key.endswith(\".to_out.0\"):\n                    diffusers_lora_key = diffusers_lora_key[:-2]\n                key_map[diffusers_lora_key] = unet_key\n    return key_map\n"
  },
  {
    "path": "ldm_patched/modules/model_base.py",
    "content": "import torch\nfrom ldm_patched.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep\nfrom ldm_patched.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation\nfrom ldm_patched.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation\nimport ldm_patched.modules.model_management\nimport ldm_patched.modules.conds\nimport ldm_patched.modules.ops\nfrom enum import Enum\nfrom . import utils\n\nclass ModelType(Enum):\n    EPS = 1\n    V_PREDICTION = 2\n    V_PREDICTION_EDM = 3\n\n\nfrom ldm_patched.modules.model_sampling import EPS, V_PREDICTION, ModelSamplingDiscrete, ModelSamplingContinuousEDM\n\n\ndef model_sampling(model_config, model_type):\n    s = ModelSamplingDiscrete\n\n    if model_type == ModelType.EPS:\n        c = EPS\n    elif model_type == ModelType.V_PREDICTION:\n        c = V_PREDICTION\n    elif model_type == ModelType.V_PREDICTION_EDM:\n        c = V_PREDICTION\n        s = ModelSamplingContinuousEDM\n\n    class ModelSampling(s, c):\n        pass\n\n    return ModelSampling(model_config)\n\n\nclass BaseModel(torch.nn.Module):\n    def __init__(self, model_config, model_type=ModelType.EPS, device=None):\n        super().__init__()\n\n        unet_config = model_config.unet_config\n        self.latent_format = model_config.latent_format\n        self.model_config = model_config\n        self.manual_cast_dtype = model_config.manual_cast_dtype\n\n        if not unet_config.get(\"disable_unet_model_creation\", False):\n            if self.manual_cast_dtype is not None:\n                operations = ldm_patched.modules.ops.manual_cast\n            else:\n                operations = ldm_patched.modules.ops.disable_weight_init\n            self.diffusion_model = UNetModel(**unet_config, device=device, operations=operations)\n        self.model_type = model_type\n        self.model_sampling = model_sampling(model_config, model_type)\n\n        self.adm_channels = unet_config.get(\"adm_in_channels\", None)\n        if self.adm_channels is None:\n            self.adm_channels = 0\n        self.inpaint_model = False\n        print(\"model_type\", model_type.name)\n        print(\"UNet ADM Dimension\", self.adm_channels)\n\n    def apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs):\n        sigma = t\n        xc = self.model_sampling.calculate_input(sigma, x)\n        if c_concat is not None:\n            xc = torch.cat([xc] + [c_concat], dim=1)\n\n        context = c_crossattn\n        dtype = self.get_dtype()\n\n        if self.manual_cast_dtype is not None:\n            dtype = self.manual_cast_dtype\n\n        xc = xc.to(dtype)\n        t = self.model_sampling.timestep(t).float()\n        context = context.to(dtype)\n        extra_conds = {}\n        for o in kwargs:\n            extra = kwargs[o]\n            if hasattr(extra, \"dtype\"):\n                if extra.dtype != torch.int and extra.dtype != torch.long:\n                    extra = extra.to(dtype)\n            extra_conds[o] = extra\n\n        model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float()\n        return self.model_sampling.calculate_denoised(sigma, model_output, x)\n\n    def get_dtype(self):\n        return self.diffusion_model.dtype\n\n    def is_adm(self):\n        return self.adm_channels > 0\n\n    def encode_adm(self, **kwargs):\n        return None\n\n    def extra_conds(self, **kwargs):\n        out = {}\n        if self.inpaint_model:\n            concat_keys = (\"mask\", \"masked_image\")\n            cond_concat = []\n            denoise_mask = kwargs.get(\"concat_mask\", kwargs.get(\"denoise_mask\", None))\n            concat_latent_image = kwargs.get(\"concat_latent_image\", None)\n            if concat_latent_image is None:\n                concat_latent_image = kwargs.get(\"latent_image\", None)\n            else:\n                concat_latent_image = self.process_latent_in(concat_latent_image)\n\n            noise = kwargs.get(\"noise\", None)\n            device = kwargs[\"device\"]\n\n            if concat_latent_image.shape[1:] != noise.shape[1:]:\n                concat_latent_image = utils.common_upscale(concat_latent_image, noise.shape[-1], noise.shape[-2], \"bilinear\", \"center\")\n\n            concat_latent_image = utils.resize_to_batch_size(concat_latent_image, noise.shape[0])\n\n            if len(denoise_mask.shape) == len(noise.shape):\n                denoise_mask = denoise_mask[:,:1]\n\n            denoise_mask = denoise_mask.reshape((-1, 1, denoise_mask.shape[-2], denoise_mask.shape[-1]))\n            if denoise_mask.shape[-2:] != noise.shape[-2:]:\n                denoise_mask = utils.common_upscale(denoise_mask, noise.shape[-1], noise.shape[-2], \"bilinear\", \"center\")\n            denoise_mask = utils.resize_to_batch_size(denoise_mask.round(), noise.shape[0])\n\n            def blank_inpaint_image_like(latent_image):\n                blank_image = torch.ones_like(latent_image)\n                # these are the values for \"zero\" in pixel space translated to latent space\n                blank_image[:,0] *= 0.8223\n                blank_image[:,1] *= -0.6876\n                blank_image[:,2] *= 0.6364\n                blank_image[:,3] *= 0.1380\n                return blank_image\n\n            for ck in concat_keys:\n                if denoise_mask is not None:\n                    if ck == \"mask\":\n                        cond_concat.append(denoise_mask.to(device))\n                    elif ck == \"masked_image\":\n                        cond_concat.append(concat_latent_image.to(device)) #NOTE: the latent_image should be masked by the mask in pixel space\n                else:\n                    if ck == \"mask\":\n                        cond_concat.append(torch.ones_like(noise)[:,:1])\n                    elif ck == \"masked_image\":\n                        cond_concat.append(blank_inpaint_image_like(noise))\n            data = torch.cat(cond_concat, dim=1)\n            out['c_concat'] = ldm_patched.modules.conds.CONDNoiseShape(data)\n\n        adm = self.encode_adm(**kwargs)\n        if adm is not None:\n            out['y'] = ldm_patched.modules.conds.CONDRegular(adm)\n\n        cross_attn = kwargs.get(\"cross_attn\", None)\n        if cross_attn is not None:\n            out['c_crossattn'] = ldm_patched.modules.conds.CONDCrossAttn(cross_attn)\n\n        return out\n\n    def load_model_weights(self, sd, unet_prefix=\"\"):\n        to_load = {}\n        keys = list(sd.keys())\n        for k in keys:\n            if k.startswith(unet_prefix):\n                to_load[k[len(unet_prefix):]] = sd.pop(k)\n\n        to_load = self.model_config.process_unet_state_dict(to_load)\n        m, u = self.diffusion_model.load_state_dict(to_load, strict=False)\n        if len(m) > 0:\n            print(\"unet missing:\", m)\n\n        if len(u) > 0:\n            print(\"unet unexpected:\", u)\n        del to_load\n        return self\n\n    def process_latent_in(self, latent):\n        return self.latent_format.process_in(latent)\n\n    def process_latent_out(self, latent):\n        return self.latent_format.process_out(latent)\n\n    def state_dict_for_saving(self, clip_state_dict=None, vae_state_dict=None, clip_vision_state_dict=None):\n        extra_sds = []\n        if clip_state_dict is not None:\n            extra_sds.append(self.model_config.process_clip_state_dict_for_saving(clip_state_dict))\n        if vae_state_dict is not None:\n            extra_sds.append(self.model_config.process_vae_state_dict_for_saving(vae_state_dict))\n        if clip_vision_state_dict is not None:\n            extra_sds.append(self.model_config.process_clip_vision_state_dict_for_saving(clip_vision_state_dict))\n\n        unet_state_dict = self.diffusion_model.state_dict()\n        unet_state_dict = self.model_config.process_unet_state_dict_for_saving(unet_state_dict)\n\n        if self.get_dtype() == torch.float16:\n            extra_sds = map(lambda sd: utils.convert_sd_to(sd, torch.float16), extra_sds)\n\n        if self.model_type == ModelType.V_PREDICTION:\n            unet_state_dict[\"v_pred\"] = torch.tensor([])\n\n        for sd in extra_sds:\n            unet_state_dict.update(sd)\n\n        return unet_state_dict\n\n    def set_inpaint(self):\n        self.inpaint_model = True\n\n    def memory_required(self, input_shape):\n        if ldm_patched.modules.model_management.xformers_enabled() or ldm_patched.modules.model_management.pytorch_attention_flash_attention():\n            dtype = self.get_dtype()\n            if self.manual_cast_dtype is not None:\n                dtype = self.manual_cast_dtype\n            #TODO: this needs to be tweaked\n            area = input_shape[0] * input_shape[2] * input_shape[3]\n            return (area * ldm_patched.modules.model_management.dtype_size(dtype) / 50) * (1024 * 1024)\n        else:\n            #TODO: this formula might be too aggressive since I tweaked the sub-quad and split algorithms to use less memory.\n            area = input_shape[0] * input_shape[2] * input_shape[3]\n            return (((area * 0.6) / 0.9) + 1024) * (1024 * 1024)\n\n\ndef unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge=0.0, seed=None):\n    adm_inputs = []\n    weights = []\n    noise_aug = []\n    for unclip_cond in unclip_conditioning:\n        for adm_cond in unclip_cond[\"clip_vision_output\"].image_embeds:\n            weight = unclip_cond[\"strength\"]\n            noise_augment = unclip_cond[\"noise_augmentation\"]\n            noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment)\n            c_adm, noise_level_emb = noise_augmentor(adm_cond.to(device), noise_level=torch.tensor([noise_level], device=device), seed=seed)\n            adm_out = torch.cat((c_adm, noise_level_emb), 1) * weight\n            weights.append(weight)\n            noise_aug.append(noise_augment)\n            adm_inputs.append(adm_out)\n\n    if len(noise_aug) > 1:\n        adm_out = torch.stack(adm_inputs).sum(0)\n        noise_augment = noise_augment_merge\n        noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment)\n        c_adm, noise_level_emb = noise_augmentor(adm_out[:, :noise_augmentor.time_embed.dim], noise_level=torch.tensor([noise_level], device=device))\n        adm_out = torch.cat((c_adm, noise_level_emb), 1)\n\n    return adm_out\n\nclass SD21UNCLIP(BaseModel):\n    def __init__(self, model_config, noise_aug_config, model_type=ModelType.V_PREDICTION, device=None):\n        super().__init__(model_config, model_type, device=device)\n        self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**noise_aug_config)\n\n    def encode_adm(self, **kwargs):\n        unclip_conditioning = kwargs.get(\"unclip_conditioning\", None)\n        device = kwargs[\"device\"]\n        if unclip_conditioning is None:\n            return torch.zeros((1, self.adm_channels))\n        else:\n            return unclip_adm(unclip_conditioning, device, self.noise_augmentor, kwargs.get(\"unclip_noise_augment_merge\", 0.05), kwargs.get(\"seed\", 0) - 10)\n\ndef sdxl_pooled(args, noise_augmentor):\n    if \"unclip_conditioning\" in args:\n        return unclip_adm(args.get(\"unclip_conditioning\", None), args[\"device\"], noise_augmentor, seed=args.get(\"seed\", 0) - 10)[:,:1280]\n    else:\n        return args[\"pooled_output\"]\n\nclass SDXLRefiner(BaseModel):\n    def __init__(self, model_config, model_type=ModelType.EPS, device=None):\n        super().__init__(model_config, model_type, device=device)\n        self.embedder = Timestep(256)\n        self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**{\"noise_schedule_config\": {\"timesteps\": 1000, \"beta_schedule\": \"squaredcos_cap_v2\"}, \"timestep_dim\": 1280})\n\n    def encode_adm(self, **kwargs):\n        clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor)\n        width = kwargs.get(\"width\", 768)\n        height = kwargs.get(\"height\", 768)\n        crop_w = kwargs.get(\"crop_w\", 0)\n        crop_h = kwargs.get(\"crop_h\", 0)\n\n        if kwargs.get(\"prompt_type\", \"\") == \"negative\":\n            aesthetic_score = kwargs.get(\"aesthetic_score\", 2.5)\n        else:\n            aesthetic_score = kwargs.get(\"aesthetic_score\", 6)\n\n        out = []\n        out.append(self.embedder(torch.Tensor([height])))\n        out.append(self.embedder(torch.Tensor([width])))\n        out.append(self.embedder(torch.Tensor([crop_h])))\n        out.append(self.embedder(torch.Tensor([crop_w])))\n        out.append(self.embedder(torch.Tensor([aesthetic_score])))\n        flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1)\n        return torch.cat((clip_pooled.to(flat.device), flat), dim=1)\n\nclass SDXL(BaseModel):\n    def __init__(self, model_config, model_type=ModelType.EPS, device=None):\n        super().__init__(model_config, model_type, device=device)\n        self.embedder = Timestep(256)\n        self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**{\"noise_schedule_config\": {\"timesteps\": 1000, \"beta_schedule\": \"squaredcos_cap_v2\"}, \"timestep_dim\": 1280})\n\n    def encode_adm(self, **kwargs):\n        clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor)\n        width = kwargs.get(\"width\", 768)\n        height = kwargs.get(\"height\", 768)\n        crop_w = kwargs.get(\"crop_w\", 0)\n        crop_h = kwargs.get(\"crop_h\", 0)\n        target_width = kwargs.get(\"target_width\", width)\n        target_height = kwargs.get(\"target_height\", height)\n\n        out = []\n        out.append(self.embedder(torch.Tensor([height])))\n        out.append(self.embedder(torch.Tensor([width])))\n        out.append(self.embedder(torch.Tensor([crop_h])))\n        out.append(self.embedder(torch.Tensor([crop_w])))\n        out.append(self.embedder(torch.Tensor([target_height])))\n        out.append(self.embedder(torch.Tensor([target_width])))\n        flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1)\n        return torch.cat((clip_pooled.to(flat.device), flat), dim=1)\n\nclass SVD_img2vid(BaseModel):\n    def __init__(self, model_config, model_type=ModelType.V_PREDICTION_EDM, device=None):\n        super().__init__(model_config, model_type, device=device)\n        self.embedder = Timestep(256)\n\n    def encode_adm(self, **kwargs):\n        fps_id = kwargs.get(\"fps\", 6) - 1\n        motion_bucket_id = kwargs.get(\"motion_bucket_id\", 127)\n        augmentation = kwargs.get(\"augmentation_level\", 0)\n\n        out = []\n        out.append(self.embedder(torch.Tensor([fps_id])))\n        out.append(self.embedder(torch.Tensor([motion_bucket_id])))\n        out.append(self.embedder(torch.Tensor([augmentation])))\n\n        flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0)\n        return flat\n\n    def extra_conds(self, **kwargs):\n        out = {}\n        adm = self.encode_adm(**kwargs)\n        if adm is not None:\n            out['y'] = ldm_patched.modules.conds.CONDRegular(adm)\n\n        latent_image = kwargs.get(\"concat_latent_image\", None)\n        noise = kwargs.get(\"noise\", None)\n        device = kwargs[\"device\"]\n\n        if latent_image is None:\n            latent_image = torch.zeros_like(noise)\n\n        if latent_image.shape[1:] != noise.shape[1:]:\n            latent_image = utils.common_upscale(latent_image, noise.shape[-1], noise.shape[-2], \"bilinear\", \"center\")\n\n        latent_image = utils.resize_to_batch_size(latent_image, noise.shape[0])\n\n        out['c_concat'] = ldm_patched.modules.conds.CONDNoiseShape(latent_image)\n\n        cross_attn = kwargs.get(\"cross_attn\", None)\n        if cross_attn is not None:\n            out['c_crossattn'] = ldm_patched.modules.conds.CONDCrossAttn(cross_attn)\n\n        if \"time_conditioning\" in kwargs:\n            out[\"time_context\"] = ldm_patched.modules.conds.CONDCrossAttn(kwargs[\"time_conditioning\"])\n\n        out['image_only_indicator'] = ldm_patched.modules.conds.CONDConstant(torch.zeros((1,), device=device))\n        out['num_video_frames'] = ldm_patched.modules.conds.CONDConstant(noise.shape[0])\n        return out\n\nclass Stable_Zero123(BaseModel):\n    def __init__(self, model_config, model_type=ModelType.EPS, device=None, cc_projection_weight=None, cc_projection_bias=None):\n        super().__init__(model_config, model_type, device=device)\n        self.cc_projection = ldm_patched.modules.ops.manual_cast.Linear(cc_projection_weight.shape[1], cc_projection_weight.shape[0], dtype=self.get_dtype(), device=device)\n        self.cc_projection.weight.copy_(cc_projection_weight)\n        self.cc_projection.bias.copy_(cc_projection_bias)\n\n    def extra_conds(self, **kwargs):\n        out = {}\n\n        latent_image = kwargs.get(\"concat_latent_image\", None)\n        noise = kwargs.get(\"noise\", None)\n\n        if latent_image is None:\n            latent_image = torch.zeros_like(noise)\n\n        if latent_image.shape[1:] != noise.shape[1:]:\n            latent_image = utils.common_upscale(latent_image, noise.shape[-1], noise.shape[-2], \"bilinear\", \"center\")\n\n        latent_image = utils.resize_to_batch_size(latent_image, noise.shape[0])\n\n        out['c_concat'] = ldm_patched.modules.conds.CONDNoiseShape(latent_image)\n\n        cross_attn = kwargs.get(\"cross_attn\", None)\n        if cross_attn is not None:\n            if cross_attn.shape[-1] != 768:\n                cross_attn = self.cc_projection(cross_attn)\n            out['c_crossattn'] = ldm_patched.modules.conds.CONDCrossAttn(cross_attn)\n        return out\n\nclass SD_X4Upscaler(BaseModel):\n    def __init__(self, model_config, model_type=ModelType.V_PREDICTION, device=None):\n        super().__init__(model_config, model_type, device=device)\n        self.noise_augmentor = ImageConcatWithNoiseAugmentation(noise_schedule_config={\"linear_start\": 0.0001, \"linear_end\": 0.02}, max_noise_level=350)\n\n    def extra_conds(self, **kwargs):\n        out = {}\n\n        image = kwargs.get(\"concat_image\", None)\n        noise = kwargs.get(\"noise\", None)\n        noise_augment = kwargs.get(\"noise_augmentation\", 0.0)\n        device = kwargs[\"device\"]\n        seed = kwargs[\"seed\"] - 10\n\n        noise_level = round((self.noise_augmentor.max_noise_level) * noise_augment)\n\n        if image is None:\n            image = torch.zeros_like(noise)[:,:3]\n\n        if image.shape[1:] != noise.shape[1:]:\n            image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], \"bilinear\", \"center\")\n\n        noise_level = torch.tensor([noise_level], device=device)\n        if noise_augment > 0:\n            image, noise_level = self.noise_augmentor(image.to(device), noise_level=noise_level, seed=seed)\n\n        image = utils.resize_to_batch_size(image, noise.shape[0])\n\n        out['c_concat'] = ldm_patched.modules.conds.CONDNoiseShape(image)\n        out['y'] = ldm_patched.modules.conds.CONDRegular(noise_level)\n        return out\n"
  },
  {
    "path": "ldm_patched/modules/model_detection.py",
    "content": "import ldm_patched.modules.supported_models\nimport ldm_patched.modules.supported_models_base\n\ndef count_blocks(state_dict_keys, prefix_string):\n    count = 0\n    while True:\n        c = False\n        for k in state_dict_keys:\n            if k.startswith(prefix_string.format(count)):\n                c = True\n                break\n        if c == False:\n            break\n        count += 1\n    return count\n\ndef calculate_transformer_depth(prefix, state_dict_keys, state_dict):\n    context_dim = None\n    use_linear_in_transformer = False\n\n    transformer_prefix = prefix + \"1.transformer_blocks.\"\n    transformer_keys = sorted(list(filter(lambda a: a.startswith(transformer_prefix), state_dict_keys)))\n    if len(transformer_keys) > 0:\n        last_transformer_depth = count_blocks(state_dict_keys, transformer_prefix + '{}')\n        context_dim = state_dict['{}0.attn2.to_k.weight'.format(transformer_prefix)].shape[1]\n        use_linear_in_transformer = len(state_dict['{}1.proj_in.weight'.format(prefix)].shape) == 2\n        time_stack = '{}1.time_stack.0.attn1.to_q.weight'.format(prefix) in state_dict or '{}1.time_mix_blocks.0.attn1.to_q.weight'.format(prefix) in state_dict\n        return last_transformer_depth, context_dim, use_linear_in_transformer, time_stack\n    return None\n\ndef detect_unet_config(state_dict, key_prefix, dtype):\n    state_dict_keys = list(state_dict.keys())\n\n    unet_config = {\n        \"use_checkpoint\": False,\n        \"image_size\": 32,\n        \"use_spatial_transformer\": True,\n        \"legacy\": False\n    }\n\n    y_input = '{}label_emb.0.0.weight'.format(key_prefix)\n    if y_input in state_dict_keys:\n        unet_config[\"num_classes\"] = \"sequential\"\n        unet_config[\"adm_in_channels\"] = state_dict[y_input].shape[1]\n    else:\n        unet_config[\"adm_in_channels\"] = None\n\n    unet_config[\"dtype\"] = dtype\n    model_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[0]\n    in_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[1]\n\n    out_key = '{}out.2.weight'.format(key_prefix)\n    if out_key in state_dict:\n        out_channels = state_dict[out_key].shape[0]\n    else:\n        out_channels = 4\n\n    num_res_blocks = []\n    channel_mult = []\n    attention_resolutions = []\n    transformer_depth = []\n    transformer_depth_output = []\n    context_dim = None\n    use_linear_in_transformer = False\n\n    video_model = False\n\n    current_res = 1\n    count = 0\n\n    last_res_blocks = 0\n    last_channel_mult = 0\n\n    input_block_count = count_blocks(state_dict_keys, '{}input_blocks'.format(key_prefix) + '.{}.')\n    for count in range(input_block_count):\n        prefix = '{}input_blocks.{}.'.format(key_prefix, count)\n        prefix_output = '{}output_blocks.{}.'.format(key_prefix, input_block_count - count - 1)\n\n        block_keys = sorted(list(filter(lambda a: a.startswith(prefix), state_dict_keys)))\n        if len(block_keys) == 0:\n            break\n\n        block_keys_output = sorted(list(filter(lambda a: a.startswith(prefix_output), state_dict_keys)))\n\n        if \"{}0.op.weight\".format(prefix) in block_keys: #new layer\n            num_res_blocks.append(last_res_blocks)\n            channel_mult.append(last_channel_mult)\n\n            current_res *= 2\n            last_res_blocks = 0\n            last_channel_mult = 0\n            out = calculate_transformer_depth(prefix_output, state_dict_keys, state_dict)\n            if out is not None:\n                transformer_depth_output.append(out[0])\n            else:\n                transformer_depth_output.append(0)\n        else:\n            res_block_prefix = \"{}0.in_layers.0.weight\".format(prefix)\n            if res_block_prefix in block_keys:\n                last_res_blocks += 1\n                last_channel_mult = state_dict[\"{}0.out_layers.3.weight\".format(prefix)].shape[0] // model_channels\n\n                out = calculate_transformer_depth(prefix, state_dict_keys, state_dict)\n                if out is not None:\n                    transformer_depth.append(out[0])\n                    if context_dim is None:\n                        context_dim = out[1]\n                        use_linear_in_transformer = out[2]\n                        video_model = out[3]\n                else:\n                    transformer_depth.append(0)\n\n            res_block_prefix = \"{}0.in_layers.0.weight\".format(prefix_output)\n            if res_block_prefix in block_keys_output:\n                out = calculate_transformer_depth(prefix_output, state_dict_keys, state_dict)\n                if out is not None:\n                    transformer_depth_output.append(out[0])\n                else:\n                    transformer_depth_output.append(0)\n\n\n    num_res_blocks.append(last_res_blocks)\n    channel_mult.append(last_channel_mult)\n    if \"{}middle_block.1.proj_in.weight\".format(key_prefix) in state_dict_keys:\n        transformer_depth_middle = count_blocks(state_dict_keys, '{}middle_block.1.transformer_blocks.'.format(key_prefix) + '{}')\n    else:\n        transformer_depth_middle = -1\n\n    unet_config[\"in_channels\"] = in_channels\n    unet_config[\"out_channels\"] = out_channels\n    unet_config[\"model_channels\"] = model_channels\n    unet_config[\"num_res_blocks\"] = num_res_blocks\n    unet_config[\"transformer_depth\"] = transformer_depth\n    unet_config[\"transformer_depth_output\"] = transformer_depth_output\n    unet_config[\"channel_mult\"] = channel_mult\n    unet_config[\"transformer_depth_middle\"] = transformer_depth_middle\n    unet_config['use_linear_in_transformer'] = use_linear_in_transformer\n    unet_config[\"context_dim\"] = context_dim\n\n    if video_model:\n        unet_config[\"extra_ff_mix_layer\"] = True\n        unet_config[\"use_spatial_context\"] = True\n        unet_config[\"merge_strategy\"] = \"learned_with_images\"\n        unet_config[\"merge_factor\"] = 0.0\n        unet_config[\"video_kernel_size\"] = [3, 1, 1]\n        unet_config[\"use_temporal_resblock\"] = True\n        unet_config[\"use_temporal_attention\"] = True\n    else:\n        unet_config[\"use_temporal_resblock\"] = False\n        unet_config[\"use_temporal_attention\"] = False\n\n    return unet_config\n\ndef model_config_from_unet_config(unet_config):\n    for model_config in ldm_patched.modules.supported_models.models:\n        if model_config.matches(unet_config):\n            return model_config(unet_config)\n\n    print(\"no match\", unet_config)\n    return None\n\ndef model_config_from_unet(state_dict, unet_key_prefix, dtype, use_base_if_no_match=False):\n    unet_config = detect_unet_config(state_dict, unet_key_prefix, dtype)\n    model_config = model_config_from_unet_config(unet_config)\n    if model_config is None and use_base_if_no_match:\n        return ldm_patched.modules.supported_models_base.BASE(unet_config)\n    else:\n        return model_config\n\ndef convert_config(unet_config):\n    new_config = unet_config.copy()\n    num_res_blocks = new_config.get(\"num_res_blocks\", None)\n    channel_mult = new_config.get(\"channel_mult\", None)\n\n    if isinstance(num_res_blocks, int):\n        num_res_blocks = len(channel_mult) * [num_res_blocks]\n\n    if \"attention_resolutions\" in new_config:\n        attention_resolutions = new_config.pop(\"attention_resolutions\")\n        transformer_depth = new_config.get(\"transformer_depth\", None)\n        transformer_depth_middle = new_config.get(\"transformer_depth_middle\", None)\n\n        if isinstance(transformer_depth, int):\n            transformer_depth = len(channel_mult) * [transformer_depth]\n        if transformer_depth_middle is None:\n            transformer_depth_middle =  transformer_depth[-1]\n        t_in = []\n        t_out = []\n        s = 1\n        for i in range(len(num_res_blocks)):\n            res = num_res_blocks[i]\n            d = 0\n            if s in attention_resolutions:\n                d = transformer_depth[i]\n\n            t_in += [d] * res\n            t_out += [d] * (res + 1)\n            s *= 2\n        transformer_depth = t_in\n        transformer_depth_output = t_out\n        new_config[\"transformer_depth\"] = t_in\n        new_config[\"transformer_depth_output\"] = t_out\n        new_config[\"transformer_depth_middle\"] = transformer_depth_middle\n\n    new_config[\"num_res_blocks\"] = num_res_blocks\n    return new_config\n\n\ndef unet_config_from_diffusers_unet(state_dict, dtype):\n    match = {}\n    transformer_depth = []\n\n    attn_res = 1\n    down_blocks = count_blocks(state_dict, \"down_blocks.{}\")\n    for i in range(down_blocks):\n        attn_blocks = count_blocks(state_dict, \"down_blocks.{}.attentions.\".format(i) + '{}')\n        for ab in range(attn_blocks):\n            transformer_count = count_blocks(state_dict, \"down_blocks.{}.attentions.{}.transformer_blocks.\".format(i, ab) + '{}')\n            transformer_depth.append(transformer_count)\n            if transformer_count > 0:\n                match[\"context_dim\"] = state_dict[\"down_blocks.{}.attentions.{}.transformer_blocks.0.attn2.to_k.weight\".format(i, ab)].shape[1]\n\n        attn_res *= 2\n        if attn_blocks == 0:\n            transformer_depth.append(0)\n            transformer_depth.append(0)\n\n    match[\"transformer_depth\"] = transformer_depth\n\n    match[\"model_channels\"] = state_dict[\"conv_in.weight\"].shape[0]\n    match[\"in_channels\"] = state_dict[\"conv_in.weight\"].shape[1]\n    match[\"adm_in_channels\"] = None\n    if \"class_embedding.linear_1.weight\" in state_dict:\n        match[\"adm_in_channels\"] = state_dict[\"class_embedding.linear_1.weight\"].shape[1]\n    elif \"add_embedding.linear_1.weight\" in state_dict:\n        match[\"adm_in_channels\"] = state_dict[\"add_embedding.linear_1.weight\"].shape[1]\n\n    SDXL = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,\n            'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,\n            'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10,\n            'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],\n            'use_temporal_attention': False, 'use_temporal_resblock': False}\n\n    SDXL_refiner = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,\n                    'num_classes': 'sequential', 'adm_in_channels': 2560, 'dtype': dtype, 'in_channels': 4, 'model_channels': 384,\n                    'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [0, 0, 4, 4, 4, 4, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 4,\n                    'use_linear_in_transformer': True, 'context_dim': 1280, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0],\n                    'use_temporal_attention': False, 'use_temporal_resblock': False}\n\n    SD21 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,\n            'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2],\n            'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True,\n            'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n            'use_temporal_attention': False, 'use_temporal_resblock': False}\n\n    SD21_uncliph = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,\n                    'num_classes': 'sequential', 'adm_in_channels': 2048, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,\n                    'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1,\n                    'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n                    'use_temporal_attention': False, 'use_temporal_resblock': False}\n\n    SD21_unclipl = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,\n                    'num_classes': 'sequential', 'adm_in_channels': 1536, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,\n                    'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1,\n                    'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n                    'use_temporal_attention': False, 'use_temporal_resblock': False}\n\n    SD15 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None,\n            'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0],\n            'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, 'num_heads': 8,\n            'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n            'use_temporal_attention': False, 'use_temporal_resblock': False}\n\n    SDXL_mid_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,\n                     'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,\n                     'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 1,\n                     'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 1, 1, 1],\n                     'use_temporal_attention': False, 'use_temporal_resblock': False}\n\n    SDXL_small_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,\n                       'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,\n                       'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 0, 0], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 0,\n                       'use_linear_in_transformer': True, 'num_head_channels': 64, 'context_dim': 1, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 0, 0, 0],\n                       'use_temporal_attention': False, 'use_temporal_resblock': False}\n\n    SDXL_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,\n                              'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 9, 'model_channels': 320,\n                              'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10,\n                              'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],\n                              'use_temporal_attention': False, 'use_temporal_resblock': False}\n\n    SSD_1B = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,\n              'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,\n              'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 4, 4], 'transformer_depth_output': [0, 0, 0, 1, 1, 2, 10, 4, 4],\n              'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,\n              'use_temporal_attention': False, 'use_temporal_resblock': False}\n\n    Segmind_Vega = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,\n              'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,\n              'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 1, 1, 2, 2], 'transformer_depth_output': [0, 0, 0, 1, 1, 1, 2, 2, 2],\n              'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,\n              'use_temporal_attention': False, 'use_temporal_resblock': False}\n\n    supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega]\n\n    for unet_config in supported_models:\n        matches = True\n        for k in match:\n            if match[k] != unet_config[k]:\n                matches = False\n                break\n        if matches:\n            return convert_config(unet_config)\n    return None\n\ndef model_config_from_diffusers_unet(state_dict, dtype):\n    unet_config = unet_config_from_diffusers_unet(state_dict, dtype)\n    if unet_config is not None:\n        return model_config_from_unet_config(unet_config)\n    return None\n"
  },
  {
    "path": "ldm_patched/modules/model_management.py",
    "content": "import psutil\nfrom enum import Enum\nfrom ldm_patched.modules.args_parser import args\nimport ldm_patched.modules.utils\nimport torch\nimport sys\n\nclass VRAMState(Enum):\n    DISABLED = 0    #No vram present: no need to move models to vram\n    NO_VRAM = 1     #Very low vram: enable all the options to save vram\n    LOW_VRAM = 2\n    NORMAL_VRAM = 3\n    HIGH_VRAM = 4\n    SHARED = 5      #No dedicated vram: memory shared between CPU and GPU but models still need to be moved between both.\n\nclass CPUState(Enum):\n    GPU = 0\n    CPU = 1\n    MPS = 2\n\n# Determine VRAM State\nvram_state = VRAMState.NORMAL_VRAM\nset_vram_to = VRAMState.NORMAL_VRAM\ncpu_state = CPUState.GPU\n\ntotal_vram = 0\n\nlowvram_available = True\nxpu_available = False\n\nif args.pytorch_deterministic:\n    print(\"Using deterministic algorithms for pytorch\")\n    torch.use_deterministic_algorithms(True, warn_only=True)\n\ndirectml_enabled = False\nif args.directml is not None:\n    import torch_directml\n    directml_enabled = True\n    device_index = args.directml\n    if device_index < 0:\n        directml_device = torch_directml.device()\n    else:\n        directml_device = torch_directml.device(device_index)\n    print(\"Using directml with device:\", torch_directml.device_name(device_index))\n    # torch_directml.disable_tiled_resources(True)\n    lowvram_available = False #TODO: need to find a way to get free memory in directml before this can be enabled by default.\n\ntry:\n    import intel_extension_for_pytorch as ipex\n    if torch.xpu.is_available():\n        xpu_available = True\nexcept:\n    pass\n\ntry:\n    if torch.backends.mps.is_available():\n        cpu_state = CPUState.MPS\n        import torch.mps\nexcept:\n    pass\n\nif args.always_cpu:\n    if args.always_cpu > 0:\n        torch.set_num_threads(args.always_cpu)\n    print(f\"Running on {torch.get_num_threads()} CPU threads\")\n    cpu_state = CPUState.CPU\n\ndef is_intel_xpu():\n    global cpu_state\n    global xpu_available\n    if cpu_state == CPUState.GPU:\n        if xpu_available:\n            return True\n    return False\n\ndef get_torch_device():\n    global directml_enabled\n    global cpu_state\n    if directml_enabled:\n        global directml_device\n        return directml_device\n    if cpu_state == CPUState.MPS:\n        return torch.device(\"mps\")\n    if cpu_state == CPUState.CPU:\n        return torch.device(\"cpu\")\n    else:\n        if is_intel_xpu():\n            return torch.device(\"xpu\")\n        else:\n            return torch.device(torch.cuda.current_device())\n\ndef get_total_memory(dev=None, torch_total_too=False):\n    global directml_enabled\n    if dev is None:\n        dev = get_torch_device()\n\n    if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'):\n        mem_total = psutil.virtual_memory().total\n        mem_total_torch = mem_total\n    else:\n        if directml_enabled:\n            mem_total = 1024 * 1024 * 1024 #TODO\n            mem_total_torch = mem_total\n        elif is_intel_xpu():\n            stats = torch.xpu.memory_stats(dev)\n            mem_reserved = stats['reserved_bytes.all.current']\n            mem_total = torch.xpu.get_device_properties(dev).total_memory\n            mem_total_torch = mem_reserved\n        else:\n            stats = torch.cuda.memory_stats(dev)\n            mem_reserved = stats['reserved_bytes.all.current']\n            _, mem_total_cuda = torch.cuda.mem_get_info(dev)\n            mem_total_torch = mem_reserved\n            mem_total = mem_total_cuda\n\n    if torch_total_too:\n        return (mem_total, mem_total_torch)\n    else:\n        return mem_total\n\ntotal_vram = get_total_memory(get_torch_device()) / (1024 * 1024)\ntotal_ram = psutil.virtual_memory().total / (1024 * 1024)\nprint(\"Total VRAM {:0.0f} MB, total RAM {:0.0f} MB\".format(total_vram, total_ram))\nif not args.always_normal_vram and not args.always_cpu:\n    if lowvram_available and total_vram <= 4096:\n        print(\"Trying to enable lowvram mode because your GPU seems to have 4GB or less. If you don't want this use: --always-normal-vram\")\n        set_vram_to = VRAMState.LOW_VRAM\n\ntry:\n    OOM_EXCEPTION = torch.cuda.OutOfMemoryError\nexcept:\n    OOM_EXCEPTION = Exception\n\nXFORMERS_VERSION = \"\"\nXFORMERS_ENABLED_VAE = True\nif args.disable_xformers:\n    XFORMERS_IS_AVAILABLE = False\nelse:\n    try:\n        import xformers\n        import xformers.ops\n        XFORMERS_IS_AVAILABLE = True\n        try:\n            XFORMERS_IS_AVAILABLE = xformers._has_cpp_library\n        except:\n            pass\n        try:\n            XFORMERS_VERSION = xformers.version.__version__\n            print(\"xformers version:\", XFORMERS_VERSION)\n            if XFORMERS_VERSION.startswith(\"0.0.18\"):\n                print()\n                print(\"WARNING: This version of xformers has a major bug where you will get black images when generating high resolution images.\")\n                print(\"Please downgrade or upgrade xformers to a different version.\")\n                print()\n                XFORMERS_ENABLED_VAE = False\n        except:\n            pass\n    except:\n        XFORMERS_IS_AVAILABLE = False\n\ndef is_nvidia():\n    global cpu_state\n    if cpu_state == CPUState.GPU:\n        if torch.version.cuda:\n            return True\n    return False\n\nENABLE_PYTORCH_ATTENTION = False\nif args.attention_pytorch:\n    ENABLE_PYTORCH_ATTENTION = True\n    XFORMERS_IS_AVAILABLE = False\n\nVAE_DTYPE = torch.float32\n\ntry:\n    if is_nvidia():\n        torch_version = torch.version.__version__\n        if int(torch_version[0]) >= 2:\n            if ENABLE_PYTORCH_ATTENTION == False and args.attention_split == False and args.attention_quad == False:\n                ENABLE_PYTORCH_ATTENTION = True\n            if torch.cuda.is_bf16_supported() and torch.cuda.get_device_properties(torch.cuda.current_device()).major >= 8:\n                VAE_DTYPE = torch.bfloat16\n    if is_intel_xpu():\n        if args.attention_split == False and args.attention_quad == False:\n            ENABLE_PYTORCH_ATTENTION = True\nexcept:\n    pass\n\nif is_intel_xpu():\n    VAE_DTYPE = torch.bfloat16\n\nif args.vae_in_cpu:\n    VAE_DTYPE = torch.float32\n\nif args.vae_in_fp16:\n    VAE_DTYPE = torch.float16\nelif args.vae_in_bf16:\n    VAE_DTYPE = torch.bfloat16\nelif args.vae_in_fp32:\n    VAE_DTYPE = torch.float32\n\n\nif ENABLE_PYTORCH_ATTENTION:\n    torch.backends.cuda.enable_math_sdp(True)\n    torch.backends.cuda.enable_flash_sdp(True)\n    torch.backends.cuda.enable_mem_efficient_sdp(True)\n\nif args.always_low_vram:\n    set_vram_to = VRAMState.LOW_VRAM\n    lowvram_available = True\nelif args.always_no_vram:\n    set_vram_to = VRAMState.NO_VRAM\nelif args.always_high_vram or args.always_gpu:\n    vram_state = VRAMState.HIGH_VRAM\n\nFORCE_FP32 = False\nFORCE_FP16 = False\nif args.all_in_fp32:\n    print(\"Forcing FP32, if this improves things please report it.\")\n    FORCE_FP32 = True\n\nif args.all_in_fp16:\n    print(\"Forcing FP16.\")\n    FORCE_FP16 = True\n\nif lowvram_available:\n    if set_vram_to in (VRAMState.LOW_VRAM, VRAMState.NO_VRAM):\n        vram_state = set_vram_to\n\n\nif cpu_state != CPUState.GPU:\n    vram_state = VRAMState.DISABLED\n\nif cpu_state == CPUState.MPS:\n    vram_state = VRAMState.SHARED\n\nprint(f\"Set vram state to: {vram_state.name}\")\n\nALWAYS_VRAM_OFFLOAD = args.always_offload_from_vram\n\nif ALWAYS_VRAM_OFFLOAD:\n    print(\"Always offload VRAM\")\n\ndef get_torch_device_name(device):\n    if hasattr(device, 'type'):\n        if device.type == \"cuda\":\n            try:\n                allocator_backend = torch.cuda.get_allocator_backend()\n            except:\n                allocator_backend = \"\"\n            return \"{} {} : {}\".format(device, torch.cuda.get_device_name(device), allocator_backend)\n        else:\n            return \"{}\".format(device.type)\n    elif is_intel_xpu():\n        return \"{} {}\".format(device, torch.xpu.get_device_name(device))\n    else:\n        return \"CUDA {}: {}\".format(device, torch.cuda.get_device_name(device))\n\ntry:\n    print(\"Device:\", get_torch_device_name(get_torch_device()))\nexcept:\n    print(\"Could not pick default device.\")\n\nprint(\"VAE dtype:\", VAE_DTYPE)\n\ncurrent_loaded_models = []\n\ndef module_size(module):\n    module_mem = 0\n    sd = module.state_dict()\n    for k in sd:\n        t = sd[k]\n        module_mem += t.nelement() * t.element_size()\n    return module_mem\n\nclass LoadedModel:\n    def __init__(self, model):\n        self.model = model\n        self.model_accelerated = False\n        self.device = model.load_device\n\n    def model_memory(self):\n        return self.model.model_size()\n\n    def model_memory_required(self, device):\n        if device == self.model.current_device:\n            return 0\n        else:\n            return self.model_memory()\n\n    def model_load(self, lowvram_model_memory=0):\n        patch_model_to = None\n        if lowvram_model_memory == 0:\n            patch_model_to = self.device\n\n        self.model.model_patches_to(self.device)\n        self.model.model_patches_to(self.model.model_dtype())\n\n        try:\n            self.real_model = self.model.patch_model(device_to=patch_model_to) #TODO: do something with loras and offloading to CPU\n        except Exception as e:\n            self.model.unpatch_model(self.model.offload_device)\n            self.model_unload()\n            raise e\n\n        if lowvram_model_memory > 0:\n            print(\"loading in lowvram mode\", lowvram_model_memory/(1024 * 1024))\n            mem_counter = 0\n            for m in self.real_model.modules():\n                if hasattr(m, \"ldm_patched_cast_weights\"):\n                    m.prev_ldm_patched_cast_weights = m.ldm_patched_cast_weights\n                    m.ldm_patched_cast_weights = True\n                    module_mem = module_size(m)\n                    if mem_counter + module_mem < lowvram_model_memory:\n                        m.to(self.device)\n                        mem_counter += module_mem\n                elif hasattr(m, \"weight\"): #only modules with ldm_patched_cast_weights can be set to lowvram mode\n                    m.to(self.device)\n                    mem_counter += module_size(m)\n                    print(\"lowvram: loaded module regularly\", m)\n\n            self.model_accelerated = True\n\n        if is_intel_xpu() and not args.disable_ipex_hijack:\n            self.real_model = torch.xpu.optimize(self.real_model.eval(), inplace=True, auto_kernel_selection=True, graph_mode=True)\n\n        return self.real_model\n\n    def model_unload(self):\n        if self.model_accelerated:\n            for m in self.real_model.modules():\n                if hasattr(m, \"prev_ldm_patched_cast_weights\"):\n                    m.ldm_patched_cast_weights = m.prev_ldm_patched_cast_weights\n                    del m.prev_ldm_patched_cast_weights\n\n            self.model_accelerated = False\n\n        self.model.unpatch_model(self.model.offload_device)\n        self.model.model_patches_to(self.model.offload_device)\n\n    def __eq__(self, other):\n        return self.model is other.model\n\ndef minimum_inference_memory():\n    return (1024 * 1024 * 1024)\n\ndef unload_model_clones(model):\n    to_unload = []\n    for i in range(len(current_loaded_models)):\n        if model.is_clone(current_loaded_models[i].model):\n            to_unload = [i] + to_unload\n\n    for i in to_unload:\n        print(\"unload clone\", i)\n        current_loaded_models.pop(i).model_unload()\n\ndef free_memory(memory_required, device, keep_loaded=[]):\n    unloaded_model = False\n    for i in range(len(current_loaded_models) -1, -1, -1):\n        if not ALWAYS_VRAM_OFFLOAD:\n            if get_free_memory(device) > memory_required:\n                break\n        shift_model = current_loaded_models[i]\n        if shift_model.device == device:\n            if shift_model not in keep_loaded:\n                m = current_loaded_models.pop(i)\n                m.model_unload()\n                del m\n                unloaded_model = True\n\n    if unloaded_model:\n        soft_empty_cache()\n    else:\n        if vram_state != VRAMState.HIGH_VRAM:\n            mem_free_total, mem_free_torch = get_free_memory(device, torch_free_too=True)\n            if mem_free_torch > mem_free_total * 0.25:\n                soft_empty_cache()\n\ndef load_models_gpu(models, memory_required=0):\n    global vram_state\n\n    inference_memory = minimum_inference_memory()\n    extra_mem = max(inference_memory, memory_required)\n\n    models_to_load = []\n    models_already_loaded = []\n    for x in models:\n        loaded_model = LoadedModel(x)\n\n        if loaded_model in current_loaded_models:\n            index = current_loaded_models.index(loaded_model)\n            current_loaded_models.insert(0, current_loaded_models.pop(index))\n            models_already_loaded.append(loaded_model)\n        else:\n            if hasattr(x, \"model\"):\n                print(f\"Requested to load {x.model.__class__.__name__}\")\n            models_to_load.append(loaded_model)\n\n    if len(models_to_load) == 0:\n        devs = set(map(lambda a: a.device, models_already_loaded))\n        for d in devs:\n            if d != torch.device(\"cpu\"):\n                free_memory(extra_mem, d, models_already_loaded)\n        return\n\n    print(f\"Loading {len(models_to_load)} new model{'s' if len(models_to_load) > 1 else ''}\")\n\n    total_memory_required = {}\n    for loaded_model in models_to_load:\n        unload_model_clones(loaded_model.model)\n        total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device)\n\n    for device in total_memory_required:\n        if device != torch.device(\"cpu\"):\n            free_memory(total_memory_required[device] * 1.3 + extra_mem, device, models_already_loaded)\n\n    for loaded_model in models_to_load:\n        model = loaded_model.model\n        torch_dev = model.load_device\n        if is_device_cpu(torch_dev):\n            vram_set_state = VRAMState.DISABLED\n        else:\n            vram_set_state = vram_state\n        lowvram_model_memory = 0\n        if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM):\n            model_size = loaded_model.model_memory_required(torch_dev)\n            current_free_mem = get_free_memory(torch_dev)\n            lowvram_model_memory = int(max(64 * (1024 * 1024), (current_free_mem - 1024 * (1024 * 1024)) / 1.3 ))\n            if model_size > (current_free_mem - inference_memory): #only switch to lowvram if really necessary\n                vram_set_state = VRAMState.LOW_VRAM\n            else:\n                lowvram_model_memory = 0\n\n        if vram_set_state == VRAMState.NO_VRAM:\n            lowvram_model_memory = 64 * 1024 * 1024\n\n        cur_loaded_model = loaded_model.model_load(lowvram_model_memory)\n        current_loaded_models.insert(0, loaded_model)\n    return\n\n\ndef load_model_gpu(model):\n    return load_models_gpu([model])\n\ndef cleanup_models():\n    to_delete = []\n    for i in range(len(current_loaded_models)):\n        if sys.getrefcount(current_loaded_models[i].model) <= 2:\n            to_delete = [i] + to_delete\n\n    for i in to_delete:\n        x = current_loaded_models.pop(i)\n        x.model_unload()\n        del x\n\ndef dtype_size(dtype):\n    dtype_size = 4\n    if dtype == torch.float16 or dtype == torch.bfloat16:\n        dtype_size = 2\n    elif dtype == torch.float32:\n        dtype_size = 4\n    else:\n        try:\n            dtype_size = dtype.itemsize\n        except: #Old pytorch doesn't have .itemsize\n            pass\n    return dtype_size\n\ndef unet_offload_device():\n    if vram_state == VRAMState.HIGH_VRAM:\n        return get_torch_device()\n    else:\n        return torch.device(\"cpu\")\n\ndef unet_inital_load_device(parameters, dtype):\n    torch_dev = get_torch_device()\n    if vram_state == VRAMState.HIGH_VRAM:\n        return torch_dev\n\n    cpu_dev = torch.device(\"cpu\")\n    if ALWAYS_VRAM_OFFLOAD:\n        return cpu_dev\n\n    model_size = dtype_size(dtype) * parameters\n\n    mem_dev = get_free_memory(torch_dev)\n    mem_cpu = get_free_memory(cpu_dev)\n    if mem_dev > mem_cpu and model_size < mem_dev:\n        return torch_dev\n    else:\n        return cpu_dev\n\ndef unet_dtype(device=None, model_params=0):\n    if args.unet_in_bf16:\n        return torch.bfloat16\n    if args.unet_in_fp16:\n        return torch.float16\n    if args.unet_in_fp8_e4m3fn:\n        return torch.float8_e4m3fn\n    if args.unet_in_fp8_e5m2:\n        return torch.float8_e5m2\n    if should_use_fp16(device=device, model_params=model_params):\n        return torch.float16\n    return torch.float32\n\n# None means no manual cast\ndef unet_manual_cast(weight_dtype, inference_device):\n    if weight_dtype == torch.float32:\n        return None\n\n    fp16_supported = ldm_patched.modules.model_management.should_use_fp16(inference_device, prioritize_performance=False)\n    if fp16_supported and weight_dtype == torch.float16:\n        return None\n\n    if fp16_supported:\n        return torch.float16\n    else:\n        return torch.float32\n\ndef text_encoder_offload_device():\n    if args.always_gpu:\n        return get_torch_device()\n    else:\n        return torch.device(\"cpu\")\n\ndef text_encoder_device():\n    if args.always_gpu:\n        return get_torch_device()\n    elif vram_state == VRAMState.HIGH_VRAM or vram_state == VRAMState.NORMAL_VRAM:\n        if is_intel_xpu():\n            return torch.device(\"cpu\")\n        if should_use_fp16(prioritize_performance=False):\n            return get_torch_device()\n        else:\n            return torch.device(\"cpu\")\n    else:\n        return torch.device(\"cpu\")\n\ndef text_encoder_dtype(device=None):\n    if args.clip_in_fp8_e4m3fn:\n        return torch.float8_e4m3fn\n    elif args.clip_in_fp8_e5m2:\n        return torch.float8_e5m2\n    elif args.clip_in_fp16:\n        return torch.float16\n    elif args.clip_in_fp32:\n        return torch.float32\n\n    if is_device_cpu(device):\n        return torch.float16\n\n    if should_use_fp16(device, prioritize_performance=False):\n        return torch.float16\n    else:\n        return torch.float32\n\ndef intermediate_device():\n    if args.always_gpu:\n        return get_torch_device()\n    else:\n        return torch.device(\"cpu\")\n\ndef vae_device():\n    if args.vae_in_cpu:\n        return torch.device(\"cpu\")\n    return get_torch_device()\n\ndef vae_offload_device():\n    if args.always_gpu:\n        return get_torch_device()\n    else:\n        return torch.device(\"cpu\")\n\ndef vae_dtype():\n    global VAE_DTYPE\n    return VAE_DTYPE\n\ndef get_autocast_device(dev):\n    if hasattr(dev, 'type'):\n        return dev.type\n    return \"cuda\"\n\ndef supports_dtype(device, dtype): #TODO\n    if dtype == torch.float32:\n        return True\n    if is_device_cpu(device):\n        return False\n    if dtype == torch.float16:\n        return True\n    if dtype == torch.bfloat16:\n        return True\n    return False\n\ndef device_supports_non_blocking(device):\n    if is_device_mps(device):\n        return False #pytorch bug? mps doesn't support non blocking\n    return True\n\ndef cast_to_device(tensor, device, dtype, copy=False):\n    device_supports_cast = False\n    if tensor.dtype == torch.float32 or tensor.dtype == torch.float16:\n        device_supports_cast = True\n    elif tensor.dtype == torch.bfloat16:\n        if hasattr(device, 'type') and device.type.startswith(\"cuda\"):\n            device_supports_cast = True\n        elif is_intel_xpu():\n            device_supports_cast = True\n\n    non_blocking = device_supports_non_blocking(device)\n\n    if device_supports_cast:\n        if copy:\n            if tensor.device == device:\n                return tensor.to(dtype, copy=copy, non_blocking=non_blocking)\n            return tensor.to(device, copy=copy, non_blocking=non_blocking).to(dtype, non_blocking=non_blocking)\n        else:\n            return tensor.to(device, non_blocking=non_blocking).to(dtype, non_blocking=non_blocking)\n    else:\n        return tensor.to(device, dtype, copy=copy, non_blocking=non_blocking)\n\ndef xformers_enabled():\n    global directml_enabled\n    global cpu_state\n    if cpu_state != CPUState.GPU:\n        return False\n    if is_intel_xpu():\n        return False\n    if directml_enabled:\n        return False\n    return XFORMERS_IS_AVAILABLE\n\n\ndef xformers_enabled_vae():\n    enabled = xformers_enabled()\n    if not enabled:\n        return False\n\n    return XFORMERS_ENABLED_VAE\n\ndef pytorch_attention_enabled():\n    global ENABLE_PYTORCH_ATTENTION\n    return ENABLE_PYTORCH_ATTENTION\n\ndef pytorch_attention_flash_attention():\n    global ENABLE_PYTORCH_ATTENTION\n    if ENABLE_PYTORCH_ATTENTION:\n        #TODO: more reliable way of checking for flash attention?\n        if is_nvidia(): #pytorch flash attention only works on Nvidia\n            return True\n    return False\n\ndef get_free_memory(dev=None, torch_free_too=False):\n    global directml_enabled\n    if dev is None:\n        dev = get_torch_device()\n\n    if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'):\n        mem_free_total = psutil.virtual_memory().available\n        mem_free_torch = mem_free_total\n    else:\n        if directml_enabled:\n            mem_free_total = 1024 * 1024 * 1024 #TODO\n            mem_free_torch = mem_free_total\n        elif is_intel_xpu():\n            stats = torch.xpu.memory_stats(dev)\n            mem_active = stats['active_bytes.all.current']\n            mem_allocated = stats['allocated_bytes.all.current']\n            mem_reserved = stats['reserved_bytes.all.current']\n            mem_free_torch = mem_reserved - mem_active\n            mem_free_total = torch.xpu.get_device_properties(dev).total_memory - mem_allocated\n        else:\n            stats = torch.cuda.memory_stats(dev)\n            mem_active = stats['active_bytes.all.current']\n            mem_reserved = stats['reserved_bytes.all.current']\n            mem_free_cuda, _ = torch.cuda.mem_get_info(dev)\n            mem_free_torch = mem_reserved - mem_active\n            mem_free_total = mem_free_cuda + mem_free_torch\n\n    if torch_free_too:\n        return (mem_free_total, mem_free_torch)\n    else:\n        return mem_free_total\n\ndef cpu_mode():\n    global cpu_state\n    return cpu_state == CPUState.CPU\n\ndef mps_mode():\n    global cpu_state\n    return cpu_state == CPUState.MPS\n\ndef is_device_cpu(device):\n    if hasattr(device, 'type'):\n        if (device.type == 'cpu'):\n            return True\n    return False\n\ndef is_device_mps(device):\n    if hasattr(device, 'type'):\n        if (device.type == 'mps'):\n            return True\n    return False\n\ndef should_use_fp16(device=None, model_params=0, prioritize_performance=True):\n    global directml_enabled\n\n    if device is not None:\n        if is_device_cpu(device):\n            return False\n\n    if FORCE_FP16:\n        return True\n\n    if device is not None: #TODO\n        if is_device_mps(device):\n            return False\n\n    if FORCE_FP32:\n        return False\n\n    if directml_enabled:\n        return False\n\n    if cpu_mode() or mps_mode():\n        return False #TODO ?\n\n    if is_intel_xpu():\n        return True\n\n    if torch.cuda.is_bf16_supported():\n        return True\n\n    props = torch.cuda.get_device_properties(\"cuda\")\n    if props.major < 6:\n        return False\n\n    fp16_works = False\n    #FP16 is confirmed working on a 1080 (GP104) but it's a bit slower than FP32 so it should only be enabled\n    #when the model doesn't actually fit on the card\n    #TODO: actually test if GP106 and others have the same type of behavior\n    nvidia_10_series = [\"1080\", \"1070\", \"titan x\", \"p3000\", \"p3200\", \"p4000\", \"p4200\", \"p5000\", \"p5200\", \"p6000\", \"1060\", \"1050\"]\n    for x in nvidia_10_series:\n        if x in props.name.lower():\n            fp16_works = True\n\n    if fp16_works:\n        free_model_memory = (get_free_memory() * 0.9 - minimum_inference_memory())\n        if (not prioritize_performance) or model_params * 4 > free_model_memory:\n            return True\n\n    if props.major < 7:\n        return False\n\n    #FP16 is just broken on these cards\n    nvidia_16_series = [\"1660\", \"1650\", \"1630\", \"T500\", \"T550\", \"T600\", \"MX550\", \"MX450\", \"CMP 30HX\", \"T2000\", \"T1000\", \"T1200\"]\n    for x in nvidia_16_series:\n        if x in props.name:\n            return False\n\n    return True\n\ndef soft_empty_cache(force=False):\n    global cpu_state\n    if cpu_state == CPUState.MPS:\n        torch.mps.empty_cache()\n    elif is_intel_xpu():\n        torch.xpu.empty_cache()\n    elif torch.cuda.is_available():\n        if force or is_nvidia(): #This seems to make things worse on ROCm so I only do it for cuda\n            torch.cuda.empty_cache()\n            torch.cuda.ipc_collect()\n\ndef unload_all_models():\n    free_memory(1e30, get_torch_device())\n\n\ndef resolve_lowvram_weight(weight, model, key): #TODO: remove\n    return weight\n\n#TODO: might be cleaner to put this somewhere else\nimport threading\n\nclass InterruptProcessingException(Exception):\n    pass\n\ninterrupt_processing_mutex = threading.RLock()\n\ninterrupt_processing = False\ndef interrupt_current_processing(value=True):\n    global interrupt_processing\n    global interrupt_processing_mutex\n    with interrupt_processing_mutex:\n        interrupt_processing = value\n\ndef processing_interrupted():\n    global interrupt_processing\n    global interrupt_processing_mutex\n    with interrupt_processing_mutex:\n        return interrupt_processing\n\ndef throw_exception_if_processing_interrupted():\n    global interrupt_processing\n    global interrupt_processing_mutex\n    with interrupt_processing_mutex:\n        if interrupt_processing:\n            interrupt_processing = False\n            raise InterruptProcessingException()\n"
  },
  {
    "path": "ldm_patched/modules/model_patcher.py",
    "content": "import torch\nimport copy\nimport inspect\n\nimport ldm_patched.modules.utils\nimport ldm_patched.modules.model_management\n\nclass ModelPatcher:\n    def __init__(self, model, load_device, offload_device, size=0, current_device=None, weight_inplace_update=False):\n        self.size = size\n        self.model = model\n        self.patches = {}\n        self.backup = {}\n        self.object_patches = {}\n        self.object_patches_backup = {}\n        self.model_options = {\"transformer_options\":{}}\n        self.model_size()\n        self.load_device = load_device\n        self.offload_device = offload_device\n        if current_device is None:\n            self.current_device = self.offload_device\n        else:\n            self.current_device = current_device\n\n        self.weight_inplace_update = weight_inplace_update\n\n    def model_size(self):\n        if self.size > 0:\n            return self.size\n        model_sd = self.model.state_dict()\n        self.size = ldm_patched.modules.model_management.module_size(self.model)\n        self.model_keys = set(model_sd.keys())\n        return self.size\n\n    def clone(self):\n        n = ModelPatcher(self.model, self.load_device, self.offload_device, self.size, self.current_device, weight_inplace_update=self.weight_inplace_update)\n        n.patches = {}\n        for k in self.patches:\n            n.patches[k] = self.patches[k][:]\n\n        n.object_patches = self.object_patches.copy()\n        n.model_options = copy.deepcopy(self.model_options)\n        n.model_keys = self.model_keys\n        return n\n\n    def is_clone(self, other):\n        if hasattr(other, 'model') and self.model is other.model:\n            return True\n        return False\n\n    def memory_required(self, input_shape):\n        return self.model.memory_required(input_shape=input_shape)\n\n    def set_model_sampler_cfg_function(self, sampler_cfg_function, disable_cfg1_optimization=False):\n        if len(inspect.signature(sampler_cfg_function).parameters) == 3:\n            self.model_options[\"sampler_cfg_function\"] = lambda args: sampler_cfg_function(args[\"cond\"], args[\"uncond\"], args[\"cond_scale\"]) #Old way\n        else:\n            self.model_options[\"sampler_cfg_function\"] = sampler_cfg_function\n        if disable_cfg1_optimization:\n            self.model_options[\"disable_cfg1_optimization\"] = True\n\n    def set_model_sampler_post_cfg_function(self, post_cfg_function, disable_cfg1_optimization=False):\n        self.model_options[\"sampler_post_cfg_function\"] = self.model_options.get(\"sampler_post_cfg_function\", []) + [post_cfg_function]\n        if disable_cfg1_optimization:\n            self.model_options[\"disable_cfg1_optimization\"] = True\n\n    def set_model_unet_function_wrapper(self, unet_wrapper_function):\n        self.model_options[\"model_function_wrapper\"] = unet_wrapper_function\n\n    def set_model_patch(self, patch, name):\n        to = self.model_options[\"transformer_options\"]\n        if \"patches\" not in to:\n            to[\"patches\"] = {}\n        to[\"patches\"][name] = to[\"patches\"].get(name, []) + [patch]\n\n    def set_model_patch_replace(self, patch, name, block_name, number, transformer_index=None):\n        to = self.model_options[\"transformer_options\"]\n        if \"patches_replace\" not in to:\n            to[\"patches_replace\"] = {}\n        if name not in to[\"patches_replace\"]:\n            to[\"patches_replace\"][name] = {}\n        if transformer_index is not None:\n            block = (block_name, number, transformer_index)\n        else:\n            block = (block_name, number)\n        to[\"patches_replace\"][name][block] = patch\n\n    def set_model_attn1_patch(self, patch):\n        self.set_model_patch(patch, \"attn1_patch\")\n\n    def set_model_attn2_patch(self, patch):\n        self.set_model_patch(patch, \"attn2_patch\")\n\n    def set_model_attn1_replace(self, patch, block_name, number, transformer_index=None):\n        self.set_model_patch_replace(patch, \"attn1\", block_name, number, transformer_index)\n\n    def set_model_attn2_replace(self, patch, block_name, number, transformer_index=None):\n        self.set_model_patch_replace(patch, \"attn2\", block_name, number, transformer_index)\n\n    def set_model_attn1_output_patch(self, patch):\n        self.set_model_patch(patch, \"attn1_output_patch\")\n\n    def set_model_attn2_output_patch(self, patch):\n        self.set_model_patch(patch, \"attn2_output_patch\")\n\n    def set_model_input_block_patch(self, patch):\n        self.set_model_patch(patch, \"input_block_patch\")\n\n    def set_model_input_block_patch_after_skip(self, patch):\n        self.set_model_patch(patch, \"input_block_patch_after_skip\")\n\n    def set_model_output_block_patch(self, patch):\n        self.set_model_patch(patch, \"output_block_patch\")\n\n    def add_object_patch(self, name, obj):\n        self.object_patches[name] = obj\n\n    def model_patches_to(self, device):\n        to = self.model_options[\"transformer_options\"]\n        if \"patches\" in to:\n            patches = to[\"patches\"]\n            for name in patches:\n                patch_list = patches[name]\n                for i in range(len(patch_list)):\n                    if hasattr(patch_list[i], \"to\"):\n                        patch_list[i] = patch_list[i].to(device)\n        if \"patches_replace\" in to:\n            patches = to[\"patches_replace\"]\n            for name in patches:\n                patch_list = patches[name]\n                for k in patch_list:\n                    if hasattr(patch_list[k], \"to\"):\n                        patch_list[k] = patch_list[k].to(device)\n        if \"model_function_wrapper\" in self.model_options:\n            wrap_func = self.model_options[\"model_function_wrapper\"]\n            if hasattr(wrap_func, \"to\"):\n                self.model_options[\"model_function_wrapper\"] = wrap_func.to(device)\n\n    def model_dtype(self):\n        if hasattr(self.model, \"get_dtype\"):\n            return self.model.get_dtype()\n\n    def add_patches(self, patches, strength_patch=1.0, strength_model=1.0):\n        p = set()\n        for k in patches:\n            if k in self.model_keys:\n                p.add(k)\n                current_patches = self.patches.get(k, [])\n                current_patches.append((strength_patch, patches[k], strength_model))\n                self.patches[k] = current_patches\n\n        return list(p)\n\n    def get_key_patches(self, filter_prefix=None):\n        ldm_patched.modules.model_management.unload_model_clones(self)\n        model_sd = self.model_state_dict()\n        p = {}\n        for k in model_sd:\n            if filter_prefix is not None:\n                if not k.startswith(filter_prefix):\n                    continue\n            if k in self.patches:\n                p[k] = [model_sd[k]] + self.patches[k]\n            else:\n                p[k] = (model_sd[k],)\n        return p\n\n    def model_state_dict(self, filter_prefix=None):\n        sd = self.model.state_dict()\n        keys = list(sd.keys())\n        if filter_prefix is not None:\n            for k in keys:\n                if not k.startswith(filter_prefix):\n                    sd.pop(k)\n        return sd\n\n    def patch_model(self, device_to=None, patch_weights=True):\n        for k in self.object_patches:\n            old = getattr(self.model, k)\n            if k not in self.object_patches_backup:\n                self.object_patches_backup[k] = old\n            setattr(self.model, k, self.object_patches[k])\n\n        if patch_weights:\n            model_sd = self.model_state_dict()\n            for key in self.patches:\n                if key not in model_sd:\n                    print(\"could not patch. key doesn't exist in model:\", key)\n                    continue\n\n                weight = model_sd[key]\n\n                inplace_update = self.weight_inplace_update\n\n                if key not in self.backup:\n                    self.backup[key] = weight.to(device=self.offload_device, copy=inplace_update)\n\n                if device_to is not None:\n                    temp_weight = ldm_patched.modules.model_management.cast_to_device(weight, device_to, torch.float32, copy=True)\n                else:\n                    temp_weight = weight.to(torch.float32, copy=True)\n                out_weight = self.calculate_weight(self.patches[key], temp_weight, key).to(weight.dtype)\n                if inplace_update:\n                    ldm_patched.modules.utils.copy_to_param(self.model, key, out_weight)\n                else:\n                    ldm_patched.modules.utils.set_attr(self.model, key, out_weight)\n                del temp_weight\n\n            if device_to is not None:\n                self.model.to(device_to)\n                self.current_device = device_to\n\n        return self.model\n\n    def calculate_weight(self, patches, weight, key):\n        for p in patches:\n            alpha = p[0]\n            v = p[1]\n            strength_model = p[2]\n\n            if strength_model != 1.0:\n                weight *= strength_model\n\n            if isinstance(v, list):\n                v = (self.calculate_weight(v[1:], v[0].clone(), key), )\n\n            if len(v) == 1:\n                patch_type = \"diff\"\n            elif len(v) == 2:\n                patch_type = v[0]\n                v = v[1]\n\n            if patch_type == \"diff\":\n                w1 = v[0]\n                if alpha != 0.0:\n                    if w1.shape != weight.shape:\n                        print(\"WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}\".format(key, w1.shape, weight.shape))\n                    else:\n                        weight += alpha * ldm_patched.modules.model_management.cast_to_device(w1, weight.device, weight.dtype)\n            elif patch_type == \"lora\": #lora/locon\n                mat1 = ldm_patched.modules.model_management.cast_to_device(v[0], weight.device, torch.float32)\n                mat2 = ldm_patched.modules.model_management.cast_to_device(v[1], weight.device, torch.float32)\n                if v[2] is not None:\n                    alpha *= v[2] / mat2.shape[0]\n                if v[3] is not None:\n                    #locon mid weights, hopefully the math is fine because I didn't properly test it\n                    mat3 = ldm_patched.modules.model_management.cast_to_device(v[3], weight.device, torch.float32)\n                    final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]]\n                    mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1), mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1)\n                try:\n                    weight += (alpha * torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1))).reshape(weight.shape).type(weight.dtype)\n                except Exception as e:\n                    print(\"ERROR\", key, e)\n            elif patch_type == \"lokr\":\n                w1 = v[0]\n                w2 = v[1]\n                w1_a = v[3]\n                w1_b = v[4]\n                w2_a = v[5]\n                w2_b = v[6]\n                t2 = v[7]\n                dim = None\n\n                if w1 is None:\n                    dim = w1_b.shape[0]\n                    w1 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w1_a, weight.device, torch.float32),\n                                  ldm_patched.modules.model_management.cast_to_device(w1_b, weight.device, torch.float32))\n                else:\n                    w1 = ldm_patched.modules.model_management.cast_to_device(w1, weight.device, torch.float32)\n\n                if w2 is None:\n                    dim = w2_b.shape[0]\n                    if t2 is None:\n                        w2 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w2_a, weight.device, torch.float32),\n                                      ldm_patched.modules.model_management.cast_to_device(w2_b, weight.device, torch.float32))\n                    else:\n                        w2 = torch.einsum('i j k l, j r, i p -> p r k l',\n                                          ldm_patched.modules.model_management.cast_to_device(t2, weight.device, torch.float32),\n                                          ldm_patched.modules.model_management.cast_to_device(w2_b, weight.device, torch.float32),\n                                          ldm_patched.modules.model_management.cast_to_device(w2_a, weight.device, torch.float32))\n                else:\n                    w2 = ldm_patched.modules.model_management.cast_to_device(w2, weight.device, torch.float32)\n\n                if len(w2.shape) == 4:\n                    w1 = w1.unsqueeze(2).unsqueeze(2)\n                if v[2] is not None and dim is not None:\n                    alpha *= v[2] / dim\n\n                try:\n                    weight += alpha * torch.kron(w1, w2).reshape(weight.shape).type(weight.dtype)\n                except Exception as e:\n                    print(\"ERROR\", key, e)\n            elif patch_type == \"loha\":\n                w1a = v[0]\n                w1b = v[1]\n                if v[2] is not None:\n                    alpha *= v[2] / w1b.shape[0]\n                w2a = v[3]\n                w2b = v[4]\n                if v[5] is not None: #cp decomposition\n                    t1 = v[5]\n                    t2 = v[6]\n                    m1 = torch.einsum('i j k l, j r, i p -> p r k l',\n                                      ldm_patched.modules.model_management.cast_to_device(t1, weight.device, torch.float32),\n                                      ldm_patched.modules.model_management.cast_to_device(w1b, weight.device, torch.float32),\n                                      ldm_patched.modules.model_management.cast_to_device(w1a, weight.device, torch.float32))\n\n                    m2 = torch.einsum('i j k l, j r, i p -> p r k l',\n                                      ldm_patched.modules.model_management.cast_to_device(t2, weight.device, torch.float32),\n                                      ldm_patched.modules.model_management.cast_to_device(w2b, weight.device, torch.float32),\n                                      ldm_patched.modules.model_management.cast_to_device(w2a, weight.device, torch.float32))\n                else:\n                    m1 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w1a, weight.device, torch.float32),\n                                  ldm_patched.modules.model_management.cast_to_device(w1b, weight.device, torch.float32))\n                    m2 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w2a, weight.device, torch.float32),\n                                  ldm_patched.modules.model_management.cast_to_device(w2b, weight.device, torch.float32))\n\n                try:\n                    weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype)\n                except Exception as e:\n                    print(\"ERROR\", key, e)\n            elif patch_type == \"glora\":\n                if v[4] is not None:\n                    alpha *= v[4] / v[0].shape[0]\n\n                a1 = ldm_patched.modules.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, torch.float32)\n                a2 = ldm_patched.modules.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, torch.float32)\n                b1 = ldm_patched.modules.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, torch.float32)\n                b2 = ldm_patched.modules.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, torch.float32)\n\n                weight += ((torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)) * alpha).reshape(weight.shape).type(weight.dtype)\n            else:\n                print(\"patch type not recognized\", patch_type, key)\n\n        return weight\n\n    def unpatch_model(self, device_to=None):\n        keys = list(self.backup.keys())\n\n        if self.weight_inplace_update:\n            for k in keys:\n                ldm_patched.modules.utils.copy_to_param(self.model, k, self.backup[k])\n        else:\n            for k in keys:\n                ldm_patched.modules.utils.set_attr(self.model, k, self.backup[k])\n\n        self.backup = {}\n\n        if device_to is not None:\n            self.model.to(device_to)\n            self.current_device = device_to\n\n        keys = list(self.object_patches_backup.keys())\n        for k in keys:\n            setattr(self.model, k, self.object_patches_backup[k])\n\n        self.object_patches_backup = {}\n"
  },
  {
    "path": "ldm_patched/modules/model_sampling.py",
    "content": "import torch\nfrom ldm_patched.ldm.modules.diffusionmodules.util import make_beta_schedule\nimport math\nimport numpy as np\n\nclass EPS:\n    def calculate_input(self, sigma, noise):\n        sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))\n        return noise / (sigma ** 2 + self.sigma_data ** 2) ** 0.5\n\n    def calculate_denoised(self, sigma, model_output, model_input):\n        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))\n        return model_input - model_output * sigma\n\n    def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):\n        if max_denoise:\n            noise = noise * torch.sqrt(1.0 + sigma ** 2.0)\n        else:\n            noise = noise * sigma\n\n        noise += latent_image\n        return noise\n\n    def inverse_noise_scaling(self, sigma, latent):\n        return latent\n\nclass V_PREDICTION(EPS):\n    def calculate_denoised(self, sigma, model_output, model_input):\n        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))\n        return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) - model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5\n\nclass EDM(V_PREDICTION):\n    def calculate_denoised(self, sigma, model_output, model_input):\n        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))\n        return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) + model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5\n\n\nclass ModelSamplingDiscrete(torch.nn.Module):\n    def __init__(self, model_config=None):\n        super().__init__()\n\n        if model_config is not None:\n            sampling_settings = model_config.sampling_settings\n        else:\n            sampling_settings = {}\n\n        beta_schedule = sampling_settings.get(\"beta_schedule\", \"linear\")\n        linear_start = sampling_settings.get(\"linear_start\", 0.00085)\n        linear_end = sampling_settings.get(\"linear_end\", 0.012)\n\n        self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=1000, linear_start=linear_start, linear_end=linear_end, cosine_s=8e-3)\n        self.sigma_data = 1.0\n\n    def _register_schedule(self, given_betas=None, beta_schedule=\"linear\", timesteps=1000,\n                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):\n        if given_betas is not None:\n            betas = given_betas\n        else:\n            betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)\n        alphas = 1. - betas\n        alphas_cumprod = torch.cumprod(alphas, dim=0)\n\n        timesteps, = betas.shape\n        self.num_timesteps = int(timesteps)\n        self.linear_start = linear_start\n        self.linear_end = linear_end\n\n        # self.register_buffer('betas', torch.tensor(betas, dtype=torch.float32))\n        # self.register_buffer('alphas_cumprod', torch.tensor(alphas_cumprod, dtype=torch.float32))\n        # self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32))\n\n        sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5\n        alphas_cumprod = torch.tensor(np.cumprod(alphas, axis=0), dtype=torch.float32)\n        self.set_sigmas(sigmas)\n        self.set_alphas_cumprod(alphas_cumprod.float())\n\n    def set_sigmas(self, sigmas):\n        self.register_buffer('sigmas', sigmas.float())\n        self.register_buffer('log_sigmas', sigmas.log().float())\n\n    def set_alphas_cumprod(self, alphas_cumprod):\n        self.register_buffer(\"alphas_cumprod\", alphas_cumprod.float())\n\n    @property\n    def sigma_min(self):\n        return self.sigmas[0]\n\n    @property\n    def sigma_max(self):\n        return self.sigmas[-1]\n\n    def timestep(self, sigma):\n        log_sigma = sigma.log()\n        dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None]\n        return dists.abs().argmin(dim=0).view(sigma.shape).to(sigma.device)\n\n    def sigma(self, timestep):\n        t = torch.clamp(timestep.float().to(self.log_sigmas.device), min=0, max=(len(self.sigmas) - 1))\n        low_idx = t.floor().long()\n        high_idx = t.ceil().long()\n        w = t.frac()\n        log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx]\n        return log_sigma.exp().to(timestep.device)\n\n    def percent_to_sigma(self, percent):\n        if percent <= 0.0:\n            return 999999999.9\n        if percent >= 1.0:\n            return 0.0\n        percent = 1.0 - percent\n        return self.sigma(torch.tensor(percent * 999.0)).item()\n\n\nclass ModelSamplingContinuousEDM(torch.nn.Module):\n    def __init__(self, model_config=None):\n        super().__init__()\n        if model_config is not None:\n            sampling_settings = model_config.sampling_settings\n        else:\n            sampling_settings = {}\n\n        sigma_min = sampling_settings.get(\"sigma_min\", 0.002)\n        sigma_max = sampling_settings.get(\"sigma_max\", 120.0)\n        sigma_data = sampling_settings.get(\"sigma_data\", 1.0)\n        self.set_parameters(sigma_min, sigma_max, sigma_data)\n\n    def set_parameters(self, sigma_min, sigma_max, sigma_data):\n        self.sigma_data = sigma_data\n        sigmas = torch.linspace(math.log(sigma_min), math.log(sigma_max), 1000).exp()\n\n        self.register_buffer('sigmas', sigmas) #for compatibility with some schedulers\n        self.register_buffer('log_sigmas', sigmas.log())\n\n    @property\n    def sigma_min(self):\n        return self.sigmas[0]\n\n    @property\n    def sigma_max(self):\n        return self.sigmas[-1]\n\n    def timestep(self, sigma):\n        return 0.25 * sigma.log()\n\n    def sigma(self, timestep):\n        return (timestep / 0.25).exp()\n\n    def percent_to_sigma(self, percent):\n        if percent <= 0.0:\n            return 999999999.9\n        if percent >= 1.0:\n            return 0.0\n        percent = 1.0 - percent\n\n        log_sigma_min = math.log(self.sigma_min)\n        return math.exp((math.log(self.sigma_max) - log_sigma_min) * percent + log_sigma_min)\n\nclass StableCascadeSampling(ModelSamplingDiscrete):\n    def __init__(self, model_config=None):\n        super().__init__()\n\n        if model_config is not None:\n            sampling_settings = model_config.sampling_settings\n        else:\n            sampling_settings = {}\n\n        self.set_parameters(sampling_settings.get(\"shift\", 1.0))\n\n    def set_parameters(self, shift=1.0, cosine_s=8e-3):\n        self.shift = shift\n        self.cosine_s = torch.tensor(cosine_s)\n        self._init_alpha_cumprod = torch.cos(self.cosine_s / (1 + self.cosine_s) * torch.pi * 0.5) ** 2\n\n        #This part is just for compatibility with some schedulers in the codebase\n        self.num_timesteps = 10000\n        sigmas = torch.empty((self.num_timesteps), dtype=torch.float32)\n        for x in range(self.num_timesteps):\n            t = (x + 1) / self.num_timesteps\n            sigmas[x] = self.sigma(t)\n\n        self.set_sigmas(sigmas)\n\n    def sigma(self, timestep):\n        alpha_cumprod = (torch.cos((timestep + self.cosine_s) / (1 + self.cosine_s) * torch.pi * 0.5) ** 2 / self._init_alpha_cumprod)\n\n        if self.shift != 1.0:\n            var = alpha_cumprod\n            logSNR = (var/(1-var)).log()\n            logSNR += 2 * torch.log(1.0 / torch.tensor(self.shift))\n            alpha_cumprod = logSNR.sigmoid()\n\n        alpha_cumprod = alpha_cumprod.clamp(0.0001, 0.9999)\n        return ((1 - alpha_cumprod) / alpha_cumprod) ** 0.5\n\n    def timestep(self, sigma):\n        var = 1 / ((sigma * sigma) + 1)\n        var = var.clamp(0, 1.0)\n        s, min_var = self.cosine_s.to(var.device), self._init_alpha_cumprod.to(var.device)\n        t = (((var * min_var) ** 0.5).acos() / (torch.pi * 0.5)) * (1 + s) - s\n        return t\n\n    def percent_to_sigma(self, percent):\n        if percent <= 0.0:\n            return 999999999.9\n        if percent >= 1.0:\n            return 0.0\n\n        percent = 1.0 - percent\n        return self.sigma(torch.tensor(percent))"
  },
  {
    "path": "ldm_patched/modules/ops.py",
    "content": "import torch\nimport ldm_patched.modules.model_management\n\ndef cast_bias_weight(s, input):\n    bias = None\n    non_blocking = ldm_patched.modules.model_management.device_supports_non_blocking(input.device)\n    if s.bias is not None:\n        bias = s.bias.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking)\n    weight = s.weight.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking)\n    return weight, bias\n\n\nclass disable_weight_init:\n    class Linear(torch.nn.Linear):\n        ldm_patched_cast_weights = False\n        def reset_parameters(self):\n            return None\n\n        def forward_ldm_patched_cast_weights(self, input):\n            weight, bias = cast_bias_weight(self, input)\n            return torch.nn.functional.linear(input, weight, bias)\n\n        def forward(self, *args, **kwargs):\n            if self.ldm_patched_cast_weights:\n                return self.forward_ldm_patched_cast_weights(*args, **kwargs)\n            else:\n                return super().forward(*args, **kwargs)\n\n    class Conv2d(torch.nn.Conv2d):\n        ldm_patched_cast_weights = False\n        def reset_parameters(self):\n            return None\n\n        def forward_ldm_patched_cast_weights(self, input):\n            weight, bias = cast_bias_weight(self, input)\n            return self._conv_forward(input, weight, bias)\n\n        def forward(self, *args, **kwargs):\n            if self.ldm_patched_cast_weights:\n                return self.forward_ldm_patched_cast_weights(*args, **kwargs)\n            else:\n                return super().forward(*args, **kwargs)\n\n    class Conv3d(torch.nn.Conv3d):\n        ldm_patched_cast_weights = False\n        def reset_parameters(self):\n            return None\n\n        def forward_ldm_patched_cast_weights(self, input):\n            weight, bias = cast_bias_weight(self, input)\n            return self._conv_forward(input, weight, bias)\n\n        def forward(self, *args, **kwargs):\n            if self.ldm_patched_cast_weights:\n                return self.forward_ldm_patched_cast_weights(*args, **kwargs)\n            else:\n                return super().forward(*args, **kwargs)\n\n    class GroupNorm(torch.nn.GroupNorm):\n        ldm_patched_cast_weights = False\n        def reset_parameters(self):\n            return None\n\n        def forward_ldm_patched_cast_weights(self, input):\n            weight, bias = cast_bias_weight(self, input)\n            return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps)\n\n        def forward(self, *args, **kwargs):\n            if self.ldm_patched_cast_weights:\n                return self.forward_ldm_patched_cast_weights(*args, **kwargs)\n            else:\n                return super().forward(*args, **kwargs)\n\n\n    class LayerNorm(torch.nn.LayerNorm):\n        ldm_patched_cast_weights = False\n        def reset_parameters(self):\n            return None\n\n        def forward_ldm_patched_cast_weights(self, input):\n            weight, bias = cast_bias_weight(self, input)\n            return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps)\n\n        def forward(self, *args, **kwargs):\n            if self.ldm_patched_cast_weights:\n                return self.forward_ldm_patched_cast_weights(*args, **kwargs)\n            else:\n                return super().forward(*args, **kwargs)\n\n    @classmethod\n    def conv_nd(s, dims, *args, **kwargs):\n        if dims == 2:\n            return s.Conv2d(*args, **kwargs)\n        elif dims == 3:\n            return s.Conv3d(*args, **kwargs)\n        else:\n            raise ValueError(f\"unsupported dimensions: {dims}\")\n\n\nclass manual_cast(disable_weight_init):\n    class Linear(disable_weight_init.Linear):\n        ldm_patched_cast_weights = True\n\n    class Conv2d(disable_weight_init.Conv2d):\n        ldm_patched_cast_weights = True\n\n    class Conv3d(disable_weight_init.Conv3d):\n        ldm_patched_cast_weights = True\n\n    class GroupNorm(disable_weight_init.GroupNorm):\n        ldm_patched_cast_weights = True\n\n    class LayerNorm(disable_weight_init.LayerNorm):\n        ldm_patched_cast_weights = True\n"
  },
  {
    "path": "ldm_patched/modules/options.py",
    "content": "\nargs_parsing = False\n\ndef enable_args_parsing(enable=True):\n    global args_parsing\n    args_parsing = enable\n"
  },
  {
    "path": "ldm_patched/modules/sample.py",
    "content": "import torch\nimport ldm_patched.modules.model_management\nimport ldm_patched.modules.samplers\nimport ldm_patched.modules.conds\nimport ldm_patched.modules.utils\nimport math\nimport numpy as np\n\ndef prepare_noise(latent_image, seed, noise_inds=None):\n    \"\"\"\n    creates random noise given a latent image and a seed.\n    optional arg skip can be used to skip and discard x number of noise generations for a given seed\n    \"\"\"\n    generator = torch.manual_seed(seed)\n    if noise_inds is None:\n        return torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device=\"cpu\")\n    \n    unique_inds, inverse = np.unique(noise_inds, return_inverse=True)\n    noises = []\n    for i in range(unique_inds[-1]+1):\n        noise = torch.randn([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device=\"cpu\")\n        if i in unique_inds:\n            noises.append(noise)\n    noises = [noises[i] for i in inverse]\n    noises = torch.cat(noises, axis=0)\n    return noises\n\ndef prepare_mask(noise_mask, shape, device):\n    \"\"\"ensures noise mask is of proper dimensions\"\"\"\n    noise_mask = torch.nn.functional.interpolate(noise_mask.reshape((-1, 1, noise_mask.shape[-2], noise_mask.shape[-1])), size=(shape[2], shape[3]), mode=\"bilinear\")\n    noise_mask = torch.cat([noise_mask] * shape[1], dim=1)\n    noise_mask = ldm_patched.modules.utils.repeat_to_batch_size(noise_mask, shape[0])\n    noise_mask = noise_mask.to(device)\n    return noise_mask\n\ndef get_models_from_cond(cond, model_type):\n    models = []\n    for c in cond:\n        if model_type in c:\n            models += [c[model_type]]\n    return models\n\ndef convert_cond(cond):\n    out = []\n    for c in cond:\n        temp = c[1].copy()\n        model_conds = temp.get(\"model_conds\", {})\n        if c[0] is not None:\n            model_conds[\"c_crossattn\"] = ldm_patched.modules.conds.CONDCrossAttn(c[0]) #TODO: remove\n            temp[\"cross_attn\"] = c[0]\n        temp[\"model_conds\"] = model_conds\n        out.append(temp)\n    return out\n\ndef get_additional_models(positive, negative, dtype):\n    \"\"\"loads additional models in positive and negative conditioning\"\"\"\n    control_nets = set(get_models_from_cond(positive, \"control\") + get_models_from_cond(negative, \"control\"))\n\n    inference_memory = 0\n    control_models = []\n    for m in control_nets:\n        control_models += m.get_models()\n        inference_memory += m.inference_memory_requirements(dtype)\n\n    gligen = get_models_from_cond(positive, \"gligen\") + get_models_from_cond(negative, \"gligen\")\n    gligen = [x[1] for x in gligen]\n    models = control_models + gligen\n    return models, inference_memory\n\ndef cleanup_additional_models(models):\n    \"\"\"cleanup additional models that were loaded\"\"\"\n    for m in models:\n        if hasattr(m, 'cleanup'):\n            m.cleanup()\n\ndef prepare_sampling(model, noise_shape, positive, negative, noise_mask):\n    device = model.load_device\n    positive = convert_cond(positive)\n    negative = convert_cond(negative)\n\n    if noise_mask is not None:\n        noise_mask = prepare_mask(noise_mask, noise_shape, device)\n\n    real_model = None\n    models, inference_memory = get_additional_models(positive, negative, model.model_dtype())\n    ldm_patched.modules.model_management.load_models_gpu([model] + models, model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:])) + inference_memory)\n    real_model = model.model\n\n    return real_model, positive, negative, noise_mask, models\n\n\ndef sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):\n    real_model, positive_copy, negative_copy, noise_mask, models = prepare_sampling(model, noise.shape, positive, negative, noise_mask)\n\n    noise = noise.to(model.load_device)\n    latent_image = latent_image.to(model.load_device)\n\n    sampler = ldm_patched.modules.samplers.KSampler(real_model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)\n\n    samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed)\n    samples = samples.to(ldm_patched.modules.model_management.intermediate_device())\n\n    cleanup_additional_models(models)\n    cleanup_additional_models(set(get_models_from_cond(positive_copy, \"control\") + get_models_from_cond(negative_copy, \"control\")))\n    return samples\n\ndef sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=None, callback=None, disable_pbar=False, seed=None):\n    real_model, positive_copy, negative_copy, noise_mask, models = prepare_sampling(model, noise.shape, positive, negative, noise_mask)\n    noise = noise.to(model.load_device)\n    latent_image = latent_image.to(model.load_device)\n    sigmas = sigmas.to(model.load_device)\n\n    samples = ldm_patched.modules.samplers.sample(real_model, noise, positive_copy, negative_copy, cfg, model.load_device, sampler, sigmas, model_options=model.model_options, latent_image=latent_image, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)\n    samples = samples.to(ldm_patched.modules.model_management.intermediate_device())\n    cleanup_additional_models(models)\n    cleanup_additional_models(set(get_models_from_cond(positive_copy, \"control\") + get_models_from_cond(negative_copy, \"control\")))\n    return samples\n\n"
  },
  {
    "path": "ldm_patched/modules/samplers.py",
    "content": "from ldm_patched.k_diffusion import sampling as k_diffusion_sampling\nfrom ldm_patched.unipc import uni_pc\nimport torch\nimport collections\nfrom ldm_patched.modules import model_management\nimport math\n\ndef get_area_and_mult(conds, x_in, timestep_in):\n    area = (x_in.shape[2], x_in.shape[3], 0, 0)\n    strength = 1.0\n\n    if 'timestep_start' in conds:\n        timestep_start = conds['timestep_start']\n        if timestep_in[0] > timestep_start:\n            return None\n    if 'timestep_end' in conds:\n        timestep_end = conds['timestep_end']\n        if timestep_in[0] < timestep_end:\n            return None\n    if 'area' in conds:\n        area = conds['area']\n    if 'strength' in conds:\n        strength = conds['strength']\n\n    input_x = x_in[:,:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]]\n    if 'mask' in conds:\n        # Scale the mask to the size of the input\n        # The mask should have been resized as we began the sampling process\n        mask_strength = 1.0\n        if \"mask_strength\" in conds:\n            mask_strength = conds[\"mask_strength\"]\n        mask = conds['mask']\n        assert(mask.shape[1] == x_in.shape[2])\n        assert(mask.shape[2] == x_in.shape[3])\n        mask = mask[:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]] * mask_strength\n        mask = mask.unsqueeze(1).repeat(input_x.shape[0] // mask.shape[0], input_x.shape[1], 1, 1)\n    else:\n        mask = torch.ones_like(input_x)\n    mult = mask * strength\n\n    if 'mask' not in conds:\n        rr = 8\n        if area[2] != 0:\n            for t in range(rr):\n                mult[:,:,t:1+t,:] *= ((1.0/rr) * (t + 1))\n        if (area[0] + area[2]) < x_in.shape[2]:\n            for t in range(rr):\n                mult[:,:,area[0] - 1 - t:area[0] - t,:] *= ((1.0/rr) * (t + 1))\n        if area[3] != 0:\n            for t in range(rr):\n                mult[:,:,:,t:1+t] *= ((1.0/rr) * (t + 1))\n        if (area[1] + area[3]) < x_in.shape[3]:\n            for t in range(rr):\n                mult[:,:,:,area[1] - 1 - t:area[1] - t] *= ((1.0/rr) * (t + 1))\n\n    conditioning = {}\n    model_conds = conds[\"model_conds\"]\n    for c in model_conds:\n        conditioning[c] = model_conds[c].process_cond(batch_size=x_in.shape[0], device=x_in.device, area=area)\n\n    control = conds.get('control', None)\n\n    patches = None\n    if 'gligen' in conds:\n        gligen = conds['gligen']\n        patches = {}\n        gligen_type = gligen[0]\n        gligen_model = gligen[1]\n        if gligen_type == \"position\":\n            gligen_patch = gligen_model.model.set_position(input_x.shape, gligen[2], input_x.device)\n        else:\n            gligen_patch = gligen_model.model.set_empty(input_x.shape, input_x.device)\n\n        patches['middle_patch'] = [gligen_patch]\n\n    cond_obj = collections.namedtuple('cond_obj', ['input_x', 'mult', 'conditioning', 'area', 'control', 'patches'])\n    return cond_obj(input_x, mult, conditioning, area, control, patches)\n\ndef cond_equal_size(c1, c2):\n    if c1 is c2:\n        return True\n    if c1.keys() != c2.keys():\n        return False\n    for k in c1:\n        if not c1[k].can_concat(c2[k]):\n            return False\n    return True\n\ndef can_concat_cond(c1, c2):\n    if c1.input_x.shape != c2.input_x.shape:\n        return False\n\n    def objects_concatable(obj1, obj2):\n        if (obj1 is None) != (obj2 is None):\n            return False\n        if obj1 is not None:\n            if obj1 is not obj2:\n                return False\n        return True\n\n    if not objects_concatable(c1.control, c2.control):\n        return False\n\n    if not objects_concatable(c1.patches, c2.patches):\n        return False\n\n    return cond_equal_size(c1.conditioning, c2.conditioning)\n\ndef cond_cat(c_list):\n    c_crossattn = []\n    c_concat = []\n    c_adm = []\n    crossattn_max_len = 0\n\n    temp = {}\n    for x in c_list:\n        for k in x:\n            cur = temp.get(k, [])\n            cur.append(x[k])\n            temp[k] = cur\n\n    out = {}\n    for k in temp:\n        conds = temp[k]\n        out[k] = conds[0].concat(conds[1:])\n\n    return out\n\ndef calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options):\n    out_cond = torch.zeros_like(x_in)\n    out_count = torch.ones_like(x_in) * 1e-37\n\n    out_uncond = torch.zeros_like(x_in)\n    out_uncond_count = torch.ones_like(x_in) * 1e-37\n\n    COND = 0\n    UNCOND = 1\n\n    to_run = []\n    for x in cond:\n        p = get_area_and_mult(x, x_in, timestep)\n        if p is None:\n            continue\n\n        to_run += [(p, COND)]\n    if uncond is not None:\n        for x in uncond:\n            p = get_area_and_mult(x, x_in, timestep)\n            if p is None:\n                continue\n\n            to_run += [(p, UNCOND)]\n\n    while len(to_run) > 0:\n        first = to_run[0]\n        first_shape = first[0][0].shape\n        to_batch_temp = []\n        for x in range(len(to_run)):\n            if can_concat_cond(to_run[x][0], first[0]):\n                to_batch_temp += [x]\n\n        to_batch_temp.reverse()\n        to_batch = to_batch_temp[:1]\n\n        free_memory = model_management.get_free_memory(x_in.device)\n        for i in range(1, len(to_batch_temp) + 1):\n            batch_amount = to_batch_temp[:len(to_batch_temp)//i]\n            input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:]\n            if model.memory_required(input_shape) < free_memory:\n                to_batch = batch_amount\n                break\n\n        input_x = []\n        mult = []\n        c = []\n        cond_or_uncond = []\n        area = []\n        control = None\n        patches = None\n        for x in to_batch:\n            o = to_run.pop(x)\n            p = o[0]\n            input_x.append(p.input_x)\n            mult.append(p.mult)\n            c.append(p.conditioning)\n            area.append(p.area)\n            cond_or_uncond.append(o[1])\n            control = p.control\n            patches = p.patches\n\n        batch_chunks = len(cond_or_uncond)\n        input_x = torch.cat(input_x)\n        c = cond_cat(c)\n        timestep_ = torch.cat([timestep] * batch_chunks)\n\n        if control is not None:\n            c['control'] = control.get_control(input_x, timestep_, c, len(cond_or_uncond))\n\n        transformer_options = {}\n        if 'transformer_options' in model_options:\n            transformer_options = model_options['transformer_options'].copy()\n\n        if patches is not None:\n            if \"patches\" in transformer_options:\n                cur_patches = transformer_options[\"patches\"].copy()\n                for p in patches:\n                    if p in cur_patches:\n                        cur_patches[p] = cur_patches[p] + patches[p]\n                    else:\n                        cur_patches[p] = patches[p]\n            else:\n                transformer_options[\"patches\"] = patches\n\n        transformer_options[\"cond_or_uncond\"] = cond_or_uncond[:]\n        transformer_options[\"sigmas\"] = timestep\n\n        c['transformer_options'] = transformer_options\n\n        if 'model_function_wrapper' in model_options:\n            output = model_options['model_function_wrapper'](model.apply_model, {\"input\": input_x, \"timestep\": timestep_, \"c\": c, \"cond_or_uncond\": cond_or_uncond}).chunk(batch_chunks)\n        else:\n            output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks)\n        del input_x\n\n        for o in range(batch_chunks):\n            if cond_or_uncond[o] == COND:\n                out_cond[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o]\n                out_count[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o]\n            else:\n                out_uncond[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o]\n                out_uncond_count[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o]\n        del mult\n\n    out_cond /= out_count\n    del out_count\n    out_uncond /= out_uncond_count\n    del out_uncond_count\n    return out_cond, out_uncond\n\n#The main sampling function shared by all the samplers\n#Returns denoised\ndef sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None):\n        if math.isclose(cond_scale, 1.0) and model_options.get(\"disable_cfg1_optimization\", False) == False:\n            uncond_ = None\n        else:\n            uncond_ = uncond\n\n        cond_pred, uncond_pred = calc_cond_uncond_batch(model, cond, uncond_, x, timestep, model_options)\n        if \"sampler_cfg_function\" in model_options:\n            args = {\"cond\": x - cond_pred, \"uncond\": x - uncond_pred, \"cond_scale\": cond_scale, \"timestep\": timestep, \"input\": x, \"sigma\": timestep,\n                    \"cond_denoised\": cond_pred, \"uncond_denoised\": uncond_pred, \"model\": model, \"model_options\": model_options}\n            cfg_result = x - model_options[\"sampler_cfg_function\"](args)\n        else:\n            cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale\n\n        for fn in model_options.get(\"sampler_post_cfg_function\", []):\n            args = {\"denoised\": cfg_result, \"cond\": cond, \"uncond\": uncond, \"model\": model, \"uncond_denoised\": uncond_pred, \"cond_denoised\": cond_pred,\n                    \"sigma\": timestep, \"model_options\": model_options, \"input\": x}\n            cfg_result = fn(args)\n\n        return cfg_result\n\nclass CFGNoisePredictor(torch.nn.Module):\n    def __init__(self, model):\n        super().__init__()\n        self.inner_model = model\n    def apply_model(self, x, timestep, cond, uncond, cond_scale, model_options={}, seed=None):\n        out = sampling_function(self.inner_model, x, timestep, uncond, cond, cond_scale, model_options=model_options, seed=seed)\n        return out\n    def forward(self, *args, **kwargs):\n        return self.apply_model(*args, **kwargs)\n\nclass KSamplerX0Inpaint(torch.nn.Module):\n    def __init__(self, model):\n        super().__init__()\n        self.inner_model = model\n    def forward(self, x, sigma, uncond, cond, cond_scale, denoise_mask, model_options={}, seed=None):\n        if denoise_mask is not None:\n            latent_mask = 1. - denoise_mask\n            x = x * denoise_mask + (self.latent_image + self.noise * sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1))) * latent_mask\n        out = self.inner_model(x, sigma, cond=cond, uncond=uncond, cond_scale=cond_scale, model_options=model_options, seed=seed)\n        if denoise_mask is not None:\n            out = out * denoise_mask + self.latent_image * latent_mask\n        return out\n\ndef simple_scheduler(model, steps):\n    s = model.model_sampling\n    sigs = []\n    ss = len(s.sigmas) / steps\n    for x in range(steps):\n        sigs += [float(s.sigmas[-(1 + int(x * ss))])]\n    sigs += [0.0]\n    return torch.FloatTensor(sigs)\n\ndef ddim_scheduler(model, steps):\n    s = model.model_sampling\n    sigs = []\n    ss = len(s.sigmas) // steps\n    x = 1\n    while x < len(s.sigmas):\n        sigs += [float(s.sigmas[x])]\n        x += ss\n    sigs = sigs[::-1]\n    sigs += [0.0]\n    return torch.FloatTensor(sigs)\n\ndef normal_scheduler(model, steps, sgm=False, floor=False):\n    s = model.model_sampling\n    start = s.timestep(s.sigma_max)\n    end = s.timestep(s.sigma_min)\n\n    if sgm:\n        timesteps = torch.linspace(start, end, steps + 1)[:-1]\n    else:\n        timesteps = torch.linspace(start, end, steps)\n\n    sigs = []\n    for x in range(len(timesteps)):\n        ts = timesteps[x]\n        sigs.append(s.sigma(ts))\n    sigs += [0.0]\n    return torch.FloatTensor(sigs)\n\ndef get_mask_aabb(masks):\n    if masks.numel() == 0:\n        return torch.zeros((0, 4), device=masks.device, dtype=torch.int)\n\n    b = masks.shape[0]\n\n    bounding_boxes = torch.zeros((b, 4), device=masks.device, dtype=torch.int)\n    is_empty = torch.zeros((b), device=masks.device, dtype=torch.bool)\n    for i in range(b):\n        mask = masks[i]\n        if mask.numel() == 0:\n            continue\n        if torch.max(mask != 0) == False:\n            is_empty[i] = True\n            continue\n        y, x = torch.where(mask)\n        bounding_boxes[i, 0] = torch.min(x)\n        bounding_boxes[i, 1] = torch.min(y)\n        bounding_boxes[i, 2] = torch.max(x)\n        bounding_boxes[i, 3] = torch.max(y)\n\n    return bounding_boxes, is_empty\n\ndef resolve_areas_and_cond_masks(conditions, h, w, device):\n    # We need to decide on an area outside the sampling loop in order to properly generate opposite areas of equal sizes.\n    # While we're doing this, we can also resolve the mask device and scaling for performance reasons\n    for i in range(len(conditions)):\n        c = conditions[i]\n        if 'area' in c:\n            area = c['area']\n            if area[0] == \"percentage\":\n                modified = c.copy()\n                area = (max(1, round(area[1] * h)), max(1, round(area[2] * w)), round(area[3] * h), round(area[4] * w))\n                modified['area'] = area\n                c = modified\n                conditions[i] = c\n\n        if 'mask' in c:\n            mask = c['mask']\n            mask = mask.to(device=device)\n            modified = c.copy()\n            if len(mask.shape) == 2:\n                mask = mask.unsqueeze(0)\n            if mask.shape[1] != h or mask.shape[2] != w:\n                mask = torch.nn.functional.interpolate(mask.unsqueeze(1), size=(h, w), mode='bilinear', align_corners=False).squeeze(1)\n\n            if modified.get(\"set_area_to_bounds\", False):\n                bounds = torch.max(torch.abs(mask),dim=0).values.unsqueeze(0)\n                boxes, is_empty = get_mask_aabb(bounds)\n                if is_empty[0]:\n                    # Use the minimum possible size for efficiency reasons. (Since the mask is all-0, this becomes a noop anyway)\n                    modified['area'] = (8, 8, 0, 0)\n                else:\n                    box = boxes[0]\n                    H, W, Y, X = (box[3] - box[1] + 1, box[2] - box[0] + 1, box[1], box[0])\n                    H = max(8, H)\n                    W = max(8, W)\n                    area = (int(H), int(W), int(Y), int(X))\n                    modified['area'] = area\n\n            modified['mask'] = mask\n            conditions[i] = modified\n\ndef create_cond_with_same_area_if_none(conds, c):\n    if 'area' not in c:\n        return\n\n    c_area = c['area']\n    smallest = None\n    for x in conds:\n        if 'area' in x:\n            a = x['area']\n            if c_area[2] >= a[2] and c_area[3] >= a[3]:\n                if a[0] + a[2] >= c_area[0] + c_area[2]:\n                    if a[1] + a[3] >= c_area[1] + c_area[3]:\n                        if smallest is None:\n                            smallest = x\n                        elif 'area' not in smallest:\n                            smallest = x\n                        else:\n                            if smallest['area'][0] * smallest['area'][1] > a[0] * a[1]:\n                                smallest = x\n        else:\n            if smallest is None:\n                smallest = x\n    if smallest is None:\n        return\n    if 'area' in smallest:\n        if smallest['area'] == c_area:\n            return\n\n    out = c.copy()\n    out['model_conds'] = smallest['model_conds'].copy() #TODO: which fields should be copied?\n    conds += [out]\n\ndef calculate_start_end_timesteps(model, conds):\n    s = model.model_sampling\n    for t in range(len(conds)):\n        x = conds[t]\n\n        timestep_start = None\n        timestep_end = None\n        if 'start_percent' in x:\n            timestep_start = s.percent_to_sigma(x['start_percent'])\n        if 'end_percent' in x:\n            timestep_end = s.percent_to_sigma(x['end_percent'])\n\n        if (timestep_start is not None) or (timestep_end is not None):\n            n = x.copy()\n            if (timestep_start is not None):\n                n['timestep_start'] = timestep_start\n            if (timestep_end is not None):\n                n['timestep_end'] = timestep_end\n            conds[t] = n\n\ndef pre_run_control(model, conds):\n    s = model.model_sampling\n    for t in range(len(conds)):\n        x = conds[t]\n\n        timestep_start = None\n        timestep_end = None\n        percent_to_timestep_function = lambda a: s.percent_to_sigma(a)\n        if 'control' in x:\n            x['control'].pre_run(model, percent_to_timestep_function)\n\ndef apply_empty_x_to_equal_area(conds, uncond, name, uncond_fill_func):\n    cond_cnets = []\n    cond_other = []\n    uncond_cnets = []\n    uncond_other = []\n    for t in range(len(conds)):\n        x = conds[t]\n        if 'area' not in x:\n            if name in x and x[name] is not None:\n                cond_cnets.append(x[name])\n            else:\n                cond_other.append((x, t))\n    for t in range(len(uncond)):\n        x = uncond[t]\n        if 'area' not in x:\n            if name in x and x[name] is not None:\n                uncond_cnets.append(x[name])\n            else:\n                uncond_other.append((x, t))\n\n    if len(uncond_cnets) > 0:\n        return\n\n    for x in range(len(cond_cnets)):\n        temp = uncond_other[x % len(uncond_other)]\n        o = temp[0]\n        if name in o and o[name] is not None:\n            n = o.copy()\n            n[name] = uncond_fill_func(cond_cnets, x)\n            uncond += [n]\n        else:\n            n = o.copy()\n            n[name] = uncond_fill_func(cond_cnets, x)\n            uncond[temp[1]] = n\n\ndef encode_model_conds(model_function, conds, noise, device, prompt_type, **kwargs):\n    for t in range(len(conds)):\n        x = conds[t]\n        params = x.copy()\n        params[\"device\"] = device\n        params[\"noise\"] = noise\n        params[\"width\"] = params.get(\"width\", noise.shape[3] * 8)\n        params[\"height\"] = params.get(\"height\", noise.shape[2] * 8)\n        params[\"prompt_type\"] = params.get(\"prompt_type\", prompt_type)\n        for k in kwargs:\n            if k not in params:\n                params[k] = kwargs[k]\n\n        out = model_function(**params)\n        x = x.copy()\n        model_conds = x['model_conds'].copy()\n        for k in out:\n            model_conds[k] = out[k]\n        x['model_conds'] = model_conds\n        conds[t] = x\n    return conds\n\nclass Sampler:\n    def sample(self):\n        pass\n\n    def max_denoise(self, model_wrap, sigmas):\n        max_sigma = float(model_wrap.inner_model.model_sampling.sigma_max)\n        sigma = float(sigmas[0])\n        return math.isclose(max_sigma, sigma, rel_tol=1e-05) or sigma > max_sigma\n\nclass UNIPC(Sampler):\n    def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):\n        return uni_pc.sample_unipc(model_wrap, noise, latent_image, sigmas, max_denoise=self.max_denoise(model_wrap, sigmas), extra_args=extra_args, noise_mask=denoise_mask, callback=callback, disable=disable_pbar)\n\nclass UNIPCBH2(Sampler):\n    def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):\n        return uni_pc.sample_unipc(model_wrap, noise, latent_image, sigmas, max_denoise=self.max_denoise(model_wrap, sigmas), extra_args=extra_args, noise_mask=denoise_mask, callback=callback, variant='bh2', disable=disable_pbar)\n\nKSAMPLER_NAMES = [\"euler\", \"euler_ancestral\", \"heun\", \"heunpp2\",\"dpm_2\", \"dpm_2_ancestral\",\n                  \"lms\", \"dpm_fast\", \"dpm_adaptive\", \"dpmpp_2s_ancestral\", \"dpmpp_sde\", \"dpmpp_sde_gpu\",\n                  \"dpmpp_2m\", \"dpmpp_2m_sde\", \"dpmpp_2m_sde_gpu\", \"dpmpp_3m_sde\", \"dpmpp_3m_sde_gpu\", \"ddpm\", \"lcm\", \"tcd\", \"edm_playground_v2.5\", \"restart\"]\n\nclass KSAMPLER(Sampler):\n    def __init__(self, sampler_function, extra_options={}, inpaint_options={}):\n        self.sampler_function = sampler_function\n        self.extra_options = extra_options\n        self.inpaint_options = inpaint_options\n\n    def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):\n        extra_args[\"denoise_mask\"] = denoise_mask\n        model_k = KSamplerX0Inpaint(model_wrap)\n        model_k.latent_image = latent_image\n        if self.inpaint_options.get(\"random\", False): #TODO: Should this be the default?\n            generator = torch.manual_seed(extra_args.get(\"seed\", 41) + 1)\n            model_k.noise = torch.randn(noise.shape, generator=generator, device=\"cpu\").to(noise.dtype).to(noise.device)\n        else:\n            model_k.noise = noise\n\n        if self.max_denoise(model_wrap, sigmas):\n            noise = noise * torch.sqrt(1.0 + sigmas[0] ** 2.0)\n        else:\n            noise = noise * sigmas[0]\n\n        k_callback = None\n        total_steps = len(sigmas) - 1\n        if callback is not None:\n            k_callback = lambda x: callback(x[\"i\"], x[\"denoised\"], x[\"x\"], total_steps)\n\n        if latent_image is not None:\n            noise += latent_image\n\n        samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options)\n        return samples\n\n\ndef ksampler(sampler_name, extra_options={}, inpaint_options={}):\n    if sampler_name == \"dpm_fast\":\n        def dpm_fast_function(model, noise, sigmas, extra_args, callback, disable):\n            sigma_min = sigmas[-1]\n            if sigma_min == 0:\n                sigma_min = sigmas[-2]\n            total_steps = len(sigmas) - 1\n            return k_diffusion_sampling.sample_dpm_fast(model, noise, sigma_min, sigmas[0], total_steps, extra_args=extra_args, callback=callback, disable=disable)\n        sampler_function = dpm_fast_function\n    elif sampler_name == \"dpm_adaptive\":\n        def dpm_adaptive_function(model, noise, sigmas, extra_args, callback, disable):\n            sigma_min = sigmas[-1]\n            if sigma_min == 0:\n                sigma_min = sigmas[-2]\n            return k_diffusion_sampling.sample_dpm_adaptive(model, noise, sigma_min, sigmas[0], extra_args=extra_args, callback=callback, disable=disable)\n        sampler_function = dpm_adaptive_function\n    else:\n        sampler_function = getattr(k_diffusion_sampling, \"sample_{}\".format(sampler_name))\n\n    return KSAMPLER(sampler_function, extra_options, inpaint_options)\n\ndef wrap_model(model):\n    model_denoise = CFGNoisePredictor(model)\n    return model_denoise\n\ndef sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={}, latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None):\n    positive = positive[:]\n    negative = negative[:]\n\n    resolve_areas_and_cond_masks(positive, noise.shape[2], noise.shape[3], device)\n    resolve_areas_and_cond_masks(negative, noise.shape[2], noise.shape[3], device)\n\n    model_wrap = wrap_model(model)\n\n    calculate_start_end_timesteps(model, negative)\n    calculate_start_end_timesteps(model, positive)\n\n    if latent_image is not None:\n        latent_image = model.process_latent_in(latent_image)\n\n    if hasattr(model, 'extra_conds'):\n        positive = encode_model_conds(model.extra_conds, positive, noise, device, \"positive\", latent_image=latent_image, denoise_mask=denoise_mask, seed=seed)\n        negative = encode_model_conds(model.extra_conds, negative, noise, device, \"negative\", latent_image=latent_image, denoise_mask=denoise_mask, seed=seed)\n\n    #make sure each cond area has an opposite one with the same area\n    for c in positive:\n        create_cond_with_same_area_if_none(negative, c)\n    for c in negative:\n        create_cond_with_same_area_if_none(positive, c)\n\n    pre_run_control(model, negative + positive)\n\n    apply_empty_x_to_equal_area(list(filter(lambda c: c.get('control_apply_to_uncond', False) == True, positive)), negative, 'control', lambda cond_cnets, x: cond_cnets[x])\n    apply_empty_x_to_equal_area(positive, negative, 'gligen', lambda cond_cnets, x: cond_cnets[x])\n\n    extra_args = {\"cond\":positive, \"uncond\":negative, \"cond_scale\": cfg, \"model_options\": model_options, \"seed\":seed}\n\n    samples = sampler.sample(model_wrap, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar)\n    return model.process_latent_out(samples.to(torch.float32))\n\nSCHEDULER_NAMES = [\"normal\", \"karras\", \"exponential\", \"sgm_uniform\", \"simple\", \"ddim_uniform\"]\nSAMPLER_NAMES = KSAMPLER_NAMES + [\"ddim\", \"uni_pc\", \"uni_pc_bh2\"]\n\ndef calculate_sigmas_scheduler(model, scheduler_name, steps):\n    if scheduler_name == \"karras\":\n        sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=float(model.model_sampling.sigma_min), sigma_max=float(model.model_sampling.sigma_max))\n    elif scheduler_name == \"exponential\":\n        sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=float(model.model_sampling.sigma_min), sigma_max=float(model.model_sampling.sigma_max))\n    elif scheduler_name == \"normal\":\n        sigmas = normal_scheduler(model, steps)\n    elif scheduler_name == \"simple\":\n        sigmas = simple_scheduler(model, steps)\n    elif scheduler_name == \"ddim_uniform\":\n        sigmas = ddim_scheduler(model, steps)\n    elif scheduler_name == \"sgm_uniform\":\n        sigmas = normal_scheduler(model, steps, sgm=True)\n    else:\n        print(\"error invalid scheduler\", scheduler_name)\n    return sigmas\n\ndef sampler_object(name):\n    if name == \"uni_pc\":\n        sampler = UNIPC()\n    elif name == \"uni_pc_bh2\":\n        sampler = UNIPCBH2()\n    elif name == \"ddim\":\n        sampler = ksampler(\"euler\", inpaint_options={\"random\": True})\n    else:\n        sampler = ksampler(name)\n    return sampler\n\nclass KSampler:\n    SCHEDULERS = SCHEDULER_NAMES\n    SAMPLERS = SAMPLER_NAMES\n\n    def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None, model_options={}):\n        self.model = model\n        self.device = device\n        if scheduler not in self.SCHEDULERS:\n            scheduler = self.SCHEDULERS[0]\n        if sampler not in self.SAMPLERS:\n            sampler = self.SAMPLERS[0]\n        self.scheduler = scheduler\n        self.sampler = sampler\n        self.set_steps(steps, denoise)\n        self.denoise = denoise\n        self.model_options = model_options\n\n    def calculate_sigmas(self, steps):\n        sigmas = None\n\n        discard_penultimate_sigma = False\n        if self.sampler in ['dpm_2', 'dpm_2_ancestral', 'uni_pc', 'uni_pc_bh2']:\n            steps += 1\n            discard_penultimate_sigma = True\n\n        sigmas = calculate_sigmas_scheduler(self.model, self.scheduler, steps)\n\n        if discard_penultimate_sigma:\n            sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])\n        return sigmas\n\n    def set_steps(self, steps, denoise=None):\n        self.steps = steps\n        if denoise is None or denoise > 0.9999:\n            self.sigmas = self.calculate_sigmas(steps).to(self.device)\n        else:\n            new_steps = int(steps/denoise)\n            sigmas = self.calculate_sigmas(new_steps).to(self.device)\n            self.sigmas = sigmas[-(steps + 1):]\n\n    def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None, force_full_denoise=False, denoise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):\n        if sigmas is None:\n            sigmas = self.sigmas\n\n        if last_step is not None and last_step < (len(sigmas) - 1):\n            sigmas = sigmas[:last_step + 1]\n            if force_full_denoise:\n                sigmas[-1] = 0\n\n        if start_step is not None:\n            if start_step < (len(sigmas) - 1):\n                sigmas = sigmas[start_step:]\n            else:\n                if latent_image is not None:\n                    return latent_image\n                else:\n                    return torch.zeros_like(noise)\n\n        sampler = sampler_object(self.sampler)\n\n        return sample(self.model, noise, positive, negative, cfg, self.device, sampler, sigmas, self.model_options, latent_image=latent_image, denoise_mask=denoise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)\n"
  },
  {
    "path": "ldm_patched/modules/sd.py",
    "content": "import torch\n\nfrom ldm_patched.modules import model_management\nfrom ldm_patched.ldm.models.autoencoder import AutoencoderKL, AutoencodingEngine\nimport yaml\n\nimport ldm_patched.modules.utils\n\nfrom . import clip_vision\nfrom . import gligen\nfrom . import diffusers_convert\nfrom . import model_base\nfrom . import model_detection\n\nfrom . import sd1_clip\nfrom . import sd2_clip\nfrom . import sdxl_clip\n\nimport ldm_patched.modules.model_patcher\nimport ldm_patched.modules.lora\nimport ldm_patched.t2ia.adapter\nimport ldm_patched.modules.supported_models_base\nimport ldm_patched.taesd.taesd\n\ndef load_model_weights(model, sd):\n    m, u = model.load_state_dict(sd, strict=False)\n    m = set(m)\n    unexpected_keys = set(u)\n\n    k = list(sd.keys())\n    for x in k:\n        if x not in unexpected_keys:\n            w = sd.pop(x)\n            del w\n    if len(m) > 0:\n        print(\"extra\", m)\n    return model\n\ndef load_clip_weights(model, sd):\n    k = list(sd.keys())\n    for x in k:\n        if x.startswith(\"cond_stage_model.transformer.\") and not x.startswith(\"cond_stage_model.transformer.text_model.\"):\n            y = x.replace(\"cond_stage_model.transformer.\", \"cond_stage_model.transformer.text_model.\")\n            sd[y] = sd.pop(x)\n\n    if 'cond_stage_model.transformer.text_model.embeddings.position_ids' in sd:\n        ids = sd['cond_stage_model.transformer.text_model.embeddings.position_ids']\n        if ids.dtype == torch.float32:\n            sd['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round()\n\n    sd = ldm_patched.modules.utils.transformers_convert(sd, \"cond_stage_model.model.\", \"cond_stage_model.transformer.text_model.\", 24)\n    return load_model_weights(model, sd)\n\n\ndef load_lora_for_models(model, clip, lora, strength_model, strength_clip):\n    key_map = {}\n    if model is not None:\n        key_map = ldm_patched.modules.lora.model_lora_keys_unet(model.model, key_map)\n    if clip is not None:\n        key_map = ldm_patched.modules.lora.model_lora_keys_clip(clip.cond_stage_model, key_map)\n\n    loaded = ldm_patched.modules.lora.load_lora(lora, key_map)\n    if model is not None:\n        new_modelpatcher = model.clone()\n        k = new_modelpatcher.add_patches(loaded, strength_model)\n    else:\n        k = ()\n        new_modelpatcher = None\n\n    if clip is not None:\n        new_clip = clip.clone()\n        k1 = new_clip.add_patches(loaded, strength_clip)\n    else:\n        k1 = ()\n        new_clip = None\n    k = set(k)\n    k1 = set(k1)\n    for x in loaded:\n        if (x not in k) and (x not in k1):\n            print(\"NOT LOADED\", x)\n\n    return (new_modelpatcher, new_clip)\n\n\nclass CLIP:\n    def __init__(self, target=None, embedding_directory=None, no_init=False):\n        if no_init:\n            return\n        params = target.params.copy()\n        clip = target.clip\n        tokenizer = target.tokenizer\n\n        load_device = model_management.text_encoder_device()\n        offload_device = model_management.text_encoder_offload_device()\n        params['device'] = offload_device\n        params['dtype'] = model_management.text_encoder_dtype(load_device)\n\n        self.cond_stage_model = clip(**(params))\n\n        self.tokenizer = tokenizer(embedding_directory=embedding_directory)\n        self.patcher = ldm_patched.modules.model_patcher.ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device)\n        self.layer_idx = None\n\n    def clone(self):\n        n = CLIP(no_init=True)\n        n.patcher = self.patcher.clone()\n        n.cond_stage_model = self.cond_stage_model\n        n.tokenizer = self.tokenizer\n        n.layer_idx = self.layer_idx\n        return n\n\n    def add_patches(self, patches, strength_patch=1.0, strength_model=1.0):\n        return self.patcher.add_patches(patches, strength_patch, strength_model)\n\n    def clip_layer(self, layer_idx):\n        self.layer_idx = layer_idx\n\n    def tokenize(self, text, return_word_ids=False):\n        return self.tokenizer.tokenize_with_weights(text, return_word_ids)\n\n    def encode_from_tokens(self, tokens, return_pooled=False):\n        if self.layer_idx is not None:\n            self.cond_stage_model.clip_layer(self.layer_idx)\n        else:\n            self.cond_stage_model.reset_clip_layer()\n\n        self.load_model()\n        cond, pooled = self.cond_stage_model.encode_token_weights(tokens)\n        if return_pooled:\n            return cond, pooled\n        return cond\n\n    def encode(self, text):\n        tokens = self.tokenize(text)\n        return self.encode_from_tokens(tokens)\n\n    def load_sd(self, sd):\n        return self.cond_stage_model.load_sd(sd)\n\n    def get_sd(self):\n        return self.cond_stage_model.state_dict()\n\n    def load_model(self):\n        model_management.load_model_gpu(self.patcher)\n        return self.patcher\n\n    def get_key_patches(self):\n        return self.patcher.get_key_patches()\n\nclass VAE:\n    def __init__(self, sd=None, device=None, config=None, dtype=None):\n        if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format\n            sd = diffusers_convert.convert_vae_state_dict(sd)\n\n        self.memory_used_encode = lambda shape, dtype: (1767 * shape[2] * shape[3]) * model_management.dtype_size(dtype) #These are for AutoencoderKL and need tweaking (should be lower)\n        self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype)\n        self.downscale_ratio = 8\n        self.latent_channels = 4\n\n        if config is None:\n            if \"decoder.mid.block_1.mix_factor\" in sd:\n                encoder_config = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0}\n                decoder_config = encoder_config.copy()\n                decoder_config[\"video_kernel_size\"] = [3, 1, 1]\n                decoder_config[\"alpha\"] = 0.0\n                self.first_stage_model = AutoencodingEngine(regularizer_config={'target': \"ldm_patched.ldm.models.autoencoder.DiagonalGaussianRegularizer\"},\n                                                            encoder_config={'target': \"ldm_patched.ldm.modules.diffusionmodules.model.Encoder\", 'params': encoder_config},\n                                                            decoder_config={'target': \"ldm_patched.ldm.modules.temporal_ae.VideoDecoder\", 'params': decoder_config})\n            elif \"taesd_decoder.1.weight\" in sd:\n                self.first_stage_model = ldm_patched.taesd.taesd.TAESD()\n            else:\n                #default SD1.x/SD2.x VAE parameters\n                ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0}\n\n                if 'encoder.down.2.downsample.conv.weight' not in sd: #Stable diffusion x4 upscaler VAE\n                    ddconfig['ch_mult'] = [1, 2, 4]\n                    self.downscale_ratio = 4\n\n                self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=4)\n        else:\n            self.first_stage_model = AutoencoderKL(**(config['params']))\n        self.first_stage_model = self.first_stage_model.eval()\n\n        m, u = self.first_stage_model.load_state_dict(sd, strict=False)\n        if len(m) > 0:\n            print(\"Missing VAE keys\", m)\n\n        if len(u) > 0:\n            print(\"Leftover VAE keys\", u)\n\n        if device is None:\n            device = model_management.vae_device()\n        self.device = device\n        offload_device = model_management.vae_offload_device()\n        if dtype is None:\n            dtype = model_management.vae_dtype()\n        self.vae_dtype = dtype\n        self.first_stage_model.to(self.vae_dtype)\n        self.output_device = model_management.intermediate_device()\n\n        self.patcher = ldm_patched.modules.model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device)\n\n    def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16):\n        steps = samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap)\n        steps += samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap)\n        steps += samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap)\n        pbar = ldm_patched.modules.utils.ProgressBar(steps)\n\n        decode_fn = lambda a: (self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)) + 1.0).float()\n        output = torch.clamp((\n            (ldm_patched.modules.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = self.downscale_ratio, output_device=self.output_device, pbar = pbar) +\n            ldm_patched.modules.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = self.downscale_ratio, output_device=self.output_device, pbar = pbar) +\n             ldm_patched.modules.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = self.downscale_ratio, output_device=self.output_device, pbar = pbar))\n            / 3.0) / 2.0, min=0.0, max=1.0)\n        return output\n\n    def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):\n        steps = pixel_samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)\n        steps += pixel_samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap)\n        steps += pixel_samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap)\n        pbar = ldm_patched.modules.utils.ProgressBar(steps)\n\n        encode_fn = lambda a: self.first_stage_model.encode((2. * a - 1.).to(self.vae_dtype).to(self.device)).float()\n        samples = ldm_patched.modules.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)\n        samples += ldm_patched.modules.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)\n        samples += ldm_patched.modules.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)\n        samples /= 3.0\n        return samples\n\n    def decode(self, samples_in):\n        try:\n            memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype)\n            model_management.load_models_gpu([self.patcher], memory_required=memory_used)\n            free_memory = model_management.get_free_memory(self.device)\n            batch_number = int(free_memory / memory_used)\n            batch_number = max(1, batch_number)\n\n            pixel_samples = torch.empty((samples_in.shape[0], 3, round(samples_in.shape[2] * self.downscale_ratio), round(samples_in.shape[3] * self.downscale_ratio)), device=self.output_device)\n            for x in range(0, samples_in.shape[0], batch_number):\n                samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device)\n                pixel_samples[x:x+batch_number] = torch.clamp((self.first_stage_model.decode(samples).to(self.output_device).float() + 1.0) / 2.0, min=0.0, max=1.0)\n        except model_management.OOM_EXCEPTION as e:\n            print(\"Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.\")\n            pixel_samples = self.decode_tiled_(samples_in)\n\n        pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1)\n        return pixel_samples\n\n    def decode_tiled(self, samples, tile_x=64, tile_y=64, overlap = 16):\n        model_management.load_model_gpu(self.patcher)\n        output = self.decode_tiled_(samples, tile_x, tile_y, overlap)\n        return output.movedim(1,-1)\n\n    def encode(self, pixel_samples):\n        pixel_samples = pixel_samples.movedim(-1,1)\n        try:\n            memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype)\n            model_management.load_models_gpu([self.patcher], memory_required=memory_used)\n            free_memory = model_management.get_free_memory(self.device)\n            batch_number = int(free_memory / memory_used)\n            batch_number = max(1, batch_number)\n            samples = torch.empty((pixel_samples.shape[0], self.latent_channels, round(pixel_samples.shape[2] // self.downscale_ratio), round(pixel_samples.shape[3] // self.downscale_ratio)), device=self.output_device)\n            for x in range(0, pixel_samples.shape[0], batch_number):\n                pixels_in = (2. * pixel_samples[x:x+batch_number] - 1.).to(self.vae_dtype).to(self.device)\n                samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).to(self.output_device).float()\n\n        except model_management.OOM_EXCEPTION as e:\n            print(\"Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.\")\n            samples = self.encode_tiled_(pixel_samples)\n\n        return samples\n\n    def encode_tiled(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):\n        model_management.load_model_gpu(self.patcher)\n        pixel_samples = pixel_samples.movedim(-1,1)\n        samples = self.encode_tiled_(pixel_samples, tile_x=tile_x, tile_y=tile_y, overlap=overlap)\n        return samples\n\n    def get_sd(self):\n        return self.first_stage_model.state_dict()\n\nclass StyleModel:\n    def __init__(self, model, device=\"cpu\"):\n        self.model = model\n\n    def get_cond(self, input):\n        return self.model(input.last_hidden_state)\n\n\ndef load_style_model(ckpt_path):\n    model_data = ldm_patched.modules.utils.load_torch_file(ckpt_path, safe_load=True)\n    keys = model_data.keys()\n    if \"style_embedding\" in keys:\n        model = ldm_patched.t2ia.adapter.StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8)\n    else:\n        raise Exception(\"invalid style model {}\".format(ckpt_path))\n    model.load_state_dict(model_data)\n    return StyleModel(model)\n\n\ndef load_clip(ckpt_paths, embedding_directory=None):\n    clip_data = []\n    for p in ckpt_paths:\n        clip_data.append(ldm_patched.modules.utils.load_torch_file(p, safe_load=True))\n\n    class EmptyClass:\n        pass\n\n    for i in range(len(clip_data)):\n        if \"transformer.resblocks.0.ln_1.weight\" in clip_data[i]:\n            clip_data[i] = ldm_patched.modules.utils.transformers_convert(clip_data[i], \"\", \"text_model.\", 32)\n\n    clip_target = EmptyClass()\n    clip_target.params = {}\n    if len(clip_data) == 1:\n        if \"text_model.encoder.layers.30.mlp.fc1.weight\" in clip_data[0]:\n            clip_target.clip = sdxl_clip.SDXLRefinerClipModel\n            clip_target.tokenizer = sdxl_clip.SDXLTokenizer\n        elif \"text_model.encoder.layers.22.mlp.fc1.weight\" in clip_data[0]:\n            clip_target.clip = sd2_clip.SD2ClipModel\n            clip_target.tokenizer = sd2_clip.SD2Tokenizer\n        else:\n            clip_target.clip = sd1_clip.SD1ClipModel\n            clip_target.tokenizer = sd1_clip.SD1Tokenizer\n    else:\n        clip_target.clip = sdxl_clip.SDXLClipModel\n        clip_target.tokenizer = sdxl_clip.SDXLTokenizer\n\n    clip = CLIP(clip_target, embedding_directory=embedding_directory)\n    for c in clip_data:\n        m, u = clip.load_sd(c)\n        if len(m) > 0:\n            print(\"clip missing:\", m)\n\n        if len(u) > 0:\n            print(\"clip unexpected:\", u)\n    return clip\n\ndef load_gligen(ckpt_path):\n    data = ldm_patched.modules.utils.load_torch_file(ckpt_path, safe_load=True)\n    model = gligen.load_gligen(data)\n    if model_management.should_use_fp16():\n        model = model.half()\n    return ldm_patched.modules.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device())\n\ndef load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_clip=True, embedding_directory=None, state_dict=None, config=None):\n    #TODO: this function is a mess and should be removed eventually\n    if config is None:\n        with open(config_path, 'r') as stream:\n            config = yaml.safe_load(stream)\n    model_config_params = config['model']['params']\n    clip_config = model_config_params['cond_stage_config']\n    scale_factor = model_config_params['scale_factor']\n    vae_config = model_config_params['first_stage_config']\n\n    fp16 = False\n    if \"unet_config\" in model_config_params:\n        if \"params\" in model_config_params[\"unet_config\"]:\n            unet_config = model_config_params[\"unet_config\"][\"params\"]\n            if \"use_fp16\" in unet_config:\n                fp16 = unet_config.pop(\"use_fp16\")\n                if fp16:\n                    unet_config[\"dtype\"] = torch.float16\n\n    noise_aug_config = None\n    if \"noise_aug_config\" in model_config_params:\n        noise_aug_config = model_config_params[\"noise_aug_config\"]\n\n    model_type = model_base.ModelType.EPS\n\n    if \"parameterization\" in model_config_params:\n        if model_config_params[\"parameterization\"] == \"v\":\n            model_type = model_base.ModelType.V_PREDICTION\n\n    clip = None\n    vae = None\n\n    class WeightsLoader(torch.nn.Module):\n        pass\n\n    if state_dict is None:\n        state_dict = ldm_patched.modules.utils.load_torch_file(ckpt_path)\n\n    class EmptyClass:\n        pass\n\n    model_config = ldm_patched.modules.supported_models_base.BASE({})\n\n    from . import latent_formats\n    model_config.latent_format = latent_formats.SD15(scale_factor=scale_factor)\n    model_config.unet_config = model_detection.convert_config(unet_config)\n\n    if config['model'][\"target\"].endswith(\"ImageEmbeddingConditionedLatentDiffusion\"):\n        model = model_base.SD21UNCLIP(model_config, noise_aug_config[\"params\"], model_type=model_type)\n    else:\n        model = model_base.BaseModel(model_config, model_type=model_type)\n\n    if config['model'][\"target\"].endswith(\"LatentInpaintDiffusion\"):\n        model.set_inpaint()\n\n    if fp16:\n        model = model.half()\n\n    offload_device = model_management.unet_offload_device()\n    model = model.to(offload_device)\n    model.load_model_weights(state_dict, \"model.diffusion_model.\")\n\n    if output_vae:\n        vae_sd = ldm_patched.modules.utils.state_dict_prefix_replace(state_dict, {\"first_stage_model.\": \"\"}, filter_keys=True)\n        vae = VAE(sd=vae_sd, config=vae_config)\n\n    if output_clip:\n        w = WeightsLoader()\n        clip_target = EmptyClass()\n        clip_target.params = clip_config.get(\"params\", {})\n        if clip_config[\"target\"].endswith(\"FrozenOpenCLIPEmbedder\"):\n            clip_target.clip = sd2_clip.SD2ClipModel\n            clip_target.tokenizer = sd2_clip.SD2Tokenizer\n            clip = CLIP(clip_target, embedding_directory=embedding_directory)\n            w.cond_stage_model = clip.cond_stage_model.clip_h\n        elif clip_config[\"target\"].endswith(\"FrozenCLIPEmbedder\"):\n            clip_target.clip = sd1_clip.SD1ClipModel\n            clip_target.tokenizer = sd1_clip.SD1Tokenizer\n            clip = CLIP(clip_target, embedding_directory=embedding_directory)\n            w.cond_stage_model = clip.cond_stage_model.clip_l\n        load_clip_weights(w, state_dict)\n\n    return (ldm_patched.modules.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device), clip, vae)\n\ndef load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, vae_filename_param=None):\n    sd = ldm_patched.modules.utils.load_torch_file(ckpt_path)\n    sd_keys = sd.keys()\n    clip = None\n    clipvision = None\n    vae = None\n    vae_filename = None\n    model = None\n    model_patcher = None\n    clip_target = None\n\n    parameters = ldm_patched.modules.utils.calculate_parameters(sd, \"model.diffusion_model.\")\n    unet_dtype = model_management.unet_dtype(model_params=parameters)\n    load_device = model_management.get_torch_device()\n    manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device)\n\n    class WeightsLoader(torch.nn.Module):\n        pass\n\n    model_config = model_detection.model_config_from_unet(sd, \"model.diffusion_model.\", unet_dtype)\n    model_config.set_manual_cast(manual_cast_dtype)\n\n    if model_config is None:\n        raise RuntimeError(\"ERROR: Could not detect model type of: {}\".format(ckpt_path))\n\n    if model_config.clip_vision_prefix is not None:\n        if output_clipvision:\n            clipvision = clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix, True)\n\n    if output_model:\n        inital_load_device = model_management.unet_inital_load_device(parameters, unet_dtype)\n        offload_device = model_management.unet_offload_device()\n        model = model_config.get_model(sd, \"model.diffusion_model.\", device=inital_load_device)\n        model.load_model_weights(sd, \"model.diffusion_model.\")\n\n    if output_vae:\n        if vae_filename_param is None:\n            vae_sd = ldm_patched.modules.utils.state_dict_prefix_replace(sd, {\"first_stage_model.\": \"\"}, filter_keys=True)\n            vae_sd = model_config.process_vae_state_dict(vae_sd)\n        else:\n            vae_sd = ldm_patched.modules.utils.load_torch_file(vae_filename_param)\n            vae_filename = vae_filename_param\n        vae = VAE(sd=vae_sd)\n\n    if output_clip:\n        w = WeightsLoader()\n        clip_target = model_config.clip_target()\n        if clip_target is not None:\n            clip = CLIP(clip_target, embedding_directory=embedding_directory)\n            w.cond_stage_model = clip.cond_stage_model\n            sd = model_config.process_clip_state_dict(sd)\n            load_model_weights(w, sd)\n\n    left_over = sd.keys()\n    if len(left_over) > 0:\n        print(\"left over keys:\", left_over)\n\n    if output_model:\n        model_patcher = ldm_patched.modules.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=model_management.unet_offload_device(), current_device=inital_load_device)\n        if inital_load_device != torch.device(\"cpu\"):\n            print(\"loaded straight to GPU\")\n            model_management.load_model_gpu(model_patcher)\n\n    return model_patcher, clip, vae, vae_filename, clipvision\n\n\ndef load_unet_state_dict(sd): #load unet in diffusers format\n    parameters = ldm_patched.modules.utils.calculate_parameters(sd)\n    unet_dtype = model_management.unet_dtype(model_params=parameters)\n    load_device = model_management.get_torch_device()\n    manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device)\n\n    if \"input_blocks.0.0.weight\" in sd: #ldm\n        model_config = model_detection.model_config_from_unet(sd, \"\", unet_dtype)\n        if model_config is None:\n            return None\n        new_sd = sd\n\n    else: #diffusers\n        model_config = model_detection.model_config_from_diffusers_unet(sd, unet_dtype)\n        if model_config is None:\n            return None\n\n        diffusers_keys = ldm_patched.modules.utils.unet_to_diffusers(model_config.unet_config)\n\n        new_sd = {}\n        for k in diffusers_keys:\n            if k in sd:\n                new_sd[diffusers_keys[k]] = sd.pop(k)\n            else:\n                print(diffusers_keys[k], k)\n    offload_device = model_management.unet_offload_device()\n    model_config.set_manual_cast(manual_cast_dtype)\n    model = model_config.get_model(new_sd, \"\")\n    model = model.to(offload_device)\n    model.load_model_weights(new_sd, \"\")\n    left_over = sd.keys()\n    if len(left_over) > 0:\n        print(\"left over keys in unet:\", left_over)\n    return ldm_patched.modules.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device)\n\ndef load_unet(unet_path):\n    sd = ldm_patched.modules.utils.load_torch_file(unet_path)\n    model = load_unet_state_dict(sd)\n    if model is None:\n        print(\"ERROR UNSUPPORTED UNET\", unet_path)\n        raise RuntimeError(\"ERROR: Could not detect model type of: {}\".format(unet_path))\n    return model\n\ndef save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, metadata=None):\n    clip_sd = None\n    load_models = [model]\n    if clip is not None:\n        load_models.append(clip.load_model())\n        clip_sd = clip.get_sd()\n\n    model_management.load_models_gpu(load_models)\n    clip_vision_sd = clip_vision.get_sd() if clip_vision is not None else None\n    sd = model.model.state_dict_for_saving(clip_sd, vae.get_sd(), clip_vision_sd)\n    ldm_patched.modules.utils.save_torch_file(sd, output_path, metadata=metadata)\n"
  },
  {
    "path": "ldm_patched/modules/sd1_clip.py",
    "content": "import os\n\nfrom transformers import CLIPTokenizer\nimport ldm_patched.modules.ops\nimport torch\nimport traceback\nimport zipfile\nfrom . import model_management\nimport ldm_patched.modules.clip_model\nimport json\n\ndef gen_empty_tokens(special_tokens, length):\n    start_token = special_tokens.get(\"start\", None)\n    end_token = special_tokens.get(\"end\", None)\n    pad_token = special_tokens.get(\"pad\")\n    output = []\n    if start_token is not None:\n        output.append(start_token)\n    if end_token is not None:\n        output.append(end_token)\n    output += [pad_token] * (length - len(output))\n    return output\n\nclass ClipTokenWeightEncoder:\n    def encode_token_weights(self, token_weight_pairs):\n        to_encode = list()\n        max_token_len = 0\n        has_weights = False\n        for x in token_weight_pairs:\n            tokens = list(map(lambda a: a[0], x))\n            max_token_len = max(len(tokens), max_token_len)\n            has_weights = has_weights or not all(map(lambda a: a[1] == 1.0, x))\n            to_encode.append(tokens)\n\n        sections = len(to_encode)\n        if has_weights or sections == 0:\n            to_encode.append(gen_empty_tokens(self.special_tokens, max_token_len))\n\n        out, pooled = self.encode(to_encode)\n        if pooled is not None:\n            first_pooled = pooled[0:1].to(model_management.intermediate_device())\n        else:\n            first_pooled = pooled\n\n        output = []\n        for k in range(0, sections):\n            z = out[k:k+1]\n            if has_weights:\n                z_empty = out[-1]\n                for i in range(len(z)):\n                    for j in range(len(z[i])):\n                        weight = token_weight_pairs[k][j][1]\n                        if weight != 1.0:\n                            z[i][j] = (z[i][j] - z_empty[j]) * weight + z_empty[j]\n            output.append(z)\n\n        if (len(output) == 0):\n            return out[-1:].to(model_management.intermediate_device()), first_pooled\n        return torch.cat(output, dim=-2).to(model_management.intermediate_device()), first_pooled\n\nclass SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):\n    \"\"\"Uses the CLIP transformer encoder for text (from huggingface)\"\"\"\n    LAYERS = [\n        \"last\",\n        \"pooled\",\n        \"hidden\"\n    ]\n    def __init__(self, version=\"openai/clip-vit-large-patch14\", device=\"cpu\", max_length=77,\n                 freeze=True, layer=\"last\", layer_idx=None, textmodel_json_config=None, dtype=None, model_class=ldm_patched.modules.clip_model.CLIPTextModel,\n                 special_tokens={\"start\": 49406, \"end\": 49407, \"pad\": 49407}, layer_norm_hidden_state=True):  # clip-vit-base-patch32\n        super().__init__()\n        assert layer in self.LAYERS\n\n        if textmodel_json_config is None:\n            textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"sd1_clip_config.json\")\n\n        with open(textmodel_json_config) as f:\n            config = json.load(f)\n\n        self.transformer = model_class(config, dtype, device, ldm_patched.modules.ops.manual_cast)\n        self.num_layers = self.transformer.num_layers\n\n        self.max_length = max_length\n        if freeze:\n            self.freeze()\n        self.layer = layer\n        self.layer_idx = None\n        self.special_tokens = special_tokens\n        self.text_projection = torch.nn.Parameter(torch.eye(self.transformer.get_input_embeddings().weight.shape[1]))\n        self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055))\n        self.enable_attention_masks = False\n\n        self.layer_norm_hidden_state = layer_norm_hidden_state\n        if layer == \"hidden\":\n            assert layer_idx is not None\n            assert abs(layer_idx) < self.num_layers\n            self.clip_layer(layer_idx)\n        self.layer_default = (self.layer, self.layer_idx)\n\n    def freeze(self):\n        self.transformer = self.transformer.eval()\n        #self.train = disabled_train\n        for param in self.parameters():\n            param.requires_grad = False\n\n    def clip_layer(self, layer_idx):\n        if abs(layer_idx) > self.num_layers:\n            self.layer = \"last\"\n        else:\n            self.layer = \"hidden\"\n            self.layer_idx = layer_idx\n\n    def reset_clip_layer(self):\n        self.layer = self.layer_default[0]\n        self.layer_idx = self.layer_default[1]\n\n    def set_up_textual_embeddings(self, tokens, current_embeds):\n        out_tokens = []\n        next_new_token = token_dict_size = current_embeds.weight.shape[0] - 1\n        embedding_weights = []\n\n        for x in tokens:\n            tokens_temp = []\n            for y in x:\n                if isinstance(y, int):\n                    if y == token_dict_size: #EOS token\n                        y = -1\n                    tokens_temp += [y]\n                else:\n                    if y.shape[0] == current_embeds.weight.shape[1]:\n                        embedding_weights += [y]\n                        tokens_temp += [next_new_token]\n                        next_new_token += 1\n                    else:\n                        print(\"WARNING: shape mismatch when trying to apply embedding, embedding will be ignored\", y.shape[0], current_embeds.weight.shape[1])\n            while len(tokens_temp) < len(x):\n                tokens_temp += [self.special_tokens[\"pad\"]]\n            out_tokens += [tokens_temp]\n\n        n = token_dict_size\n        if len(embedding_weights) > 0:\n            new_embedding = torch.nn.Embedding(next_new_token + 1, current_embeds.weight.shape[1], device=current_embeds.weight.device, dtype=current_embeds.weight.dtype)\n            new_embedding.weight[:token_dict_size] = current_embeds.weight[:-1]\n            for x in embedding_weights:\n                new_embedding.weight[n] = x\n                n += 1\n            new_embedding.weight[n] = current_embeds.weight[-1] #EOS embedding\n            self.transformer.set_input_embeddings(new_embedding)\n\n        processed_tokens = []\n        for x in out_tokens:\n            processed_tokens += [list(map(lambda a: n if a == -1 else a, x))] #The EOS token should always be the largest one\n\n        return processed_tokens\n\n    def forward(self, tokens):\n        backup_embeds = self.transformer.get_input_embeddings()\n        device = backup_embeds.weight.device\n        tokens = self.set_up_textual_embeddings(tokens, backup_embeds)\n        tokens = torch.LongTensor(tokens).to(device)\n\n        attention_mask = None\n        if self.enable_attention_masks:\n            attention_mask = torch.zeros_like(tokens)\n            max_token = self.transformer.get_input_embeddings().weight.shape[0] - 1\n            for x in range(attention_mask.shape[0]):\n                for y in range(attention_mask.shape[1]):\n                    attention_mask[x, y] = 1\n                    if tokens[x, y] == max_token:\n                        break\n\n        outputs = self.transformer(tokens, attention_mask, intermediate_output=self.layer_idx, final_layer_norm_intermediate=self.layer_norm_hidden_state)\n        self.transformer.set_input_embeddings(backup_embeds)\n\n        if self.layer == \"last\":\n            z = outputs[0]\n        else:\n            z = outputs[1]\n\n        if outputs[2] is not None:\n            pooled_output = outputs[2].float()\n        else:\n            pooled_output = None\n\n        if self.text_projection is not None and pooled_output is not None:\n            pooled_output = pooled_output.float().to(self.text_projection.device) @ self.text_projection.float()\n        return z.float(), pooled_output\n\n    def encode(self, tokens):\n        return self(tokens)\n\n    def load_sd(self, sd):\n        if \"text_projection\" in sd:\n            self.text_projection[:] = sd.pop(\"text_projection\")\n        if \"text_projection.weight\" in sd:\n            self.text_projection[:] = sd.pop(\"text_projection.weight\").transpose(0, 1)\n        return self.transformer.load_state_dict(sd, strict=False)\n\ndef parse_parentheses(string):\n    result = []\n    current_item = \"\"\n    nesting_level = 0\n    for char in string:\n        if char == \"(\":\n            if nesting_level == 0:\n                if current_item:\n                    result.append(current_item)\n                    current_item = \"(\"\n                else:\n                    current_item = \"(\"\n            else:\n                current_item += char\n            nesting_level += 1\n        elif char == \")\":\n            nesting_level -= 1\n            if nesting_level == 0:\n                result.append(current_item + \")\")\n                current_item = \"\"\n            else:\n                current_item += char\n        else:\n            current_item += char\n    if current_item:\n        result.append(current_item)\n    return result\n\ndef token_weights(string, current_weight):\n    a = parse_parentheses(string)\n    out = []\n    for x in a:\n        weight = current_weight\n        if len(x) >= 2 and x[-1] == ')' and x[0] == '(':\n            x = x[1:-1]\n            xx = x.rfind(\":\")\n            weight *= 1.1\n            if xx > 0:\n                try:\n                    weight = float(x[xx+1:])\n                    x = x[:xx]\n                except:\n                    pass\n            out += token_weights(x, weight)\n        else:\n            out += [(x, current_weight)]\n    return out\n\ndef escape_important(text):\n    text = text.replace(\"\\\\)\", \"\\0\\1\")\n    text = text.replace(\"\\\\(\", \"\\0\\2\")\n    return text\n\ndef unescape_important(text):\n    text = text.replace(\"\\0\\1\", \")\")\n    text = text.replace(\"\\0\\2\", \"(\")\n    return text\n\ndef safe_load_embed_zip(embed_path):\n    with zipfile.ZipFile(embed_path) as myzip:\n        names = list(filter(lambda a: \"data/\" in a, myzip.namelist()))\n        names.reverse()\n        for n in names:\n            with myzip.open(n) as myfile:\n                data = myfile.read()\n                number = len(data) // 4\n                length_embed = 1024 #sd2.x\n                if number < 768:\n                    continue\n                if number % 768 == 0:\n                    length_embed = 768 #sd1.x\n                num_embeds = number // length_embed\n                embed = torch.frombuffer(data, dtype=torch.float)\n                out = embed.reshape((num_embeds, length_embed)).clone()\n                del embed\n                return out\n\ndef expand_directory_list(directories):\n    dirs = set()\n    for x in directories:\n        dirs.add(x)\n        for root, subdir, file in os.walk(x, followlinks=True):\n            dirs.add(root)\n    return list(dirs)\n\ndef load_embed(embedding_name, embedding_directory, embedding_size, embed_key=None):\n    if isinstance(embedding_directory, str):\n        embedding_directory = [embedding_directory]\n\n    embedding_directory = expand_directory_list(embedding_directory)\n\n    valid_file = None\n    for embed_dir in embedding_directory:\n        embed_path = os.path.abspath(os.path.join(embed_dir, embedding_name))\n        embed_dir = os.path.abspath(embed_dir)\n        try:\n            if os.path.commonpath((embed_dir, embed_path)) != embed_dir:\n                continue\n        except:\n            continue\n        if not os.path.isfile(embed_path):\n            extensions = ['.safetensors', '.pt', '.bin']\n            for x in extensions:\n                t = embed_path + x\n                if os.path.isfile(t):\n                    valid_file = t\n                    break\n        else:\n            valid_file = embed_path\n        if valid_file is not None:\n            break\n\n    if valid_file is None:\n        return None\n\n    embed_path = valid_file\n\n    embed_out = None\n\n    try:\n        if embed_path.lower().endswith(\".safetensors\"):\n            import safetensors.torch\n            embed = safetensors.torch.load_file(embed_path, device=\"cpu\")\n        else:\n            if 'weights_only' in torch.load.__code__.co_varnames:\n                try:\n                    embed = torch.load(embed_path, weights_only=True, map_location=\"cpu\")\n                except:\n                    embed_out = safe_load_embed_zip(embed_path)\n            else:\n                embed = torch.load(embed_path, map_location=\"cpu\", weights_only=True)\n    except Exception as e:\n        print(traceback.format_exc())\n        print()\n        print(\"error loading embedding, skipping loading:\", embedding_name)\n        return None\n\n    if embed_out is None:\n        if 'string_to_param' in embed:\n            values = embed['string_to_param'].values()\n            embed_out = next(iter(values))\n        elif isinstance(embed, list):\n            out_list = []\n            for x in range(len(embed)):\n                for k in embed[x]:\n                    t = embed[x][k]\n                    if t.shape[-1] != embedding_size:\n                        continue\n                    out_list.append(t.reshape(-1, t.shape[-1]))\n            embed_out = torch.cat(out_list, dim=0)\n        elif embed_key is not None and embed_key in embed:\n            embed_out = embed[embed_key]\n        else:\n            values = embed.values()\n            embed_out = next(iter(values))\n    return embed_out\n\nclass SDTokenizer:\n    def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, pad_to_max_length=True):\n        if tokenizer_path is None:\n            tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"sd1_tokenizer\")\n        self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path)\n        self.max_length = max_length\n\n        empty = self.tokenizer('')[\"input_ids\"]\n        if has_start_token:\n            self.tokens_start = 1\n            self.start_token = empty[0]\n            self.end_token = empty[1]\n        else:\n            self.tokens_start = 0\n            self.start_token = None\n            self.end_token = empty[0]\n        self.pad_with_end = pad_with_end\n        self.pad_to_max_length = pad_to_max_length\n\n        vocab = self.tokenizer.get_vocab()\n        self.inv_vocab = {v: k for k, v in vocab.items()}\n        self.embedding_directory = embedding_directory\n        self.max_word_length = 8\n        self.embedding_identifier = \"embedding:\"\n        self.embedding_size = embedding_size\n        self.embedding_key = embedding_key\n\n    def _try_get_embedding(self, embedding_name:str):\n        '''\n        Takes a potential embedding name and tries to retrieve it.\n        Returns a Tuple consisting of the embedding and any leftover string, embedding can be None.\n        '''\n        embed = load_embed(embedding_name, self.embedding_directory, self.embedding_size, self.embedding_key)\n        if embed is None:\n            stripped = embedding_name.strip(',')\n            if len(stripped) < len(embedding_name):\n                embed = load_embed(stripped, self.embedding_directory, self.embedding_size, self.embedding_key)\n                return (embed, embedding_name[len(stripped):])\n        return (embed, \"\")\n\n\n    def tokenize_with_weights(self, text:str, return_word_ids=False):\n        '''\n        Takes a prompt and converts it to a list of (token, weight, word id) elements.\n        Tokens can both be integer tokens and pre computed CLIP tensors.\n        Word id values are unique per word and embedding, where the id 0 is reserved for non word tokens.\n        Returned list has the dimensions NxM where M is the input size of CLIP\n        '''\n        if self.pad_with_end:\n            pad_token = self.end_token\n        else:\n            pad_token = 0\n\n        text = escape_important(text)\n        parsed_weights = token_weights(text, 1.0)\n\n        #tokenize words\n        tokens = []\n        for weighted_segment, weight in parsed_weights:\n            to_tokenize = unescape_important(weighted_segment).replace(\"\\n\", \" \").split(' ')\n            to_tokenize = [x for x in to_tokenize if x != \"\"]\n            for word in to_tokenize:\n                #if we find an embedding, deal with the embedding\n                if word.startswith(self.embedding_identifier) and self.embedding_directory is not None:\n                    embedding_name = word[len(self.embedding_identifier):].strip('\\n')\n                    embed, leftover = self._try_get_embedding(embedding_name)\n                    if embed is None:\n                        print(f\"warning, embedding:{embedding_name} does not exist, ignoring\")\n                    else:\n                        if len(embed.shape) == 1:\n                            tokens.append([(embed, weight)])\n                        else:\n                            tokens.append([(embed[x], weight) for x in range(embed.shape[0])])\n                    #if we accidentally have leftover text, continue parsing using leftover, else move on to next word\n                    if leftover != \"\":\n                        word = leftover\n                    else:\n                        continue\n                #parse word\n                tokens.append([(t, weight) for t in self.tokenizer(word)[\"input_ids\"][self.tokens_start:-1]])\n\n        #reshape token array to CLIP input size\n        batched_tokens = []\n        batch = []\n        if self.start_token is not None:\n            batch.append((self.start_token, 1.0, 0))\n        batched_tokens.append(batch)\n        for i, t_group in enumerate(tokens):\n            #determine if we're going to try and keep the tokens in a single batch\n            is_large = len(t_group) >= self.max_word_length\n\n            while len(t_group) > 0:\n                if len(t_group) + len(batch) > self.max_length - 1:\n                    remaining_length = self.max_length - len(batch) - 1\n                    #break word in two and add end token\n                    if is_large:\n                        batch.extend([(t,w,i+1) for t,w in t_group[:remaining_length]])\n                        batch.append((self.end_token, 1.0, 0))\n                        t_group = t_group[remaining_length:]\n                    #add end token and pad\n                    else:\n                        batch.append((self.end_token, 1.0, 0))\n                        if self.pad_to_max_length:\n                            batch.extend([(pad_token, 1.0, 0)] * (remaining_length))\n                    #start new batch\n                    batch = []\n                    if self.start_token is not None:\n                        batch.append((self.start_token, 1.0, 0))\n                    batched_tokens.append(batch)\n                else:\n                    batch.extend([(t,w,i+1) for t,w in t_group])\n                    t_group = []\n\n        #fill last batch\n        batch.append((self.end_token, 1.0, 0))\n        if self.pad_to_max_length:\n            batch.extend([(pad_token, 1.0, 0)] * (self.max_length - len(batch)))\n\n        if not return_word_ids:\n            batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens]\n\n        return batched_tokens\n\n\n    def untokenize(self, token_weight_pair):\n        return list(map(lambda a: (a, self.inv_vocab[a[0]]), token_weight_pair))\n\n\nclass SD1Tokenizer:\n    def __init__(self, embedding_directory=None, clip_name=\"l\", tokenizer=SDTokenizer):\n        self.clip_name = clip_name\n        self.clip = \"clip_{}\".format(self.clip_name)\n        setattr(self, self.clip, tokenizer(embedding_directory=embedding_directory))\n\n    def tokenize_with_weights(self, text:str, return_word_ids=False):\n        out = {}\n        out[self.clip_name] = getattr(self, self.clip).tokenize_with_weights(text, return_word_ids)\n        return out\n\n    def untokenize(self, token_weight_pair):\n        return getattr(self, self.clip).untokenize(token_weight_pair)\n\n\nclass SD1ClipModel(torch.nn.Module):\n    def __init__(self, device=\"cpu\", dtype=None, clip_name=\"l\", clip_model=SDClipModel, **kwargs):\n        super().__init__()\n        self.clip_name = clip_name\n        self.clip = \"clip_{}\".format(self.clip_name)\n        setattr(self, self.clip, clip_model(device=device, dtype=dtype, **kwargs))\n\n    def clip_layer(self, layer_idx):\n        getattr(self, self.clip).clip_layer(layer_idx)\n\n    def reset_clip_layer(self):\n        getattr(self, self.clip).reset_clip_layer()\n\n    def encode_token_weights(self, token_weight_pairs):\n        token_weight_pairs = token_weight_pairs[self.clip_name]\n        out, pooled = getattr(self, self.clip).encode_token_weights(token_weight_pairs)\n        return out, pooled\n\n    def load_sd(self, sd):\n        return getattr(self, self.clip).load_sd(sd)\n"
  },
  {
    "path": "ldm_patched/modules/sd1_clip_config.json",
    "content": "{\n  \"_name_or_path\": \"openai/clip-vit-large-patch14\",\n  \"architectures\": [\n    \"CLIPTextModel\"\n  ],\n  \"attention_dropout\": 0.0,\n  \"bos_token_id\": 0,\n  \"dropout\": 0.0,\n  \"eos_token_id\": 2,\n  \"hidden_act\": \"quick_gelu\",\n  \"hidden_size\": 768,\n  \"initializer_factor\": 1.0,\n  \"initializer_range\": 0.02,\n  \"intermediate_size\": 3072,\n  \"layer_norm_eps\": 1e-05,\n  \"max_position_embeddings\": 77,\n  \"model_type\": \"clip_text_model\",\n  \"num_attention_heads\": 12,\n  \"num_hidden_layers\": 12,\n  \"pad_token_id\": 1,\n  \"projection_dim\": 768,\n  \"torch_dtype\": \"float32\",\n  \"transformers_version\": \"4.24.0\",\n  \"vocab_size\": 49408\n}\n"
  },
  {
    "path": "ldm_patched/modules/sd1_tokenizer/merges.txt",
    "content": "#version: 0.2\ni n\nt h\na n\nr e\na r\ne r\nth e</w>\nin g</w>\no u\no n\ns t\no r\ne n\no n</w>\na l\na t\ne r</w>\ni t\ni n</w>\nt o</w>\nr o\ni s</w>\nl e\ni c\na t</w>\nan d</w>\ne d</w>\no f</w>\nc h\no r</w>\ne s</w>\ni l\ne l\ns t</w>\na c\no m\na m\nl o\na n</w>\na y</w>\ns h\nr i\nl i\nt i\nf or</w>\nn e\nð Ł\nr a\nh a\nd e\no l\nv e</w>\ns i\nu r\na l</w>\ns e\n' s</w>\nu n\nd i\nb e\nl a\nw h\no o\nd ay</w>\ne n</w>\nm a\nn o\nl e</w>\nt o\nou r</w>\ni r\ng h\nw it\ni t</w>\ny o\na s\ns p\nth is</w>\nt s</w>\nat i\nyo u</w>\nwit h</w>\na d\ni s\na b\nl y</w>\nw e\nth e\nt e\na s</w>\na g\nv i\np p\ns u\nh o\nm y</w>\n. .\nb u\nc om\ns e</w>\ner s</w>\nm e\nm e</w>\nal l</w>\nc on\nm o\nk e</w>\ng e\nou t</w>\nen t</w>\nc o\nf e\nv er\na r</w>\nf ro\na u\np o\nc e</w>\ngh t</w>\nar e</w>\ns s</w>\nfro m</w>\nc h</w>\nt r\nou n\non e</w>\nb y</w>\nd o\nt h</w>\nw or\ner e</w>\nk e\np ro\nf or\nd s</w>\nb o\nt a\nw e</w>\ng o\nh e\nt er</w>\nin g\nd e</w>\nb e</w>\nati on</w>\nm or\na y\ne x\nil l</w>\np e\nk s</w>\ns c\nl u\nf u\nq u\nv er</w>\nðŁ ĺ\nj u\nm u\nat e</w>\nan d\nv e\nk ing</w>\nm ar\no p\nh i\n.. .</w>\np re\na d</w>\nr u\nth at</w>\nj o\no f\nc e\nne w</w>\na m</w>\na p\ng re\ns s\nd u\nno w</w>\ny e\nt ing</w>\ny our</w>\nit y</w>\nn i\nc i\np ar\ng u\nf i\na f\np er\nt er\nu p</w>\ns o</w>\ng i\non s</w>\ng r\ng e</w>\nb r\np l\n' t</w>\nm i\nin e</w>\nwe e\nb i\nu s</w>\nsh o\nha ve</w>\nto day</w>\na v\nm an\nen t\nac k</w>\nur e</w>\nou r\nâ Ģ\nc u\nl d</w>\nlo o\ni m\nic e</w>\ns om\nf in\nre d</w>\nre n\noo d</w>\nw as</w>\nti on</w>\np i\ni r</w>\nth er</w>\nt y</w>\np h\nar d</w>\ne c\n! !</w>\nm on\nmor e</w>\nw ill</w>\nt ra\nc an</w>\nc ol\np u\nt e</w>\nw n</w>\nm b\ns o\nit i\nju st</w>\nn ing</w>\nh ere</w>\nt u\np a\np r\nbu t</w>\nwh at</w>\nal ly</w>\nf ir\nm in\nc a\nan t</w>\ns a\nt ed</w>\ne v\nm ent</w>\nf a\nge t</w>\nam e</w>\nab out</w>\ng ra\nno t</w>\nha pp\nay s</w>\nm an</w>\nh is</w>\nti me</w>\nli ke</w>\ng h</w>\nha s</w>\nth an\nlo ve</w>\nar t</w>\nst e\nd ing</w>\nh e</w>\nc re\nw s</w>\nw at\nd er</w>\nit e</w>\ns er\nac e</w>\nag e</w>\nen d</w>\nst r\na w\nst or\nr e</w>\nc ar\nel l</w>\nal l\np s</w>\nf ri\np ho\np or\nd o</w>\na k\nw i\nf re\nwh o</w>\nsh i\nb oo\ns on</w>\nel l\nwh en</w>\nil l\nho w</w>\ngre at</w>\nw in\ne l</w>\nb l\ns si\nal i\nsom e</w>\nðŁ Ĵ\nt on\nd er\nle s</w>\np la\nï ¸\ne d\ns ch\nh u\non g</w>\nd on</w>\nk i\ns h</w>\nan n\nc or\n. .</w>\noun d</w>\na z\nin e\nar y</w>\nfu l</w>\nst u\nou ld</w>\nst i\ng o</w>\nse e</w>\nab le</w>\nar s</w>\nl l</w>\nm is\nb er\nc k</w>\nw a\nen ts</w>\nn o</w>\nsi g\nf e</w>\nfir st</w>\ne t</w>\nsp e\nac k\ni f</w>\nou s</w>\n' m</w>\nst er</w>\na pp\nan g\nan ce</w>\nan s</w>\ng ood</w>\nb re\ne ver\nthe y</w>\nt ic\ncom e</w>\nof f\nb ack</w>\nas e</w>\ning s</w>\nol d</w>\ni ght</w>\nf o\nh er</w>\nhapp y</w>\np ic\nit s</w>\nv ing</w>\nu s\nm at\nh om\nd y</w>\ne m\ns k\ny ing</w>\nthe ir</w>\nle d</w>\nr y</w>\nu l\nh ar\nc k\nt on</w>\non al</w>\nh el\nr ic\nb ir\nvi e\nw ay</w>\nt ri\nd a\np le\nb ro\nst o\noo l</w>\nni ght</w>\ntr u\nb a\nre ad\nre s</w>\nye ar</w>\nf r\nt or\nal s</w>\nc oun\nc la\nt ure</w>\nv el\nat ed</w>\nle c\nen d\nth ing</w>\nv o\nic i\nbe st</w>\nc an\nwor k</w>\nla st</w>\naf ter</w>\nen ce</w>\np ri\np e</w>\ne s\ni l</w>\nâĢ ¦</w>\nd re\ny s</w>\no ver</w>\ni es</w>\nðŁ ij\ncom m\nt w\nin k</w>\ns un\nc l\nli fe</w>\nt t\na ch\nl and</w>\ns y\nt re\nt al\np ol\ns m\ndu c\ns al\nf t</w>\n' re</w>\nch e\nw ar\nt ur\nati ons</w>\nac h</w>\nm s</w>\nil e</w>\np m</w>\nou gh</w>\nat e\nst ar\nwee k</w>\n! !!</w>\nc lu\nth ere</w>\nn er</w>\nt om\ns el\nï¸ ı</w>\nwor ld</w>\nv es</w>\nc am\ngo t</w>\nin ter\nof f</w>\nu m</w>\nton ight</w>\no ther</w>\nh ou\nloo k</w>\nj e\ni d</w>\nsi on</w>\nbe au\nat t\nel i\nor t</w>\nre c\nf f\nst er\nsu pp\ng en\nbe en</w>\nil y</w>\nte am</w>\nm m\ni c</w>\npe op\nit t\nat s</w>\non ly</w>\nmb er</w>\nen g\nb ri\nm p\nk now</w>\nb ur\nb ar\nin s</w>\nlo w</w>\nsh e</w>\nro w</w>\nâ Ŀ\nt ro\npeop le</w>\nvi a</w>\nlo w\nag a\nbe t\nx t</w>\nf ac\nch ar\ne ar\nw al\ns en\nf am\nb le</w>\nn ati\nis h</w>\nn or\ng ame</w>\nli ve</w>\ns co\nle y</w>\nd on\nic k</w>\nb all</w>\nver y</w>\nthe se</w>\np an\ni a</w>\nat ing</w>\nc r\na re\ng ir\nma ke</w>\nst re\nsho w</w>\n. \"</w>\nf l\nu p\nd r\nthan ks</w>\nil li\nw om\nst s</w>\ni g\ns ur\never y\nc ur\nvie w</w>\nle t</w>\nin to</w>\nmo st</w>\nn a\nin di\ng ar\nha d</w>\ns ou\nv ed</w>\nan t\niti on</w>\nma de</w>\nf ol\nun i\nit ed</w>\nðŁ ı\nic al</w>\nth r\nread y</w>\nch ec\nd ra\nk es</w>\nboo k</w>\ne p</w>\nsi c</w>\nmor ning</w>\nne ws</w>\nc au\nc t</w>\nw ell</w>\nan c\npho to</w>\nth an</w>\nor s</w>\nbir th\ng g\nou t\nne xt</w>\nsom e\nen ing</w>\nstor y</w>\nch ri\ndo wn</w>\nhom e</w>\nf fe\nfre e</w>\nd a</w>\nb or\nf il\nci al</w>\nthan k</w>\nsi de</w>\nle ar\nqu e\nl ine</w>\nt en\nat es</w>\nye ars</w>\nm y\npho to\nbeau ti\nri ght</w>\nn u\nfor m\nshi p</w>\nb an\nth er\nd ays</w>\ng am\nas on</w>\ng y</w>\nðŁ İ\nbirth day</w>\nse t</w>\nic k\ne t\nst ill</w>\ncom ing</w>\nta ke</w>\nðŁ ĩ\nb b\ns ol\ns on\nd en\ne p\nmu sic</w>\nthe m</w>\nde n</w>\nwh y</w>\nf oo\nc ra\nam az\nw n\nh ol\nt ting</w>\nw r\nu e</w>\nma g\nc ro\nl an\nc lo\nb ra\na k</w>\ns ing</w>\nc al\nre ad</w>\n' ve</w>\njo h\nb ab\nd ri\nb lo\nbi g</w>\ner ic\nin t</w>\nt or</w>\ntr y</w>\nl a</w>\nle g\nhou se</w>\nm ic\nv al\nbeauti ful</w>\nl itt\nchec k</w>\nne w\nver s\ns w\nar i\npla y\nh er\nâĢ ĵ</w>\nw in</w>\nm a</w>\ncon gr\nsch ool</w>\nf un\n. @</w>\nhe al\nic h</w>\nd el\nwh ere</w>\nl on\nke t</w>\ntw o</w>\nmu ch</w>\nwat ch</w>\nv en\nd ed</w>\na st</w>\nk ed</w>\nb as\ngo ing</w>\nm p</w>\ne ver</w>\nw ays</w>\nro o\nde sig\nl y\ns ed</w>\nto p</w>\nl in\nch an\nto o</w>\nit ing</w>\nd ent</w>\ngh ts</w>\nt y\nsp o\nne ed</w>\nb lu\nin st\nbe ing</w>\nâĿ ¤\nw el\nl s</w>\nhi m</w>\nm ay</w>\nst ing</w>\nn a</w>\nel y</w>\nlitt le</w>\ng a\nn at\ntom or\nm c\nh on\nw ant</w>\na ir\npi c</w>\nam eric\np er</w>\nle ss</w>\nwee k\nve l</w>\na h</w>\nc ap\nch am\ng er\nti m\ntomor row</w>\nne ss</w>\nst ate</w>\nh al\nser v\nz e</w>\no s</w>\np at\nv is\nex c\ns in\nf f</w>\nc ity</w>\nc en\nan y\nb el\nsu mm\nt in\nw ould</w>\nloo king</w>\nk o\nce le\nfam ily</w>\nm er\npo w\nhel p</w>\nbu s\nc o</w>\nc le\nsel f</w>\nen s</w>\nic s</w>\nth o\nan i\nch o\nle ad\nb s</w>\nt wee\nth ink</w>\nfor e</w>\nch il\nvi de\ndi d</w>\nal e</w>\nch i\nv il\nen ds</w>\nw ing</w>\np as\n' ll</w>\nv ol\ns a</w>\ng s</w>\nman y</w>\nj ec\nbe fore</w>\ngra ph\nn y</w>\nur ing</w>\nw il\nd d\nbu il\nf av\nst ed</w>\ntr an\nl ing</w>\nou d</w>\nd ge</w>\nfi el\nnati onal</w>\nst a\nc er\nw ere</w>\nin a</w>\nse ason</w>\nc ou\nn ed</w>\namaz ing</w>\nti ons</w>\ncele br\nn s</w>\na th\nhe ad</w>\ns day</w>\nd ar\nlo c\nv in\nan other</w>\ng oo\ns at\nn y\njo in</w>\npre s\ns es</w>\ns ing\nan a</w>\nin ing</w>\n.. ..</w>\nc our\nï¸ ı\nac t</w>\ncau se</w>\nli ght</w>\nam s</w>\nt a</w>\nb al\nf c</w>\nhi gh</w>\noff ici\nt t</w>\nchri st\nd ic\nd ay\nra l</w>\nh or\n: )</w>\nvi si\nn am\no b\nma s</w>\ngh t\nre ally</w>\nt un\nfin d</w>\nthr ough</w>\npor t</w>\nu t\nti ve</w>\nst y\nn e</w>\nor e</w>\nðŁĺ Ĥ\nsupp ort</w>\nne ver</w>\nev en</w>\nðŁ Ķ\nh a</w>\ny a</w>\nl d\nu k</w>\nr an\nj am\nwi th\nme di\nd es</w>\nne y</w>\nch ing</w>\nal e\nh y\nk in\n! !\nd y\npl ace</w>\nal so</w>\nb le\nwh ich</w>\nbl ack</w>\nb li\ns ay</w>\npar k</w>\npl ay</w>\nir e</w>\nvide o</w>\nweek end</w>\na il\nke y</w>\np t</w>\nw ard</w>\nfri day</w>\nd in\nine ss</w>\ng ro\nb en\nal ways</w>\nt ball</w>\nag o</w>\nm il\nc y\npro duc\ndi sc\nun der\nple ase</w>\nsp or\nfu ll</w>\ne y</w>\nðŁ Ļ\nis e</w>\niti es</w>\nc at\nk no\nu se</w>\nfo re\nk er</w>\nar t\nhi gh\nop en</w>\ns an\ne f\nour s</w>\nsh ed</w>\nst ri\nd ro\naga in</w>\ni m</w>\nðŁ ĵ\nen jo\nfu n</w>\nge tting</w>\np en\ng er</w>\nc li\nan y</w>\never y</w>\ne u\nwom en</w>\nâ ľ\ne st</w>\nc ould</w>\nr y\n\" @</w>\nth ou\nsh a\ncomm un\nb er</w>\nd ents</w>\ndi s\nwh ile</w>\naw ay</w>\ndi o</w>\nh am\ng la\nd ate</w>\nk a</w>\nmis s</w>\nun ch</w>\nw on\nin f\nroo m</w>\ng a</w>\nre al</w>\nex per\ndi rec\nsh ould</w>\nsp r\ng ol\nl ong</w>\nbet ter</w>\nor i\ne y\ni ence</w>\nil s</w>\nz z\nh an\nf ound</w>\nv s</w>\nâ Ļ\npo st</w>\nti c</w>\npar t</w>\nm en\nren ce</w>\nce ss</w>\nv ic\ns il\nsho p</w>\nðŁĺ Ĥ</w>\nf ood</w>\nv al</w>\nsti c</w>\ny ou\ns ays</w>\ne lec\nst ar</w>\no c\nl and\ni d\nc tion</w>\nfiel d</w>\ns of\nst art</w>\nwat er</w>\nfri ends</w>\non es</w>\nðŁ Į\nf la\nf ar\nwh ite</w>\npar ty</w>\nin st</w>\ngr ou\nt v</w>\nevery one</w>\nm ent\nj a\nch a\npr in\nan ts</w>\nd uring</w>\nl at\nl ar\nwe st</w>\nth en</w>\nk a\ny oun\nin sp\nin te\nwe en</w>\nvisi t</w>\naga inst</w>\nre le\nhe ad\nc es</w>\nto wn</w>\nloo ks</w>\nth re\nre gi\nren t</w>\npro jec\ngir l</w>\nse ar\nw o\nm om\nc ar</w>\nh un\npu bli\nd i</w>\np le</w>\nc all</w>\nc ri\nu m\nfor d</w>\nper fe\nfri end</w>\nh ard</w>\nssi on</w>\nte st</w>\npla ying</w>\nar ound</w>\nbe cause</w>\nke ts</w>\nme et</w>\nsat ur\nar ti\nwor k\nj un\nv en</w>\nr un\nme mber</w>\npor t\nsu per\nt wit\ns am\nel s</w>\nt ly</w>\nad v\nati ve</w>\nat h</w>\ns ure</w>\nav ail\nla r</w>\ns qu\nar ds</w>\nev ent</w>\nm en</w>\nl l\no ver\nlo gy</w>\nit al</w>\ntim es</w>\nm al\nb ack\nc oo\nma king</w>\nst ru\nâ ģ\nit u\nsh ar\ng an</w>\nc as\ns n\nsumm er</w>\npic ture</w>\nf an\nh in\nchrist mas</w>\nc y</w>\npr oud</w>\ncham pi\ndesig n</w>\npp ing</w>\nho pe</w>\nc a</w>\navail able</w>\nma y\nwe d\nphoto graph\nspe cial</w>\nsal e</w>\nsto p</w>\ner y</w>\na we\nal ity</w>\nhi story</w>\nam a</w>\npre si\nb ru\nwor king</w>\nd one</w>\nd r</w>\nk en</w>\nfe at\nw ood</w>\nate st</w>\nsun day</w>\nmo vi\nvel y</w>\ns le\nf ace</w>\nsp ec\nstu dents</w>\nb y\nha m</w>\nsp on\nbus iness</w>\nd at\ni e</w>\ni p\nso ci\ng lo\nh and\nre cor\nr s</w>\nme e\nke ep</w>\np ur\nheal th</w>\nsh e\ncom ple\ngo d</w>\nda vi\ncol lec\nli st\nr a</w>\nclu b</w>\nt ers</w>\nin clu\nth ings</w>\npl an\nâ ĺ\njoh n</w>\nsh ing</w>\nat ul\nso on</w>\nblu e</w>\ng or\nsatur day</w>\nw on</w>\ncongr atul\nse e\nâĿ¤ ï¸ı</w>\ntho se</w>\nðŁĺ į</w>\nfin al</w>\nd ou\nit h</w>\no wn</w>\nro ad</w>\nt our</w>\na st\nindi a</w>\nti l</w>\nn d</w>\nf er\nfav or\nsu l\nlear n</w>\nfir e</w>\nju st\ngrou p</w>\na h\nr ac\nbo dy</w>\nu r</w>\nc are</w>\nà ¸\np lo\no h</w>\npo s\ngi ve</w>\nte ch\nsu b\nc ent\ner ing</w>\ny m\nil ity</w>\nf ic\nlon don</w>\nv ir\ngu ys</w>\nb a</w>\nðŁ ¤\nbab y</w>\nsc re\nðŁĺ į\ntru mp</w>\nun der</w>\nchan ge</w>\ni an</w>\ncol le\nss es</w>\nl er</w>\nss ed</w>\nn ice</w>\nann oun\npow er</w>\ns ar\na king</w>\nmin i\ns li\ns wee\nk ar\nfu l\nc ru\nac tion</w>\na ther</w>\n) .</w>\nst and\nde vel\na a\ng an\nle ft</w>\nlo l</w>\nre l\ntran s\nm ents</w>\nin t\ne f</w>\nman ag\ndi g\ngen er\ndo wn\np au\nti v\nk u\nth ur\nk en\nst on</w>\nf ans</w>\ntal k</w>\ntwee t</w>\nt oo\nsty le</w>\npro te\nse con\nfr on\nawe some</w>\ng l\np al\nne t\ns or\nla u\ng on\nsin ce</w>\nt ty</w>\nser ies</w>\nme mor\nb eli\nfil m</w>\ndi d\ndi es</w>\no t\ncongratul ations</w>\np ra\ne ve</w>\nw oo\noffici al</w>\nsu c\nin cre\nb on\npar t\npp ed</w>\ncla ss</w>\nsi ve</w>\nbo y</w>\ncu l\nperfe ct</w>\nt ou\nd am\nwel come</w>\nfoo tball</w>\nh i</w>\np ap\nwa it</w>\nad a</w>\ncongr ats</w>\nyoun g</w>\nexc ited</w>\nre ce\nj an\nv a</w>\nre d\nst ra\nmedi a</w>\n' d</w>\ndo es</w>\nle t\nmu l\nill s</w>\ngre en</w>\nm el\nto ge\nfu ture</w>\nye ster\nvers ity</w>\nfor m</w>\nta in</w>\ni de\nch es</w>\nki ds</w>\nqu i\nha ha\nde ta\nbi g\nfavor ite</w>\ngir ls</w>\ncon tin\ndo m</w>\nsear ch</w>\nu al</w>\na ir</w>\nd ers</w>\nmon th</w>\nc er</w>\nyester day</w>\ncommun ity</w>\nad e</w>\ndo g</w>\nvil le</w>\nic es</w>\nd eli\nsy ste\nru n</w>\nis m</w>\nhe art</w>\nc up</w>\nen ti\nfe w</w>\npresi dent</w>\ne ds</w>\nun til</w>\nfe sti\no k\nf lo\nsa id</w>\nol e</w>\nme d\ntra vel</w>\n £</w>\nph one</w>\ntoge ther</w>\nfa st</w>\nlo t</w>\ngam es</w>\nsh ir\nbet ween</w>\ny es</w>\nth ers</w>\ndo ing</w>\nm ac\nat or</w>\nb and</w>\nfol low\nprojec t</w>\ndevel op\ndi ffe\ncon fe\nspe ci\nca st</w>\ny s\nbo ard</w>\nr d</w>\ni al</w>\nsh oo\nr am\nha ving</w>\nsh are</w>\nfol low</w>\non e\nn ame</w>\nm r</w>\npu t</w>\ndisc u\nor y</w>\nc ame</w>\nou s\ns ite</w>\ntwit ter</w>\nt b\nt it\nfin ally</w>\nz ed</w>\nsu per</w>\ncom pan\nus ing</w>\nall s</w>\nli st</w>\nr is</w>\nsho t</w>\ng al\nt ar\nde l</w>\njoh n\nâĢ Ķ</w>\nsome thing</w>\nra m</w>\ninte re\nwh e\nb it</w>\nðŁ į\nstre et</w>\noun d\na i\ntic kets</w>\nmovi e</w>\nre al\nk y\nta king</w>\no pp\nc c</w>\nl am\nm oun\nin ve\nbl ack\nus ed</w>\non line</w>\ny or\nloc al</w>\ngu e\nc ks</w>\no w\nge st</w>\nbo ys</w>\nilli on</w>\ncon t\nre ci\nin ed</w>\neu ro\nno w\nse en</w>\np h</w>\nte ach\nde f\nsou th</w>\nsu ch</w>\naw ard</w>\nmu st</w>\nis su\nca re\nfe el</w>\np lu\nl atest</w>\nspor ts</w>\nwe b\nte x\ne ment</w>\ns k</w>\nfi c</w>\nw an\nte ch</w>\no t</w>\nbo x</w>\nn er\nfre e\nt al</w>\na sh\nc ase</w>\nho t</w>\nwon der\nmee ting</w>\ner a</w>\nch all\nðŁ IJ\njo b</w>\nil i\nc ool</w>\nj our\nth s</w>\nm o</w>\nf el\ndi e</w>\nmic ha\ne le\nte am\nserv ice</w>\nst and</w>\nma kes</w>\np ing</w>\near ly</w>\ncom es</w>\ne k</w>\nho li\nv ers</w>\nag ue</w>\ns au\nthre e</w>\nmon day</w>\nfa shi\nsome one</w>\nth ro\nse a</w>\nb ad</w>\nsupp or\ntur n</w>\nur y</w>\nm ing</w>\nphotograph y</w>\nn ic\nmar k</w>\npre tty</w>\nss ing</w>\nwat ching</w>\nme mb\nar ri\ncoun ty</w>\nbe ach</w>\nfr an\ncen ter</w>\npol ice</w>\nb at\npubli c</w>\nt an\npre ss</w>\ns af\ns y</w>\nge ts</w>\nro y\nn ers</w>\ny our\nbu y</w>\nst ers</w>\nsho w\nas ed</w>\nchil dre\naf ric\nin es</w>\nsp ace</w>\nsc ri\nh all</w>\npa in\nar ing</w>\nhom e\nm ur\nheal th\nch ed</w>\ns and\nrece i\ngu y</w>\ne a\nameric an</w>\nre si\nchildre n</w>\n- -\ni ri\ning ton</w>\ncoun try</w>\nro ss</w>\nle n</w>\nann a</w>\nboo ks</w>\nb c</w>\ne ce</w>\nd om\nlo vely</w>\nk h\npe t\ng y\ng ri\nst age</w>\noff ice</w>\nro ck</w>\nm on</w>\nb ay</w>\nt able</w>\nsu n</w>\nm ed</w>\nth in\nl or\nf low\n( @</w>\nuni versity</w>\nstor e</w>\nfron t</w>\ngoo d\nz a</w>\nvo te</w>\nnor th</w>\nhe y</w>\nan im\nor der</w>\nmi d\nwith out</w>\na de\nre member</w>\nmar ket</w>\n? ?</w>\nmu s\ntra ining</w>\ne duc\nbu t\nco ver</w>\nst an\nsc en\nb la\nbre ak\nl ou\ns ame</w>\ng old</w>\na in</w>\no s\nbo th</w>\nl it\nver n\na i</w>\nal bu\np a</w>\nenjo y</w>\nbe g\nell ing</w>\nthur sday</w>\ninf o</w>\ns an</w>\nameric a</w>\nha ir</w>\nte l</w>\nmar ch</w>\ncon cer\ncolle ge</w>\nconfe rence</w>\nap p</w>\nh our</w>\nch ang\nâ ļ\ns our\nol s</w>\nwe ather</w>\nw ar</w>\np hi\nfesti val</w>\nsecon d</w>\ncu te</w>\npr ac\nen er\nstr y</w>\nle a\npol it\ns av\nse n</w>\no w</w>\nm i</w>\nne ar</w>\nou ght</w>\nz e\nco ffe\nw illi\nd an\nse y</w>\ndavi d</w>\ne se</w>\nf an</w>\nde ci\nthe at\nno v\nati on\ntr ac\nsc i\nre view</w>\nc el\ne m</w>\nu n</w>\nju ly</w>\nor ig\nti on\nd ru\nform er</w>\nst ay</w>\naf ter\nin v\ntoo k</w>\ndat a</w>\nb al</w>\ntu es\nd an</w>\nev ening</w>\nðŁĺĤ ðŁĺĤ\nd ol\nu res</w>\npro vi\nt s\ne st\nsig n</w>\nj ac\nu k\ns ong</w>\nye t</w>\nbo w\nin du\nj ap\nh oo\npo int</w>\nany one</w>\nz y</w>\ni st</w>\nh ur\nit al\nbuil ding</w>\nwom an</w>\nch ur\nj er\nper for\nco ach</w>\nle ague</w>\nce ss\nne t</w>\ni mag\nnati on\nbr it\nqu e</w>\naw ards</w>\nag es</w>\nwor ks</w>\nc ed</w>\nman ce</w>\nl ate</w>\nig n</w>\nmon ey</w>\ntru e</w>\ni i</w>\nt ell</w>\npl ac\np ac\nas y</w>\nwor ld\nbe hin\nim port\nread ing</w>\ngra m</w>\ngi ving</w>\nme t</w>\nh it</w>\nfor ward</w>\nst om\npres ent\njun e</w>\nso cial</w>\nno on</w>\nmar t\nhal f</w>\ns we\ngo vern\nk er\ndeta ils</w>\nli sh</w>\n_ _\nac y</w>\nsi a</w>\nber t</w>\nf all</w>\n! !!!</w>\n) ,</w>\nth i\nd iti\nsp ort</w>\nk ing\nf it\nst af\nc at</w>\nmu se\ncen tr\ny er</w>\ncon tro\nb loo\nwal k</w>\nac tu\ndid n</w>\nli m\nlear ning</w>\nre search</w>\nwed ne\nau th\nh ours</w>\nk y</w>\nf ar</w>\nh en\n.. ..\nit ch\nri l</w>\nstr ong</w>\nsk y</w>\nque sti\njam es</w>\nr on\nd g\nf ur\nc in\ndo es\napp ro\nmar ke\ntu res</w>\nful ly</w>\nch at</w>\nbehin d</w>\nte m\nfin i\nmis sion</w>\nb att\nfe el\nhe av\nevery thing</w>\nb ar</w>\nw ish</w>\npre mi\ni ma\nexper ience</w>\ne ach</w>\nre port</w>\nswee t</w>\ntic s</w>\nspr ing</w>\nre spon\nsyste m</w>\nvic tor\nl in</w>\nsa w</w>\nal ready</w>\ngh ter</w>\nf le\nã ĥ\nbr ing</w>\nalbu m</w>\n- -</w>\nell s</w>\nst an</w>\nto m</w>\ninter national</w>\nw ent</w>\nan ni\nmat ch</w>\npp er</w>\nst one</w>\nsm all</w>\nra in</w>\nfashi on</w>\nare a</w>\nv an\nag ram</w>\nk o</w>\nthou ght</w>\nwor th</w>\nv an</w>\nm er</w>\ncoffe e</w>\nit es</w>\ng n\narti st</w>\nc on</w>\nar ch\nc ir\nse cre\ngr ound</w>\nis o\nh and</w>\nco m</w>\nbri dge</w>\nh s</w>\nx i\nl ink</w>\npu l\nsp l\nr ace</w>\nf li\nri ver</w>\ng as</w>\ndi sco\nd al\nplay er</w>\nf it</w>\nphoto s</w>\nit y\no k</w>\nj or\ntr a</w>\nap ril</w>\nad s</w>\na di\nsol u\nbeau ty</w>\ndo or</w>\nme ss\nup date</w>\nali a</w>\nsch o\nen ed</w>\nmom ent</w>\nsco t\nsc ience</w>\ni or</w>\nti es</w>\nac ross</w>\nous ly</w>\nsh es</w>\ndoes n</w>\np age</w>\nwat er\nm illion</w>\ncla ssi\nl ic\nca st\nform ation</w>\nmicha el</w>\nell o</w>\ns mo\nin ts</w>\nvi sion</w>\nop ening</w>\nld n</w>\nau str\ntues day</w>\nwin ner</w>\npo ssi\nr ound</w>\nshir t</w>\ndi t</w>\nb o</w>\nu es</w>\nil led</w>\nal ong</w>\ntri p</w>\nstar ting</w>\nim pro\nk an\nper son</w>\nno t\nre co\nne eds</w>\nc le</w>\nli e</w>\nre st</w>\nr ing</w>\nwin ter</w>\nsi mp\nmo m</w>\nbe er</w>\nfac e\ntor s</w>\nus a</w>\ncollec tion</w>\nge or\nse ssion</w>\ntr ying</w>\nla s</w>\nla ke</w>\nj en\norig in\nstu dent</w>\nse cur\nv in</w>\npic s</w>\nex pe\ncom p\ngon na</w>\ne qu\nb ad\nle y\na u</w>\nmemb ers</w>\nbre ak</w>\nw all</w>\ngi c</w>\ndin ner</w>\nbu l\ninsp ir\nr i</w>\nmin d</w>\nic a</w>\nwin ning</w>\ntal king</w>\nt ren\ns is</w>\nt en</w>\nwonder ful</w>\ns now</w>\nhe ar</w>\nth om\nno thing</w>\ngu i\nst in\nblo g</w>\nfe st</w>\nb un\nle e</w>\nwar ds</w>\nch ance</w>\ndre ss</w>\nre n</w>\npau l</w>\np es</w>\ntech no\nru ssi\nc ard</w>\ne ast</w>\nmar i\nw ine</w>\nt i</w>\nla w</w>\nstr ic\nk i</w>\nap e</w>\nau gu\npro fe\nas h</w>\ncour se</w>\nma il</w>\nren tly</w>\nd un\nm un\nlo ve\nis land</w>\ndri ve</w>\ns l\nend ed</w>\nma in</w>\nlo st</w>\nnat ure</w>\nâĿ¤ ï¸ı\nch ic\nre por\np in\npr o</w>\nst ation</w>\nce p\nta kes</w>\ncompan y</w>\ngo es</w>\non d</w>\nma ch\nra dio</w>\nd ad</w>\nro ck\nj a</w>\np ay\nchampi on\ne e\nin de\ntt a</w>\nati c</w>\nt ab\nbeli eve</w>\nener gy</w>\nz i\nt at\nwor d</w>\non ce</w>\nre sul\ny l\nand re\nan o</w>\ninst agram</w>\nclo se</w>\nt am\ncu stom\nw a</w>\ncon om\nsho ws</w>\nli fe\nk in</w>\nro b\nt age</w>\nn ation</w>\nal most</w>\nlist en</w>\nsa ve</w>\nre li\nac e\nmar y</w>\ntre e</w>\nfor get</w>\nj ack\nwa iting</w>\ndirec tor</w>\nh ill</w>\nbor n</w>\nte mp\nf l</w>\nst e</w>\non a</w>\nsing le</w>\nwedne sday</w>\nun ited</w>\nin o</w>\n@ _</w>\nne l</w>\ncelebr ate</w>\nen ding</w>\nde al</w>\nj i</w>\ncan ada</w>\nhu ge</w>\ntr ack</w>\nâĢ ¢</w>\nf y</w>\nfan ta\nan g</w>\nyor k</w>\nrele ase</w>\np un\nep iso\nwor ds</w>\nt our\np ack\ni gh\nclassi c</w>\nperfor mance</w>\nke t\nafter noon</w>\nrecor d</w>\nwin s</w>\npro ble\nâĿ ¤</w>\nf our</w>\nb ed</w>\nban k</w>\nd ance</w>\ns la\ncal led</w>\nmi ght</w>\na p</w>\npa st</w>\nðŁ ļ\ndiffe rent</w>\nit e\ngi ft</w>\nssi ve</w>\nchur ch</w>\nc us</w>\npro gram</w>\nho tel</w>\nic e\nma d\nsecur ity</w>\nen ge</w>\nd c</w>\nen ough</w>\nst a</w>\ne ty</w>\nde ad</w>\ng un\nhe ar\nm ir\nhu man</w>\ngre ss</w>\noun ds</w>\npi ece</w>\nbre aking</w>\ngar den</w>\nfi ght</w>\nvie ws</w>\nf ish</w>\nstar ted</w>\nrun ning</w>\ngre en\nser i\ns m</w>\nas k</w>\nd or\nde ath</w>\ne conom\ner i\nir d</w>\ns er</w>\nl unch</w>\nâģ ¦\nbo x\nnat u\nba se\nb an</w>\nf al\nglo bal</w>\nwil d\nwo w</w>\nout side</w>\nmo ve</w>\nle ad</w>\nan al\nmuse um</w>\non g\nha w\npow er\nthan k\nb ac\nchar ac\ncam pa\ndig ital</w>\nr o</w>\nop er\nde v\nw ol\np ati\nf a</w>\nm ale</w>\npap er</w>\nill ing</w>\nc s</w>\nâ ĥ\neduc ation</w>\nta ken</w>\ne ffe\nm ou\ns ad\n\" .</w>\nbas ed</w>\nstaf f</w>\ninclu ding</w>\nli ving</w>\na c</w>\nch ina</w>\nmo b\nstor m</w>\nlu ck</w>\nph il\no o</w>\ny n\ntra vel\nk el\nti al</w>\npr ice</w>\nboo k\nimport ant</w>\nbi o\np ool</w>\nny c</w>\nf ab\nlo ad</w>\n? !</w>\nchall enge</w>\ncr y\nser ve</w>\nwe ar</w>\nbu s</w>\nta in\nnu mber</w>\nro r</w>\nk at\ni z\nth ough</w>\nho sp\nm m</w>\nfa ir</w>\nut es</w>\nho t\npo p</w>\nfi ed</w>\ncam p\ndevelop ment</w>\nli br\nc ali\nem s</w>\nâģ¦ @</w>\nb ol\nis ed</w>\nstand ing</w>\nmo del</w>\nit a</w>\ng le</w>\nbro wn</w>\nima ge</w>\nve red</w>\nfor ce</w>\no il</w>\npar tic\nsh u\nda ily</w>\nla w\nse c\ncla ss\ncam p</w>\nholi day</w>\ncl in\nk ers</w>\npres ent</w>\ngam e\nincre di\ner ship</w>\ninter view</w>\nb ill</w>\ndu e</w>\nand y</w>\nab o\nin nov\nke y\nac ade\np il\nmo der\nst ars</w>\nbr and</w>\nf er</w>\nwee ks</w>\ncon si\npr e</w>\nsa fe\nwr it\ndi um</w>\nla unch</w>\nmarke ting</w>\nann ual</w>\nas si\ncour t</w>\nla dy</w>\nc ted</w>\nand a</w>\nin side</w>\nchil d</w>\nopp or\nsm ith</w>\ncentr e</w>\ngu e</w>\nâģ ©</w>\nf ren\nst y</w>\nfor t</w>\nent ly</w>\nis n</w>\nke ep\nto ber</w>\non y</w>\nbo y\nal d</w>\ncol la\nde mo\nle vel</w>\ncom pet\nad o</w>\nb our\nfanta stic</w>\nm ate</w>\ns u</w>\nsou th\noppor tun\nvers ary</w>\nlat er</w>\nbu d\nface book</w>\nla un\nster n</w>\np it\n! \"</w>\nma j\ngr am\ntb t</w>\nfi re\nhapp y\na ks</w>\nwh ole</w>\nactu ally</w>\nill er</w>\nell a</w>\nlo ts</w>\nal ex\nan ge\nlan ds</w>\nðŁĺ Ń\nen ter\nr ou\nepiso de</w>\np ed</w>\nin ten\nsh ire</w>\nwh o\npl an</w>\nh o</w>\nca ke</w>\nwe st\nmag az\nfre sh</w>\nc c\nn ar\nch ris</w>\nwr iting</w>\nw er</w>\nn om\nl o</w>\nmi dd\ndre am</w>\no l</w>\nti onal</w>\nde b\n> ></w>\nbe come</w>\ns i</w>\ngr and</w>\nall ing</w>\nhi stor\nri de</w>\ni red</w>\nsaf e</w>\nque en</w>\nci l</w>\nin tro\nvi l</w>\nd ani\n.. .\nar tic\nst at\nsh ort</w>\nor ing</w>\nsel fi\nmis si\ndo c\nb it\ng all\nb om\ni re\nse lec\nd ition</w>\nðŁĶ ¥</w>\nfri end\nbe at</w>\ngh ting</w>\nðŁĺ Ĭ</w>\npe ace</w>\nex hi\nant a</w>\nab ility</w>\nil lu\nj on\nqu ality</w>\ntri bu\nm es</w>\nplay ers</w>\nfa ir\ncu t</w>\nc ab\nsuc cess</w>\nb i</w>\nsu s</w>\npro mo\nsch e\nan ge</w>\nic o</w>\ncomm it\ncat ch</w>\nill a</w>\nkin d</w>\nfeel ing</w>\nqu o\ns ay\nanni versary</w>\nspo t</w>\nmo ther</w>\nan e</w>\np end\nyour self</w>\nop s</w>\napp le</w>\nmin utes</w>\np o</w>\ngr and\nri es</w>\nha ha</w>\ncare er</w>\ned ition</w>\nde c\nric k</w>\nam i</w>\nconcer t</w>\niti ve</w>\nge ous</w>\nd ly</w>\nt te</w>\nadv ent\ni g</w>\nli ghts</w>\nak er</w>\nsk y\nâĥ £</w>\nr ay</w>\nfini shed</w>\nw ay\ns d\nac coun\nðŁĴ ķ</w>\nck y</w>\nch el\nlit er\npain ting</w>\nlo s</w>\nst un\ntechno logy</w>\nn as\nma r</w>\nb il\nafric a</w>\nki e</w>\ney es</w>\ngol f</w>\nplu s</w>\nni a</w>\nit ec\nserv ices</w>\nwed ding</w>\nkno wn</w>\nte le\n.. ...</w>\nstar ts</w>\npa ren\nw ants</w>\nati onal</w>\nmon ths</w>\nwin do\nfav our\ner t</w>\nmagaz ine</w>\nex clu\nre ve\nb c\norigin al</w>\ne ss\nn al</w>\nan ti\nst ro\nt ice</w>\nstu dy</w>\nà ¤\nv ac\nnation al\nfi ve</w>\nra in\nve ment</w>\nu te</w>\nver se</w>\nem er\nar my</w>\npossi ble</w>\ngue ss</w>\nval ley</w>\nther n</w>\ncro w\nm r\ncol or</w>\non to</w>\npic k</w>\ncle ar</w>\ndar k</w>\nt ac\nwan ted</w>\nit ting</w>\ncan cer</w>\ngovern ment</w>\ndi e\nri se</w>\nz ing</w>\ncol d</w>\nf oun\nstu dio</w>\nstr ation</w>\nbro ther</w>\na head</w>\nsh el\nmic ro\nic ally</w>\nd au\nsig ned</w>\nvi ol\na x\nas se\ni o\nw re\nspl ay</w>\nch ick\naugu st</w>\npl at\nti ps</w>\nsp i\nhu man\ne asy</w>\nlo gi\nmi ke</w>\ngro w\nag re\nw w\nsh ad\nmo tiv\nwi de</w>\ntur ns</w>\nom g</w>\nv ar\nde fin\nsu g\nj im\nðŁĶ ¥\nt d</w>\ncampa ign</w>\nnam ed</w>\nre tweet</w>\nco p\nt v\nle av\nk is\ndou ble</w>\ns mar\nissu e</w>\nvil la\nin formation</w>\nli es</w>\nsto ck</w>\nn t</w>\ndi stric\nsh or\nmi x\ner o\nse p\nme x\nsee ing</w>\nli ve\nre min\nco de</w>\ng ur\ns c</w>\nwil d</w>\nl un\nh ood</w>\nspo t\nfa ther</w>\nfore ver</w>\nup d\ntra f\nf ly</w>\nne ed\ngra du\ntra in</w>\nma ke\ns ab\nbe y\nsi ze</w>\nlead er</w>\ntal ks</w>\ne u</w>\nlo g\nfo x</w>\ngor geous</w>\nle ss\nle ts</w>\nsur pri\nmy self</w>\nno te</w>\nli ves</w>\nf ru\nlo ved</w>\nse ver\nde m\nj i\nso c\nh old</w>\ndo gs</w>\nn i</w>\nâ ŀ\nlea ve</w>\nair port</w>\nben ef\nex pl\nshi ps</w>\ncomple te</w>\nach i\ngre at\nvin tage</w>\nj ack</w>\nro c\nwoo d\npri v\noff er</w>\ney e</w>\nver sion</w>\nte a</w>\nco ach\noff ic\nw ell\ng en</w>\ns at</w>\nh h\nyou th</w>\no x\n? \"</w>\nm t</w>\nmi x</w>\ng g</w>\nd le</w>\nnatu ral</w>\nbuil d</w>\nbreak fast</w>\nthin king</w>\ntheat re</w>\nmo on</w>\nber g</w>\ngo als</w>\ngeor ge</w>\nen e\nexc ell\nil ing</w>\ntun e</w>\ny ed</w>\ng ate</w>\nm it\nnet work</w>\njo e</w>\nh ello</w>\nf b</w>\ntu be</w>\nwe aring</w>\nath le\nstru c\nhar d\ngla ss</w>\ng ers</w>\nthro w\ng es</w>\nb t\nindu stry</w>\nmanag ement</w>\nali st</w>\ngo al</w>\nstre am</w>\ny el\na vi\nici ous</w>\no thers</w>\ns ki\nchri sti\nbir d</w>\ne sc\nm in</w>\ntr o</w>\nl t</w>\nj an</w>\nim p\nri ghts</w>\nsh a</w>\nor gan\ncent ral</w>\nar a</w>\nro ll</w>\nfavour ite</w>\nche ster</w>\nel se</w>\np ay</w>\ncar s</w>\nm ine</w>\nste p</w>\nprac tice</w>\nmaj or</w>\nh ang\nðŁĺ ĺ</w>\nn on</w>\nv ari\neng ine\nvol un\ndi a</w>\ni led</w>\narch itec\np ink</w>\nd s\nth y</w>\nwa sh\nweb site</w>\nba g</w>\ncontro l</w>\nel li\nf ra\nan sw\nd ence</w>\ny u\nr on</w>\nol a</w>\ng in\ndr in\nli c</w>\ncou ple</w>\nsp ar\ng on</w>\ncre ate</w>\nc t\ncelebr ating</w>\nde ep</w>\ne at</w>\nte e</w>\nvo ice</w>\ndro p</w>\nvis it\nat ors</w>\nsta dium</w>\nf t\nw is\nro l\ngra de</w>\nfam il\npo ints</w>\nre pre\nw as\ntraf fic</w>\njap an</w>\nor g\nhon or</w>\ntex as</w>\nman u\nâĻ ¥</w>\nsafe ty</w>\nre r</w>\nb ag\nem plo\nrele ased</w>\nre gu\nak a</w>\nn av\nro le</w>\nsen ior</w>\nspec t</w>\ncro ss</w>\nlin es</w>\nbe st\np ack</w>\ns in</w>\nti e</w>\nmis sing</w>\nsun set</w>\nli ber\nis ing</w>\nj ay\nsk i</w>\nchampion ship</w>\nac tiv\nla dies</w>\nplay ed</w>\ny y\npu bl\nal o\npri de</w>\ns r\npa ki\nlu x\nsur vi\nck ed</w>\ne ts</w>\ncho col\naustr alia</w>\npar is</w>\nmi les</w>\nh at\nment al</w>\nal a</w>\nme an</w>\nmob ile</w>\nen a</w>\nin si\nf ound\nchi ef</w>\nt ag\nincredi ble</w>\nre turn</w>\nà ©\ngoo gle</w>\nfren ch</w>\ncre w</w>\nhal lo\nali an</w>\nj az\nch er</w>\nsil ver</w>\nnor th\neng lish</w>\nbase ball</w>\nc af\nlim ited</w>\nfollow ing</w>\napp reci\near th</w>\nk ir\nve mber</w>\nw ed</w>\np tion</w>\ng ed</w>\noc tober</w>\nfl ori\nc r</w>\nen cy</w>\nga ve</w>\nlor d</w>\nstu ff</w>\nber ry</w>\npo st\nsm ile</w>\nbro ad\nst ate\ngg er</w>\nme ans</w>\nic y</w>\ngu n</w>\ny o</w>\nma ster</w>\nbur g</w>\nhan ds</w>\nni e</w>\n/ /</w>\nuni on</w>\nbrit ish</w>\nbig gest</w>\ndistric t</w>\nam ing</w>\nh il\no ce\nper son\npas s</w>\nen vir\nscho ols</w>\narri ved</w>\nanc es</w>\ninsp ired</w>\nex pla\nbe n</w>\nlibr ary</w>\nbo tt\nam p\nste ph\ncont act</w>\nb ang\nm s\ncali for\nt old</w>\nbatt le</w>\nb b</w>\nchic ago</w>\nâľ ¨</w>\nstr ate\nsh i</w>\nde ce\n- )</w>\nad d</w>\nla b\nj ones</w>\nleg end</w>\ncast le</w>\ning er</w>\nst ance</w>\nbe l</w>\nur a</w>\nre fu\nlead ers</w>\npo t\nse x\nh ic\nartic le</w>\nki d</w>\nfr ance</w>\nx x</w>\nex e\ngui de</w>\nvolun te\npr int</w>\nal i</w>\nce o</w>\ntwee ts</w>\nw x</w>\nscen e</w>\nvol u\nant i</w>\nh an</w>\nas soci\nshar ing</w>\nro se</w>\nmini ster</w>\nsh er\nin ste\ncle an\ndemo cr\npo ster</w>\nsk in</w>\np sy\npro per\ncra zy</w>\ni am\no re\nin i</w>\nany thing</w>\npo d\nmo ving</w>\ncl ick</w>\nex plo\ncom b\ncra ft</w>\nf i</w>\nbloo d</w>\nis ra\npubl ic\nd ent\nol ym\neng land</w>\na si\nch er\nfac t</w>\nenvir on\nhar ry</w>\ng one</w>\nme dic\nenjo ying</w>\njust ice</w>\nj r</w>\nindi an</w>\nwi fe</w>\ns ound</w>\nt es</w>\ndra wing</w>\np al</w>\nide a</w>\ncr it\nju li\nil er</w>\nwar m</w>\ncl ar\nthou ghts</w>\ndef en\ncoun cil</w>\nintro duc\ndi ed</w>\njan u\nan i</w>\ns end</w>\nli er</w>\nm l\nintere sting</w>\ntra de</w>\nwin d</w>\nb ay\ns ac\nanc y</w>\nsour ce</w>\nb es</w>\norg ani\nar ly</w>\nlar ge</w>\nff ici\nta g</w>\nu t</w>\nde sp\no es</w>\ntit le</w>\nsy m\npic tures</w>\nop en\nwom en\nsho wing</w>\nri a</w>\nle ast</w>\nlead ership</w>\ncur rent</w>\nelec tr\nval ent\nlist ening</w>\nc key</w>\ngener al</w>\nde ser\ndu ce</w>\n; )</w>\nc ent</w>\nðŁĺį ðŁĺį\nsco tt</w>\npo or</w>\nselfi e</w>\nev ents</w>\ni on</w>\nwr ong</w>\nde v</w>\nh ill\nsep te\ncul ture</w>\nl ine\nsor ry</w>\ns ent</w>\nsi ster</w>\nce pt</w>\nk ri\nno vember</w>\nar i</w>\nannoun ce</w>\nz ation</w>\nbr an\ng ent\nd u</w>\nl en\nper s\nf m</w>\nmart in</w>\no p</w>\ne mb\nom e\nmidd le</w>\nsuc cess\npe ter</w>\njanu ary</w>\nf lu\nrac ing</w>\nd av\nbi ke</w>\nðŁı »</w>\npe t</w>\nshoo t</w>\nprofe ssi\nfeat uring</w>\nsepte mber</w>\nnow playing</w>\nsta ur\nz a\non ic</w>\nqu ick</w>\nbas ke\nspe aking</w>\nmil it\nz er</w>\nchick en</w>\nb ell</w>\ns ad</w>\nco ast</w>\nlo ving</w>\ny ers</w>\nd j</w>\npan el</w>\nver age</w>\ns wit\nic ks</w>\nb ou\ncalifor nia</w>\ns am</w>\nparen ts</w>\ner o</w>\nk illed</w>\nph ys\njo bs</w>\nmi gr\nan th\ne mo\nhallo ween</w>\nand er\nc m</w>\ncompet ition</w>\ne ag\ns ket\nsp ir\nmay be</w>\nexclu sive</w>\napp e\njour ney</w>\nscre en</w>\nfor d\ni o</w>\nh ate</w>\nu g\nsou l</w>\nher o</w>\nsoci ety</w>\nsy n\ngu it\nn h\nd j\nas es</w>\nim pre\nti me\nsal es</w>\nd d</w>\nf ts</w>\nsumm it</w>\nstun ning</w>\nom s</w>\ntur ned</w>\ncle an</w>\nsof t</w>\nbe at\nre staur\nde red</w>\nen ces</w>\nma gic</w>\ndi o\nsh ine</w>\ngu est</w>\nhealth y</w>\nexhi b\nstor ies</w>\npo pu\nn is</w>\nel a</w>\nbel ow</w>\nfun ny</w>\nresul ts</w>\ns ne\ncur rently</w>\nar d\ndown load</w>\nf light</w>\nm al</w>\nf ine</w>\np ad\nch u\nent ed</w>\nh at</w>\nðŁij ı\nste ve</w>\nj o</w>\nmar k\nr at\nb all\np c</w>\np on\nb by</w>\no li\nar ts</w>\nas ure</w>\nbow l</w>\natt ack</w>\nmi c</w>\nde ar</w>\nran ge</w>\nen ter</w>\nchocol ate</w>\nbr illi\nac cess</w>\n, \"</w>\n? ??</w>\nch ap\ncon st\nt n\nmat ter</w>\nblu e\ngall ery</w>\nem p\nwork shop</w>\nlead ing</w>\ny ours</w>\nbaske tball</w>\nw anna</w>\nth u\n_ _</w>\nmar ri\nsle ep</w>\nbi a</w>\nch e</w>\nma d</w>\nimp act</w>\no wn\nsi r</w>\nchan nel</w>\neuro pe</w>\ne sp\nk itch\nhosp ital</w>\nw ra\nroy al</w>\nf s</w>\nne u\nqu ar\nne y\nac ks</w>\nch ase</w>\npp y</w>\nst al\nat ely</w>\nti m</w>\ndece mber</w>\nr are</w>\nper form\ncre am</w>\nwe ight</w>\nch oo\nni ght\nha ven</w>\nfr anc\nkh an</w>\nbuil t</w>\nhel ping</w>\ntru st</w>\nty pe</w>\ngol den</w>\nta x</w>\ns now\ns wi\ndi sa\nquesti ons</w>\nve y</w>\nli ght\nc n\ncl oud</w>\nthom as</w>\nag ed</w>\nsh ou\nte ams</w>\ngr an\nre ason</w>\na a</w>\nyou tube</w>\nv p</w>\npi zz\nmanag er</w>\nbur y</w>\ncre dit</w>\ntre at</w>\nma x</w>\ni k\nma in\ng ing</w>\nde ad\npro bab\nye ah</w>\nã Ĥ\nbr and\nso li\npl ant</w>\nta yl\ngir l\nðŁĺ Ń</w>\nnam ent</w>\nau to\nmess age</w>\nko re\nn ur\nter r\nag u\nma p</w>\nsen ting</w>\nlo ves</w>\ngi ves</w>\ng ab\nz en</w>\nro bert</w>\ncon fir\nw ars</w>\no m</w>\nsta in\ncam era</w>\nand er</w>\nwon der</w>\na b</w>\nca p</w>\ns old</w>\nsu it</w>\nwal king</w>\ncontin ue</w>\neffe c\ndau ghter</w>\nd anc\ncha in</w>\nmul ti\nki d\ny an\nchampi on</w>\nv o</w>\nta ins</w>\nho st</w>\nmin i</w>\nmis sed</w>\nre sc\nly n\nfin ish</w>\ndel icious</w>\ns as\ntayl or</w>\ni b\npro mis\nproduc ts</w>\nmoun tain</w>\nflori da</w>\nregi ster</w>\ntre at\nrec ent</w>\nfe male</w>\nboo th</w>\nmat t</w>\nve hic\ns op\nmo tor\nsuppor ting</w>\nphi c</w>\nex tre\ndr ink</w>\nlan e</w>\nth ird</w>\np s\ncon stru\nce re\nfar m</w>\nðŁİ ī</w>\ntu red</w>\nðŁij ī</w>\nc ats</w>\na j\ngi e</w>\nshoo ting</w>\nas ked</w>\npaki stan</w>\nam e\nm b</w>\ng il\nleg al</w>\nsqu are</w>\nin vol\ndra w</w>\noo oo\n!! !!\nopportun ity</w>\np y\ne i\nb ts</w>\nteach er</w>\ncharac ter</w>\njohn son</w>\nbr on\nly wood</w>\nch ine\nc ing</w>\nc ine\nd ge\ngam ing</w>\nrussi a</w>\nci a</w>\nquo te</w>\nric h</w>\ngo v\nflow ers</w>\nsp iri\nst in</w>\ngrow th</w>\nðŁı ¼</w>\ncomm er\nj uni\nmu m</w>\nr an</w>\ns na\na ren\nc b\nac tor</w>\ncol or\nsi t</w>\npa ir</w>\nch i</w>\nbo w</w>\nacade my</w>\nhel d</w>\nr ang\nme tal</w>\ny l</w>\nac tive</w>\nprobab ly</w>\nt ch</w>\nneed ed</w>\nspe e\ncho ice</w>\nital y</w>\nry an</w>\nðŁĩ º\nflow er</w>\nv it\nm n</w>\nfound ation</w>\nb ak\nsi ons</w>\nne igh\nf loo\nhe ard</w>\nre mo\nfre sh\ning ing</w>\nre f\nto wn\ncl ou\nje sus</w>\nspiri t</w>\ncou ldn</w>\nz es</w>\nðŁĴ Ļ</w>\nwilli ams</w>\npro ce\nmoder n</w>\npro cess</w>\nsho es</w>\ncre ated</w>\ntri c</w>\nissu es</w>\nann e</w>\natt en\nde but</w>\nh r</w>\nn it\nsti g\na po\ne ps</w>\nz u\nã Ģ\nsi x</w>\ncar ds</w>\nlan gu\nfam ous</w>\ntour nament</w>\nse l</w>\ne bay</w>\ny n</w>\nst on\nk ick\nannoun ced</w>\nk am\nvo c\nbrilli ant</w>\nhou se\nche ese</w>\nwar ri\nmus ic\nho ckey</w>\nðŁĺĤ ðŁĺĤ</w>\nsk ills</w>\nau tom\nsmar t</w>\nmed ical</w>\nmon y</w>\ne x</w>\ngu ar\ngi ve\npers onal</w>\nven tion</w>\nal li\npre ss\nflo or</w>\nm c</w>\nvictor y</w>\nhi m\nsimp le</w>\nth or\nðŁĩº ðŁĩ\nta il</w>\nlu cky</w>\nale x</w>\nqu ite</w>\nbo t\nssi ons</w>\nchall eng\nc ann\namaz on</w>\nh ell</w>\nb ought</w>\n) :</w>\ned y</w>\nsecre t</w>\nproduc tion</w>\ninde pend\nde fe\nad ded</w>\np r</w>\np ag\nbe d\ngre atest</w>\nwith in</w>\nj ay</w>\nðŁ ¥\nire land</w>\nre ly</w>\ns d</w>\nte xt</w>\ndri ving</w>\npro gram\nspe ed</w>\ncol um\nstr on\nà ©</w>\nfore st</w>\nâ ĸ\nmach ine</w>\nco in</w>\nsc ar\noun t</w>\nbi e</w>\n¡ ï¸ı</w>\npor tra\ncomm on</w>\nwre st\nrecei ved</w>\nkno w\ninve st\npl ans</w>\nac cor\nad op\nter y</w>\nre ali\np p</w>\nk al\nart work</w>\nme an\ngo d\ninste ad</w>\nan ci\nmotiv ation</w>\nas ing</w>\ninspir ation</w>\nup coming</w>\npolit ical</w>\neuro pe\nm ers</w>\nheav y</w>\nðŁij į</w>\nfe bru\nscot land</w>\nou gh\nb t</w>\nbo ss</w>\nsche du\nspe ak</w>\nn ick\nu red</w>\nin o\ne k\nri sk</w>\ntor y</w>\npres ents</w>\nb on</w>\nru g\nst ates</w>\nexhib ition</w>\nil o\nm ill\nbr ought</w>\n: -)</w>\ntou ri\ncom e\noffici ally</w>\nchampi ons</w>\ndo ors</w>\nre p\npo se</w>\nex tra</w>\nk ings</w>\nsoc cer</w>\nsqu ad</w>\napp lic\nat a</w>\nsome times</w>\nt ari\nexcell ent</w>\nðŁĺ ĺ\nstra ight</w>\ncar ol\nri p</w>\nâĢ į\ngra phic</w>\nm ol\nelec tion</w>\nfebru ary</w>\nas ons</w>\nl i</w>\ndi r\nm t\nn ick</w>\nu su\nm rs</w>\ncom ics</w>\ninst itu\ncor por\nv i</w>\nðŁĻ ı\ntu ral</w>\ndi se\nac ci\nwe are\nam ong</w>\nsho pping</w>\nt ill</w>\nwh at\ncha ir</w>\nsp an\nchine se</w>\ninnov ation</w>\njo y</w>\nk it</w>\ncent ury</w>\nob ama</w>\nph ili\nf c\nre ach</w>\nc iti\nul ous</w>\nn on\nd ang\nhapp ening</w>\nbur n</w>\np el\nor ange</w>\nd v\nk ick</w>\ncla im\ning ham</w>\nph y</w>\nno v</w>\npod cast</w>\nwh i\nni ghts</w>\near lier</w>\nbe ar</w>\nla h</w>\nexc iting</w>\nor a</w>\ngi ven</w>\ns lo\nmemor ies</w>\ncontin ues</w>\nproduc t</w>\ngh o\nc d\nkno ws</w>\nðŁİ ī\npubli shed</w>\ndiscu ss</w>\ny ard</w>\ni phone</w>\ntri es</w>\nw all\nfe b</w>\nare n</w>\ntru th</w>\nwin ners</w>\ntu re\nditi onal</w>\nmilit ary</w>\nproble m</w>\nm and\ndo g\nlo ss</w>\nc ric\ncan adi\nve ter\nvilla ge</w>\n\" ,</w>\ny r</w>\nun g</w>\ndon ald</w>\nag ing</w>\nbir ds</w>\nsci enti\nle s\nth is\nregi on</w>\ntic al</w>\nitt en</w>\nil a</w>\nðŁĺ İ</w>\nd ad\ndi am\nabo ve</w>\nst ren\nli t</w>\np ir\nla b</w>\nfo cus</w>\nbus y</w>\nd ur\napp ly</w>\ns ma\nauth or</w>\nac i\nexe cu\ndom in\nre la\njack son</w>\nat o</w>\nwash ington</w>\nðŁĻ Į\nk ill</w>\npopu lar</w>\nce ment</w>\nro ad\ne ating</w>\nloc ation</w>\nv ent\nar re\nn an\ncu sto\nadvent ure</w>\nor din\nspor t\nul t</w>\nlo ck</w>\nquesti on</w>\ndri ver</w>\nland sc\non i\nk ins</w>\np d\njor dan</w>\nte red</w>\nk k\na f</w>\nchil d\ns p</w>\njust in</w>\nen i\ns elling</w>\nz o\nwh it\nbo ston</w>\npartic ip\nsig ning</w>\nhapp ened</w>\nhe at</w>\nm am\ndre ams</w>\nlo ws</w>\ngra ph</w>\nthe day</w>\nhead ing</w>\nbr o</w>\nble ssed</w>\nvi c</w>\nve gas</w>\nh d</w>\nin ning</w>\nro man\nand ro\nden ti\nu se\nc it\npro gress</w>\nwrit er</w>\nbo b</w>\nff s</w>\ngro wing</w>\nb ly</w>\naw are\nex am\nsp ent</w>\nbe t</w>\nsc ore</w>\nbey ond</w>\ndo cu\nad el\ns f\ncou ra\ncolla bor\nin c</w>\npriv ate</w>\nbo at</w>\n* *</w>\nz one</w>\np ha\nb ill\nto tal</w>\nplan ning</w>\nto wards</w>\nplac es</w>\npre view</w>\ncre ative</w>\ndam n</w>\nide as</w>\nse ems</w>\npo ten\nsay ing</w>\ndi splay</w>\ns w</w>\na qu\nlou is</w>\nby e</w>\nli l</w>\ne mail</w>\nwe stern</w>\nger many</w>\nell er</w>\nre s\nf ant\nment ary</w>\nde als</w>\nric hard</w>\njer sey</w>\nstren g\nra d\npizz a</w>\nmon d</w>\nw are</w>\nl ac\ng i</w>\nar chi\nc d</w>\nyel low</w>\nrec ently</w>\nre ach\nà ¹\nkitch en</w>\ndesig ned</w>\ntr y\ng al</w>\nrestaur ant</w>\nat ure</w>\nw w</w>\nj as\nl ma\nðŁij Į</w>\npa in</w>\nav o\nmin ute</w>\nsch ol\nther ap\ntic ket</w>\nd ry</w>\njap an\nditi ons</w>\nter ri\nsel ves</w>\nhapp en</w>\nt up</w>\nma g</w>\ncop y</w>\nsh er</w>\nfree dom</w>\nf ile</w>\nspeci ally</w>\ntor onto</w>\nlo ad\ng ary</w>\nre y</w>\nansw er</w>\nlo y\ncau ght</w>\npri ze</w>\nu ne\nfic ation</w>\nni ger\nsy d\ntou ch</w>\nfeat ure</w>\njaz z</w>\nrecor ds</w>\nhim self</w>\ndi sh</w>\nro ber\nspot ted</w>\nma ster\nwa ve</w>\nfin als</w>\nbu ll\nfor um</w>\nal d\nre comm\nch a</w>\na e</w>\nd oo\ninst ru\ntru ly</w>\nl g\nin k\nbro thers</w>\nde st</w>\nj im</w>\nm it</w>\nclo sed</w>\nis on</w>\ntri ed</w>\ns anta</w>\naf fe\nw an</w>\nhor se</w>\ng row</w>\ncamp us</w>\nrel ation\nnati ve</w>\njour n\ngo v</w>\no ct</w>\nk it\nb ound</w>\npart ner</w>\nre ma\ncrow d</w>\n! )</w>\nc alls</w>\nra il\nqu ali\nsolu tion</w>\ncon test</w>\ncon vers\nsn ap\nb ase</w>\nin iti\nta x\ny e</w>\nent repre\nit or</w>\nconstru ction</w>\nfoo d\npresent ed</w>\nn ings</w>\ncli mate</w>\nk m</w>\nmo del\nb j\nblo ck</w>\npresent ation</w>\ndre am\nfi x\nc alling</w>\nbus ine\ncon gress</w>\nunder stand</w>\nwe b</w>\nval ue</w>\nï¸ı âĥ£</w>\nmex ico</w>\nit ely</w>\nki m</w>\nchar ity</w>\nref lec\nbl an\nfl ying</w>\nanal y\nfamil ies</w>\nb and\nreci pe</w>\ncelebr ation</w>\nac cep\nar y\nto t\ng b</w>\nintere sted</w>\ncap tain</w>\nâĻ ¥\nti p</w>\nab sol\nbra z\ninve stig\no logy</w>\nde c</w>\ntru ck</w>\nver ing</w>\nc lear\ndon t</w>\ngo tta</w>\nad vis\nbeg ins</w>\nma ss\nde scri\nblo ck\nk im\ndavi d\nson gs</w>\nmemor ial</w>\nfeat ures</w>\nsu stain\n' .</w>\ngra b</w>\njo se\nv a\ncon serv\nse ts</w>\nman chester</w>\nfi ghting</w>\nde gre\nag a</w>\nin d</w>\nsle ep\npos ition</w>\nha ir\nsig ns</w>\npol icy</w>\nit o</w>\nal ert</w>\nst am\nsp end</w>\nw y\nabsol ut\nd m</w>\nanim al</w>\nmy ster\nsuccess ful</w>\nproble ms</w>\nro bo\nk ay\ngar den\np d</w>\nmay or</w>\nd ale</w>\nt ol\noff ers</w>\nvis iting</w>\nfriend ly</w>\ntre es</w>\noffic er</w>\naccoun t</w>\nke vin</w>\nðŁij į\ngi ant</w>\ncontin u\ncon su\ntr act</w>\nn fl</w>\nðŁĺ Ĭ\nh q</w>\nb ility</w>\na ar\ndis ney</w>\nte en</w>\non ed</w>\nwh ite\ntra iler</w>\nde dic\nal one</w>\nabsolut ely</w>\ndig ital\nwilli am</w>\nin ation</w>\ns wa\ne e</w>\nenti re</w>\nger man</w>\nro ll\nh its</w>\nco st</w>\nst ay\nth a</w>\nali ve</w>\naccor ding</w>\nco t\nliter ally</w>\nher it\nre ti\nhaha ha</w>\nexper i\nli kes</w>\ng t</w>\nste el</w>\n__ __\nch air\nchristi an</w>\nto wer</w>\ndiffe rence</w>\nm d</w>\ntre ss</w>\nmi d</w>\nprin ce</w>\nafric an</w>\nfe der\nfoo t</w>\ncar ri\nser ved</w>\nr ice</w>\nsh all</w>\nfeat ured</w>\nck er</w>\nrec ru\npo e\nsen se</w>\nni fic\ncom edy</w>\ncont ent</w>\nf at\npo sted</w>\ncon tribu\ntim ate</w>\nli ver\nmb le</w>\ninter net</w>\nag e\neurope an</w>\ncl ing</w>\ngla d</w>\nff ic\nsc o</w>\nak es</w>\nel le</w>\nter min\nton y</w>\np ale\ncol our</w>\nseri ous</w>\npat ri\nmovi es</w>\nb m\nprofessi onal</w>\nad o\nal u\nbr inging</w>\nf alls</w>\nisra el</w>\nter m</w>\nlangu age</w>\nbro ok\nman n</w>\ncommun ic\ncan not</w>\nac ti\np he\ny an</w>\nentrepre ne\ntur key</w>\nlog ical</w>\nlon g\nar m</w>\nur s</w>\nwork ers</w>\ning ly</w>\ngg s</w>\nri c</w>\ntu al</w>\nrecei ve</w>\nop ens</w>\nge ar</w>\nsoci al\nfe et</w>\nc king</w>\nad ver\nfin an\nfe els</w>\nsp la\nh r\nea ster</w>\nbra in</w>\nã ģ\nfi g\nle dge</w>\nne arly</w>\nprote ct</w>\nma ssive</w>\ne th\naw a\nðŁĺ ģ</w>\ny rs</w>\naware ness</w>\ndefin itely</w>\nk n\nimag ine</w>\nk u</w>\nsyste ms</w>\nðŁij ı</w>\nf as\nli k\nprovi de</w>\nam o\ndisco ver</w>\ninf lu\nma ker</w>\ng az\nfit ness</w>\nstre et\ner s\nte d\nw c\nys is</w>\npos itive</w>\nhel ped</w>\nque st</w>\nandre w</w>\nbra d\nb in\nhang ing</w>\nl ing\nbri ght</w>\nse ction</w>\nma ss</w>\nðŁĻ Į</w>\nfollow ers</w>\nho sting</w>\ntem por\nfla g</w>\na ve</w>\nlet ter</w>\nk ur\nre qui\nof ten</w>\ncry p\nsu ff\nâļ ½\nrussi an</w>\ntreat ment</w>\nal le\nha y\nl an</w>\nkeep ing</w>\nhol y</w>\npower ful</w>\npre dic\nfun d</w>\ne specially</w>\nwindo w</w>\nje wel\nil y\nðŁĴ ľ</w>\ngener ation</w>\napp a\nseri ously</w>\no d\nðŁĺĤðŁĺĤ ðŁĺĤ</w>\ncer ti\niri sh</w>\nðŁij Į\nmi ami</w>\nbe th</w>\nv ity</w>\nse cu\nche f</w>\ncri me</w>\ngraph y</w>\nma x\narti sts</w>\nre volu\ngu ard</w>\nspee ch</w>\nu c\nupd ates</w>\nfac es</w>\nst ant</w>\nchang ed</w>\nrepor ts</w>\nlow er</w>\npe ar\nn c</w>\nk il\nloo ked</w>\nspe aker</w>\ns f</w>\nre spect</w>\nok ay</w>\noce an</w>\ns itting</w>\narchitec ture</w>\ntra il</w>\nse at</w>\ni ra\nle g</w>\njapan ese</w>\nd am</w>\nu lar</w>\nsw im\npolit ics</w>\nfinan cial</w>\nol d\nmou th</w>\nat temp\nde stin\nfi shing</w>\natten tion</w>\nme m\nchang es</w>\ndeci ded</w>\nreli gi\ng in</w>\nc av\nz z</w>\nad am</w>\nma c</w>\nwr ite</w>\nbeg in</w>\nsc ul\nal ter\nis s</w>\nath on</w>\nimag es</w>\nm oo\njo ined</w>\nðŁĺ ī</w>\nâŀ ¡ï¸ı</w>\npas sed</w>\nmu sli\nh ir\nlar gest</w>\ncam er\ncom ic</w>\ngh ted</w>\nrug by</w>\nbur gh</w>\ngg ing</w>\nte sting</w>\npre par\nlau gh\nal ed</w>\nimpro ve</w>\nbeli ev\nadv ice</w>\nsha res</w>\nhe art\ntur ning</w>\ns b</w>\nt el\ncaf e</w>\nn es</w>\ndani el</w>\npat ter\nt z</w>\nse tt\npar k\nc and\nst ick</w>\nhapp ens</w>\nbri an</w>\nne west</w>\ne pic</w>\nad or\nki es</w>\nwar ning</w>\nanim als</w>\ncusto m</w>\nar c\ndi an</w>\ngol d\ncor e</w>\nt f</w>\nc ity\npan ts</w>\nre ality</w>\ncon fi\nin ju\nfo x\ngu il\nk new</w>\nâĺ º\ncor rec\nitu de</w>\nd den</w>\n. #</w>\nre duc\npas s\nf on\ny a\now ner</w>\nre turns</w>\nn c\ne ast\nap ol\nin sur\nth o</w>\nsi m\njuni or</w>\nbe e</w>\nang el\natt le</w>\nelec tric</w>\nhor ror</w>\ncra sh</w>\ne ye\npat h</w>\nsou thern</w>\nemplo ye\nge o\nt an</w>\nha z\nr ally</w>\nðŁı »\nproper ty</w>\nwas n</w>\nenjo yed</w>\ngre y</w>\ng as\nbre w\nnor thern</w>\nhol ding</w>\ng p</w>\nta ke\nch art</w>\nly n</w>\ndr ama</w>\nz o</w>\npa id</w>\nthrow back</w>\ncu p\ndiscu ssion</w>\ndown town</w>\nw ill\nle w\nb is\nt ary</w>\nbre ad</w>\nup on</w>\nr ate</w>\nteach ers</w>\nit ation</w>\nanc ed</w>\ncy cle</w>\nchoo se</w>\nd c\nir an</w>\nco w\nda ve</w>\nra ise</w>\nprin cess</w>\nfa ith</w>\n- ></w>\nindu stri\nsp ain</w>\nguit ar</w>\nfac ts</w>\nm n\nsp en\ncour te\ngo tt\nprojec ts</w>\nau di\no sc\npe ter\ns and</w>\nintere st</w>\nhapp iness</w>\nven ue</w>\nsol di\nsurpri se</w>\npoten tial</w>\nper io\ncustom er</w>\ni i\ng ni\nmanu fac\ne co\nbro ken</w>\nsing er</w>\nvel s</w>\nwal es</w>\nhu s\nin j\nf our\ntal ent</w>\nd ying</w>\nmat the\nfil m\njo ining</w>\ns ell</w>\nj ar\nlma o</w>\nsur ger\nbb c\nsour ces</w>\nau stin</w>\nni k\nchar les</w>\nf am</w>\nprin ci\nange l</w>\ncas h</w>\nlo t\no red</w>\npla ys</w>\npl ate</w>\ndon e\nmemor y</w>\nbr ings</w>\nn ba</w>\nsolu tions</w>\nteach ing</w>\ngr ace</w>\ncir cu\nhel ps</w>\nfoun der</w>\nmar y\nexpl ore</w>\nde cor\npar ts</w>\nch o</w>\ninte gr\nha u\nis es</w>\npu tting</w>\nin er</w>\nr it\nv y</w>\nmic hel\nblu es</w>\nevery day</w>\nfor ms</w>\nbi o</w>\nye ar\np in</w>\nt ter</w>\nspr ing\n) )</w>\npo t</w>\nal ing</w>\nperform ing</w>\nsh an\nplan et</w>\nmus ical</w>\nhead s</w>\nit alian</w>\nstru gg\nâĢį âĻ\nw ings</w>\npu mp\nh h</w>\ntr ou\na id</w>\npri me</w>\near th\npa int</w>\nmon t\nam y</w>\nbb c</w>\nfab ulous</w>\nfru it</w>\nandro id</w>\nbour ne</w>\ncere mony</w>\nenti al</w>\n? ?\ndeb ate</w>\non ing</w>\ndra ft</w>\nsol ar</w>\nt x</w>\nj am</w>\ncor n\n!! !!!</w>\nbro o\nmil k</w>\npo sed</w>\no hi\nmo vement</w>\nb ren\npart ner\np g</w>\net te</w>\nar ies</w>\nsh out</w>\nn g</w>\nleav ing</w>\nt ells</w>\nsen s\nta ste</w>\nkel ly</w>\nwor l\ngy m</w>\nric h\ne gy\npi d</w>\nma s\nâ Ĥ\ncourte sy</w>\nfran k</w>\nincre ase</w>\nwr itten</w>\npp ers</w>\nre l</w>\nha i</w>\ns as</w>\ns ound\ntt i</w>\nw ich</w>\nri ver\n.. .\"</w>\na g</w>\nfel low</w>\nro me</w>\nsm all\ngen cy</w>\nic an</w>\nlux ury</w>\npro of</w>\nme t\nwild life</w>\nmom ents</w>\nra ther</w>\ncor ner</w>\ncom pe\ncanadi an</w>\nlik ely</w>\ntherap y</w>\nli am\neconom ic</w>\nindi e\nrou te</w>\nfi ght\nho pe\nse tting</w>\nant ly</w>\ncro ss\nfant asy</w>\nde e\nsket ch</w>\ncomp li\nym i</w>\nru les</w>\nengine ering</w>\nfig ure</w>\nro w\n. ,</w>\nf w</w>\nsyd ney</w>\nw ou\nt ation</w>\ndre w</w>\nus es</w>\nthe re\nsp read</w>\nstruc ture</w>\npat rick</w>\nappa rently</w>\nro s\nh ills</w>\nw we</w>\nann y</w>\ncom mission</w>\ndi v\nf ying</w>\ncon sul\nanal ysis</w>\nex i\nten nis</w>\nvehic le</w>\nðŁĺŃ ðŁĺŃ\nas s</w>\nhigh ly</w>\nop ened</w>\nb ann\nðŁĴ Ļ\nmp h</w>\nwi shing</w>\nv or</w>\nfi f\ngive away</w>\nr r\nra y\nje ss\ng at\nic ymi</w>\nx it</w>\nhigh est</w>\nyor k\npi e</w>\ninvol ved</w>\nhigh er</w>\nri e</w>\nmal ay\nint elli\ndesp ite</w>\nche e\nsar ah</w>\nbe an</w>\nreco gni\nar sen\ntal ented</w>\npas sion</w>\nic h\nab c</w>\nlead s</w>\ndise ase</w>\nv is</w>\nse c</w>\npre senting</w>\nm illi\nhol e</w>\nsho ts</w>\nde part\nsurger y</w>\ngov t</w>\nb in</w>\ndu al</w>\ne vi\nlon ger</w>\nev ol\nscre en\nportra it</w>\net c</w>\nlo se</w>\nch at\np en</w>\np i</w>\nom a</w>\ns ick</w>\ner c\ncompan ies</w>\nen try</w>\nplan e</w>\ngr y</w>\nven e\nliver pool</w>\npremi ere</w>\nsha red</w>\na red</w>\nfil ms</w>\nir a</w>\nholi days</w>\ncric ket</w>\nici an</w>\nv ing\n. )</w>\nul timate</w>\ndi vision</w>\ncon duc\nse pt</w>\nfor ces</w>\nmon t</w>\ns mart\ndisa pp\nsun shine</w>\nin d\nb less</w>\nma de\ncol ors</w>\nfran k\nir on</w>\nbott le</w>\ns go\nm ood</w>\nj ason</w>\ner ic</w>\nbir th</w>\nte en\nrespon se</w>\ntar get</w>\nstate ment</w>\nfe ar</w>\nth el\nal um\nar ab\nbl in</w>\ndirec tion</w>\nste ps</w>\ner ial</w>\nwor ked</w>\nat l\nðŁĴ ķ\nfel t</w>\npol i</w>\nscen es</w>\nhom es</w>\nb ell\ne at\nate ful</w>\nt in</w>\nl ace</w>\nfol ks</w>\np se</w>\nan n</w>\nwis dom</w>\nfa v</w>\nbut ter\ns r</w>\nare as</w>\nsm oo\nbi z</w>\ndg es</w>\napp o\nmo re\nthe m\neffe ct</w>\nwindo ws</w>\nsun ny</w>\ncap ital</w>\ntot ally</w>\nc ities</w>\ngr ant</w>\nmb ers</w>\ns low</w>\nau tu\nil ities</w>\nw ro\nri sing</w>\nst ics</w>\nviol ence</w>\ni gh</w>\nqu ot\nh it\nt c</w>\nherit age</w>\nbu ff\nne s\nz ar\nden tial</w>\nex ac\ned ge</w>\nde ep\naren a</w>\nbe came</w>\nbenef its</w>\nmar ks</w>\nmb er\na z</w>\nam es</w>\npre ci\ndra gon</w>\nre g\nd ings</w>\ndo s</w>\nðŁĴ ª\nn el\ns ity</w>\nme al</w>\ndi st\nleg end\npur chase</w>\npic al</w>\nst ick\nf at</w>\ndu ba\nprofe ss\ncar to\npro f</w>\ncoun tries</w>\nrespon si\nse qu\nfa b</w>\ntribu te</w>\nhon ored</w>\nprac tic\npur ple</w>\nan ton\npa red</w>\nt ough</w>\nsumm er\nenviron ment</w>\ns ons</w>\nðŁĻ ı</w>\nm ps</w>\ngi es</w>\nher oes</w>\nt elling</w>\nhen ry</w>\nf en\nknow ledge</w>\nĢ ï¸ı</w>\nf r</w>\nne g\nu re\nac king</w>\nhear ts</w>\ns oo\nhol lywood</w>\nju mp\nsau ce</w>\nschedu le</w>\ntur n\nyo ga</w>\ncre ating</w>\nc ket</w>\ncre ek</w>\nâ Ń\ncustom ers</w>\nma dri\ngu l\nasse mb\nmoun t</w>\nc ell</w>\nto p\nst al</w>\ndav is</w>\nt wi\nsig n\npremi er</w>\niti ons</w>\nhe aring</w>\nun k</w>\npati ents</w>\napp ear\nheav en</w>\nal ty</w>\ndoc tor</w>\na e\nplat form</w>\nje ff</w>\nðŁĵ ·</w>\nregi onal</w>\nbi d</w>\nbox ing</w>\nex ten\nor ity</w>\na w</w>\nw ise</w>\nil le</w>\nsever al</w>\nbi e\ns itu\nsy ria</w>\nâľ ħ</w>\nremin der</w>\nenter tain\nli on</w>\npart ners</w>\nin n</w>\nph ar\nf au\npl s</w>\nexpe cted</w>\nsug ar</w>\ndeci sion</w>\ns b\nch ron\nassoci ation</w>\nleav es</w>\nvis ited</w>\nsh ap\nðŁĴ ĸ</w>\nfur ther</w>\nh ann\nw i</w>\nrun s</w>\nl er\nfun ding</w>\nfil led</w>\n.. ....</w>\ntin y</w>\nhan g</w>\nor g</w>\nco ol\nse min\nðŁı Ĩ</w>\nspon s\nnav y</w>\nsa int</w>\ndru g</w>\nd al</w>\nr oun\nco vered</w>\ntra ditional</w>\ninvest ment</w>\nde te\nal ism</w>\nf low</w>\nn is\nsun rise</w>\nfe at</w>\nf ted</w>\nwe ird</w>\nje re\nve gan</w>\nmedic ine</w>\nan o\nac cu\ndeli very</w>\ntemp le</w>\nchang ing</w>\nwil son</w>\nphili pp\nre fe\nn d\nis er</w>\ng ay</w>\nr and\nati ves</w>\nt ely</w>\np and\nintelli g\ng are\nam bas\nde mon\ncommit tee</w>\nstrate gy</w>\nrefu ge\nbud get</w>\nprote c\npi er\nex press</w>\nnom in\neconom y</w>\nal low\nic on</w>\ngal ax\no h\nindi vi\ndem and</w>\nvir gin\nlu ke</w>\nali sts</w>\nman i\ns mi\nju dge</w>\nent y</w>\nmic hi\nresul t</w>\nam ed</w>\nspe aks</w>\n' ,</w>\nhou ston</w>\nsh in\nb ing</w>\nfl y\nch em\nau to</w>\nv as\nge t\nar m\nthank s\nd in</w>\ngan g</w>\nx x\nsi on\nloc ated</w>\np l</w>\njo sh</w>\nin fo\njo ins</w>\nadver ti\not d</w>\nel d</w>\nsi e</w>\nre asons</w>\nv ent</w>\nðŁĩºðŁĩ ¸</w>\nâ ł\nconvers ation</w>\nstu di\nðŁĶ¥ ðŁĶ¥\ngo s</w>\ns ounds</w>\nun it</w>\nmu sc\nge l</w>\nack ed</w>\npac i\nco s</w>\nde re\nu u\na o</w>\nla m</w>\ninspir ing</w>\nar ms</w>\ntw are</w>\nmat ters</w>\nad dic\ndu de</w>\nex t\ncri sis</w>\nb ath</w>\nme et\nsing h</w>\nexpe ct</w>\ndel hi</w>\nresc ue</w>\nwor st</w>\nau g</w>\nshi pping</w>\nser ving</w>\nst o</w>\ndar k\nac es</w>\nhistor ic</w>\nlandsc ape</w>\ndesig ner</w>\nb illion</w>\ngr ateful</w>\nwa ke</w>\ne ve\nm iller</w>\nhou sing</w>\ndy nam\nis co</w>\nbe ha\nsh op\npr ou\ne as\na sia</w>\ne ding</w>\nk on\ndepart ment</w>\naw ar\nmar ine</w>\nin ci\nphotograph er</w>\nta pe</w>\nlo go</w>\nr ings</w>\nd it\n-- --\nvin yl</w>\nw c</w>\nvo ting</w>\nse ven</w>\nambas sad\ndal las</w>\nt u</w>\ncom ment</w>\nk ra\nb les</w>\nw ag\nu d</w>\nau dio</w>\nstri ke</w>\noffici al\no ts</w>\nme tho\nto ols</w>\nra di\nal an</w>\nhun t</w>\nwat ched</w>\na ke</w>\nfa ke</w>\ndrin king</w>\nmer ry</w>\nm l</w>\nb day</w>\nri o</w>\nni ke</w>\nc ant</w>\nre pe\nco stu\nmur der</w>\nak ers</w>\nch ers</w>\nou ts</w>\nbeg inning</w>\nso s</w>\nad es</w>\nn in\nnot es</w>\nwro te</w>\nsol o</w>\nc i</w>\nli ghting</w>\nur ban</w>\nbre xit</w>\natt end</w>\nshir ts</w>\npla yo\nac tress</w>\npl ic\nstand ard</w>\nquot es</w>\npar ade</w>\nanci ent</w>\n ©</w>\ntur ing</w>\nre e</w>\npri mary</w>\nfla sh</w>\nciti z\nmat es</w>\nste in</w>\nz i</w>\nclin ton</w>\nsk in\ngen e\nhu m\ng ar</w>\nt le</w>\ny i\nfo cu\nde an</w>\npl ants</w>\ncy ber\nb u</w>\nom e</w>\nho p</w>\nad dress</w>\nti x</w>\ngi fts</w>\nrelation ship</w>\nsub scri\nfe ed</w>\nexac tly</w>\nhaw ks</w>\nex o</w>\nstre ss</w>\ns n</w>\narre sted</w>\nan e\nsof tware</w>\nz ero</w>\nthe me</w>\nmu mb\nim migr\nmi a</w>\nmake up</w>\nple asure</w>\nuni vers\nhar b\neng ine</w>\nap er</w>\nr in\nbr a</w>\ninstitu te</w>\nle ather</w>\nal th</w>\nsing ing</w>\nco s\ngh ty</w>\nme as\nst ic\nsi de\ninsur ance</w>\nco t</w>\npit ch</w>\nmoun tains</w>\ncri min\nsu pre\nvalent ine</w>\nat er</w>\nwou ldn</w>\nsc ale</w>\nrel ated</w>\nre gar\nstar tup</w>\npack ed</w>\nmi ke\nweek ly</w>\np ts</w>\ncoun t</w>\nha r</w>\ngott en</w>\nmin d\nber lin</w>\ncon ditions</w>\nswit ch</w>\ncor n</w>\nsa ve\ng li\nemer gency</w>\ntun ed</w>\nsto ck\ndiscu ssing</w>\nevery body</w>\ns day\nwhe ther</w>\nwrest ling</w>\nec es</w>\ngen der</w>\nch en\nðŁij Ģ</w>\nmadri d</w>\nmar athon</w>\ne gg</w>\ni er</w>\nth x</w>\nas king</w>\nkore a</w>\nwol f</w>\nay a</w>\ng m</w>\ng au\nat ory</w>\nv r</w>\ngra ss</w>\nk illing</w>\nb ble</w>\nur o</w>\nun i</w>\ne th</w>\nsh ore</w>\nth en\nre ale\nbot tom</w>\nex erc\nk ar</w>\nor ies</w>\nad ri\nsan ds</w>\nse x</w>\n. '</w>\nvolunte ers</w>\nper form</w>\npar liam\ninclu de</w>\ndeli ghted</w>\nexecu tive</w>\nfu el</w>\nkis s</w>\nã ħ\nchar ge</w>\nh u</w>\nca kes</w>\nve t</w>\ng lu\nagre e</w>\npr ices</w>\nn au\nh l</w>\ng ru\nra j\nstreng th</w>\nb ic\nsp ending</w>\nal es</w>\nav en\nb last</w>\n: (</w>\nyo f\nnor mal</w>\nsi x\nqu ick\nse a\nd aw\nmee ts</w>\nlo vers</w>\nupd ated</w>\npo tat\ncomple ted</w>\ncoo k</w>\nopportun ities</w>\np ure</w>\norgan ic</w>\ntem per\nc am</w>\navo id</w>\npar king</w>\nduba i</w>\nand o</w>\ndi stri\nto y</w>\ncomple tely</w>\ndon ald\ntri al</w>\nbas s</w>\nb oun\nback ground</w>\nv as</w>\nmar vel</w>\nlu m</w>\nru s</w>\nt ool</w>\ncom missi\nthrow back\nfin ding</w>\nis lam\n! ?</w>\nst op\ne vil</w>\nor al</w>\nresi dents</w>\ni denti\no ak\nðŁİ ¶</w>\nl il\nspan ish</w>\nchap ter</w>\nsto pped</w>\ndirec t</w>\nho sted</w>\npic ked</w>\nlab our</w>\nlew is</w>\ndefen se</w>\nà ®\nhealth care</w>\nwh is\nmat h</w>\npe ak</w>\nra ised</w>\nfi x</w>\nbu ll</w>\nth ir\nchel sea</w>\nfol k</w>\ntr e</w>\ncan di\npau l\nei ther</w>\nad am\npoe try</w>\njewel ry</w>\nðŁ ¦\npr ay</w>\nØ §\ng c</w>\no z</w>\nwi shes</w>\nfore ign</w>\nsun g</w>\nlear ned</w>\nen e</w>\nn ing\nmicha el\nillu stration</w>\nlegend ary</w>\nw av\nb au\nðŁļ ¨</w>\ncal end\nstre ets</w>\nâ Ĩ\nmon ster</w>\nbu ck\ng r</w>\nscho ol\nba th\nwa ste</w>\nne ck\nha wa\nbe ach\nre plac\njec t</w>\non er</w>\nfac tory</w>\ncoun t\nðŁĵ ¸</w>\nmor gan</w>\nder ing</w>\nse an</w>\nsteph en</w>\nde p\nno vel</w>\nvide os</w>\nic al\npress ure</w>\narsen al</w>\nex pre\nir s</w>\ntren ding</w>\nss a</w>\nfla sh\nre sear\nthr ough\nprofess or</w>\nscul p\nto s</w>\ngg ed</w>\nmm a</w>\nbe e\na pe\nhun ter</w>\nam i\nhe i\npla stic</w>\nbu cks</w>\nuni verse</w>\nle gen\nniger ia</w>\nple ased</w>\nri s\nthin ks</w>\nautu mn</w>\ni ds</w>\nd is</w>\nanth ony</w>\nðŁı ½</w>\nak ed</w>\ngla sses</w>\nfin ance</w>\nz er\nk as\ncon tract</w>\nnu mbers</w>\nsh aw\npartner ship</w>\nt il\nlaun ched</w>\ns al</w>\nvictor ia</w>\ntheat er</w>\nusu al</w>\nnam es</w>\nperio d</w>\neli za\ni th\nbar cel\nro cks</w>\nbag s</w>\nmat e\ndistri bu\nj on</w>\ndi ffic\nali zed</w>\ncur ren\nsco red</w>\nb ha\ndu blin</w>\nro se\nin ted</w>\nsoli d</w>\nbeha vi\nwal ker</w>\nsimp ly</w>\ngarden s</w>\nhead ed</w>\nin i\nohi o</w>\nwe ap\nf o</w>\ngl en\ne state</w>\nran dom</w>\nth under\nthr u</w>\nk ill\njac ket</w>\nit i</w>\nentertain ment</w>\nthanks giving</w>\nent al</w>\nen coura\nel o\na ther\ntan k</w>\nhigh lights</w>\nf ting</w>\nru le</w>\nmodel s</w>\nbor der</w>\nbj p</w>\nhus band</w>\nin done\nken ya</w>\nbe ars</w>\nal o</w>\nn inten\npi x\nstr o</w>\nor ders</w>\nsal ad</w>\nro ads</w>\nn or</w>\nl ation</w>\nsop hi\nðŁı ¼\npi eces</w>\nb one</w>\nmin s</w>\ninclu des</w>\nnu tr\nphi l</w>\ns ent\nfun dra\nga in</w>\nbor ough</w>\nn ad\nmon day\nactiv ity</w>\nit ems</w>\nbe coming</w>\nken ne\nde tro\ncar di\ngue sts</w>\nu x</w>\nworld wide</w>\nsever e</w>\nnew s\nthank ful</w>\nfic tion</w>\nve ge\nm all</w>\nsi an</w>\ner al</w>\ninj ury</w>\nle e\nmen u</w>\ndanc ing</w>\nscot ti\nexam ple</w>\n( #</w>\nna i\nstudi os</w>\nba i\nðŁĴ Ľ</w>\nj av\ndiam ond</w>\nvin ce</w>\nric k\nprote ction</w>\nlin col\ncham ps</w>\nappro ach</w>\nd ar</w>\nm ile</w>\nclou ds</w>\nje ff\nin fin\nl ers</w>\np les</w>\npe ace\ngo p</w>\nâĻ ¡</w>\ntech n\nstr a</w>\na verage</w>\nef fort</w>\nintroduc ing</w>\ndi versity</w>\naustr alian</w>\nam p</w>\nboo st</w>\ns ke\npati ent</w>\nappreci ate</w>\nici ans</w>\npu r</w>\nf ell</w>\nwoo ds</w>\nillu str\nðŁ ĸ\nag ency</w>\nac tions</w>\nbrit ain</w>\nunder way</w>\nse attle</w>\nel and</w>\nag o\nf ill</w>\nstre aming</w>\npro test</w>\nchalleng es</w>\nky o</w>\net sy</w>\ncoo king</w>\nexper t</w>\nru ss\nrain bow</w>\ncommer cial</w>\nsp in\nbe ats</w>\nc ry</w>\nval u\nel i</w>\nth row</w>\ngr ams</w>\nle vels</w>\nmichi gan</w>\nc ad\nador able</w>\nconst itu\nw s\npu b</w>\nmid night</w>\nth at\nnet fli\nbraz il</w>\ndie go</w>\nregu lar</w>\njo y\nâĤ ¬</w>\nli qu\nea stern</w>\nk ni\nfl at</w>\nn p</w>\nbro wn\nw er\nse y\ntt ers</w>\nac ting</w>\nv anc\ncy cling</w>\nprogram me</w>\nra w</w>\ncomple x</w>\ntat too</w>\nthrowback thursday</w>\nse ssions</w>\nro oms</w>\nsi ght</w>\nspeci es</w>\nbom b</w>\nlau gh</w>\nke eps</w>\nmo on\noffic ers</w>\ncon ver\nt r</w>\nha sh\nt ack\nri ous</w>\nad ap\na j</w>\nreco gn\nex po</w>\nsug ge\nconfir med</w>\nrol ling</w>\ndre ssing</w>\nic t</w>\nfri day\nph ones</w>\nri dge</w>\ncon cept</w>\nro y</w>\nke ys</w>\nef for\nc ate\nk ne\nev en\nl ay</w>\ncommun ities</w>\nmo d\nn az\nevery where</w>\nal ab\nbit coin</w>\nban ks</w>\nout door</w>\nfeder al</w>\nsto res</w>\nh p</w>\nc al</w>\nm ely</w>\nsig nific\nbe ar\nre public\nclo ser</w>\nal lah</w>\npic k\nx d</w>\npal ace</w>\nch ill</w>\nb am\ner ous</w>\nun a</w>\nal len</w>\nout standing</w>\nolym pic</w>\nsupp ly</w>\nfi gu\nv au\nl p</w>\nchar lie</w>\nun es</w>\n> >></w>\nlegen ds</w>\nici al</w>\nco ast\nbenef it</w>\nmul ti</w>\nf its</w>\nfar mers</w>\nam ount</w>\nsi sters</w>\nhar ve\nhon ey</w>\nque en\nb ers</w>\npl ann\nâŃ IJ\nm u</w>\nbarcel ona</w>\nal ber\nstat us</w>\nre main</w>\nex tra\nc andy</w>\nvi ous</w>\nâľ Į\no v\nwarri ors</w>\n-- ></w>\nju mp</w>\nam ar\nx mas</w>\nstu dies</w>\ni ors</w>\nk or\ndon ate</w>\npre p\nfi sh\nim a</w>\npain ted</w>\nad mini\nco splay</w>\nspor ts\ndro ps</w>\nfi ghter</w>\nevi dence</w>\nðŁĴ ª</w>\nla ke\nro b</w>\ncine ma</w>\npro file</w>\nà ±\nstan ds</w>\nleg acy</w>\nsh ape</w>\nro of</w>\nci vil</w>\ni ans</w>\nsy l\nsh am\nvo ted</w>\nre tail</w>\nph illi\nli sted</w>\ndu ty</w>\nn b\nth es</w>\nf are</w>\nau ction</w>\nffici al</w>\nstor ms</w>\nd p</w>\nl oun\nsh ops</w>\nal y\nani me</w>\nmulti ple</w>\nðŁĺį ðŁĺį</w>\npsy cho\nje an</w>\nap art\ncandi date</w>\ngg y</w>\ncon f</w>\njose ph</w>\nw ick</w>\nme at</w>\nfr ame</w>\nc l</w>\nfor got</w>\nph y\nf ing\nli ed</w>\nre p</w>\nse ed</w>\nf all\nu fc</w>\nnu t</w>\nlin d\nmo de</w>\nfiel ds</w>\nen ce\ns ley</w>\nðŁ¤ Ķ</w>\nch ill\nfollow ed</w>\nannoun ces</w>\ncor ru\ntro phy</w>\nthem selves</w>\nac le</w>\nal du\nk ong</w>\nl on</w>\ns v\nbro ke</w>\nander son</w>\nta i\nstor y\ntempor ary</w>\nactiv ities</w>\nk ati\nari z\ncry stal</w>\nspo ke</w>\nextre mely</w>\ntra ding</w>\nðŁĴ ļ</w>\nà ¼\nin ch</w>\ned in\nout fit</w>\nequ ip\nma di\nform ed</w>\nbe ef</w>\npo p\nti ger</w>\nthis day</w>\nti red</w>\nneigh b\nre tro\nis a</w>\nun t</w>\nt as\nkan sas</w>\nde st\nsecon ds</w>\nta y\nhur ric\no u</w>\ngalax y</w>\ndad dy</w>\nbro w\nbur ger</w>\nen ced</w>\nde sk</w>\nac cur\nsecre tary</w>\nel ite</w>\nk ab\nch in\ntouri sm</w>\nbud dy</w>\nici de</w>\ndre ssed</w>\nu d\nvac ation</w>\nche ers</w>\ncom for\ncharac ters</w>\nj et</w>\nbu ying</w>\nl ins</w>\nn ap\nreale state</w>\nli e\naf c</w>\ni ii</w>\nf ame</w>\nn r\nb at</w>\nag ent</w>\nma kers</w>\nâĢ ¼\nsec tor</w>\nop ti\nle on\ndi et</w>\npra yer</w>\nhi p</w>\nmi r</w>\nle x\nbr y\nan a\npas sing</w>\nw en\nreco very</w>\nak i</w>\npo pul\nres ort</w>\nmar ia</w>\nstu ck</w>\nread s</w>\nti er</w>\nperfe c\nnetfli x</w>\np oo\ncham p</w>\no c</w>\nre duce</w>\nwe red</w>\ncomm ents</w>\ncla im</w>\nacci dent</w>\ns ag\nh ack\nsal t</w>\nkin da</w>\nk iller</w>\ni os</w>\nz y\nex change</w>\nlec ture</w>\neng er</w>\nic king</w>\nt au\nreve als</w>\npri son</w>\nz om\ngh an</w>\nu l</w>\njour nal</w>\ni ot</w>\ntr in\njon a\ngovern or</w>\ncap e</w>\nquar ter</w>\nspec tive</w>\nimpre ssive</w>\nbab ies</w>\nt x\nm ill</w>\no y\nhar ri\njo int</w>\nsu e</w>\ncollabor ation</w>\ntren d</w>\nrevolu tion</w>\nre new\nalum ni</w>\nge tt\nsh ell</w>\nsun day\nent u\nni c</w>\ndonald trump</w>\nblock chain</w>\npaci fic</w>\nexpla ins</w>\nsp y</w>\nad voc\npar adi\nto f\nstar ring</w>\np av\nfe ed\nbr ac\nsmo ke</w>\nham p\ny am\nto kyo</w>\nsi mon</w>\nd h\ne ffici\nphys ical</w>\nn j</w>\nell i</w>\ns low\ngradu ate</w>\nameric ans</w>\nti fy</w>\nf red</w>\nap ore</w>\nfin ds</w>\nrob in\nwe t</w>\nnot ice</w>\nse mi</w>\nun ve\nk om\npil ot</w>\nscre ening</w>\nda ily\nðŁĴ Ĺ</w>\nroy al\nsp a</w>\nvo tes</w>\nn ag\nwh ate\natt ending</w>\nexper im\nad dition</w>\nk ate</w>\nsto l</w>\nm ali\nfoo t\nchri st</w>\nch an</w>\nde e</w>\nlic en\nglo bal\nmo ore</w>\nti a</w>\nbri gh\nmyster y</w>\ny ay</w>\nâĿ¤ï¸ı âĿ¤ï¸ı\ncre ati\nme chan\nclo ck</w>\ndi c</w>\nâĢ Ķ\npp er\nal ph\nthrough out</w>\nal low</w>\nre sources</w>\nselec tion</w>\nham il\nbb q</w>\naa aa\nvirgin ia</w>\ndis ney\nen g</w>\nso red</w>\ndrin ks</w>\nf ancy</w>\nconsi der</w>\nend a</w>\njan e</w>\nhand made</w>\ndu l\non tari\ni us</w>\ns ville</w>\ncolor ado</w>\nwhate ver</w>\nwhe el</w>\npromis e</w>\nne ver\ndesig ns</w>\nab ly</w>\nsex ual</w>\nvanc ou\nat i</w>\ncon vention</w>\ncul tural</w>\nsing apore</w>\npro mo</w>\nload ed</w>\ngla sgo\npp l</w>\nn oo\nke e</w>\nste m</w>\nmen tion</w>\ni do\ncru ise</w>\nri ding</w>\nbe comes</w>\nbe y</w>\nâļ½ ï¸ı</w>\ntw in</w>\ndedic ated</w>\nna sh\nde si\nwork out</w>\njen ni\ni v\ngrou ps</w>\nrela x\npho eni\nli ft</w>\nmix ed</w>\nm ck\np c\nmu st\nme tro</w>\nci es</w>\ny ar\na im\nang er</w>\ni e\nrec y\nmarri ed</w>\ndro pped</w>\neng ag\nle st</w>\nambassad or</w>\nop h\nde s\nw ick\nassi stant</w>\nnat ur\nfa il</w>\nl td</w>\nshor t\nk ap\nsha w</w>\nbi gger</w>\nrema ins</w>\ncrit ical</w>\nsur vey</w>\nco verage</w>\ner son</w>\nwin d\nn b</w>\nbil ly</w>\nlet es</w>\nac ts</w>\njim my</w>\nat lan\nal and</w>\nt c\nimport ance</w>\ndam age</w>\nf g</w>\nstor age</w>\ntw t</w>\nbon d</w>\nbal ance</w>\ncr ying</w>\npu ppy</w>\nvo te\npu sh</w>\nðŁĴ ľ\npol y\nme l</w>\nlon don\nterr ori\neffec tive</w>\ncorpor ate</w>\natl anta</w>\njac o\nnas a</w>\ngre ek</w>\nsen ate</w>\ni sh\nev a</w>\nintellig ence</w>\neffor ts</w>\nal co\nk un\nh all\ndi ag\nclaim s</w>\nfir st\nh b\nba e</w>\nv ul\npu ll</w>\n °</w>\nse par\nspe ed\nvic ti\non thisday</w>\naudi ence</w>\nr ates</w>\nte ach</w>\nfil ming</w>\nbu sh</w>\nson g\ny um\nbr un\nra ine</w>\naw a</w>\npar ks</w>\nð Ŀ\nra bb\nra ch\nra id</w>\nreach ed</w>\nra il</w>\nmo ves</w>\nselec ted</w>\nfr i</w>\nra ising</w>\nom y</w>\nst ones</w>\nsu k</w>\nfranc isco</w>\ncas es</w>\ncap it\ncon fu\nw tf</w>\npo ke\nequip ment</w>\ngre g\ness ential</w>\noff ering</w>\nne x\npi es</w>\nbe c\ncre ation</w>\nchair man</w>\ncro wn</w>\nw al</w>\njohn ny</w>\nshi ft</w>\nne ck</w>\nban g</w>\nbir d\nðŁĺ ı</w>\ndu ck</w>\nre serve</w>\nde pu\nma sters</w>\nover all</w>\nno tic\nju ice</w>\nsne ak</w>\nche er</w>\ncla sses</w>\neag les</w>\nn ca\ncar pet</w>\nci vil\ncoach es</w>\nhar ris</w>\nu ps</w>\nb alls</w>\ndec or</w>\nmar tin\nro s</w>\nv ice</w>\nannoun cement</w>\nwho se</w>\nti gers</w>\nste red</w>\nc ts</w>\ndr am\nste el\nyoun g\ninst all\nsupp o\nrecor ding</w>\nde ck</w>\nse ats</w>\nl der</w>\nang le</w>\nbo t</w>\nsty les</w>\nelec tions</w>\nfor tun\nn ab\nbut ter</w>\nari an</w>\nka sh\nin ner</w>\nou red</w>\nbe ast</w>\nwe i\nic onic</w>\nexper ts</w>\nne cess\nb eng\njam es\nli a</w>\ngre ece</w>\nðŁĵ ·\nðŁĺ ģ\ngood bye</w>\nm itch\ntw ice</w>\nmumb ai</w>\nste am</w>\nru sh</w>\nmed al</w>\nne tt</w>\nfashi on\nt ar</w>\nr s\nsav ing</w>\nric ul\nl m\nsleep ing</w>\nbrook lyn</w>\nmis s\nsen ding</w>\ndisco vered</w>\nsp here</w>\nof theday</w>\nk icks</w>\nmissi ons</w>\nw right</w>\ner n\nght ly</w>\ni ous</w>\nmel bourne</w>\nstar tu\nmo ved</w>\ncar ry</w>\nd ak\nag ues</w>\nbel gi\ne ma\nway ne</w>\ndo t</w>\ner ie</w>\npe l</w>\nit unes</w>\nmatthe w</w>\nno body</w>\nest ab\ncal m</w>\nwin ds</w>\nlu c\nprep are</w>\ntren ds</w>\nexerc ise</w>\nadv ant\nðŁĴ ¯</w>\nathle tics</w>\napp s</w>\nc tions</w>\nadv ance</w>\nlaun ches</w>\nlitt le\nreal donaldtrump</w>\neliza beth</w>\ncarol ina</w>\nhu b</w>\nhi dden</w>\nn w</w>\nus er</w>\npol l</w>\ngreat er</w>\nmo st\nf ed</w>\np at</w>\nlife style</w>\ns ati\nsco res</w>\nmarri age</w>\nl r</w>\naven ue</w>\nde serve</w>\nri f\nðŁ Ĺ\nwat ch\nchampion ships</w>\ngr ay</w>\nen ni\ncot ton</w>\ng om\nwhe re\npack age</w>\nsu m\nab solu\nnew ly</w>\nfoo ds</w>\nty ler</w>\nassemb ly</w>\nmusli m</w>\nban k\nre memb\nop tions</w>\nproduc er</w>\nland o</w>\nfun ds</w>\nu pper</w>\nshad ow</w>\npro gre\nco p</w>\ning e</w>\nleg s</w>\ndetro it</w>\nhill ary</w>\njo se</w>\ngi ants</w>\nsou p</w>\nsustain able</w>\nt us</w>\nclo thes</w>\nroc king</w>\nn z</w>\nmin ne\nmat eri\nbru ce</w>\near t\nca sting</w>\nindepend ent</w>\nthou sands</w>\nta h</w>\nde cl\nveter ans</w>\nli ons</w>\nwra p</w>\nâĢ ¦\nde ss\nbl ing</w>\nst ine</w>\ne ggs</w>\no on</w>\nclo sing</w>\nz ay\nat t</w>\nbac on</w>\nfa il\nariz ona</w>\nde pre\ngho st</w>\nnew sp\nw ers</w>\nvi p</w>\nli ked</w>\nid ent\nvolunte er</w>\nad ult</w>\npu pp\ncir cle</w>\nmat erial</w>\ndegre e</w>\ngro wn</w>\nboo m</w>\ncalend ar</w>\nsu r</w>\nvie wing</w>\nath letes</w>\nch and\nre ll</w>\nasi an</w>\nen tr\nvol ley\nvicti ms</w>\nbo dy\nm ama</w>\ntrans fer</w>\nge ek</w>\nin dic\nsav ed</w>\nma i\ng ent</w>\nit s\nloun ge</w>\nk ol\nthe ory</w>\nsitu ation</w>\nis lands</w>\nar th\nz oo</w>\nfloo d</w>\nvi ously</w>\nshow ed</w>\nparliam ent</w>\nch ev\nel ine</w>\nat trac\nab ad</w>\nta il\nh rs</w>\nlu s</w>\npor tu\ngor y</w>\nprovi des</w>\nto ys</w>\nde ath\nin fe\nan ce\ng le\nli am</w>\nlo ver</w>\nhu d\ndv d</w>\nreve aled</w>\ng w\nre ment</w>\nca the\nl ying</w>\nra dio\nder by</w>\nstor s</w>\nche mi\nhosp it\nâľ ¨\n' :</w>\nilo ve\nle mon</w>\nre public</w>\ns ni\nne ss\ndo or\nre action</w>\npre gn\nfla v\nschol ar\nspo tify</w>\nis ation</w>\nvis ual</w>\naw are</w>\nspon sored</w>\njo ke</w>\nless ons</w>\nleg is\nlo ck\nsi mil\nðŁĺ ĭ</w>\nkin d\nla y\nma h\nho ping</w>\nvancou ver</w>\nas er</w>\nclean ing</w>\ngal a</w>\nthre at</w>\nla p\nach e</w>\nro mance</w>\nex pen\nre post</w>\nz am\ne pi\nmir ror</w>\no ak</w>\nad ul\nbat man</w>\ns lu\nl c</w>\nvie wed</w>\nre views</w>\nd ates</w>\nindone sia</w>\nacti vi\noff en\nlea f</w>\ni si\nag ricul\ncostu me</w>\ns ites</w>\nspir itu\nappear ance</w>\nir y</w>\nst air\napplic ation</w>\nspec tac\nic ity</w>\nski es</w>\nhand le</w>\npun k</w>\nparadi se</w>\nt n</w>\nde al\nprovi ding</w>\ndo c</w>\nrecei ving</w>\nbre w</w>\nmicro soft</w>\nà ¶\nfer r\nme tro\nth ail\ny um</w>\ncar ter</w>\nà ¡\ngent le\nbre aks</w>\ncoo per\nshow case</w>\ncu tting</w>\negy pt</w>\nbab y\nsemin ar</w>\ngl ori\nss on</w>\nfa ve</w>\nre hear\nlo tte</w>\nla dy\nal as\npre p</w>\ndeli vered</w>\nnu clear</w>\nir o</w>\nengag ement</w>\nat ta\ncon ven\nz an\ngl ory</w>\nhol ds</w>\nbusine sses</w>\nstr ange</w>\nsch e</w>\nit self</w>\ngra d</w>\nmar kets</w>\nf alling</w>\nst ats</w>\nge on</w>\nbu dd\nli s\nshe et</w>\nthi si\nco lo\ndeser t</w>\nregi stration</w>\nig n\nexpla in</w>\ninter ior</w>\nla ws</w>\nwrit ers</w>\nspr ings</w>\nk r\nfri ed</w>\nblo om\ninf ra\na o\ncre d\npa st\nline up</w>\nbo o</w>\nbre a\nboo ts</w>\ncelebr ity</w>\natt acks</w>\nbro ok</w>\nev es</w>\nex cu\ncher ry</w>\noo p</w>\nfas cin\nboy friend</w>\nse as\nn ine</w>\neffec ts</w>\npo wered</w>\nk ha\nðŁĺ Ģ</w>\nsh out\ncon dition</w>\ni j\nher o\nenter pri\nwin ter\napplic ations</w>\nsho e</w>\ng el\nbatt le\npro grams</w>\nw art</w>\nðŁĴ ¥</w>\nra p</w>\nho l</w>\ndang erous</w>\ndi a\ncoun ter</w>\nric s</w>\ni or\nk night</w>\nco at</w>\nemo tional</w>\nat ures</w>\nd as</w>\nwhe el\nfore cast</w>\ntran sport</w>\nglasgo w</w>\nking dom</w>\nprepar ing</w>\nim medi\nff in</w>\nawar ded</w>\nprin ting</w>\nro man</w>\nfight ers</w>\nany more</w>\nbel t</w>\np ine</w>\nwin e\nx i</w>\nemploye es</w>\nlogi es</w>\nal led</w>\nde mo</w>\nbirth day\nange les</w>\nlo g</w>\ndri vers</w>\nneck lace</w>\nk ath\ns it\nathle te</w>\nef s</w>\ns burg</w>\npur pose</w>\nresi stance</w>\nrele ases</w>\nt is</w>\nvari ous</w>\ndeli ver</w>\nch al\ns anc\nopp o\ncra w\nneu ro\ndr a</w>\nsuppor ters</w>\nsna p</w>\ndiffic ult</w>\nswe ar</w>\nlogi st</w>\npa th\nattemp t</w>\nà ¥\nswim ming</w>\nste ve\nhur t</w>\ninclu ded</w>\nb ap\nwa re\nðŁĴ ĭ</w>\nend ers</w>\nja ke</w>\nle eds</w>\ncli mb\nl b</w>\nim ple\nli sa</w>\nclo thing</w>\nðŁĺ İ\nd t</w>\ncom pla\nsw ing</w>\nstra w\nv als</w>\nk le</w>\nus ers</w>\nstor m\ncu ts</w>\nontari o</w>\np an</w>\nhand some</w>\ni ow\nar gu\nchec king</w>\nscotti sh</w>\nĶ ï¸ı</w>\nsi er</w>\nem ma</w>\npo d</w>\npatter n</w>\nde sh</w>\nen h\ned ward</w>\nt ing\nk h</w>\nhal f\nlincol n</w>\nmo ther\nal leg\nr c</w>\nvolley ball</w>\nd n</w>\ng ay\nall y\nle ton</w>\ngro ve</w>\nl oud</w>\nadv anced</w>\nre spec\ncli ent</w>\nsupre me</w>\nthail and</w>\nho w\ngi g</w>\nto i\ndo t\ndol lar</w>\nðŁij ĩ</w>\np it</w>\nr b</w>\nh n</w>\nproduc ed</w>\ngg ers</w>\nâĨ Ĵ</w>\nml b</w>\ncan vas</w>\nfin eart\nus d</w>\nin the\np son</w>\nactu al</w>\ns l</w>\nt b</w>\nip ad</w>\nen sure</w>\nu mb\nw d</w>\nsk a</w>\nmar s</w>\nk end\nf eli\nth ing\ncount down</w>\nabsolu te</w>\nr out\ndra l</w>\np y</w>\ninju red</w>\nmin t</w>\nhun ting</w>\nmm er</w>\ns age</w>\nli gh\nac ity</w>\nex pan\nmur ray</w>\nar o\nsec ure</w>\nfour th</w>\neag le</w>\nreli ef</w>\nst akes</w>\nindustri al</w>\nclar k</w>\nunder standing</w>\nsee m</w>\npl enty</w>\nsil ver\ncla u\nthre at\nsa il\npro duce</w>\nab str\nis is</w>\nb r</w>\neng ers</w>\nwor ry</w>\nbie ber</w>\ns j\njust in\nreali ze</w>\nky le</w>\nesp n</w>\nfil ter</w>\ns ch</w>\nty pes</w>\ngame dev</w>\nd ing\ntwit ter\nsoldi ers</w>\np om\ncar bon</w>\ny ards</w>\nchild hood</w>\nri ed</w>\nke l</w>\nele ph\nt ons</w>\nkey note</w>\nqui et</w>\nwi re\npo sting</w>\nis sa</w>\nrepre senting</w>\nbac ks</w>\nalex ander</w>\ncelebr ates</w>\nta ining</w>\n| |</w>\nch or\nesc ape</w>\npe ek</w>\nti ves</w>\nfiel d\nssi e</w>\nim pac\nspons or</w>\nr c\nwe dd\ncann ab\nsi des</w>\ntrac ks</w>\ncom par\ncon trac\ntechn ical</w>\nbi ble</w>\nexpl oring</w>\nsh are\ntra v\nn ate</w>\nill o</w>\nsc ru\nm ingham</w>\ngun s</w>\nof the\nsh ame</w>\nse es</w>\nca tho\nac cess\nce l</w>\nrepor ted</w>\n »</w>\nmari o</w>\np ad</w>\nhope fully</w>\nou se</w>\ny on</w>\ndisapp o\nol o</w>\np itt\npa c</w>\nga p</w>\ncru sh</w>\ns g</w>\nk le\nge m</w>\nemp ire</w>\ndir ty</w>\na is\navi ation</w>\nze aland</w>\nfac ing</w>\nhigh way</w>\nd anny</w>\nspi der</w>\not ta\nðŁĺ Ħ</w>\nw y</w>\ncol ours</w>\nin fl\nco sts</w>\nolym pics</w>\nau s</w>\nh m</w>\nho ward</w>\npas ses</w>\nlau ren</w>\nmu sh\nop in\nr ho\ndisc ount</w>\noper ation</w>\nem ily</w>\nmm m</w>\ncham ber</w>\nd il\nto yo\nshi p\nsam u\npic tured</w>\nun ic\npo l</w>\nkeep er</w>\ncarto on</w>\nst en\nig nor\nn ations</w>\nn l</w>\nta sting</w>\ndeta il</w>\noffici als</w>\nmo tor</w>\nfranc is</w>\ned itor</w>\nðŁij ĩ\npe ts</w>\nrang ers</w>\nt g\nr n</w>\nw ri\nnic hol\ni se\nspo ts</w>\nani e</w>\nchec k\ntri ple</w>\nku mar</w>\nspe akers</w>\nic ing</w>\npre pared</w>\nab use</w>\nfriend ship</w>\nmon th\nswi m</w>\nair e</w>\nsc ent</w>\nhamil ton</w>\nindi an\nj es\nyum my</w>\nte ars</w>\nda wn</w>\ni zed</w>\nworl ds</w>\nðŁ ķ\nb illi\nst one\nn hs</w>\nba sic</w>\np or</w>\nst le</w>\nir on\nol der</w>\ncle vel\ne ing</w>\nðŁĺįðŁĺį ðŁĺį</w>\nprin ts</w>\nfir m</w>\nair craft</w>\nfin est</w>\ndevel op</w>\naar on</w>\nt z\ngra ham</w>\nown ers</w>\nfo li\nless on</w>\nqu es</w>\nbab e</w>\ncra ft\nph en\nju n</w>\nbir mingham</w>\nv ine</w>\nll er</w>\ni an\nfineart america</w>\nevol u\nst ab\nim per\nwar d\ncom ic\nwi z\ninv ited</w>\ndu ke</w>\nmat ch\npor ts</w>\nro ger</w>\ndiag no\nke pt</w>\nte st\nvis u\nr hy\nso c</w>\nto x\nb aker</w>\nsur face</w>\nco vers</w>\nman s</w>\nb its</w>\nx box</w>\nff le</w>\nn an</w>\ngar d\nh art</w>\nwat ers</w>\nv illa</w>\nre tro</w>\nlight ning</w>\ncatho lic</w>\ndemocr acy</w>\nneigh bor\npen n\ncr an\njona than</w>\nla ura</w>\nvi bes</w>\nsu b</w>\ncoach ing</w>\nclear ly</w>\nuk raine</w>\nbra ve</w>\ncommit ment</w>\nt all</w>\nmar t</w>\nra p\nmo di</w>\nsco tt\nbro s</w>\nshow er</w>\nðŁı ¾</w>\nâĺº ï¸ı</w>\ncou sin</w>\nappro ach\nbr e</w>\ncom pos\nhil ari\nphil ly</w>\ng ad\nquick ly</w>\nri an</w>\nt m</w>\nvir tual</w>\nhou ses</w>\nk t</w>\nphoeni x</w>\nw ire</w>\nff y</w>\nb unch</w>\nanc ing</w>\ntal e</w>\nsnap chat</w>\nstar ter</w>\nh t</w>\nk icking</w>\nap art</w>\nth y\n) !</w>\nblo gger</w>\nit z</w>\ncom fort</w>\nang els</w>\nw ash</w>\n\" :</w>\nar gent\nre quest</w>\nhon est\nmi ghty</w>\nbo bby</w>\nk g</w>\nro l</w>\nthou se</w>\nex po\nh c</w>\ntab les</w>\nmag ical</w>\npo sts</w>\nde m</w>\nn w\nor lando</w>\nab er\n* **</w>\nðŁĺ ľ</w>\nenviron mental</w>\ntrans formation</w>\nmi le\nw ic\nhir ing</w>\nma ine</w>\nbo ar\nr ying</w>\nti s\nnit ure</w>\ntwee ted</w>\nanton io</w>\nopin ion</w>\nfin ale</w>\ndi y</w>\nf is\nth in</w>\ntrou ble</w>\nle go</w>\nfi les</w>\nqu art\nsp a\ncurren cy</w>\ncli mate\nfan art</w>\nrail way</w>\nsp ace\nban ds</w>\ndani el\nmo tion</w>\nl eng\nhol der</w>\noc cu\nmar ie</w>\ncathe dral</w>\nbu zz\nbi es</w>\nnas car</w>\nbm w</w>\nbat tery</w>\nchar lotte</w>\ndoc tor\nzz le</w>\nse ven\nin san\nd dy</w>\nst en</w>\nlab or</w>\nthr illed</w>\nse ren\ndocu mentary</w>\nwav es</w>\ncer tain</w>\ncan did\nallow ed</w>\nninten do</w>\nstar wars</w>\nta p</w>\nhome made</w>\nd les</w>\nther ing</w>\nbre e\nemp ty</w>\npi ano</w>\npos iti\ncoun try\npor k</w>\npu ts</w>\nper ry</w>\nm atic</w>\nspot light</w>\nti st</w>\nor ities</w>\nwe alth</w>\nc p\nbar bar\ncommit ted</w>\nas sau\npro fit</w>\ne ight</w>\nhu l\nfini shing</w>\nrun ner</w>\nss o</w>\ninsp ec\nchar ged</w>\nchrist op\nlo sing</w>\nco al</w>\nho o</w>\nele v\nde le\nmo ham\ndon ation</w>\nc able</w>\nclin ic</w>\nj in\nmanag ed</w>\nter ing</w>\nâ ¬\nur ban\ndepu ty</w>\nbb er</w>\nbur n\nacade mic</w>\no tt</w>\nsta ke</w>\nit er\nsto wn</w>\nack er</w>\nadvent ures</w>\nad ams</w>\ngre g</w>\npro m</w>\nvo l</w>\nac qu\ncon gre\npa int\ncitiz ens</w>\nc all\naf ford\nv c</w>\nas ks</w>\nthe tic</w>\nindepend ence</w>\nâ Ľ\nh itting</w>\nbl on\nfu ture\nâ ı\nin no\ngen e</w>\nbo ards</w>\ndi stance</w>\nse t\nre mem\nth al\npre vent</w>\nl ang\nob jec\nsu sp\nmat t\nin duc\nbor o</w>\npi one\nre di\nvir tu\nprin ted</w>\nsco pe</w>\nshar k</w>\nsuc ce\na stron\nil legal</w>\nj ag\nc ting</w>\nine e</w>\nat o\nrob in</w>\nnutr ition</w>\nb f</w>\ndu tch</w>\nb n</w>\nfur niture</w>\nfor gotten</w>\nat ar</w>\nru p\nhy per\nbran ch</w>\ncommunic ation</w>\ndegre es</w>\non ia</w>\nun cle</w>\npromo te</w>\nor che\nwi i</w>\nj s</w>\nbut ton</w>\nma jor\nc bs</w>\nbri stol</w>\npremi um</w>\nordin ary</w>\ne dit</w>\nm g</w>\nwe ed</w>\nst even</w>\n: '\ngu s</w>\nte s\ncap tured</w>\ndru gs</w>\ndo w\nwr ites</w>\nbi shop</w>\nwhe els</w>\nali zation</w>\ndisco very</w>\nw r</w>\nrach el</w>\nne il</w>\nhy dr\ncu test</w>\nentreprene ur</w>\nkore an</w>\nore gon</w>\nul ty</w>\nperfec tly</w>\nsuppor ted</w>\nhistor ical</w>\nt wins</w>\nell y\nwe l</w>\nde vil</w>\nin come</w>\nscienti sts</w>\nde leg\nh en</w>\non i</w>\nic ed</w>\ngi o</w>\ncur ry</w>\nreve al</w>\ne g\nbuff alo</w>\nn ol\nop era</w>\ncamer on</w>\nhaha haha\nj ab\ngradu ation</w>\ncra ig</w>\nr al\ni f\norgani zation</w>\nle ge</w>\ng ang\nsu d\nedin burgh</w>\nl ack</w>\nfli es</w>\ng ate\nthr ones</w>\nq b</w>\nthe real\ne leg\npp in</w>\nc les</w>\njam ie</w>\ntn am</w>\ncryp to\nou l</w>\np ages</w>\na se\nroo ts</w>\nstu pid</w>\na did\nboo t</w>\nprote in</w>\ns ap\nsi um</w>\nsu s\nend or\nfun ction</w>\ndon t\nen na</w>\nch y</w>\nsqu e</w>\nwor ker</w>\nm tv\ne a</w>\nk an</w>\nðŁĴ ļ\nmu s</w>\nprofessi on\nt to</w>\noper ations</w>\nal lo\nc tor</w>\ninv ite</w>\nsc and\nou th</w>\nz im\nlin ks</w>\ncli ents</w>\nsam sung</w>\ndiscu sses</w>\nn ell</w>\nul tra</w>\nsome where</w>\nste wart</w>\nine t</w>\nde z</w>\nb out</w>\nfac tor</w>\nti an</w>\ntr ans</w>\njere my</w>\nd b</w>\nðŁĩ ¬\nor n</w>\ndevelop ing</w>\nspo l</w>\ncoo per</w>\nma u\nrememb ering</w>\ntre k</w>\nfamil y\nsen iors</w>\nfo ster</w>\natt ended</w>\nw ing\ntrans form\nele mentary</w>\nhor iz\nli sting</w>\nmalay sia</w>\nit ch</w>\nwarri or</w>\nphilipp ines</w>\nruss ell</w>\nm end\niniti ative</w>\ncre ep\nto ps</w>\nbr iti\na ur\nshar p</w>\nadverti sing</w>\nug ly</w>\nachi ev\nmateri als</w>\nbu g</w>\ndev ice</w>\nbon us</w>\nfac ility</w>\ncol e</w>\nnh l</w>\ny as\nplann ed</w>\npol e</w>\nexcell ence</w>\ntr ick</w>\ncon fl\nr p</w>\nachi eve</w>\nlo an</w>\nswa g</w>\njess ica</w>\nho we\np our</w>\nsc u\nz oo\nr ated</w>\ndre sses</w>\nre bel\nmex ican</w>\nco ordin\nme ss</w>\natlan tic</w>\nt l</w>\nosc ar</w>\nwal ks</w>\nphar mac\ninvestig ation</w>\n... #</w>\ncc i</w>\neas ily</w>\nmonday motivation</w>\ny ment</w>\nau ti\nfor ced</w>\nar med</w>\ncolle agues</w>\npap ers</w>\npro per</w>\nsha ke\nbu c\nle an</w>\nexhi bit</w>\ne vement</w>\nco tt\nbi z\nsp er\nk ent</w>\nsw an\n/ @</w>\ngirl friend</w>\nhaw k</w>\nâĺ Ģï¸ı</w>\nmon o\nðŁĴ Ľ\nstat ue</w>\nðŁĺ ³</w>\nra s</w>\nte eth</w>\npreci ous</w>\nt ile</w>\np am\nswi ft</w>\nv ali\nno se</w>\ndr unk</w>\nexperi ences</w>\ncome back</w>\ngen ius</w>\nwor se</w>\nsh ef\nra d</w>\ned it\nhon our</w>\nau spol</w>\nlar ry</w>\nh ire</w>\ngor don</w>\nachi evement</w>\n.... ....\nsu icide</w>\nalter native</w>\nsu p</w>\nsur roun\nsha ke</w>\nke ith</w>\npe pper</w>\ntur k\ncrimin al</w>\nbe ck\nsu m</w>\nw alls</w>\ncn n</w>\nan tic\nof fe\ncol li\nwin es</w>\nhigh light</w>\nhawa ii</w>\nemb ar\nl fc</w>\nðŁĩ ®\nm v</w>\n> >\nat mo\nwor d\ncar l\nshout out</w>\nbre wing</w>\nì Ŀ\ndo f\ns ic\nhot test</w>\ncol on\nhh h</w>\nshu t</w>\nlow ing</w>\nvolu me</w>\napart ment</w>\nagre ement</w>\nde stro\nwe e</w>\nreligi ous</w>\niow a</w>\nro d</w>\nland ing</w>\nre present\nðŁĵ· :</w>\nla s\nusu ally</w>\nh l\nc ac\nsal v\nal ong\nlaugh ing</w>\nbe ans</w>\nremin ds</w>\npha se</w>\nsome body</w>\nma sk</w>\nran ked</w>\ndest roy\nsc i</w>\nâĢ¼ ï¸ı</w>\ngab ri\nle o</w>\nro a\nfa iled</w>\nsi l</w>\nrefuge es</w>\nre vi\nr ing\nber ries</w>\ncoo kies</w>\ny y</w>\nconserv ation</w>\nsh ab\nhuman s</w>\nde termin\na in\nni all</w>\nas su\nmb a</w>\nfro m\nextre me</w>\nvic es</w>\ncommer ce</w>\nght ful</w>\nor dered</w>\nsuppor ts</w>\nre cap</w>\nv or\ndro pping</w>\ncorrec t</w>\npay ing</w>\nmean ing</w>\nn j\nqui z</w>\n\" #</w>\nbusine ss\nðŁĩ® ðŁĩ\nindi gen\ndu st</w>\nbox es</w>\nbl ind</w>\nx xx</w>\nzz y</w>\nðŁĩ¬ ðŁĩ\nss els</w>\ns ant\ndd le</w>\nhilari ous</w>\ndesig n\nwonder ing</w>\nvehic les</w>\nk re\nju d\nrece ption</w>\npar ker</w>\nà Ń\npri vi\nhy dro\nsof tball</w>\npol lu\nlo cked</w>\nba h\ne ar</w>\nscri pt</w>\ndi vi\nbr ace\ngeor ge\nthe ast</w>\nbel o\nj al\ntion ary</w>\ndent al</w>\nroc ket</w>\npur ch\nsh ak\nmanufac turing</w>\ne z</w>\nit is</w>\ncon cep\ntb all\nch s</w>\ndirec ted</w>\npra yers</w>\noo k</w>\nphil os\nvari ety</w>\nche ss</w>\nser ver</w>\ng and\nbal ti\nðŁĵ ¸\nsel y</w>\ncru z</w>\nspectac ular</w>\nbur ning</w>\nre present</w>\ni z</w>\nt one</w>\nmer ce\nh ell\nbed room</w>\nestab li\nbo l</w>\ncom mon\nãĥ »\nab or\nkit ty</w>\nhei ghts</w>\nre pair</w>\nwilli am\nqu ake</w>\nalab ama</w>\npopul ation</w>\nre v\nre tt</w>\ni sts</w>\nn ite</w>\nle m</w>\na ha</w>\nclevel and</w>\nr m</w>\npo ver\nob se\nmon tre\nman ia</w>\n ®</w>\ncon ne\ncar ni\nsh ah</w>\nf y\nu a</w>\nsc or\nstrugg le</w>\nbo b\n' '</w>\nappro pri\ndeci de</w>\nff ed</w>\nca ster</w>\ns ort</w>\nhun gry</w>\ndra g\nا Ù\ngr ounds</w>\nd w\nsli ghtly</w>\ncar din\ndead line</w>\nbron ze</w>\nweb in\nbar ry</w>\nsil ence</w>\ne uro</w>\nop tion</w>\near n</w>\nðŁĴ ĸ\nhowe ver</w>\nna ren\nna ils</w>\nbath room</w>\nv ine\nph d</w>\nmin ing</w>\ngar age</w>\n( )</w>\nshou lder</w>\ndefe at</w>\ndi r</w>\no v</w>\nliber ty</w>\nple as\nx on</w>\ncom pre\na v</w>\nj in</w>\nab les</w>\nsil ent</w>\nfam ili\nvis its</w>\ndi pl\nha bit\nmilli ons</w>\nregar ding</w>\ninnov ative</w>\nsen ator</w>\nr ts</w>\nv on</w>\nk l\nwh il\nrequi red</w>\nâĿ Ħ\nlu v</w>\npresi dential</w>\npo cket</w>\nhun dre\nsho wn</w>\nfro zen</w>\nto ward</w>\nfa st\nconfi dence</w>\nr ough</w>\nindivi dual</w>\nqu et</w>\nðŁı ½\ndom e\nfi fa</w>\nengine er</w>\nz en\nre mix</w>\nðŁĺ ĥ</w>\npl ant\nmin or</w>\nrobin son</w>\nas y\npul led</w>\ncer tain\npotat o</w>\n( :</w>\npre s</w>\noc ca\nw it</w>\nit em</w>\nsi e\nd ating</w>\nthom pson</w>\nown ed</w>\nan u\nvi e</w>\nte dly</w>\ngood night</w>\nex cept</w>\nðŁĮ Ł</w>\nira q</w>\nki e\nren ces</w>\nli p</w>\nsimil ar</w>\nsau di</w>\nvi g\narth ur</w>\npic ks</w>\nmil an</w>\nhon da</w>\nma xi\no g</w>\nste st</w>\nar ch</w>\nanaly tics</w>\nba sti\npear l</w>\nter ry</w>\nhor se\nast ro\nac ce\nlaun ching</w>\ninter national\ns no\nta sty</w>\nden ver</w>\nir l</w>\npe te</w>\ntor n\nadvant age</w>\nvar sity</w>\n\" \"</w>\nsol e</w>\ng c\nlan g</w>\ndemon str\nol ds</w>\nun ity</w>\nne ts</w>\ninsp ire</w>\ncre te</w>\nnash ville</w>\nnel son</w>\ne ter\nwal k\nhy un</w>\nm ack\ntre as\nsee king</w>\nra ge</w>\nbru sh</w>\nab and\nwhil st</w>\nco con\nh ong</w>\nshel ter</w>\ni p</w>\npossi bly</w>\nso o</w>\nit ed\nâ Ħ\nrac es</w>\nwar ming</w>\nqu in\ntele vision</w>\nmat ches</w>\nra pi\nment al\npal m</w>\njenni fer</w>\nrol ls</w>\nindi ana</w>\nb ars</w>\ncat ching</w>\nresc u\ncandid ates</w>\nfa re\nâł Ģ</w>\nse o</w>\nvie tnam</w>\nalph a</w>\nmichel le</w>\nvisi ble</w>\nre gre\nwn ed</w>\napp le\nli p\nf fe</w>\nli z\nyork shire</w>\nha il</w>\nse asons</w>\nbe gan</w>\nm d\nk c</w>\nla p</w>\nfascin ating</w>\nhel p\nur y\nu ms</w>\nnu ts</w>\nse m\nalong side</w>\nbri dge\nori al</w>\no ve\nworld cup</w>\nbriti sh\ncomfor table</w>\ni ve</w>\nhot els</w>\nfair s</w>\nhor ri\nso x</w>\nd ining</w>\nstre am\nbar ri\nss y</w>\nw im\nter ms</w>\nv u\npe re\nl ens</w>\nwal ked</w>\nr or\nl ars</w>\nshi eld</w>\ndou bt</w>\npro to\ncro ssing</w>\nme ant</w>\nmedi um</w>\nad ding</w>\ne b</w>\nche ap</w>\nfun c\npap er\nbran ds</w>\nry an\nfeed back</w>\ncol lins</w>\nun known</w>\ntro pical</w>\nsand wich</w>\nfal len</w>\nfor mu\nselec t</w>\nlo ads</w>\nansw ers</w>\nor i</w>\nmag a</w>\nd or</w>\ndu o</w>\nali e</w>\ndru m</w>\nur i</w>\nde er</w>\nsou l\nsh ut\nâĺ º</w>\nsto len</w>\ndon ated</w>\nbu zz</w>\npatri ots</w>\nha l</w>\nna sty</w>\nnomin ated</w>\nmon te\nki a</w>\nth ri\ning u\nte sts</w>\npe tro\nðŁij ij</w>\nho sts</w>\nne st</w>\nto pic</w>\npat ch</w>\nm my</w>\nhu gh\nab ilities</w>\nma the\ns miles</w>\ng b\nag enda</w>\ninsi ghts</w>\nchi p</w>\nph an\nfail ure</w>\ndg ers</w>\nha i\nsignific ant</w>\nsho ck</w>\nru ral</w>\ngl am\nfigu res</w>\npot us</w>\no ta</w>\nmini stry</w>\nappe ars</w>\nfe ar\nr h\nameric an\nh att\nson y</w>\nfi res</w>\ne di\nn ou\ne qui\nwh en\nunivers al</w>\nmad ness</w>\ni x</w>\nsculp ture</w>\nb ach</w>\nt to\nswe den</w>\net a</w>\nen to</w>\ndevelop ed</w>\nmonth ly</w>\nma ps</w>\nra h</w>\nle d\ndel ta</w>\nsa ints</w>\nis lam</w>\nben ch</w>\nfif th</w>\nv ard</w>\nso cks</w>\nwel coming</w>\nj e</w>\ntur ner</w>\nv b</w>\nad i</w>\nnor way</w>\nad y</w>\nhurric ane</w>\npor sche</w>\ntra dition</w>\nex am</w>\nnewsp aper</w>\nlu ci\na ver\nide al</w>\nd na</w>\nmadi son</w>\nðŁ §\nwit ness</w>\nac ou\ninsi ght</w>\nsi mon\nrobo t</w>\nsna ke</w>\nn bc</w>\nac o</w>\nro ss\nsh ment</w>\nreligi on</w>\nch ann\nin su\ncamp bell</w>\ninst alled</w>\nwe ather\nhor ses</w>\nol i</w>\nrober t\nk az\nðŁı Ģ</w>\nveter an</w>\nth read</w>\nquar ter\nea sier</w>\ncap ture</w>\nhi pho\nlaw rence</w>\nroman tic</w>\npas sion\ncl ay</w>\nox ford</w>\nth ai</w>\nstu dying</w>\nfi a</w>\nelec ted</w>\nmost ly</w>\nc b</w>\ntu mb\nâĢįâĻ Ĥ\nx l</w>\nsh an</w>\nfa ster</w>\nev ans</w>\nsli de</w>\nsh ri\nsee k</w>\nmi es</w>\nchemi stry</w>\npump kin</w>\ntu m</w>\n, ,</w>\nro om\nfi red</w>\nli ps</w>\npres ence</w>\naf f\nbrew ery</w>\narri ve</w>\nsw ag\nphoto graph</w>\npen gu\nchi ps</w>\nat tor\nval ues</w>\naccur ate</w>\ncon temporary</w>\nprinci pal</w>\ncannab is</w>\nari o</w>\nany where</w>\ngi a</w>\ndemocr ats</w>\nbuil dings</w>\nli ved</w>\nap s</w>\nneg ative</w>\nm are</w>\nbal lo\nli on\ndiam on\nloo k\nre form</w>\ntom my</w>\nil la\ntre ats</w>\nhundre ds</w>\nport land</w>\nwor thy</w>\nex cep\nar ia</w>\nido l</w>\nbe er\ncd n\ny u</w>\naw k\nðŁĩ ¨\nc ells</w>\nà ³\nident ity</w>\ndra wn</w>\nde vil\nf inger</w>\nth am</w>\nðŁij Ĭ\near ned</w>\nfin tech</w>\ndol ph\ntwee ting</w>\nevolu tion</w>\nðŁĵ į</w>\nest im\nm vp</w>\nn one</w>\nðŁĩºðŁĩ ¸\ntoyo ta</w>\nau x</w>\nmar in\nb old</w>\nl bs</w>\nste ak</w>\nmur phy</w>\nit able</w>\nlou is\nsol ve</w>\npi a</w>\nsk ir\nill ino\nwebin ar</w>\nban ana</w>\nlo v\nth on</w>\nvo ters</w>\nafford able</w>\ndefe ated</w>\nlm fa\nair lines</w>\nsuper b</w>\nany way</w>\ndeb t</w>\nbo red</w>\nver si\nme tal\nresponsi ble</w>\nm k</w>\ns se</w>\nf ay\ncau sed</w>\nf p</w>\nrecomm end</w>\npla za</w>\nspor ting</w>\nalli ance</w>\nau stri\nn n\nt ours</w>\nsurpri sed</w>\narti f\nth under</w>\nsur ve\nwor e</w>\nbri ef</w>\nnecess ary</w>\nz ie</w>\nash ley</w>\ndra ke</w>\nr t\nkni fe</w>\nim mun\nchar ges</w>\na the\nbri de</w>\nrep ly</w>\ng av\nbroad cast</w>\npu er\nbrace let</w>\ncap acity</w>\nharve st</w>\nid k</w>\nperfor man\nd ding</w>\nil ers</w>\npar a</w>\njam a\npro vince</w>\nch in</w>\nid ers</w>\nhar i</w>\nte aser</w>\nch en</w>\nre stor\nr at</w>\nfl at\ncol om\nðŁĴ ŀ</w>\nðŁĩ¨ ðŁĩ\nsmoo th</w>\nr t</w>\np itch\nstay ing</w>\nisra eli</w>\nt cot</w>\nper spective</w>\ndo ck</w>\nopen er</w>\nlo vel\nx o</w>\nclass room</w>\nl ington</w>\ngo al\nkenne dy</w>\nsh am</w>\nsp aces</w>\nmitch ell</w>\nhome coming</w>\nuk i</w>\nclaim ed</w>\nrecru it\ning o</w>\nmu fc</w>\nmon it\ng roo\nresi dent</w>\nper cent</w>\nper man\notta wa</w>\nint ment</w>\nan xi\nstand ards</w>\nwor ship</w>\nsche me</w>\nf x</w>\npot 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poor</w>\nhi king</w>\nsuppo sed</w>\nro of\nre as\ntra in\nti ght</w>\ntru mp\nbas ically</w>\nr r</w>\nea red</w>\nsee ds</w>\nentr ance</w>\nc p</w>\nwi e</w>\nson ic</w>\nvic tim</w>\nhe re\ne h</w>\near rings</w>\nsal mon</w>\narc tic</w>\nan ne\ndou gla\ncorru ption</w>\nhann ah</w>\nha sn</w>\nvo ices</w>\ncon ce\natt a</w>\nfle et</w>\nclin ical</w>\ndemocr atic</w>\nton y\nst ood</w>\nle f\ntwit ch</w>\na il</w>\nhonest ly</w>\nincre ased</w>\ndro me</w>\ndon na</w>\naccep ted</w>\nvisit ors</w>\nap ar\nad or</w>\np ar</w>\njer ry</w>\nra i\nbrand on</w>\nab u\n!! !!!!</w>\nme me</w>\nin gh\nglori ous</w>\nb hu\npu mp</w>\nj ol\nli ke\nfi sher</w>\nma z\nag an</w>\ndestin ation</w>\nplay list</w>\nle tters</w>\ngen u\nbr ace</w>\ncelebr ated</w>\nbann er</w>\nr he\ndra gon\nðŁĺ ħ</w>\nsig nature</w>\ngre y\nâľ Ķï¸ı</w>\nal ice</w>\nbe red</w>\nph er\nber n\nca th\nga thering</w>\nsc oring</w>\ninflu ence</w>\nsm iling</w>\nde pt</w>\nlo cal\na x</w>\nac u\nreti rement</w>\nhon or\nher self</w>\nchem ical</w>\nasse ss\ny all</w>\nfre qu\nappreci ation</w>\nac a</w>\ncho ir</w>\ncu z</w>\nso il</w>\nc il\nrepor ting</w>\nu h</w>\nenterpri se</w>\ngr at\njaco b</w>\nru m\nfe e</w>\nj ak\nsp in</w>\nbi kes</w>\nphi a</w>\nste re\np is\nbloo d\nt att\nra ft</w>\nwar ren</w>\nsh eri\nback stage</w>\nmar sh\nhash tag</w>\nther ine</w>\nre in\ngame day</w>\nguar an\nreci pes</w>\nmin ds</w>\nstron ger</w>\nissu ed</w>\nbic y\nn ak\nment ed</w>\nsc ary</w>\nu x\npre vious</w>\ntt le</w>\nth ats</w>\nac tors</w>\nu ma</w>\ntin a</w>\nbun ny</w>\npromo tion</w>\nu ss</w>\noli ver</w>\nmontre al</w>\nwhat s\nappreci ated</w>\nla kes</w>\nexcu se</w>\nkno wing</w>\npri zes</w>\nmusc le</w>\nshad es</w>\nsco t</w>\ning redi\nelectr onic</w>\nju an</w>\ncomb at</w>\ns ri</w>\ne h\nturk ish</w>\nl om\nstri kes</w>\npri son\nre e\npo pe</w>\nvi d</w>\nol dest</w>\ndol l</w>\nsw iss</w>\ncerti fied</w>\ncli p</w>\nre turning</w>\nlat or</w>\nle igh</w>\ntt es</w>\nwat 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i\nemploye e</w>\ncom mu\nalas ka</w>\nal an\nfe ast</w>\ndg ing</w>\nban king</w>\nmanu el</w>\nslow ly</w>\ntru cks</w>\nmc car\noo o</w>\nsc rat\norche stra</w>\nindivi du\nm x</w>\nbre ath</w>\nstair s</w>\nequ ality</w>\nbla ke</w>\nloc ations</w>\ncocon ut</w>\nbalti more</w>\naa a</w>\nl c\nðŁı Ĩ\nhar vey</w>\nresi st</w>\nimmigr ation</w>\nadid as</w>\nfil i\nre f</w>\nlg bt</w>\nmo s</w>\npp i</w>\nken ny</w>\nterr or\nban e</w>\napol is</w>\ns g\nsocial media</w>\nka i</w>\nhon est</w>\nas sas\nbol lywood</w>\nâĢįâĻ Ģï¸ı</w>\nferr ari</w>\nhor n</w>\ncryp to</w>\nbo om\nmainten ance</w>\ni di\ns man</w>\nw l</w>\next ended</w>\nin sul\nve s\ngo sp\ntr i</w>\npi g</w>\ntar ge\ncel er\nst ati\nsm h</w>\nri dic\nappe al</w>\n? )</w>\ncon clu\ncos me\nshe ep</w>\nchristop her</w>\nen thusi\npo lish</w>\nme ts</w>\noun ded</w>\nsustain ability</w>\ncreati vity</w>\ncon crete</w>\nra i</w>\nali en</w>\nble ss\nte es</w>\nclu b\nro t</w>\nbo s</w>\nex ist</w>\nperfe ction</w>\nlu 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land</w>\nover night</w>\njourn alist</w>\nser ves</w>\nvol can\n.... ...</w>\nplo t</w>\nnic ol\ncar rying</w>\nmag ne\ntre asure</w>\nex p\nbe ver\nðŁĺ ¢</w>\nmar ty\nmo le\ndon ations</w>\nrecogni zed</w>\nb h\ndu s</w>\nsh ann\nal do</w>\nsuccess fully</w>\nent e</w>\nðŁĺĤðŁĺĤ ðŁĺĤðŁĺĤ\ncab inet</w>\ncu is\ntit led</w>\nd as\nso l</w>\nstrate gies</w>\ndeli vering</w>\nad ds</w>\nani an</w>\nne ther\nðŁĴ ĥ\ncon tain\nsu its</w>\npa irs</w>\nto dd</w>\nrel la</w>\nro pe</w>\nci o</w>\ncro p</w>\npaint ings</w>\nsu z\nre jec\nbu st</w>\nd h</w>\nfra ud</w>\nm h\ncontro l\nje al\ndestroy ed</w>\nal lows</w>\nwo ol\nminneso ta</w>\nom en\nj u</w>\nsympo sium</w>\nd af\nlim it</w>\naccoun ts</w>\nload ing</w>\ninter n\nre solution</w>\nhol land</w>\nqu al\nmeet ings</w>\ngra ve</w>\ncam ping</w>\nv am\nre nov\nliber al</w>\nam ber</w>\ngre e\nhu mb\nfe ver</w>\nel ing</w>\nbroo ks</w>\nà ²\nbe th\nad ed</w>\nal t\nro e</w>\nperform ed</w>\njo sh\nfrank lin</w>\nnic ole</w>\nde 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l</w>\nda ve\ncare ers</w>\nopp os\nl loy\nin ter</w>\ndo pe</w>\nre su\nj agu\nsh ade</w>\nin dy</w>\non ist</w>\nrel ations</w>\nag en\nab le\ninci dent</w>\nme ter</w>\nshar ma</w>\nid r</w>\npro ve</w>\nimmedi ately</w>\ntro ops</w>\nam an</w>\ng low</w>\ngaz a</w>\nblo cks</w>\nperson al\nchron ic\nall er\nsi d\nsh r\nwhats app</w>\nlu cy</w>\nar chae\nho u</w>\njourn alism</w>\nour selves</w>\ngo t\nthe med</w>\nshap ed</w>\nwe ak</w>\ncas ual</w>\nleng th</w>\nsla m</w>\nab bey</w>\ne v</w>\ncoun ter\nest a</w>\nreci pi\ncha pel</w>\nexpan sion</w>\nsel f\nsuff ering</w>\nsp ice</w>\nn z\nsp art\ndesp er\nboo king</w>\nquart ers</w>\ny on\nðŁĴ Ĺ\np k\ncontinu ed</w>\n- #</w>\nman hatt\ntal ked</w>\nsh en\ncom bo</w>\nhybri d</w>\nje ans</w>\nliqu id</w>\nse al</w>\nre tweets</w>\nac celer\ncollec tive</w>\nt as</w>\n: ))</w>\nprofession als</w>\nra w\no tt\nsu san</w>\nir ing</w>\nokla homa</w>\nre ven\nsurvi val</w>\ncre ator</w>\ntran sit</w>\nst ac\nsur f\ni k</w>\ned 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th</w>\nli pse</w>\nn g\nri p\nmel o\nsand y</w>\nðŁijı ðŁijı\nvin cent</w>\nnu t\nhu g</w>\nwho le\ncre ates</w>\n? ???</w>\nâĿ¤ï¸ı âĿ¤ï¸ı</w>\nbak ed</w>\nup grade</w>\nrober ts</w>\nhar a</w>\ncarib bean</w>\nauth entic</w>\nmb s</w>\nmosco w</w>\nattor ney</w>\nwi ki\nch lo\nhu ll</w>\ncor k</w>\n\" !</w>\nsty lish</w>\nðŁĵ¸ :</w>\ndi ary</w>\nimpro ving</w>\nex pand\nbri ght\npollu tion</w>\nk nights</w>\nperson ality</w>\nchec ked</w>\nfac ilities</w>\nz el\nbow ling</w>\ngu er\nðŁİ Ĥ</w>\non going</w>\nun its</w>\nhoo k</w>\nbe ck</w>\nconfl ict</w>\nto dd\nfar ming</w>\neduc ational</w>\nk ak\ncla y\nstro ke</w>\nbel ly</w>\nexplo re\nmill enni\nth m</w>\nloo p</w>\nsm s</w>\nconsi st\ncir ca</w>\nbr yan</w>\nd ab\nyoun ger</w>\nsoli dar\npp a</w>\nexperi enced</w>\nb ella</w>\nbo ard\nshef field</w>\nsteph en\nconsu mer</w>\nsub mit</w>\nspon sor\nt ang\nag gre\ncomb ined</w>\ntrac king</w>\nsand ers</w>\nb az\nsurvi ve</w>\nfer red</w>\nequ al</w>\nse p</w>\nre ed</w>\nstr 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ssi\nhe ight</w>\nmedi eval</w>\nimpro vement</w>\nke es</w>\nprac tical</w>\ncar d\nde par\nhu n</w>\nom ing</w>\ncal gary</w>\nste l</w>\nbu bble</w>\ngur u</w>\nma h</w>\nunex pe\nn h</w>\ned a</w>\nme at\ni ge</w>\nsi o</w>\ngod dess</w>\nin ches</w>\ntun es</w>\nbr itt\nsti on</w>\nra j</w>\nâĻ «</w>\nmer cy</w>\nðŁĴ ĺ</w>\nsen ds</w>\ni est</w>\npol ici\nval e</w>\nreduc ed</w>\nas ap</w>\nvi jay</w>\ndefen sive</w>\ncelebr ations</w>\nri ders</w>\nmed itation</w>\nhar mon\ng ing\n ¡</w>\nprogram ming</w>\nin au\nsud den\nm h</w>\nreplac ement</w>\nsk u\nj ar</w>\ngra des</w>\nta st\nk itt\nbrand ing</w>\nk aw\nboo t\nf ought</w>\np ays</w>\ng f</w>\niz ation</w>\nho p\nk k</w>\nactivi st</w>\nv end\ncoast al</w>\ncha os</w>\nðŁĶ ´</w>\nse me\nbill board</w>\nli fting</w>\ncu mb\nsc al\nðŁĸ ¤</w>\nstru ck</w>\nl v\nindie dev</w>\nbeat en</w>\njun gle</w>\nal right</w>\ndestin y</w>\nm ing\nk c\nch ances</w>\nom an</w>\nq atar</w>\ncra f\ntra ined</w>\npri x</w>\nchar m</w>\no 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p</w>\nli ving\nun e</w>\ndescri be</w>\nca res</w>\nman ila</w>\nsha wn</w>\nac ted</w>\nbas h</w>\nst even\nre st\npet ition</w>\ndiv ine</w>\nwel sh</w>\nrac e\nplatin um</w>\nðŁĮ ¸</w>\np b</w>\nextra ordinary</w>\nsolidar ity</w>\nm all\non ion</w>\nschedu led</w>\ngame of\nfer gu\nde ms</w>\nnor m\np k</w>\ntri als</w>\npolici es</w>\npubli shing</w>\nst ole</w>\nfron t\ncharac ter\nvan ia</w>\nex ce\nsti e</w>\nsc a</w>\nresi dential</w>\nsa iling</w>\nðŁĶ¥ðŁĶ¥ ðŁĶ¥</w>\nspons ors</w>\nth ick</w>\nchampag ne</w>\nshe pher\ncontinu ing</w>\nven ice</w>\nper th</w>\nna p</w>\na ster\ny ak\nun limited</w>\ncho ices</w>\nne o</w>\nhi v</w>\nrepor ter</w>\nbru ssels</w>\nf old</w>\ndy s\nse mi\nla wn</w>\nit alia</w>\nwi fi</w>\nas k\nem ed</w>\nfr ame\nmonit oring</w>\nste ad</w>\ni da\ngr in\nis a\nfli p</w>\nre stric\noffen sive</w>\natta ched</w>\ndi sh\nwh y\nphilli ps</w>\ngre et\np als</w>\nmix tape</w>\nv ou\nfiel der</w>\nspar k</w>\nalber ta</w>\ng len</w>\nca sh\ns ri\nu ri\nro dri\nentreprene urs</w>\nclimate change</w>\np sy</w>\nd le\nem ents</w>\nlin ked</w>\nnether lands</w>\nacci dentally</w>\noppos ition</w>\nvel vet</w>\nra ys</w>\nc w</w>\nom o</w>\nm f</w>\nlmfa o</w>\nnewsle tter</w>\n: )\ntoi let</w>\nliter ature</w>\ndi sp\nphili p</w>\nuni form</w>\nsudden ly</w>\nhead er</w>\ncool er</w>\n-- -</w>\nprou d\nbri g\nnis san</w>\nscienti st</w>\nj ah</w>\ncon centr\npac ks</w>\nappo inted</w>\nso ap</w>\neng age</w>\ncho se</w>\nâĻ ¡\nse tup</w>\njeal ous</w>\nhar ry\ng ation</w>\ntun nel</w>\nte mp</w>\nosc ars</w>\ndec ade</w>\nrecomm ended</w>\nchild ren\nab a</w>\nanxi ety</w>\nve ments</w>\nsal on</w>\npho too\norgani z\nmach ines</w>\nab s</w>\nvil le\nhy pe</w>\nti ff\nemer ging</w>\nav geek</w>\n[ #</w>\ncontribu tion</w>\nbra dy</w>\nre sto\ng mail</w>\nfit z\nphoto shoot</w>\nhel met</w>\nh t\neleg ant</w>\nug anda</w>\nnur sing</w>\nor leans</w>\npen n</w>\nna h</w>\nfoo tage</w>\nem a</w>\nw o</w>\nw ad\nconcer ns</w>\nve re\nre mark\nwho ever</w>\nstr ang\np t\nqu it</w>\nsh ang\nhistor y\ns ick\nperman ent</w>\nill ness</w>\ncol d\nvisi on\nhe m</w>\nar row</w>\ncon vic\npin k\noc cup\nbal d\nex hau\nu of\nam o</w>\non t</w>\nãĥ »</w>\nadop t</w>\nla id</w>\nsmo ked</w>\ninter pre\ness enti\nassoci ated</w>\nb d</w>\nbb y\nfi er\ninst all</w>\ndipl om\ncon diti\nc f</w>\nw ak\nany a</w>\ngr aci\nfi sher\ns ss</w>\nap r</w>\nil it\nmus ician</w>\nsymph ony</w>\ncor d</w>\nh ack</w>\nle gi\nl v</w>\nbless ings</w>\nhum or</w>\nsc ra\ne ti\nmin ster</w>\ntrav elling</w>\nbu sh\njewell ery</w>\nli me</w>\n!! !\npregn ant</w>\npe e</w>\nlo b\ncap ital\nip a</w>\npen cil</w>\nla bor\nduc ks</w>\nprou dly</w>\nwedd ing\ndere k</w>\nm w</w>\npe g</w>\nvalent ine\nan gu\nre treat</w>\npro spect</w>\ndang er</w>\nvul ner\nup set</w>\n, #</w>\nsr k</w>\nx im\nthur sday\nn fl\nkis ses</w>\nre ds</w>\ncr ack\nre ward</w>\nc u</w>\nko k</w>\nme te\naband oned</w>\nit t</w>\nme als</w>\nsp ell</w>\nstan bul</w>\ndel ays</w>\nru m</w>\nle op\ngu m</w>\nno va</w>\nsuper man</w>\nch ick</w>\nm is</w>\ndram atic</w>\ninno cent</w>\nr ounds</w>\nre c</w>\nauti sm</w>\nbangla desh</w>\nmor al</w>\nmo vie\nsp oo\nk la\nâĥ £\nou ting</w>\nmess i</w>\nab road</w>\nloo kin</w>\na im</w>\nq i</w>\nst ack</w>\ncolla ge</w>\nà ¯\nhud son</w>\nsc an</w>\nho e</w>\nch au\noc cur\ncomm ander</w>\nho les</w>\nðŁİ Ħ</w>\nbi as</w>\nv on\nstick er</w>\nma k\nresponsi bility</w>\ncolum bus</w>\nsa int\ned mon\nrac ism</w>\nfar ms</w>\nw en</w>\ngul f</w>\nmay o</w>\n!!!! !!!!\ncorpor ation</w>\nba chel\nel a\ninter nal</w>\nje ep</w>\nfol lows</w>\ndi alogue</w>\nde rer</w>\nsmart phone</w>\nhe len</w>\nrich mond</w>\nequ ity</w>\ns land</w>\nb g</w>\nne ar\nav i</w>\nmemph is</w>\nwe ir\ndiscu ssed</w>\nbad ge</w>\np up</w>\nmi stake</w>\nphen omen\nun ite</w>\nðŁ Ľ\nde pic\nri des</w>\nin augu\nn at</w>\nsof twitter</w>\ncomb ination</w>\ngosp el</w>\nâļ ¾\nad mission</w>\nretro gaming</w>\nðŁIJ ¾</w>\nsch u\nmb o</w>\njun ction</w>\nal arm</w>\nà ¦\ngr ac\nkh ali\nk ul\nm ale\ncap tion</w>\nwi sh\nte re\ncor ps</w>\nru bber</w>\nplay station</w>\ner in</w>\neffici ent</w>\nl or</w>\njo kes</w>\nin ary</w>\nnor man</w>\nlu is</w>\ninaugu ral</w>\nch ed\nâļ½ ï¸ı\ndi p</w>\nto e</w>\nstr at\naa c</w>\nam u\npi er</w>\nco tt</w>\ncomm and</w>\ntt en\nsn oo\ncu be</w>\nclo ses</w>\nclass ical</w>\ns word</w>\nexpre ssion</w>\nreach ing</w>\nn app\nco st\naffe ct</w>\nric o</w>\ngi f\nbrea the</w>\ntri be</w>\nor tho\nh ay</w>\nl g</w>\nfri es</w>\nn m</w>\nhi ding</w>\nrichar ds</w>\nen de\nmic ro</w>\ncapit ol</w>\ncop y\nro m\nregi me</w>\nmary land</w>\ntax i</w>\ndi al</w>\nembar ra\nun believ\nch t</w>\nv s\nelim in\no dd</w>\npen ny</w>\nsound track</w>\nl ings</w>\ntrans ition</w>\nrema ining</w>\na is</w>\nmali k</w>\n? !?</w>\nrand om\ndef end</w>\nul tra\ntru m</w>\ndanc er</w>\nst ol\ndri ve\na ver</w>\nro ast</w>\ndefin ition</w>\nse an\nexcit ement</w>\npartic ul\nsu rely</w>\nsh av\nber y</w>\ndi shes</w>\ncom m</w>\nis ol\ni am</w>\nob li\ngho st\nhugh es</w>\nchi efs</w>\nb as</w>\nconserv ative</w>\nspeci al\nfe min\nsh ri</w>\nn ancy</w>\ninte l</w>\ntu ne\nðŁĩ ª\njo el</w>\ngg le</w>\nmo to</w>\nðŁĺ Ķ</w>\nbu ck</w>\nd ag\nantic ip\nmont ana</w>\ngu id\nfro g</w>\nec raft</w>\nop e</w>\ndri ves</w>\nnu mer\nx y</w>\ncolor ful</w>\nwednesday wisdom</w>\nillu min\nbey on\ninau gur\ndeep ly</w>\npre fer</w>\nfor tune</w>\ncoo ked</w>\nti ble</w>\nâĺ ķ\nswe ater</w>\nit ter</w>\ntt y\nu i</w>\ngi e\ncom plic\n~ ~\ntax es</w>\ncu ps</w>\ndi verse</w>\nsam anth\nâłĢ âłĢ\nba king</w>\nsy mp\nwa i\nbe half</w>\nmer cur\ntravel s</w>\nðŁİī ðŁİ\nor ia</w>\neng aged</w>\njump ing</w>\nreti red</w>\nn aked</w>\np uni\nspeed way</w>\nsci ences</w>\nrehear sal</w>\non ym\ndy ou\npl ates</w>\nr ati\nkri sh\njaz z\ncar ol</w>\nra f</w>\npen alty</w>\ntim eline</w>\nru by</w>\nengine ers</w>\nra f\nbel le</w>\ndo se</w>\nche on</w>\nesc ap\nme g\nran k</w>\nor d</w>\nme gan</w>\nmer ch</w>\nec lipse</w>\nâĺº ï¸ı\nple dge</w>\nkir k</w>\nper si\nleice ster</w>\nsa k\nw k\nsaf ely</w>\nyy y</w>\nje t\npromis ed</w>\nj c</w>\nen ne</w>\nno ah</w>\nre no\nre a</w>\nðŁĺĤðŁĺĤ ðŁĺĤðŁĺĤ</w>\ntra il\nðŁij Ģ\nf d</w>\nsoo o</w>\nri min\nw k</w>\nภ²\ni al\nx ox\nbis cu\nd ale\nfan dom</w>\nparticip ating</w>\nfla g\nprivi lege</w>\npe ach</w>\nmach ine\nbo ston\ngro ss</w>\no g\nmir acle</w>\nadop tion</w>\nu ss\nmon sters</w>\nbe ij\nclar ke</w>\npu shing</w>\npra ying</w>\nar o</w>\nd n\nell is</w>\napol lo</w>\nod ds</w>\nrefuge e</w>\nto w\nb p</w>\nðŁĩ¬ðŁĩ §</w>\nh end\napp eared</w>\nmemb ership</w>\npe an\ndu m</w>\nviol ent</w>\nv y\npotat oes</w>\naw w</w>\ngreet ings</w>\nt ts</w>\nac on</w>\nsh ane</w>\nphotograph ed</w>\ncra b</w>\ntemper atures</w>\ncu ba</w>\nc fc</w>\nwel com\nhe l</w>\nin nings</w>\nm k\nco de\nkno ck</w>\ngra ss\nswe dish</w>\np ta</w>\nick y</w>\nv at\nlin ing</w>\ns q</w>\nsa p</w>\nar c</w>\nannoun cing</w>\nsk ins</w>\ncit yof\nbr ing\nco x</w>\ngam er</w>\nit arian</w>\ni da</w>\nh d\nros se</w>\nsad ly</w>\nge o</w>\nâļ ¡ï¸ı</w>\ntag s</w>\nfa ther\nchan ge\nl ance</w>\nwhis key</w>\nadel aide</w>\nte c</w>\nstick ers</w>\nmarke t\nclass y</w>\nbad ass</w>\nflo rence</w>\nlin er</w>\nfro st</w>\nk ate\nac on\nscand al</w>\nes sex</w>\nðŁĺ ı\nvi vi\ndr ill</w>\nblo ggers</w>\nrecomm end\nd ha\nac res</w>\nro ma</w>\nbu y\ngro cer\ner ia</w>\nma har\nff er</w>\npatter ns</w>\nver i\ncom pu\nst ev\nang a</w>\nment or</w>\ndo o</w>\nit ali\ncdn poli</w>\non ly\nconduc t</w>\nelec tro\nde f</w>\nwh ale</w>\nprepar ation</w>\nbicy cle</w>\nvi ral</w>\nturn out</w>\nbra ss</w>\nqu ad\nhospit ality</w>\npack aging</w>\nden cy</w>\nceme tery</w>\nabo ard</w>\ndre aming</w>\npic ture\nt all\ninv ent\nad mi\no e</w>\ntem ps</w>\nqu an\nfun dam\npro mp\nresi dence</w>\nmu d</w>\nsour i</w>\nâĦ ¢</w>\ngraff iti</w>\ngi f</w>\nd nd</w>\ncom p</w>\ns war\npe eps</w>\npale stine</w>\ndevil s</w>\nsan g</w>\nassi 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a</w>\ncolo red</w>\nda h</w>\nassoci ate</w>\nint el\ns eller</w>\np u</w>\nstu ffed</w>\nac s</w>\nb s\nsh in</w>\ncooper ation</w>\ncertific ate</w>\nab u</w>\ningredi ents</w>\nre v</w>\nin ge\nel der\nchristi an\nbun dle</w>\nth ic</w>\ndir t</w>\nbeij ing</w>\ncomm it</w>\nted dy</w>\ned u</w>\nto day\ns field</w>\nw yn\nconfir ms</w>\nlo o</w>\nj v</w>\nene ss</w>\nal pha\nvir us</w>\nari um</w>\ngr ind</w>\nbri dges</w>\nintroduc tion</w>\npol ls</w>\nbac ter\nz ach</w>\ntermin al</w>\nra iders</w>\nfla vor</w>\nzom bie</w>\nvo d\nsp reading</w>\ngameof thrones</w>\neffici ency</w>\nlat ely</w>\nale m</w>\ntwee t\ncri mes</w>\ncl er\nde y</w>\ndg ed</w>\nhy un\npay ments</w>\ncir cus</w>\nðŁĺŃ ðŁĺŃ</w>\nmis souri</w>\nlu b</w>\nepiso des</w>\nc age</w>\npo s</w>\nmat ching</w>\ntumb lr</w>\nlin ed</w>\nge st\nam bi\nnar r\ning ton\nregu l\nblo wn</w>\nis le</w>\nco co\non don</w>\njoshu a</w>\ntour ing</w>\nsm a</w>\nsau sage</w>\nbest friend</w>\nbo eing</w>\ndesi re</w>\nsav age</w>\nra pper</w>\nde vo\nte ar</w>\ntake over</w>\ncow boys</w>\npo ker</w>\npar ag\npp e</w>\nh int</w>\nwe ars</w>\nse th</w>\nro les</w>\nl anc\nman ga</w>\nform at</w>\nfl yer</w>\nc ay\nmo or</w>\nba ke</w>\nspla sh</w>\nv ad\nker ala</w>\nproce eds</w>\nsil ly</w>\nreflec tion</w>\ndi str\nwi d\nsu it\nci vic</w>\nyan kees</w>\nby n</w>\nmigr ation</w>\ndi stin\nor ch\nfe mini\nquali fying</w>\ntu ri\no be\nhun dred</w>\ncra p</w>\nwan g</w>\nmathe mat\nbu re\nexpo sure</w>\nfergu son</w>\nseme ster</w>\nre serv\npl ym\na hu\nfac ial</w>\nwa x</w>\nwor ried</w>\nca b</w>\nvi o\nas a</w>\nco d</w>\nto pics</w>\np cs</w>\nhal o</w>\nrescu ed</w>\nhoriz on</w>\nar k\nâļ ª\nhol ly</w>\nel f</w>\nul ti\npu p\nquali fied</w>\nattend ance</w>\nati vely</w>\ndestro y</w>\ny c</w>\nfor th</w>\nphotoo ftheday</w>\nc ents</w>\nic eland</w>\nmeas ures</w>\nde sk\nport folio</w>\nartic les</w>\ndirec tors</w>\ndat ab\ne w\ncreep y</w>\noun ding</w>\nhon oured</w>\nmi st</w>\nj it\nmen tioned</w>\nport able</w>\niti c</w>\nd ann\nfriday feeling</w>\nam id</w>\nti ger\nscri p\nhelicop ter</w>\nhard ware</w>\nexpl or\nwork place</w>\naustri a</w>\nbeat les</w>\nber nar\nspi der\ndisc o</w>\ncul t</w>\nlim its</w>\nshor tly</w>\nfin al\nnin ja</w>\nlu ke\nle bron</w>\nwal mart</w>\no il\nvan illa</w>\nshi re\nye g</w>\nak y</w>\nc s\nbl er</w>\ncollec ted</w>\nt g</w>\nrol led</w>\nspeci als</w>\nb ff</w>\npier re</w>\nsh im\nvi er</w>\nflash back</w>\nrestor ation</w>\nindividu als</w>\npro d</w>\nfre aking</w>\ntu rer</w>\no a</w>\nre fre\nmor oc\ngre et</w>\nre yn\ncare ful</w>\nour ing</w>\nu sh\nis d</w>\ng ill</w>\nvie w\nthunder storm</w>\nb led</w>\npic nic</w>\nguar di\npi g\nar k</w>\nsyl vania</w>\nbann ed</w>\nu cl\nvi jay\nori um</w>\nav engers</w>\nbeliev es</w>\neu r</w>\nmonu ment</w>\nconcer ned</w>\nla bs</w>\nber g\na ap\nvi sh\nsing les</w>\ncan cel\nz el</w>\nar ab</w>\nru th</w>\ntoo th</w>\nar ta</w>\nsh af\nchair s</w>\nr ack</w>\ndise ases</w>\ncrow d\ncl y\nfle x</w>\nchrist ma\nartif icial</w>\ntom at\nfin e\ndra ws</w>\nadvoc ate</w>\nfran ce\nÙ Ĭ\nðŁĺ ³\nheav y\ns our</w>\ncompre hen\nno ble</w>\naa p</w>\nhin du</w>\ncor al</w>\ng ars</w>\now en</w>\nn l\nst all</w>\nyel low\nmar ina</w>\nin ver\nsuppor t\ntou gh\npromis es</w>\npi e\nmaster piece</w>\nsco re\nfor ce\nmor tg\ncrypto currency</w>\no x</w>\nr ors</w>\nrock in</w>\npro vin\nho g\nno stal\noak land</w>\npat rick\ninclu sion</w>\ntra ffic\nah med</w>\na ha\nlux ury\ncon secu\nde mon</w>\nâĸ º</w>\nb lowing</w>\nst ag\n: \"</w>\nencoura ge</w>\nben e\nsku ll</w>\ndo dge</w>\nbu ster</w>\nkin son</w>\nwit ne\ner ror</w>\nlo west</w>\nfel low\nà °\nsh re\nbl ur\nvir gin</w>\ncompos er</w>\nsli p</w>\nmor nings</w>\nga ins</w>\ntab le\ngra in</w>\nari st</w>\nbraz ilian</w>\nw we\ntu es</w>\nribb on</w>\nan ag\ndi st</w>\nsac rif\nem brace</w>\nentreprene ur\naf fili\nde o</w>\nt ali\ntouri st</w>\nfat al</w>\nì Ĭ\nautom atic</w>\nðŁĩ µ\nwe ak\nwel fare</w>\nconfir m</w>\nbenjam in</w>\nfi ghts</w>\nalleg ed</w>\nme ad\nstrugg ling</w>\npro secu\nche f\nà ¨\npropos al</w>\ner n</w>\nðŁĺ Ħ\ndy k</w>\non gs</w>\nhon g\nm ack</w>\nmel on</w>\non ent</w>\nru sh\nd ap\ntol er\npro pag\nc ze\ntrans lation</w>\nwal let</w>\ncott age</w>\nsa il</w>\nconstitu tion</w>\nðŁĴ Ģ</w>\nmun ici\nfav or</w>\nstorm hour</w>\ni h\nðŁĺ Į</w>\napproach ing</w>\npin ned</w>\nj ed\nniger ian</w>\nn ach\nsh at\nparticul arly</w>\nmc don\ncamer as</w>\nanni e</w>\nadmini str\nhe at\nelectr ical</w>\nchar ming</w>\ngib son</w>\nbouti que</w>\nex posed</w>\nac tor\npil low</w>\nbeach es</w>\ngenu ine</w>\nmargare t</w>\nben nett</w>\nlou isi\npos itions</w>\nel y\nshin y</w>\nten tion</w>\narchitec t</w>\nren tal</w>\nac qui\ngoo gle\nsub way</w>\nmom ent\nðŁļ ¨\nri m</w>\nmetho ds</w>\ncy cli\nnor folk</w>\nÙ Ī\nover whel\nra pid</w>\nwe ar\nhappy birthday</w>\nprogre ssive</w>\nðŁĴ ¥\nco gn\npap a</w>\nf ool</w>\nphilosoph y</w>\npol ar</w>\njim my\nwi g</w>\nðŁĴ ĭ\noper ating</w>\nreduc tion</w>\nph i</w>\nfla gs</w>\nto the\no di\na res</w>\nk oo\nk ang\nar kansas</w>\nash ton</w>\nwimble don</w>\nsci fi</w>\nattrac tive</w>\nmississi ppi</w>\nlogi sts</w>\nral ph</w>\nla bel\ngradu ates</w>\nma ha\nhome town</w>\nâľĮ ï¸ı</w>\nfoun ded</w>\non the\nli z</w>\ntrans l\nmini mum</w>\npre sti\nta m</w>\ngener ations</w>\nre bel</w>\njourn alists</w>\npar am\nmc m</w>\nacry lic</w>\ndeath s</w>\ntes la</w>\nw t</w>\nbry ant</w>\njer us\ni stanbul</w>\nmuham mad</w>\nri ley</w>\nk ris</w>\nwork shops</w>\nis o</w>\ncoun ts</w>\nstre t\nprote cted</w>\ntrin ity</w>\nman ual</w>\nr hin\nr il\npleas ant</w>\nle mon\nner d</w>\nhar der</w>\ndar ren</w>\nbur y\nra h\nbas is</w>\nmi gu\nocca sion</w>\nli sts</w>\nâĿ¤ï¸ıâĿ¤ï¸ı âĿ¤ï¸ı</w>\ne b\nde cre\nhamp ton</w>\nìĿ ´\ntra vis</w>\ntrans form</w>\npuer to</w>\nnh l\nav oc\ntri ps</w>\nunexpe cted</w>\nve t\ndi dyou\nbar ber</w>\nst ages</w>\nm son</w>\nre presented</w>\nfor t\nl al\npp le</w>\nnic ely</w>\nignor e</w>\nqu il\nqu inn</w>\nh k</w>\ncarri er</w>\nremin ded</w>\nam ong\npass enger</w>\nel len</w>\ngue z</w>\nsc ape</w>\nmu ral</w>\nyoun gest</w>\nma sh\nd ill\nrout ine</w>\nstain less</w>\njack son\ngand hi</w>\nth al</w>\non ers</w>\nedit orial</w>\nconvers ations</w>\nsd ale</w>\nautom ation</w>\ni ke\nา à¸\nðŁĩ ª</w>\nhau l</w>\nla ying</w>\nmen tions</w>\nam en</w>\nabor tion</w>\ni bi\ncoun ties</w>\nca therine</w>\nman ds</w>\njam e\nroll er</w>\nau t</w>\nn am</w>\no logical</w>\ncep tion</w>\nran king</w>\ntox ic</w>\nsn acks</w>\nvictor ian</w>\nbang kok</w>\npsycho logy</w>\nre g</w>\nang ela</w>\nrespon d</w>\nsty le\nsophi e</w>\ndak ota</w>\nachiev ed</w>\nmar ked</w>\nimper ial</w>\nin as</w>\nglo ves</w>\nsli m</w>\nconfi dent</w>\natt acked</w>\ngg er\nlon ely</w>\nvalentine sday</w>\nre b\ncraft beer</w>\norig in</w>\nzim bab\nce iling</w>\nte ens</w>\nother wise</w>\nw b</w>\nf ers</w>\nday sof\nadvis or</w>\ny ah</w>\nâĻ ª</w>\nen 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ly</w>\nili on</w>\nas i</w>\nleg it</w>\nco pe</w>\nm cla\nrecy cling</w>\nlar ger</w>\nðŁĴ ĵ</w>\npat ric\ngener ous</w>\nja red</w>\np f</w>\nmol ly</w>\nthom as\nju dges</w>\nh b</w>\nsor ts</w>\nbl vd</w>\no ven</w>\nenter ing</w>\nplan es</w>\nbe et\nintegr ation</w>\nboo ked</w>\nfre ed\nver n</w>\nash es</w>\nto pped</w>\nde pot</w>\nwelcom ed</w>\nren a</w>\nm ick</w>\nd and\nsee ks</w>\ngam er\nran kings</w>\nren e</w>\nmu t\nwhis ky</w>\nfire fighters</w>\ngu es</w>\nga ther</w>\ntour ney</w>\nde men\ny ang</w>\nnew ton</w>\nautom otive</w>\nback yard</w>\ndeta iled</w>\nmi st\nto bac\nfi ber</w>\nun usual</w>\ngrat itude</w>\nsp are</w>\nne ys</w>\n: *</w>\nper i\nflo ating</w>\nfin alist</w>\ndon ating</w>\ndre ss\nbro ad</w>\nbe the\neconom ics</w>\ntai wan</w>\ned wards</w>\nplu g</w>\npra iri\nval en\nbab a</w>\nf ad\nan as</w>\nhar per</w>\ndis order</w>\napp lied</w>\np att\nbi kin\nli ver</w>\ncu ri\ncarol ine</w>\nann er</w>\njuli an</w>\nwal king\nmal col\nscreen shot</w>\nco ding</w>\nskin care</w>\nactivi sts</w>\nmyster ious</w>\nex act</w>\nblo cking</w>\nmercur y</w>\nbat ter\ndu mp\nâľ Į</w>\nen se\nli sh\nridic ulous</w>\nprote sters</w>\nðŁĻ Ī\nlu st</w>\nswe at</w>\nas s\nali ke</w>\nco dy</w>\nre ments</w>\nwin ds\nas pir\nvi enna</w>\npra y\n.. .@</w>\nbo i</w>\ncand le</w>\nassi sts</w>\nte e\nder son</w>\np ony</w>\nf ence</w>\ncon spir\nâĺħ âĺħ\noo th</w>\ne pic\nba rely</w>\na unt</w>\nb am</w>\ndiamon ds</w>\nend less</w>\nscre ens</w>\ncan cer\ngr o</w>\np st</w>\npro spec\nmo sque</w>\nhelp ful</w>\nou ri\nbro ther\ngu jar\ncri sti\nine z</w>\nto wers</w>\nad dresses</w>\ngra y\nbur ton</w>\nre tweeted</w>\nðŁ¤ Ķ\nn ity</w>\ndu ck\nsuper vis\njo an</w>\nkin der\nsanc tu\npi ed</w>\nâı °</w>\nł ï¸ı</w>\nm ati\nreven ge</w>\nce ster</w>\neli fe</w>\ndesig ners</w>\nback ed</w>\nbo li\nwei ght\ncou ch</w>\nsu res</w>\ns its</w>\nshri mp</w>\nla gos</w>\nauth orities</w>\nos ity</w>\nhol ly\ncompu ting</w>\nfac tors</w>\nab e</w>\npan els</w>\nram ad\nsent ence</w>\nmissi on\nhol m</w>\nr b\nd ads</w>\nshang hai</w>\nmon ey\nshe ets</w>\nsk ate</w>\nthre w</w>\ncup cakes</w>\ninfin ite</w>\nl is</w>\npractic ing</w>\ness ay</w>\nka i\nas ci\nmo b</w>\nu gh</w>\nhol mes</w>\nre gg\nik h</w>\nmo ck</w>\ncollec tions</w>\npe p\no va</w>\nsal t\nnan dez</w>\nco y\nthre ats</w>\ntex ts</w>\ncin nam\npregn ancy</w>\npen ding</w>\nstam p</w>\nflow er\ng is</w>\nagre ed</w>\npay ne</w>\nro ver</w>\nph ra\nsof t\nf fin\nfa thers</w>\npass engers</w>\naw ays</w>\nal a\nh es</w>\nli van</w>\nin s\nsamu el</w>\ningu i\nh of</w>\nj j</w>\nchen nai</w>\ncat al\nom ic</w>\nhe ath\nni ece</w>\npump ed</w>\nintegr ated</w>\nare l</w>\nno m</w>\nproduc tivity</w>\nwan ting</w>\nvis a</w>\ndi ana</w>\ntw il\nit v</w>\ncam ps</w>\nro wing</w>\nd ley</w>\nblack and\ngu ards</w>\nb ells</w>\nre verse</w>\nvi be</w>\nric ky</w>\nmo ss</w>\nny t</w>\nâĺ Ģï¸ı\nel le\ntro y</w>\ncu dd\nev an\nwomen s\nfo to</w>\nmi stakes</w>\nwick ed</w>\nmi l</w>\nc led</w>\nme mes</w>\nco smo\nschol ar</w>\nren o</w>\nðŁĺ Ģ\nv ents</w>\n# âĢ¦</w>\nterrori sts</w>\nca sey</w>\ncardin als</w>\nðŁĺĬ ðŁĺĬ\nvenezu ela</w>\nbol a</w>\nliter acy</w>\nt w</w>\nen o</w>\ncon tains</w>\nau stin\nfin anci\nev an</w>\nhar vard</w>\norigin ally</w>\nchev ro\nher ald</w>\nnott ingham</w>\nmanag ers</w>\nâŀ ¡</w>\naccep ting</w>\nwal sh</w>\ntutor ial</w>\nentrepreneur ship</w>\nyach t</w>\nrequi rements</w>\nglen n</w>\npe de\nunfortun ately</w>\nach ing</w>\ndais y</w>\ngi an</w>\nnight mare</w>\nâĿ Ĺ\nr ina</w>\nb art</w>\nema ils</w>\noppo site</w>\nwho m</w>\nsa ke</w>\npu zzle</w>\nda shi\npar ty\nblan ket</w>\nbus es</w>\nlo re\nbeau ty\nreas on\npun jab</w>\nwinds or</w>\nfunc tional</w>\nexi sting</w>\nhel lo\ngli mp\ncon vin\nla k\nscre aming</w>\nrebec ca</w>\nbli ss</w>\nnorth west</w>\ninfin ity</w>\ncosme tics</w>\npul ling</w>\ncoffe e\npl ing</w>\nop ho\ncolom bia</w>\ninterior design</w>\n( +</w>\nemo tions</w>\nsa c</w>\nsun glasses</w>\nsav es</w>\nd f\nsix th</w>\nal y</w>\nðŁĺ »</w>\nde en</w>\ndev ast\npolit icians</w>\nlac rosse</w>\ng u</w>\npe i</w>\njav a</w>\ncomb ine</w>\ncoal ition</w>\ner ts</w>\nsurvi v\nch ad</w>\nstri an</w>\nn n</w>\nde vi\ncoun c\nconcer n</w>\ncontro ller</w>\nbre ast\nj ury</w>\ntu m\nintroduc es</w>\nla di\nmobi le\nal z\nste ady</w>\nnur ses</w>\nh acking</w>\non line\noce an\nðŁİ Ħ\na am\nju ven\nic c</w>\nlouisi ana</w>\nar te</w>\nstreet art</w>\nis on\nwn s</w>\nfr m</w>\np anda</w>\nno ir</w>\nmain tain</w>\ndel ay\nsymp toms</w>\nthor n\nge ome\nter n</w>\ncarri ed</w>\np ru\npan or\nas sy</w>\nper u</w>\nclou d\nsp ra\npe di\ne ste\ntag ged</w>\nðŁĺ Ŀ</w>\nshado ws</w>\nnaz i</w>\nا٠Ħ\ncor ri\nâĻ¥ âĻ¥\nj ad\nðŁĩ «\nform al</w>\nspo ken</w>\nðŁĮ ŀ</w>\nenjo y\nlo pez</w>\nout look</w>\nin ho</w>\nw ander\nÙ ħ\nma ya</w>\npe e\nd ine</w>\nãĢ ij</w>\nbrief ing</w>\nsuppor ter</w>\nar ily</w>\nght ers</w>\nnatur ally</w>\ndoctor who</w>\nj en</w>\nv ar</w>\nnew year</w>\nre se\nsi mm\nre x\ncon sequ\ntomat oes</w>\nbur st</w>\nbra vo</w>\nbur gers</w>\ncr acking</w>\nnor theast</w>\nbi om\nmush room</w>\nmar que\ndou ble\nni er</w>\nv ag\ntw enty</w>\nkey board</w>\nwin ni\njama ica</w>\npar ish</w>\n: -\nmental health</w>\nali zing</w>\nren der</w>\nwa king</w>\nðŁİ Ĥ\ng ly\nna than\nwa shing</w>\nmel issa</w>\njun g</w>\nloy al</w>\nchil i</w>\nsong writer</w>\nguit arist</w>\nbo wie</w>\nneighb ors</w>\nonym ous</w>\nas set</w>\nta i</w>\nhead quarters</w>\nðŁĮ Ī</w>\ni hear\nci gare\nsur g\n) \"</w>\nre pl\ndar ling</w>\nðŁĻ Ħ</w>\nz ak\nsa re\nãħ ĭ\nmic key</w>\nware house</w>\nmass age</w>\nine es</w>\ndid nt</w>\ni w\nhur ts</w>\neng aging</w>\nmag ic\nwomen in\nk itten</w>\nmor s</w>\nc art</w>\ntit ans</w>\ncolle ague</w>\ncompe ting</w>\ner an</w>\nk hal\nmar ble</w>\ndem and\ndel ight</w>\net ary</w>\nbli zz\nlou ise</w>\nm ls</w>\nfini shes</w>\nexperim ent</w>\nconduc ted</w>\nelectr onics</w>\nitt ers</w>\ncar ing</w>\nwh ats</w>\nsym bol</w>\njun g\ne cu\npi x</w>\ncon text</w>\nchar ger</w>\nðŁĺ ĩ</w>\nre ig\nfra g\në ĭ\nch ad\ntru e\nker ry</w>\ndef ending</w>\na int</w>\nau ton\ncheck out</w>\nbar nes</w>\nless ly</w>\nd t\nm me</w>\nclou dy</w>\nsecond ary</w>\nare z</w>\n_ :</w>\napp a</w>\nconst ant</w>\n\" )</w>\nve ts</w>\njo b\ni ent</w>\nðŁĺŃðŁĺŃ ðŁĺŃ</w>\nm j\nfren ch\ndi ver\ndavi es</w>\nhh hh</w>\ne book</w>\n๠ī</w>\nmar iti\nbree ze</w>\nsusp ended</w>\nmat o\nvi et</w>\nra hu\nse i\nbol t</w>\nen ary</w>\nle is\nkar l</w>\nfr amed</w>\nexpla ining</w>\nab c\nde aling</w>\nnat o</w>\nja ke\nexp and</w>\nleon ard</w>\nestabli shed</w>\ndu b</w>\nar men\nel led</w>\nvoc al</w>\nnichol as</w>\nori ent\nk yo\nillustr ated</w>\nah h</w>\ndanc ers</w>\nmilli on\nge ta\npo pp\nas u\nmur dered</w>\ngi ble</w>\nsto ked</w>\ngri ffin</w>\nmaxi mum</w>\nadri an</w>\nen counter</w>\nther o\ndavid son</w>\nðŁį »</w>\nholi day\nev o</w>\nasse ts</w>\ncar son</w>\nmemor able</w>\nâļ ½</w>\nob am\nrepresent ative</w>\ncb d</w>\ntr icks</w>\nvo gue</w>\nvo ice\nmm mm</w>\nsebasti an</w>\ncli f\nath y</w>\npar alle\nðŁ¤ ·\npa k\nev acu\ne ats</w>\nا Ø\ntou ched</w>\norgan ised</w>\nspir its</w>\ncan ad\ngui ded</w>\nframe work</w>\nðŁĮ Ł\npe d\nnatur al\nag ar\nreplac ed</w>\nanch or</w>\nti t</w>\nsha h\norgan is\nsuper ior</w>\nr n\nch ro\neric a</w>\nst ill\ncor on\nchu ck\nloc ks</w>\nor gan</w>\nro sen\nsc am</w>\nben ed\n/ #</w>\nke en</w>\ntre vor</w>\nvamp ire</w>\nsor ted</w>\n! '</w>\naf ford</w>\nin tro</w>\ngr ace\nðŁĺ ľ\nsau r</w>\nkick starter</w>\ninflu en\nv u</w>\ny up</w>\npo c\nðŁİ ¥</w>\na ar</w>\ns ang\ntre k\net sy\ntb h</w>\nscre am</w>\nchevro let</w>\npix el</w>\nshepher d</w>\nan or\ngabri el</w>\ntw ood</w>\nsd cc</w>\nme ters</w>\ndevelop ers</w>\nclo sure</w>\nv w</w>\ntwit ch\nì Ĺ\nse oul</w>\npr ice\nho g</w>\nn ish</w>\nhill ary\nscrat ch</w>\nin cen\nwag on</w>\ndis ability</w>\npan ther</w>\nch ats</w>\ng d\nwit z</w>\nsus sex</w>\nl ate\nden mark</w>\nger ald</w>\ncancel led</w>\nnet te</w>\ni x\nnav al</w>\nbap tist</w>\nte t</w>\ny ad\nma th\nho y</w>\nr andy</w>\npo int\nintel lec\nfru its</w>\nw ool</w>\ngu in\npr on\nthe ft</w>\ncon dem\nmar ry</w>\nn ola</w>\narchitec ts</w>\ncin cin\nroc kets</w>\ngentle man</w>\nex plan\nt ate</w>\ndo e</w>\nra ises</w>\nwild life\nw l\ninsi der</w>\nblan c</w>\nw p</w>\nfor sale</w>\nny c\npo well</w>\nunbeliev able</w>\npen s\ngoo dies</w>\nmu stang</w>\np ens</w>\nst ays</w>\nsqu ash</w>\nxox o</w>\nnear by</w>\never ton</w>\nco co</w>\nle agu\nk han\nstu d</w>\nsouth west</w>\ncon struc\ns worth</w>\ncro atia</w>\nle a</w>\nsu ms</w>\naim s</w>\ne an</w>\nvan ess\niti ous</w>\npa thy</w>\narc ade</w>\nb end</w>\nsugge sts</w>\nsac ram\nroy als</w>\nri er</w>\nem ir\nin cl</w>\nan k\nclar k\nri ght\nvac c\nठ¾</w>\ntan e\nli b</w>\nu sc\nsal es\nhu h</w>\ns ally</w>\nver a</w>\np ga</w>\ngro ws</w>\ndru m\ntre e\neth ics</w>\nsug gest</w>\nis ab\nse aled</w>\npre viously</w>\nanim ated</w>\nab du\nri ses</w>\nglo b\npre dat\nscar f</w>\ndel ic\nom ar</w>\nll i</w>\nsx sw</w>\npy thon</w>\nne bra\nfun k</w>\nreflec t</w>\npav ilion</w>\ntic ally</w>\nch asing</w>\nbak ery</w>\ninva sion</w>\nko h\nbeliev ed</w>\nco hen</w>\ncon qu\ncra fts</w>\nnat i</w>\ncle ver</w>\ngovern ance</w>\nsam ples</w>\nfa ils</w>\nâ Ķ\nti mo\nr itu\nstri king</w>\ninclu sive</w>\nsho cking</w>\ncan t\nrequi res</w>\ndra wings</w>\nภŃ\npurch ased</w>\ndu m\nz ach\nwar ner</w>\ncon sole</w>\nman sion</w>\nfoun tain</w>\ncircu m\ne sh</w>\nis land\nmil k\npro fits</w>\nhali fax</w>\nri val\nâľĪ ï¸ı</w>\njen ny</w>\nsand ra</w>\nny e</w>\nk elly\ny al</w>\nqu ad</w>\nno s</w>\ninste in</w>\nfin alists</w>\nmid fielder</w>\ncu e</w>\nexcep tional</w>\na an</w>\nsa pp\ngett in</w>\nsa a</w>\nf ati\nsl ice</w>\nvol k\ns wal\nla sting</w>\nsum mary</w>\nit as</w>\nsm o</w>\ns z\nâĺ Ĩ</w>\nip l</w>\nfl ames</w>\nene ws</w>\nha v\nhoo die</w>\npitch er</w>\nwin dy</w>\nre vol\ncentr al\nton ite</w>\nðŁİī ðŁİī</w>\nsol ved</w>\nmil wau\norganiz ations</w>\nwee ts</w>\nre fin\ns th\nãĥ ¼\nel in</w>\nton a</w>\ncinnam on</w>\nðŁİ ¨</w>\nðŁİ ģ</w>\nron aldo</w>\npen insu\nome ga</w>\nel ds</w>\ndesig ning</w>\ne igh\nblu et\nben z</w>\nnu g\nash a</w>\nrobo ts</w>\nsu dan</w>\nchoo sing</w>\nen do\nser ge\nclo sely</w>\nhand y</w>\nfing er\nbe ing\nar te\nsurvi ved</w>\nfl ame</w>\nmile stone</w>\ngu t</w>\nd war\nfu tures</w>\né e</w>\nel o</w>\nfri dge</w>\neli c</w>\nou ch</w>\nu b</w>\np v</w>\ntit an\ncol lar</w>\nst ation\nnev ada</w>\naur ora</w>\nr d\ndun can</w>\nâģ ł</w>\nbri en</w>\nmar sh</w>\nÐ ¾\nto tal\nch ry\ns ers</w>\nsu ffe\nra chel\ncolle ge\nto days</w>\ncour ts</w>\nch it\nre united</w>\ngym na\ngen esis</w>\nbe side</w>\nre presentation</w>\nch ant</w>\ncollec tor</w>\nra k\nath ens</w>\nni gh\nmun ich</w>\nlangu ages</w>\nfl u</w>\nparticip ation</w>\n__ _</w>\nc v\nspec trum</w>\nso da</w>\nco ver\nrefe ren\nab bo\nap a</w>\npublic ation</w>\ned m</w>\nmon ica</w>\nar my\nðŁļ Ģ</w>\ndiv or\ndr y\nstre ams</w>\nrobo tics</w>\nci der</w>\nbull ying</w>\nappro val</w>\nsto ke</w>\nplat forms</w>\nsier ra</w>\nex tin\ni b</w>\nha yes</w>\nsucce ed</w>\nsuff er</w>\nat ically</w>\nda i\nlyn ch</w>\nh ound</w>\ndel ines</w>\nack now\nd ated</w>\nexclu sively</w>\nhe res</w>\nfac ilit\ndam aged</w>\nchar ter</w>\nla kers</w>\nfal con</w>\nunve iled</w>\nwel ove\ne ase</w>\npati ence</w>\nl one</w>\ngent le</w>\ngene tic</w>\nproduc ing</w>\ng our\nshann on</w>\nbil ities</w>\nzimbab we</w>\np int</w>\ndau ghters</w>\nliter ary</w>\nbel le\ncl am\nsurroun ded</w>\nk any\nne il\npir ate</w>\nrang er</w>\nhb d</w>\nnat alie</w>\nbel ong</w>\nolym pi\nemb assy</w>\nsc ol\nen er</w>\nak in</w>\nlo ren\nb h</w>\n: /</w>\ndi va</w>\nden im</w>\nhi pp\nðŁĩµ ðŁĩ\narn old</w>\n? '</w>\nwe ren</w>\nem power\ndis abled</w>\nman or</w>\nrasp berry</w>\nb af\naw ful</w>\ndru mmer</w>\nkar dashi\nn ash</w>\nmachine learning</w>\nch u</w>\nrebel s</w>\ntim ing</w>\nmon roe</w>\nton gue</w>\nran ge\npup ils</w>\nre ss</w>\namaz on\nb z</w>\nhar ley</w>\npal mer</w>\nballo on</w>\ns ings</w>\nic ec\nj b</w>\nc ers</w>\ng ps</w>\nwhi st\nri se\nl t\noo oo</w>\nc attle</w>\nshoo ter</w>\nvod ka</w>\nuc l</w>\nmt g</w>\nle sli\njon as</w>\ndi spo\nat ric</w>\nste in\nvintag e\nfir ms</w>\nflo yd</w>\ncow boy</w>\nsoo oo</w>\nis aac</w>\nwar craft</w>\ndisney land</w>\nbeauti ful\nbe am</w>\nfranch ise</w>\nbu n</w>\nk ag\nan on</w>\ntur bo</w>\nswee p</w>\nmade in\nkar achi</w>\ndete ctive</w>\npenn sylvania</w>\ncontro versi\nvitam in</w>\na side</w>\nchron ic</w>\ndescri bes</w>\nremo val</w>\nha h</w>\nap er\nten ed</w>\nu to</w>\nbad ly</w>\nmir ac\nf ry</w>\nye a</w>\nin jec\nther mal</w>\ncomp act</w>\nth or</w>\nte ed</w>\nur gent</w>\nl ite</w>\ng illi\nsop hom\nic o\nche m</w>\np m\nfor k</w>\nfre ak</w>\nch ak\nrecipi ent</w>\ni y\nni k</w>\nmodel ing</w>\nc ans</w>\nðŁı Ģ\ndel ux\nse am\nsurviv ors</w>\nrad ical</w>\ninvestig ating</w>\nreli able</w>\nf m\ntur t\nligh thouse</w>\nto ol\ngo wn</w>\n) )\nbo ts</w>\nauto graph</w>\na id\nbu ffe\nh mm</w>\nhorri ble</w>\nssi onal</w>\nann i</w>\n๠Ģ\nk its</w>\nsch i\neter nal</w>\nhu ss\nsens itive</w>\nr u</w>\ntast es</w>\nchec ks</w>\nim o</w>\npor tion</w>\nsk ate\ne den</w>\nhalf time</w>\nfri ed\nri hanna</w>\nti se</w>\nfl ick\nca in</w>\ns gt</w>\nâľ Ķ</w>\nsh au\nsta ined</w>\nra ffle</w>\ndro ve</w>\nsal man\nprinci ples</w>\nsh o</w>\nar u\nje ss</w>\ngu ine\ngar bage</w>\nmy an\njel ly</w>\ndis ru\nz ia</w>\nq ld</w>\nent ries</w>\nla v\nfle w</w>\nad mit</w>\nobjec ts</w>\ncomp are</w>\nny times</w>\ncann es</w>\np n</w>\nsuff ol\nro c</w>\nd ana</w>\ne gg\nhi st</w>\ncoun sel\n' !</w>\nphy si\nimag ination</w>\nad just\nexplo sion</w>\nplym outh</w>\nhor ror\nelli ott</w>\nbour ne\nde x</w>\nbre ed</w>\nau dio\nlob ster</w>\ndisappo inted</w>\nnation wide</w>\n( (</w>\nincre ases</w>\naustr ali\nce dar</w>\nstar ing</w>\nrac ial</w>\ne is\ng mt</w>\nvisi ons</w>\nstay ed</w>\ndiscu ssions</w>\nde an\ncur tis</w>\nmai den</w>\nstel lar</w>\nhapp iest</w>\nh wy</w>\npre season</w>\ncar av\nmon days</w>\nhospit als</w>\nglimp se</w>\nschol ars</w>\nja i</w>\nter race</w>\nann a\ngoo se</w>\ngra ded</w>\nlot us</w>\nhun g</w>\ngrocer y</w>\nstam ps</w>\nemper or</w>\nsc oop</w>\nin ser\nc as</w>\nexist ence</w>\nhe al</w>\nfal cons</w>\nmar vel\nreduc ing</w>\nterri fic</w>\nmagne tic</w>\nperfor ms</w>\nbar re\np us</w>\ntre ating</w>\nic on\nw h</w>\ndecla red</w>\ntra uma</w>\ndo d\ncome dian</w>\nnik on</w>\nbu gs</w>\nas m</w>\nmont gom\nibi za</w>\ncomprehen sive</w>\nha s\nsan ti\nfellow ship</w>\nda sh\np sal\nlouis ville</w>\nsp y\nfau lt</w>\nd the\nfi led</w>\nvi sta</w>\nde sc\nfe ars</w>\nyou tu\nsp s</w>\nes p</w>\nri g</w>\ncri me\nber ger</w>\nwonder land</w>\nk ent\nin formed</w>\nstev ens</w>\nmy th</w>\nast on</w>\nir i</w>\nvisit or</w>\nat ri\nproduc ers</w>\nal la\nperson ally</w>\nsepar ate</w>\nagen cies</w>\naf ri\nil an\nspo ke\nn ina</w>\nsqu ad\ndi ves</w>\nde pend\nli v\nfier ce</w>\nenter taining</w>\ncha in\nsc at\nbor ders</w>\npal ette</w>\nsp ro\nos is</w>\nder by\ntobac co</w>\nzi o</w>\nwilli e</w>\nju vent\nzoo m</w>\nhol y\nenti rely</w>\naf e</w>\nmart inez</w>\nbe ds</w>\npe a</w>\nbull dogs</w>\nðŁĩª ðŁĩ\nib m</w>\nne on</w>\nethiop ia</w>\nteam mates</w>\nplan ting</w>\ntw er\nany time</w>\nfor bes</w>\nó n</w>\nrun way</w>\nner vous</w>\nro ger\np ile</w>\nch anc\napo caly\nu w\no i</w>\ndr ought</w>\nterrit ory</w>\nbr ick\ncre atures</w>\ngo in</w>\nw aff\ngre n\nsou theast</w>\nje an\nam bul\ned ited</w>\nstra p</w>\nc v</w>\naar on\nãĥ» ãĥ»\nt su\ndescri ption</w>\nkin dly</w>\nclu tch</w>\nim mer\nen or\nwomen sday</w>\nor ange\nra g\nob vious</w>\nhy der\nchann els</w>\nman go</w>\nme yer</w>\nra ining</w>\nge tty</w>\npil gri\ncoordin ator</w>\nup load</w>\nninten do\ndon uts</w>\nsan chez</w>\napp arel</w>\nj r\nzz i</w>\n, @</w>\njeff erson</w>\naccessi ble</w>\ngreat ly</w>\ne id</w>\niniti al</w>\nbudd ha</w>\npar is\nma scot</w>\nâ¬ĩ ï¸ı</w>\nsch war\nsi ri\nsp inning</w>\nmortg age</w>\ne cho</w>\nend ange\nge dly</w>\nchlo e</w>\nenh ance</w>\nkar nat\nk ry\nexplo res</w>\nðŁĴ ģ\naf fair</w>\nic als</w>\nall a</w>\ndar t\ndolph ins</w>\ndiffe rences</w>\nsquir rel</w>\nau gh</w>\ndr ones</w>\nell en\nre store</w>\npa w\nun for\npi ke</w>\nhil ton</w>\ncolla b</w>\nconsu mers</w>\nco inci\nout comes</w>\npp p</w>\na q\ncoup on</w>\nli est</w>\nsi ms</w>\nk ho\nav es</w>\nspo on</w>\npu dding</w>\ncor byn</w>\nhat ers</w>\nex ams</w>\nsla ve</w>\n. !</w>\np sa</w>\napp les</w>\ntam il</w>\nse d\nco ke</w>\nzz o</w>\nlo sange\ncar bon\ncla ir</w>\n... )</w>\nk hu\ncra ig\nexplor ation</w>\nsanctu ary</w>\nsu e\nal way\ndemen tia</w>\nwon ders</w>\nsuper hero</w>\npakistan i</w>\nbrown s</w>\nbluet ooth</w>\nlo cker</w>\nmar c\nev entu\ndelux e</w>\nrodri guez</w>\nâĿ¤ âĿ¤</w>\nro bb\nðŁĴ ¦</w>\nlin ux</w>\nten s</w>\nintellig ent</w>\nse ed\nvo ter</w>\ns ler</w>\npe aks</w>\ninter n</w>\nteen age</w>\npeninsu la</w>\nhand ling</w>\nti e\ncou sins</w>\nwen dy</w>\nme e</w>\nà¹Ģ à¸\ndin o</w>\nðŁĴ °</w>\nðŁĺ ĥ\nze e</w>\ns bury</w>\ntrage dy</w>\nb k</w>\nbo re\nz in\nwar ns</w>\nidi ot</w>\ntou ching</w>\ncontin ental</w>\ntac os</w>\nsaf ari</w>\nwa shed</w>\npo dium</w>\nmorri son</w>\nfore sts</w>\nc bc\nal on\npartic ular</w>\nbe ads</w>\ninv ented</w>\nlo ch</w>\nli ghter</w>\nwhere ver</w>\ni de</w>\ndocu ments</w>\na we</w>\nk r</w>\nno where</w>\nmin er\nst it\nro x\ncontribu te</w>\nhar dy</w>\ncl an</w>\nob ject</w>\nca it\nðŁĴķ ðŁĴķ</w>\nhapp ier</w>\nvege tables</w>\nt art</w>\ng ag\nnom inee</w>\nheav ily</w>\npan ic</w>\nj d</w>\nthere sa</w>\nat m</w>\nu ph\ns fc</w>\nsu ri\ndrin k\nn al\nre vel\nk l</w>\navoc ado</w>\nnom ination</w>\nma donna</w>\nshar on</w>\nmalcol m</w>\ncontrol led</w>\nsh ers</w>\nrevi val</w>\nlegis lation</w>\nshoo ts</w>\nn in</w>\ncomm entary</w>\npro s</w>\nhuman rights</w>\nstr anger</w>\nmit ch</w>\npipel ine</w>\nleg ally</w>\nth u</w>\ngil bert</w>\ntol l</w>\ngran ted</w>\ngh s</w>\nir anian</w>\nrefre shing</w>\ndu k</w>\nab i</w>\npri me\njose ph\nmo sa\nstati stics</w>\nproduc tions</w>\nmer ry\npat el</w>\nsa x\nhuman itarian</w>\nstruc tures</w>\ne missions</w>\ntown s</w>\nfre el\nster ing</w>\nrat ings</w>\nalle gedly</w>\ncab in</w>\nst l\nw ade</w>\nfl yers</w>\ntri m</w>\npromis ing</w>\nz u</w>\nbal lot</w>\ncompar ison</w>\nfree ze</w>\nou ter</w>\ngreat ness</w>\nas sign\nsnow y</w>\nr ale\ntor ies</w>\nmed iter\nkno ck\nconsult ant</w>\ncincin nati</w>\nanaly st</w>\nsc oo\nje ws</w>\nappro xim\npu re\nportra its</w>\ncy rus</w>\nation al\nlo ans</w>\nacqu is\nel u\naccep table</w>\nuni on\nwater color</w>\nru st</w>\nbatt les</w>\nper fu\nseas onal</w>\nser ial</w>\nmind set</w>\nri ot</w>\nfel d</w>\nenni al</w>\nclo set</w>\npri est</w>\ntan ks</w>\nint l</w>\nscre w</w>\nbu m</w>\nab dul\nou x</w>\nexpla ined</w>\nric a</w>\nimag ing</w>\nlaw yers</w>\nbu ried</w>\nãĥ»ãĥ» ãĥ»</w>\near l</w>\nâĢ ķ</w>\nl ton</w>\nresto red</w>\nstri pes</w>\nfo ss\nde mands</w>\nste aling</w>\nalex is</w>\nmun d</w>\nak er\nur us</w>\nwar dro\nhu gs</w>\ngen re</w>\ne go</w>\nÙ Ħ\nparticip ated</w>\nbab es</w>\nban quet</w>\nti ous</w>\nhe mi\nds b</w>\nlo st\nmilwau kee</w>\njen ner</w>\nge m\nou tra\nlo ses</w>\nid i</w>\nre ps</w>\nðŁİ §</w>\nregu lation</w>\nfla w\nf ang\nvibr ant</w>\nram p</w>\nra ins</w>\nwell being</w>\nso viet</w>\nvie wers</w>\nde po\nlibr aries</w>\nbi go\nser y</w>\ng ill\nde struction</w>\nco z</w>\nc x</w>\nbri dal</w>\nal ds</w>\nplan ted</w>\namate ur</w>\nlu d\nche ering</w>\nshow cas\npro file\ni u\nver tical</w>\npack ers</w>\nwiz ard</w>\nski p</w>\ns light</w>\nbe au</w>\nair ways</w>\nmu ch\nre ra</w>\nðŁĮ Ĭ</w>\nab sor\npati o</w>\npack ages</w>\ns ells</w>\nment ally</w>\nðŁĺ ¢\nreyn olds</w>\nk are\ntri bun\nwal t</w>\nkn it</w>\nta ste\nsur rey</w>\nboun ce</w>\ncre ature</w>\nb are</w>\nbet ting</w>\nsu re\nmi ley</w>\nlaugh s</w>\nal ore</w>\ncy n\nt l\narti st\nann ah</w>\nwar mer</w>\ndynam ics</w>\nlunch time</w>\nmariti me</w>\nvulner able</w>\nðŁĴ ĥ</w>\nwol ver\ndur ham</w>\nconst antly</w>\nam in\nsi bl\n: @</w>\nbul let\nk ach\nangel o</w>\nwil der\ndoo m</w>\ndesk top</w>\nlaw suit</w>\nk ca</w>\nhen derson</w>\ninv iting</w>\nbet ty</w>\nta wards</w>\nra fa\nle aked</w>\nand i</w>\nge ms</w>\naf l</w>\nvel o\nmediter ran\npro be</w>\nto tten\nsteph anie</w>\nsn ation</w>\ncom be</w>\nq s</w>\nover come</w>\nassas sin\nra v\nfil ip\nwinni peg</w>\nsh il\ndetermin ed</w>\nk as</w>\nou tre\nregre t</w>\ngui des</w>\naa a\nðŁĺ Ī\nwi ves</w>\nmani fe\ner ly</w>\nsm y\nsh ima</w>\nx ing</w>\npix el\njac ob\nac commod\nto y\non o</w>\npo o</w>\nti er\nan swe\nðŁĴ ģ</w>\nro sa</w>\nle ase</w>\nbel ongs</w>\nth ar\neventu ally</w>\nnei ther</w>\ngo a</w>\nski ing</w>\nat ra</w>\nag h</w>\nbroad casting</w>\nf ury</w>\npy ram\nd ice</w>\nvolk swag\nwom ens</w>\nprovi der</w>\nbom bs</w>\nmiss ile</w>\nwhi p</w>\nd ick\nnor we\nback up</w>\nel der</w>\nmat ure</w>\nconcer ts</w>\ngi ous</w>\nsque e\ngood morning</w>\nbra ves</w>\n^ _\nau ssie</w>\nlun a</w>\nmal es</w>\nhe ck</w>\nfor tn\nrome o</w>\nsteel ers</w>\np n\npe er</w>\nre presents</w>\n «</w>\nkat y</w>\nmigu el</w>\nrequ ire</w>\ncha ins</w>\nl ur\nimmedi ate</w>\nti mber\nâĸ¶ ï¸ı</w>\nadvoc acy</w>\nex port</w>\nan z\ntiff any</w>\nauth or\nðŁİ Ī</w>\ndu des</w>\nchil ly</w>\nhi d</w>\nhar m</w>\nbu g\nmon ster\nterri er</w>\ntu c\nstory telling</w>\nta k</w>\nin ti\nimmigr ants</w>\nb is</w>\nreach es</w>\ncom passion</w>\njohn ny\ncontribu tions</w>\nðŁIJ ¶\nmechan ical</w>\nimpre ssion</w>\nran ks</w>\nko be</w>\nmen ting</w>\nbloss om</w>\npab lo</w>\nbuil 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Ĺ</w>\nne o\nalu min\nweek ends</w>\nnebra ska</w>\nco des</w>\ndelay ed</w>\nbrun o</w>\npro ven</w>\nin c\ni ght\nfl an\nor o</w>\nlam bert</w>\nregu lat\nw f\nmassach use\nkardashi an</w>\nbern ard</w>\nfi esta</w>\nvolcan o</w>\ngrand pa</w>\nanc a</w>\nd re</w>\nst itu\nmean ing\nfo am</w>\nau ck\nat ed\nr l</w>\nhot el\npers ons</w>\ndy nasty</w>\nell or</w>\nma i</w>\nam ne\nsty ling</w>\navi er</w>\ne g</w>\nvege tarian</w>\n, âĢ¦</w>\nfoun ders</w>\nsta in</w>\ng d</w>\ncy cles</w>\nsky line</w>\ntrac tor</w>\nexi sts</w>\ntra l</w>\nkid ney</w>\nmar il\ninst ag\nse tte</w>\naddic t</w>\ntri angle</w>\nflash back\ncontroversi al</w>\nz on</w>\np ins</w>\ni as</w>\ntr ay</w>\ntown ship</w>\ndeleg ates</w>\nsp am</w>\nh ms</w>\ncr ane</w>\npeop les</w>\no lo\nfac tion</w>\nbut es</w>\non ica</w>\ndeleg ation</w>\nnew profile\neli er</w>\nmc a</w>\nw and\ng ely</w>\nlosange les</w>\nber ke\nti ve\ndis rup\nzz a</w>\ncas a</w>\njor dan\nford shire</w>\nga thered</w>\nic 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aka</w>\ntit an</w>\nwh ar\njer seys</w>\nre fur\nheav en\ngri p</w>\npan ama</w>\npre li\nglu ten</w>\no dd\ncont ent\npon ti\ntion ing</w>\ne commerce</w>\nfeder ation</w>\nflaw less</w>\nge ar\nti res</w>\nby r\npol ice\ncu ban</w>\ntri butes</w>\ntic ul\nchur ches</w>\nnur sery</w>\ndi aries</w>\nmuse ums</w>\nsnapp ed</w>\ni van\nwi ght</w>\ntouri sts</w>\nramad an</w>\nt rent</w>\nprophe t</w>\nwon dered</w>\nfocu sing</w>\nhi d\nic ons</w>\ni q\nambul ance</w>\npi st\nfun niest</w>\ntime less</w>\nsr ilan\nbu ys</w>\nki ds\ncolour ful</w>\na shi\nch ir\nmu m\nðŁĵ ļ</w>\nlet ter\nx en\nreut ers</w>\npre serve</w>\nin ting</w>\nste p\nfu ji\nuni ver\ni u</w>\nshow down</w>\npo ems</w>\nsurveill ance</w>\nsuspec ted</w>\nta e</w>\nsol ving</w>\ntom b</w>\nmother sday</w>\ncar pen\nrecru it</w>\npil ots</w>\nbro c\nmix ing</w>\nfri days</w>\nty r\nrepresent atives</w>\ntra pped</w>\nabdu l</w>\nfree style</w>\nclu ster</w>\nâļ łï¸ı</w>\nk d</w>\nsk ill\npit t</w>\nex o\ncommer 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music</w>\ni van</w>\nðŁİ ¤</w>\nle u\npatri ot</w>\nman it\nlan ca\nhome decor</w>\nde ar\nsig ma</w>\nti de\nstr ings</w>\nv ita</w>\nsequ el</w>\ntry na</w>\ninve stigate</w>\nbor is</w>\nve gan\nbarri er</w>\nmind fulness</w>\nweb b</w>\nhu stle</w>\nin da</w>\ntan zania</w>\nstr ay</w>\ntex as\nc ag\ndiagno sis</w>\nwom an\ng w</w>\nob session</w>\nl ative</w>\nnu fc</w>\nfl ynn</w>\nmoment um</w>\nsof a</w>\nwal d</w>\nvege table</w>\ntu cker</w>\nsupp er</w>\nse ab\nar ro\nse ag\nven ting</w>\ncounc ill\nsp lat\ncal cul\n.. #</w>\ncom fy</w>\nodi sha</w>\nsto pp\nwar fare</w>\nca es\nà ¨\nco y</w>\nprice less</w>\nin sec\nðŁĺ Ľ</w>\ncontro ls</w>\nempower ment</w>\ndatasci ence</w>\nper pe\ngen ic</w>\ne res</w>\ntru deau</w>\nman o\nsla very</w>\nexpand ing</w>\nma he\nfa iling</w>\ns aga</w>\nphotograph s</w>\ncre st</w>\nre on</w>\nsurf ing</w>\nhi e</w>\nðŁį Ģ</w>\nja e</w>\nfel lows</w>\nsouth ampton</w>\nsol om\nce ster\ntab ility</w>\nhor n\nse ct</w>\nhe e</w>\ncole man</w>\nat las</w>\nexplo rer</w>\nconsul tation</w>\ncopy right</w>\norgani zing</w>\nden ied</w>\nmon keys</w>\nnoo dles</w>\nbr is</w>\nfl or\ndou gh\nbon ds</w>\nsho cked</w>\neco system</w>\ncare fully</w>\nw m</w>\napart ments</w>\ncur ve</w>\nsan diego</w>\nmust ard</w>\ncomm en\ncere mon\ne ch\nru th\nðŁĻĮ ðŁı»</w>\nhawa i\nfil med</w>\nte ar\nas ingly</w>\nca ir\nwat t</w>\ninstru ment</w>\nou tta</w>\nye ol</w>\nriver side</w>\në °\n. :</w>\nnor wich</w>\nalo g</w>\nmigr ants</w>\nnew man</w>\nri de\nspr ink\ntarge ting</w>\nbeli eve\ntor ch</w>\nreflec ts</w>\nper mission</w>\nff man</w>\nene mies</w>\nbas ics</w>\nse ized</w>\nsun days</w>\nle i\nhass an</w>\nen do</w>\nh c\nst ad\nle ments</w>\nkk kk\nnan o\nshar k\nman a</w>\non ic\ntreat ments</w>\near ly\ncollabor ative</w>\nshu ttle</w>\nbran ches</w>\nmis ses</w>\nmained cm</w>\nap ers</w>\nky le\ncarri e</w>\nleis ure</w>\nsh et\nbir ding</w>\nadv ances</w>\nðŁĵ Ŀ</w>\npopu lar\ndi ane</w>\na be\nre war\nneigh bour\nk pop</w>\nremem brance</w>\nplay ground</w>\nru b\nkrish na</w>\ne bola</w>\ninqu iry</w>\nep a</w>\nlu min\norgan isation</w>\nabra ham</w>\nnorm ally</w>\npre ten\njan et</w>\nw t\nðŁĴ İ</w>\nencoura ging</w>\na stic</w>\nbu mp</w>\nsyd ney\ns z</w>\nss ss</w>\ngar rett</w>\nðŁĵ »</w>\nconsul ting</w>\nroman ia</w>\nspo tting</w>\nchanc ellor</w>\nar ma\npresti gious</w>\nðĿ IJ\nt ad\ncry st\ncompe tit\nrati o</w>\ncat aly\nbro w</w>\nj ur\nvi king</w>\ncommu te</w>\ny day</w>\nla yers</w>\ndu mb\nesc al\ngenoci de</w>\nf ill\ngu pta</w>\nste pping</w>\nse i</w>\nfo to\nwild cats</w>\ncol i</w>\nprojec t\near nings</w>\nst r</w>\nge ons</w>\ncomple tion</w>\nb m</w>\ndecor ated</w>\ncraw ford</w>\naf ghan</w>\nsc are</w>\nvisi bility</w>\nhi b\ndirec tion\nstro ll</w>\nchrist ina</w>\nalter nate</w>\ncl are</w>\nsty list</w>\nbe hold</w>\ns ance</w>\nleop ard</w>\nacqui red</w>\nnarr ative</w>\nash i</w>\nthe a\n?? ??\npe as</w>\nat ch</w>\nsli des</w>\nle en</w>\nrenew able</w>\neng lish\nqu ir\nco aster</w>\nr x</w>\nfo ols</w>\nmatch day</w>\nmis m</w>\namaz ing\nz ig\nke ting</w>\nwon t</w>\nto wel</w>\ndi ab\nsta ke\nn m\nmel t</w>\ne than</w>\ngra pe</w>\npolit ician</w>\nsm en</w>\ní ĺ\nre o\nwedd ings</w>\ncat cher</w>\nor acle</w>\nme mo\nðŁĮ ´</w>\nec k</w>\nrob bie</w>\nnorwe gian</w>\noper ator</w>\nam or</w>\nse wing</w>\nju l</w>\nx ie</w>\nu v</w>\nfif ty</w>\nme ga\ntatt oo\nliber als</w>\nu pri\ntraffic king</w>\nrichard son</w>\nsu v</w>\nki p</w>\nmess y</w>\ntremend ous</w>\ngl ou\ncour tney</w>\nla d\nstere o\nmy ers</w>\ni dio\n^_ ^</w>\nman ning</w>\ndy e</w>\nw d\nthr one</w>\njun k</w>\nas u</w>\nprovin cial</w>\nk ook</w>\nwr c</w>\nfine art</w>\nhamp shire</w>\nrenais sance</w>\nb red</w>\nfall out</w>\ns j</w>\nsn l</w>\nal am</w>\ntor ture</w>\nfy i</w>\nsh ines</w>\npa w</w>\nch ar</w>\nhen ry\nc row</w>\naci ous</w>\ndi an\npa ige</w>\nba re\nstock holm</w>\nscen ery</w>\nðŁĩ ·\njef frey</w>\npu sh\ndecor ation</w>\nne d\ncu te\nbrig ade</w>\nlaven der</w>\ninv ites</w>\ne sports</w>\nvo ir</w>\ndri ed</w>\ntran spl\nsur geon</w>\nno vels</w>\npul ls</w>\nson y\nlun ar</w>\nman e</w>\ni vy</w>\nfru str\ndor set</w>\nsa i\ntor res</w>\nssi on\nshut down</w>\nsuggesti ons</w>\nwrit ing\ne o\nbattle field</w>\nu ga</w>\nðŁIJ ¾\nvac u\nspl ac\ng it\nu g</w>\nhigh land</w>\n% )</w>\nmer maid</w>\nsacram ento</w>\nta ils</w>\np w</w>\nka h\nt ell\nenh anced</w>\nì ķ\nauck land</w>\ncru el\nðŁ¤ ©</w>\nau dre\nsail or</w>\ngram mar</w>\ng love</w>\nde on</w>\ninfl am\nfresh ly</w>\nk ell\nzi p</w>\nchristi e</w>\nmil d</w>\ndi xon</w>\ninstru ctor</w>\ng ence</w>\nãħ ł\nsub jec\nconstitu tional</w>\ncrow ds</w>\nin visible</w>\nru ins</w>\nda k</w>\nsi p</w>\npla que</w>\np ouring</w>\ncomple x\nz ine</w>\nste ad\nf let\ntrans mission</w>\nlo way</w>\nar un\nincre asingly</w>\nau d\ntransp aren\ncro wned</w>\nsc oun\nblizz ard</w>\nlux u\nfi ers</w>\nachieve ments</w>\nhun ters</w>\nrock ed</w>\nbas 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order</w>\nlu x</w>\ncu cu\nðŁijı ðŁijı</w>\nt art\nâĿ¤âĿ¤ âĿ¤</w>\narab ic</w>\nrapi dly</w>\nar rang\nall en\ntravel tuesday</w>\npa ws</w>\nflo ws</w>\nst ability</w>\nflu id</w>\nca pp\ncan berra</w>\nuu uu\nsp ani\ndemon stration</w>\nm la</w>\nplac ement</w>\nm w\npresi dents</w>\nawe som\nbever ly</w>\nani st</w>\nne al</w>\nfather sday</w>\nreferen dum</w>\nla hore</w>\no aks</w>\ndeb bie</w>\nhalf way</w>\ngho sts</w>\nde bor\nmatthe ws</w>\nfi at</w>\nt fw</w>\npre sen\nrob i</w>\nde d\nbro ck</w>\nlaugh ed</w>\nam ounts</w>\nbam boo</w>\nkinder garten</w>\neat en</w>\nmtv hottest</w>\nbreak out</w>\nu sic</w>\nfra ser</w>\nlegis lative</w>\np ang\nmodu le</w>\nsam my</w>\ngo ver</w>\near ns</w>\nexpe dition</w>\ngar h</w>\nconcep ts</w>\nchar lie\nla va</w>\nbachel or</w>\nveg gies</w>\ndeter mine</w>\nel lie</w>\nun locked</w>\nfru it\ndal la\ncou pe</w>\nwash ington\ndepo sit</w>\niv ory</w>\npau la</w>\nchic ag\ngu cci</w>\nðŁİ ĥ</w>\ncul tiv\npier ce</w>\nli fted</w>\nstu mb\nre cover</w>\nmusc les</w>\nconduc ting</w>\ncb s\nmcla ren</w>\nsophi a</w>\ncel lu\noce ans</w>\nup loaded</w>\ngame play</w>\nmal dives</w>\nkim ber\navo i\nrac er</w>\nca ine</w>\ncav s</w>\nh ana</w>\nli ga</w>\nra ven</w>\ninter vention</w>\ninaugur ation</w>\noo h</w>\nat traction</w>\nmerchandi se</w>\ntune in</w>\nli king</w>\njuni ors</w>\nint ended</w>\natt acking</w>\naqu arium</w>\ni wd</w>\ncomp onents</w>\nsur ing</w>\ncent u\nyogur t</w>\nðŁı ĥ\nshow room</w>\nop tical</w>\nty our\nju dge\nyi eld</w>\nan to\npl c</w>\ntransparen cy</w>\nrecy cled</w>\nchi ef\nar om\nambassad ors</w>\nplan et\nâĿĦ ï¸ı\nom ed</w>\nvaness a</w>\ncour t\nmar gar\nhal ey</w>\nv r\nreg ina</w>\npd ates</w>\nhi span\nlive stream</w>\nâģ £</w>\nya hoo</w>\ngal la\nsecu red</w>\nw ir\nbene ath</w>\noff l</w>\nn il\nam b</w>\nye g\nout let</w>\nu te\npe ep</w>\nlind say</w>\nbent ley</w>\n... !</w>\nhe el</w>\ntrilo gy</w>\nvo s</w>\nty re</w>\nthere fore</w>\ntor onto\nab i\nsimp li\nja e\nexten sive</w>\neleph ants</w>\ns or</w>\norient ation</w>\nim peach\nre play</w>\nconstru cted</w>\npeter son</w>\npa is\npor ted</w>\ncustom s</w>\ncolla p\nad u\nhigh lands</w>\nsal em</w>\nshel by</w>\nko vic</w>\nstra in</w>\nro sie</w>\nsen ators</w>\nsnap s</w>\nbo bb\nsuz uki</w>\nbla des</w>\nk p</w>\nlo lo\ngener ate</w>\nsi ght\nma e\nstruc tural</w>\npredic t</w>\njump ed</w>\nah mad</w>\nsun g\njust ice\ngla m</w>\nvol vo</w>\njubi lee</w>\nde tention</w>\nlo sses</w>\npu ri\nevery time</w>\nÐ °\nra o</w>\ned ge\nli mer\nrese mb\nhar old</w>\nre tri\nsacri fic\nsurpri ses</w>\nam c</w>\nsrilan ka</w>\nbar bie</w>\nmen s\nfin n</w>\nag s</w>\nukrain ian</w>\nem brac\nî IJ\nflav ors</w>\nhom er</w>\nlau re\nou th\npr iced</w>\nver de</w>\nfir m\nah s</w>\ncu b\ntre y</w>\npar anor\npro fit\nin dv\nwho a</w>\nhar sh</w>\nal ot</w>\ncrit ics</w>\nhu bby</w>\nfi gur\ngi ra\nca stro</w>\nchan el</w>\nin put</w>\norigin als</w>\nten ant</w>\nyy yy</w>\nture rs</w>\nlincol n\nco on</w>\nlear n\nch ou\nac are</w>\no les</w>\ndin er</w>\nhy p\nbizar re</w>\nmc r</w>\nlet sgo\ndecor ating</w>\nðŁĮ İ</w>\nal ison</w>\nar vin\nf d\nreha b</w>\nmccar thy</w>\nlot tery</w>\nda h\nminne apolis</w>\neli gible</w>\ndiagno sed</w>\nemer ald</w>\ndestin ations</w>\ns ans</w>\nor y\nbla zers</w>\nn v</w>\nba il</w>\ndigital art</w>\nno c\nmal ta</w>\nsol ar\npi pes</w>\nalleg ations</w>\nno ck</w>\npo pe\nbri d\npremi er\nn x</w>\npresent ations</w>\nef a</w>\nbo ws</w>\nval ve</w>\nopp onent</w>\nĮ ë\nvisu al\ning le</w>\ncate gor\ne ter</w>\npo is\ndan i</w>\nat tract</w>\nneu tral</w>\nth ene\ncra shes</w>\nfred die</w>\nut ili\nc st</w>\nawak ening</w>\nslo ven\nquali fy</w>\npro of\nfair y\nle v\nfre ight</w>\nenjo ys</w>\ncup cake</w>\nflav our</w>\nâ ķ\nprotec tive</w>\nðŁijı ðŁı»</w>\nis u\nad mir\nh mmm</w>\ncontinu ous</w>\nai res</w>\nrap tors</w>\nshowcas ing</w>\ny uk\npa ste</w>\nfollow er</w>\ninstru ctions</w>\nsp ru\n@ __</w>\nthe o\ndebu ts</w>\nve tte</w>\nsto w</w>\nes of\nach ed</w>\nsul tan</w>\nsand wich\nsom alia</w>\nfranc o</w>\ncar ne\nflu ffy</w>\nal pine</w>\njas mine</w>\nhe ated</w>\nviol in</w>\nple ss</w>\ndivor ce</w>\nper former</w>\nphi es</w>\nport sm\ndar a</w>\nkir by</w>\nlo p</w>\nchill i</w>\nfor th\nsky pe</w>\nðŁĩ®ðŁĩ ¹</w>\ncelebr ities</w>\ned y\nve e</w>\npo ison</w>\ney el\ngra bs</w>\nssi c</w>\nun o</w>\nwester n\nrail road</w>\nam er\nnumer ous</w>\ns v</w>\nfo w\nfi st</w>\nâĢ ĭ\nreque sts</w>\nmar tial</w>\nem my</w>\naccept ance</w>\nlau ra\nภ´</w>\ner up\nhyun dai</w>\nout lander</w>\nu tt\nwrest le\nesp resso</w>\ndemand ing</w>\ng dp</w>\ngeo graphy</w>\nsas kat\ntro ll</w>\nconfe der\nsu es</w>\nse m</w>\nbe ts</w>\nt ful</w>\nto sh</w>\nteach es</w>\ncol oured</w>\ngal way</w>\nmac y</w>\ndis orders</w>\nbb cra\nat em\nfen der</w>\nlit ter</w>\ne sh\nprovi ders</w>\nrenov ation</w>\nnomin ate</w>\nps g</w>\nnomin ations</w>\njen na</w>\nshar p\nsome day</w>\nz ur\nbra ins</w>\nche shire</w>\npre y</w>\nhu go</w>\n ¿</w>\nto ken</w>\nr v\ncar r</w>\ntac tical</w>\nzel da</w>\nkay la</w>\nfern ando</w>\nphotograph ers</w>\nj our</w>\numb rella</w>\nwoo dy</w>\ncongress man</w>\ndu mp</w>\nle vy</w>\nju an\nd azz\nsign als</w>\nla in</w>\nan u</w>\nmic hel</w>\npor ch</w>\nal den\nsibl ings</w>\ny ale</w>\npe el</w>\nsw ick</w>\ngg in</w>\nll c</w>\nk ale\ns con\nil d</w>\npat reon</w>\nre el</w>\nqu in</w>\nwit t</w>\nmar ty</w>\nmoo dy</w>\nton i</w>\nder y</w>\ng ators</w>\nspeci fically</w>\ndd in</w>\nly on</w>\ntr ick\nmeado ws</w>\np j</w>\nbor gh\nvi k</w>\ntu r</w>\nbron x</w>\npu ff</w>\nlan tern</w>\nðŁ¤ ¦\ng ently</w>\nbe stie</w>\nfac t\nrefu sed</w>\nfas ci\nmp y</w>\nðŁĶ µ</w>\ncross over</w>\nmead ow</w>\nindian apolis</w>\nduc ation</w>\nsle y\nloo m</w>\nmix er</w>\nnew music</w>\nfilm maker</w>\nprosper ity</w>\nli m</w>\nweek end\ncre amy</w>\nneu tr\nlu ther</w>\nh v\nnor thern\ntw o\nh ra</w>\ncat ches</w>\nappear ances</w>\nha bit</w>\nkitt ens</w>\nn v\nilla c</w>\ninf an\nregar dless</w>\nliz ard</w>\ndun k</w>\ncur tain</w>\nac om\nin tu\nve z</w>\ne min\nfl ats</w>\ncalend ars</w>\nem power</w>\nru ined</w>\nhun gary</w>\nvi d\nwe x\nu lum</w>\naber deen</w>\no sa</w>\nk t\nma ssi\nse emed</w>\ns den</w>\n' ?</w>\ntele phone</w>\nde fi\ninsp ires</w>\nme ow</w>\nz ones</w>\nbl ind\npl y\ntuc son</w>\nadvent ure\nge d\noy ster</w>\nðŁijıðŁijı ðŁijı</w>\nout put</w>\ntt t</w>\nmetal lic</w>\nsma sh\nucl a</w>\nsco ts</w>\nperfe ct\nlu cy\nregular ly</w>\nsp ic\nrel ative</w>\nath ers</w>\nmis e</w>\nbatt ling</w>\ndeci des</w>\nmat a</w>\noccu pied</w>\nrandom ly</w>\ncat softwitter</w>\ngi an\nball y\nal ties</w>\nal lies</w>\nim men\nsy rac\nðŁĴľ ðŁĴľ\nl lan\nau r</w>\nk ut\nlam ar</w>\naffe cts</w>\nn ra</w>\nstar war\nðŁ¤ ĺ</w>\nsc ram\nen chan\npro cess\nluxu rious</w>\nar ray</w>\nsher lock</w>\ncomp ati\ndor f</w>\nstre ss\nm su</w>\ns with\nsal a</w>\nsof instagram</w>\nfo il</w>\nunder stood</w>\nqu ay</w>\nr p\nc ade</w>\nja w</w>\nen ab\nen coun\nðŁİī :</w>\ndo ck\nsatur n</w>\nmu ll\nlay out</w>\nra rely</w>\nhapp ily</w>\nfix ture</w>\nor ph\nover looking</w>\nher bs</w>\nm itt\npil lar</w>\nnol an</w>\npe tty</w>\nstr y\nu i\nmu k\no res</w>\no vers</w>\ná µ\nre creation</w>\nwe sley</w>\nri t</w>\nkejri wal</w>\nsto cking</w>\ng v</w>\nsubscri bers</w>\nmoo se</w>\nma e</w>\nber t\nopp re\nassign ment</w>\nu ro\nhigh lighting</w>\ncal vin</w>\nwe igh</w>\ncambo dia</w>\nav on</w>\nke m</w>\ndis abilities</w>\nread y\nchar gers</w>\np ads</w>\niz ing</w>\nilli an</w>\ntru ste\ncol leges</w>\nassoci ates</w>\nalban y</w>\nmil ton</w>\ncr on\nbu r</w>\nhar dly</w>\nsi ghts</w>\nanti ques</w>\ne cho\nsurpri singly</w>\nha iti</w>\ncap t</w>\nph p</w>\nop io\nine quality</w>\nequ al\nken y\nsch mid\nautograph s</w>\nren t\nqu er\ncit rus</w>\nchalleng ed</w>\nte c\nepi de\nfe st\nz hou</w>\nli me\ncitizen ship</w>\ncry stal\nconvin ced</w>\nmess enger</w>\ncopen hagen</w>\nâĿĹ ï¸ı</w>\nwar ran\ndevelop ments</w>\nï¸ı âĥ£\nfore x</w>\nhi ro\nsne akers</w>\nxi de</w>\nvi va</w>\nstere o</w>\nbat ting</w>\nss el\nho st\nbeng al\ncritic ism</w>\nq c</w>\ncr un\nattemp ted</w>\nry e</w>\ndetermin ation</w>\ncre ations</w>\nd read\nlabel s</w>\npos se\nanc er</w>\njoh an\nsi ster\npartner ships</w>\nles bian</w>\nk st</w>\nguaran tee</w>\nbar o\nfix ing</w>\nma son\nm ous</w>\nchem icals</w>\nt less</w>\nbio diversity</w>\npar o\nbhar at</w>\nac ol\nrefu ge</w>\nen te\nt iti\ndys sey</w>\nrespon ds</w>\nlef to\nin er\nse vel\nrahu l</w>\nol ine</w>\nfrank fur\ncho reo\nenjoy able</w>\nc to</w>\nstrugg les</w>\nwood land</w>\nheavy weight</w>\ngen s</w>\nrece p\nac cred\nðŁĺ ¡</w>\ntrans formed</w>\nlist en\nat op</w>\nn k</w>\nsur ge</w>\nbe re\ngover nor\nprison ers</w>\nclau de</w>\nt ill\nmu lator</w>\nemo tion</w>\nwater loo</w>\nstar t\nðŁĩ º</w>\nclean ed</w>\ngrand mother</w>\nfear less</w>\nafric an\nastron omy</w>\nðŁı ģ</w>\nภĻ\nthe world</w>\nsu itable</w>\nanth ony\nk and\ntt en</w>\nmeaning ful</w>\ndisc lo\njaco bs</w>\nà ¸\ntom linson</w>\nghe tti</w>\nty pho\nsub stan\nas co</w>\nte k\nnag ar</w>\nmu d\nam on\nvacc ine</w>\nf ty</w>\nfle sh</w>\nno el</w>\ninfl ation</w>\nportu gue\nglam our</w>\ntra m</w>\nv re</w>\nte qu\nroun dup</w>\nw yn</w>\nrejec ted</w>\nmosa ic</w>\nsi ghting</w>\ncal f</w>\no ta\ncom position</w>\ngo pro</w>\ngonz ale\ne ed</w>\nb ard</w>\ntu e</w>\neffec tively</w>\nwe en\nal to</w>\nri bs</w>\nrel ate</w>\nthir sty</w>\nfu rious</w>\ndi m</w>\nch ard</w>\nperfu me</w>\ns ny\nchur chill</w>\nk of\nmaster class</w>\nwa ve\nðŁĶ µ\ner in\nown s</w>\nto be\nsk illed</w>\nte m</w>\ngo f\nen i</w>\ntor i</w>\ncra zy\nl ick</w>\nresi stant</w>\nici al\nag ar</w>\n! :</w>\ng ali\ndel aware</w>\nbl itz</w>\nkoh li</w>\npu ck</w>\navail ability</w>\nhi malay\ninflu ential</w>\ncro chet</w>\nvictor i\nread ing\nho bby</w>\nvie t\nj as</w>\nen gra\nsk ul\nðŁĩ² ðŁĩ\neduc ate</w>\ntech 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les</w>\nko wski</w>\ninter action</w>\nsh u</w>\nbou le\nic an\nj are\nhan k</w>\nleg itim\nk nicks</w>\nkar ma</w>\nrecei ver</w>\nper ks</w>\nu h\nsta ir</w>\nsun i\nlabor atory</w>\ngra ves</w>\nvoc als</w>\noo t</w>\nc ture</w>\nthri ve</w>\ntic o</w>\nãĥ ³\nb w\ncarto ons</w>\nmcdon alds</w>\ndra w\ny ung</w>\npl er</w>\nli d</w>\neth ical</w>\ngroo ve</w>\nent a</w>\ninternational womensday</w>\npat ron</w>\nwor ries</w>\nðŁİ ħ\nðŁij ĭ</w>\nka therine</w>\ndi az</w>\ntor i\nbach chan</w>\ntru st\nmin eral</w>\nic om\nbuil ders</w>\nbor n\ncol oring</w>\nlat te</w>\nca se\nrevolu tion\ntra der</w>\nox id\nchi pot\ninst antly</w>\nsou thern\nse hun</w>\npro b\nher nandez</w>\nlis bon</w>\nhu awe\np ong</w>\nme a</w>\nro oney</w>\nwheel chair</w>\nke en\nbe tt\ncor in\nregulat ory</w>\ndi splac\nka ren\nsch em\nsun sets</w>\nwh ales</w>\nremin is\nhe p\nhi de\nmar cel\npand ora</w>\ndo yle</w>\nth fc</w>\not to</w>\nno kia</w>\ntrans gender</w>\nko v\nhawai ian</w>\nsha ve</w>\nso vere\nexc er\nnick i</w>\npu g</w>\nst or</w>\nro th\nwee t</w>\nleg al\ndig nity</w>\npo w</w>\nhom age</w>\nðŁĩ³ ðŁĩ\ns re\ncan on\nla x\nwo ah</w>\nquart z</w>\nñ a</w>\ngree ting</w>\nflick r</w>\nnai robi</w>\nadvoc ates</w>\nan c</w>\nvi i</w>\neu gene</w>\nth ra\nc re</w>\nel an\npen sion</w>\nth letics</w>\nton i\nre agan</w>\nx v</w>\nsto re\nben ch\nhar lem</w>\ntodd ler</w>\nsent enced</w>\nâĻ¥ ï¸ı\nglob ally</w>\nche aper</w>\nu f\nma m</w>\nnic o</w>\nik u</w>\ntho u</w>\nni st</w>\ndam i\nth ala</w>\nrho des</w>\nsal e\nbow ls</w>\nâ Ī\nlas vegas</w>\nsanc tions</w>\nadm ire</w>\nmat ched</w>\nun able</w>\ntravel er</w>\nele ven</w>\nstraw berries</w>\nâĢĶâĢĶ âĢĶâĢĶ\nstu dio\njac ques</w>\nim s</w>\nvalu ed</w>\ns no</w>\ncheese cake</w>\nn xt</w>\ne os</w>\ns x</w>\nf x\nton ic</w>\nhat ch</w>\nchic ks</w>\ngra ds</w>\nhand ic\nr ory</w>\nas p\nri pped</w>\ndenti st</w>\nn en\nlu fc</w>\nâľ Ĭ</w>\ndi ge\nhop kins</w>\nsher man</w>\nf da</w>\nfor all</w>\nash ley\nstr and</w>\nh y</w>\nliqu or</w>\nbuffe t</w>\ness ence</w>\nphar ma</w>\nsuri ya</w>\nðŁĴĻ ðŁĴĻ\nfesti vals</w>\nz an</w>\nre fresh\npur ple\nuni forms</w>\nkenne th</w>\n= )</w>\nas an</w>\nhel sin\ntransform ers</w>\nk ali\nperson alized</w>\nchal k</w>\nbo bby\nâ Į\nthe mes</w>\ndepar ture</w>\nprin t\nillustr ations</w>\nqui et\nagre es</w>\ngri ff\nØ ³\nm iti\ntoge ther\nconven ience</w>\nab ar\ncar lo\nturt les</w>\ninfo sec</w>\nsome what</w>\nar lington</w>\nscholar ships</w>\nemir ates</w>\nmu ms</w>\nst ella</w>\nauton om\nfe ather</w>\ng ore</w>\nnom inees</w>\nfragr ance</w>\nÑ Ĥ\nw ong</w>\nthea stern</w>\ngr e</w>\nz illa</w>\nis i</w>\nbump er</w>\ngo o</w>\ndo zens</w>\nab duc\nâļª ï¸ı</w>\no ils</w>\ndon ors</w>\nsil icon</w>\ni pod</w>\nfortn ite</w>\nðŁĴ ¨</w>\ntor o</w>\nspark ling</w>\nconsci ousness</w>\npal a</w>\nnu m\nmoun ted</w>\nffin s</w>\nthi eves</w>\nteam mate</w>\npra b\nom er</w>\nta pes</w>\nbo d\nmit su\nste w</w>\ne re\np bs</w>\ntu sc\nlo we</w>\nra de</w>\nparliam entary</w>\nh m\ned gar</w>\nðŁijĩ ðŁijĩ\nto a\na gh\nhon i</w>\ns late</w>\nge ek\nap t</w>\nhard t</w>\nta p\nhoriz on\ngrow th\nmake over</w>\nhi l</w>\npaper back</w>\nid an</w>\nreha bil\ngi u\npossi bilities</w>\nlet tu\nfran co\nbo ss\nach er</w>\ndoes nt</w>\nmo e</w>\nta ker</w>\nhuss ain</w>\nml k</w>\ndi l</w>\nth ia</w>\nham a</w>\nreal ised</w>\nraven s</w>\ncurric ulum</w>\nm ith</w>\nk night\nted x\nr v</w>\nisai ah</w>\ncumb ria</w>\nbirth days</w>\nf ing</w>\npre z</w>\nmu barak</w>\nexquis ite</w>\nclear ance</w>\ny en</w>\npar i\nev o\nà º\nmodi fied</w>\napp lying</w>\nimple ment</w>\ndisco vering</w>\nchap man</w>\nindie game</w>\ndis k</w>\ncrowd funding</w>\nmach in\nli vel\nsty led</w>\nâĿ Į</w>\nma king\nrehear sals</w>\nnutr iti\nsubscri ption</w>\nand ro</w>\ncre ators</w>\ncar ries</w>\nky lie</w>\ncam den</w>\nappren tice</w>\ntax pay\nc ca</w>\ntuesday thoughts</w>\npis sed</w>\ner man</w>\ndete c\nfreed om\nmer i\n.. !</w>\npsal m</w>\nsun light</w>\nper spec\nbe ings</w>\nbook store</w>\nrock star</w>\nfun ctions</w>\np ence</w>\nfav es</w>\nz n</w>\nobam acare</w>\nsp ill</w>\ncoven try</w>\npi geon</w>\npi vo\nba it</w>\nkol kata</w>\nav al\ndon or</w>\nwa h</w>\nprivi leg\ntra ditions</w>\nrajas than</w>\nten ess</w>\nportugue se</w>\nyn es</w>\ntack les</w>\nde fic\ntor n</w>\npol ling</w>\nthor ne</w>\nin a\nbened ict</w>\nbar ry\ncal ories</w>\nver dict</w>\nsave the\nnor ton</w>\noff ice\nmain stream</w>\nimpro ves</w>\nfr on</w>\nrespon ding</w>\nreal tor</w>\nscotti sh\nde clar\nr l\nshi v\nsupp lier</w>\nre sting</w>\nswee ts</w>\nqu i</w>\n. âĢ¦</w>\nwhit ney</w>\nstartu p\nthank you\nteach er\nh alls</w>\nha ve\nhand made\npro ving</w>\nquar tet</w>\nro chester</w>\nli an</w>\nvirtu al\nmend es</w>\nof icial</w>\nmid lands</w>\nx box\nmeas uring</w>\no vo</w>\naccommod ation</w>\nbri des</w>\ncollegi ate</w>\nintellec tual</w>\nin car\nni ag\nðŁį ·</w>\nsf w</w>\ncoco a</w>\nco ats</w>\ncivil ians</w>\npresi dency</w>\nmat rix</w>\nsweethe art</w>\ntri athlon</w>\nwag ner</w>\nra dic\nplann er</w>\nthe o</w>\nexecu tion</w>\nk um\nthe walkingdead</w>\nsc ar</w>\nro tation</w>\nblo gging</w>\nbom b\nre son\nbb les</w>\nst are</w>\nassi sted</w>\ne do</w>\nbrand ed</w>\nwar nings</w>\nthor pe</w>\nacknow le\nsatis fied</w>\nsho res</w>\nri d\ndor a</w>\nphys ically</w>\nbi gh\nappro ves</w>\nha h\nric al</w>\nvers atile</w>\npret end</w>\nlu m\nab hi\nye e\nsp it\nãĢ Į</w>\ndj s</w>\nash tra</w>\nj t</w>\nven ues</w>\ngram mys</w>\ncy clo\ntr acker</w>\nover watch</w>\nrepl ica</w>\nel yn</w>\nnr l</w>\nlind sey</w>\nhom o\nballo ons</w>\nkitch en\nsi s\nam os</w>\nende av\nðŁĴ »</w>\na rec\nthu g</w>\nhoo ked</w>\nhr c</w>\nnew york\nbur gh\nameric as</w>\npatric ia</w>\nug u</w>\nap athy</w>\nha st\npsy chi\ncor k\npetro l</w>\nðŁİ ¬</w>\nak u\npo pping</w>\npsycho logical</w>\nau x\ng ma</w>\ncad illac</w>\nwa ste\nauth ent\nbri stol\nnam e\nque er</w>\nto ber\njer ry\ncom in</w>\nch ant\nprivileg ed</w>\nop ar\nlo ser</w>\ntex t\nmar ker</w>\nstri es</w>\nequ ally</w>\nak i\nchrist mas\ngare th</w>\nble w</w>\nem ma\nimag in\nse als</w>\nche at</w>\nconditi oning</w>\nj ana</w>\nren s</w>\ndar ies</w>\no asis</w>\ndisc ounts</w>\ncoun cil\ni ka</w>\nshir ley</w>\nvou cher</w>\nal ps</w>\nw x\nq r</w>\ndri ft</w>\nattemp ting</w>\nut c</w>\nØ ª\ngonzale z</w>\nm f\njo ker</w>\nparalle l</w>\npa re\naspe cts</w>\nproce du\nn p\nam a\nrale igh</w>\nbright en</w>\ngu ire</w>\nradi ation</w>\ncre scent</w>\nho b\nil le\nstr and\nv ore</w>\nn ard</w>\nche st\ndi wali</w>\nav atar</w>\nal der\nd ling</w>\npa thetic</w>\nðŁĴ ĺ\nspir it\njor ge</w>\nfilm making</w>\nðŁĻı ðŁĻı\nchalleng er</w>\nb j</w>\ndown town\nht ml</w>\nade qu\ntwi sted</w>\nin ely</w>\n( '</w>\nwra ps</w>\noper ational</w>\ny ne</w>\nn us</w>\nmag net</w>\nmarket place</w>\nhealth ier</w>\nsnap shot</w>\ndam on</w>\ninter ven\nfe derer</w>\now ls</w>\nbiscu its</w>\nj p\nro deo</w>\nblue 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sh</w>\nke yes</w>\nal ma</w>\ninduc tion</w>\nk w</w>\nk har\nsl ick</w>\nbri de\ne ur\ncra ving</w>\nbook ings</w>\nch es\ntr unk</w>\nvern on</w>\nsp her\ncryst als</w>\nrel atively</w>\npom pe\nuni ons</w>\nval ley\npar a\nw ant\nok c</w>\nde af</w>\nser gio</w>\nlen non</w>\nsh ay</w>\ncr a</w>\nv at</w>\nhe e\nt we\nliqu id\npol y</w>\nðŁİ ģ\nb ent</w>\nbe aring</w>\nmotor sport</w>\nbar be\nte sti\nhan i</w>\nfin ancing</w>\nastron aut</w>\nwater colour</w>\nri sh\ncomic con</w>\ngar t\nwr ong\nber n</w>\nit an</w>\nste pped</w>\nfil ters</w>\nc low\nme x</w>\ndem ons</w>\nall o</w>\nexpand ed</w>\ncomm and\net ers</w>\ngo ats</w>\nsi ri</w>\ny r\npot tery</w>\nmari on</w>\ni le\nel an</w>\nsan to</w>\nperson a</w>\ndu ke\nhom eless\nli ghted</w>\nwheel er</w>\nchang er</w>\ncab bage</w>\nsur real</w>\nham burg</w>\nsma shed</w>\nstr an\nk not</w>\ni art\nob i</w>\nbe dro\ndi al\nth ick\nb ingo</w>\nfu s</w>\nvacu um</w>\ncon ve\nati ve\naccur acy</w>\naccoun t\nre fer\nri z\nspider man</w>\nban a</w>\nr ite</w>\nu b\nab s\nmedic al\nlin k\nsi em\n> >>></w>\nbe tra\ng lowing</w>\nre actions</w>\npupp et</w>\nspa ghetti</w>\nang s</w>\nre medi\npray for\nroy ce</w>\nchar lotte\n£ ï¸ı</w>\ngh et\naffe cting</w>\nro de</w>\nsoci alist</w>\nmo ses</w>\naz i</w>\no it\nre porters</w>\ncd t</w>\nap ing</w>\ns nat\nminim al</w>\nwa ist</w>\nsie ge</w>\n>> >>\nri g\nschmid t</w>\nh are</w>\nec a</w>\nthor n</w>\nhe mp</w>\nes the\ncly de</w>\nth a\ndon ut</w>\nmoham ed</w>\nling erie</w>\nle gg\ncarpen ter</w>\nperform ers</w>\nde a</w>\nimag ined</w>\ncur se</w>\nla sh</w>\nct r</w>\nagu a</w>\nro ar</w>\ngr i</w>\nro le\nj fk</w>\nresur rec\nroosevel t</w>\nmaril yn</w>\nsm alle\nwill is</w>\nwa ited</w>\nchar ities</w>\nthe res</w>\nli k</w>\norigin al\ncar i\nc ough</w>\ncru ci\nla gun\ncontra st</w>\nk ou\narm our</w>\nre moving</w>\nt ent\nmaz da</w>\nbri ghter</w>\nthi ef</w>\ncor ner\ntequ ila</w>\nbuzz ing</w>\nal bi\np am</w>\naz ure</w>\ndisc 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i</w>\ncommuni st</w>\nregi ment</w>\nele anor</w>\nanticip ated</w>\nðŁijĮ ðŁı»</w>\nthephoto hour</w>\nran o</w>\nsurvi ving</w>\nsimul ation</w>\ndaw son</w>\nar in</w>\naqu a</w>\nm or</w>\nâĢ¦ .</w>\ncin o</w>\nira qi</w>\nsh az\ndun dee</w>\nwe s\ndra u\nhann ah\ns news</w>\noccup ation</w>\nste en</w>\nx m</w>\nang les</w>\nsett ings</w>\ngur u\nkno x\nor ca</w>\nshap ing</w>\nw ent\ndr illing</w>\nzz ie</w>\nbr i</w>\nkis sing</w>\nfin d\nma ine\nâŃIJï¸ı âŃIJï¸ı\nðŁĮ į</w>\nlar ry\nbu sted</w>\nta vern</w>\nacti vely</w>\n- \"</w>\nreplac ing</w>\nno d</w>\nun lock</w>\n. \"\nâŀ ¤</w>\naffili ate</w>\nto w</w>\nl n</w>\nhappy newyear</w>\ndi f\nj m</w>\ngreen wich</w>\ncontro versy</w>\ndaw g</w>\ncon dol\nsav annah</w>\ncompens ation</w>\ntouch down</w>\nte o</w>\namb itious</w>\nembro i\nconvic ted</w>\niart g</w>\nbar ack\ntr ance</w>\ntestim ony</w>\nau dition</w>\nthum b</w>\nmy ths</w>\nbe x\nque z</w>\norch id</w>\nden y</w>\nentit led</w>\nhoo d\ngr ant\nin 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c</w>\nas parag\nne st\ncycli sts</w>\nfe l</w>\nìĬ ¤\noverwhel ming</w>\npey ton</w>\nj it</w>\na post\nmb le\nble eding</w>\nneighbour hood</w>\na very</w>\nexpre ssions</w>\nmac donald</w>\ngi gs</w>\nmon ds</w>\nillu sion</w>\nn ct</w>\ncam ero\nover head</w>\nmy th\nol y\nvi o</w>\net v</w>\nlau rie</w>\nunve iling</w>\npri or\ncon n</w>\niron man</w>\ndi ff</w>\nday in\ncrit ici\ncon go</w>\nre vision</w>\nwal e</w>\ndirec tor\np ines</w>\nblack pink</w>\ngar ner</w>\ncur ated</w>\nmanit oba</w>\nh ac\ncommon ly</w>\nbar ton</w>\n.... #</w>\nmor tality</w>\nlive smatter</w>\nphilos op\nshor ter</w>\ncon vince</w>\nfre ak\nvend ors</w>\ninsi ghtful</w>\nel ly</w>\nsens ors</w>\ne led</w>\ns berg</w>\nweight loss</w>\nu kip</w>\nsp ur</w>\npriv ate\nqu a</w>\nss c</w>\n, ...</w>\nsupervis or</w>\nadvis er</w>\namaz ingly</w>\nless er</w>\nat es\nmah on</w>\noooo oo</w>\nsar as\npmo india</w>\nwaff le</w>\nun ders</w>\ntoler ance</w>\nsculp tures</w>\nher sh\nkno cking</w>\nsmo ke\ncathol ic\ngri m\ntra veled</w>\nfli p\nge off</w>\ndinosa urs</w>\nsle pt</w>\nscar let</w>\nok i</w>\ncompla int</w>\nob sc\nnam i\nla g</w>\ncross fit</w>\nu fc\nmc cain</w>\nrefe ree</w>\nsad ness</w>\npen ny\nli eu\nmo de\nki er\nvol s</w>\nw is</w>\nel on</w>\nshe a</w>\nba o</w>\nson ia</w>\ncla ire\nem manuel</w>\nmoist ure</w>\ndi gest</w>\nvi ii</w>\nt eller</w>\nch on\naccess ory</w>\nnight club</w>\nfoss il\naw an</w>\nhu sky</w>\nab original</w>\nbrand on\nffici ent</w>\ncou gars</w>\nste d\nad mitted</w>\nigno red</w>\ncontent marketing</w>\nag as\nv ase</w>\nexecu ted</w>\nnegoti ations</w>\nshe ad</w>\nn and\ntab lets</w>\ngo th</w>\nts al</w>\nd fw</w>\non ep\nprotec tor</w>\nsp ho\ngaz ette</w>\nandre as</w>\nss er</w>\ncomp ilation</w>\nha v</w>\ncontain ers</w>\nbro ker</w>\nsoc al</w>\nporcel ain</w>\nhy uk</w>\nair ing</w>\nðŁĴ °\npubli sher</w>\nscen ario</w>\nspart ans</w>\nre viewing</w>\nitu des</w>\ned el\npear son</w>\nba sh\nmau i</w>\na ad\nðŁĮ Ĭ\nli u</w>\nul ate</w>\nprogram mes</w>\nfav our</w>\nweb design</w>\nreal ty</w>\nmotiv ational</w>\ncro sses</w>\n' ...</w>\nbus ch</w>\nadjust able</w>\nar jun</w>\nmist ak\ndimen sion</w>\npi stol</w>\nweigh s</w>\nen y</w>\nunve il</w>\nindy car</w>\ngor don\nf ade</w>\nfran ken\nqual ities</w>\nbet t</w>\nloc ate</w>\nker r</w>\nsp c</w>\nconfu sion</w>\nne e\nluck y\nbas es</w>\ndep ends</w>\nfire fighter</w>\nol a\nre t\nmar oon</w>\nðŁĶ Ĭ</w>\nw am\ndefin ing</w>\nwhe at\nbi l</w>\né s</w>\nb hai</w>\npsy ch</w>\nta u</w>\nic ans</w>\nthi k</w>\nob ile</w>\ninspec tor</w>\nìĨ Įë\nill on</w>\ngo s\nev angel\nfa i\nsi st</w>\nvoc ation</w>\nbur ge\nchi stan</w>\nrenew ed</w>\nenthusi asm</w>\nen ting</w>\nag ri\nike a</w>\nm sc</w>\naero space</w>\nsens iti\nmemo ir</w>\nhosp ice</w>\nco caine</w>\nder ry</w>\nmechan ics</w>\nĦ à¸\ntin o</w>\nreduc es</w>\ncollec tors</w>\nin justice</w>\nsupp re\nv ana</w>\nab un\nnap a</w>\nsu sa</w>\nos lo</w>\ne ff\nen core</w>\nlic ence</w>\nched dar</w>\nz al\nmoun t\nðŁĴ IJ</w>\nthreat ens</w>\n!! \"</w>\narchi e</w>\nfu tsal</w>\nscu ba</w>\njo s\ngn on</w>\nse xi\ns official</w>\ncompar ing</w>\ndomin ant</w>\ntof theday</w>\nfa it</w>\npropos als</w>\ngi ft\ny as</w>\ncn c</w>\nl r\nha b\nreser voir</w>\nbeli efs</w>\ngener al\nmar ti\nt d\nest e</w>\nì ł\nwi l</w>\nðŁij ¯</w>\nðŁĶ «</w>\nsp x</w>\net work</w>\nexcer pt</w>\ne instein</w>\nhir o</w>\nsil hou\nteam ed</w>\nper ception</w>\ncorri dor</w>\nmental health\nhin ts</w>\nben ny</w>\ninduc ted</w>\nsw x</w>\nwi desp\nspe ak\ncher yl</w>\ndru g\nðŁĺ ķ</w>\nh f</w>\nasparag us</w>\nmyster ies</w>\nfitz gerald</w>\noff er\ntherap ist</w>\ncare er\ndam aging</w>\nts d</w>\nper u\nwei bo</w>\ny ay\nphoeni x\ndisc re\nmac book</w>\nbar ker</w>\nstig ma</w>\nsp read\nroc kies</w>\nkang ar\nbri dg\npa i\nbi shop\nta iled</w>\ncapsu le</w>\nðŁĴ ĵ\nge of\nroy ale</w>\nshort listed</w>\no ste\nash amed</w>\nch app\nkey e</w>\ncl a</w>\nscreen shot\naustri an</w>\nnati ve\nen ight</w>\njuli et</w>\nmichel e</w>\nðŁĮ ´\ntravel ers</w>\npi l</w>\nfootball er</w>\nwin chester</w>\nðŁĻ Ħ\nazer bai\ngold eng\norganis ations</w>\ninterpre tation</w>\npredat or</w>\nofthe week</w>\nlo gan\npok é\nmari e\ncal la\nt nt</w>\ncin de\nge tic</w>\nfit fam</w>\ngra v\now ens</w>\nðŁĮ ±</w>\nshoot out</w>\nsal is\ncommissi ons</w>\nco he\np tic</w>\nni xon</w>\nhi a</w>\namb ition</w>\nmar ine\ncruel ty</w>\nt k</w>\ncru de</w>\nsal ty</w>\njim a</w>\nmon go\nir ony</w>\non wards</w>\narre sts</w>\nstrang ers</w>\nig er</w>\ncycli st</w>\nra g</w>\nexten ds</w>\ntra dio</w>\nbour g</w>\nmo i\nel la\ne able</w>\nlex us</w>\nau l\nder a</w>\nhistor ian</w>\nmor ton</w>\nti ff</w>\nman ner</w>\nko t</w>\nd k\npo inted</w>\nmar qu\na an\nen ey</w>\ndu blin\non poli</w>\nem ili\nsecre t\nfl o</w>\nâļ ¡</w>\nba j\nste ep</w>\naccompan ied</w>\nrum ours</w>\ndev i</w>\npurch asing</w>\nfi g</w>\npu b\nsch oo\nautonom ous</w>\ngo alie</w>\nx ia</w>\nautom atically</w>\nre vers\nter o\nfu ku\ntitan ic</w>\nshoo k</w>\nsand als</w>\nsee kers</w>\nexc av\nnor dic</w>\nbigo live</w>\nba ke\nr att\nz ak</w>\nne p\nðŁĺ ¤</w>\ncand y\nbilli ons</w>\nbook worm</w>\npp et</w>\nà ³\nsur faces</w>\nsc ars</w>\nphil ip\ndo gg</w>\nci gars</w>\nco te</w>\ntransl ated</w>\ncur ator</w>\nsin dh</w>\nhan gover</w>\nbre wer</w>\non es\nel ton</w>\nðŁĴª ðŁı¼</w>\nmar cu\nelli ot</w>\nrigh te\ndi oce\nru ss</w>\nrail ways</w>\ngrand son</w>\nas cen\napo logy</w>\nawa it</w>\nmob ili\nre spir\nparti san</w>\noli vi\nstri ke\nyo o</w>\nwhite house</w>\nexpre ssed</w>\npu ps</w>\nbed ford</w>\ncul tur\nfro gs</w>\nfly ing\ncav ali\nc ds</w>\nfri ger\nstreet photography</w>\nre solve</w>\ntali ban</w>\nkan g</w>\ncru shing</w>\nju m\nðŁĺ Ĵ\nwilliam son</w>\ntan g</w>\ncur ly</w>\nt man</w>\nveter an\nfa ire</w>\nartificial intelligence</w>\nun anim\npre n\nback drop</w>\nfr ances</w>\noc cer</w>\ndoro thy</w>\nwork ing\nar thr\nconver ted</w>\nday light</w>\nserv ant</w>\npad dle</w>\ncompla ining</w>\nthir ty</w>\nnad al</w>\nak u</w>\nibra him</w>\nad dressed</w>\np iss</w>\ngreen house</w>\nbatt alion</w>\nsi mulator</w>\nout lets</w>\nembroi dery</w>\nðŁĵ ±</w>\nfis cal</w>\nger ard</w>\nsas sy</w>\nðŁİī ðŁİīðŁİī</w>\nvent ures</w>\nmer it</w>\npublic ity</w>\nðŁij Ī</w>\nsophistic ated</w>\nc tu\nconven tional</w>\ncondol ences</w>\nisra el\ntra dition\nar an\nte ss</w>\ngla d\nðŁĺĬ ðŁĺĬ</w>\ncorrec tion</w>\nge on\nam d</w>\nor ship</w>\nbe ast\nch ment</w>\nì ŀ\nnic o\nwk nd</w>\nwel s</w>\ncushi on</w>\nbeli e\nvo c</w>\nidio ts</w>\nunder neath</w>\npu ma</w>\ncorn ell</w>\nen ation</w>\nlu l\nswa ch\nab ig\nu rer</w>\nmi e\nform erly</w>\nca f</w>\ner nal</w>\nchor us</w>\njuli us</w>\nsen ator\nâľ į\nwh ir\nsalv ador</w>\nph d\nuni fied</w>\nboo ster</w>\ngraph ical</w>\nw rec\nson ny</w>\nmi z\ndere rs</w>\ns all</w>\nven s</w>\ntusc any</w>\nwi d</w>\ny ong</w>\nkur ds</w>\nw az\ntrol ls</w>\nmac ro\ncat urday</w>\npre ssing</w>\nsa sha</w>\ncent ennial</w>\ngu sts</w>\nem c\nbe fore\nden ise</w>\ncu st\nðŁĵ ¢</w>\nlo oo\nbase l</w>\neng land\ny olo</w>\nar du\nmanife sto</w>\ndo ha</w>\nì ľ\nkni ves</w>\nbourne mouth</w>\nbi bl\nbar b</w>\nal icia</w>\nØ ©</w>\ncom er</w>\ncycl one</w>\ng it</w>\nane ws</w>\ncharacter i\nvent ura</w>\nin tra\nsf giants</w>\nhu t\nbe a</w>\ndar win</w>\nell er\nal v\nre ese</w>\nbl y\nkar an</w>\nconclu sion</w>\nman ny</w>\nfla kes</w>\nunite blue</w>\nnad u</w>\nco pp\ned ges</w>\nlanca shire</w>\ni als</w>\no tta</w>\nphilipp e</w>\nl ent\nche e</w>\nment ors</w>\nfesti val\nan ism</w>\ncompli mentary</w>\nr j</w>\npu g\nd ine\nwe i</w>\ncli ffs</w>\nsar my</w>\nti veness</w>\ntreas ury</w>\nil and</w>\nafter math</w>\nrabb i</w>\nou n</w>\nbou quet</w>\nherit age\nzi on</w>\nsur render</w>\nshen an\nin ks</w>\nkar l\ngh ty\npol icing</w>\nexam ination</w>\nce y</w>\nper su\nmeasure ment</w>\nhydro gen</w>\nlu han</w>\nâłĢâłĢ âłĢâłĢ\nwar i</w>\nо Ð\nj y\nfow ler</w>\nmis h</w>\nal fre\nâĺ ij\nbb naija</w>\ncat alogue</w>\nrecogn ised</w>\nsa ver</w>\nhu skies</w>\ncol in\nmun do</w>\nsi va</w>\np ng</w>\ndiscoun ted</w>\nman utd</w>\nfre sno</w>\nde vin</w>\nprelimin ary</w>\ntro phies</w>\npla stics</w>\ndu g</w>\npro cu\nindi go</w>\ng ard</w>\ndy lan\npit ches</w>\nground breaking</w>\nin son</w>\nbl ac\nan thology</w>\nf h</w>\nexpl ic\nr ard</w>\nadmi ral</w>\nso chi</w>\nla shes</w>\nsplen did</w>\nen vy</w>\nad v</w>\nsex y\nfestiv ities</w>\nstic king</w>\nbi b</w>\nthr ill</w>\nop p</w>\nari el</w>\nbotan ical</w>\nendur ance</w>\nfe males</w>\nbr icks</w>\nvat ican</w>\nblack pool</w>\nber mu\nbr ough</w>\nroll er\nbi d\nsue de</w>\nsloven ia</w>\nmm ing\nml b\nmed alist</w>\ndi ans</w>\nrehabil itation</w>\nne on\ns go</w>\nli thu\nram os</w>\nz ed\npi anist</w>\ninten sive</w>\nbroad band</w>\nstu dy\npeter sburg</w>\nlu ca</w>\nah hhh</w>\nphys ician</w>\ndill on</w>\ntele com</w>\ngri ef</w>\nmu n</w>\nac ro\nsi ded</w>\ns ly</w>\nblo ws</w>\nclassic cars</w>\ntri um\nar gy\n? :</w>\nh ri\nmarsh mal\nâĢ ĵ\nto pping</w>\nwar saw</w>\ntran sc\npreserv ation</w>\nb av\nre friger\nexperim ents</w>\nä º\ngl it\nsli ga</w>\ng age</w>\nfac tor\nflav ours</w>\nbr ony</w>\nsp o</w>\ncook book</w>\ncarri age</w>\naw ay\nny fw</w>\non ian</w>\nw g\nsimp sons</w>\nro lex</w>\nðŁı ¿</w>\ncro sby</w>\nãħ ¤\ncre di\nsyn dic\npu bs</w>\nali fe</w>\npoor ly</w>\nmac ed\nðŁĺ ŀ</w>\nbehin dthe\nw enger</w>\nn ats</w>\nðŁİ Ł</w>\nrubb ish</w>\nprocedu res</w>\ntypho on</w>\nopho bia</w>\ner do\nfu el\nvi era</w>\nbu mps</w>\nmillenni um</w>\nnew zealand</w>\nlec tures</w>\nit on</w>\nmil ky</w>\nrespon ded</w>\nê °\nlandsc ape\n.. @</w>\nbo ther</w>\nâĸ ¶</w>\nz hang</w>\nhuawe i</w>\ntu ition</w>\ns worn</w>\nin u\ny or</w>\npa olo</w>\nau ditions</w>\nab il\nmalay sian</w>\nho ps</w>\nfe athers</w>\nmp le</w>\nau ts</w>\nã o</w>\nboun ty</w>\nic he</w>\nì ĺ\nsh q</w>\npin ot</w>\nge ars</w>\ndisapp ear\nvideo games</w>\nt na</w>\nalzheim er</w>\nðŁĮ ŀ\na ji</w>\nunder wear</w>\nswit ching</w>\nsign age</w>\no scar\nec on</w>\ndro w\ncl int</w>\npl ated</w>\ngun dy</w>\nemb lem</w>\nho es</w>\nici st</w>\nnel ly</w>\njuni or\nroad show</w>\nminer als</w>\nat le\nalexand ria</w>\nac claimed</w>\nv ell\nshi va</w>\nad he\nen ne\namne sty</w>\nh ounds</w>\ncouncill or</w>\nðŁĴ ¦\naes the\npart nering</w>\ninflu enced</w>\nmag no\nfl are</w>\nextin ction</w>\ncivil ian</w>\nmaje sty</w>\nva il</w>\nlaw makers</w>\nrac ks</w>\nmc c</w>\nori an</w>\nsp ices</w>\ner rors</w>\nmay er</w>\nco ca</w>\npa i</w>\ns ooooo</w>\nreti ring</w>\nba thro\nðŁĻĮ ðŁĻĮ\nâĸ ª\nsu f\nendor sement</w>\nbuil ding\nbroo ch</w>\npal la\narvin d\nag ent\nkar ate</w>\nr hi\nc tv\nta ine\num m</w>\nba x\nreig ns</w>\nuni of\nenterpri ses</w>\nadel e</w>\nfla ke</w>\nat tire</w>\nbru ce\nba hamas</w>\ngra vy</w>\nsa in\nche ek</w>\ntri vi\nlo v</w>\ne en</w>\nbb lo\nlady gaga</w>\nitt a</w>\n. \"-</w>\ndu stin</w>\nobserv atory</w>\neigh th</w>\nbloom berg</w>\nkh s</w>\nf cc</w>\ngi st</w>\ncommemor ate</w>\nve er\nsexu ality</w>\ned c</w>\nnic ole\nvac ancy</w>\nu ser\nson a</w>\n:' (</w>\ndipl oma</w>\nt end</w>\nup grades</w>\nÅ Ł\njura ssic</w>\ncardi ac</w>\ndr s</w>\nwidesp read</w>\nà ł</w>\ndail ies</w>\nvend or</w>\nsim plicity</w>\nwi der</w>\nlen ses</w>\nsupp lements</w>\nde pos\nob served</w>\nvin es</w>\nparti ally</w>\nrenew al</w>\ncollabor ate</w>\nali g\nfin ity</w>\nph u\nzz y\npe tit</w>\nðŁĵ ħ</w>\nz in</w>\ni gu\nsm ack\nfall on</w>\nðŁĵ £</w>\nback wards</w>\ncomp onent</w>\no so</w>\ncompati ble</w>\nbin ding</w>\nzur ich</w>\nthom e</w>\nw ounds</w>\nly ric</w>\nfresh men</w>\nsne aky</w>\nfi bro\ndi et\nemplo yer</w>\nin sect</w>\nh ated</w>\nsch er</w>\nraz or</w>\nn sw\nboo ker</w>\ncalifor ni\nav fc</w>\n °\npreten ding</w>\npep si</w>\nal is\nun titled</w>\nk art</w>\ngrand parents</w>\ne the\no ck</w>\nlux emb\nvisu als</w>\nsmall business</w>\nabdul lah</w>\nmin ho</w>\nsu baru</w>\nh ra\nreve aling</w>\nheart breaking</w>\nclar ity</w>\nam g</w>\nsl r</w>\n** **\nâŀ ĸ\nrecor d\nici ary</w>\nmin ded</w>\nye h</w>\nexce ssive</w>\nknu ck\nicec ream</w>\ntru th\nev ic\nta stic</w>\nant arc\nren dering</w>\n, ,\nmit t</w>\nloren zo</w>\nst patrick\nbound ary</w>\nzi g</w>\nvo cab\nosa ka</w>\nfur n\ntu n</w>\ngu l</w>\ns ounding</w>\nblo gger\nutter ly</w>\ng af\nadv ancing</w>\nl cd</w>\nmar gin</w>\nlifel ong</w>\nsolst ice</w>\nsh ra\nwa its</w>\nple ar\nbre ach</w>\nen ligh\nad er</w>\nitt le</w>\nc ation</w>\nho on</w>\nstu died</w>\n?? ???</w>\nk ash</w>\nev angeli\nps l</w>\nwei ghts</w>\nmet als</w>\nty res</w>\ntur no\nwi e\ncar b</w>\ng ale</w>\nse al\nsun ite</w>\nam ic</w>\npatter son</w>\ná n</w>\neu ph\nup stairs</w>\nquali fiers</w>\nkhali fa</w>\napple music</w>\nìĨĮë ħ\nvau ghan</w>\nal ter</w>\ncru iser</w>\nmu a</w>\nt ana</w>\nkat rina</w>\nid ols</w>\nspo iled</w>\nsecre tly</w>\nfi bre</w>\npart nered</w>\num es</w>\ngi ov\ncom et</w>\nscreenshot saturday</w>\nk eller</w>\nfil tr\nfe t\ncon way</w>\npe u\nbad minton</w>\ngi d</w>\nm ound</w>\ndon key</w>\nbu ff</w>\nlea ther\nlar gely</w>\nbro ch\nint ments</w>\nam use\nr k</w>\nsto ve</w>\nimpac ted</w>\ncon t</w>\ncr acks</w>\nprison er</w>\nbar i\ncontrac tor</w>\nori oles</w>\ndomin ate</w>\npol ar\nam elia</w>\ndr c</w>\nðŁijĮ ðŁijĮ</w>\nvi st</w>\nsu arez</w>\ninjec tion</w>\nblo oms</w>\nðŁļ¨ ðŁļ¨</w>\nsti ff</w>\npay pal</w>\nsno wing</w>\nthur sdays</w>\ngoo se\nwe dge</w>\neduc ated</w>\nweak ness</w>\nde cker</w>\nabud ha\nbree zy</w>\nÛ Į\nhope ful</w>\no bi\nrai der</w>\ngh am\nde u\nse ve\npar tly</w>\nfu t\ninfu sed</w>\nmer ri\nthan e</w>\nsome time</w>\nhu e</w>\nme in</w>\ncre dit\nsli ding</w>\nran de</w>\ncher ry\ndead pool</w>\nsh ol\nar am</w>\nunder wood</w>\nsky e</w>\ndistur bing</w>\nm nt</w>\npoli shed</w>\nguardi ans</w>\nha dn</w>\npic asso</w>\nari us</w>\nak shay\nir ri\nj h</w>\nhapp en\nla kh</w>\ndal ton</w>\nat the\ns well</w>\nmar sha</w>\nre h\ncour s</w>\nj kt</w>\ntop us</w>\nserv ice\nr ink</w>\nhack ers</w>\ndono van</w>\nhor o\ntc m\nmay hem</w>\ncha se\ndev ops</w>\nken sing\nsc up</w>\nsh ere</w>\nquali fication</w>\nc live</w>\nton g</w>\nn ancy\nmar is\nder dale</w>\nber man</w>\ncinde rella</w>\njol ly</w>\nci c</w>\nloo t</w>\ncollecti bles</w>\nhom icide</w>\ng ge\nepide mic</w>\nsu ites</w>\nmu ddy</w>\ngi mme</w>\ne rec\n- *</w>\ntal la\nlis le</w>\nembro ide\nðŁĩ© ðŁĩª</w>\nveriz on</w>\nve ctor</w>\nbe anie</w>\narti san</w>\nga in\nflo res</w>\nvi gil\nu so</w>\nðŁĻı ðŁı½</w>\ngrin ding</w>\ngh er\nair ports</w>\nrespon sive</w>\nshaf t</w>\ncan cel</w>\nceremon ies</w>\ne me</w>\nat ari</w>\nbru shes</w>\neag er</w>\nbo hemi\nchildren s</w>\nyan kee</w>\nma a</w>\nsuspen se</w>\nmor an</w>\nmac ar\nsun flower</w>\ncre w\nvo id</w>\nke ar\nfashi oned</w>\njen nings</w>\nsunday funday</w>\nsub missions</w>\nme ad</w>\nher man</w>\nwa i</w>\ncrit ically</w>\nle um</w>\nbaek hyun</w>\nfor cing</w>\nco bra</w>\nãģ ®\nacqu ire</w>\nal k</w>\nge ology</w>\npri mar\nimport antly</w>\nire z</w>\nbunde sliga</w>\ncuri osity</w>\nsen a</w>\nstric t</w>\ncon soli\nwin ters</w>\nven om</w>\nchelten ham</w>\nðŁį º</w>\ncen a</w>\nt at</w>\nba in</w>\nglo ver</w>\nunder cover</w>\nas ses</w>\ncar n\nmemorial day</w>\nam eli\ni rene</w>\nch on</w>\nsyn thesis</w>\nspe edy</w>\nmitsu bi\nsla yer</w>\ncompos ite</w>\nunder stands</w>\npe w\ninter rup\nhen ri</w>\nmor row</w>\nan om\nthof july</w>\ng lee</w>\nthre e\nðŁĺ ®</w>\nand hi</w>\nch att\nrenew ables</w>\nye s\ntrans fers</w>\n!!!! !!!!</w>\nbab u</w>\ndu ter\nlo ops</w>\npe ers</w>\no ilers</w>\npau lo</w>\nic ation</w>\nh mu</w>\nwar a</w>\nmer cer</w>\nhom eland</w>\nfu ji</w>\nale y</w>\nyear book</w>\nre m</w>\nre en\nab sur\nbo is</w>\n] :</w>\ncaes ar</w>\nshot gun</w>\nkur dish</w>\no ren\nra e\nanci es</w>\nty pic\nf h\ndef ault</w>\nre plic\nlu k</w>\ntrans actions</w>\nr ys</w>\ninfan try</w>\nðŁį ¾</w>\ncho w</w>\nchick ens</w>\nba gh\nwy att</w>\nay e\ngg i</w>\nbre ws</w>\ned itions</w>\nmi ra\ncommen cement</w>\npre su\nperis cope</w>\nic hi\nguatem ala</w>\nzam bia</w>\npain ts</w>\nwit ches</w>\nwan i</w>\nun dere\ncro y\nvo ws</w>\nus mc</w>\nhear ted</w>\ntheat res</w>\nshu ffle</w>\nle vel\nmul tic\nsquee ze</w>\nfer n</w>\napp et\npost al</w>\nmal t</w>\non board</w>\nld nt</w>\nco o</w>\ns sc\nk ac\nðŁĺ ĩ\nsc rap</w>\nmar cos</w>\ndeal ers</w>\nann u\nmill er\nco ve\nul ary</w>\nvladi mir</w>\nbe ef\nth ur</w>\npick led</w>\nse same</w>\nbengal uru</w>\nmo tt</w>\nkathle en</w>\nhi st\nno tor\ndr ank</w>\ndu chess</w>\nsnow fall</w>\ne ff</w>\ntin y\nj n</w>\nsy our\nspeci alists</w>\nscot us</w>\nbay lor</w>\neve rest</w>\nmali bu</w>\npre m</w>\nharm ful</w>\nl ali\nb ates</w>\ng ye\ndifferen ti\nand ra</w>\ngeome try</w>\nel over</w>\nblack out</w>\n== ==\nko ta</w>\ninter act</w>\nasi an\nla yo\nsamu rai</w>\nfi del\nexhau sted</w>\ngla di\npd t</w>\nspher ic</w>\nanti qu\nguit ar\nstu ri\nho pper</w>\nang le\nf ills</w>\nsla p</w>\nmi th\nrod ney</w>\nong i</w>\nin som\npre venting</w>\ncassi dy</w>\nap ho\nore gon\nlo in</w>\nham mond</w>\ncontribu ting</w>\nf n</w>\ngar ri\nori on</w>\ncomp elling</w>\nescap ing</w>\naim ing</w>\nplu mb\nbi stro</w>\nbe asts</w>\nconcer ning</w>\nbo e</w>\ndo pp\nshop local</w>\nstumb led</w>\nâĤ ¹</w>\nnaz is</w>\nâĢįâĻĤ ï¸ı\ngest ure</w>\nwar ts</w>\nus open</w>\nhi ggins</w>\nchar li\nhang s</w>\nbom bers</w>\n° :</w>\nfe eds</w>\nc ch\nst il\nnic ola</w>\nðŁĵ º\nclam ation</w>\ntro pic\naf ro</w>\nou k</w>\nexpen ses</w>\nder rick</w>\nal ine</w>\nfa w\nreg ard</w>\nim er</w>\nsat in</w>\nthi um</w>\nry der</w>\npear l\nte ss\nmm mmm</w>\nsen ses</w>\nðŁĩ ¹\npositi ve\nexhau st</w>\noccu r</w>\nnor ris</w>\nlil ly</w>\nis les</w>\ndirec ting</w>\nyo fficial</w>\ncount less</w>\nsam ar\non stage</w>\nflo ck</w>\nmir rors</w>\narch er</w>\nmo i</w>\nk d\nvi v\nin os</w>\nsi kh</w>\nle i</w>\nsen sory</w>\nbr its</w>\nkno x</w>\nchest nut</w>\nop y</w>\ncoli seum</w>\nz af\ndi vin\nadap ter</w>\n:) ))</w>\ntem ple\nku n</w>\nhel mets</w>\nt df</w>\ngu ide\nm old</w>\no ids</w>\nlu ther\nhe is\nmonaster y</w>\nsp ree</w>\nk lu\nbrit ney</w>\njagu ars</w>\ngre ats</w>\nc cc</w>\nky rie</w>\nmachin ery</w>\ncric ket\nre ro</w>\nab o</w>\naspir ing</w>\nsemi finals</w>\nale ss\nsig natures</w>\nvar d\nme th\nher bal</w>\nhol den</w>\nking dom\nap or\nreg gie</w>\nore o</w>\npalestin ians</w>\nem mys</w>\nsec tional</w>\nro i</w>\nney mar</w>\nqu el</w>\ncu ll\nl ka</w>\nhaz el</w>\nestim ate</w>\nul ties</w>\ngo w\nbe a\npurch ases</w>\nbel ts</w>\nprotec ts</w>\nm é\ngue ssing</w>\nbb o</w>\nclau dia</w>\nfr acking</w>\njon ny</w>\nel k</w>\ncel tic\nal mighty</w>\nra je\ncourty ard</w>\nig i</w>\ncan es</w>\nðŁĴª ðŁı»</w>\nbank rup\nle thal</w>\nâľĮ ï¸ı\ngraphic design</w>\nvad er</w>\npenc ils</w>\nrough ly</w>\ndan te</w>\nm fg</w>\nconst ell\ncam el</w>\nj b\nbloss oms</w>\nen to\nbalo chistan</w>\ncine mato\nill ard</w>\njer sey\ncon sent</w>\ndent ed</w>\ncon templ\nsch er\nhol i</w>\nlou gh\nst our</w>\na yo\nbegin ners</w>\ncur b</w>\nv hs</w>\na jax</w>\ndu ff</w>\nav eng\ndom est\ncommit ting</w>\nai red</w>\ncha p</w>\nhedge hog</w>\ndisappo inting</w>\nfreel ance</w>\nin land</w>\nchar ms</w>\nðŁĺį âĿ¤ï¸ı</w>\nai sh\nm x\nbuck le</w>\nti dal</w>\nper mit</w>\nbo ating</w>\nra cha\nkend rick</w>\nb ello</w>\nb hi</w>\nple a</w>\nestim ates</w>\nl b\napo logies</w>\njay a</w>\nbb l</w>\nast oni\ninter state</w>\nmain taining</w>\nel bow</w>\nmu p</w>\nep it\nðŁĺ ¡\nviol ations</w>\ndef end\nbe h\nsl c</w>\nam ir</w>\npur i</w>\nti um</w>\nfi fa\nblur ry</w>\nscri m\nðŁĻı ðŁı¾</w>\nma ple\nrel atives</w>\nâĺ Ŀ\ncho c</w>\ncon nor\n⾨ ⾨</w>\nwhi sp\nlist ings</w>\nma ze</w>\nthan king</w>\nri dd\ngrass roots</w>\nshi fting</w>\ndesper ately</w>\ngor illa</w>\nden i\nju les</w>\nstra th\ng ley</w>\nja in</w>\nbu ick</w>\nt anner</w>\nðŁĴ Ŀ</w>\nga e</w>\npri m\nit ors</w>\nn ano</w>\nsepar ation</w>\narmen ia</w>\nbor deaux</w>\nðŁ ħ\npj net</w>\nbu rial</w>\ne bon\nglo ss</w>\nre new</w>\ngri er</w>\nspe eds</w>\ncomic books</w>\nsym boli\npur poses</w>\nãħł ãħł\nspati al</w>\nno table</w>\nci on</w>\nn ps</w>\nho ffman</w>\nnor man\nrt g</w>\ndu sty</w>\nsitu ated</w>\ntr an</w>\nk fc</w>\nem en</w>\nnic kel</w>\nhast ings</w>\nsett ling</w>\ngr it\nl ena</w>\nw aw\nart s\ngu m\nca regi\nle wis\nsapp hire</w>\nrememb er\nembed ded</w>\nt lc</w>\nbl at\nserge ant</w>\nel sa</w>\nboot camp</w>\nbow man</w>\nphoto graphic</w>\npill ars</w>\ndirection ers</w>\nclassi fied</w>\nno is\nve er</w>\nbarre ls</w>\nwh oop</w>\nðŁĺ± ðŁĺ±\nfe male\npetro leum</w>\nmedi a\ne fc</w>\npoké mon</w>\nठķ</w>\nenthusi astic</w>\nvar un</w>\npro files</w>\npedi atric</w>\nacci dents</w>\ncon rad</w>\njan g</w>\njo jo</w>\nac or\nob server</w>\nl f</w>\nlive stock</w>\nfor gi\nfo s</w>\nel m</w>\nan and</w>\ngo e\nc ere</w>\navoi ding</w>\ngri t</w>\nom an\nthank fully</w>\nscat tered</w>\nnick y</w>\ncylin der</w>\nchees y</w>\ndi ver</w>\nmahe sh</w>\ncav es</w>\near liest</w>\nqu inte\nsubjec ts</w>\nb end\ngul f\nvocali st</w>\nglu e</w>\npat ches</w>\nun stopp\nsny der</w>\ndemonstr ating</w>\npi o</w>\nhor ns</w>\nwic kets</w>\nand the\nr ama</w>\nyo on</w>\nstra ight\nbed time</w>\nor ang\nbul lets</w>\nsa urus</w>\nmin ers</w>\ninci dents</w>\n! ...</w>\nðŁİ ¸</w>\nag ers</w>\nhand les</w>\nstat es\nin ity</w>\nd ons</w>\nincredi ble\nemin em</w>\navi v</w>\nru dy</w>\nmoz art</w>\nfolk lore\nappli ances</w>\nmt l</w>\nfre y\ndi as\nhu a</w>\npage ant</w>\nstri ve</w>\nim prison\nbul lish</w>\nr ana</w>\nal erts</w>\nbb mas</w>\nhy per</w>\nderby shire</w>\nre cre\nre dd\ndebor ah</w>\ncosmo s</w>\nlaw son</w>\nmel anie</w>\npsy cho</w>\nho or\ndoo dles</w>\nsni per</w>\nshad y</w>\nman tle</w>\ncanadi an\nnew year\ninter actions</w>\nsepar ated</w>\ncor ds</w>\nspiritu ality</w>\nap u\nit o\np ct</w>\npel osi</w>\nrebel lion</w>\nse iz\nwor cester\nsec tors</w>\nul i</w>\nsan ta\nÐ µ\nðŁĩªðŁĩ ¸</w>\nbi ased</w>\nclass ical\ngam ma</w>\ndee plear\nemer ge</w>\nback er</w>\nsur ance</w>\nhand crafted</w>\nðŁİ ¥\nfranc is\nmill an</w>\nic i</w>\ncro wn\nwo w\nstri ped</w>\nun fair</w>\nrelax ation</w>\n³ ï¸ı\nembrac ing</w>\nshe alth</w>\npale o</w>\nmartin i</w>\ndist illery</w>\nwr ink\nor k\nna th\nhay ley</w>\ncour thouse</w>\nsi ber\nsa di\nquiet ly</w>\nmel t\nm sm</w>\nme h</w>\nsmart phones</w>\nrel ent\npp ing\nwar wick</w>\nco logne</w>\ngli a</w>\ncot ton\npro g</w>\nlon e\nip sw\nstar ters</w>\nexpan ds</w>\nu mp\nsu ed</w>\nski pper</w>\ninfe ctions</w>\ning le\nà ¡</w>\ncler k</w>\ndemonstr ate</w>\nac ar\nðŁĺĤðŁĺĤ ðŁĺĤ\nti bet</w>\nbun s</w>\nalo m</w>\ndemol ition</w>\nssi a</w>\ng st</w>\n[ ]</w>\nso ar</w>\nâĺ Ģ</w>\nðŁĺ ª</w>\nðŁĵ Ĭ</w>\ndee pest</w>\nbeyon d\nare t</w>\natt ends</w>\nactiv ated</w>\ndi mit\nâļª ï¸ı\nhigh lighted</w>\nmagaz ines</w>\nrum or</w>\naz za</w>\nsteph ens</w>\ndol ph</w>\nsho ckey</w>\nmat s</w>\nwe av\nmel an\nserv ers</w>\ntra um\nku sh\næ Ĺ\nbab ys\npa z</w>\na al\nla use</w>\nbreak ers</w>\ncanter bury</w>\nul ture</w>\nmi ri\neuro s</w>\ntane ous</w>\nimpre ssions</w>\ndu tch\nil d\ngh i</w>\npur due</w>\nadequ ate</w>\nl p\nsy ner\nang ler</w>\ndu rable</w>\ngal ore</w>\nro wn\nmg mt</w>\nðŁĵ Į</w>\nlu cia</w>\nâĺij ï¸ı</w>\nzay n\nbor row</w>\n. (</w>\nnorth umber\ncru sh\neng a</w>\nsu sh\nextra vag\nt out</w>\nma hal</w>\nali stic</w>\nther mo\ngall eries</w>\nes se</w>\nchi bi</w>\nattrac tions</w>\nlex ington</w>\nlegislat ure</w>\ndocu mented</w>\nresi den\nbrow nies</w>\nw f</w>\nst ool</w>\nplan ets</w>\nsho ppers</w>\nconduc tor</w>\nms p</w>\ntr icky</w>\nfru ity</w>\nend ra</w>\nfeel the\nwhi pped</w>\nhair style</w>\nre fer</w>\noo k\noc topus</w>\naudi ences</w>\nku mar\nafter no\nop tim\nc fl</w>\nni p</w>\ngen i\nalpha bet</w>\nann ab\nlam in\naccep ts</w>\nl ng</w>\nðŁĺ «</w>\nt ine</w>\nac om</w>\ncheer leaders</w>\nt k\ngr on\nv g</w>\nk ung</w>\nja x\ndha bi</w>\nr ss</w>\nmack enzie</w>\nbeir ut</w>\nclean up</w>\ngy psy</w>\nst ell\nbur ger\nhurric anes</w>\neduc ation\nst ina</w>\nâĻ¡ âĻ¡\nunfortun ate</w>\njere mi\nbad ger</w>\nat ers</w>\n: âĢ¦</w>\nter ra\nsubli me</w>\nstu d\ny mca</w>\nmr u</w>\nduter te</w>\nbren nan</w>\nbul b</w>\nmel o</w>\nyl on</w>\nhack er</w>\nc red</w>\ngu d</w>\nas an\npad 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missions</w>\nconstitu ency</w>\nu pper\nwoo t</w>\nallo y</w>\nse ve</w>\nlu b\nun comfortable</w>\ned win</w>\nab re\nd wight</w>\nar che\nvirtu ally</w>\nsp ol\npri e\nai i</w>\ner r\nswit ch\nbar ack</w>\nse ok</w>\ncou l\nwn t</w>\npou l\no live\ncaffe ine</w>\ncardi ff\nnotor ious</w>\nde mp\nex cess</w>\nbar r</w>\nt ford</w>\na jay\nbump ed</w>\nmy thology</w>\nshel ley</w>\nfal con\nshakespe are\nmust angs</w>\nno ted</w>\nbon e\ncivil ization</w>\nsy d</w>\npar sons</w>\nun official</w>\nhy ped</w>\nsp ends</w>\noppo sed</w>\nv ings</w>\nspace x</w>\nnoti fication</w>\ndeci ding</w>\nbio tech</w>\nout si\nsal ah</w>\n! .</w>\nfe d\nss y\nc ms</w>\nbad gers</w>\ncr o</w>\nela ine</w>\nn ba\ndy our\nn ant</w>\nhoney moon</w>\nclimb ed</w>\nconom y</w>\nath a</w>\nm ell\nne bula</w>\nnature photography</w>\njuli e\nbm x</w>\ninve sted</w>\nmon o</w>\nlieu tenant</w>\nwat kins</w>\ntechn ician</w>\no se</w>\nka e\nì Ľ\nmc queen</w>\npre ach</w>\ntrav eller</w>\nflexi bility</w>\nze bra</w>\nreta iler</w>\np ant</w>\nben der</w>\nbrand t</w>\nsqu id</w>\nwar rant</w>\nveri fied</w>\ncas s</w>\npier cing</w>\nhon ours</w>\nt ying</w>\nmor ris\nkis sed</w>\nop rah</w>\npanor amic</w>\nme i\nsplat oon</w>\nwich ita</w>\nari as</w>\ngal li\nindy ref</w>\ngood times</w>\nathe ist</w>\nconfe ssion</w>\now ski</w>\nre pping</w>\nad ditions</w>\nmechan ism</w>\nz im</w>\nj ans</w>\nsu f</w>\ncho pped</w>\nbeg innings</w>\nvitam ins</w>\nãħ¤ ãħ¤\nor th\npo les</w>\nru b</w>\nantarc tica</w>\nindie film</w>\nweb cam</w>\nket ch\nbre tt\ncle ment\nher on</w>\ndefe ating</w>\nhydr o</w>\nbuc ket\nwand ering</w>\nsid ney</w>\nfuture of\nb inge</w>\non ies</w>\nknock out</w>\nadministr ator</w>\nsyn the\nl ent</w>\njan i</w>\nbar ley</w>\npremier league</w>\nner ds</w>\ncr m</w>\nbra s</w>\nbot any</w>\nevol ved</w>\nrot ter\nro wed</w>\ntum or</w>\nweal thy</w>\n Ń</w>\nmon arch</w>\nli shed</w>\nda hl</w>\nðŁİ ĥ\nbu ch\nken yan</w>\nØ §</w>\nred ness</w>\nassemb led</w>\nse mit\nhud der\nshro p\nran i</w>\nlear ning\nmor y</w>\niti a</w>\ngeo graphic</w>\nworl dof\nf b\npho sp\nboo gie</w>\nam ped</w>\n? ...</w>\nche w</w>\ndwar f</w>\nar us</w>\ns sen</w>\nru sty</w>\nrecru its</w>\nh k\ngar de</w>\napp lause</w>\nvol umes</w>\ninvol ves</w>\nta c</w>\nhand bag</w>\ntrans late</w>\nffe l</w>\nse ym\naqu atic</w>\ntrans fer\nzo di\nand r\nacade mia</w>\ncr ater</w>\nte z</w>\nar se</w>\nadap t</w>\ncol oni\nsnow man</w>\nmal i</w>\nhang in</w>\ndi schar\noy sters</w>\npho e\ncolon el</w>\nw ba</w>\nhispan ic</w>\nthri ving</w>\nsh y\nag les</w>\nsales force</w>\ncre me</w>\nso les</w>\nla fayette</w>\nâ ī\nter ia</w>\nach a</w>\nsp erson</w>\ngo go</w>\ncar ly</w>\nthe ore\nam ore</w>\nvo x</w>\naf t</w>\nãĤ ¹\nstap le</w>\nmu ffin</w>\ndi agram</w>\nino x</w>\nsu stained</w>\nav ent\nme ta</w>\narbit r\ndec ay</w>\nado le\nÐ ½\nec ol\nph o</w>\nn k\no cu\ngr anny</w>\nç a</w>\nluxemb our\nstad t</w>\nalber to</w>\nle vit\nam as\nd x\nor phan\nco bb</w>\nas c\nlo gy\nimmen se</w>\nchan ts</w>\noff line</w>\np ent</w>\nbre x\nw inger</w>\nplan e\ni el</w>\nnichol s</w>\nca thy</w>\nnar uto</w>\nlow ed</w>\n/ //</w>\nignor ance</w>\ncat astro\nyou ts</w>\nsch en\nbuil d\nhaz i</w>\ns ine\ncritical role</w>\ndu g\ndete ct</w>\nlo gs</w>\nen amel</w>\nstpatrick sday</w>\ned die\nco pa</w>\ncigare ttes</w>\nho ff</w>\nkay a</w>\nla goon</w>\nra pha\nair borne</w>\nchoo se\npuer tor\nke v\ngui ding</w>\nfro sty</w>\nbor ough\nmir a</w>\nðŁİ Ĭ\ncade t</w>\nanu sh\nyo gi</w>\ne ger</w>\nfl ing</w>\nslo pe</w>\nnin th</w>\nwe ston</w>\nfoot wear</w>\nf n\nmay weather</w>\na am</w>\npla in\nstair case</w>\nwitne sses</w>\nwork outs</w>\nro bust</w>\ndex ter</w>\nco hort</w>\nðŁļ Ĺ</w>\nsp ell\nha ze</w>\no om\norgan ising</w>\nwild fire</w>\ncont acts</w>\nav on\nmin o</w>\nupd ating</w>\nðŁį »\nli thium</w>\ning ual</w>\nk is</w>\nau ga</w>\nlo com\nde duc\nu da</w>\nth ak\nboy le</w>\nmp er</w>\nhot tie</w>\neri k\nre vised</w>\nis la</w>\ntravel photography</w>\noo za</w>\nen qui\nconfe rences</w>\nclo ver</w>\ng room</w>\ncur ves</w>\nlive on\nper f</w>\ndisplac ed</w>\nbo log\nxx xx</w>\nðŁĺ© ðŁĺ©\nte al</w>\nve ssels</w>\nrain forest</w>\ncal ci\npan ther\ngira ffe</w>\nta sted</w>\nimag ery</w>\npad res</w>\nday time</w>\nbas s\nri pe</w>\nopio id</w>\nnu e\nvin yl\ninvent or</w>\nsen s</w>\nprocess or</w>\nmu t</w>\ngad gets</w>\nbibl ical</w>\nshann on\njacqu eline</w>\ncar y</w>\nthe resistance</w>\nali en\nn vi\nco sy</w>\nbi har</w>\nfo ley</w>\nren d</w>\nmu gs</w>\nfa ken\ncl one</w>\nni allo\ngra bbed</w>\nchi hu\npower house</w>\nn tt</w>\nchero kee</w>\nspon ge\nimple menting</w>\nrh ine\nle one</w>\nðŁį Ģ\npret tiest</w>\ninfra red</w>\nimpro v</w>\nswit ched</w>\ntu bes</w>\ncon tr\nbl k</w>\nprojec ted</w>\nbe aver</w>\nyo t\nbbcra dio</w>\nthi gh</w>\nper secu\napologi ze</w>\nw ack\npo ster\noli ver\naz a</w>\nlou d\n( ?)</w>\nf the\nwomen shi\nspar row</w>\nblu sh</w>\nus able</w>\nsc ales</w>\nit ative</w>\npeu ge\nne eding</w>\nlegg ings</w>\nglam orous</w>\nmat ur\nc z\nwat t\nda b</w>\ntam ar\net sym\nbau er</w>\nheart felt</w>\nh n\nelse where</w>\nbir ch</w>\nalu mini\nhu ck\ne me\nj l</w>\ntraf ford</w>\nd z</w>\npor tions</w>\nana sta\narthr itis</w>\nesp n\nber gen</w>\nviol ation</w>\nyo shi\nc z</w>\nnorthumber land</w>\nclo sures</w>\nðŁĩ¯ ðŁĩ\nsmi ley</w>\nr w</w>\ntel ugu</w>\ninten si\ngre gg</w>\nve ga</w>\ndun geon</w>\nsouth bound</w>\nba il\ndomin ican</w>\nsemi final</w>\nchap ters</w>\nh itch\nvan ity</w>\ntrans iti\nrecomm ends</w>\nsati sf\nbar ca</w>\nqueen s\n( (\nde struc\nstra it</w>\nra vi\ndess erts</w>\nin tru\nhar am</w>\nk os</w>\nfo e</w>\nfat ty</w>\npais ley</w>\nmagn itude</w>\ndri dge</w>\ncom ey</w>\nschem es</w>\nvision ary</w>\nour t</w>\ndown loaded</w>\nðŁĻĮ ðŁı½</w>\ngd pr</w>\nlan i</w>\np wc</w>\ngu ad\nnic est</w>\nstake holders</w>\nre ferred</w>\ngeorge town</w>\narvind kejriwal</w>\nschnei der</w>\nin 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ption</w>\nsam e\nfra u\ntru ffle</w>\nem bo\nroman s</w>\nbla sts</w>\ntechno logical</w>\npr at\nb sb</w>\ny ar</w>\ntren dy</w>\nac l</w>\nal ad\nðŁį ģ</w>\no hh</w>\nbankrup t\ntho ven</w>\nregar ds</w>\nis er\nwar wick\nvine yards</w>\nreal m</w>\nniallo fficial</w>\ndo ta</w>\nge mini</w>\nto do</w>\nv able</w>\n¨ ¨\nla u</w>\nwre ath</w>\nju ve</w>\nnat asha</w>\nle ver</w>\nlor i</w>\nhor ser\ncc tv</w>\nair bnb</w>\nes anders</w>\nsin clair</w>\nema biggest\nhigh school</w>\ncon test\noptimi stic</w>\nt te\nðŁĴķ ðŁĴķ\nss d</w>\nye e</w>\nhel ena</w>\ncon sen\nric ks</w>\njes se\nan ic</w>\nðŁİ ¯</w>\nre acts</w>\nro be</w>\nindepend ence\nvol tage</w>\nm ington</w>\ns ant</w>\nà¸Ļ à¸\n-------- --------\nsentin el</w>\nke tt\nrehear sing</w>\naaaa aaaa\nsof the\nstir ling</w>\nsear ch\nwi gan</w>\nstand out</w>\nsna il</w>\npent agon</w>\nÄ ģ\nch lor\ncru st</w>\nnet any\nchemi st</w>\ndisapp eared</w>\nric ardo</w>\nsp iders</w>\nbo se</w>\nwar ren\nme ssing</w>\nbann 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'</w>\ntail gate</w>\nnoti fications</w>\nå ¤\npas sive</w>\ntrous ers</w>\nbalo ch</w>\nro ther\ntypic ally</w>\nà ¥\nsp it</w>\nwi z</w>\nsic ily</w>\ntechnic ally</w>\nex pose</w>\nst age\nhu bb\ncre am\ncap s</w>\npo ke</w>\nsle ek</w>\nju ne\ntempor arily</w>\nde z\nawak ens</w>\nl ame</w>\n_ -</w>\nji ha\ntues days</w>\nadvis ed</w>\nadvis ors</w>\nexi sted</w>\ndis agree</w>\nnews room</w>\nlo sers</w>\nworld tour</w>\ndr ying</w>\nal di</w>\nhar ness</w>\nfoot print</w>\nhobb it</w>\np mln</w>\ni ro\nque red</w>\nasse ss</w>\ngaz e</w>\nsa b</w>\nth ian</w>\ní Ĭ\nti f</w>\nob serve</w>\nev il\ndra wer</w>\nswee p\ncor y\nco dy\nkyo to</w>\ncal lum</w>\nn inj\nlau rent</w>\nbe i</w>\nsket ching</w>\ncustom ized</w>\ndu r</w>\nregre ts</w>\nknox ville</w>\nìķ Ħ\nmess aging</w>\ngrac ie</w>\nabun dance</w>\nbi dding</w>\nbre wed</w>\nfl ouri\ntherapeu tic</w>\nalt itude</w>\nho gs</w>\nbur ner</w>\nelec tro</w>\nwonder fully</w>\nhe ater</w>\npost pon\nli very</w>\nr all\nad 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g</w>\nde pending</w>\ns ation</w>\nevol ving</w>\ninter cep\ncen sus</w>\ntof the\nre en</w>\nmendo za</w>\ntrum pet</w>\nmarke ters</w>\nan it\nðŁĻ Ĭ\nnorth western</w>\nv la\nfoto gra\nblackand white\nche wan</w>\nwi g\ntro om</w>\nginger bread</w>\nk n</w>\nro mero</w>\nn fc</w>\nor chi\nfun ko</w>\nsour ce\nf s\nra ped</w>\no st\ntar ot</w>\nann ually</w>\nðŁĺ ¬\nr ill</w>\ndel av\n.. !!</w>\nse s\ncan n</w>\nmedic are</w>\nph el\nape x</w>\nguardi an\nrema ined</w>\nr pm</w>\na ñ\nstory month</w>\ninstag ood</w>\nneighb our</w>\np ing\nsem ite</w>\nmy stic</w>\nas cot</w>\nmat er</w>\nhand ful</w>\ndang ers</w>\nti d</w>\nana heim</w>\nopol y</w>\nsh allow</w>\nnami bia</w>\ntor ia</w>\nprocu rement</w>\nbig bang</w>\nannoun cements</w>\nprosecu tor</w>\nbeng als</w>\nsal le</w>\nen roll\nga stro\nsugge stion</w>\nba k</w>\nha ul\nbudd hism</w>\nberni esanders</w>\nflu te</w>\nfati gue</w>\ncyn thia</w>\ncho i</w>\nir win</w>\ngu a</w>\nstr ous</w>\nh p\nba p</w>\nsatisf 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y\ntuesday motivation</w>\nðŁĵ ļ\nthe flash</w>\nroc ket\nmo dest</w>\nchihu ahu\non na\nk sa</w>\nhur dles</w>\nca ve\nfail ures</w>\nsp lit\nbo ho</w>\ngur l</w>\ndisappo int</w>\nho ward\nnug get</w>\nfran z</w>\nstal ert</w>\nkaz akh\nfor getting</w>\nsch ri\nag ate</w>\nam at</w>\neve rett</w>\ndu et</w>\nveter inary</w>\njuli an\nch ills</w>\nbra ve\nghost busters</w>\nlan do\ngre ets</w>\nprofit able</w>\nd é\nti r\nze e\nom en</w>\npd x\ngray son</w>\nhar i\nfix es</w>\nstab bing</w>\nswim mer</w>\nsymb ols</w>\ncompli ments</w>\npo se\nfunc tioning</w>\nth nx</w>\ngi r</w>\ncorpor ations</w>\nbar low</w>\nlo e</w>\noff season</w>\ndistin ctive</w>\nmarvel ous</w>\nnik on\nenri que</w>\nky u</w>\nja ws</w>\namo to</w>\nlom bar\ntravel blogger</w>\nfa h\nouri sm</w>\ntri stan</w>\nso e</w>\nce ase</w>\nðŁı ħ</w>\nz ac\nmck enzie</w>\ntaxpay ers</w>\nswim suit</w>\nbl o</w>\nles ley</w>\nkan sas\nw ks</w>\nki el</w>\nprovo king</w>\nmy les</w>\nstr ing\nkangar oo</w>\ngalac 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ts</w>\nam ir\ni phone\nci gar\nen du\nre i</w>\nel abor\nr inging</w>\njohn son\ncharacteri stics</w>\nsal oon</w>\nalgori thms</w>\ntal kin</w>\nm tn\ndi ve\nregion als</w>\nff ice</w>\nhat i</w>\ndeviant art</w>\nso tto</w>\nshir o</w>\nl ama</w>\nk we\nf aded</w>\npor ting</w>\ntu mmy</w>\nest ates</w>\nbuen os</w>\nðŁ¦ ģ</w>\nbeli ever</w>\npen etr\ndar n</w>\nsp ite</w>\ncan opy</w>\nfashi oni\nt illa</w>\npet als</w>\neli jah</w>\nbra wl</w>\nmarty r</w>\në°©íĥĦ ìĨĮëħĦëĭ\nmid town</w>\neric h</w>\nd apper</w>\nsm town</w>\nme gam\nww w\nle le</w>\non s\ncat fish</w>\nfir th</w>\nfossil friday</w>\nball park</w>\nth aw\npot ent</w>\nilli e</w>\ncre ep</w>\ncar p</w>\nso ap\ngun dam</w>\ninfe c\nyy yyy</w>\nठ¨</w>\nz ag\nrit t</w>\ncalcu lator</w>\nbo ca</w>\nok o</w>\nto ad</w>\nthreat en</w>\nrefin ed</w>\nolym pic\naccompli shment</w>\nbacter ial</w>\na ji\ntat um</w>\nfeli z\nshe ed</w>\nj at\nth ic\njam al</w>\nðĿ ĺ\nlin a</w>\nðŁIJ ¯</w>\njo king</w>\nyot po</w>\npin ch</w>\nak ron</w>\nher b\nmotiv ation\nli a\nho stage</w>\ncre ek\ngam ble</w>\nruss ell\npatt i</w>\nfo tos</w>\nc pc</w>\nbro ken\nback the\ncla ys</w>\nu mm\nstock ton</w>\nmat ernal</w>\nü r\nla kel\ncent ury\nbe k</w>\ninfe cted</w>\nภ¡\nsmack down</w>\nman ned</w>\nta hoe</w>\nsm es</w>\nbas a</w>\nsu la</w>\naugu sta</w>\n. *</w>\nrohing ya</w>\ngre ed</w>\ncounsel or</w>\nsilhou ette</w>\ngra vit\ncla use</w>\n' -</w>\nbo bc\nocca sions</w>\nnow adays</w>\ndic tat\nbe ard\nn ally</w>\nbrigh test</w>\nkab ul</w>\ninc india</w>\ndhan ush\narchae ological</w>\nche ape\nmizz ou</w>\nd hi</w>\nov ski</w>\nbax ter</w>\nasse mble</w>\nà ¢\ngi gi</w>\nac am\nwis ely</w>\nhaz ard\nnorth ampton</w>\nâľĪ ï¸ı\nme th</w>\nbla sting</w>\nre unite</w>\nmu lus</w>\nali zes</w>\nt read\nmil a</w>\ned ward\nko va</w>\npe sto</w>\nðŁij ¶\nvit z</w>\nhydrau lic</w>\nrefurbi shed</w>\nmo tel</w>\nisab ella</w>\nhom me</w>\nsever ance</w>\nuph ol\nmis erable</w>\nf ari\nlat ter</w>\nef er</w>\ncrack ers</w>\nes l</w>\nac io</w>\nyy j</w>\nin an</w>\nec b</w>\nz ind\npan as\ntru cking</w>\nre ed\nsh aker</w>\nburge ss</w>\nem pire\nag nes</w>\nn ington</w>\nart works</w>\nfr s</w>\nti le\nbi ome\neu n</w>\nch ong</w>\nameric ana</w>\ngod father</w>\ngo blin</w>\ni shi\n! ).</w>\ntemp ted</w>\ngen omics</w>\nmand ate</w>\nck y\nðŁĴĻ ðŁĴĽ</w>\nsom ali</w>\nbr andy</w>\nin ven\nspoke sperson</w>\npc b</w>\nyu an</w>\nh g</w>\nfa z\nstarwar s\nro wan</w>\nblue grass</w>\ndon g\nd day</w>\ntrin idad</w>\ner ton</w>\nban ning</w>\nre tention</w>\ncu red</w>\ntober fest</w>\nre set</w>\nwe is\ndeta ched</w>\nbehindthe scenes</w>\nimmun ity</w>\nph a</w>\nbra y\nðŁij ½</w>\nran cho</w>\nram say</w>\nest onia</w>\nnd tv</w>\n] .</w>\ncab aret</w>\ntar o</w>\nd v</w>\nshow cases</w>\nplu m\nðŁij ¸\nson oma</w>\npre pa\nmemor ab\ne stu\ndrive way</w>\nu les</w>\nmagn us</w>\nx r</w>\nnn n</w>\nmuch as</w>\nen ge\nstre amed</w>\nfore stry</w>\naudio book</w>\ntro y\nreck less</w>\nkil om\nru ler</w>\nra k</w>\nproce ssion</w>\ni ons</w>\npo ole</w>\nnoc tur\nwh s</w>\nfarm house</w>\nper a</w>\npar me\nhypocri sy</w>\ns ics</w>\nv ant\ncas k</w>\nholi stic</w>\nau st\nÐ ¿\nin do\nðŁij© âĢį\ndi so\ndisp atch</w>\nol sen</w>\nmake it\nen nis</w>\ncent re\nar range</w>\nðŁĮ ¼</w>\nsal ted</w>\nea siest</w>\nf ate\nreg atta</w>\nmo zz\nac an</w>\nsin i</w>\ng ically</w>\nch ops</w>\nchick en\nwork in</w>\nha gg\ninvol ve</w>\nwee ds</w>\nbook day</w>\nwake up\nky r\nmichel in</w>\nfu ss</w>\nre juven\nvac ancies</w>\nincar cer\nm st</w>\nsc ents</w>\nsovere ign</w>\nkick er</w>\nà §\nbo d</w>\nâĢĶ ></w>\nsa h</w>\nmob il\nshrop shire</w>\noph one</w>\ndress er</w>\nmis suni\nhep burn</w>\ni mo\nfoli age</w>\ndiagno stic</w>\nas san\ncycl ing\nguil t</w>\nc sa</w>\npuertor ico</w>\nwin elover</w>\nwake field</w>\ndo ggy</w>\nk he\npa pp\nco g\nal lot\ncu ck\npoe tic</w>\nmi o</w>\nre vit\nmag ician</w>\nç ¥\nant enna</w>\nwest wood</w>\nmber g</w>\nlux e</w>\noat meal</w>\nØ ¬\nte at\nffe e</w>\nsear ches</w>\nl ly</w>\nplu to</w>\nel on\nlet tering</w>\ninno cence</w>\nfa i</w>\nann on</w>\ntelang ana</w>\nma it\nneu ral</w>\ncan ni\nar oma</w>\na stor\nfe x</w>\nco cac\nmon etary</w>\nf ent\nun sure</w>\n' @</w>\nindi rec\nteh ran</w>\nisol ation</w>\nli bs</w>\nmake up\nmerce des\nff y\nhe tero\nde o\nsco m</w>\ncur sed</w>\nveteran sday</w>\nfranken stein</w>\nshre ws\nde co\nge ese</w>\nlefto ver</w>\nha did</w>\nvari able</w>\nacade mics</w>\ncarol in\nunder going</w>\nvari ation</w>\nna h\nssi er</w>\ngamer sunite</w>\npur suing</w>\nemer ged</w>\nll ers</w>\ncontrol ling</w>\nro aring</w>\nmete or\nvol t</w>\ndaw gs</w>\nbe aver\nis life</w>\nbathro oms</w>\naci onal</w>\npre vent\nlake district</w>\nin als</w>\ny ani</w>\ngra bbing</w>\nsac ks</w>\nle z</w>\nsw ay\nk ool</w>\ntime s\nklo pp</w>\nla de</w>\ncon cord</w>\nresul ted</w>\nrevi ve</w>\nrecon ciliation</w>\nol and</w>\naz z</w>\ngir o</w>\nmand arin</w>\nde en\nnutriti onal</w>\nis coming</w>\nvan i</w>\naw www</w>\nder ived</w>\nlove your\nstop the\nshou ting</w>\nnov ak</w>\nðŁĻĮ ðŁı¾</w>\nlo af\ndispla ying</w>\nsunday with\nma guire</w>\nch eri\nðŁı Ł</w>\nre match</w>\nqu ic\nÚ ©\ny in\nðŁĺ ¹\nili ve</w>\nz ip\nour ke</w>\ndown loads</w>\nsw at</w>\nmissi ss\ncare rs</w>\nt ment</w>\nproper ty\nhahahaha haha</w>\ngi bbs</w>\nsur rey\nar ise</w>\ntic ism</w>\nsti a</w>\nir ling</w>\nfro g\nco se</w>\nbas sist</w>\nfore ig\nlea u</w>\npil lows</w>\nhol la</w>\neli e</w>\ndisclo sure</w>\npeanu ts</w>\ninte ch</w>\nww c</w>\nplun ge</w>\ntrium ph\ncor i\nsli ppers</w>\nðŁĻı ðŁĻı</w>\nneutr ality</w>\nma re\nhair y</w>\ngang ster</w>\nhu mming\ncust ard</w>\nmer lin</w>\nale a</w>\ns by\ndam p</w>\nmo han\nver bal</w>\nj st</w>\ngu tted</w>\nb jor\nun finished</w>\nðŁĩ¯ðŁĩ µ</w>\nun happy</w>\nâļ« ï¸ı\nby pass</w>\nat su</w>\nfis cher</w>\nsa v</w>\nafric ans</w>\nre use</w>\nmid way</w>\ndemo lished</w>\nger rard</w>\nher cules</w>\nÄ 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e</w>\nsquir rels</w>\nse ize</w>\n: #</w>\nle k\nti mb\nst acy</w>\nnas daq</w>\nrepe atedly</w>\nbr at</w>\nmi ghty\ncompetit or</w>\nmah one</w>\nde si</w>\no ke\nbm w\nshi e</w>\nf cb\ncheape st</w>\nminim alist</w>\npar amount</w>\nn ate\nhar as\ninsan ity</w>\nlat eral</w>\nment ality</w>\nmo zam\nta pped</w>\nyad av</w>\nu sp\nb way</w>\nthe od\nbil t</w>\nra ids</w>\nem press</w>\nadap ted</w>\npat ron\nnut shell</w>\nag ra\nbe aded</w>\nsundaywith marsha</w>\nvi king\nproce ed\nmain tained</w>\nthinkbig sundaywithmarsha</w>\nsn es</w>\nmus ica</w>\nto wer\nch ab\nbo k\nsm t</w>\ninsul t</w>\nharve sting</w>\nwindo w\nru ther\nbe ige</w>\ndec al</w>\nindic ate</w>\nma iling</w>\nri ft</w>\npo le\nander son\nch oral</w>\nsp ride</w>\nl ili\nev elyn</w>\nimrankhan pti</w>\n.... \"</w>\nke red</w>\nun dp</w>\nwater falls</w>\nse ars</w>\nle mans</w>\nworld series</w>\nri el</w>\nani e\napp ar\nscore rs</w>\nlam p\na than</w>\nphys icians</w>\nqu inoa</w>\nrefu sing</w>\nvu itton</w>\nunle ash</w>\ns la</w>\npat i</w>\nshou ts</w>\ninten tions</w>\nfo amed</w>\neurope an\nneighbor hoods</w>\nme er\nman son</w>\ndu h</w>\nbr at\ncon es</w>\nbow l\nkazakh stan</w>\nठ¿</w>\nin appropriate</w>\ndel hi\nketch up</w>\nful ton</w>\ns ys</w>\nconsul t</w>\ngar field</w>\nto go</w>\nf ml</w>\nf led</w>\nb ds</w>\nfacilit ate</w>\nree bok</w>\nselfi e\nelev ate</w>\nactiv ate</w>\nbi ble\nca wx</w>\nb ys</w>\ncam ille</w>\nsy ou\nsk ool</w>\nher t\nw bc</w>\nple dges</w>\nrecor der</w>\npo sh</w>\nac re\nso aking</w>\nmat il\nv sco\nshoot ings</w>\npla r</w>\ne con\nðŁĻĮ ðŁı»\nrashi d</w>\nu bi\nðŁ¤ ¤</w>\nsw inging</w>\nwi pe</w>\nrap tor</w>\nm su\nmusic video</w>\ndur ham\nat tic</w>\napar ty</w>\nfe tus</w>\nactiv ation</w>\naa z</w>\nmotiv ate</w>\nðŁĴķ ðŁĴķðŁĴķ</w>\nj al</w>\nठ®</w>\nag on\nsche er</w>\nstal ker</w>\nfo ster\naz zo</w>\ntele gram</w>\nvi gor\ns laugh\nscreen shots</w>\nentrepre neu\nkri stin</w>\ninten tion</w>\nch illi\nfr action</w>\ndon a</w>\nge a</w>\ntc u</w>\ns ite\nla k</w>\nem il\nd nt</w>\nbor o\nwil kinson</w>\nre cu\nato day</w>\nt anya</w>\nbl anco</w>\ncd n</w>\nbrilli antly</w>\ng cc</w>\nac c\nevacu ated</w>\nther ine\nden ny</w>\ncait lin</w>\nshe pard</w>\npou ch</w>\nhand held</w>\nsou theastern</w>\nha a</w>\nà ´\nre solutions</w>\nled ger</w>\nsr in\nr ar\nshat tered</w>\nchim ney</w>\nim with\nmete or</w>\nhand led</w>\nra ke\ntown send</w>\nen han\nshi py\nduc t</w>\ntw x</w>\ninflam matory</w>\nwar hammer</w>\ntheat rical</w>\ngro s\nsk ar</w>\nsco tty</w>\nni el</w>\ntit o</w>\ntin i</w>\nconne ction</w>\n_ .</w>\ngoldeng lobes</w>\nsha q</w>\nðŁı ³ï¸ı\nhall way</w>\nfron ts</w>\neffec tiveness</w>\ngla ston\nd hs</w>\nex pi\nto h</w>\nc pl</w>\nsc s</w>\nre o</w>\nha g\nresemb lance</w>\nhor an</w>\nabu sive</w>\nqu er</w>\nvirtu e</w>\ncho lester\na q</w>\nshan e\nm ce\ncarri ers</w>\ndi stress</w>\nre wind</w>\n ¡\nvoo doo</w>\nint act</w>\nann o</w>\nðŁĺ ¤\npi led</w>\nadi a</w>\nãĥ ³</w>\nen 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eyeon</w>\nnick imin\ngb p</w>\nà µ\nw ap\nsec co</w>\nma shed</w>\nðŁĴ¥ ðŁĴ¥\naugu stine</w>\ndiss ol\ndic tator</w>\nâ ĵ\nvi per</w>\ned fringe</w>\nvau x\nhard work</w>\nbook let</w>\nno x</w>\nchi ff\nðŁĴ ¨\nobserv ations</w>\nxbox one</w>\nu sher</w>\nke er\nlu p</w>\ndal las\ncal gary\nma dra\ndi ous</w>\nk bs</w>\nwood ward</w>\nhero ine</w>\nlu mber\nsea world</w>\no ws</w>\nmc ke\nmaver ick</w>\ngu la</w>\ncross roads</w>\nfan g</w>\ns ade</w>\nnik ol\nchee tah</w>\nme c</w>\npp g</w>\ner ick\nðŁİ µ\ntox ic\nbj j</w>\nviol a</w>\nsp ire</w>\nch ino</w>\ntra vis\ninstitu tional</w>\nha as</w>\nlow ry</w>\nw ac\nea e</w>\nhu mid</w>\nmp ton</w>\nru ck\nje w</w>\nc ine</w>\nzim mer\nse f</w>\nbhar at\nfre es\naam ir</w>\nðŁĴ ħ\nz inc</w>\nwan e</w>\nmulti player</w>\nroyal wedding</w>\ne el</w>\npreci pit\nqu ery</w>\nkimber ly</w>\nisa bel</w>\nful fill</w>\nig an</w>\nvau l\npan e</w>\nsc y\ndig it</w>\ngun n</w>\nu tah\ndog day</w>\nfi on\nxia omi</w>\nda c</w>\nel ast\ncha vez</w>\nro blo\ng ine\nten th</w>\nab h\nke to\nhur dle</w>\nna dia</w>\nmemorab ilia</w>\nha bs</w>\nqu an</w>\nh w\nhv ac</w>\npix ar</w>\nec cle\nkram er</w>\naccu ses</w>\nðŁĴļ ðŁĴļ\nper se\nmean time</w>\nwa hl\natle tico</w>\nâĢ¢âĢ¢ âĢ¢âĢ¢\nott oman</w>\nno vo\nk us</w>\nconne cted</w>\ntru sts</w>\nd mv</w>\nspen cer\nrahu lg\ndo ve\nsto kes</w>\nbolog na</w>\nenthusi asts</w>\nà ª\nrockstar games</w>\nted cruz</w>\ndu ras</w>\ns acked</w>\nlate x</w>\nimmer sive</w>\ncer t</w>\nlu cin\nprinci pals</w>\nfa res</w>\nsa ils</w>\nfar n\nam ent</w>\nsaf fron</w>\nquent in</w>\ncheck point</w>\nfer ris</w>\nex cur\nðŁijī ðŁı¼</w>\nbai ley\nse h\nter re</w>\nmad am</w>\ns band</w>\nwan derers</w>\ncumber batch</w>\nyy c\ndigit ally</w>\nblackandwhite photography</w>\nroll in</w>\nmoroc can</w>\nðŁĮ ħ</w>\ndin ner\nd well\nto om\nm ye\nez ra</w>\ncp fc</w>\nwar hol</w>\nme er</w>\njon ah</w>\nno aa</w>\ns gate</w>\nso on\nsecu lar</w>\ng ating</w>\nti o</w>\ndri ver\nsi ssy</w>\nassan ge</w>\nta th\ned mund</w>\nbobc ats</w>\nra ji\npo stage</w>\nstu ds</w>\nm gm</w>\nkat o</w>\nedin burgh\nmeet the\nshir t\nfa a</w>\nmens fashion</w>\nsp reads</w>\nwi m</w>\ncar ts</w>\nphoe be</w>\nj ars</w>\nbot swana</w>\nÙ Ĥ\ned war\nsk ar\nri ve\ngu sty</w>\nc tv</w>\nferdin and</w>\nsu therland</w>\nnickimin aj</w>\nk v\nsi us</w>\nbee ch</w>\nre z\ndesi res</w>\non ial</w>\ncamp o</w>\nquar ry</w>\nlor raine</w>\ngil more</w>\nig gy</w>\nµ ï¸ı</w>\nho pping</w>\navi z</w>\nðŁĮ º\nuni sex</w>\ndedic ate</w>\natt itudes</w>\nste er</w>\njun kie</w>\nrail way\ny b</w>\nwhi sper</w>\nkey an</w>\nk us\nju g</w>\ndi x</w>\na ins</w>\nsum mon\nov ich</w>\nsy ed</w>\nher ald\nma ison</w>\nme ded</w>\nwild flower\nmain land</w>\nri sky</w>\nru kh</w>\nover looked</w>\nki c</w>\ndestro ys</w>\nnam an</w>\nki p\nz ano</w>\nchampion sleague</w>\nban dit</w>\nquin cy</w>\nsmi le\ncal vin\nopen ings</w>\nta pp\nol ulu</w>\nspec tro\naccred ited</w>\nap k</w>\npra ised</w>\nbar nett</w>\npol len</w>\npremi ered</w>\nselen agomez</w>\ntou red</w>\nscreen ings</w>\nuu u</w>\nmis o\nen se</w>\nadam lambert</w>\nguel ph</w>\nhar yana</w>\nhu tto\nle ar</w>\nl tc</w>\npo ached</w>\nbrex it\næ Ŀ\ntt c</w>\npa vement</w>\nmon gers</w>\nro e\nad ers</w>\nling ton\nparticip ant</w>\nca red</w>\nga il</w>\ny ates</w>\nlan tic</w>\ndash board</w>\njo o</w>\nfeli pe</w>\nssi onist</w>\nbu m\ns end\na eri\nthu gs</w>\nluci fer</w>\na he\ndete ctor</w>\nfil ly</w>\ngas oline</w>\nham per</w>\nhump day</w>\nthe ta</w>\nthe band</w>\nfore casts</w>\no hhh</w>\nlo bb\nhol l\ncp u</w>\naz u\nad ar\nhai ley</w>\nbu b\ncar t\nquo ted</w>\nan archy</w>\npan cre\ntwit art</w>\nal den</w>\nst ash</w>\nthe less</w>\nor ni\nbelie bers</w>\nmor mon</w>\npartic le</w>\navi ation\n⬠Ĩ\nwebcam toy</w>\nsad dened</w>\ncru is\nham let</w>\nn ct\nroll ins</w>\nmarque e</w>\nsaw yer</w>\nreli ance</w>\na ura</w>\ndi ec\nsoo thing</w>\nsig nings</w>\nak is</w>\nà ³</w>\nat kins</w>\naer op\nðŁĮ ¿\ny ab\nsh ari\ncon nol\ndu bbed</w>\nmanufac ture</w>\nconvin cing</w>\nfeelthe bern</w>\nra u\npu lit\non ec\ngem stone</w>\nur ging</w>\nbag u\nga h</w>\naci ds</w>\nfi anc\nzodi ac</w>\nsn oop</w>\nher rera</w>\niniti ated</w>\nven ge\nprofess ors</w>\npro di\nstron ger\ne mission</w>\nbb a</w>\nhal le\nta pp</w>\nhaw an</w>\nwh im\ncompe ted</w>\nmyr tle</w>\nir port</w>\ncold play</w>\nach e\nske p\nm son\nss ic\ncalli graphy</w>\nswim mers</w>\nme y</w>\npp c</w>\nthri ft</w>\npo c</w>\nre places</w>\ncommu ter</w>\nâģ¦ âģ¦@</w>\ngo ers</w>\nlo gue</w>\npara dig\nbas kets</w>\nsensiti vity</w>\njoh an</w>\natl antis</w>\n& &</w>\nsuit case</w>\nanxi ous</w>\nl h\nstr i</w>\ngal loway</w>\nstre ad</w>\nwar den</w>\ngr ounded</w>\nffici ency</w>\nli feat\nreli c</w>\ndisgu ise</w>\nisland ers</w>\nf cofficial</w>\nclassical music</w>\nb mc</w>\nen field</w>\nbi que</w>\noak ley</w>\nbat man\nsla ying</w>\nner ves</w>\nmul tit\ncalci um</w>\nprojec tor</w>\nscott 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ic\ninsp ire\ntu g</w>\nha re\nrepe ated</w>\nex pon\nlol li\nrho de</w>\npre cin\ninstall ations</w>\ninstag ram\naz ar</w>\ni es\nsole ly</w>\ndu kes</w>\nmission ary</w>\nvan guard</w>\nfursuit friday</w>\non d\npol ari\nma st</w>\nhar an</w>\njos é</w>\njack ed</w>\nec oun\nal ities</w>\nne ph\nra vel</w>\nmoder ated</w>\nsco w\ns fb</w>\nuru guay</w>\nas o</w>\nni g\nau du\np ints</w>\nlat ina</w>\nben z\nm itting</w>\nchar ted</w>\nmat ology</w>\ncit ro\nbiop ic</w>\nðŁij Ń</w>\ndjo kovic</w>\nfox y</w>\nagu il\nso to</w>\nan ada</w>\nsin king</w>\nsc rap\nhair s</w>\nbethan y</w>\nfact friday</w>\nðŁIJ IJ</w>\nunlea shed</w>\n) (</w>\ncontra dic\nram on</w>\ncoast line</w>\ny ong\nsn sd</w>\nli gan</w>\np ome\nmit age</w>\nge tt</w>\nwat i</w>\nri sk\nso aring</w>\nbru sh\nf pl</w>\nav an\nå Ĩ\nlar son</w>\nsh ear\nmul til\nblu r</w>\nmulti media</w>\nchun ky</w>\npar i</w>\nn ani</w>\nweir d\ncholester ol</w>\nchar les\ndream ed</w>\ntan ning</w>\npuzz les</w>\nfr am\nhand ball</w>\nch ag\nbeli ze</w>\nal u</w>\nbang s</w>\nÑ Ħ\ndetec tives</w>\nmc g</w>\nish q\nbo thered</w>\nsaf c</w>\nmp ing</w>\nten eri\ng ays</w>\nsail or\nan gi\nmul ticul\ngue ssed</w>\nros é</w>\nhigh ways</w>\nbro om</w>\nchatt anoo\n- '</w>\nsee ker</w>\non ed\nat f</w>\nlu c</w>\n> <</w>\nbar i</w>\nper cep\njewel ry\nas ph\nsor row</w>\nsl ing\nmam moth</w>\njac kie\në §\nwilt shire</w>\nsa o</w>\ncan cell\nim paired</w>\ntor ial</w>\nbre ed\nguy en</w>\njud ice</w>\ntit le\npro spective</w>\napplic ants</w>\nðŁį Ĭ</w>\nepis cop\ne id\nb yo\nstock ings</w>\nðŁĴĥ ðŁĴĥ\nll p</w>\nsna g</w>\nkeep it\nl ough</w>\nol son</w>\nmatur ity</w>\n!! !\"</w>\ncop ter</w>\ni sha</w>\nbl i</w>\nwil mington</w>\ntr youts</w>\nth ai\nðŁ¥ ³</w>\npe bble</w>\nkra ft</w>\nf p\n º</w>\nssi vely</w>\nli vin</w>\ncontest ants</w>\ntex tures</w>\njo an\nh dr</w>\nfilm festival</w>\nprov ence</w>\nwi do\nop end\nc si</w>\nsto wn\ncro ati\nad just</w>\nhost ile</w>\nanaly sts</w>\nil an</w>\ncu 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e</w>\ng ong</w>\nfi e\nmetho do\nfer ries</w>\nðŁ¤Ķ ðŁ¤Ķ</w>\nexplore rs</w>\nload er</w>\nattrac ted</w>\nil ton</w>\ngodd amn</w>\npi azza</w>\ndoc tr\nsav ing\nparagra ph</w>\nvisu alization</w>\nmay ors</w>\nwork flow</w>\nack les</w>\nðŁĺĤðŁĺĤðŁĺĤðŁĺĤ ðŁĺĤðŁĺĤðŁĺĤðŁĺĤ\nठ¸</w>\ntwer k</w>\nclu t\nlo ver\nte ases</w>\nsi an\no te\ndeter ior\naccor d</w>\nl fw</w>\nswar ovski</w>\nnat al</w>\ntra ps</w>\nk ina</w>\nanaly ze</w>\nlaye red</w>\nbever ages</w>\nun it\nran som\npe shaw\ndest ined</w>\nastro logy</w>\nsi pping</w>\nmiley cyrus</w>\ncam ino</w>\nmarshmal low</w>\nbli ss\nout back</w>\nfa q</w>\nint oler\nhumil ity</w>\npo ppin</w>\nhallo ween\nmon tene\nop hy\nnu n</w>\ntattoo ed</w>\na as\nðŁĮ ³</w>\ndale y</w>\nqual ity\ndu sa</w>\nfisher men</w>\nswi f\nter rac\nst au\nle in</w>\ntrol ling</w>\nship ment</w>\ngarden er</w>\nmarch madness</w>\nhead band</w>\ngr t</w>\nbur nett</w>\nw and</w>\n!!!! !!!!!</w>\ngh e</w>\ndu x</w>\nhu d</w>\nwar ner\nðŁĩ ¦</w>\nex ile</w>\nrescu e\nrat a</w>\nd han</w>\nduc ati</w>\ndro wn</w>\nbl ends</w>\nspi e\nalli gator</w>\nsimul taneously</w>\nbroo ke\nu ke</w>\nk har</w>\ncomm union</w>\nri ka</w>\nford fc</w>\nchin atown</w>\nyou rown\nme y\ncan al\nsyste matic</w>\nde pri\nox ford\nan il\nw ut</w>\nequ ation</w>\nbe z\nfle ur</w>\nthe good\nlang ley</w>\nad ity\ned ith</w>\nal fie</w>\nо ÑĤ\nen cry\nbr ill</w>\nex emp\nce sar</w>\nmb ling</w>\nab ri\nsc icom\nj ing</w>\nschool ing</w>\nmi ka\nmechan isms</w>\nimpromp tu</w>\nrhe a</w>\nmoo re\ncrime a</w>\nbe sto\nwri ght\nel ders</w>\nro ds</w>\nkam al</w>\nfolkl ore</w>\nbe et</w>\nmini on</w>\nreli eve</w>\nthr o</w>\nteam usa</w>\npas cal</w>\nmade with\nboli via</w>\nitt i</w>\nfree bies</w>\ndesi red</w>\nbest selling</w>\nl iness</w>\nla den</w>\nke ane</w>\nmi sts</w>\nhipp ie</w>\natta chment</w>\n@ /</w>\nse w</w>\nflan agan</w>\nâĿĹ ï¸ı\nsupre mac\nstl cards</w>\nsi as</w>\nq u</w>\nrh ys</w>\nste ep\nval leys</w>\nv w\npav ing</w>\ndisp at\nal ison\npor te</w>\nid u</w>\nnew sc\nsoc ket</w>\nmo s\nco star\nre vo\nprote ins</w>\nstanley cup</w>\nm cal\near ring</w>\nse cs</w>\nmc lean</w>\ncap ric\nnick elo\nad en\nv c\nshou se</w>\nadap tive</w>\nmaxi mize</w>\nentertain er</w>\npro se</w>\ngri ffi\nsix teen</w>\nlam ar\nmi rage</w>\nsaudi arabia</w>\nawe ather</w>\nru st\nin filtr\nfashion week</w>\nðŁĺĬðŁĺĬ ðŁĺĬ</w>\nselec tive</w>\nbubb le\na den</w>\nfen nel</w>\ndeci sive</w>\nm ta</w>\nmock ing\nmb les</w>\nst amp\nmu le</w>\nbernar do</w>\ngr in</w>\npo tt\nj ingle</w>\nvet tel</w>\ncolom bian</w>\ncam o\nmotivation monday</w>\nba han</w>\np ly</w>\ndh ary</w>\nk ami</w>\nx men</w>\nsleep er</w>\ngar a</w>\nmy sti\nconfi dential</w>\nconflic ts</w>\np neu\nce s\ninsur tech</w>\nclean se</w>\nme rely</w>\nva is</w>\ntu x\nthe great\nshar on\nma j</w>\nhol a</w>\neco systems</w>\naj ay</w>\naa j\nhu sh</w>\nhar mon</w>\nbackto school</w>\nwiki leaks</w>\nreflec ted</w>\nðŁĺ ĵ</w>\ncommemor ating</w>\nac et\nbuck ingham</w>\nmessi ah</w>\ntu ous</w>\nhor net</w>\nto be</w>\nd q</w>\nhe ine\nmi g</w>\npl ate\nnichol son</w>\nsp ie</w>\ncumber land</w>\nnor mal\npho bia</w>\nhappy halloween</w>\ncity fc</w>\nmc el\ngilli an</w>\nke to</w>\nlu de</w>\nde mise</w>\nsu ga</w>\nstr ate</w>\nmcgr ath</w>\nvisit scotland</w>\nfoo led</w>\ncb r</w>\ngc se</w>\ncol ori\npo td</w>\nmissuni verse</w>\nfin ances</w>\nma poli</w>\nfor ks</w>\nØ ´\ncann on\nmedic inal</w>\nðŁĹ ĵ</w>\nkh o</w>\nwre ck\npan to</w>\nbag el</w>\ngu ll</w>\nsyndic ate</w>\nic y\npr c</w>\nki en</w>\nzi ka</w>\nti sh</w>\npe ta</w>\nc co</w>\nli za</w>\nch ut\nex traction</w>\nel g\ngl i</w>\nfu eled</w>\npos it\nrespec tively</w>\nleice ster\nbr ink</w>\nvulner ability</w>\nim ported</w>\ne sha</w>\nðŁ¦ ħ</w>\nr ural\nre ll\ngam ing\natlan tic\naband on</w>\nno ah\nre solved</w>\npro state</w>\naller gic</w>\nps d</w>\nâĺ ¹\ndun geon\nfang irl</w>\nillumin ated</w>\nm hs</w>\nwhite sox</w>\nd ently</w>\nck o</w>\nendor se</w>\nover ly</w>\ndazz ling</w>\nprior iti\nnight life</w>\nut il\nbe have</w>\nflam en\neast bound</w>\nðŁĴ Ł</w>\nilove you</w>\ngov uk</w>\nmozam bique</w>\nalle gi\ndr i</w>\ntestim onial</w>\nath s</w>\nì§ Ģ\nmm y\nshab by</w>\npro secco</w>\nfriend ships</w>\ncal am\ndam ages</w>\noff set</w>\njura ssic\njun o</w>\narre ll</w>\nðŁĴ ©</w>\ninterven tions</w>\ndare devil</w>\ncar ver</w>\nrun away</w>\nran e</w>\ntruste es</w>\nha ute</w>\ndep ths</w>\nðŁİ Ń</w>\nme in\nsacrific es</w>\ncon cier\nne sting</w>\ni zzy</w>\nme tam\nilove my\nur ine</w>\ndu lu\nmal hotra</w>\nve ins</w>\nnight ly</w>\nco at\nan di\nhe witt</w>\nlon el\nci ble</w>\nwr ite\njen nie</w>\nsant ac\nĸ ï¸ı</w>\nstr ato\nsingapo re\nsop rano</w>\nkri sten\ncheer ful</w>\nflee twood</w>\nfa iri\nm eli\nwa st\ntur nt</w>\nsfor sale</w>\nsc rolling</w>\nangel ina</w>\nren dition</w>\njeric ho</w>\nnick y\nor b\nfla vo\npatri ot\nash eville</w>\nsick ness</w>\nre fund</w>\naggre ssion</w>\nb pl</w>\nãĥ ĥ\nelu sive</w>\nthi story</w>\nhang er</w>\nbu ffs</w>\nvil las</w>\nat kinson</w>\nsp h\nja it\ndecl ined</w>\nwo k</w>\nsupre macy</w>\noo tball</w>\ney ang</w>\nðŁİ ĵ\ns ford</w>\nath i</w>\nconsu me</w>\nroad ster</w>\ne so</w>\nu pro\nreci pe\nau f</w>\nuc i</w>\nar on</w>\noo oh</w>\ncs go</w>\nre ich</w>\nmc d</w>\nmin ute\nladi es\npun k\nrut gers</w>\nmee k</w>\nariz on\nta j\nland lord</w>\nde gra\nautu mn\nlyn x</w>\nus f</w>\nb hi\nfairy tale</w>\ndongha e</w>\nbet sy</w>\nexplo ded</w>\nchen nai\nop a</w>\npro tag\nbr ant\nðŁĵ °:</w>\ng f\npal li\nðŁı¼ âĢįâĻĢï¸ı</w>\nsu t</w>\nill ini</w>\ncolum nist</w>\nshir tless</w>\nde centr\nsear ched</w>\nec or\nbu ggy</w>\ns ack\nðŁĺĤ ðŁĺŃ\nde t\nther i\nor naments</w>\nbring back\nto v</w>\nquarter finals</w>\nic he\ncon stra\ngi er</w>\nbuchan an</w>\nvi x\nkay aking</w>\nmu stread</w>\nswal low</w>\nmel b</w>\nsc af\nop al</w>\nmay oral</w>\nhar at</w>\nðŁ¦ ĭ</w>\nschedu les</w>\nid f</w>\nha gue</w>\nro z\na ah</w>\nd mc</w>\ndu plic\nca che</w>\norph an</w>\nfrac ture</w>\nrec on</w>\nch av\nbun nies</w>\nal ain</w>\nmustaf a</w>\nðŁİ Ļ\nvac ations</w>\ndynam ite</w>\ntex ted</w>\nbroad caster</w>\nðŁĴ £</w>\nste amed</w>\nrock er</w>\ndi etary</w>\nluxury travel</w>\ninaugur ated</w>\nsa wards</w>\nvaugh n</w>\nlincoln shire</w>\nclick ed</w>\nkra ja</w>\nf anc\nremo ves</w>\nlayo ffs</w>\nmc far\nbre eds</w>\nwin nie</w>\njon ghyun</w>\nincen tive</w>\nvari ations</w>\npat ton</w>\natur day</w>\npersist ent</w>\npr un\npi ers</w>\ndal es</w>\næ ĸ\nbreast feeding</w>\nr ance</w>\nta wa</w>\nĤ âĸ\nmur doch</w>\ncap tive</w>\nthi stle</w>\nnic a</w>\ncommod ity</w>\ncou ldnt</w>\nboard walk</w>\ngraci ous</w>\npractiti oners</w>\nn gc</w>\nscru m</w>\nner o</w>\ncamoufla ge</w>\ncol on</w>\nhe i</w>\nphys icist</w>\nsaturday morning</w>\nten er</w>\nsi won</w>\ncolum ns</w>\nbru ne\ny vr</w>\nba ir\nreti res</w>\nhal am\ncab er\nshaz am</w>\nmin u\ncas cade</w>\nmilk shake</w>\ngri d\nd ren\nvin cent\nso dium</w>\nplat ter</w>\ncheer leader</w>\nchen ko</w>\ny ak</w>\nelimin ated</w>\nty po</w>\ny man</w>\nre think</w>\nâĿ Ĺ</w>\nts ville</w>\nbernardo kath</w>\nex tr\nðŁĺģ ðŁĺģðŁĺģ</w>\nta o\nre per\nmo ths</w>\nem powered</w>\nc iting</w>\ntranspor ted</w>\nmon ks</w>\nsan at\ncle ars</w>\nbachelore tte</w>\ncamp bell\nracha el</w>\nhar le\nhand ler</w>\nclimb s</w>\ninter ference</w>\nrele ase\nsh and\nr bs</w>\nhr h</w>\nãģ ª\nval le</w>\nr é\nsli me</w>\nw akes</w>\nchu bby</w>\nslo an</w>\nel ves</w>\nath en\nattor neys</w>\nmicro scope</w>\nston er</w>\nsc aling</w>\no be</w>\nc out\nse man\nmid week</w>\nbal sam\nðŁĺį âĿ¤</w>\nti ful</w>\nv ish</w>\nlo tta</w>\nri pping</w>\nre mn\nti re\nle ap\nha vent</w>\nla by\nhi mach\nwhisp ers</w>\nwe in\nðŁİ ¸\nwild flowers</w>\nse le\nu cc</w>\nli ability</w>\naz ine</w>\nsw ings</w>\nk ya</w>\nta ir\nre main\ne do\nflo ps</w>\npoc ket\ngrand ad</w>\nexam iner</w>\ngr is</w>\nffe ct</w>\nðŁijĬ ðŁı»</w>\nstud ded</w>\nheart beat</w>\nde acon</w>\nfirm ly</w>\ninfec tious</w>\nste f\nout lines</w>\nle asing</w>\ncla ws</w>\nsen se\ntab s</w>\nhoo t</w>\nmo sul</w>\nspa wn</w>\nco a</w>\nhog warts</w>\nve in</w>\nalban ia</w>\nmanu el\nb ino\nvaux hall</w>\nscot land\ngo bucks</w>\nmat ty</w>\nphy sio</w>\ntor ino</w>\nconst able</w>\ninvestig ated</w>\ns lower</w>\nmistak en</w>\nbay er</w>\nwild fires</w>\nvo ic\nx on\ntime to\nchas sis</w>\nbar ric\npi on</w>\nbald head</w>\nwoo k</w>\nregi str\ndra fts</w>\nb hs</w>\nli gue</w>\nl ick\nstaf fordshire</w>\nbaf ta</w>\ndar ry\nje anne</w>\nven ding</w>\ncor p\n⼠³ï¸ı</w>\nkid dos</w>\nfen way</w>\nca o</w>\nwest bound</w>\nðŁĺ Ļ</w>\ndv r</w>\nquick er</w>\nbla h</w>\ngoo die</w>\nðŁĴĭ ðŁĴĭ</w>\nvo x\nesp er\nfac ade</w>\ncor relation</w>\nred bull</w>\nrou p</w>\ndecl ining</w>\nchi ve</w>\nmc gee</w>\ntur o</w>\nin der</w>\nf eller</w>\nfu g\nil ysm</w>\nmar di</w>\npeshaw ar</w>\nki eran</w>\nine ma</w>\nmeat balls</w>\npe ck</w>\ndepre ssing</w>\nsen sing</w>\ngi z\ndd ington</w>\nspring watch</w>\nro aming</w>\nyellow stone</w>\nhorse shoe</w>\nam man</w>\nweek day</w>\nol or</w>\nðŁ¥ °\nboo sts</w>\nspr int\nscar ves</w>\nje e\nbee tro\ncl an\nall the\nìĦ ¸ë\nenlighten ment</w>\nado be\nre generation</w>\n? @</w>\ncont ag\nyach ts</w>\nto u</w>\nmor a</w>\nen voy</w>\nr ani\ngo li\ndhanush kraja</w>\nwood working</w>\nstreng ths</w>\nse di\ndisc s</w>\nar ina</w>\nsc on</w>\nlit e\nano ther\nðŁ¥ Ĭ</w>\nye men\ngu ern\nsav vy</w>\nlo yed</w>\nbiom ed\nheart break</w>\ncomra des</w>\nmilli e</w>\npat ch\nun f\njar vis</w>\nbl aming</w>\ncommemor ation</w>\nge y</w>\nå ¥\ncardio vascular</w>\nalig ned</w>\ndocu ment\n. ?</w>\naesthe tics</w>\nem u</w>\nthe irs</w>\nle h</w>\nps ic\nsi f</w>\npl ateau</w>\nex pend\ndomin ating</w>\nrob es</w>\nmauriti us</w>\nexcep tionally</w>\nhom er\ndiscover ies</w>\nbra un</w>\nten nant</w>\ninsul in</w>\nðŁİ ®</w>\ncar bs</w>\nte as</w>\n? !\"</w>\nzi e\nfranco is</w>\nbrow sing</w>\nth ol\ncla rence</w>\nhel per</w>\nob tained</w>\ncas sie</w>\nle es\n! ,</w>\npome gran\nhu bs</w>\npresti ge</w>\n] [</w>\nmach er</w>\nbott led</w>\npun ch\npi pe\no ch\ngall ons</w>\ndeliver ies</w>\nu ra\nun day</w>\nmon de</w>\ndepic ts</w>\nre gency</w>\noutra geous</w>\nkhal ed</w>\ncar o</w>\nhe arti\nza g</w>\ndevelop mental</w>\nover coming</w>\nstati stical</w>\nflavo red</w>\nfor ds</w>\ncre atives</w>\nlau rence</w>\ndi as</w>\nsun screen</w>\nin ked</w>\npre acher</w>\nn ul\nimpac ting</w>\nauti stic</w>\nâļ Ķï¸ı</w>\no ss\npel icans</w>\ncele ste</w>\nv b\nru mp</w>\nmc gra\nfair fax</w>\nhu mor\nbbc news</w>\nrow ling</w>\ncal der\nseam less</w>\nag ne\np ti\nmix ed\nt shirts</w>\nmer ci</w>\nb tob</w>\nwomen instem</w>\ngenealo gy</w>\npre ven\nl our\ncra dle</w>\ngi use\nÐ ¾</w>\nchron o\nfair ness</w>\nchocol ate\ntor y\nas da</w>\npre scott</w>\nstret ched</w>\nal man\nu il</w>\nre charge</w>\nin tre\nob st\nhosp ital\nhay ward</w>\nteneri fe</w>\nfried man</w>\nvap 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h</w>\nprepa redness</w>\nstation ary</w>\nincorpor ated</w>\ndy er</w>\nsarato ga</w>\ncele sti\n: \"\nantibio tics</w>\nor gs</w>\ninde fin\nap ron</w>\nи Ð\nfif teen</w>\nno f\nðŁĶ Ŀ</w>\nph x</w>\nte ga</w>\nm z\norganiz ational</w>\non air</w>\nband ung</w>\npleas ures</w>\nmor i</w>\nsecre tari\nrac coon</w>\nca shi\npil ates</w>\nk on</w>\ngeof frey</w>\nla o</w>\nkam p</w>\ndepart ments</w>\nback packing</w>\nan am\nà «\ncrack down</w>\naun ty</w>\non do</w>\nli zzie</w>\nph ers</w>\ncu n</w>\nðŁĩ ±\nk pop\npu t\ninten tional</w>\nconnol ly</w>\nbar clays</w>\nhs fb</w>\nswin don</w>\nu ku\ns ally\na int\nâľ ħ\npen ang</w>\nup lifting</w>\nepile psy</w>\ninter ro\nbun gal\ngo ku</w>\nblue berries</w>\nठ¦</w>\nu ssia</w>\nsil ky</w>\nmou red</w>\ni stic</w>\nbri efs</w>\nme ats</w>\ngo b\nch aser</w>\nstate wide</w>\npra sad</w>\ngl itch</w>\nar in\nban ff</w>\nmemb er\nðŁĺŃ âĿ¤ï¸ı</w>\nlo ving\nhall a</w>\nภ¡</w>\nsmo kers</w>\nyak u\nscicom m</w>\nphysi o\nsw ol\nlem ons</w>\ngel ato</w>\nch ool</w>\ncapit als</w>\nki stan</w>\nti ghts</w>\nspi kes</w>\ntrav ellers</w>\nik lan</w>\ncommissi oning</w>\nar ine</w>\nemabiggest fans</w>\nempha sis</w>\nfront line</w>\npad dock</w>\ndestruc tive</w>\nba ha\nl inger</w>\nje wish\nshet land</w>\nmc gin\nmon key\nko z\ns one</w>\nraj ini\nte h</w>\ny en\nc vs</w>\nmasqu er\ngir ly</w>\nwe sle\nwas nt</w>\nbro dy</w>\ntermin ator</w>\ngil le\nmag gi\nbir die</w>\njeopar dy</w>\ncu bic</w>\nvm ware</w>\nintric ate</w>\nan up\nto pia</w>\neast on</w>\nsab res</w>\ninvestig ates</w>\nbu sting</w>\nbil ingual</w>\nvalent ino</w>\nin format\nfer re\nadvent ur\nhydr ate</w>\nfor sy\naz iz</w>\nsan to\ne de\nwhist ler</w>\ncontinu ously</w>\nd ham\nun used</w>\nji had</w>\naddic tive</w>\nvi dy\ndo b\ni do</w>\nfi ed\nni versary</w>\nn one\nfu er\nðŁĺį ðŁĺĺ\ncoven ant</w>\nprin table</w>\nimmac ulate</w>\no em</w>\ncl t\nserv ants</w>\nconsu med</w>\nun released</w>\nsc um</w>\npack aged</w>\nme re\nìĦ¸ë ¸\nto by\nta f\nspo ons</w>\nme al\nf ball</w>\nfair field</w>\njan et\nsilver stone</w>\ndart mouth</w>\nfollow me</w>\nvoy ager</w>\nkom bat</w>\nanni ver\nene w\nmag dal\nho ve</w>\nsa th\ngrizz ly</w>\ncar di</w>\ngart ner</w>\nsand y\nkan ye\npost ure</w>\npo ign\nim pulse</w>\nradio logy</w>\nhoriz ons</w>\nsi am\naish war\n= =></w>\nno che</w>\ntr is</w>\nel yn\ncom me</w>\ndu i</w>\nce c\ncouncill ors</w>\ncudd ling</w>\ncreep ing</w>\nloc ke</w>\nmanag es</w>\ntrans ferred</w>\nne cks</w>\ndi er\ndan o</w>\nv ick</w>\nlun ches</w>\nd he\nen sures</w>\ncri ss</w>\nul ster\nbann on</w>\ncont enders</w>\nsp am\nsweet ness</w>\nmed al\nhon duras</w>\narc tic\nultra sound</w>\nin fr\ndisco vers</w>\nei ffel</w>\nca sters</w>\nru ben</w>\ndu st\nawe ed</w>\natri um</w>\nlest we\nse ared</w>\nðŁĵº :</w>\nty ne</w>\nex changes</w>\nlittle mix</w>\nl le</w>\nastron auts</w>\nhersh ey</w>\nwork day</w>\nkno b</w>\nso v</w>\nre signs</w>\ntoday show</w>\nder man</w>\nan th</w>\naf c\nta 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ina</w>\nwild flower</w>\nathle tic\nphotograph y\npe sh\nca hill</w>\nchi lean</w>\ngou l\nfi oren\nðŁij ¶</w>\nz il</w>\nsk im\nbad oo</w>\ndeli a</w>\ntre ble</w>\nn cc\nðŁĩ¦ ðŁĩ\na house</w>\nbul lock</w>\nsol itude</w>\nا٠Ĩ</w>\ncan cers</w>\nfutureof work</w>\nhu tch</w>\nwater shed</w>\nwar mongers</w>\nsp illed</w>\ncolom bo</w>\nmo th\nassoci ations</w>\nweigh ed</w>\nglobal goals</w>\nnot just\nchrist i</w>\ntor g</w>\nswe ating</w>\nman eu\nclu sters</w>\nâĢ¼ï¸ı âĢ¼ï¸ı</w>\nta ped</w>\nul y\ntru sting</w>\nyu suf</w>\nte in</w>\nra b</w>\n, ,,,</w>\nsin ai</w>\naudi ble</w>\nexplic it</w>\ncro wns</w>\nsch iz\nat least</w>\nðŁĹ £\nde bra</w>\nje suit</w>\nene gger</w>\nz hen</w>\none sie</w>\ni it</w>\nss f</w>\ngur gaon</w>\nchak ra</w>\nbear cats</w>\nk ran\nk awa</w>\nreque sting</w>\nhan over</w>\ng end\nsor os</w>\nmer cy\nlovel y\ndo omed</w>\ntim my</w>\nku z\nul l\nab ram\nsa ison</w>\nãĥ «\nclean ers</w>\nre mo</w>\ncircu its</w>\nbar red</w>\no th\nmo 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s</w>\n- (</w>\nspor ty</w>\nun aware</w>\nreven ues</w>\nchristop her\nbank sy</w>\nav an</w>\nev apor\ncom press\neyel iner</w>\nto dos</w>\nbuff y</w>\nrenewable energy</w>\nly rical</w>\nar chan\nrapi st</w>\nfair trade</w>\nlma ooo</w>\nbeat z</w>\npro active</w>\nla pse</w>\nir ical</w>\nrevers al</w>\npo de\nmcin tyre</w>\nmac au</w>\nãĥ ķãĤ\nnash grier</w>\nf sa</w>\ng all</w>\nçĶ Ł\nperpe tr\nil ya</w>\nconfigur ation</w>\n% ;</w>\nstr ange\nrac i\nภĩ</w>\npic kups</w>\nkov sky</w>\nmam mal</w>\nw ps</w>\ng able</w>\ncompar ative</w>\nz h\nsave our\nda vey</w>\non etsy</w>\nmu ssels</w>\nmis er\ncri stina</w>\nelectr on</w>\ncra ve</w>\nlo ren</w>\nprecipit ation</w>\nm z</w>\nðŁį «</w>\nvin cen\nsnow board</w>\nno ida</w>\nah n</w>\nmarin ated</w>\ng tr</w>\ntown hall</w>\nmin is\nbethe l</w>\nadv an\nsu ra\nshi el\nfur ry\nðŁĺĤðŁĺĤðŁĺĤðŁĺĤ ðŁĺĤðŁĺĤ</w>\nlyn d\nso il\nsc ence</w>\nsen eca</w>\nshar jah</w>\ndick ens</w>\ncredenti als</w>\nav ar\nper k</w>\nrequ iring</w>\npre fer\nj ian</w>\nde ca</w>\nr ach</w>\ning for\ndel e</w>\nbe ep</w>\nðŁĴ »\ncis ely</w>\nhu ddle</w>\ngreen sboro</w>\nhaw king</w>\nho ax</w>\nhang ar</w>\nç ľ\nmis o</w>\nlo vin\ngre ta</w>\nab ad\nlogi e</w>\nat an</w>\nsnow flake</w>\nmahe sh\nfear the\nal kal\nbobb lehead</w>\nba hn</w>\nju dged</w>\nfu tu\nfeli x\nðŁį ĵ</w>\npi ke\nder iv\nnotic es</w>\nau er</w>\ndis super</w>\nor da\nwi pes</w>\nam ino</w>\nstri kers</w>\nfoo tb\ndram as</w>\npun ching</w>\nscore less</w>\nheming way</w>\nbi h</w>\nbal lad</w>\nchat ter\nam mo</w>\nkle in\nfabric ation</w>\nkari m</w>\nz end\nhi sto\nvol ta</w>\nrock y\nmarke ter</w>\nxtre me</w>\nsequ encing</w>\nparadig m</w>\ncle ats</w>\nboom ing</w>\nâģł âģł</w>\nblock ade</w>\npromp ts</w>\nyogh urt</w>\npur pose\nnu r</w>\nregu late</w>\nnois y</w>\ning rid</w>\nbird watching</w>\nbar tender</w>\nÙ ĥ\nwor dof\ncha otic</w>\nshor ty</w>\nel dest</w>\nz app\nonceupon atime</w>\nfl yo\nrit os</w>\nmike quind\nðŁIJ ´</w>\nregi stering</w>\n. ]</w>\nad ol\ngg gg</w>\npur ge</w>\nkid lit</w>\nar bor\nval ves</w>\nsynago gue</w>\no th</w>\nunanim ous</w>\nveri fication</w>\ndar rell</w>\nãģ Ħ\nvander bilt</w>\ntape stry</w>\npro sper</w>\ndid dy</w>\ndra fting</w>\nde cep\nmarqu is</w>\nst int</w>\nmichael jackson</w>\npee led</w>\nmen us</w>\nbb b</w>\nsc are\nema il\nwri gley</w>\nit is\nf ell\nsome thin</w>\nbar ra</w>\ned gar\ndi pping</w>\npu ddle</w>\nsla de</w>\nlear ner</w>\njal en</w>\nðŁ§ IJ</w>\nthe daily\nmikequind azzi</w>\nju x\niq bal</w>\nmckin ney</w>\nra iser</w>\nef an\ndr one\ncat o</w>\npic ket</w>\ncro we</w>\nl att\nuk o</w>\ngiuse ppe</w>\nhin i</w>\nsynthe si\nponti fex</w>\nsong writing</w>\nto d</w>\nswit ches</w>\ndin ners</w>\nh q\ngabri elle</w>\npensac ola</w>\ncir cle\nexpo ses</w>\nev s</w>\nriyad h</w>\npro men\no ck\nsa j\ncit ation</w>\nbrew co</w>\njo si\nep aper</w>\ndri f\npoint less</w>\ntang led</w>\ncri pp\nline ups</w>\nfairi es</w>\ndaz e</w>\nmour n</w>\nbla dder</w>\nsal z\nbur undi</w>\nbook mark</w>\nthe people</w>\nsub sequ\nprinci pal\nsk er</w>\ncourt ney\na oki</w>\nrac ers</w>\nad m</w>\nmom a</w>\ncritical role\nhou n</w>\nshed ding</w>\nsa ka</w>\nace ous</w>\nmck ay</w>\nhus bands</w>\n ½</w>\nme da</w>\naccu sations</w>\nro sel\nnc is</w>\nwitne ssing</w>\nor ama</w>\ngo ds\nhil ton\nel man</w>\nÃŃ n</w>\nmeg ap\ncra ven</w>\nannoun cer</w>\ncrit eri\nsheffiel dissuper</w>\nmilit ant</w>\nconsu l</w>\nhoo ded</w>\naby ss</w>\nb x</w>\nma dam\nlo cu\nmary am\nmanic ure</w>\ngrat is</w>\nac tresses</w>\nros ario</w>\nthis dayin\nking ly</w>\ngn ome</w>\ncel ine</w>\nr ous\nhe el\nlil ac</w>\nvish al</w>\nab h</w>\nthor ns</w>\ns ls</w>\nne al\nconstruc ting</w>\nbe ren\ns lang</w>\nma ins</w>\nfar ra\nsar ko\npai ge\ngu iller\nl ala</w>\nice berg</w>\nnou n</w>\nplann ers</w>\nu mmm</w>\nou ses</w>\nill ary</w>\nma an</w>\nbox ing\nzi pper</w>\nsrin agar</w>\nmigu el\no str\nmp o</w>\nresponsi bly</w>\nlan terns</w>\nappli ance</w>\nx b</w>\ngren ade</w>\nneglec t</w>\ndy sle\nham mock</w>\nne ctar</w>\nwit cher</w>\nr gv</w>\ndi ence</w>\nser bian</w>\nseed ed</w>\ncru z\nbi sh\nsp he\ne q</w>\nsky rim</w>\nalge bra</w>\nphil ately</w>\nbungal ow</w>\nge off\ny ves</w>\ndemand ed</w>\nconsider ations</w>\nthe vamp\npawan kalyan</w>\nco ded</w>\ngrit ty</w>\nerup tion</w>\nse infeld</w>\nuni denti\nëĭ Ī\nwor m\nac us</w>\nse ung</w>\ndun g</w>\nro land\nsu d</w>\ndi visions</w>\nab lanc\nshor test</w>\nj f</w>\np oun\nplant based</w>\nbe to</w>\ntough er</w>\nmc o</w>\ndon et\nmark us</w>\nv fl</w>\nðŁı ł</w>\nopen ing\nco ward</w>\ncaber net</w>\no xi\nburle sque</w>\nsand ra\nsu mo</w>\nconsi st</w>\ntho t</w>\ncay man</w>\nmotor ola</w>\ngutier rez</w>\nd slr</w>\ny w\nno bel\nnov ice</w>\nmoms demand</w>\ngrun ge</w>\nsp or</w>\nd cc</w>\npre sses</w>\nsli st</w>\nallot ment</w>\nvoc ational</w>\nft c</w>\npu ja</w>\nlo ven\nutt arak\ntan dem</w>\nsh ep\ncome dians</w>\nanat om\ncant wait</w>\nhealthye ating</w>\nwest side</w>\nmar gins</w>\nchi ang</w>\nasbe stos</w>\nstupi dity</w>\nproble matic</w>\nfit bit</w>\n: $</w>\nceil ings</w>\nshu a</w>\nprotec tions</w>\nbio tic</w>\nbeng ali</w>\nre sts</w>\nbien nale</w>\ntim o</w>\ncul min\ne minent</w>\naffe ction\nunbeliev ably</w>\nindividu ally</w>\ncanvas sing</w>\nwh itt\nnov asco\nchin son</w>\nh pe</w>\ngo w</w>\ngloucester shire</w>\npa o</w>\nthresh old</w>\nchev ron</w>\ns ine</w>\nwe ther\npp ie</w>\naqu ino</w>\nantwer p</w>\nâĸ ¬\npo on\ninst af\nequ ine</w>\ncinemato graphy</w>\nnbaf inals</w>\nvali ant</w>\nkil kenny</w>\nte rence</w>\nsyste mic</w>\nsr l</w>\np ound\nmade ira</w>\npl ough\ntre cht</w>\nmat ed</w>\nmp d</w>\nransom ware</w>\nph in</w>\nli qui\nbb ce\nboom er\ni standwith\ncon ju\nr te\nnar a</w>\nfoo lish</w>\nda shing</w>\nvier nes</w>\nbr ite</w>\nda u</w>\njuni per</w>\nai da</w>\nyou now</w>\nra zer</w>\nde i\nrepe ating</w>\ncomfor ting</w>\nadjac ent</w>\ne to</w>\nca sted</w>\nchat ur\nmu er\nsyn th\nsan itary</w>\nmac le\nindepend ent\nlaw ful</w>\ne erie</w>\nh or</w>\nðŁĴ Ń</w>\nam rit\nvel o</w>\nstation ery</w>\nmu f\nmay may</w>\ncontempl ating</w>\nelabor ate</w>\ngre gor\ndri es</w>\nac col\nภļ\nschwarz enegger</w>\nill nesses</w>\nday break</w>\nfollow back</w>\ncollu sion</w>\nelectr onic\njo vi</w>\nhiro shima</w>\nta w\nhom ec\nmic ah</w>\nqu itting</w>\nfro sting</w>\nben fica</w>\nhel i\ns ical</w>\npic cad\ncorpor ate\nment orship</w>\nyou are\nsing er\nshi va\nru ne\ning er\nri um</w>\nplay able</w>\ndoo p</w>\nwil low\nter re\nni p\nat d</w>\nwar bler</w>\nprofession ally</w>\ner ase</w>\nproce ed</w>\npedestri ans</w>\nmis chief</w>\nben ding</w>\nalas kan</w>\nc kett</w>\nmo p</w>\ndd les</w>\nshut ter</w>\nge ared</w>\natene o</w>\nma deline</w>\ng ations</w>\no sha</w>\nder ick</w>\nsw ild\nan gry\npat ents</w>\nhun k</w>\ndecre ased</w>\nfr y\nðŁĴĸðŁĴĸ ðŁĴĸ</w>\nsal on\nquant ities</w>\nd ario</w>\nni gel\nku ma</w>\njen n\nhapp ye\nxx x\nrex perience</w>\npro s\nau sch\nrele ssly</w>\nham burger</w>\nfuku shima</w>\ner ne\nstat ec\nren d\nmay field</w>\nj one\nlef ty</w>\nbern stein</w>\nsm il\ngener ates</w>\nfore station</w>\nband its</w>\nta yo</w>\nr ca</w>\nac ci</w>\nrodri go</w>\nkn app</w>\nelo vers</w>\nvege tation</w>\nu ral</w>\nle ft\nħ ï¸ı</w>\nworl dre\nsur i</w>\nembar k</w>\nw son</w>\nba you</w>\nmu ller</w>\nmo vers</w>\nðŁķ º\npresby ter\nl f\ncre e\nbat b</w>\nsal am</w>\ndemonstr ations</w>\nan ec\nn pc</w>\nit ics</w>\nto graphy</w>\nre inst\nthur st</w>\ntal e\noff ences</w>\nsmart city</w>\nbro tha</w>\nofthe year</w>\nin valuable</w>\near n\nðŁijı ðŁı½</w>\nkre mlin</w>\ngra dy</w>\ntown fc</w>\nguern sey</w>\nma ha</w>\ncontag ious</w>\ndre x\nbe en\n( £</w>\nnati vity</w>\nk tm</w>\nsomer halder</w>\ncomp ounds</w>\níķ ĺ\n\" âĢ¦</w>\naf g</w>\nott news</w>\nh ound\nfire fly</w>\ncil an\ndonet sk</w>\nvolunte ered</w>\nak ira</w>\nè ª\nsing ul\nst h</w>\ndro wned</w>\nmand o</w>\nhe ir</w>\nðŁİīðŁİ Ī</w>\ntax is</w>\ny uki</w>\nvel d</w>\nk ans</w>\nel k\nran ts</w>\nhash tag\nt eng\nro g</w>\na at\ngru b</w>\ne ber\nin india</w>\ncolo ssus</w>\nsig ni\nso ever</w>\nmile stones</w>\nder o</w>\ndifferen tial</w>\nphu ket</w>\nmaster mind</w>\nan gh\nmel ani\nbro ker\nactor vijay</w>\nstun ned</w>\ncontinu ity</w>\naf fl\nvo cal\nperenni al</w>\nfianc é</w>\nin complete</w>\nhun ts</w>\nre issue</w>\ndomin ates</w>\ntur meric</w>\nro am</w>\nri on</w>\nbag ged</w>\nnas sau</w>\nfu t</w>\nx ox</w>\nnational trust</w>\njo ye\nsan o</w>\nhearth stone</w>\ndis respect</w>\nle es</w>\nh se</w>\nsiber ian</w>\noffe e</w>\nre stock</w>\nwolf gang</w>\nre gan</w>\nplan o</w>\nun wind</w>\nre par\nmil le</w>\n] ,</w>\nskul l\nfat ally</w>\nconcep tual</w>\nðŁĮ ²\nf é\nber to</w>\nb ms</w>\nu a\nmag na</w>\nnotre dame</w>\nle te</w>\nla undering</w>\nheartw arming</w>\nbuffe tt</w>\ngo at\npe abo\nwind mill</w>\nv ac</w>\ncontinu ally</w>\naz alea</w>\nmem brane</w>\ncan cels</w>\nmake yourown\nathe red</w>\np to</w>\ntor pe\nðŁĺ ł</w>\nðŁĴ §</w>\nsc ares</w>\nle aking</w>\nz et\npix els</w>\nac i</w>\nkh il\nmarath i</w>\nðŁĻı ðŁı½\nu la\ntam u</w>\nchandi garh</w>\nz agre\naa b</w>\npronoun ced</w>\naubre y</w>\nsand er</w>\npun ta</w>\nhar low</w>\nic elan\ncelebr atory</w>\nso t</w>\nunci ation</w>\nstru ly\nmc dowell</w>\ndeepi ka</w>\nremin ders</w>\nmy stical</w>\nct c</w>\nchat ted</w>\ns ica</w>\nbar gains</w>\nch hat\nru bin</w>\nm net</w>\noiland gas</w>\npel ican</w>\no at</w>\nmor ality</w>\nk our\ni h</w>\nnu clear\ngc u</w>\nric her</w>\nvene zia</w>\nm ma\nle ith</w>\nac company</w>\nrich mond\nsports net</w>\nba ahu\nsmu ggling</w>\nmm i</w>\nðŁĩ®ðŁĩ ª</w>\ntwi sts</w>\nsahi b</w>\n.... .\namb itions</w>\nil lo\nhistor ical\nfo rec\nshow biz</w>\npon ies</w>\nchas ers</w>\nremo del\nwill ing\nprince sses</w>\nam ple</w>\ncushi ons</w>\nac les</w>\nlot r</w>\nda ch\nan the\nin corporate</w>\nnew bury</w>\nki ri\nfried rich</w>\nab v</w>\nball ers</w>\nalber t\nðŁij Ń\nlet i</w>\nnan op\nci de</w>\nanal o\nn sf</w>\n)) ))</w>\ngriffi ths</w>\nvalen ci\nro ano\nfun run</w>\nbabys itting</w>\nca day</w>\nent re\nu ck</w>\nslu g</w>\ntic al\nthe sims</w>\nro ar\ncar ney</w>\ng am</w>\nsto we</w>\nfi d\nbun ny\nsham rock</w>\npe cu\nmol ina</w>\ngo cougs</w>\ncon tributes</w>\ntransform ation\nmo y</w>\nv aj\nsever y\nantioxid ants</w>\nthir teen</w>\nsight seeing</w>\nl j\nreversi ble</w>\nodd ly</w>\nhoo kah</w>\nnou vel\nhal al</w>\nfe i</w>\nstab les</w>\nmul t\nho pped</w>\nbra ids</w>\ninter change</w>\nghana ian</w>\nww ww\neth no\ncon junction</w>\nago v</w>\nye ti</w>\nearth and\nts p</w>\ncon serve</w>\nheir loom</w>\nmetaph or</w>\nwoo f\ntor io</w>\nself less</w>\nn wa</w>\nem ilia</w>\nyl ene</w>\ny xe</w>\ngi ar\nmoder ating</w>\npro bz</w>\nb fi</w>\nne er\ndu mmy</w>\nhanuk kah</w>\nwe bber</w>\nk v</w>\neye brow</w>\ndag ger</w>\nsu mp\nra ges</w>\nork ney</w>\ntb o</w>\nhal sey</w>\nassign ments</w>\ntr onic</w>\nscri b\nco on\nan war</w>\n# âĢİ</w>\njal ape\nflori da\nqu aid</w>\nhaw keyes</w>\nâĻ¡ âĻ¡</w>\nstreet car</w>\nro g\ndat lantic\ngran ola</w>\nun changed</w>\nexpect ation</w>\nÙ ĩ\nmar lin</w>\ngu mmy</w>\nðŁĻı ðŁı¾\nawareness month</w>\noil painting</w>\nmu th</w>\nper ch</w>\njun to</w>\nvilla gers</w>\nmor g\nche ated</w>\nweb comic</w>\nthe future</w>\nd ps</w>\nla kings</w>\nmen tioning</w>\nvo or\nident ities</w>\naccor d\nmc gu\nl pga</w>\nrum our</w>\nmassi vely</w>\nm pls</w>\nheal y</w>\nd ate\nsp oli</w>\nre visited</w>\non t\nal and\nscru tiny</w>\nlakel and</w>\nbl ending</w>\n< /</w>\nan kara</w>\njami edor\nmetab olic</w>\nf ences</w>\nann y\nå ħ\nsemic on\noo tt</w>\nspace ship</w>\nwack y</w>\nle ta</w>\nap ac</w>\nshe e</w>\nin herit\ndo res</w>\nðŁĩ¨ðŁĩ ¦\ngent e</w>\ntw ick\nri ms</w>\ngal ve\nde ville</w>\nking fisher</w>\nscorpi o</w>\now l\nal ar\nvari an</w>\nðŁĹ ĵ\nvene tian</w>\nstar dust</w>\nthen orth</w>\nq ing</w>\nhar rington</w>\nconsul ate</w>\nspectac le</w>\nho bbs</w>\ntur ks</w>\ngre er</w>\nmat ing</w>\nðŁİ Ģ\nðŁĮ Ģ</w>\ndirec ts</w>\ní ĭ\npompe o</w>\nvo iced</w>\nla os</w>\ntz u</w>\npro me\npri sm</w>\nmer c\nfortun ately</w>\nbc fc</w>\nmcdon nell</w>\nnot sorry</w>\nsmi led</w>\nt ba</w>\nfor war\nmid term</w>\ndar by</w>\nwe instein</w>\nup grading</w>\nwol ff</w>\nbron co</w>\ncab ello</w>\nðŁ¥ ĩ\nfi able</w>\nshar pe</w>\nbat tered</w>\nsat o</w>\nmyth ical</w>\ninstap ic</w>\npre pped</w>\neni um</w>\ne spo\ndi aper</w>\nexplan ations</w>\nwho pping</w>\nragn ar\npe el\nantibio tic</w>\nl acks</w>\nharri son\nli sm</w>\nau l</w>\nqu ail</w>\nmartin a</w>\nsent encing</w>\nsc ams</w>\ndi di</w>\ntr onics</w>\nãħł ãħł</w>\ngo ff</w>\nza in\nparam ore</w>\ncha ined</w>\nclin ton\nli ff</w>\ncott ages</w>\nem on</w>\nreve rend</w>\nconsu mer\nce an\nt any\nlum pur</w>\ne bay\nsto ol\nðŁĺ» ðŁĺ»\nta pro\nh ath</w>\nmodern art</w>\njust ine</w>\nprover b</w>\napp y</w>\ntra x</w>\nmani fest</w>\nam bu\nnai k</w>\npe pp\nr sd</w>\nmer chants</w>\nkitch ener</w>\nshi fted</w>\nli zz\nâĺħâĺħ âĺħâĺħ\nâĢĶâĢĶâĢĶâĢĶ âĢĶâĢĶâĢĶâĢĶ\nuto pia</w>\ntom o</w>\nou ted</w>\ncom ers</w>\nchiroprac tic</w>\nbook club</w>\ncin dy\npro hibition</w>\nse uss</w>\në¯ ¼\nthin kin</w>\nrr rr</w>\ngo fund\nt ack</w>\nom b</w>\ncatastro phic</w>\nling u\nguild ford</w>\nbo td</w>\nॠĭ</w>\nplan ter</w>\n^ ^\nwin k\nkath mandu</w>\nsto ppers</w>\nsmooth ies</w>\nre efs</w>\nhin d\nbell amy</w>\nĦ ë\nwaste water</w>\nvo or</w>\nnat l</w>\n! ]</w>\nre el\ny ap</w>\nscoo by</w>\nwork space</w>\ncorin thians</w>\nbl un\nobli gation</w>\ng bbo</w>\ndy son</w>\ncra vings</w>\nell ington</w>\ndap l</w>\nwre xham</w>\nearthand clouds</w>\nuk runchat</w>\npositi oned</w>\nkal b</w>\nfour square</w>\njo ck</w>\nim pending</w>\neven ing\nath y\npro claimed</w>\nc ites</w>\nann apolis</w>\nsan i</w>\nmar th\nir l\naccom mo\nka a</w>\nfin a</w>\ny aa</w>\ndi sper\nec ar\nbha k\nwill y\nðŁĺĢ ðŁĺĢ</w>\nmcder mott</w>\nmo j\ngener ational</w>\nu said</w>\ntrain ing\nlon ely\nlo res</w>\nimpe cc\nâĢ IJ</w>\nbeav ers</w>\nma ki</w>\nhe b</w>\naap l</w>\nå ı\nwolver hampton</w>\nleader board</w>\nme u</w>\nc fa</w>\neaster n\nhu r</w>\ncivil war</w>\nou rage</w>\nhor ned</w>\nle high</w>\nawar ds\nevi dent</w>\ngi gab\nr ous</w>\nma del\nro byn</w>\nur gently</w>\nk ors</w>\nen as</w>\nheis man</w>\nbam bam</w>\nfab ian</w>\nf om\nevalu ating</w>\nassemb ly\nout sourcing</w>\nhun tsville</w>\nðŁĶ ª</w>\njusti fied</w>\ncashi er</w>\nsp aper\nbuc keye</w>\nanaly tical</w>\nillumin ati</w>\nau tho\no j</w>\nsha de\ngeel ong</w>\nwh ey</w>\nhe aton</w>\nterri bly</w>\nele k\nun charted</w>\nsd live</w>\nmoto cross</w>\nher mes</w>\ndar shan</w>\ndar lington</w>\ncash mere</w>\ngri pping</w>\ncilan tro</w>\npun ish</w>\n... :</w>\nðŁĴ Ħ</w>\ninst ance</w>\nder i\nlo bal</w>\nmuk her\nsp ar</w>\nthin ker</w>\nfre mont</w>\ncom piled</w>\ncolor ado\nvig ne</w>\nsm d</w>\nwhe ad</w>\nvilla ge\nle ek</w>\nformula e</w>\nta res</w>\npersist ence</w>\n?? ????</w>\nped ago\nhe z\nalzheim ers</w>\nvul ture</w>\noff ence</w>\nis great</w>\nsuff ra\nkick in</w>\nh mmmm</w>\nbroad way\nï¸ı @</w>\nart i</w>\nalli son\nendor ses</w>\nry u</w>\nlolli pop</w>\nsoy bean</w>\nkend all\ncer a</w>\ninv ade</w>\n( ðŁĵ·:</w>\nconver ter</w>\ncar pets</w>\nho bo\nfr it\npe ac\nes qu\nern an</w>\nou f</w>\nan il</w>\ndi ffer</w>\nch ing\nbre cht</w>\nsp g</w>\ndaven port</w>\nstra va</w>\nsever n</w>\nn gos</w>\nstor ians</w>\nfe te</w>\nparame dic</w>\nj hb</w>\nal amo</w>\nsne aking</w>\ngold coast</w>\nroof s</w>\nisi l</w>\ndepic ted</w>\nprojec tions</w>\nnu mb\no ss</w>\nep i</w>\nglu cose</w>\nzid ane</w>\ninfin iti</w>\níĺ Ħ</w>\nran som</w>\nton ics</w>\nfal k\ng ler</w>\nou tw\nre ss\nweek ly\nthe on</w>\nn ole</w>\nðŁĩªðŁĩ º</w>\nvol ley</w>\nsum mar\nneg ativity</w>\nsam son</w>\nye w</w>\naus votes</w>\nju l\nju dy\nf art</w>\npra yed</w>\npal ate</w>\nmulticul tural</w>\ndouble header</w>\ncycl ones</w>\npier re\nãģ ¨\nâĺ łï¸ı</w>\nrt w</w>\nconver ting</w>\nwir ral</w>\nl ari\nir relevant</w>\naustin mahone</w>\nan che</w>\nya an</w>\nsd f</w>\n$ .</w>\nexplo ding</w>\nulti mate\nprof ici\ngofund me</w>\ncell ence</w>\nep stein</w>\nbul lied</w>\nsep tic</w>\nà® ¤</w>\nlu mber</w>\ncu ff\nvsco cam</w>\npl or\nภ¥\nse ok\nro to\nvenezu elan</w>\nsor ta</w>\nspir ited</w>\ndaniel padilla</w>\nteam sisd</w>\nradio active</w>\nicelan dic</w>\nðŁĴ ¤\nver e</w>\naccommo date</w>\nshi pp\not ter\nol ina</w>\ne go\nsu la\nsan antonio</w>\nde as</w>\nsimil arities</w>\nâļ ¾</w>\ny om\nbro ward</w>\nå °\ncan cun</w>\nveri fy</w>\non te</w>\ncandle light</w>\nìł ķ\ninf ants</w>\naz am</w>\nðŁĺ °</w>\nle ven</w>\nun stable</w>\nbloom ington</w>\nx ford</w>\ncon tour</w>\ny p</w>\ninnov ator</w>\nhistor ies</w>\npo y</w>\nlolo lol</w>\nex pires</w>\ncat alo\nbill boards</w>\nan ab\nel ic\nnovasco tia</w>\nfa ire\nìĿ ´</w>\nrock well</w>\ngr ille</w>\naz tec</w>\njoh or</w>\nur struly\nfi ren\ndun lop</w>\nid le</w>\nport man</w>\njo es</w>\ntx hsfb</w>\nhol m\ncham ele\nunder world</w>\nlo ss\nti em\ntherap ists</w>\npast ure</w>\npa ste\ning now</w>\nvul can</w>\nra gon</w>\nlar kin</w>\no shi</w>\nho co</w>\nchild hood\numb rel\nsuccess or</w>\nkath y\niz en</w>\n° ï¸ı</w>\nshare holders</w>\nol ga</w>\nai b</w>\nhe ap</w>\nfl aming</w>\nro u</w>\nair tel</w>\nrat t</w>\nz ane</w>\nvo w</w>\nthor ough</w>\nsn ag\npar th</w>\nun conscious</w>\nve y\nnew release</w>\ngh ee</w>\ncroati an</w>\nfacilit ating</w>\nswan son</w>\nastor ia</w>\nto logy</w>\nmaster y</w>\nðŁ¤ ij</w>\nbil bao</w>\ntrou pe</w>\nthe ori\nchey enne</w>\nro tt\nshore line</w>\ngra sso</w>\nmaster chef</w>\n+ )</w>\nvi x</w>\nellen show</w>\nas g</w>\nan ak\nku ya</w>\nsafar ilive</w>\ndebu ting</w>\nblu m</w>\nlist ener</w>\nv ins</w>\nbook shelf</w>\nsmart cities</w>\nmakeyourown lane</w>\n; ;\nðŁIJ ¯\nri zz\non ward</w>\nbull dog\nbear ish</w>\nvir uses</w>\nfri gh\nlin den</w>\nwe iser</w>\nsn t</w>\ngon a</w>\ndre sden</w>\nfl anders</w>\ncu k</w>\nwheel ing</w>\nba u</w>\natu esday</w>\nsurf ers</w>\nswi ft\nmc call</w>\narbitr ation</w>\naw d</w>\nmon c\nb ine</w>\nat x\nre fr\nmi ro\npo sey</w>\nn are\nrit ter</w>\nâģ ¦</w>\nplay book</w>\nblow out</w>\nsports manship</w>\ns oooooo</w>\nmalay alam</w>\ngri ms\nbur bank</w>\ninfin ity\nsar gent</w>\noit nb</w>\njoseph ine</w>\nski pping</w>\npar kin\nexcur sion</w>\nsemin ars</w>\njo har</w>\npar tridge</w>\npost game</w>\nll ll\nblan che</w>\ntemp ting</w>\nm na</w>\nlu ka</w>\nis ers</w>\nto ffee</w>\nbar ron</w>\nhe mmings</w>\nsa e</w>\ngo hawks</w>\ncu pid</w>\nli mbs</w>\ncon se\nun common</w>\nz ada</w>\nhead shot</w>\nso ils</w>\npione er\nmam ma</w>\nsem itic</w>\npan dey</w>\njamiedor nan</w>\nspl its</w>\nvel a</w>\nson i\nra ff\nt mobile</w>\nâŀ ĸ</w>\npra wns</w>\nlit er</w>\nenjo yment</w>\negg plant</w>\ntu b\ncultur al\nus ic\nsuspici on</w>\nsy cam\nsumm ed</w>\nma du\nho ck\nup wards</w>\neye ing</w>\nri ve</w>\nassas sins</w>\nâĤ ¬\nout fy</w>\nchi ves</w>\nt ner</w>\nla is</w>\npor ridge</w>\nsad dest</w>\nw cc</w>\nvick i</w>\nsna ils</w>\nbiz italk</w>\nmill an\nðŁĮ į\nsam oa</w>\nj ing\nmi key\ngu j\nchel ms\neli gibility</w>\narma da</w>\nthro p</w>\nsurger ies</w>\nãĤ ¿\nmo hawk</w>\nex its</w>\nme m</w>\nis lington</w>\nc me</w>\nland fill</w>\nkait lyn</w>\nðŁİ ¼\ncombin ations</w>\ntomorrow land</w>\nver b</w>\ncor a</w>\npre cisely</w>\nna om\nðŁĨ ķ</w>\nshr ink</w>\nsof tly</w>\nmerce de\nmand el\npoo dle</w>\nball erina</w>\nsop h</w>\njux ta\ny at</w>\nary an</w>\nhesit ate</w>\nlo wered</w>\ngu lar</w>\ndungeon sand\nron an</w>\nmy ri\nsp f</w>\nmen opau\ngra sp</w>\npa thi\nfe asi\nfla w</w>\nshi story</w>\nste ward\ngg le\nfay re</w>\ncli que</w>\ncredi bility</w>\nyo g\nsec tion\nmu sko\nse ville</w>\nno tt</w>\ncal m\nmate o</w>\nindic ted</w>\nfi ba</w>\nby l</w>\nlin o</w>\nu kin\n!! #</w>\nenig ma</w>\nsiri us</w>\nbu sc\nðŁį Ĭ\nmac kerel</w>\npsal ms</w>\na at</w>\ntomorrow spaper\nðŁĺ ĸ</w>\np fc</w>\n........ ...</w>\nshre k</w>\nmul let</w>\no sh</w>\ndanger ously</w>\nimmen sely</w>\nam ur\nðŁį Ĥ\npro por\nsy a</w>\nlondon marathon</w>\nabo ve\nobli gatory</w>\npro v</w>\nra cha</w>\nalex is\npri mary\nsh h</w>\nether net</w>\nd stv</w>\ncou gar\nun lucky</w>\nni l</w>\nsteak house</w>\nmel a</w>\nfc bayern</w>\ncause way</w>\nca therine\nfluore scent</w>\nnx t\nto kyo\nau sp\nreleg ation</w>\nqui zz\nshored itch</w>\nproud tobe\npromo s</w>\ninter acting</w>\nhome brew</w>\nda esh</w>\nw pg</w>\nstead ily</w>\nprovin ces</w>\nbal lots</w>\ni ah</w>\nal to\n< <<</w>\nyou u</w>\nri ley\nprefe rence</w>\ntra verse</w>\nincen se</w>\nam munition</w>\nho dges</w>\n# @</w>\nhail state</w>\ntart an</w>\nwitch craft</w>\nvent ilation</w>\nliber tarian</w>\n! âĢ¦</w>\now es</w>\n% !</w>\nong chang</w>\nbru shing</w>\nle ic\nfi ber\nunder attack</w>\ndown load\nex pir\nhy o</w>\npompe y</w>\nmc bride</w>\ny ag\nstre e\ncom bat\nten ding</w>\nai ra\ngug gen\nab ra</w>\nin na</w>\nfli ps</w>\naw al</w>\nm ach</w>\ndol lar\ninspir ations</w>\nz um</w>\no du\nit ty</w>\nvideo game</w>\naqu aman</w>\nhar u</w>\nbel fast\nje b</w>\nbut ch</w>\nus gs</w>\ncalcu lus</w>\ngo yal</w>\nmor gen</w>\nx finity</w>\nstand up\ncontrac ep\nsab re</w>\nna be\nin secure</w>\ngener ously</w>\nepit ome</w>\nl w</w>\nt ca</w>\nnarr atives</w>\ndon nell</w>\npand as</w>\nber gh</w>\ntu t</w>\nker al\nfel icity</w>\nbr ampton</w>\nquinte t</w>\nnom ore\nðŁĶ ij</w>\nlo i</w>\nalham dulil\nðŁĶ¥ ðŁĶĹ</w>\nston er\nshaw l</w>\nclin ical\nbren dan\ngon e\nfla wed</w>\ntri ppy</w>\nj g</w>\nal location</w>\npo aching</w>\nve vo</w>\nmo cks</w>\nlef tist</w>\nbon uses</w>\ncondem ned</w>\nabil ity\nst ating</w>\nmicrobi ome</w>\nbio logist</w>\nfor you</w>\nwahl berg</w>\nss or</w>\nift ar</w>\nw ul\nÑĦ оÑĤ\npom er\nme me\nver te\ntre ll</w>\ntra it</w>\nin let</w>\nhormon es</w>\ndeliber ately</w>\nvill ar\nbattle ship</w>\np bl</w>\ntw enti\nho kies</w>\ndal ail\nsay a</w>\nmay fair</w>\nhan s\ndie ts</w>\n⾨ ⾨\nod in</w>\nhot spur</w>\npap i</w>\nk ana</w>\nk amp\nfin na</w>\nflo tus</w>\nti ans</w>\nunic orns</w>\ntribe ca</w>\nchang ers</w>\nfore ground</w>\nout a</w>\ninv aders</w>\ngett ys\ntomorrowspaper stoday</w>\nmac millan</w>\nhand written</w>\nw fp</w>\nu de</w>\nstate of\nbase d\nâĺģ ï¸ı</w>\ncas m</w>\npsy ched</w>\nhistor ians</w>\nfol d\nd da</w>\nag grav\np ans</w>\ngreen way</w>\nau sv\nðŁĺ ¶</w>\nshradd ha\ninde x\nbe sti\nzim mer</w>\nt ness</w>\neye shadow</w>\not te</w>\ngo ts</w>\ndistribu ting</w>\npro min\nyo l</w>\nace a</w>\ntram rahim</w>\nhoo per</w>\nsupre me\njam min</w>\nintu itive</w>\nquali fications</w>\nsli m\nsid di\njay ne</w>\ntri pping</w>\ng tx</w>\npun s</w>\ne manuel</w>\nom g\nmid summer</w>\nin to\nsuccul ent</w>\nri en</w>\nnew mexico</w>\no or</w>\nhoo king</w>\nin f</w>\nðŁ¤ Ŀ</w>\nflir ting</w>\nna hi</w>\ng friend</w>\nt ps</w>\nhel ix</w>\nz s</w>\non ie</w>\nct 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i</w>\npha ses</w>\nd jan\nbe am\nop ry</w>\ni shed</w>\nthe ellenshow</w>\nthe st</w>\nhabit ats</w>\nto ons</w>\nmclau ghlin</w>\nri pper</w>\nmicro biology</w>\ntal aga</w>\nclu eless</w>\nss u</w>\ncro che\nbro mance</w>\nlonge vity</w>\nzagre b</w>\nprev ented</w>\ntra ve\nspo ilt</w>\ndarry l</w>\nmigra ine</w>\nal cat\ndd dd</w>\nvi v</w>\nser pent</w>\nmat tel</w>\njam a</w>\ncon quest</w>\nî Ħ\nsam sung\npresbyter ian</w>\nket ch</w>\nfire fox</w>\nmo tif</w>\nle c</w>\ncho pping</w>\ncher no\nj ann\nðŁIJ °\npro lon\nwake up</w>\nconver gence</w>\nmersey side</w>\nheart broken</w>\nlo oming</w>\nhal lucin\nmai ze</w>\ncommun ism</w>\nmo h</w>\ntwitter storians</w>\nserge y</w>\nres eller</w>\nfavor able</w>\ned gy</w>\nre iter\nmal aga</w>\nlive me</w>\nka hn</w>\npul sion</w>\nbig g</w>\nkim kardashian</w>\nati o</w>\ntyr anny</w>\nru ption</w>\nq ant\npro ven\nby z\npu shaw\nkri stin\ne er\ntar dis</w>\nri z</w>\nawak en</w>\nmi ko</w>\nun documented</w>\npath finder</w>\nindirec t</w>\nresemb les</w>\nh ler</w>\nconce aled</w>\nscand al\nre im\nd nb</w>\ncr itters</w>\nattend ant</w>\napprentice ships</w>\naa u</w>\nscre amed</w>\nl su\nfa h</w>\nhar bour\ned d</w>\nbat sman</w>\nli ss</w>\nmi sha</w>\nspani el</w>\nit f</w>\nadvan cement</w>\nfa c</w>\nclose up</w>\ncecil ia</w>\nmedi c</w>\nnarcis si\nlav ish</w>\ngi ac\nma ys</w>\nle it\nwine wednesday</w>\npushaw ard\nlet to</w>\ncurren ts</w>\nbug atti</w>\nout ine</w>\nw j</w>\nun do</w>\nler osis</w>\ndevo tional</w>\nðŁij «</w>\non na</w>\nfais al</w>\nsa una</w>\nhimach al</w>\nam ii\nà® ®</w>\ndi zzy</w>\nscreen writing</w>\nph x\nsp n\nick i</w>\nag irl</w>\nfi shes</w>\nwb z</w>\npi m</w>\nbo ar</w>\nac id\n! ..</w>\nrocke feller</w>\nn ga</w>\ndra stically</w>\nsimpli fy</w>\ndru mming</w>\nautum nal</w>\ngur mee\nlor de</w>\njo ann\ngive up</w>\nb our</w>\nam ura</w>\nder land</w>\nsim pler</w>\nwat son\ntri dent</w>\nconcor dia</w>\nbel lum</w>\nbre k\ndum plings</w>\nvi on</w>\ndungeonsand dragons</w>\nsp ri\nascen sion</w>\nwil datlantic\nu st\nrob ins</w>\nlegi on\ninsi st</w>\njar o</w>\ngue ss\nso b</w>\nbigh it</w>\npool side</w>\nnegoti ating</w>\nmc gill</w>\nbil d</w>\ntechn icians</w>\nmiti gation</w>\najay devgn</w>\nb to</w>\nant en\ncosmo politan</w>\nðŁĺĬðŁĺĬ ðŁĺĬðŁĺĬ\npatri oti\ntemp er</w>\npromen ade</w>\nnav ajo</w>\nnam m</w>\nwrink les</w>\ndc fc</w>\nle ach</w>\nbru nette</w>\nr f\ncout inho</w>\nal ti\ntradition ally</w>\nop tome\nna z</w>\naccord ingly</w>\nrec ard</w>\nde ets</w>\nsw ell\npo sure</w>\nwhit ening</w>\nstrang er\nilli on\nhere ford</w>\nu wu</w>\nro bber</w>\ncotsw olds</w>\ncl en\ngor ge\nnam aste</w>\nre lish</w>\ngri ff</w>\nadren aline</w>\nbla sio</w>\nval e\nê ²\ntoler ate</w>\nrail minindia</w>\njen sen\nho ven</w>\nel lu\nob sole\neisen hower</w>\nunidenti fied</w>\nthan niversary</w>\nbody guard</w>\nØ ¯</w>\ni dge</w>\nsch al\nstock port</w>\nsn i</w>\nre taining</w>\npo po</w>\npix ie</w>\noli thic</w>\nki er</w>\nha jj</w>\nsa z</w>\ncor bin</w>\n!!!! !!!!!!</w>\nv it</w>\nme gat\nde h</w>\ncircu it\naf fleck</w>\ntheore tical</w>\nhope less</w>\nu ab</w>\nslu mp</w>\nb ice\njam med</w>\nlet stalk</w>\ncan i\nside ways</w>\nlabyrin th</w>\nre fs</w>\nha hn</w>\njare d\nðŁį ¹</w>\njam bo\nph yl\nenhan cement</w>\nc tr\nful lest</w>\nse ye</w>\ndo ba</w>\ncho ic\nyo s</w>\ncb j</w>\nandr é</w>\nre watch</w>\npri ma\ndoctr ine</w>\nfor gets</w>\nu hm</w>\nar ound\nu le</w>\nart lovers</w>\nshi raz</w>\nhar th</w>\nex tor\nÅ ¡\nunexpec tedly</w>\neli us</w>\ny x</w>\nem my\nse ac\nðŁijĩðŁijĩ ðŁijĩ</w>\ncorrec ted</w>\ncom bu\nwom anc\ncou gh\nwhat son\npubli shes</w>\ndivers ity\nback bone</w>\nlock down</w>\nmesmeri zing</w>\nnor te</w>\nma b</w>\ndesig ner\ní ģ\nra gh\nmole cules</w>\nget outside</w>\nthe beatles</w>\nsemicon duc\nnach o</w>\nlun es</w>\nham mers</w>\nsul tan\no on\nfe ren\natt ach</w>\nar qu\nuttarak hand</w>\ns ash</w>\n; -\ntre ad</w>\ni ko\nar 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ise</w>\nkend ra</w>\ndat o</w>\nchees es</w>\npar ma</w>\nburn out</w>\na stra</w>\npre toria</w>\nadjust ment</w>\nuk u</w>\nsl o</w>\nli ken\nfav ors</w>\ncli ve\nbe ets</w>\nsnow donia</w>\ngo tv</w>\nsy n</w>\nopen house</w>\npan i</w>\nportra yed</w>\nsl ated</w>\nme cca</w>\nren al</w>\nsupportsmall streamers</w>\nstaf fs</w>\nda o</w>\nbi ker\nvik tor</w>\ntit us</w>\nadmi red</w>\nðŁĵ ±\nhurric an\nhe ats</w>\ngl ory\nphoto genic</w>\nmer i</w>\nde por\nburn ham</w>\nor angu\ndj ing</w>\nimpre ssionism</w>\nign ition</w>\nca i</w>\nw ynn</w>\nde pe\ncove ted</w>\ncolla gen</w>\nsau s</w>\nor nam\nadministr ators</w>\nss on\nnh politics</w>\nhahahaha hahahaha\naspir ations</w>\nr gb</w>\nswol len</w>\nso we\nsc r</w>\ndiver gent</w>\nhou ghton</w>\nhan oi</w>\nd ory</w>\nni ki</w>\nland ry</w>\nb cci</w>\nðŁijĮ ðŁijĮ\nis mail</w>\ntri pod</w>\nher d\nbhat t</w>\ndress age</w>\ntab by</w>\ningu ish</w>\nhur on</w>\nà³ į</w>\nà ł\nto das</w>\nevangel ical</w>\nchor ds</w>\nst 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play</w>\naver aged</w>\nedmon ton\nsteph an</w>\nsister hood</w>\ncru sted</w>\nstag gering</w>\nmethodo logy</w>\ncongress woman</w>\nc abo\ntri ggers</w>\nmil ky\ngli de</w>\ntooth paste</w>\nroom mates</w>\nnu ff</w>\ngu am</w>\nsprink les</w>\nalternati ve\nwat fordfc</w>\nuof t</w>\nhal ey\ncont acted</w>\nbun dy</w>\npro stitu\ngh ar\npre ston\non site</w>\nhil ar\ng ts</w>\nc att\nhamp stead</w>\n? ?!</w>\nðŁĩ§ ðŁĩ\nbbc qt</w>\naless andro</w>\nresi st\nma idan</w>\nt ko</w>\nshad ing</w>\npin up</w>\ngal lo\nsin u\nat ec\nfun k\nac lu</w>\nstri des</w>\nrhy me</w>\nwet land</w>\nbbc springwatch</w>\nt ins</w>\nwild card</w>\nst our\nflamen co</w>\npau la\nonto logy</w>\ngang sta</w>\nam ade\nãĤ «\nt bs</w>\nskelet al</w>\nrun ner\njard in</w>\nharri er</w>\nhun ted</w>\nz hen\nbelievein film</w>\nde mean\nau diti\nre start</w>\nchon dri\nâĿ¤ï¸ı ðŁĴĻ</w>\nmcla ren\nga b</w>\nsh um\nau sa</w>\nlewi sham\ny pg</w>\nk jv</w>\nfur nished</w>\ndor o</w>\nbon ded</w>\nmor ty</w>\nlat 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fs</w>\nfo sters</w>\neat ers</w>\ndu es</w>\n_ /\nlymph oma</w>\nsf am</w>\nme gal\nafri di</w>\nag ic</w>\np amp\njeal ousy</w>\nðŁijĮ ðŁı¼\ncalcul ate</w>\nnapp ing</w>\ng ale\nðŁ¦ Ħ</w>\nlub bock</w>\nassu med</w>\nren ting</w>\níĥ ľ\nsubur b</w>\nãĤ ·\ntech nic</w>\nu cla\nin front</w>\ngar net</w>\nster oids</w>\nstri ving</w>\nho war\nmo ver</w>\nle ton\nbull do\nis in</w>\nci ao</w>\nsn z</w>\nfore front</w>\nd ams</w>\nmid wife</w>\nma wards</w>\ncla pton</w>\nwe in</w>\nsubsi dies</w>\nspr oud</w>\nrother ham</w>\nphan tom\nar ach\nspi el</w>\nrac ket</w>\nsel amat</w>\nno on\nl bc</w>\nenti ally</w>\nðŁĴ ¸\nsil ve\nm oud</w>\nkine tic</w>\ny asi\nðŁİ ©</w>\no ol\nmi ku</w>\ni za</w>\nfer a</w>\nflo ren\nbarber shop</w>\ngroo t</w>\nz est</w>\nne ars</w>\nstan is\nz and\npolice man</w>\njuris dic\nform ations</w>\nappar atus</w>\nsp d\narti fact</w>\nto sc\nmotiv ating</w>\nwomanc rush\nre dro\ndiagno stics</w>\nra za</w>\nout fitters</w>\nel xn</w>\ndod gy</w>\nry n</w>\nsh d</w>\northo don\nol de</w>\njay anti</w>\nbal ances</w>\nquic kest</w>\ncan ton\nfriday reads</w>\n! *</w>\nna a</w>\na ak\nðŁĶ ·</w>\nbehavi ors</w>\nrasp berries</w>\nä »\npolit ical\ncam il\nå ľ\ndi k</w>\nast ounding</w>\nlie be</w>\nnovel ty</w>\ntur moil</w>\nsul ly</w>\nspring break</w>\nhon ouring</w>\ncc g</w>\nðŁı Ĵ</w>\nmy little\nky c</w>\npro ms</w>\nðŁķ Ĭ</w>\nà ¨</w>\nbi ge\nav ril</w>\nðŁĩµðŁĩ °</w>\nmari on\nas ants</w>\nsur ya</w>\noc tag\nluf than\nac ron\nfayette ville</w>\nti que</w>\nlove s\nen ca</w>\nde kalb</w>\nta ver\nde vote\naux iliary</w>\njoh annes</w>\ntread mill</w>\nay an\nqu r</w>\ndonald son</w>\ncher yl\n\" ....</w>\ns ven\nkir sty</w>\ngun ners</w>\nra dish</w>\no ahu</w>\nv sky</w>\ni ble</w>\ncon course</w>\nb ps</w>\nelo qu\nash ford</w>\nte bow</w>\nroblo x</w>\nma da</w>\ndri ving\nth day</w>\nspro ject</w>\nm ms</w>\nband ed</w>\n. !!</w>\nlibr arians</w>\nflan nel</w>\nintoler ance</w>\nher al\nç µ\nneme sis</w>\nlist a</w>\ntar ak</w>\ncry pt</w>\nstar plus</w>\nvish nu</w>\nsc ale\ncr is</w>\n% ),</w>\nj illian</w>\nregg ae\npegas us</w>\nol in\nip ment</w>\nman ic</w>\nl fc\ngodd ard</w>\nite am</w>\nparl our</w>\nanch ors</w>\nlee minho</w>\ntalla hassee</w>\nant it\nd ho\nkid ney\ny ash</w>\nbatt led</w>\naz ad</w>\ngar is</w>\nfaul kner</w>\nsni ff</w>\npapar azzi</w>\ned m\nphy llis</w>\ncon tested</w>\naa ay</w>\nse ca</w>\nk ton</w>\nvel ve\nrain ier</w>\nfor um\ntam pab\nho sp</w>\ntrac tors</w>\nox fordshire</w>\nno tion</w>\nguang zhou</w>\nðŁĺ ¯</w>\nref ill</w>\nwednesday motivation</w>\nsli der</w>\nmukher jee</w>\npr att\nfon taine</w>\nalph on\naf ar</w>\nts i</w>\npest icides</w>\nfi ends</w>\nmo cking</w>\nbra w\ntran sat\ndo ses</w>\nco res</w>\nhom ophobia</w>\ndocu menting</w>\nzlat an</w>\ncon doms</w>\ns é\nsun set\nkun st</w>\nton ga</w>\nภª\nv ation</w>\nsp ray\nchow der</w>\nra ps</w>\npalla dium</w>\nnor wood</w>\nmusic history</w>\nhoo ker</w>\nsi si</w>\nosp rey</w>\nph ys</w>\nconce ded</w>\nbob cat</w>\nar mad\nze it\nÙ Ħ</w>\nðŁĺģ ðŁĺģ\nmer idi\nðŁĩ· ðŁĩº</w>\ncorn wall\n! ),</w>\ntouch downs</w>\nze it</w>\nchal et</w>\nmm m\nal che\ngor illa\nfo ss</w>\nati ku</w>\nlumin ous</w>\nivan ka</w>\nbe ek</w>\nsta res</w>\nsw iss\nâĿ¤âĿ¤ âĿ¤âĿ¤\nscru bs</w>\nme ath</w>\ngusta v</w>\njo gging</w>\nconfe tti</w>\nas os</w>\ners fc</w>\nbreit bart</w>\napplic able</w>\nautho red</w>\nya ho\nh in</w>\ndisplac ement</w>\nj v\nðŁĮ¹ ðŁĮ¹\not c</w>\nnon profits</w>\ndiec ast</w>\ngu sto</w>\ninte stin\nc ages</w>\nme en\nlu kas</w>\nmoon ey</w>\nðŁĺ ·\nvery day</w>\ntor ah</w>\nis sion</w>\nwa c</w>\nlever aging</w>\nish able</w>\ncu se</w>\nle wood</w>\nmay an</w>\nturn table</w>\nju ice\ntru sty</w>\ntu p\neti quette</w>\nsupervis ors</w>\nstu n</w>\ngu zman</w>\nconfe ren\nric o\nfe ast\nback ward</w>\npol aris</w>\nmic he\njo g\nh ing\nfield house</w>\nvel ing</w>\nsho cker</w>\nesc ence</w>\nठ¾\nvi be\nanasta sia</w>\nmar ched</w>\nkill ing\nĶ ë\nfe tt</w>\nexop lan\n... (</w>\nsnow day</w>\nlo h</w>\nir ani</w>\nla khs</w>\ndel a</w>\npo caly\nboom ers</w>\ndictat orship</w>\nac er\ntur keys</w>\nquarter final</w>\nmuskete ers</w>\nðŁĴĽ ðŁĴļ\nsf x</w>\nmuseum week</w>\nsc ala</w>\nri sis</w>\n( ðŁĵ·</w>\nãĢ Ĥ</w>\nz ies</w>\nbo eh\nhu es</w>\nlu sci\ndol a</w>\nimpeach trump</w>\nroo d</w>\ndon caster\ntor re</w>\nhero es\nfo yer</w>\ntar i</w>\nblur red</w>\nke w\nfrank ly</w>\ndro id</w>\nap al\nÐ ¼\ny af\nbre t\npar agu\ncac ao</w>\nðŁĻĮ ðŁı¾\nru e\nhead aches</w>\nshaw ty</w>\nchar ley</w>\npal er\ngo wns</w>\ncorrec tional</w>\nðŁĺ© ðŁĺ©</w>\nbreaking bad</w>\nol ing</w>\nda p</w>\nendeav our</w>\ncit adel</w>\ntra d</w>\nincumb ent</w>\nmedit ate</w>\nfoo ted</w>\nðŁĴ µ</w>\nshab bat</w>\ndayof the\nwil lem</w>\ngal way\nto red</w>\nmarri age\nf illion</w>\nsleeve less</w>\naud itor</w>\njin young</w>\ninvin cible</w>\nkad una</w>\na and\nvolcan oes</w>\nmon eti\nindie gogo</w>\nbuccane ers</w>\nðŁijī ðŁı½</w>\nãĢ Ĥ\nlay ton</w>\ncuck oo</w>\nhu mber</w>\nbuzz er</w>\nÏ ī</w>\nto re\nstra ins</w>\nsto m</w>\npa ine</w>\ns we</w>\ndu ff\nz ou\nsi mi</w>\nli pp\nur n</w>\nse agu\nðŁĶ ®</w>\nsun dae</w>\nhi c</w>\nðŁĺ ¨</w>\nbull pen</w>\nu per\nflyo ver</w>\nal dridge</w>\nglo bes</w>\nali es</w>\nken zie</w>\nge es</w>\ny cle</w>\nsp lin\nmag enta</w>\nj ha</w>\nbal u\ngh orn</w>\nti pper\nwick er</w>\ntaste of\ncon clave</w>\nch ale</w>\ninv asi\ncat er</w>\ndio xide</w>\nme gab\nwin n</w>\nat p\ntransform ative</w>\nnest led</w>\nhi g\nbri dging</w>\nlil ies</w>\nchee red</w>\nbad dest</w>\nsc rolls</w>\nreal is</w>\ndipl o</w>\nðŁĶ «\nconce ssion</w>\nprefe rences</w>\nexplo des</w>\ner gon\nintroduc tory</w>\nine au</w>\nch af\nsom es</w>\nland rover</w>\nspir ation</w>\nsex y</w>\nsco recard</w>\nillustr ates</w>\nsoul mate</w>\nwi en</w>\ninter disciplinary</w>\nfore casting</w>\nent ities</w>\nglu ed</w>\nen lar\ncur t</w>\npercep tions</w>\nboot leg</w>\nmi re\nasho k</w>\nv az\nhor ne</w>\ncal le</w>\nac ulture</w>\nther oy\nnight time</w>\noc al</w>\ncharacter design</w>\nar mist\nðŁĺı ðŁĺı</w>\nyah oo\nac eae</w>\nto se</w>\neven to</w>\nsou t\nnay anth\nwh om\nv are\nri gging</w>\ngen us</w>\nhi ve\ncom mands</w>\nsti e\nday a</w>\nethan ol</w>\nen f\nhi fi</w>\nflu ence</w>\ncle mson\nre invent</w>\nthermom eter</w>\nhumor ous</w>\nemer ging\naci ón</w>\nðŁĺĺ ðŁĺį</w>\ns ity\nhaw ke</w>\naccompan ying</w>\nt ility</w>\nðŁĺ ª\nre cess</w>\nprotag onist</w>\nl ery</w>\ndun dal\nint l\nbritt any\nq bs</w>\noff the\nmarri ages</w>\nhow to\nviol ated</w>\nadel aide\nwit t\nlanc er</w>\npak v\nhu me</w>\nst ade</w>\nbra gging</w>\nou tright</w>\nad c</w>\nsuper st\nreal time</w>\ncu res</w>\ngarden ers</w>\nero ck</w>\ndale jr</w>\nver o</w>\nbar tol\nmo ti\nmc fly</w>\nv pn</w>\nst ink</w>\nover rated</w>\nguer ra</w>\ne tis\nath ome</w>\ntwd family</w>\nth ab\ntn x</w>\nrafa el\nfamily travel</w>\nx ley</w>\nsat anic</w>\nequ ations</w>\nru dy\nwal dorf</w>\nstan i</w>\ntu be\nmeas les</w>\nzimmer man</w>\nobli gations</w>\ni ously</w>\nbow ser</w>\ntrans former</w>\nsho ppe</w>\nshak en</w>\ngh ouse</w>\nto d\nke tball</w>\nshare holder</w>\nmar ca</w>\nkp mg</w>\nak an</w>\ngiven chy</w>\ncoast al\nau th</w>\nroller coaster</w>\nmar ches</w>\ncoordin ate</w>\ncine ma\napprentic es</w>\npar lor</w>\nmit o\nmen on</w>\nconsider able</w>\nbar re</w>\nglo ss\nenh ances</w>\njaz eera</w>\nfal mouth</w>\nthra sh</w>\nstat en</w>\nk zn</w>\neng el\nsamanth ap\nflo ppy</w>\nsal om\nðŁıĨ ðŁıĨ</w>\nw ack</w>\ndeliber ate</w>\nosc ill\nherit ag\ndu sted</w>\norni thology</w>\npad dle\nfer ns</w>\nbar un\ncl ans</w>\nanticip ate</w>\na ay\nmat ically</w>\né ĩ\ntu mble</w>\npost man</w>\nunic ef\ntro tter</w>\nop d</w>\nleaf let</w>\nge ist</w>\ncease fire</w>\nscre ws</w>\ncre ation\nwal nuts</w>\nlongh orns</w>\nunder statement</w>\nab b</w>\nproxim ity</w>\nna x\nun ity\nturn pike</w>\norda ined</w>\ndub step</w>\nchak ra\nme ch</w>\nlove her</w>\nlook alike</w>\ndonne in\nvir on\nÙ Ī</w>\nbang ers</w>\nvari ants</w>\nout dated</w>\nin ta</w>\ncri sto</w>\nsp elt</w>\nfood and\nf on</w>\nstefan i</w>\nmargin al</w>\nhu tton</w>\nti ara</w>\ntel ford</w>\nqu en</w>\nfair grounds</w>\nque tta</w>\nmikha il</w>\nheal er</w>\nv ball</w>\nty re\nunder grad</w>\ngl end\nhom ers</w>\nscri bed</w>\nmain tains</w>\npo che\nmis sal</w>\nmar ko</w>\nu as</w>\ná n\nsh p</w>\ncon vey</w>\npad re</w>\nsab a</w>\npu glia</w>\nmadhu ri\npa xton</w>\nchap lain</w>\nn ago\nca si\n... !!!</w>\nfli rt</w>\nsal eh</w>\nk are</w>\ndi re\nstam ped</w>\nextre me\nðŁĺĥ ðŁĺĥ</w>\nho ppy</w>\nguadalu pe</w>\nadvant aged</w>\neu char\np low</w>\nun n</w>\nmac qu\nport land\ncla sh\npe s\nlou bout\ny p\nkeep ing\narca dia</w>\nfran kie\nfi u</w>\nde th</w>\nencyclo pedia</w>\nsi ze\ninve sts</w>\nðŁį ©</w>\ngeo logical</w>\nfran ç\ncon front</w>\nðŁĺ ¥\nd ys</w>\naf m</w>\ntex an</w>\ngraph ene</w>\nrepost app</w>\nac f</w>\nur sula</w>\ngaz a\ndd led</w>\nfu m</w>\nwsb tv</w>\nm be\nfron tiers</w>\nchrono graph</w>\nke s\ninter faith</w>\ntab oo</w>\nspar ta</w>\nwon do</w>\nflori st</w>\nem braces</w>\nca w\nno el\narch ers</w>\nðŁIJ ·</w>\nroman o</w>\nban an\nsh akers</w>\nmelo dies</w>\ngeo thermal</w>\nse phora</w>\nìļ °\nоР´\npro c\nhand shake</w>\npan de\npopul ated</w>\nslow down</w>\nhor tons</w>\nregistr ations</w>\nun deni\nlan ts</w>\npas sover</w>\nthak ur</w>\nli ef</w>\nadhe sive</w>\npe tal\nmicro scopy</w>\nmemph is\nconfir ming</w>\nair drop</w>\nmesm er\nperce ived</w>\nming le</w>\nlifel ine</w>\ngh j\nworcester shire</w>\npas sions</w>\nach er\nel lar</w>\nah o</w>\nfiren ze</w>\nbar ang\nletter man</w>\nhat field</w>\nlu cha</w>\nje ter</w>\ne shop\nwilliam s\nhoro scope</w>\npre de\neast bourne</w>\ndur ga</w>\ndi version</w>\nal trin\nseis mic</w>\npremi osm\nnar co\nti r</w>\nori g</w>\nor m</w>\nland fall</w>\nci ous</w>\nlin do</w>\nmax ine</w>\nx ico</w>\ntra y\nos wald</w>\nc ba</w>\nric otta</w>\nn cr</w>\nmar au\nภ²</w>\ngladi ator</w>\nch ery</w>\nlun g\nu me\npo psic\nlon ging</w>\ncan als</w>\nta ya</w>\ndecentr alized</w>\nsho pp\npres sures</w>\nmahar aj</w>\neti had</w>\nwal greens</w>\nsucce ssion</w>\nsign aling</w>\nli g</w>\nstaf fer</w>\nnorth korea</w>\ndef ying</w>\nas ma</w>\nde g</w>\nperi meter</w>\noak ville</w>\nm sk\nbalti more\nrece ip\nde ple\nðŁĺŃ ðŁĺĤ</w>\njambo ree</w>\n> .<</w>\nrsp b\npuni sher</w>\nconsider ably</w>\nin tothe\npari sian</w>\nacceler ated</w>\npolye ster</w>\nlow es</w>\nfr ying</w>\nsauté ed</w>\nmou ths</w>\nseychel les</w>\nra x</w>\ngo dis\ndak ota\nhouse wives</w>\nthe me\nmat inee</w>\nblack bird</w>\nye sung</w>\npre fers</w>\npelle gr\nin ated</w>\ntrun ks</w>\nstronger together</w>\nre pet\nre pairing</w>\nped als</w>\ntoler ant</w>\nher r</w>\ndun ne</w>\nindic ation</w>\ndecat ur</w>\nb tv</w>\nexhibit ors</w>\nik on\nfriday motivation</w>\nbra gg</w>\nlive tweet</w>\nal ves</w>\nwomens art</w>\nforeig ners</w>\nwal lets</w>\nmin dy</w>\nlan ey</w>\nbb in</w>\ntv miaw</w>\nlif ter</w>\ntar get\ntam e\ndr ou\nastro photography</w>\nmp c</w>\ng pu</w>\nnord strom</w>\nfric tion</w>\nrun off</w>\nlov able</w>\nsp nfamily</w>\next ingui\nbloo dy\nsch el</w>\narti stry</w>\nsw ish</w>\nscar ce</w>\nph ils</w>\nmax im</w>\npos sum</w>\ncom promised</w>\nsty li\nsc fc</w>\nis sa\nbirmin gham\nsket ched</w>\nangel ica</w>\nordin ance</w>\nje ts\nconqu er\nðŁĺ IJ\nonline shopping</w>\ns ori\nreason ably</w>\nnue stro</w>\nar turo</w>\nch l</w>\nbenef ici\nspho to</w>\nwel t</w>\nni kk\nðŁ¤ ŀ</w>\ndan ao</w>\nfor mid\nas se</w>\naf irst</w>\nâľ Ĥ\ngil lette</w>\nas sor\nan onym\nsel ca</w>\nfe mi</w>\nbear able</w>\ny and\nar mory</w>\ncre pe</w>\nceltic fc</w>\nbra vo\nin expensive</w>\nde lec\nge cko</w>\nnew market</w>\nsnow flakes</w>\nkab ir</w>\ncon tra</w>\ncan ning</w>\nmor pho\ngar wal</w>\nðŁĴĥ ðŁı»</w>\nfight ing\nmu tation</w>\nwoo dy\nju gg\ngr aces</w>\npremiosm tvmiaw</w>\nkenne dy\ngu p\nsa e\nop ha\noff spring</w>\nfini sher</w>\nbet ts</w>\nspan ning</w>\nmar j\nh one</w>\nsh ing\ncontin ents</w>\nsamanthap rabhu</w>\nun related</w>\nl acy</w>\nexplo sions</w>\nbenjam in\nsophi e\nno ting</w>\nmicro soft\nas sen</w>\na hoy</w>\ni ker</w>\nho fer</w>\nmo e\nah madi\nyan n</w>\nan ak</w>\nma hi</w>\nbe u\naha h</w>\ncreep er</w>\nbaahu bali</w>\nam at\npri ory</w>\nhaw keye</w>\ndeloit te</w>\nsko da</w>\nprint making</w>\nassemb ling</w>\nmirac ulous</w>\nno ch</w>\nsw o\nleg a</w>\noper ates</w>\nborder lands</w>\neli e\nstron gh\nrep tiles</w>\npir ate\nun fold</w>\n ¯\nqual comm</w>\nun predictable</w>\not r</w>\nrose wood</w>\ndirec tional</w>\ncounsel ors</w>\ncorn ell\nliber ated</w>\nj ad</w>\nir regular</w>\nbulgar ian</w>\nhigh ness</w>\nvodaf one</w>\nsw ild</w>\nmini mize</w>\ngra zie</w>\n๠ĩ</w>\nr stats</w>\nstre ep</w>\nome tric</w>\nhumb le\nlu mp</w>\nl ille</w>\nb ü\nhome depot</w>\ntripad visor</w>\nki wan\na via</w>\ner z</w>\nex ico</w>\ndu f\nblu men\nmi zing</w>\nar ma</w>\nin im\ncon stan\nsor a</w>\nju al</w>\nau n</w>\ntw ell\ntren ches</w>\nher a</w>\nr k\npo plar</w>\nrecipe oftheday</w>\nll an</w>\nbhu ban\nshort ages</w>\ning don</w>\nbridge water</w>\nðŁIJ ĺ</w>\nfortn ite\ncam den\nun cture</w>\npro w\ncolon ies</w>\nt ks</w>\nn go\nb hm</w>\nlive pd\nspl ace</w>\nsli ke\nhappye aster</w>\nter rence</w>\nrevol ver</w>\nj ed</w>\nyy yy\noffice of\nm ts</w>\nexist ential</w>\nr ourke</w>\nexplore bc</w>\nsse d\npri est\nvix en</w>\nsi ding</w>\nk pa</w>\na har</w>\nju ic\nob struc\nforen sics</w>\nuk mfg</w>\ncancell ation</w>\nwe ary</w>\nab q</w>\nele c</w>\npri zed</w>\ndeb ts</w>\nme zz\nsalv atore</w>\nm dc\ngre tte</w>\nc gc</w>\nth on\nsnow storm</w>\nts ch</w>\ncook ery</w>\nå ¹\nwa xing</w>\nn acional</w>\nmur s</w>\nra ve\ncap es</w>\nger main</w>\ndri pping</w>\nsub mitting</w>\nome lette</w>\niter ation</w>\naj es</w>\nshim mer</w>\nfu eling</w>\nðŁĩ§ ðŁĩª</w>\nli po\nbo bble</w>\nun follow</w>\nislam ist</w>\nhi ber\ncat s\nagentsof shield</w>\nsen si\n____ _</w>\nster ia</w>\ninst al\nausp icious</w>\nhar row</w>\nover land</w>\nfemini sts</w>\ninst ant\nchar iot</w>\nblind ness</w>\nsp ed</w>\nsc arec\nnu it</w>\nmini atures</w>\nho seok</w>\nglo ck</w>\nfifa worldcup</w>\ne te\ndis m</w>\nwe iner</w>\nex foli\near ts</w>\nภĶ</w>\nmy art</w>\nman il\niss ant</w>\nform a</w>\nin cu\nbuffal ob\nin tim\nmc cul\nanj ali</w>\npo po\nun doub\nhil a</w>\nfun gal</w>\nthank ful\nfu tur\nen dish</w>\nren ds</w>\nth ar</w>\nshe ff\nring o</w>\nnichol ls</w>\nio wa\npo tom\ncl ams</w>\nãģ Ħ</w>\nacon f</w>\nstadi ums</w>\ndi mp\ndi k\nresiden ces</w>\ndo v</w>\ncaric ature</w>\nseagu ll</w>\nkl m</w>\nconfe ss</w>\nsla pped</w>\ncele b\nturb ines</w>\npp v</w>\nnur ture</w>\nel ab</w>\n.... .#</w>\ntu ff</w>\nde press\nal far\namii bo</w>\ndi spon\ne wing</w>\nque er\nfriend s\nfor re\nâĺ ¼</w>\nsw t</w>\naqu arius</w>\nhead liner</w>\ncur d</w>\nfi gs</w>\no tters</w>\nlove fl</w>\nkare em</w>\ngo vegan</w>\nfri yay</w>\nconsol ation</w>\nat ri</w>\nì§ Ħ</w>\nâĺĿ ï¸ı</w>\npoly ne\ngu ed</w>\no ya</w>\nla us\nintestin al</w>\ncam illa</w>\nscal p</w>\npi r</w>\nleed s\nhorri fying</w>\nbore tum</w>\ndand elion</w>\nfer rer</w>\nell ic\nas x</w>\nso ren\nre loaded</w>\nale ague</w>\nnavig ator</w>\nine tte</w>\nadd ams</w>\nal chemist</w>\nak shay</w>\ndystop ian</w>\nawe c</w>\nn aya</w>\nal isa</w>\nai led</w>\nag or\navi ator</w>\nali zer</w>\nsmo bile</w>\nfindyour park</w>\ncop ying</w>\nto ddy</w>\nsh ti</w>\nmon ger</w>\ncal houn</w>\nnap kin</w>\nbreak up</w>\ny atra</w>\nse thu\nric hi\neras mus</w>\nfer ry\nam ore\nprac tise</w>\nbo bo</w>\npower point</w>\noo se</w>\nli ffe</w>\nchin a\nsh ka</w>\nfad navis</w>\ndu ane</w>\nwar on\nfal se\nðŁļ Ĥ</w>\nwa shes</w>\ndisc ip\n==== ====\ng k\nab b\nstub born</w>\nmedi eval\np ci</w>\nðŁį ª</w>\nmaril yn\nh yo\nman di\ncr i</w>\nprede cess\ncontinu ation</w>\nom usic</w>\ns lat\nwh al\nmall ory</w>\nbon n</w>\nshen zhen</w>\nca i\nâĺ ĥ\nsa fest</w>\nfor wards</w>\ndra wers</w>\nbla sted</w>\nsle e</w>\nmor phe\nmb ta</w>\ndumb ass</w>\nÑĦоÑĤ о</w>\nalhamdulil lah</w>\nec lub</w>\nal beit</w>\nheal ey</w>\nayurve da</w>\nadverti sed</w>\ncro cs</w>\nitt les</w>\nbry son</w>\nbe i\nnj pw</w>\nhonore e</w>\nfu sed</w>\nðŁĶ ĺ</w>\nmul tin\nn aga</w>\nde parts</w>\nko p</w>\nkin o</w>\njhar khand</w>\ned na</w>\nax le</w>\nmil ton\nsupremac ist</w>\nmarrake ch</w>\ndomin ic\ntran script</w>\n] [#</w>\n: ).</w>\nwo c</w>\nsur rounds</w>\no gil\nleaf lets</w>\nco well</w>\nwhe w</w>\ntru de</w>\nproli fer\nsucce s\nsports man</w>\ncon dom</w>\npo che</w>\nk up\nimprison ment</w>\n{ }</w>\nscram bled</w>\nå Ľ\nka ine</w>\ncell phone</w>\nmetam or\ncon i\nremn ants</w>\nee z</w>\ndown pour</w>\nafterno on\nexerc ising</w>\nber ser\narchitec ture\nwick low</w>\nm ns</w>\nis p</w>\nbo c</w>\nn iss</w>\nmn wild</w>\nstu mble</w>\nr si</w>\nlu ffy</w>\nsil en\ndd ad</w>\nbul lies</w>\nhaw ker</w>\nbb cc\nscu ba\ne pp\nque ts</w>\nfor aging</w>\npal let</w>\nha di</w>\ncinemato grapher</w>\ncat chers</w>\nto aster</w>\nk hi\nlite coin</w>\nkid lit\namher st</w>\nmaur icio</w>\nip ad\nmar malade</w>\nfe y\ndon nelly</w>\ng to</w>\nest as</w>\ncere bral</w>\nant grasso</w>\nzz led</w>\nvir gil</w>\nswa pped</w>\nðŁĺħ ðŁĺħ</w>\nno dapl</w>\ngreate st\nnhl bruins</w>\nfra ser\nb mo</w>\nane w\n. âĿ¤ï¸ı</w>\nse gregation</w>\nremark ably</w>\nmccor mick</w>\nlo gger</w>\ner as</w>\ncontrac ting</w>\nâłĢ âłĢ</w>\nyor ks</w>\nuku lele</w>\ntouch screen</w>\nde cked</w>\nben n</w>\nsouth wark</w>\nra vin\nnu mis\nðŁ¤ Ļ</w>\nru t</w>\ngre co</w>\neth ic</w>\nred neck</w>\nar r\nt cs</w>\nih ri\nðŁĩ« ðŁĩ·\nl k\ninher ited</w>\nzy k</w>\nviadu ct</w>\nmarty red</w>\nhi gu\nss n</w>\nbe in\nstreet style</w>\nfer gie</w>\nbank of\næĹ ¥\nstake holder</w>\nexempl ary</w>\ncre ss</w>\ness a</w>\nero tica</w>\nintre pid</w>\ngom es</w>\nbra un\nbethan y\nbang tan</w>\npulmon ary</w>\nm illing</w>\ndoctor ate</w>\ntrump russia</w>\nठ°\ns ani\nbl att</w>\npla u\ndepri ved</w>\nt le\nful ly\nbour n</w>\nst ak\nlufthan sa</w>\nkio sk</w>\nfar oo\ndef y</w>\nbad an</w>\nðŁĺĺ âĿ¤ï¸ı</w>\nrit z\ntri sha</w>\nran ds</w>\nmiddle sex</w>\narab s</w>\npro j</w>\nsport scenter</w>\nrepe ats</w>\niv f</w>\nbleed blue</w>\nas sure</w>\no bs</w>\nterrit orial</w>\nele n</w>\nbever ley</w>\nann ah\nâĿ¤ï¸ıâĿ¤ï¸ı âĿ¤ï¸ıâĿ¤ï¸ı\nz l</w>\nfor good</w>\nscience fiction</w>\ngla u\nson ya</w>\npri th\nst weets</w>\nmix ers</w>\nmari o\nant elope</w>\nwriting community</w>\nwent z</w>\nden ham</w>\nbe di</w>\nsf o</w>\nharley davidson</w>\nlook book</w>\nimmuno therapy</w>\nor phe\nes ville</w>\ned ged</w>\ntas k\nsb ball</w>\ncorro sion</w>\nkilom eters</w>\nco sting</w>\nplay back</w>\nke ke</w>\ndi visi\nu ter\nre location</w>\nyel led</w>\npen g</w>\nup beat</w>\nser ve\nâļ ł</w>\nhal en</w>\nstir ring</w>\nreh man</w>\nen v</w>\nschu macher</w>\nfrag ment</w>\nalkal ine</w>\nsb k</w>\nresil i\nshare 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rust</w>\npull man</w>\nr fid</w>\nsep sis</w>\nle ed</w>\nfri ght</w>\nroun ding</w>\nne b\nph ins</w>\nai sha</w>\nutili zing</w>\nsqu ats</w>\ngold smith</w>\nj ic</w>\nbo ks</w>\nvau s</w>\ni po\nexclu sion</w>\ntari ff</w>\npo kes</w>\nmin al</w>\nland s\nen force</w>\nwashington dc</w>\nor char\ng x</w>\nmar ys</w>\ney our\naussi e\nbak ers</w>\nun popular</w>\nlatin os</w>\nlar ge\npu tnam</w>\nbol o</w>\nwa de\npel o</w>\ndi zz\nob struction</w>\nfla ppy</w>\nweare the\ndepend ence</w>\npajam a</w>\ne te</w>\ny ann\ne wan</w>\ndisc la\na ay</w>\nkar ina</w>\ne ic\nan trim</w>\nw soc</w>\nneg atively</w>\nkai do</w>\nfotogra fia</w>\ndh ru\ncolo ssal</w>\nmcle od</w>\nk wang</w>\nmani pu\nex hilar\nus atoday</w>\nsummer slam</w>\nco les\ntapro om</w>\nunbeat able</w>\nde ma\ntic ks</w>\nk ling\nfil s</w>\ncampaig ners</w>\nภķ\nbrew ster</w>\naudu bon</w>\nqu ay\nch s\nki gali</w>\nd ler</w>\nstrength ens</w>\nsom al\nsign ingday</w>\ngol ds</w>\npig ment</w>\norche stral</w>\ng q\nlin kin\nðŁı ĩ</w>\nta w</w>\nalgar ve</w>\nho v</w>\near le</w>\ngold fish</w>\nam ig\nex er\nben in</w>\ndru id</w>\nðŁIJ ¸</w>\nshe m</w>\nquat tro</w>\nmer cen\nmen te\nincorpor ating</w>\nbon anza</w>\nstate fair</w>\nen de</w>\nconcep tions</w>\ne es</w>\nâĻ¥ï¸ı âĻ¥ï¸ı\nd son</w>\nfire arm</w>\norb ital</w>\nwe h</w>\nmulti p\nfo b</w>\nrequi em</w>\np light</w>\nthou se\nsa id\noc re</w>\nremem brance\nn old</w>\nchi pping</w>\nbe v\ner t\nca thy\nsy m</w>\nri ggs</w>\nm ley</w>\ndialo gues</w>\nsl ender</w>\nhow l</w>\ngau teng</w>\nwd w</w>\nto bi\nsmo kes</w>\nim plo\nb pm</w>\nad n</w>\nmom basa</w>\ncap sul\nbloom field</w>\nartic ul\ncle o</w>\ngoog led</w>\nflu ffy\nl ard</w>\nen zyme</w>\nve sti\nibra hi\nfl ame\ne mea</w>\nout ages</w>\ndispro por\nble ak</w>\nan sel\nick er</w>\nst louis\nstock market</w>\ngood friday</w>\nsau lt</w>\nstal led</w>\npro m\nep som</w>\nb é\nthe se\nsau ces</w>\nme w</w>\nlit fest</w>\npre d\nre u</w>\nkar ak\nsi enna</w>\nell in</w>\nbio technology</w>\nï¸ıâĥ£ -</w>\ntac tic</w>\nsa in</w>\npor k\nmon za</w>\nka j</w>\nlu sh\ncompart ment</w>\nchang ing\nshraddha kapoor</w>\nfo al</w>\nar tem\ncu ando</w>\ncan ola</w>\nori ente\nme sse</w>\nd ited</w>\nbr c</w>\nbox er\nbbc two</w>\ns st</w>\nment day</w>\nem ing</w>\nde wey</w>\nkof i</w>\nâŀĸâŀĸ âŀĸâŀĸ\nreali zation</w>\nsmo l</w>\ntw ood\nsan je\nflag staff</w>\nber wick</w>\ncor set</w>\ncan ary\nwhistle blower</w>\net ched</w>\ncom posing</w>\nsquee zed</w>\nbow er</w>\nauto desk</w>\nne h\nmathi eu</w>\nba ja\nÅ Ĥ\nhy dra</w>\nda im\nam eri\ninsi sted</w>\nmer lot</w>\ngar ros</w>\nheart news</w>\ngaine sville</w>\ncut ler</w>\nbo de</w>\nðŁĺī ðŁĺī</w>\nlew es</w>\nscoun try</w>\ng sa</w>\nus u</w>\ncc m</w>\ngod awgs</w>\nphara oh</w>\ncra e</w>\nmor ley</w>\nhyp noti\nf ades</w>\nneur ons</w>\nfu zz</w>\ning co</w>\nhigh landers</w>\nstar k\nvig ne\npac kets</w>\namar illo</w>\nreu ben</w>\ninsul ts</w>\nbas ic\nvec tor\nn me</w>\nac ruz</w>\ntro s</w>\ntransm itter</w>\nðŁĺ ŀ\ninterpre t</w>\nðŁĺ ²\npre quel</w>\nmc gowan</w>\ndis semin\nðŁĴĺ ðŁĴĺ</w>\nmascul inity</w>\nindie gamedev</w>\nali ve\nte t\npe tal</w>\nema iled</w>\nar med\nko o</w>\nhe er</w>\nba ird</w>\nsuper junior</w>\nmetro polis</w>\ndelav in\ndecl ines</w>\nstit utes</w>\nÛ ģ\np tbo</w>\ng lan\ncho res</w>\ne aling</w>\nchri ssy</w>\nste mc\nvi an\nassassin ated</w>\npron ounce</w>\nilleg als</w>\ndiscover y\ncav ill</w>\nfri fotos</w>\nf al</w>\nso i</w>\nsabot age</w>\nt int</w>\np dc</w>\nðŁİīðŁİ Ī\nãĤ Ĭãģ\nji o</w>\nendeav or</w>\nin sig\ncommit tees</w>\nshe arer</w>\nme tz</w>\nmar rying</w>\nh dd</w>\ng by</w>\nfre t</w>\ntri sh\npu l</w>\nscrip ted</w>\nsa ki</w>\nl w\nke ye\nshim i</w>\nnan aimo</w>\nca h</w>\nà «</w>\ntem pered</w>\nici an\ndu gg\ndish washer</w>\nair field</w>\ns rugby</w>\ngr inch</w>\ny st\nr ms</w>\nmahat ma</w>\nlan kan</w>\ndisc ar\ndige stion</w>\nno des</w>\nl ls</w>\nom ic\ngu tter</w>\ntis garh</w>\nfeder ico</w>\nelection day</w>\nbo he\nmaster card</w>\nfire ball</w>\nâľ Ķï¸ı\noy ster\np ong\ndo k\nen route</w>\nm vc</w>\nbeat the\nali stair</w>\nshu b</w>\nsh aming</w>\ncherno byl</w>\nghi bli</w>\nthe s\npin ion</w>\nd bs</w>\nsal ts</w>\nic tion</w>\nepi ph\nnc pol</w>\nin convenience</w>\nwhit ley</w>\ninspec ting</w>\nwood ley</w>\nwi ener</w>\nskil let</w>\nno les</w>\nm ca\nh ina</w>\na sha\nwilling ness</w>\nwell ness\ntam ed</w>\nshow time\ndis advantaged</w>\nber nat\nus n</w>\nmission aries</w>\ncoun selling</w>\narrog ant</w>\nquant itative</w>\nleg alization</w>\nho dge</w>\nenergye fficiency</w>\ncameron dallas</w>\npos sessions</w>\np bb</w>\nharris burg</w>\nv g\nhindu ism</w>\nhappy thanksgiving</w>\nfi b</w>\nre acting</w>\ntweeta picture\npol iti\nmu ppet</w>\nhur rah</w>\npac e\ncoast guard</w>\nguar ded</w>\nas am\npar ry</w>\nfore very\nx q</w>\noom f</w>\nke anu</w>\nj ind\nri st</w>\ncustomer service</w>\nsac red\nðŁĺ º</w>\nton er</w>\noccur rence</w>\nmat u\nval dez</w>\nred d</w>\nis ak\npower rangers</w>\npe asant</w>\nraj ini</w>\nabra ham\ne mil</w>\ncar do</w>\ntr il\nhair styles</w>\nobsole te</w>\nsam pler</w>\ndirec tive</w>\ndelavin kisses</w>\nver ton</w>\nglo s</w>\nsp ay\npaler mo</w>\ncom ets</w>\nman ziel</w>\nchicag of\nski pped</w>\npic torial</w>\nh ant\nb mi</w>\na ol</w>\nre opens</w>\npad dling</w>\ndevo s</w>\nfra ud\nbas eline</w>\nque ues</w>\nsp ired</w>\nsn are</w>\neu ve</w>\ndescri ptions</w>\ndaisi es</w>\nca ching</w>\ngall eria</w>\ntri mmed</w>\nstin o</w>\nrecy cla\nic ular</w>\nbir ken\nraw lings</w>\nfli x</w>\nchic as</w>\nb gt</w>\nlik eli\nargy ll</w>\nthel ove\nga ston</w>\nbl anca</w>\nha k</w>\nf one</w>\nsailor moon</w>\nh aci\nima c</w>\nfl yn\nde can\nbel les</w>\nap ic\nzo g</w>\ntaun ton</w>\ncon stance</w>\nlasag na</w>\nker nel</w>\nin ka</w>\nhar bor\ncollec tively</w>\ncalcul ated</w>\nav ille</w>\nshil pa</w>\npur du\ngi mm\nfun er\na est</w>\npembroke shire</w>\nnighting ale</w>\nn unes</w>\nhyper tension</w>\nhu bert</w>\nsli ders</w>\ninfer tility</w>\ncomm ended</w>\ntransat lantic</w>\nmetr ical</w>\n!! @</w>\nÅ Ł</w>\nss g</w>\nbac ca</w>\ninver ted</w>\nfun factfriday</w>\nit ans</w>\nalbu m\nacqu ainted</w>\nri er\nwhel an</w>\nsar ab\nmu e</w>\nsnoo ze</w>\npi ff</w>\nagre eing</w>\nsp itting</w>\njer maine</w>\nn ye\nâľı ï¸ı</w>\nam bush</w>\nze ph\ncon greg\nunivers ity\ns app</w>\nwann abe</w>\npat rice</w>\nib d</w>\ndo glo\nfri dges</w>\nsun d</w>\nking ston\nar gon\nkam en</w>\nhardro ck</w>\nds ley</w>\ndo lores</w>\nì °\nota ku</w>\npi ping</w>\nbe having</w>\nâŃIJï¸ıâŃIJï¸ı âŃIJï¸ı</w>\nblue bird</w>\nan sari</w>\nteapo t</w>\nfire work</w>\ncro p\nlog ans</w>\nty ped</w>\nthick ness</w>\nig ers\nc fp</w>\ndys functional</w>\ncontra sting</w>\net ty</w>\naston martin</w>\ntx st</w>\ndra grace</w>\nat tributes</w>\nmarath on\nmanu scripts</w>\njohn stone</w>\nðŁĺ± ðŁĺ±</w>\nbo er</w>\nay u</w>\naru gula</w>\npoo rest</w>\ncon du\nassu mption</w>\nanag h</w>\nno h</w>\ndelav in</w>\nsit ter</w>\ng ö\nmor ow</w>\nkick start</w>\ncom i\ngl acial</w>\nghe ad</w>\nba in\nker shaw</w>\nen dof\nfre ud</w>\nom at\ni af</w>\nhu g\nsign up</w>\neach other</w>\ndefin ite</w>\ntu bing</w>\nshak ira</w>\nðŁijı ðŁı½\nuu uu</w>\nsw in</w>\nsham bles</w>\nol as</w>\nsk ell</w>\nbrit ain\nkn w</w>\nclu tter</w>\nom y\nj ens</w>\nhang ed</w>\ncity scape</w>\nscra ps</w>\nun locking</w>\ndead liest</w>\ner no</w>\nbreast cancer\na it</w>\ninspec t</w>\nfu ri\nðŁĴ Į</w>\nku d\nju le\nor ah</w>\nmi ds</w>\nm dt</w>\nbur gring</w>\nr attle\npu sa</w>\nstal k\ncle ans</w>\niss ance</w>\nz ek</w>\nworth it</w>\nnam eis\nmusko ka</w>\ncouncil man</w>\nurban art</w>\nbar rac\nun solved</w>\ntu l</w>\ng ita</w>\nwhite board</w>\nsoy beans</w>\nem ent\ncont i</w>\nsaturday motivation</w>\nconveni ently</w>\ndoc king</w>\nt ado</w>\nâı ©</w>\nsp ino\npuppy love</w>\npo f\nfabric ated</w>\nrobb ers</w>\nadop ts</w>\nti fied</w>\nkk r</w>\nindulg ence</w>\nnotic eable</w>\nmacqu arie</w>\nchap el\nsensu al</w>\nki ko</w>\nmelan oma</w>\nlore tta</w>\nli ance</w>\nab en\nsp lus</w>\nga al</w>\nac ele\nlib dems</w>\ncompar isons</w>\nðŁĮ µ</w>\nrhy thms</w>\nmer y</w>\nen capsul\nnap ier</w>\nðŁijĮ ðŁijĮðŁijĮ</w>\nðŁij IJ</w>\nplat z</w>\nfre sno\nre formed</w>\nran bir</w>\nel it\nthe best\nbhu shan</w>\nvin nie</w>\nimpro vised</w>\ns ittin</w>\nre created</w>\ne ba</w>\nec ker</w>\nac rob\npon te</w>\ncor d\ngi ddy</w>\neur usd</w>\nfe ver\nintu ition</w>\ngar i\ndum mies</w>\nbud weiser</w>\namend ments</w>\nte tra\nsch nit\nay as</w>\nmar ys\nci st</w>\nk ani\nker mit</w>\nðŁĺ±ðŁĺ± ðŁĺ±</w>\ntin ker</w>\nstrol ling</w>\ndi visional</w>\nniger i\nomin ous</w>\nmenstru al</w>\nkar ab\nk hy\nbw fc</w>\npan handle</w>\nl illi\nwell er</w>\nstra pped</w>\nson the\ntransfer ring</w>\nethe real</w>\nsne aks</w>\nru dol\ngab les</w>\njac king</w>\ncin code\nfor tune\ncanadi ens</w>\ncon for\nab normal</w>\nfrank lin\ntit a</w>\nmu la</w>\npersi st</w>\ncu ties\nki el\nðŁĩ± ðŁĩ\nher mann</w>\naw k</w>\nfi asco</w>\nko to\nwe ta</w>\nhi ker</w>\nbudd y\npreven tive</w>\nmcgra w</w>\ngame boy</w>\nforsy th</w>\ntop shop</w>\nsi ob\nsad h\nin tram\nfollow art</w>\nso aps</w>\ndragon ball\nou x\nmorri son\n๠ĥ\nlu bric\nadul thood</w>\nmorri sons</w>\nâļ łï¸ı\nher mo\nta ka</w>\nstall one</w>\nmis use</w>\nteam gb</w>\nra gha\ncon fined</w>\nat y\nhom ophobic</w>\nnw o</w>\nsky news</w>\nho ya</w>\nac rosse</w>\nwi iu</w>\npur ée</w>\njed dah</w>\nðŁ¤ §</w>\nadvis ers</w>\nph ine</w>\nan is\nscrump tious</w>\në° ķ\nc ke</w>\nvin y\nter m\ns dc</w>\no do</w>\nhome school</w>\nvas c</w>\nleop ards</w>\ndebor ah\nillic it</w>\ncur ran</w>\nas roma</w>\nnau ght</w>\nmar ig\nbrand i</w>\nem p</w>\nðŁĺį ðŁijĮ</w>\nî Į\nsu spend</w>\nlu z</w>\niniti ation</w>\nsch aft</w>\njensen ackles</w>\ncraw ler</w>\npost doc</w>\ndes ks</w>\ntrail blazer</w>\nden omin\ntri x</w>\nno ise\npo et\n± ï¸ı</w>\ns mug</w>\nvol atile</w>\nproof s</w>\npharmac ist</w>\nsardin ia</w>\nmash able</w>\nkim chi</w>\nco ed</w>\nschal ke</w>\ndoo dled</w>\nc sw</w>\nsh ur\nro x</w>\ndo k</w>\nchris brown</w>\nmathemat ician</w>\nab ound</w>\nang elic</w>\nrock ford</w>\nd ole</w>\nyor kers</w>\nms n</w>\ng man\nxavi er\nbor rowing</w>\nmark ings</w>\nlongh orn</w>\nk ja\ndiver ted</w>\nmm it</w>\neuph oria</w>\nay yy</w>\nte a\npa h\nck i</w>\nun cut</w>\nli ven\nky ung</w>\nfan art\nmer ing</w>\nred ding</w>\namo vie</w>\ngri di\nc thulhu</w>\nschol arly</w>\nju dah</w>\nth bewithyou</w>\neu calyp\nðŁIJ ķ</w>\nhert fordshire</w>\ncour troom</w>\nby u\nauc tioned</w>\nple ase\nmar cia</w>\nê° ĵ\nsucce eded</w>\nel as</w>\narvin d</w>\nt lot</w>\nsaig on</w>\nre tt\nra kesh</w>\nfd ny</w>\nas en\nse bring</w>\ngladi ators</w>\nyou know</w>\nv lad</w>\ngol a</w>\npar ap\nÑĢ и\nsab cnews</w>\none team</w>\noh l</w>\nsun e</w>\nri j\ncd c\nstar gate</w>\nrun down</w>\nplat o</w>\nph c</w>\nchat ter</w>\nra viol\nmn f</w>\nmand ala</w>\nli et</w>\nภķ</w>\nmari a\nhun gover</w>\nconsoli dation</w>\nfer rell</w>\ntradition al\nilove art</w>\ngal ap\nðŁı Į\nque zon</w>\nespa ña</w>\nðŁĩ¨ðŁĩ Ń</w>\nho bby\nsteam boat</w>\nmali gn\nguil lau\npro hi\nits me\níĥ Ģ\nin scription</w>\nal z</w>\nmari an\nk ade</w>\nmm on</w>\nadju sting</w>\nne sts</w>\nintern ally</w>\nci r</w>\nvik ram\nmal ala</w>\nk ph</w>\nfel icia</w>\nthe real</w>\ncap tivity</w>\nat is</w>\nmarcor ubio</w>\nkale ido\nche v</w>\nmano j</w>\nle more</w>\ngent ri\nvi ps</w>\ntro pe</w>\n\" âĢĶ</w>\npair ings</w>\nmal nutrition</w>\nfr ay</w>\ndesig nation</w>\nbrun omars</w>\naz e\ntor rential</w>\npan zer</w>\nga il\nunder the\nthe ological</w>\nschizoph re\ndazz le</w>\nfreder ic</w>\nmo par</w>\nad illa</w>\nso ggy</w>\nra un\nmedi ocre</w>\ncolo rec\ni fe\np inst\nblu ef\n ²</w>\nworld water\ngir oud</w>\nclar inet</w>\nad olf</w>\ntar antino</w>\nreceip ts</w>\nassu mp\nðŁij Ł</w>\ncoffe es</w>\nâľĬ ðŁı¾</w>\ndu plex</w>\ns of</w>\nr x\nlin o\ntimber wolves</w>\npan dit</w>\nmo tm</w>\ne ga</w>\nay ama</w>\nach s</w>\noutsi der</w>\nll en\nco er\ntil ly</w>\ncheese burger</w>\nma ds</w>\nple dis</w>\nemp ty\nnational parks</w>\naz iz\np mi</w>\njun kies</w>\nf ener\nsq n</w>\nè s</w>\ngener ation\ncleop atra</w>\nbhuban es\nmosqu es</w>\nty free</w>\npopp ins</w>\ntw c</w>\nor well</w>\nn age</w>\nka whi</w>\nhol low\ndal ai</w>\n¨¨ ¨¨\nou ro\nm health</w>\ngi on</w>\naz o</w>\nvis as</w>\nreneg ade</w>\nre ic\nw sop</w>\nðŁĴļ ðŁĴĽ</w>\ne chel\ntox icity</w>\nmü n\nbun k</w>\nstimul ating</w>\nasth our</w>\n\\ '</w>\nep h</w>\nende mic</w>\ncn bc\nshrin king</w>\npeabo dy</w>\nmichel angelo</w>\ncan yon\nwal e\nsu mi</w>\nsi ders</w>\ninu it</w>\n? .</w>\nprofession alism</w>\ndr acing</w>\nplat oon</w>\np ons</w>\nout bound</w>\nmaple leafs</w>\nde sol\ncen cy</w>\na than\nver ma</w>\nru bbing</w>\nok an\nðŁij ł</w>\nmull ins</w>\nauthent ic\nÅ į\nalman ac</w>\nga ia</w>\nbb q\non imo</w>\nke h\nty a</w>\ntou ts</w>\ny av\nre posit\n, .</w>\nwi ght\nse eyou\ncal lof\ndone sia</w>\nbar gaining</w>\ngr anth\nsd su</w>\namphi theater</w>\np su\nre watching</w>\nwine tasting</w>\npeak district</w>\ndete cting</w>\nthur man</w>\nphe e</w>\nèª ķ\nu mich\nre r\nsculp ted</w>\ngo le\nname sake</w>\nðŁĶ ģ</w>\nserv icing</w>\nbau gh</w>\npu gh</w>\npen cil\ndar th\nmunch kin</w>\nat orium</w>\nten ers</w>\nsun y</w>\nrolling stones</w>\nmag ing</w>\nstar rer</w>\ni dris</w>\nfe instein</w>\nag ron\nâĺºï¸ı âĺºï¸ı</w>\nsupervis ed</w>\nchamele on</w>\naggre gate</w>\nsucce ssive</w>\nmo gul</w>\ninst yle</w>\npol dark</w>\ncustom e\nohio state</w>\nha ya</w>\nci des</w>\nbroker age</w>\nangel ou</w>\nfifa wwc</w>\nde forestation</w>\nal ton\npam ph\nhu gged</w>\nho bo</w>\nchange able</w>\nku ber\nbur roughs</w>\ndemon etisation</w>\ncape cod</w>\nvers atility</w>\nor ice</w>\nle ila</w>\nwomenin science</w>\ntu a</w>\nhe dges</w>\nembarrass ment</w>\nali fe\nso ars</w>\nni ghter</w>\nhy mn</w>\ngi pp\nchas u\ntech s</w>\nni all\nk illa</w>\nhi ka</w>\ncam els</w>\nvalu e\n ¢</w>\nsc oops</w>\nmah moud</w>\nclu sive</w>\nadri ana</w>\npac o</w>\noz il</w>\nun as</w>\ntransl ations</w>\nwhispe rer</w>\ns bi</w>\nbu xton</w>\nbio tics</w>\nindi ffe\nken ney</w>\nk lar\net ching</w>\nbarra best</w>\ninst ability</w>\nse ine</w>\nvo tel\nblo gged</w>\nwhis key\nmy space</w>\nt ant\nlan dia</w>\ngive back</w>\nillu s</w>\naw ak</w>\nac ab\nf bloggers</w>\ncloud computing</w>\nblat ant</w>\nsyri ans</w>\nband ra</w>\nsty n</w>\nan em\nke ted</w>\nkar thik</w>\nbarun sob\npin ot\ngu bernat\ngay e</w>\narti ste</w>\ni fied</w>\nconven tions</w>\nhu an</w>\ngeni uses</w>\neeee ee</w>\nfol ly</w>\nsomer ville</w>\npride month</w>\nðŁĩºðŁĩ¸ ðŁĩºðŁĩ¸</w>\nchemo therapy</w>\npaul s\nbak ar</w>\nìĦ¸ë¸ IJ</w>\ntaiwan ese</w>\nfol lo\nc ss\nre ign\nnn nn</w>\nfla un\ncatastro phe</w>\niti es\nfrag ments</w>\nextre mists</w>\nym oun\ncar men\neze kiel</w>\nconne cting</w>\nse h</w>\nman ta</w>\nremodel ing</w>\nwe ymouth</w>\nat oms</w>\nce m</w>\nne well</w>\nlu mi\nthe open</w>\nmo c\nmili band</w>\ng land</w>\nz shq</w>\nmag gie\nmani acs</w>\nm sp\nad y\ncre ams</w>\nle anne</w>\ne sta\npy g\naf finity</w>\npray er\ndun bar</w>\nligh troom</w>\nac adi\nwyn onna\nroman tic\nstate dept</w>\nsick le</w>\nwh os\nlam o\net our</w>\nfin ity\nshru b</w>\nshar pen\npun dit</w>\ned on</w>\naf ore\nmar s\njeff ery</w>\nter ps</w>\nmedal list</w>\nkath arine</w>\naccu sing</w>\nta z\nroy d</w>\nfrom home</w>\nconfron tation</w>\nalle gh\nðŁijī ðŁijī</w>\nrefresh er</w>\nran veer</w>\nnever land</w>\njo jo\nlu crative</w>\nen am\nca ver\npa edi\nman jaro</w>\nflu ids</w>\nthe ssal\noppre ssed</w>\nmu ss\njoh anna</w>\nØ ®\ncn g</w>\nbuil dthe\nsett les</w>\ns ith</w>\nfu ego</w>\ncl amp</w>\nar ag\npay er</w>\nted x</w>\nmand y\ninter stellar</w>\nfr c</w>\nch and</w>\nb cc</w>\nmo lo\nlen til</w>\njohan sson</w>\ngrims by</w>\nnature lovers</w>\nðŁļ¨ ðŁļ¨ðŁļ¨</w>\nshin de</w>\nx in</w>\ninternational dayof\ntransiti onal</w>\nsat a</w>\ncad dy</w>\nwo d</w>\nif u</w>\nha ys</w>\nholl yo\nj ang\nir c</w>\nco im\ngrad able</w>\n\" \"\nðŁį ´\nঠ¾</w>\na el\nn yo\nwest lake</w>\ntime out</w>\nsof i\nphenom ena</w>\ncultiv ation</w>\nag no\nun armed</w>\nso t\ncon j\ngen o\nroyal navy</w>\nnutriti on\nfair mont</w>\nti relessly</w>\nsn g</w>\nre ty</w>\nmic a</w>\nlu cent</w>\nslo ane</w>\ndroo l</w>\nriz al</w>\nod ell</w>\ncritici zed</w>\n. '\"</w>\nla ze</w>\ndeser ted</w>\nco der</w>\npra s</w>\nl illian</w>\nitiner ary</w>\ndav y</w>\nan ap\nwhi pping</w>\nhobo ken</w>\nkare ena</w>\nçľ Ł\nvi us</w>\nter n\nnan tucket</w>\nmis understood</w>\nbu laga</w>\nst ant\nchin ook</w>\nz am</w>\nreli es</w>\nd ss</w>\ned mond</w>\nsket chy</w>\nm ell</w>\nfe x\nrec tor</w>\ndist ill\nday dream</w>\nwine maker</w>\nri pley</w>\nbillion aires</w>\nhel ene</w>\nati f</w>\ncul prit</w>\nbertr and</w>\nwou ldnt</w>\nma pped</w>\nv ak</w>\ngla dly</w>\nparliam ent\nkidlit art</w>\nware ness\ngoli ath</w>\nâĨ ĵ</w>\nview point</w>\ntat ted</w>\nfu ls</w>\ndor sey</w>\nang lers</w>\nli ds</w>\nki ya</w>\nbow les</w>\nbe h</w>\nb ite</w>\ncompati bility</w>\nance stral</w>\npro x\nbeha ved</w>\ngubernat orial</w>\nch field</w>\nsab an</w>\nz h</w>\nteen y</w>\nshibu ya</w>\nholli day</w>\npan cy</w>\nâĿĦï¸ı âĿĦï¸ı\nseun gri</w>\n? ,</w>\nðŁĩ¦ ðŁĩ·</w>\nim itation</w>\nimpac tful</w>\nany i</w>\ngene vie\nañ os</w>\nbate man</w>\ngli der</w>\naf ar\nra sheed</w>\neffor tless</w>\nsh war</w>\ndach sh\ner un</w>\nat os</w>\nkin i</w>\nch d</w>\nkha ki</w>\nk lin</w>\nfelici dades</w>\nbel o</w>\nas l</w>\nto ppers</w>\nfin ley</w>\nstac ey\nrigor ous</w>\nkar ting</w>\nle ppard</w>\ncar michael</w>\nbe ret</w>\nc se</w>\nak hi\nmer ingue</w>\nab an\nha ke\nger i\ner jee</w>\nre sto</w>\ncomm anders</w>\npr it\nfl or</w>\nad ven\nex termin\nremain der</w>\nå IJ\nes g</w>\nmartin o</w>\nlulla by</w>\n| @</w>\nmi gn\nin store</w>\nbig bang\ncor di\ncau ley</w>\nante bellum</w>\ndg ate</w>\ncro ck\nspan dex</w>\nscaf folding</w>\nore os</w>\nê°ĵ ìĦ¸ë¸IJ</w>\npom ona</w>\nma uro</w>\nuni versi\nre mi</w>\naf ootball</w>\nt ant</w>\nsm alls</w>\nne h</w>\nworl do\ntropic al\nmor ph</w>\njav elin</w>\ngla r</w>\narqu itec\nreminis cent</w>\ntu bs</w>\nspide y</w>\nmake u\nsyl la\nprogressi ves</w>\nblo t</w>\nshor ten</w>\nkeep in</w>\nch ak</w>\nang st</w>\nsuper food</w>\ndecad ent</w>\nston y\nneuro logical</w>\nar boretum</w>\nann ak\nfe ma</w>\nper cu\ndis respectful</w>\nsmall biz\nlo x</w>\nco om\nc sc\nbs bi\npre valence</w>\nhim ss</w>\nesp an\nmo ga\nfr ampton</w>\nsky map</w>\nmas se\nlevi athan</w>\n( ).</w>\nnoctur nal</w>\ncar ameli\nang or</w>\namne sia</w>\noutsi ders</w>\nshe alth\nrhin o\nant ag\nag io</w>\nðŁĴ° ðŁĴ°\ntake me\nkab addi</w>\nc si\nm sh\ncoch rane</w>\nthessal oni\nsil a</w>\nha us\ndu sting</w>\nobe se</w>\nmack lemore</w>\nmani sh\nlen in</w>\nm dc</w>\ngro wn\nshef field\ns rs</w>\nke le\ncar son\nch um</w>\ndah lia</w>\ncan tore</w>\nopp o</w>\nhow ling</w>\ncyber crime</w>\nsur realism</w>\nsc ran\nfa iz\nthre n</w>\nrac ists</w>\nr out</w>\npk not</w>\nse mana</w>\nsin i\nmc cull\nma chi\nalfon so</w>\ny b\nsar dar</w>\nkend rick\nden g</w>\nreci pro\non f</w>\ndoom sday</w>\nbri bery</w>\ncustom iz\nart is</w>\nc pi</w>\nðŁĻĪ ðŁĻĪ</w>\nsla va</w>\nlet te\nen s\nâĿ¤ï¸ı ðŁĺĺ</w>\ncra yon</w>\nad an</w>\ntr c</w>\nmigr ate</w>\nsimp son\nrow ers</w>\nking sley</w>\nfarmers market</w>\nshee han</w>\nne phe\nbor non\ncar ton</w>\nmic key\nall ure</w>\nu lu\nsli pknot</w>\nheb do</w>\ngui do</w>\ndog celebration</w>\nonline marketing</w>\nacceler ating</w>\n) ..</w>\norigin ated</w>\nmacar oni</w>\ned tech\nout field</w>\nmit z\ndisc us</w>\nadverti ser</w>\nman or\nha shi</w>\ndescri p\ncap ita</w>\nful bright</w>\nrecep tor</w>\ncon n\ncon ey</w>\nspion age</w>\nr attle</w>\npre st\nu li\nblog post</w>\nacker ay</w>\n) âĢ¦</w>\nred velvet</w>\nmat th\ninspir ing\nb sd</w>\nker ri\npo con\nmil lar</w>\nre pur\naccent ure</w>\nä ¹\nram bo</w>\nragnar ok</w>\ndele ting</w>\nbritish museum</w>\npat ory</w>\nleip zig</w>\nflori an</w>\nsci fi\nin ers</w>\nbr ate</w>\nyo y</w>\nmelis sa\nab er</w>\nma sa</w>\npo te</w>\nmosquit oes</w>\ntranspl ant\nr pa</w>\n; ))</w>\nbast ille</w>\nyl an</w>\njoye ux</w>\nmelo dic</w>\ncap tions</w>\natri st</w>\nroch dale</w>\ngott i</w>\npew die\ncuties aturday</w>\nwho is\naqu aculture</w>\ntiv a</w>\nsp el\nhe ss</w>\nha ji</w>\nfred die\nco per\nbrand o</w>\nv k</w>\nphoto book</w>\n* ,</w>\nmy dayin\nmicha ela</w>\nbrune i</w>\nsr ini\nin te</w>\nÄ ±</w>\nde ol</w>\nd fc</w>\nsepar ately</w>\nbun d</w>\nve sts</w>\nto c\nme ck\nrein forced</w>\nconstra ints</w>\ncar roll\nsq ft</w>\nre ver</w>\ncam per\nbird man</w>\nin action</w>\ngener ators</w>\ntriumph ant</w>\npe sts</w>\no vo\ngy pt</w>\nal amo\nsc aled</w>\nsuresh pp\nsd n</w>\nis mo</w>\ngi os</w>\n) @</w>\njustic eleague</w>\nrestaur ant\ngab i</w>\nden gue</w>\nnext gen</w>\nexemp li\nap ex\ninspir ational\ndown side</w>\nkid z</w>\nu pl\net na</w>\nalvar o</w>\nfel dman</w>\nbar net</w>\nm ha</w>\nes ch</w>\nbloo ded</w>\n>>>> >>>>\nkan i</w>\nho fficial</w>\ncasablanc a</w>\nbir ds\nty ga</w>\nsw amp\no day</w>\nnew castle\nnb ap\nci sion</w>\ncho ols</w>\naf lo\nne p</w>\nmon ton</w>\nak b</w>\nsuper model</w>\ndown time</w>\nth os</w>\nsc wx</w>\nsnoo py</w>\nag greg\nyo ke</w>\nnor cal</w>\nwe tt</w>\nprolon ged</w>\nme tast\nbeat er</w>\nf ta</w>\nt lap</w>\ndisgu sted</w>\ny h</w>\nvoice over</w>\nitch y</w>\nip c</w>\nðŁİ ¾\nphe asant</w>\nstra its</w>\nram pant</w>\nj g\nfer til\nassu res</w>\nfortun es</w>\nsal inas</w>\nliz ards</w>\nkett le\ni bs</w>\ncyn thi\nhe g\nmc cr\nsoccer oos</w>\nhappen ings</w>\ncor den</w>\nðŁĺĤ ðŁijĮ</w>\nt ches</w>\negre t</w>\nwolver ines</w>\ncongratul ated</w>\nho gg</w>\nbott ling</w>\nwr i</w>\nfer ri\nbo sch\naf ire</w>\nog den</w>\ns jo\nj dm</w>\nsv t</w>\ncon tex\ntol lywood</w>\nmin k</w>\nme se</w>\nsuper sonic</w>\nop oulos</w>\nå ¸\nâĶ ģ\nknuck le</w>\ngu ise</w>\ngam i</w>\nchu cky</w>\nz inger</w>\nradi al</w>\ncompla ined</w>\nbo da</w>\nfe tal</w>\ndiscipl ines</w>\ncor ro</w>\nðŁĩ®ðŁĩ ¹\nop ted</w>\nfiltr ation</w>\nad nan</w>\nem cee</w>\nmi stre\ninsom ni\nfer gus</w>\ntra jec\non don\nmed tech</w>\ntanger ine</w>\nmadra s</w>\ngru e\ncab s</w>\nz hu\nsureshpp rabhu</w>\ninsul ated</w>\nday swild</w>\npp m</w>\nband ai</w>\nv 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i</w>\nweather channel</w>\ngh c</w>\n: ...</w>\nta ft</w>\nawe ather\nal isation</w>\nbru tal\nbliss ful</w>\nnik ola</w>\nmal icious</w>\nq m</w>\nmpg vip</w>\nbro die</w>\nbl itz\napplau d</w>\ndri bb\nv ague</w>\ndog go</w>\ntransl ating</w>\ninterpre ted</w>\nhat ched</w>\nge tyour\nbenefici aries</w>\nspar ring</w>\ncaes ars</w>\naw illiams</w>\nla hat</w>\nbro ke\nti mp\nvirtu es</w>\nrel ying</w>\npie tro</w>\nk tn\nici sts</w>\npab lo\nlou i\na ag\npn pp\ncha st\npul ses</w>\nfini sh\nusair force</w>\ntype writer</w>\nthomp son\ndog s\nut to</w>\nãģ į\nsand al</w>\nnew ly\ndo ge</w>\nz w</w>\nwan kers</w>\nne gr\nmu cha</w>\ndetermin es</w>\nblack fish</w>\nsk unk</w>\nmu ps</w>\ninstru ment\nphy to\ndaysto go</w>\nskin ned</w>\nhai der</w>\ncon ten\nðŁIJ¾ ðŁIJ¾</w>\nwe iler</w>\nundoub tedly</w>\nchair ing</w>\nwall is</w>\nsh ard</w>\nzind abad</w>\nadul t\nabsor ption</w>\npre sto</w>\ndeplo ying</w>\ndrum mond</w>\nbattle front</w>\nseag ulls</w>\nhow dy</w>\njuda ism</w>\ndes de</w>\npart ition</w>\nâľ Ŀ\nno logy</w>\nnational bestfriend\nlesn ar</w>\nfilm fare</w>\nco asts</w>\nchristen sen</w>\nac an\nmb u</w>\nco pped</w>\nru bble</w>\nsw c</w>\nfun nier</w>\nfar ther</w>\nwhere as</w>\nnano technology</w>\nwith stand</w>\npil low\nbow ers</w>\nto pe</w>\nit ly</w>\ncon fit</w>\nma kar\ncomfor ts</w>\nbo sh</w>\ncli pper</w>\nbal la\nsti k</w>\nmil b</w>\nsafe guard</w>\nmusi que</w>\neas port\nya z</w>\npad ded</w>\nbad er</w>\nfore ign\nchop in</w>\narchi ve\no ka\ntran sporting</w>\ntml talk</w>\naj it</w>\nconsequ ence</w>\nsc roo\nff o</w>\ncollabor ated</w>\npug chat</w>\nye mi</w>\njav ed</w>\nau burn\no of</w>\nma w</w>\nsau cer</w>\nmiti gate</w>\ni les</w>\nevangeli st</w>\nter ie</w>\nre cl\nindic tment</w>\ncat a</w>\nbright ness</w>\nmay the</w>\nwhim sical</w>\nun lv</w>\nkey word</w>\ncu min</w>\nmed way</w>\nwest world</w>\ntra w\nim posing</w>\nform ity</w>\ncoul ter</w>\nab z</w>\nny pd\ngrass i</w>\nkel sey\nqld pol</w>\nclock work</w>\nf dr</w>\ndi anne</w>\nâĺ ij</w>\nad h</w>\np ann\nbra vely</w>\nae ge\nun lawful</w>\nver di</w>\npocaly pse</w>\nphar o\nkar la</w>\nreson ance</w>\nma stiff</w>\nla dak\nbu u\nma iled</w>\nhi i</w>\ncraw ley</w>\ntor rent</w>\nmach ado</w>\nliby an</w>\neffort lessly</w>\nfal sely</w>\nq vist</w>\nke ef</w>\ncraf thour</w>\ncheri shed</w>\nval kyrie</w>\ns ari\nkal amaz\nbe he\nðŁĮ Ļ\nth im\nro ddy</w>\ncol trane</w>\nbut chers</w>\nach im</w>\nwk end</w>\nawk ward\ncab rera</w>\n:) )))</w>\nfran c</w>\ndecl an</w>\ncon dos</w>\na ja\npandor amusic</w>\nchar ter\nph ill\nmon trose</w>\nhatch back</w>\nhandic app\ngre aves</w>\neucalyp tus</w>\nut most</w>\nt son\nbur ton\nmid wives</w>\nin cur\nðŁĺį #</w>\nmoo d\ncompre ssed</w>\ntom a\nmust ang\nmo g</w>\nas ana</w>\nte stic\nsho tel</w>\nin sol\ncor sair</w>\nnh q</w>\nben ny\nsm ma</w>\nkap ur</w>\nin con\njon as\nener gies</w>\ndon al\nas ad</w>\nse z</w>\nn pa</w>\narchi ved</w>\nstimul ate</w>\ndo p\nhy d</w>\ngri eving</w>\nãĥ Ī\nron a</w>\nwhy te</w>\ntree house</w>\nss ell</w>\nsand ro</w>\nko bo</w>\nther most\nse clu\nhi ya</w>\nge ez</w>\nmam as</w>\nprisc illa</w>\nflav oured</w>\nfas s\nw old</w>\nmaker space</w>\ncospla y\np tv</w>\nhappy valentinesday</w>\nsequo ia</w>\nlove craft</w>\ngu an</w>\nd tm</w>\nci i</w>\nyoko hama</w>\npos thum\nre q</w>\nðŁĶµ âļªï¸ı</w>\ngalat asar\ndol by</w>\nhamp tons</w>\ndisturb ance</w>\nstone henge</w>\nok c\ndisrup ting</w>\nmonth sary</w>\njun gle\nhead lights</w>\ndu stin\nmicro sof\nhappy mothersday</w>\nko ko\ngra zi\nte sto\nna idu</w>\nmal ay</w>\nari al</w>\nru mb\nab oo</w>\nhar man</w>\ntra pe\nspo ils</w>\nje ho\ngo dly</w>\nlock screen</w>\nz un\npi ous</w>\nma gento</w>\nl enders</w>\nprob able</w>\ncorpor al</w>\nm our</w>\naw al\nsu a</w>\ncall me\nton ne</w>\ngo vin\ndevast ation</w>\nx j</w>\ngear box</w>\nwar lock</w>\nper me\nit ate</w>\ngaza underattack</w>\ndu val</w>\nparas ite</w>\nclement e</w>\nle th</w>\ni va</w>\nfro zen\ntho les</w>\nto bin</w>\ncair n</w>\ns ill\nluc kiest</w>\nconver ts</w>\nst ale</w>\npan cra\neuro pale\nwis dom\nsch ur\nì ¶\nverti go</w>\nbi j\nu bc\nnu re\nrighte ousness</w>\nmt c</w>\nfactor y\nver st\nrevers ed</w>\nhur i</w>\nhee chul</w>\nfab er</w>\nar r</w>\nul ous\nven om\nph at</w>\ngreen ery</w>\nbra dy\nà ¦\n: ((</w>\nnever giveup</w>\ndi sha</w>\nmo ta</w>\nhealth care\ndun ham</w>\ndex po</w>\nden zel</w>\nbb ins</w>\nf ics</w>\nwh am\nmc g\neli an</w>\nwat a</w>\nstr alia</w>\ntel lu\npe sky</w>\nspin off</w>\nar moured</w>\nre acted</w>\ndo fficial</w>\nte du</w>\nsag ar</w>\nmor ally</w>\nparalle led</w>\nfi os</w>\ndow ner</w>\ndau gh\nre do</w>\nworld cup\ntari q</w>\nbar ne\nglaci ers</w>\noc cult</w>\nbarbar ian</w>\nher mosa</w>\n!! !)</w>\ny ur\ninter nation\np ss</w>\nsit u</w>\np int\namerican air</w>\nsw am</w>\ndopp ler</w>\nðŁĴĻ ðŁĴľ</w>\ncincode mayo</w>\nle van\nhell enic</w>\nmc ne\nju di\nyu h</w>\nst x</w>\nqu are</w>\nðŁĺĤ 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el\nror y\ngol die</w>\nfi rec\nun noticed</w>\npecu liar</w>\nsch a\nker son</w>\nmour ns</w>\nliquid ity</w>\nqu ipment</w>\nhi bs</w>\nar s\naeron au\nslide show</w>\nsla bs</w>\ndelici ousness</w>\nsk itchen</w>\nhta fc</w>\nfull erton</w>\ncre ighton</w>\naer ob\nprocrastin ation</w>\naz ores</w>\nwhite hall</w>\nuss occer</w>\nmedi ation</w>\ndjoker nole</w>\nand me</w>\num en</w>\nnoxi ous</w>\njo ss</w>\nili fe</w>\nanni vers\nsudan ese</w>\net res</w>\nunder mine</w>\nwhole foods</w>\ndiso be\nkor i</w>\nade le\neli z\ncan ti\nal on</w>\ngymna sium</w>\nsarko die</w>\nmeteoro logist</w>\nyl de</w>\nste en\nstamp collecting</w>\nnas al</w>\nlo tt</w>\nfran ks</w>\nex ol</w>\nack i</w>\ngood year</w>\nanimal rights</w>\ny les</w>\nvio lets</w>\nmm es</w>\ns thel\nra pping</w>\ntu scan</w>\nwai ver</w>\ntur ner\neat local</w>\nnorthe asthour</w>\nanim ations</w>\ntom morow</w>\nt sh\nff ame</w>\nbra e\npe tron\nglam our\nbr yn</w>\nd cs</w>\nbal es</w>\nðŁĶ ¶\nbro v\nbre v</w>\nb 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items</w>\nda res</w>\n**** ****\nðŁij Ĩ\ncompre h\njin x</w>\nas well</w>\niri e</w>\ncircul ating</w>\nðŁIJ ¥</w>\nover board</w>\ncultiv ate</w>\nrhe tt</w>\noriente ering</w>\nca k</w>\nbal kans</w>\ns itt\njas min\nbritney spears</w>\nro tor</w>\nse aling</w>\ng bc</w>\noc ci\nf as</w>\neman cip\ncom er\nwar time</w>\ntic kle</w>\nson ny\npac es</w>\nlog g</w>\nat rix</w>\nsr p</w>\ng win\ndo bbs</w>\nuz be\nthe wanted</w>\ndru sh</w>\nex tru\nm icky</w>\nhonore es</w>\ndar win\nre dux</w>\nmm j</w>\nram i</w>\njalape ño</w>\nio c</w>\ndo ver\nju ju</w>\nwhit ney\ns eng\nen ly</w>\nau ch</w>\narchipel ago</w>\nvigil ant</w>\nman gal\nwil dest</w>\nparano id</w>\nhal i</w>\nbb ly</w>\nsanc tioned</w>\nreal ms</w>\ncon co\nu ddin</w>\nc sk</w>\nplay time</w>\nlibr a</w>\nsav ag\noc tane</w>\nrec tan\nre turn\npar rish</w>\nmor rha\ncc p</w>\nc mu</w>\nsa iled</w>\nse vent\nro sie\npil ing</w>\nhe w</w>\nboar ded</w>\nseg ments</w>\nneph ro\n( .</w>\ncr ats</w>\nbak es</w>\nðŁį 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ham</w>\nwe arec\npic collage</w>\nn ach</w>\nh ace</w>\npois oned</w>\nlo th</w>\nag na</w>\nadel rey</w>\nguar dia</w>\npoli shing</w>\npeace keeping</w>\nd all</w>\np isa</w>\nla pland</w>\nprocess ors</w>\nde andre</w>\nso bs</w>\np once</w>\ndra ins</w>\nc be</w>\nðŁİ¥ :</w>\nspla sh\nmeat ball</w>\nfon tana</w>\nworcester shirehour</w>\nne v</w>\nbri sk</w>\nb int\nac r</w>\npo x</w>\ncay enne</w>\nskril lex</w>\nj fc</w>\nhahahaha hahaha</w>\ngla s</w>\nen gul\ntempor al</w>\noni zed</w>\ncon cre\ncom pose</w>\nvibr ations</w>\nplant ers</w>\nfer t</w>\ncriticalrole fanart</w>\nt bli\nsch allenge</w>\nhuck abee</w>\nmunici pal\niam bic\nradi os</w>\nne vis</w>\ndura bility</w>\nmc cla\nhorse back</w>\ninst itutes</w>\nful fill\natta ch\nate ur</w>\nak an\nresi sting</w>\nillumin ation</w>\nhand le\nhair care</w>\nom ent</w>\nmacle od</w>\nka iser\ng no</w>\nbear down</w>\nly f</w>\ngl omer\ndistor tion</w>\nz m</w>\nsan k</w>\nroo sters</w>\nis now</w>\nas ports</w>\nag 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j\neradic ate</w>\ndeli ght\ny go\nglam ping</w>\nvic a</w>\ndu ggan</w>\ncoun ters</w>\ncf d</w>\nsc our\nreact js</w>\npu ram</w>\nparas ites</w>\nin ki\nvill en\nstel la\nli mbo</w>\nang as</w>\nk cr\nðŁĴļðŁĴļ ðŁĴļ</w>\nvap ori\nmum ford</w>\noli gar\nà ¼\nal oo</w>\nboo ties</w>\nad r</w>\nk elli</w>\ndru mmers</w>\nav ici\nnature uk</w>\nron al\nin trac\nun splash</w>\nle che</w>\ng oma</w>\nel ine\nenvir o</w>\nbi onic</w>\nbu eno</w>\nmi k</w>\nav in\nstar ling</w>\nem powers</w>\ncake day</w>\nboy cot\nðŁĴļ ðŁĴļ</w>\nðŁĮ¸ ðŁĮ¸\nv ach\nm ci\nfractu res</w>\nger i</w>\nsk ing\nexclu ded</w>\nlu ce</w>\nja ve\nig gy\nevi den\naki stan</w>\na wn</w>\nmor als</w>\nluci fer\nha ban\ntumb ling</w>\nsunday motivation</w>\nmo sley</w>\ncaptain america</w>\nsch icago</w>\nthe one</w>\nmo td</w>\nd ts</w>\nðŁIJ ¼</w>\nrep ell\nii i\nlocu st</w>\ngeo spatial</w>\nmer sey</w>\nimmer se</w>\ndesc end</w>\nber nade\nj s\nboat sales</w>\nwin der</w>\ncran k\nsing leton</w>\ncandid acy</w>\nben 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ant</w>\nenqu ire</w>\nca ir</w>\nabur ger</w>\ntru n</w>\ngreen berg</w>\nchau han</w>\nir ina</w>\nsh ani\ntrend setter</w>\npre tt\nzaf ar</w>\nalo ve\nv ici\npan ic\nno o</w>\nlu stre</w>\ndisrup ted</w>\nbal lis\nson sof\nmon si\ninst ac\nake st</w>\nëĭ ¤\nkw ame</w>\nhorror movies</w>\ndistric t\nsau cy</w>\nmb an</w>\nar mies</w>\nwith drawn</w>\nmed ics</w>\nloft us</w>\ner oom</w>\nbe kind</w>\nar ns</w>\nall on</w>\nun ison</w>\ndavi ds</w>\ncr at</w>\nnicot ine</w>\nso or\nsm x</w>\non co\ncospla ying</w>\nzombi es\nhar ms</w>\ne ger\nro sy</w>\nmoon shine</w>\nfe in\nce tt</w>\ndu brov\nreg ents</w>\nben itez</w>\nðŁijıðŁı¼ ðŁijıðŁı¼</w>\nste c</w>\nm alia</w>\nprioriti ze</w>\nic eland\nft se</w>\nv amo\nlam ont</w>\nhomo sexuality</w>\nbre es</w>\nregu i</w>\ncb p</w>\nte j</w>\nsky sports</w>\ndeter gent</w>\nsha sta</w>\nde rel\nconserv ancy</w>\ncolori zed</w>\naccol ades</w>\nvis o</w>\nshow your\nnan ow\nbice ps</w>\nus ability</w>\nbi m\ndailys ketch</w>\npearl 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life</w>\nme squ\njohn cena</w>\neuro league</w>\nsab er\nmaster ful</w>\nyar ra</w>\ncogn ition</w>\njacob son</w>\nabo lic</w>\nsir loin</w>\nshuk la</w>\nmoj ito</w>\nsu pere\nst weet</w>\nme z</w>\ne sa\nrudol f</w>\ngur a</w>\nwhere you\ntt m</w>\nwin s\ntrust worthy</w>\nny k</w>\nbra den</w>\ntable top\ngood food</w>\nes on\nbe k\nlingui stic</w>\ngra ys</w>\nch ath\nh cs</w>\nmon i\nde ans</w>\ncu ssions</w>\nch ell</w>\nslo ws</w>\nhe mi</w>\nd app\nshar pie</w>\nboo sters</w>\na os</w>\nstr ack</w>\nse dona</w>\nmu eller\nhard wick</w>\nor nate</w>\nthor a</w>\nsal ud</w>\no twol\nch um\nmi ho</w>\nfor age</w>\nthel ittle\ntear ful</w>\nones elf</w>\nmin dy\nsm g</w>\ngmb h</w>\nemer ald\nðŁĶ´ âļªï¸ı\ntu tti</w>\nrecep tions</w>\nre vising</w>\ni brox</w>\ntope ka</w>\nsal ami</w>\nexpan se</w>\ni books</w>\ndob son</w>\ncli o</w>\nat s\nðŁļ Į</w>\nmo ha\nis ance</w>\nshu tters</w>\nmoo t</w>\njan ine</w>\nmarvel comics</w>\njor dani\npos er</w>\nkenne th\nhy ung\nde 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sh</w>\nsowe to</w>\nmp lo\nal ai</w>\nsab i</w>\nraq qa</w>\nwf tv</w>\nstro ller</w>\nian somerhalder</w>\nðŁĶ ª\nan on\nmo seley</w>\n! ?!?</w>\nsta king</w>\nmol y</w>\ncar tri\nc sg</w>\nast or</w>\ntransc end\nma er\nde ux</w>\ncow girl</w>\nsas k\npun ter</w>\nma ken\no ates</w>\nlove tt</w>\ngrow ler</w>\nsag in\nv n\nssi ble</w>\nofficeof rg</w>\ny mc\nsab ar\nfaul ty</w>\nap ha</w>\nak on</w>\nðŁij «\nsnow don</w>\nae w</w>\nraise the\nðĿ ĵ\ngrue some</w>\nclement ine</w>\nsp ing</w>\nlat a</w>\nworlden viron\nmi mic\ncan aria</w>\nbakhtawar bz</w>\nao a</w>\nfal a\nãĤ Ń\navi va</w>\nyou uuu</w>\nthi gh\nla dders</w>\ngu mbo</w>\ntz ky</w>\nfu zz\nplastic pollution</w>\nest ate\nstrength ened</w>\nk ant</w>\ndr in</w>\ncal vert</w>\ntransform ational</w>\nfrigh tened</w>\nmac lean</w>\nelited angerous</w>\near thy</w>\nt son</w>\nto da</w>\nj nu</w>\n.. ,</w>\nmic hal\ni ban\nje ong\nis real</w>\nsim coe</w>\nexclu sives</w>\nblue bells</w>\nben e</w>\nte u\npil 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dova</w>\nmin n</w>\nma bel</w>\nconferen cing</w>\nbas ing\nof er\nob si\nhamill himself</w>\ncare less</w>\nbrief ed</w>\ninhe rent</w>\npar ish\ndub nation</w>\ntown sville</w>\nsar awak</w>\ngee ky</w>\ndoncaster isgreat</w>\nwas abi</w>\ngu p</w>\nphen o\ndra inthe\ncarrie underwood</w>\nble eds</w>\nbbc world</w>\nane w</w>\nalta f</w>\ndul wich</w>\nani ston</w>\nw ti</w>\nsumat ra</w>\ngra fton</w>\nbl n</w>\nme ster</w>\nbode ga</w>\nre go</w>\nes q</w>\nan jo</w>\nsump tuous</w>\nmai sie</w>\nï¿ ½\nwil t</w>\njak ob</w>\nel vis\nse pul\nmu ster</w>\nair pollution</w>\npresident e</w>\nhappy monday</w>\nexten sively</w>\nfl ondon</w>\nt ls</w>\nplay ing\npe ed</w>\ndin ho</w>\nvar dy</w>\npi ka</w>\nn iro</w>\nau cus</w>\nðŁį ¦\nnu ll</w>\nel ondon</w>\njuvent us\nimag ines</w>\ndis ab\nlit o</w>\nd ura</w>\nwork places</w>\npromo te\nmc caf\nwood work</w>\nwaw x</w>\nà® ª</w>\ntt ino</w>\nshar i</w>\nsem per\nbetter together</w>\nðŁijĬ ðŁı»\nze bra\npon dering</w>\nen chil\nho m</w>\ncosm ic\ntan z\nmo cked</w>\nec cc</w>\nath ed</w>\nabo lish</w>\nprop eller</w>\nparis agreement</w>\nassemb lies</w>\nindu stry\nfraudul ent</w>\npe sa</w>\nchang min</w>\nax x\nðŁĴ µ\nirr ational</w>\ncu sa</w>\nramad han</w>\nocta via</w>\non elove</w>\njac ki\nbar ak\ntaxi der\nseri ous\nnathan fillion</w>\nmc en\nch k</w>\npo part</w>\ngrav ity\ncopp ola</w>\nreading fc</w>\nillu sions</w>\nj ig</w>\nww x</w>\nre sh</w>\nex porting</w>\nbuzz ard</w>\nâĻ ¤</w>\np cm</w>\nlan apar\nko s\narom as</w>\nantal ya</w>\nww dc</w>\nven a</w>\nphil a</w>\nball in\nðŁij Ħ</w>\nquin ta</w>\nma o\nf ery</w>\neigh ty</w>\nsentim ents</w>\nsafe guarding</w>\nr wa</w>\npu ffs</w>\nluc ille</w>\nde cath\nsl u</w>\nnu gent</w>\nde ter</w>\nbraz il\nze iss</w>\nsuper bowl\nsubsi dy</w>\nalter n\nhi dalgo</w>\nenz ymes</w>\nä ½\ntag ne</w>\nhair dresser</w>\nadri en</w>\nwalk out</w>\noppo ses</w>\ncan tina</w>\nbed side</w>\naf an\nðŁĶ Ĺ\nprophe tic</w>\ndan es</w>\nun successful</w>\nsuper charged</w>\npk k</w>\nexem ption</w>\nhart le\nsecu lar\ncli pping</w>\nbr s</w>\nunited way\nc net</w>\npat chy</w>\nha gan</w>\ne en\nâļ ľ\nvar a</w>\nsym pathi\nnever trump</w>\naffir mation</w>\nom f</w>\nny cfc</w>\nma ja</w>\nsur ro\nkeer th\nup scale</w>\nsandal wood</w>\nmon archy</w>\nkno bs</w>\nå ĭ\npo tholes</w>\nhunger games</w>\nter races</w>\nna sir</w>\ncoun sell\nwelcome to\nwa q\nse aman</w>\nm ita</w>\nstun ningly</w>\non theroad</w>\nin ability</w>\n) !!</w>\nbon go</w>\nant v</w>\nsp ut\nworldenviron mentday</w>\nresu sc\ny td</w>\nfi m</w>\neun hyuk</w>\nsa chin\nrose anne</w>\ncler mont</w>\nape c</w>\nam ina</w>\nv ening</w>\nn antes</w>\nal most\nsin us</w>\nex as</w>\nty l</w>\nti en</w>\nple ad</w>\nlanc s</w>\nbur naby</w>\nre k\njo om\nobserv ers</w>\ndisco graphy</w>\ncl g</w>\nâĻ ¦</w>\nsn ack\nr ti</w>\no ily</w>\ncrystal li\nbru te</w>\nweb development</w>\ntopp ings</w>\nla f\nan is</w>\nad der</w>\nreli ving</w>\ncar 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les</w>\ncu tt</w>\ncli mbers</w>\në¦ ¬\nsle g</w>\nsn f</w>\nmac ys</w>\nhydr ating</w>\ngrou pe</w>\npo yn\nmou lin</w>\nhg tv</w>\nlmfa ooo</w>\nsulph ur</w>\nasdfghj kl</w>\nannab elle</w>\nhump back</w>\nbra ved</w>\nviswas am</w>\nmulti purpose</w>\nhu midi\nescor ted</w>\nbarb ican</w>\nf ad</w>\ncor sa</w>\nðŁ¤ «</w>\npi ppa</w>\nhere to\ncan y\nser gi\nor cas</w>\no vie\ned ou\ns any\nglob alization</w>\nman cini</w>\nfood truck</w>\nf is</w>\ndefi brill\nsch re\nsma fia</w>\nlove wins</w>\nla ut\nk aka</w>\nhol lande</w>\ngame on</w>\nresurg ence</w>\nout side\nolympi ad</w>\nint an\nabstr action</w>\nrapi d\npal om\ncal le\njas min</w>\nattack ers</w>\nswag g</w>\nmit ra</w>\nky lo</w>\nà® ²</w>\nher mitage</w>\ngor do</w>\ne ira</w>\nso sfam</w>\nroll out</w>\nexc ite</w>\nsy nod</w>\nmer rill</w>\nc als</w>\nas sa</w>\nliveli hoods</w>\nju ve\nthe black\ngopack go</w>\nant lers</w>\nalban ian</w>\nwool ly</w>\nqu iche</w>\npuri fication</w>\nare th</w>\nsmar thome</w>\nne 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ment</w>\ncu di</w>\nce p</w>\nðŁĺį ðŁĺĤ</w>\ntra d\nsuper rugby</w>\ned win\nwh iche\nvi meo</w>\nlay ne</w>\nin vigor\nhe he\ndubrov nik</w>\nbie ber\nu tr\nsham an</w>\nop ers</w>\nham ill</w>\nen ig</w>\ndi f</w>\nar um</w>\nscrap book</w>\nmin h</w>\ndiver gence</w>\nmckin non</w>\nlife time\nguter res</w>\nwil le\nple as</w>\npatt y\nmic ron\nk z\ndom aine</w>\nru sher</w>\nm ds</w>\nches ney</w>\nscrew driver</w>\nâģ© ,</w>\nsle dge</w>\nhau er</w>\nchan a</w>\nstam ina</w>\nsprink ler</w>\npl n</w>\nhe ff\nbol ton\nom on\ncar rington</w>\naccor dion</w>\njor ge\ninter ception</w>\nin puts</w>\ngu ll\ntran scription</w>\nvanu atu</w>\nit ical</w>\neth os</w>\ntic h</w>\nspac ey</w>\npee king</w>\nu mi\nha ger\npsycho tic</w>\nilli an\nilli a</w>\nbonnar oo</w>\nan ese</w>\npu c\nlaghate parth</w>\nen hall</w>\neconom ical</w>\ndre dge</w>\n% -</w>\nu we</w>\ntu bular</w>\nscoun cil</w>\npe asants</w>\nfl er</w>\ntumb ler</w>\nhe p</w>\nford ham</w>\nrow ley</w>\niniti 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am</w>\ngoo oo</w>\nfor sa\nelec tra</w>\naf s</w>\nâĹ Ĩ</w>\ntri fe\nsn azzy</w>\nfo lia</w>\nand olan</w>\nafter dark</w>\nwood son</w>\nstra de</w>\nlitt lest</w>\no gun\ncon wy</w>\nco wards</w>\nðŁĺĤðŁĺĤðŁĺĤðŁĺĤ ðŁĺĤðŁĺĤðŁĺĤ</w>\níĬ ¸\nse ul\nmur phy\ndun ks</w>\nkapil shar\njo achim</w>\nwom ack</w>\nequal ity\naver ages</w>\na ine\nðŁ¦ Ī</w>\ntac ular</w>\ndis ability\nu ked\nmid century</w>\nbar thol\nteas ers</w>\ntab ern\nnj caa</w>\nsp out</w>\nop i</w>\nku bball</w>\nbl om\nso ar\npopu lism</w>\nmeth yl\nðŁijĬ ðŁı¼\no spre\nalo ils</w>\nðŁĵ ĸ\nðŁĮ ļ\nx er\nsp illing</w>\npubl ica</w>\ncar dam\nadi sh</w>\nsa cha</w>\np kg</w>\nbu da</w>\nlyric ist</w>\ni bc</w>\ngru mp\nho ver</w>\nhal ep</w>\nanti body</w>\nanem one</w>\nâĻ¥âĻ¥ âĻ¥âĻ¥\nm cl\nlitho graph</w>\ncc u</w>\ns fest</w>\npath ic</w>\ncalli ster</w>\notta wa\ngun sn\nrut ger\nhali but</w>\nen vision</w>\ndifferenti ate</w>\nðŁļĢ ðŁļĢ\npir an\nlat el\nuc n</w>\ntrou bad\nra ine\nfierc ely</w>\nlearn english</w>\nlea 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ses</w>\ndro gba</w>\nbex hill</w>\nban kno\nwin ona</w>\nsi eg\np gs</w>\nhahah ha</w>\nagu chi</w>\nsu bram\nmirac le\nde sch\nli bre\nba cher</w>\nent ine</w>\nbbcra di\nlou dest</w>\nr ps</w>\npi erc\nfr yer</w>\nstorm trooper</w>\nrafael nadal</w>\npas co</w>\nexhau stion</w>\nepic onetsy</w>\nrc tid</w>\nkel lie</w>\nga ines</w>\nd bz</w>\nsm riti\ns bridge</w>\nlim ited\ncla w\ntechnic al\nbio graphical</w>\nado red</w>\nภ°</w>\nexclu de</w>\nac adia</w>\nkey boards</w>\nfur man</w>\nso ca</w>\nsur u</w>\nni ps</w>\nsw aps</w>\nserver less</w>\nrun e</w>\npu ffy</w>\nnorth ampton\nnish ings</w>\nhen der\ncartri dges</w>\ngun shot</w>\nðŁĵ ¹</w>\nfil ament</w>\nrespon dents</w>\npey ton\nmountaine er</w>\nmer ging</w>\nlife span</w>\nintimid ation</w>\np afc</w>\nnl wx</w>\nexpan sive</w>\npur r\nf ck</w>\nca e</w>\nat ti\ntele thon</w>\nso hn</w>\nmend el\nlo pes</w>\ndor i</w>\nun broken</w>\nte red\ntast ings</w>\nin active</w>\ndisin tegr\nt assel</w>\nshare the\npi ano\nis lay</w>\nair space</w>\nz awa</w>\nricci ardo</w>\nming ton\nfresh er</w>\ncur ry\nre vs</w>\npharo ah</w>\nh mv</w>\nexhilar ating</w>\nwh oo</w>\nlin kin</w>\nkri spy</w>\ncompeten cy</w>\nste wards</w>\nne bu\nkat su\nad mins</w>\nbaz ar</w>\nas ar</w>\ngiving back</w>\ns summit</w>\nsong z</w>\nlin us</w>\nraj kumar</w>\nfarm ington</w>\nfanta sia</w>\nðŁĺ´ ðŁĺ´</w>\nso bri\nlis se</w>\nbarry more</w>\npri sm\nblo b</w>\nsen ew\nmono xide</w>\nexp ire</w>\neigh teen</w>\ndi pper</w>\nxi ao</w>\nkil t</w>\nhin ch\nbbc sport</w>\nbam boo\np ter\nex al\nðŁ¦ ĭ\nham lin</w>\nexpe ditions</w>\nstar gazing</w>\nfood security</w>\nwy lie</w>\nul f</w>\nst ingly</w>\non storm</w>\nlo eb</w>\nbro ome</w>\nbn ha</w>\npancre atic</w>\neli ve\n!!!!!!!! !!!</w>\nther apper</w>\northo pedic</w>\navengers endgame</w>\nantit rust</w>\nìļ °</w>\ngo te</w>\nom d</w>\noff side</w>\ngy llen\nwin eries</w>\nwhite water</w>\nad l</w>\nlu pita</w>\nexce eds</w>\nconsi sted</w>\nchew bacca</w>\nash leigh</w>\nnhl jets</w>\nis san\nsh ld</w>\nhay at</w>\ncran berries</w>\nðŁ¤ĺ ðŁı½</w>\nrock the\nspring training</w>\nfall out\ndairy free</w>\nwa j</w>\nun decided</w>\nso wn</w>\nrc n</w>\nnorth wales</w>\nhtt r</w>\nfu mble</w>\nd its</w>\ncomp elled</w>\npopu list</w>\nmin ted</w>\nblan chett</w>\n. ''</w>\npro pulsion</w>\nm illa</w>\nau berg\nher tz</w>\nh ta</w>\nu daipur</w>\nserendip ity</w>\nazte cs</w>\nals ace</w>\nðŁIJ ij</w>\nlu n</w>\nsho es\nchar li</w>\ngar za</w>\nðŁĴ Ł\npro biotics</w>\nfox tv</w>\nol is</w>\nmi ff\nloc alized</w>\ndiffu ser</w>\nsi gue</w>\nfun ko\nrend ous</w>\nðŁĴ ij</w>\njeky ll</w>\n"
  },
  {
    "path": "ldm_patched/modules/sd1_tokenizer/special_tokens_map.json",
    "content": "{\n  \"bos_token\": {\n    \"content\": \"<|startoftext|>\",\n    \"lstrip\": false,\n    \"normalized\": true,\n    \"rstrip\": false,\n    \"single_word\": false\n  },\n  \"eos_token\": {\n    \"content\": \"<|endoftext|>\",\n    \"lstrip\": false,\n    \"normalized\": true,\n    \"rstrip\": false,\n    \"single_word\": false\n  },\n  \"pad_token\": \"<|endoftext|>\",\n  \"unk_token\": {\n    \"content\": \"<|endoftext|>\",\n    \"lstrip\": false,\n    \"normalized\": true,\n    \"rstrip\": false,\n    \"single_word\": false\n  }\n}\n"
  },
  {
    "path": "ldm_patched/modules/sd1_tokenizer/tokenizer_config.json",
    "content": "{\n  \"add_prefix_space\": false,\n  \"bos_token\": {\n    \"__type\": \"AddedToken\",\n    \"content\": \"<|startoftext|>\",\n    \"lstrip\": false,\n    \"normalized\": true,\n    \"rstrip\": false,\n    \"single_word\": false\n  },\n  \"do_lower_case\": true,\n  \"eos_token\": {\n    \"__type\": \"AddedToken\",\n    \"content\": \"<|endoftext|>\",\n    \"lstrip\": false,\n    \"normalized\": true,\n    \"rstrip\": false,\n    \"single_word\": false\n  },\n  \"errors\": \"replace\",\n  \"model_max_length\": 77,\n  \"name_or_path\": \"openai/clip-vit-large-patch14\",\n  \"pad_token\": \"<|endoftext|>\",\n  \"special_tokens_map_file\": \"./special_tokens_map.json\",\n  \"tokenizer_class\": \"CLIPTokenizer\",\n  \"unk_token\": {\n    \"__type\": \"AddedToken\",\n    \"content\": \"<|endoftext|>\",\n    \"lstrip\": false,\n    \"normalized\": true,\n    \"rstrip\": false,\n    \"single_word\": false\n  }\n}\n"
  },
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\"ðŁĺħ</w>\": 9188,\n  \"ðŁĺħðŁĺħ</w>\": 39078,\n  \"ðŁĺĨ\": 16541,\n  \"ðŁĺĨ</w>\": 10943,\n  \"ðŁĺĨðŁĺĨ</w>\": 39503,\n  \"ðŁĺĩ\": 21694,\n  \"ðŁĺĩ</w>\": 13091,\n  \"ðŁĺĪ\": 14377,\n  \"ðŁĺĪ</w>\": 9756,\n  \"ðŁĺĪðŁĺĪ</w>\": 44473,\n  \"ðŁĺī\": 9740,\n  \"ðŁĺī</w>\": 4955,\n  \"ðŁĺīðŁĺī</w>\": 40430,\n  \"ðŁĺĬ\": 4692,\n  \"ðŁĺĬ</w>\": 3020,\n  \"ðŁĺĬâĿ¤ï¸ı</w>\": 43606,\n  \"ðŁĺĬðŁĺĬ\": 12838,\n  \"ðŁĺĬðŁĺĬ</w>\": 20842,\n  \"ðŁĺĬðŁĺĬðŁĺĬ</w>\": 28685,\n  \"ðŁĺĬðŁĺĬðŁĺĬðŁĺĬ\": 35519,\n  \"ðŁĺĭ\": 12391,\n  \"ðŁĺĭ</w>\": 7203,\n  \"ðŁĺĭðŁĺĭ</w>\": 33304,\n  \"ðŁĺĮ\": 19221,\n  \"ðŁĺĮ</w>\": 12163,\n  \"ðŁĺį\": 1796,\n  \"ðŁĺį#</w>\": 42357,\n  \"ðŁĺį.</w>\": 48579,\n  \"ðŁĺį</w>\": 1754,\n  \"ðŁĺįâĿ¤</w>\": 29122,\n  \"ðŁĺįâĿ¤ï¸ı</w>\": 21945,\n  \"ðŁĺįðŁijĮ</w>\": 41005,\n  \"ðŁĺįðŁĴķ</w>\": 35946,\n  \"ðŁĺįðŁĶ¥</w>\": 46648,\n  \"ðŁĺįðŁĺĤ</w>\": 48715,\n  \"ðŁĺįðŁĺį\": 3663,\n  \"ðŁĺįðŁĺį</w>\": 6471,\n  \"ðŁĺįðŁĺįðŁĺį\": 30614,\n  \"ðŁĺįðŁĺįðŁĺį</w>\": 7703,\n  \"ðŁĺįðŁĺįðŁĺįðŁĺį\": 16603,\n  \"ðŁĺįðŁĺįðŁĺįðŁĺį</w>\": 18925,\n  \"ðŁĺįðŁĺįðŁĺįðŁĺįðŁĺį</w>\": 32078,\n  \"ðŁĺįðŁĺįðŁĺįðŁĺįðŁĺįðŁĺįðŁĺįðŁĺį\": 48683,\n  \"ðŁĺįðŁĺĺ\": 29646,\n  \"ðŁĺįðŁĺĺ</w>\": 19849,\n  \"ðŁĺįðŁĺŃ</w>\": 39555,\n  \"ðŁĺİ\": 7426,\n  \"ðŁĺİ</w>\": 4345,\n  \"ðŁĺİðŁĺİ</w>\": 24048,\n  \"ðŁĺİðŁĺİðŁĺİ</w>\": 39742,\n  \"ðŁĺı\": 11624,\n  \"ðŁĺı</w>\": 6909,\n  \"ðŁĺıðŁĺı</w>\": 38151,\n  \"ðŁĺIJ\": 38586,\n  \"ðŁĺIJ</w>\": 19618,\n  \"ðŁĺij\": 32469,\n  \"ðŁĺij</w>\": 18937,\n  \"ðŁĺĴ\": 20792,\n  \"ðŁĺĴ</w>\": 11702,\n  \"ðŁĺĵ</w>\": 28733,\n  \"ðŁĺĶ\": 19532,\n  \"ðŁĺĶ</w>\": 11432,\n  \"ðŁĺķ\": 45741,\n  \"ðŁĺķ</w>\": 20602,\n  \"ðŁĺĸ</w>\": 35006,\n  \"ðŁĺĺ\": 4240,\n  \"ðŁĺĺ</w>\": 3352,\n  \"ðŁĺĺâĿ¤</w>\": 48409,\n  \"ðŁĺĺâĿ¤ï¸ı</w>\": 39150,\n  \"ðŁĺĺðŁĺį</w>\": 38176,\n  \"ðŁĺĺðŁĺĺ\": 15663,\n  \"ðŁĺĺðŁĺĺ</w>\": 10507,\n  \"ðŁĺĺðŁĺĺðŁĺĺ</w>\": 20208,\n  \"ðŁĺĺðŁĺĺðŁĺĺðŁĺĺ</w>\": 44892,\n  \"ðŁĺĻ\": 36201,\n  \"ðŁĺĻ</w>\": 29209,\n  \"ðŁĺļ\": 24897,\n  \"ðŁĺļ</w>\": 19102,\n  \"ðŁĺĽ\": 24550,\n  \"ðŁĺĽ</w>\": 15745,\n  \"ðŁĺľ\": 13226,\n  \"ðŁĺľ</w>\": 7830,\n  \"ðŁĺľðŁĺľ</w>\": 43065,\n  \"ðŁĺĿ\": 20064,\n  \"ðŁĺĿ</w>\": 12970,\n  \"ðŁĺŀ\": 40458,\n  \"ðŁĺŀ</w>\": 21103,\n  \"ðŁĺŁ</w>\": 46947,\n  \"ðŁĺł</w>\": 34094,\n  \"ðŁĺŃ\": 2962,\n  \"ðŁĺŃ</w>\": 3915,\n  \"ðŁĺŃâĿ¤ï¸ı</w>\": 29567,\n  \"ðŁĺŃðŁĴķ</w>\": 46306,\n  \"ðŁĺŃðŁĺĤ</w>\": 38505,\n  \"ðŁĺŃðŁĺį</w>\": 36893,\n  \"ðŁĺŃðŁĺŃ\": 5300,\n  \"ðŁĺŃðŁĺŃ</w>\": 11834,\n  \"ðŁĺŃðŁĺŃðŁĺŃ\": 44089,\n  \"ðŁĺŃðŁĺŃðŁĺŃ</w>\": 13116,\n  \"ðŁĺŃðŁĺŃðŁĺŃðŁĺŃ\": 19793,\n  \"ðŁĺŃðŁĺŃðŁĺŃðŁĺŃ</w>\": 27322,\n  \"ðŁĺŃðŁĺŃðŁĺŃðŁĺŃðŁĺŃ</w>\": 43366,\n  \"ðŁĻ\": 1478,\n  \"ðŁĻĢ</w>\": 43092,\n  \"ðŁĻĤ\": 32006,\n  \"ðŁĻĤ</w>\": 14860,\n  \"ðŁĻĥ\": 27222,\n  \"ðŁĻĥ</w>\": 15652,\n  \"ðŁĻĦ\": 20648,\n  \"ðŁĻĦ</w>\": 13049,\n  \"ðŁĻħ\": 42702,\n  \"ðŁĻĨ\": 30050,\n  \"ðŁĻĨ</w>\": 35730,\n  \"ðŁĻĪ\": 12661,\n  \"ðŁĻĪ</w>\": 9516,\n  \"ðŁĻĪðŁĻĪ</w>\": 41796,\n  \"ðŁĻĬ\": 23684,\n  \"ðŁĻĬ</w>\": 16636,\n  \"ðŁĻĭ\": 19193,\n  \"ðŁĻĭ</w>\": 30274,\n  \"ðŁĻĮ\": 4366,\n  \"ðŁĻĮ</w>\": 4855,\n  \"ðŁĻĮðŁı»\": 26756,\n  \"ðŁĻĮðŁı»</w>\": 15799,\n  \"ðŁĻĮðŁı¼\": 26584,\n  \"ðŁĻĮðŁı¼</w>\": 15364,\n  \"ðŁĻĮðŁı½\": 36660,\n  \"ðŁĻĮðŁı½</w>\": 22962,\n  \"ðŁĻĮðŁı¾\": 38023,\n  \"ðŁĻĮðŁı¾</w>\": 26466,\n  \"ðŁĻĮðŁĻĮ\": 21202,\n  \"ðŁĻĮðŁĻĮ</w>\": 30430,\n  \"ðŁĻĮðŁĻĮðŁĻĮ</w>\": 37127,\n  \"ðŁĻı\": 4260,\n  \"ðŁĻı</w>\": 5503,\n  \"ðŁĻıðŁı»\": 25100,\n  \"ðŁĻıðŁı»</w>\": 16650,\n  \"ðŁĻıðŁı¼\": 31163,\n  \"ðŁĻıðŁı¼</w>\": 18952,\n  \"ðŁĻıðŁı½\": 34103,\n  \"ðŁĻıðŁı½</w>\": 21540,\n  \"ðŁĻıðŁı¾\": 34277,\n  \"ðŁĻıðŁı¾</w>\": 21979,\n  \"ðŁĻıðŁĻı\": 18227,\n  \"ðŁĻıðŁĻı</w>\": 26510,\n  \"ðŁĻıðŁĻıðŁĻı</w>\": 31702,\n  \"ðŁļ\": 2730,\n  \"ðŁļ¨\": 12198,\n  \"ðŁļ¨</w>\": 6056,\n  \"ðŁļ¨ðŁļ¨\": 36487,\n  \"ðŁļ¨ðŁļ¨</w>\": 21440,\n  \"ðŁļ¨ðŁļ¨ðŁļ¨</w>\": 41515,\n  \"ðŁļ©</w>\": 44514,\n  \"ðŁļ«</w>\": 35291,\n  \"ðŁļ²</w>\": 37085,\n  \"ðŁļ´\": 30825,\n  \"ðŁļ¶\": 46060,\n  \"ðŁļĢ\": 22400,\n  \"ðŁļĢ</w>\": 13542,\n  \"ðŁļĢðŁļĢ\": 49033,\n  \"ðŁļĤ</w>\": 38949,\n  \"ðŁļĮ</w>\": 46891,\n  \"ðŁļĹ\": 33054,\n  \"ðŁļĹ</w>\": 22783,\n  \"ðŁļĺ</w>\": 35825,\n  \"ðŁļĻ</w>\": 48487,\n  \"ðŁĽ\": 11306,\n  \"ñ\": 173,\n  \"ñ</w>\": 429,\n  \"ò\": 174,\n  \"ò</w>\": 430,\n  \"ó\": 175,\n  \"ó</w>\": 431,\n  \"ô\": 176,\n  \"ô</w>\": 432,\n  \"õ\": 177,\n  \"õ</w>\": 433,\n  \"ö\": 178,\n  \"ö</w>\": 434,\n  \"÷\": 179,\n  \"÷</w>\": 435,\n  \"ø\": 180,\n  \"ø</w>\": 436,\n  \"ù\": 181,\n  \"ù</w>\": 437,\n  \"ú\": 182,\n  \"ú</w>\": 438,\n  \"û\": 183,\n  \"û</w>\": 439,\n  \"ü\": 184,\n  \"ü</w>\": 440,\n  \"ý\": 185,\n  \"ý</w>\": 441,\n  \"þ\": 186,\n  \"þ</w>\": 442,\n  \"ÿ\": 187,\n  \"ÿ</w>\": 443,\n  \"Ā\": 188,\n  \"Ā</w>\": 444,\n  \"ā\": 189,\n  \"ā</w>\": 445,\n  \"Ă\": 190,\n  \"Ă</w>\": 446,\n  \"ă\": 191,\n  \"ă</w>\": 447,\n  \"Ą\": 192,\n  \"Ą</w>\": 448,\n  \"ą\": 193,\n  \"ą</w>\": 449,\n  \"Ć\": 194,\n  \"Ć</w>\": 450,\n  \"ć\": 195,\n  \"ć</w>\": 451,\n  \"Ĉ\": 196,\n  \"Ĉ</w>\": 452,\n  \"ĉ\": 197,\n  \"ĉ</w>\": 453,\n  \"Ċ\": 198,\n  \"Ċ</w>\": 454,\n  \"ċ\": 199,\n  \"ċ</w>\": 455,\n  \"Č\": 200,\n  \"Č</w>\": 456,\n  \"č\": 201,\n  \"č</w>\": 457,\n  \"Ď\": 202,\n  \"Ď</w>\": 458,\n  \"ď\": 203,\n  \"ď</w>\": 459,\n  \"Đ\": 204,\n  \"Đ</w>\": 460,\n  \"đ\": 205,\n  \"đ</w>\": 461,\n  \"Ē\": 206,\n  \"Ē</w>\": 462,\n  \"ē\": 207,\n  \"ē</w>\": 463,\n  \"Ĕ\": 208,\n  \"Ĕ</w>\": 464,\n  \"ĕ\": 209,\n  \"ĕ</w>\": 465,\n  \"Ė\": 210,\n  \"Ė</w>\": 466,\n  \"ė\": 211,\n  \"ė</w>\": 467,\n  \"Ę\": 212,\n  \"Ę</w>\": 468,\n  \"ę\": 213,\n  \"ę</w>\": 469,\n  \"Ě\": 214,\n  \"Ě</w>\": 470,\n  \"ě\": 215,\n  \"ě</w>\": 471,\n  \"Ĝ\": 216,\n  \"Ĝ</w>\": 472,\n  \"ĝ\": 217,\n  \"ĝ</w>\": 473,\n  \"Ğ\": 218,\n  \"Ğ</w>\": 474,\n  \"ğ\": 219,\n  \"ğ</w>\": 475,\n  \"Ġ\": 220,\n  \"Ġ</w>\": 476,\n  \"ġ\": 221,\n  \"ġ</w>\": 477,\n  \"Ģ\": 222,\n  \"Ģ</w>\": 478,\n  \"Ģï¸ı\": 9668,\n  \"Ģï¸ı</w>\": 5511,\n  \"ģ\": 223,\n  \"ģ</w>\": 479,\n  \"ģà¸\": 15016,\n  \"Ĥ\": 224,\n  \"Ĥ</w>\": 480,\n  \"Ĥâĸ\": 29036,\n  \"ĤâĸĤâĸ\": 30832,\n  \"ĥ\": 225,\n  \"ĥ</w>\": 481,\n  \"Ħ\": 226,\n  \"Ħ</w>\": 482,\n  \"Ħà¸\": 20537,\n  \"Ħë\": 34462,\n  \"Ħëĭ\": 25170,\n  \"ħ\": 227,\n  \"ħ</w>\": 483,\n  \"ħï¸ı</w>\": 33950,\n  \"Ĩ\": 228,\n  \"Ĩ</w>\": 484,\n  \"ĩ\": 229,\n  \"ĩ</w>\": 485,\n  \"Ī\": 230,\n  \"Ī</w>\": 486,\n  \"ī\": 231,\n  \"ī</w>\": 487,\n  \"īï¸ı</w>\": 37463,\n  \"Ĭ\": 232,\n  \"Ĭ</w>\": 488,\n  \"Ĭãģ\": 30294,\n  \"ĭ\": 233,\n  \"ĭ</w>\": 489,\n  \"ĭãģ\": 36218,\n  \"ĭãĤ\": 45737,\n  \"Į\": 234,\n  \"Į</w>\": 490,\n  \"ĮãĤĬãģ\": 45969,\n  \"ĮãĤĬãģŁãģĦ</w>\": 47021,\n  \"Įë\": 17003,\n  \"į\": 235,\n  \"į</w>\": 491,\n  \"İ\": 236,\n  \"İ</w>\": 492,\n  \"ı\": 237,\n  \"ı</w>\": 493,\n  \"IJ\": 238,\n  \"IJ</w>\": 494,\n  \"ij\": 239,\n  \"ij</w>\": 495,\n  \"Ĵ\": 240,\n  \"Ĵ</w>\": 496,\n  \"ĵ\": 241,\n  \"ĵ</w>\": 497,\n  \"Ķ\": 242,\n  \"Ķ</w>\": 498,\n  \"Ķë\": 37978,\n  \"Ķï¸ı\": 24395,\n  \"Ķï¸ı</w>\": 7443,\n  \"ķ\": 243,\n  \"ķ</w>\": 499,\n  \"ķãĤ\": 26609,\n  \"ķï¸ı</w>\": 44853,\n  \"ĸ\": 244,\n  \"ĸ</w>\": 500,\n  \"ĸï¸ı</w>\": 28877,\n  \"Ĺ\": 245,\n  \"Ĺ</w>\": 501,\n  \"ĺ\": 246,\n  \"ĺ</w>\": 502,\n  \"Ļ\": 247,\n  \"Ļ</w>\": 503,\n  \"ļ\": 248,\n  \"ļ</w>\": 504,\n  \"Ľ\": 249,\n  \"Ľ</w>\": 505,\n  \"ľ\": 250,\n  \"ľ</w>\": 506,\n  \"ľë\": 39810,\n  \"Ŀ\": 251,\n  \"Ŀ</w>\": 507,\n  \"ŀ\": 252,\n  \"ŀ</w>\": 508,\n  \"Ł\": 253,\n  \"Ł</w>\": 509,\n  \"ŁãģĦ</w>\": 46023,\n  \"ł\": 254,\n  \"ł</w>\": 510,\n  \"łï¸ı\": 27899,\n  \"łï¸ı</w>\": 12715,\n  \"łĪ\": 43364,\n  \"Ń\": 255,\n  \"Ń</w>\": 511\n}\n"
  },
  {
    "path": "ldm_patched/modules/sd2_clip.py",
    "content": "from ldm_patched.modules import sd1_clip\nimport torch\nimport os\n\nclass SD2ClipHModel(sd1_clip.SDClipModel):\n    def __init__(self, arch=\"ViT-H-14\", device=\"cpu\", max_length=77, freeze=True, layer=\"penultimate\", layer_idx=None, dtype=None):\n        if layer == \"penultimate\":\n            layer=\"hidden\"\n            layer_idx=-2\n\n        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"sd2_clip_config.json\")\n        super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={\"start\": 49406, \"end\": 49407, \"pad\": 0})\n\nclass SD2ClipHTokenizer(sd1_clip.SDTokenizer):\n    def __init__(self, tokenizer_path=None, embedding_directory=None):\n        super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1024)\n\nclass SD2Tokenizer(sd1_clip.SD1Tokenizer):\n    def __init__(self, embedding_directory=None):\n        super().__init__(embedding_directory=embedding_directory, clip_name=\"h\", tokenizer=SD2ClipHTokenizer)\n\nclass SD2ClipModel(sd1_clip.SD1ClipModel):\n    def __init__(self, device=\"cpu\", dtype=None, **kwargs):\n        super().__init__(device=device, dtype=dtype, clip_name=\"h\", clip_model=SD2ClipHModel, **kwargs)\n"
  },
  {
    "path": "ldm_patched/modules/sd2_clip_config.json",
    "content": "{\n  \"architectures\": [\n    \"CLIPTextModel\"\n  ],\n  \"attention_dropout\": 0.0,\n  \"bos_token_id\": 0,\n  \"dropout\": 0.0,\n  \"eos_token_id\": 2,\n  \"hidden_act\": \"gelu\",\n  \"hidden_size\": 1024,\n  \"initializer_factor\": 1.0,\n  \"initializer_range\": 0.02,\n  \"intermediate_size\": 4096,\n  \"layer_norm_eps\": 1e-05,\n  \"max_position_embeddings\": 77,\n  \"model_type\": \"clip_text_model\",\n  \"num_attention_heads\": 16,\n  \"num_hidden_layers\": 24,\n  \"pad_token_id\": 1,\n  \"projection_dim\": 1024,\n  \"torch_dtype\": \"float32\",\n  \"vocab_size\": 49408\n}\n"
  },
  {
    "path": "ldm_patched/modules/sdxl_clip.py",
    "content": "from ldm_patched.modules import sd1_clip\nimport torch\nimport os\n\nclass SDXLClipG(sd1_clip.SDClipModel):\n    def __init__(self, device=\"cpu\", max_length=77, freeze=True, layer=\"penultimate\", layer_idx=None, dtype=None):\n        if layer == \"penultimate\":\n            layer=\"hidden\"\n            layer_idx=-2\n\n        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"clip_config_bigg.json\")\n        super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype,\n                         special_tokens={\"start\": 49406, \"end\": 49407, \"pad\": 0}, layer_norm_hidden_state=False)\n\n    def load_sd(self, sd):\n        return super().load_sd(sd)\n\nclass SDXLClipGTokenizer(sd1_clip.SDTokenizer):\n    def __init__(self, tokenizer_path=None, embedding_directory=None):\n        super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g')\n\n\nclass SDXLTokenizer:\n    def __init__(self, embedding_directory=None):\n        self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory)\n        self.clip_g = SDXLClipGTokenizer(embedding_directory=embedding_directory)\n\n    def tokenize_with_weights(self, text:str, return_word_ids=False):\n        out = {}\n        out[\"g\"] = self.clip_g.tokenize_with_weights(text, return_word_ids)\n        out[\"l\"] = self.clip_l.tokenize_with_weights(text, return_word_ids)\n        return out\n\n    def untokenize(self, token_weight_pair):\n        return self.clip_g.untokenize(token_weight_pair)\n\nclass SDXLClipModel(torch.nn.Module):\n    def __init__(self, device=\"cpu\", dtype=None):\n        super().__init__()\n        self.clip_l = sd1_clip.SDClipModel(layer=\"hidden\", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False)\n        self.clip_g = SDXLClipG(device=device, dtype=dtype)\n\n    def clip_layer(self, layer_idx):\n        self.clip_l.clip_layer(layer_idx)\n        self.clip_g.clip_layer(layer_idx)\n\n    def reset_clip_layer(self):\n        self.clip_g.reset_clip_layer()\n        self.clip_l.reset_clip_layer()\n\n    def encode_token_weights(self, token_weight_pairs):\n        token_weight_pairs_g = token_weight_pairs[\"g\"]\n        token_weight_pairs_l = token_weight_pairs[\"l\"]\n        g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g)\n        l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)\n        return torch.cat([l_out, g_out], dim=-1), g_pooled\n\n    def load_sd(self, sd):\n        if \"text_model.encoder.layers.30.mlp.fc1.weight\" in sd:\n            return self.clip_g.load_sd(sd)\n        else:\n            return self.clip_l.load_sd(sd)\n\nclass SDXLRefinerClipModel(sd1_clip.SD1ClipModel):\n    def __init__(self, device=\"cpu\", dtype=None):\n        super().__init__(device=device, dtype=dtype, clip_name=\"g\", clip_model=SDXLClipG)\n"
  },
  {
    "path": "ldm_patched/modules/supported_models.py",
    "content": "import torch\nfrom . import model_base\nfrom . import utils\n\nfrom . import sd1_clip\nfrom . import sd2_clip\nfrom . import sdxl_clip\n\nfrom . import supported_models_base\nfrom . import latent_formats\n\nfrom . import diffusers_convert\n\nclass SD15(supported_models_base.BASE):\n    unet_config = {\n        \"context_dim\": 768,\n        \"model_channels\": 320,\n        \"use_linear_in_transformer\": False,\n        \"adm_in_channels\": None,\n        \"use_temporal_attention\": False,\n    }\n\n    unet_extra_config = {\n        \"num_heads\": 8,\n        \"num_head_channels\": -1,\n    }\n\n    latent_format = latent_formats.SD15\n\n    def process_clip_state_dict(self, state_dict):\n        k = list(state_dict.keys())\n        for x in k:\n            if x.startswith(\"cond_stage_model.transformer.\") and not x.startswith(\"cond_stage_model.transformer.text_model.\"):\n                y = x.replace(\"cond_stage_model.transformer.\", \"cond_stage_model.transformer.text_model.\")\n                state_dict[y] = state_dict.pop(x)\n\n        if 'cond_stage_model.transformer.text_model.embeddings.position_ids' in state_dict:\n            ids = state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids']\n            if ids.dtype == torch.float32:\n                state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round()\n\n        replace_prefix = {}\n        replace_prefix[\"cond_stage_model.\"] = \"cond_stage_model.clip_l.\"\n        state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix)\n        return state_dict\n\n    def process_clip_state_dict_for_saving(self, state_dict):\n        replace_prefix = {\"clip_l.\": \"cond_stage_model.\"}\n        return utils.state_dict_prefix_replace(state_dict, replace_prefix)\n\n    def clip_target(self):\n        return supported_models_base.ClipTarget(sd1_clip.SD1Tokenizer, sd1_clip.SD1ClipModel)\n\nclass SD20(supported_models_base.BASE):\n    unet_config = {\n        \"context_dim\": 1024,\n        \"model_channels\": 320,\n        \"use_linear_in_transformer\": True,\n        \"adm_in_channels\": None,\n        \"use_temporal_attention\": False,\n    }\n\n    latent_format = latent_formats.SD15\n\n    def model_type(self, state_dict, prefix=\"\"):\n        if self.unet_config[\"in_channels\"] == 4: #SD2.0 inpainting models are not v prediction\n            k = \"{}output_blocks.11.1.transformer_blocks.0.norm1.bias\".format(prefix)\n            out = state_dict[k]\n            if torch.std(out, unbiased=False) > 0.09: # not sure how well this will actually work. I guess we will find out.\n                return model_base.ModelType.V_PREDICTION\n        return model_base.ModelType.EPS\n\n    def process_clip_state_dict(self, state_dict):\n        replace_prefix = {}\n        replace_prefix[\"conditioner.embedders.0.model.\"] = \"cond_stage_model.model.\" #SD2 in sgm format\n        state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix)\n\n        state_dict = utils.transformers_convert(state_dict, \"cond_stage_model.model.\", \"cond_stage_model.clip_h.transformer.text_model.\", 24)\n        return state_dict\n\n    def process_clip_state_dict_for_saving(self, state_dict):\n        replace_prefix = {}\n        replace_prefix[\"clip_h\"] = \"cond_stage_model.model\"\n        state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix)\n        state_dict = diffusers_convert.convert_text_enc_state_dict_v20(state_dict)\n        return state_dict\n\n    def clip_target(self):\n        return supported_models_base.ClipTarget(sd2_clip.SD2Tokenizer, sd2_clip.SD2ClipModel)\n\nclass SD21UnclipL(SD20):\n    unet_config = {\n        \"context_dim\": 1024,\n        \"model_channels\": 320,\n        \"use_linear_in_transformer\": True,\n        \"adm_in_channels\": 1536,\n        \"use_temporal_attention\": False,\n    }\n\n    clip_vision_prefix = \"embedder.model.visual.\"\n    noise_aug_config = {\"noise_schedule_config\": {\"timesteps\": 1000, \"beta_schedule\": \"squaredcos_cap_v2\"}, \"timestep_dim\": 768}\n\n\nclass SD21UnclipH(SD20):\n    unet_config = {\n        \"context_dim\": 1024,\n        \"model_channels\": 320,\n        \"use_linear_in_transformer\": True,\n        \"adm_in_channels\": 2048,\n        \"use_temporal_attention\": False,\n    }\n\n    clip_vision_prefix = \"embedder.model.visual.\"\n    noise_aug_config = {\"noise_schedule_config\": {\"timesteps\": 1000, \"beta_schedule\": \"squaredcos_cap_v2\"}, \"timestep_dim\": 1024}\n\nclass SDXLRefiner(supported_models_base.BASE):\n    unet_config = {\n        \"model_channels\": 384,\n        \"use_linear_in_transformer\": True,\n        \"context_dim\": 1280,\n        \"adm_in_channels\": 2560,\n        \"transformer_depth\": [0, 0, 4, 4, 4, 4, 0, 0],\n        \"use_temporal_attention\": False,\n    }\n\n    latent_format = latent_formats.SDXL\n\n    def get_model(self, state_dict, prefix=\"\", device=None):\n        return model_base.SDXLRefiner(self, device=device)\n\n    def process_clip_state_dict(self, state_dict):\n        keys_to_replace = {}\n        replace_prefix = {}\n\n        state_dict = utils.transformers_convert(state_dict, \"conditioner.embedders.0.model.\", \"cond_stage_model.clip_g.transformer.text_model.\", 32)\n        keys_to_replace[\"conditioner.embedders.0.model.text_projection\"] = \"cond_stage_model.clip_g.text_projection\"\n        keys_to_replace[\"conditioner.embedders.0.model.logit_scale\"] = \"cond_stage_model.clip_g.logit_scale\"\n\n        state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace)\n        return state_dict\n\n    def process_clip_state_dict_for_saving(self, state_dict):\n        replace_prefix = {}\n        state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, \"clip_g\")\n        if \"clip_g.transformer.text_model.embeddings.position_ids\" in state_dict_g:\n            state_dict_g.pop(\"clip_g.transformer.text_model.embeddings.position_ids\")\n        replace_prefix[\"clip_g\"] = \"conditioner.embedders.0.model\"\n        state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix)\n        return state_dict_g\n\n    def clip_target(self):\n        return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLRefinerClipModel)\n\nclass SDXL(supported_models_base.BASE):\n    unet_config = {\n        \"model_channels\": 320,\n        \"use_linear_in_transformer\": True,\n        \"transformer_depth\": [0, 0, 2, 2, 10, 10],\n        \"context_dim\": 2048,\n        \"adm_in_channels\": 2816,\n        \"use_temporal_attention\": False,\n    }\n\n    latent_format = latent_formats.SDXL\n\n    def model_type(self, state_dict, prefix=\"\"):\n        if \"v_pred\" in state_dict:\n            return model_base.ModelType.V_PREDICTION\n        else:\n            return model_base.ModelType.EPS\n\n    def get_model(self, state_dict, prefix=\"\", device=None):\n        out = model_base.SDXL(self, model_type=self.model_type(state_dict, prefix), device=device)\n        if self.inpaint_model():\n            out.set_inpaint()\n        return out\n\n    def process_clip_state_dict(self, state_dict):\n        keys_to_replace = {}\n        replace_prefix = {}\n\n        replace_prefix[\"conditioner.embedders.0.transformer.text_model\"] = \"cond_stage_model.clip_l.transformer.text_model\"\n        state_dict = utils.transformers_convert(state_dict, \"conditioner.embedders.1.model.\", \"cond_stage_model.clip_g.transformer.text_model.\", 32)\n        keys_to_replace[\"conditioner.embedders.1.model.text_projection\"] = \"cond_stage_model.clip_g.text_projection\"\n        keys_to_replace[\"conditioner.embedders.1.model.text_projection.weight\"] = \"cond_stage_model.clip_g.text_projection\"\n        keys_to_replace[\"conditioner.embedders.1.model.logit_scale\"] = \"cond_stage_model.clip_g.logit_scale\"\n\n        state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix)\n        state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace)\n        return state_dict\n\n    def process_clip_state_dict_for_saving(self, state_dict):\n        replace_prefix = {}\n        keys_to_replace = {}\n        state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, \"clip_g\")\n        if \"clip_g.transformer.text_model.embeddings.position_ids\" in state_dict_g:\n            state_dict_g.pop(\"clip_g.transformer.text_model.embeddings.position_ids\")\n        for k in state_dict:\n            if k.startswith(\"clip_l\"):\n                state_dict_g[k] = state_dict[k]\n\n        replace_prefix[\"clip_g\"] = \"conditioner.embedders.1.model\"\n        replace_prefix[\"clip_l\"] = \"conditioner.embedders.0\"\n        state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix)\n        return state_dict_g\n\n    def clip_target(self):\n        return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLClipModel)\n\nclass SSD1B(SDXL):\n    unet_config = {\n        \"model_channels\": 320,\n        \"use_linear_in_transformer\": True,\n        \"transformer_depth\": [0, 0, 2, 2, 4, 4],\n        \"context_dim\": 2048,\n        \"adm_in_channels\": 2816,\n        \"use_temporal_attention\": False,\n    }\n\nclass Segmind_Vega(SDXL):\n    unet_config = {\n        \"model_channels\": 320,\n        \"use_linear_in_transformer\": True,\n        \"transformer_depth\": [0, 0, 1, 1, 2, 2],\n        \"context_dim\": 2048,\n        \"adm_in_channels\": 2816,\n        \"use_temporal_attention\": False,\n    }\n\nclass SVD_img2vid(supported_models_base.BASE):\n    unet_config = {\n        \"model_channels\": 320,\n        \"in_channels\": 8,\n        \"use_linear_in_transformer\": True,\n        \"transformer_depth\": [1, 1, 1, 1, 1, 1, 0, 0],\n        \"context_dim\": 1024,\n        \"adm_in_channels\": 768,\n        \"use_temporal_attention\": True,\n        \"use_temporal_resblock\": True\n    }\n\n    clip_vision_prefix = \"conditioner.embedders.0.open_clip.model.visual.\"\n\n    latent_format = latent_formats.SD15\n\n    sampling_settings = {\"sigma_max\": 700.0, \"sigma_min\": 0.002}\n\n    def get_model(self, state_dict, prefix=\"\", device=None):\n        out = model_base.SVD_img2vid(self, device=device)\n        return out\n\n    def clip_target(self):\n        return None\n\nclass Stable_Zero123(supported_models_base.BASE):\n    unet_config = {\n        \"context_dim\": 768,\n        \"model_channels\": 320,\n        \"use_linear_in_transformer\": False,\n        \"adm_in_channels\": None,\n        \"use_temporal_attention\": False,\n        \"in_channels\": 8,\n    }\n\n    unet_extra_config = {\n        \"num_heads\": 8,\n        \"num_head_channels\": -1,\n    }\n\n    clip_vision_prefix = \"cond_stage_model.model.visual.\"\n\n    latent_format = latent_formats.SD15\n\n    def get_model(self, state_dict, prefix=\"\", device=None):\n        out = model_base.Stable_Zero123(self, device=device, cc_projection_weight=state_dict[\"cc_projection.weight\"], cc_projection_bias=state_dict[\"cc_projection.bias\"])\n        return out\n\n    def clip_target(self):\n        return None\n\nclass SD_X4Upscaler(SD20):\n    unet_config = {\n        \"context_dim\": 1024,\n        \"model_channels\": 256,\n        'in_channels': 7,\n        \"use_linear_in_transformer\": True,\n        \"adm_in_channels\": None,\n        \"use_temporal_attention\": False,\n    }\n\n    unet_extra_config = {\n        \"disable_self_attentions\": [True, True, True, False],\n        \"num_classes\": 1000,\n        \"num_heads\": 8,\n        \"num_head_channels\": -1,\n    }\n\n    latent_format = latent_formats.SD_X4\n\n    sampling_settings = {\n        \"linear_start\": 0.0001,\n        \"linear_end\": 0.02,\n    }\n\n    def get_model(self, state_dict, prefix=\"\", device=None):\n        out = model_base.SD_X4Upscaler(self, device=device)\n        return out\n\nmodels = [Stable_Zero123, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXLRefiner, SDXL, SSD1B, Segmind_Vega, SD_X4Upscaler]\nmodels += [SVD_img2vid]\n"
  },
  {
    "path": "ldm_patched/modules/supported_models_base.py",
    "content": "import torch\nfrom . import model_base\nfrom . import utils\nfrom . import latent_formats\n\nclass ClipTarget:\n    def __init__(self, tokenizer, clip):\n        self.clip = clip\n        self.tokenizer = tokenizer\n        self.params = {}\n\nclass BASE:\n    unet_config = {}\n    unet_extra_config = {\n        \"num_heads\": -1,\n        \"num_head_channels\": 64,\n    }\n\n    clip_prefix = []\n    clip_vision_prefix = None\n    noise_aug_config = None\n    sampling_settings = {}\n    latent_format = latent_formats.LatentFormat\n\n    manual_cast_dtype = None\n\n    @classmethod\n    def matches(s, unet_config):\n        for k in s.unet_config:\n            if s.unet_config[k] != unet_config[k]:\n                return False\n        return True\n\n    def model_type(self, state_dict, prefix=\"\"):\n        return model_base.ModelType.EPS\n\n    def inpaint_model(self):\n        return self.unet_config[\"in_channels\"] > 4\n\n    def __init__(self, unet_config):\n        self.unet_config = unet_config\n        self.latent_format = self.latent_format()\n        for x in self.unet_extra_config:\n            self.unet_config[x] = self.unet_extra_config[x]\n\n    def get_model(self, state_dict, prefix=\"\", device=None):\n        if self.noise_aug_config is not None:\n            out = model_base.SD21UNCLIP(self, self.noise_aug_config, model_type=self.model_type(state_dict, prefix), device=device)\n        else:\n            out = model_base.BaseModel(self, model_type=self.model_type(state_dict, prefix), device=device)\n        if self.inpaint_model():\n            out.set_inpaint()\n        return out\n\n    def process_clip_state_dict(self, state_dict):\n        return state_dict\n\n    def process_unet_state_dict(self, state_dict):\n        return state_dict\n\n    def process_vae_state_dict(self, state_dict):\n        return state_dict\n\n    def process_clip_state_dict_for_saving(self, state_dict):\n        replace_prefix = {\"\": \"cond_stage_model.\"}\n        return utils.state_dict_prefix_replace(state_dict, replace_prefix)\n\n    def process_clip_vision_state_dict_for_saving(self, state_dict):\n        replace_prefix = {}\n        if self.clip_vision_prefix is not None:\n            replace_prefix[\"\"] = self.clip_vision_prefix\n        return utils.state_dict_prefix_replace(state_dict, replace_prefix)\n\n    def process_unet_state_dict_for_saving(self, state_dict):\n        replace_prefix = {\"\": \"model.diffusion_model.\"}\n        return utils.state_dict_prefix_replace(state_dict, replace_prefix)\n\n    def process_vae_state_dict_for_saving(self, state_dict):\n        replace_prefix = {\"\": \"first_stage_model.\"}\n        return utils.state_dict_prefix_replace(state_dict, replace_prefix)\n\n    def set_manual_cast(self, manual_cast_dtype):\n        self.manual_cast_dtype = manual_cast_dtype\n"
  },
  {
    "path": "ldm_patched/modules/utils.py",
    "content": "import torch\nimport math\nimport struct\nimport ldm_patched.modules.checkpoint_pickle\nimport safetensors.torch\nimport numpy as np\nfrom PIL import Image\n\ndef load_torch_file(ckpt, safe_load=False, device=None):\n    if device is None:\n        device = torch.device(\"cpu\")\n    if ckpt.lower().endswith(\".safetensors\"):\n        sd = safetensors.torch.load_file(ckpt, device=device.type)\n    else:\n        if safe_load:\n            if not 'weights_only' in torch.load.__code__.co_varnames:\n                print(\"Warning torch.load doesn't support weights_only on this pytorch version, loading unsafely.\")\n                safe_load = False\n        if safe_load:\n            pl_sd = torch.load(ckpt, map_location=device, weights_only=True)\n        else:\n            pl_sd = torch.load(ckpt, map_location=device, pickle_module=ldm_patched.modules.checkpoint_pickle)\n        if \"global_step\" in pl_sd:\n            print(f\"Global Step: {pl_sd['global_step']}\")\n        if \"state_dict\" in pl_sd:\n            sd = pl_sd[\"state_dict\"]\n        else:\n            sd = pl_sd\n    return sd\n\ndef save_torch_file(sd, ckpt, metadata=None):\n    if metadata is not None:\n        safetensors.torch.save_file(sd, ckpt, metadata=metadata)\n    else:\n        safetensors.torch.save_file(sd, ckpt)\n\ndef calculate_parameters(sd, prefix=\"\"):\n    params = 0\n    for k in sd.keys():\n        if k.startswith(prefix):\n            params += sd[k].nelement()\n    return params\n\ndef state_dict_key_replace(state_dict, keys_to_replace):\n    for x in keys_to_replace:\n        if x in state_dict:\n            state_dict[keys_to_replace[x]] = state_dict.pop(x)\n    return state_dict\n\ndef state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=False):\n    if filter_keys:\n        out = {}\n    else:\n        out = state_dict\n    for rp in replace_prefix:\n        replace = list(map(lambda a: (a, \"{}{}\".format(replace_prefix[rp], a[len(rp):])), filter(lambda a: a.startswith(rp), state_dict.keys())))\n        for x in replace:\n            w = state_dict.pop(x[0])\n            out[x[1]] = w\n    return out\n\n\ndef transformers_convert(sd, prefix_from, prefix_to, number):\n    keys_to_replace = {\n        \"{}positional_embedding\": \"{}embeddings.position_embedding.weight\",\n        \"{}token_embedding.weight\": \"{}embeddings.token_embedding.weight\",\n        \"{}ln_final.weight\": \"{}final_layer_norm.weight\",\n        \"{}ln_final.bias\": \"{}final_layer_norm.bias\",\n    }\n\n    for k in keys_to_replace:\n        x = k.format(prefix_from)\n        if x in sd:\n            sd[keys_to_replace[k].format(prefix_to)] = sd.pop(x)\n\n    resblock_to_replace = {\n        \"ln_1\": \"layer_norm1\",\n        \"ln_2\": \"layer_norm2\",\n        \"mlp.c_fc\": \"mlp.fc1\",\n        \"mlp.c_proj\": \"mlp.fc2\",\n        \"attn.out_proj\": \"self_attn.out_proj\",\n    }\n\n    for resblock in range(number):\n        for x in resblock_to_replace:\n            for y in [\"weight\", \"bias\"]:\n                k = \"{}transformer.resblocks.{}.{}.{}\".format(prefix_from, resblock, x, y)\n                k_to = \"{}encoder.layers.{}.{}.{}\".format(prefix_to, resblock, resblock_to_replace[x], y)\n                if k in sd:\n                    sd[k_to] = sd.pop(k)\n\n        for y in [\"weight\", \"bias\"]:\n            k_from = \"{}transformer.resblocks.{}.attn.in_proj_{}\".format(prefix_from, resblock, y)\n            if k_from in sd:\n                weights = sd.pop(k_from)\n                shape_from = weights.shape[0] // 3\n                for x in range(3):\n                    p = [\"self_attn.q_proj\", \"self_attn.k_proj\", \"self_attn.v_proj\"]\n                    k_to = \"{}encoder.layers.{}.{}.{}\".format(prefix_to, resblock, p[x], y)\n                    sd[k_to] = weights[shape_from*x:shape_from*(x + 1)]\n    return sd\n\nUNET_MAP_ATTENTIONS = {\n    \"proj_in.weight\",\n    \"proj_in.bias\",\n    \"proj_out.weight\",\n    \"proj_out.bias\",\n    \"norm.weight\",\n    \"norm.bias\",\n}\n\nTRANSFORMER_BLOCKS = {\n    \"norm1.weight\",\n    \"norm1.bias\",\n    \"norm2.weight\",\n    \"norm2.bias\",\n    \"norm3.weight\",\n    \"norm3.bias\",\n    \"attn1.to_q.weight\",\n    \"attn1.to_k.weight\",\n    \"attn1.to_v.weight\",\n    \"attn1.to_out.0.weight\",\n    \"attn1.to_out.0.bias\",\n    \"attn2.to_q.weight\",\n    \"attn2.to_k.weight\",\n    \"attn2.to_v.weight\",\n    \"attn2.to_out.0.weight\",\n    \"attn2.to_out.0.bias\",\n    \"ff.net.0.proj.weight\",\n    \"ff.net.0.proj.bias\",\n    \"ff.net.2.weight\",\n    \"ff.net.2.bias\",\n}\n\nUNET_MAP_RESNET = {\n    \"in_layers.2.weight\": \"conv1.weight\",\n    \"in_layers.2.bias\": \"conv1.bias\",\n    \"emb_layers.1.weight\": \"time_emb_proj.weight\",\n    \"emb_layers.1.bias\": \"time_emb_proj.bias\",\n    \"out_layers.3.weight\": \"conv2.weight\",\n    \"out_layers.3.bias\": \"conv2.bias\",\n    \"skip_connection.weight\": \"conv_shortcut.weight\",\n    \"skip_connection.bias\": \"conv_shortcut.bias\",\n    \"in_layers.0.weight\": \"norm1.weight\",\n    \"in_layers.0.bias\": \"norm1.bias\",\n    \"out_layers.0.weight\": \"norm2.weight\",\n    \"out_layers.0.bias\": \"norm2.bias\",\n}\n\nUNET_MAP_BASIC = {\n    (\"label_emb.0.0.weight\", \"class_embedding.linear_1.weight\"),\n    (\"label_emb.0.0.bias\", \"class_embedding.linear_1.bias\"),\n    (\"label_emb.0.2.weight\", \"class_embedding.linear_2.weight\"),\n    (\"label_emb.0.2.bias\", \"class_embedding.linear_2.bias\"),\n    (\"label_emb.0.0.weight\", \"add_embedding.linear_1.weight\"),\n    (\"label_emb.0.0.bias\", \"add_embedding.linear_1.bias\"),\n    (\"label_emb.0.2.weight\", \"add_embedding.linear_2.weight\"),\n    (\"label_emb.0.2.bias\", \"add_embedding.linear_2.bias\"),\n    (\"input_blocks.0.0.weight\", \"conv_in.weight\"),\n    (\"input_blocks.0.0.bias\", \"conv_in.bias\"),\n    (\"out.0.weight\", \"conv_norm_out.weight\"),\n    (\"out.0.bias\", \"conv_norm_out.bias\"),\n    (\"out.2.weight\", \"conv_out.weight\"),\n    (\"out.2.bias\", \"conv_out.bias\"),\n    (\"time_embed.0.weight\", \"time_embedding.linear_1.weight\"),\n    (\"time_embed.0.bias\", \"time_embedding.linear_1.bias\"),\n    (\"time_embed.2.weight\", \"time_embedding.linear_2.weight\"),\n    (\"time_embed.2.bias\", \"time_embedding.linear_2.bias\")\n}\n\ndef unet_to_diffusers(unet_config):\n    num_res_blocks = unet_config[\"num_res_blocks\"]\n    channel_mult = unet_config[\"channel_mult\"]\n    transformer_depth = unet_config[\"transformer_depth\"][:]\n    transformer_depth_output = unet_config[\"transformer_depth_output\"][:]\n    num_blocks = len(channel_mult)\n\n    transformers_mid = unet_config.get(\"transformer_depth_middle\", None)\n\n    diffusers_unet_map = {}\n    for x in range(num_blocks):\n        n = 1 + (num_res_blocks[x] + 1) * x\n        for i in range(num_res_blocks[x]):\n            for b in UNET_MAP_RESNET:\n                diffusers_unet_map[\"down_blocks.{}.resnets.{}.{}\".format(x, i, UNET_MAP_RESNET[b])] = \"input_blocks.{}.0.{}\".format(n, b)\n            num_transformers = transformer_depth.pop(0)\n            if num_transformers > 0:\n                for b in UNET_MAP_ATTENTIONS:\n                    diffusers_unet_map[\"down_blocks.{}.attentions.{}.{}\".format(x, i, b)] = \"input_blocks.{}.1.{}\".format(n, b)\n                for t in range(num_transformers):\n                    for b in TRANSFORMER_BLOCKS:\n                        diffusers_unet_map[\"down_blocks.{}.attentions.{}.transformer_blocks.{}.{}\".format(x, i, t, b)] = \"input_blocks.{}.1.transformer_blocks.{}.{}\".format(n, t, b)\n            n += 1\n        for k in [\"weight\", \"bias\"]:\n            diffusers_unet_map[\"down_blocks.{}.downsamplers.0.conv.{}\".format(x, k)] = \"input_blocks.{}.0.op.{}\".format(n, k)\n\n    i = 0\n    for b in UNET_MAP_ATTENTIONS:\n        diffusers_unet_map[\"mid_block.attentions.{}.{}\".format(i, b)] = \"middle_block.1.{}\".format(b)\n    for t in range(transformers_mid):\n        for b in TRANSFORMER_BLOCKS:\n            diffusers_unet_map[\"mid_block.attentions.{}.transformer_blocks.{}.{}\".format(i, t, b)] = \"middle_block.1.transformer_blocks.{}.{}\".format(t, b)\n\n    for i, n in enumerate([0, 2]):\n        for b in UNET_MAP_RESNET:\n            diffusers_unet_map[\"mid_block.resnets.{}.{}\".format(i, UNET_MAP_RESNET[b])] = \"middle_block.{}.{}\".format(n, b)\n\n    num_res_blocks = list(reversed(num_res_blocks))\n    for x in range(num_blocks):\n        n = (num_res_blocks[x] + 1) * x\n        l = num_res_blocks[x] + 1\n        for i in range(l):\n            c = 0\n            for b in UNET_MAP_RESNET:\n                diffusers_unet_map[\"up_blocks.{}.resnets.{}.{}\".format(x, i, UNET_MAP_RESNET[b])] = \"output_blocks.{}.0.{}\".format(n, b)\n            c += 1\n            num_transformers = transformer_depth_output.pop()\n            if num_transformers > 0:\n                c += 1\n                for b in UNET_MAP_ATTENTIONS:\n                    diffusers_unet_map[\"up_blocks.{}.attentions.{}.{}\".format(x, i, b)] = \"output_blocks.{}.1.{}\".format(n, b)\n                for t in range(num_transformers):\n                    for b in TRANSFORMER_BLOCKS:\n                        diffusers_unet_map[\"up_blocks.{}.attentions.{}.transformer_blocks.{}.{}\".format(x, i, t, b)] = \"output_blocks.{}.1.transformer_blocks.{}.{}\".format(n, t, b)\n            if i == l - 1:\n                for k in [\"weight\", \"bias\"]:\n                    diffusers_unet_map[\"up_blocks.{}.upsamplers.0.conv.{}\".format(x, k)] = \"output_blocks.{}.{}.conv.{}\".format(n, c, k)\n            n += 1\n\n    for k in UNET_MAP_BASIC:\n        diffusers_unet_map[k[1]] = k[0]\n\n    return diffusers_unet_map\n\ndef repeat_to_batch_size(tensor, batch_size):\n    if tensor.shape[0] > batch_size:\n        return tensor[:batch_size]\n    elif tensor.shape[0] < batch_size:\n        return tensor.repeat([math.ceil(batch_size / tensor.shape[0])] + [1] * (len(tensor.shape) - 1))[:batch_size]\n    return tensor\n\ndef resize_to_batch_size(tensor, batch_size):\n    in_batch_size = tensor.shape[0]\n    if in_batch_size == batch_size:\n        return tensor\n\n    if batch_size <= 1:\n        return tensor[:batch_size]\n\n    output = torch.empty([batch_size] + list(tensor.shape)[1:], dtype=tensor.dtype, device=tensor.device)\n    if batch_size < in_batch_size:\n        scale = (in_batch_size - 1) / (batch_size - 1)\n        for i in range(batch_size):\n            output[i] = tensor[min(round(i * scale), in_batch_size - 1)]\n    else:\n        scale = in_batch_size / batch_size\n        for i in range(batch_size):\n            output[i] = tensor[min(math.floor((i + 0.5) * scale), in_batch_size - 1)]\n\n    return output\n\ndef convert_sd_to(state_dict, dtype):\n    keys = list(state_dict.keys())\n    for k in keys:\n        state_dict[k] = state_dict[k].to(dtype)\n    return state_dict\n\ndef safetensors_header(safetensors_path, max_size=100*1024*1024):\n    with open(safetensors_path, \"rb\") as f:\n        header = f.read(8)\n        length_of_header = struct.unpack('<Q', header)[0]\n        if length_of_header > max_size:\n            return None\n        return f.read(length_of_header)\n\ndef set_attr(obj, attr, value):\n    attrs = attr.split(\".\")\n    for name in attrs[:-1]:\n        obj = getattr(obj, name)\n    prev = getattr(obj, attrs[-1])\n    setattr(obj, attrs[-1], torch.nn.Parameter(value, requires_grad=False))\n    del prev\n\ndef copy_to_param(obj, attr, value):\n    # inplace update tensor instead of replacing it\n    attrs = attr.split(\".\")\n    for name in attrs[:-1]:\n        obj = getattr(obj, name)\n    prev = getattr(obj, attrs[-1])\n    prev.data.copy_(value)\n\ndef get_attr(obj, attr):\n    attrs = attr.split(\".\")\n    for name in attrs:\n        obj = getattr(obj, name)\n    return obj\n\ndef bislerp(samples, width, height):\n    def slerp(b1, b2, r):\n        '''slerps batches b1, b2 according to ratio r, batches should be flat e.g. NxC'''\n        \n        c = b1.shape[-1]\n\n        #norms\n        b1_norms = torch.norm(b1, dim=-1, keepdim=True)\n        b2_norms = torch.norm(b2, dim=-1, keepdim=True)\n\n        #normalize\n        b1_normalized = b1 / b1_norms\n        b2_normalized = b2 / b2_norms\n\n        #zero when norms are zero\n        b1_normalized[b1_norms.expand(-1,c) == 0.0] = 0.0\n        b2_normalized[b2_norms.expand(-1,c) == 0.0] = 0.0\n\n        #slerp\n        dot = (b1_normalized*b2_normalized).sum(1)\n        omega = torch.acos(dot)\n        so = torch.sin(omega)\n\n        #technically not mathematically correct, but more pleasing?\n        res = (torch.sin((1.0-r.squeeze(1))*omega)/so).unsqueeze(1)*b1_normalized + (torch.sin(r.squeeze(1)*omega)/so).unsqueeze(1) * b2_normalized\n        res *= (b1_norms * (1.0-r) + b2_norms * r).expand(-1,c)\n\n        #edge cases for same or polar opposites\n        res[dot > 1 - 1e-5] = b1[dot > 1 - 1e-5] \n        res[dot < 1e-5 - 1] = (b1 * (1.0-r) + b2 * r)[dot < 1e-5 - 1]\n        return res\n    \n    def generate_bilinear_data(length_old, length_new, device):\n        coords_1 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1,1,1,-1))\n        coords_1 = torch.nn.functional.interpolate(coords_1, size=(1, length_new), mode=\"bilinear\")\n        ratios = coords_1 - coords_1.floor()\n        coords_1 = coords_1.to(torch.int64)\n        \n        coords_2 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1,1,1,-1)) + 1\n        coords_2[:,:,:,-1] -= 1\n        coords_2 = torch.nn.functional.interpolate(coords_2, size=(1, length_new), mode=\"bilinear\")\n        coords_2 = coords_2.to(torch.int64)\n        return ratios, coords_1, coords_2\n\n    orig_dtype = samples.dtype\n    samples = samples.float()\n    n,c,h,w = samples.shape\n    h_new, w_new = (height, width)\n    \n    #linear w\n    ratios, coords_1, coords_2 = generate_bilinear_data(w, w_new, samples.device)\n    coords_1 = coords_1.expand((n, c, h, -1))\n    coords_2 = coords_2.expand((n, c, h, -1))\n    ratios = ratios.expand((n, 1, h, -1))\n\n    pass_1 = samples.gather(-1,coords_1).movedim(1, -1).reshape((-1,c))\n    pass_2 = samples.gather(-1,coords_2).movedim(1, -1).reshape((-1,c))\n    ratios = ratios.movedim(1, -1).reshape((-1,1))\n\n    result = slerp(pass_1, pass_2, ratios)\n    result = result.reshape(n, h, w_new, c).movedim(-1, 1)\n\n    #linear h\n    ratios, coords_1, coords_2 = generate_bilinear_data(h, h_new, samples.device)\n    coords_1 = coords_1.reshape((1,1,-1,1)).expand((n, c, -1, w_new))\n    coords_2 = coords_2.reshape((1,1,-1,1)).expand((n, c, -1, w_new))\n    ratios = ratios.reshape((1,1,-1,1)).expand((n, 1, -1, w_new))\n\n    pass_1 = result.gather(-2,coords_1).movedim(1, -1).reshape((-1,c))\n    pass_2 = result.gather(-2,coords_2).movedim(1, -1).reshape((-1,c))\n    ratios = ratios.movedim(1, -1).reshape((-1,1))\n\n    result = slerp(pass_1, pass_2, ratios)\n    result = result.reshape(n, h_new, w_new, c).movedim(-1, 1)\n    return result.to(orig_dtype)\n\ndef lanczos(samples, width, height):\n    images = [Image.fromarray(np.clip(255. * image.movedim(0, -1).cpu().numpy(), 0, 255).astype(np.uint8)) for image in samples]\n    images = [image.resize((width, height), resample=Image.Resampling.LANCZOS) for image in images]\n    images = [torch.from_numpy(np.array(image).astype(np.float32) / 255.0).movedim(-1, 0) for image in images]\n    result = torch.stack(images)\n    return result.to(samples.device, samples.dtype)\n\ndef common_upscale(samples, width, height, upscale_method, crop):\n        if crop == \"center\":\n            old_width = samples.shape[3]\n            old_height = samples.shape[2]\n            old_aspect = old_width / old_height\n            new_aspect = width / height\n            x = 0\n            y = 0\n            if old_aspect > new_aspect:\n                x = round((old_width - old_width * (new_aspect / old_aspect)) / 2)\n            elif old_aspect < new_aspect:\n                y = round((old_height - old_height * (old_aspect / new_aspect)) / 2)\n            s = samples[:,:,y:old_height-y,x:old_width-x]\n        else:\n            s = samples\n\n        if upscale_method == \"bislerp\":\n            return bislerp(s, width, height)\n        elif upscale_method == \"lanczos\":\n            return lanczos(s, width, height)\n        else:\n            return torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method)\n\ndef get_tiled_scale_steps(width, height, tile_x, tile_y, overlap):\n    return math.ceil((height / (tile_y - overlap))) * math.ceil((width / (tile_x - overlap)))\n\n@torch.inference_mode()\ndef tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device=\"cpu\", pbar = None):\n    output = torch.empty((samples.shape[0], out_channels, round(samples.shape[2] * upscale_amount), round(samples.shape[3] * upscale_amount)), device=output_device)\n    for b in range(samples.shape[0]):\n        s = samples[b:b+1]\n        out = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device=output_device)\n        out_div = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device=output_device)\n        for y in range(0, s.shape[2], tile_y - overlap):\n            for x in range(0, s.shape[3], tile_x - overlap):\n                s_in = s[:,:,y:y+tile_y,x:x+tile_x]\n\n                ps = function(s_in).to(output_device)\n                mask = torch.ones_like(ps)\n                feather = round(overlap * upscale_amount)\n                for t in range(feather):\n                        mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1))\n                        mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1))\n                        mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1))\n                        mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1))\n                out[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += ps * mask\n                out_div[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += mask\n                if pbar is not None:\n                    pbar.update(1)\n\n        output[b:b+1] = out/out_div\n    return output\n\nPROGRESS_BAR_ENABLED = True\ndef set_progress_bar_enabled(enabled):\n    global PROGRESS_BAR_ENABLED\n    PROGRESS_BAR_ENABLED = enabled\n\nPROGRESS_BAR_HOOK = None\ndef set_progress_bar_global_hook(function):\n    global PROGRESS_BAR_HOOK\n    PROGRESS_BAR_HOOK = function\n\nclass ProgressBar:\n    def __init__(self, total):\n        global PROGRESS_BAR_HOOK\n        self.total = total\n        self.current = 0\n        self.hook = PROGRESS_BAR_HOOK\n\n    def update_absolute(self, value, total=None, preview=None):\n        if total is not None:\n            self.total = total\n        if value > self.total:\n            value = self.total\n        self.current = value\n        if self.hook is not None:\n            self.hook(self.current, self.total, preview)\n\n    def update(self, value):\n        self.update_absolute(self.current + value)\n"
  },
  {
    "path": "ldm_patched/pfn/__init__.py",
    "content": ""
  },
  {
    "path": "ldm_patched/pfn/architecture/DAT.py",
    "content": "# pylint: skip-file\nimport math\nimport re\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.utils.checkpoint as checkpoint\nfrom einops import rearrange\nfrom einops.layers.torch import Rearrange\nfrom torch import Tensor\nfrom torch.nn import functional as F\n\nfrom .timm.drop import DropPath\nfrom .timm.weight_init import trunc_normal_\n\n\ndef img2windows(img, H_sp, W_sp):\n    \"\"\"\n    Input: Image (B, C, H, W)\n    Output: Window Partition (B', N, C)\n    \"\"\"\n    B, C, H, W = img.shape\n    img_reshape = img.view(B, C, H // H_sp, H_sp, W // W_sp, W_sp)\n    img_perm = (\n        img_reshape.permute(0, 2, 4, 3, 5, 1).contiguous().reshape(-1, H_sp * W_sp, C)\n    )\n    return img_perm\n\n\ndef windows2img(img_splits_hw, H_sp, W_sp, H, W):\n    \"\"\"\n    Input: Window Partition (B', N, C)\n    Output: Image (B, H, W, C)\n    \"\"\"\n    B = int(img_splits_hw.shape[0] / (H * W / H_sp / W_sp))\n\n    img = img_splits_hw.view(B, H // H_sp, W // W_sp, H_sp, W_sp, -1)\n    img = img.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)\n    return img\n\n\nclass SpatialGate(nn.Module):\n    \"\"\"Spatial-Gate.\n    Args:\n        dim (int): Half of input channels.\n    \"\"\"\n\n    def __init__(self, dim):\n        super().__init__()\n        self.norm = nn.LayerNorm(dim)\n        self.conv = nn.Conv2d(\n            dim, dim, kernel_size=3, stride=1, padding=1, groups=dim\n        )  # DW Conv\n\n    def forward(self, x, H, W):\n        # Split\n        x1, x2 = x.chunk(2, dim=-1)\n        B, N, C = x.shape\n        x2 = (\n            self.conv(self.norm(x2).transpose(1, 2).contiguous().view(B, C // 2, H, W))\n            .flatten(2)\n            .transpose(-1, -2)\n            .contiguous()\n        )\n\n        return x1 * x2\n\n\nclass SGFN(nn.Module):\n    \"\"\"Spatial-Gate Feed-Forward Network.\n    Args:\n        in_features (int): Number of input channels.\n        hidden_features (int | None): Number of hidden channels. Default: None\n        out_features (int | None): Number of output channels. Default: None\n        act_layer (nn.Module): Activation layer. Default: nn.GELU\n        drop (float): Dropout rate. Default: 0.0\n    \"\"\"\n\n    def __init__(\n        self,\n        in_features,\n        hidden_features=None,\n        out_features=None,\n        act_layer=nn.GELU,\n        drop=0.0,\n    ):\n        super().__init__()\n        out_features = out_features or in_features\n        hidden_features = hidden_features or in_features\n        self.fc1 = nn.Linear(in_features, hidden_features)\n        self.act = act_layer()\n        self.sg = SpatialGate(hidden_features // 2)\n        self.fc2 = nn.Linear(hidden_features // 2, out_features)\n        self.drop = nn.Dropout(drop)\n\n    def forward(self, x, H, W):\n        \"\"\"\n        Input: x: (B, H*W, C), H, W\n        Output: x: (B, H*W, C)\n        \"\"\"\n        x = self.fc1(x)\n        x = self.act(x)\n        x = self.drop(x)\n\n        x = self.sg(x, H, W)\n        x = self.drop(x)\n\n        x = self.fc2(x)\n        x = self.drop(x)\n        return x\n\n\nclass DynamicPosBias(nn.Module):\n    # The implementation builds on Crossformer code https://github.com/cheerss/CrossFormer/blob/main/models/crossformer.py\n    \"\"\"Dynamic Relative Position Bias.\n    Args:\n        dim (int): Number of input channels.\n        num_heads (int): Number of attention heads.\n        residual (bool):  If True, use residual strage to connect conv.\n    \"\"\"\n\n    def __init__(self, dim, num_heads, residual):\n        super().__init__()\n        self.residual = residual\n        self.num_heads = num_heads\n        self.pos_dim = dim // 4\n        self.pos_proj = nn.Linear(2, self.pos_dim)\n        self.pos1 = nn.Sequential(\n            nn.LayerNorm(self.pos_dim),\n            nn.ReLU(inplace=True),\n            nn.Linear(self.pos_dim, self.pos_dim),\n        )\n        self.pos2 = nn.Sequential(\n            nn.LayerNorm(self.pos_dim),\n            nn.ReLU(inplace=True),\n            nn.Linear(self.pos_dim, self.pos_dim),\n        )\n        self.pos3 = nn.Sequential(\n            nn.LayerNorm(self.pos_dim),\n            nn.ReLU(inplace=True),\n            nn.Linear(self.pos_dim, self.num_heads),\n        )\n\n    def forward(self, biases):\n        if self.residual:\n            pos = self.pos_proj(biases)  # 2Gh-1 * 2Gw-1, heads\n            pos = pos + self.pos1(pos)\n            pos = pos + self.pos2(pos)\n            pos = self.pos3(pos)\n        else:\n            pos = self.pos3(self.pos2(self.pos1(self.pos_proj(biases))))\n        return pos\n\n\nclass Spatial_Attention(nn.Module):\n    \"\"\"Spatial Window Self-Attention.\n    It supports rectangle window (containing square window).\n    Args:\n        dim (int): Number of input channels.\n        idx (int): The indentix of window. (0/1)\n        split_size (tuple(int)): Height and Width of spatial window.\n        dim_out (int | None): The dimension of the attention output. Default: None\n        num_heads (int): Number of attention heads. Default: 6\n        attn_drop (float): Dropout ratio of attention weight. Default: 0.0\n        proj_drop (float): Dropout ratio of output. Default: 0.0\n        qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set\n        position_bias (bool): The dynamic relative position bias. Default: True\n    \"\"\"\n\n    def __init__(\n        self,\n        dim,\n        idx,\n        split_size=[8, 8],\n        dim_out=None,\n        num_heads=6,\n        attn_drop=0.0,\n        proj_drop=0.0,\n        qk_scale=None,\n        position_bias=True,\n    ):\n        super().__init__()\n        self.dim = dim\n        self.dim_out = dim_out or dim\n        self.split_size = split_size\n        self.num_heads = num_heads\n        self.idx = idx\n        self.position_bias = position_bias\n\n        head_dim = dim // num_heads\n        self.scale = qk_scale or head_dim**-0.5\n\n        if idx == 0:\n            H_sp, W_sp = self.split_size[0], self.split_size[1]\n        elif idx == 1:\n            W_sp, H_sp = self.split_size[0], self.split_size[1]\n        else:\n            print(\"ERROR MODE\", idx)\n            exit(0)\n        self.H_sp = H_sp\n        self.W_sp = W_sp\n\n        if self.position_bias:\n            self.pos = DynamicPosBias(self.dim // 4, self.num_heads, residual=False)\n            # generate mother-set\n            position_bias_h = torch.arange(1 - self.H_sp, self.H_sp)\n            position_bias_w = torch.arange(1 - self.W_sp, self.W_sp)\n            biases = torch.stack(torch.meshgrid([position_bias_h, position_bias_w]))\n            biases = biases.flatten(1).transpose(0, 1).contiguous().float()\n            self.register_buffer(\"rpe_biases\", biases)\n\n            # get pair-wise relative position index for each token inside the window\n            coords_h = torch.arange(self.H_sp)\n            coords_w = torch.arange(self.W_sp)\n            coords = torch.stack(torch.meshgrid([coords_h, coords_w]))\n            coords_flatten = torch.flatten(coords, 1)\n            relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]\n            relative_coords = relative_coords.permute(1, 2, 0).contiguous()\n            relative_coords[:, :, 0] += self.H_sp - 1\n            relative_coords[:, :, 1] += self.W_sp - 1\n            relative_coords[:, :, 0] *= 2 * self.W_sp - 1\n            relative_position_index = relative_coords.sum(-1)\n            self.register_buffer(\"relative_position_index\", relative_position_index)\n\n        self.attn_drop = nn.Dropout(attn_drop)\n\n    def im2win(self, x, H, W):\n        B, N, C = x.shape\n        x = x.transpose(-2, -1).contiguous().view(B, C, H, W)\n        x = img2windows(x, self.H_sp, self.W_sp)\n        x = (\n            x.reshape(-1, self.H_sp * self.W_sp, self.num_heads, C // self.num_heads)\n            .permute(0, 2, 1, 3)\n            .contiguous()\n        )\n        return x\n\n    def forward(self, qkv, H, W, mask=None):\n        \"\"\"\n        Input: qkv: (B, 3*L, C), H, W, mask: (B, N, N), N is the window size\n        Output: x (B, H, W, C)\n        \"\"\"\n        q, k, v = qkv[0], qkv[1], qkv[2]\n\n        B, L, C = q.shape\n        assert L == H * W, \"flatten img_tokens has wrong size\"\n\n        # partition the q,k,v, image to window\n        q = self.im2win(q, H, W)\n        k = self.im2win(k, H, W)\n        v = self.im2win(v, H, W)\n\n        q = q * self.scale\n        attn = q @ k.transpose(-2, -1)  # B head N C @ B head C N --> B head N N\n\n        # calculate drpe\n        if self.position_bias:\n            pos = self.pos(self.rpe_biases)\n            # select position bias\n            relative_position_bias = pos[self.relative_position_index.view(-1)].view(\n                self.H_sp * self.W_sp, self.H_sp * self.W_sp, -1\n            )\n            relative_position_bias = relative_position_bias.permute(\n                2, 0, 1\n            ).contiguous()\n            attn = attn + relative_position_bias.unsqueeze(0)\n\n        N = attn.shape[3]\n\n        # use mask for shift window\n        if mask is not None:\n            nW = mask.shape[0]\n            attn = attn.view(B, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(\n                0\n            )\n            attn = attn.view(-1, self.num_heads, N, N)\n\n        attn = nn.functional.softmax(attn, dim=-1, dtype=attn.dtype)\n        attn = self.attn_drop(attn)\n\n        x = attn @ v\n        x = x.transpose(1, 2).reshape(\n            -1, self.H_sp * self.W_sp, C\n        )  # B head N N @ B head N C\n\n        # merge the window, window to image\n        x = windows2img(x, self.H_sp, self.W_sp, H, W)  # B H' W' C\n\n        return x\n\n\nclass Adaptive_Spatial_Attention(nn.Module):\n    # The implementation builds on CAT code https://github.com/Zhengchen1999/CAT\n    \"\"\"Adaptive Spatial Self-Attention\n    Args:\n        dim (int): Number of input channels.\n        num_heads (int): Number of attention heads. Default: 6\n        split_size (tuple(int)): Height and Width of spatial window.\n        shift_size (tuple(int)): Shift size for spatial window.\n        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True\n        qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set.\n        drop (float): Dropout rate. Default: 0.0\n        attn_drop (float): Attention dropout rate. Default: 0.0\n        rg_idx (int): The indentix of Residual Group (RG)\n        b_idx (int): The indentix of Block in each RG\n    \"\"\"\n\n    def __init__(\n        self,\n        dim,\n        num_heads,\n        reso=64,\n        split_size=[8, 8],\n        shift_size=[1, 2],\n        qkv_bias=False,\n        qk_scale=None,\n        drop=0.0,\n        attn_drop=0.0,\n        rg_idx=0,\n        b_idx=0,\n    ):\n        super().__init__()\n        self.dim = dim\n        self.num_heads = num_heads\n        self.split_size = split_size\n        self.shift_size = shift_size\n        self.b_idx = b_idx\n        self.rg_idx = rg_idx\n        self.patches_resolution = reso\n        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)\n\n        assert (\n            0 <= self.shift_size[0] < self.split_size[0]\n        ), \"shift_size must in 0-split_size0\"\n        assert (\n            0 <= self.shift_size[1] < self.split_size[1]\n        ), \"shift_size must in 0-split_size1\"\n\n        self.branch_num = 2\n\n        self.proj = nn.Linear(dim, dim)\n        self.proj_drop = nn.Dropout(drop)\n\n        self.attns = nn.ModuleList(\n            [\n                Spatial_Attention(\n                    dim // 2,\n                    idx=i,\n                    split_size=split_size,\n                    num_heads=num_heads // 2,\n                    dim_out=dim // 2,\n                    qk_scale=qk_scale,\n                    attn_drop=attn_drop,\n                    proj_drop=drop,\n                    position_bias=True,\n                )\n                for i in range(self.branch_num)\n            ]\n        )\n\n        if (self.rg_idx % 2 == 0 and self.b_idx > 0 and (self.b_idx - 2) % 4 == 0) or (\n            self.rg_idx % 2 != 0 and self.b_idx % 4 == 0\n        ):\n            attn_mask = self.calculate_mask(\n                self.patches_resolution, self.patches_resolution\n            )\n            self.register_buffer(\"attn_mask_0\", attn_mask[0])\n            self.register_buffer(\"attn_mask_1\", attn_mask[1])\n        else:\n            attn_mask = None\n            self.register_buffer(\"attn_mask_0\", None)\n            self.register_buffer(\"attn_mask_1\", None)\n\n        self.dwconv = nn.Sequential(\n            nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1, groups=dim),\n            nn.BatchNorm2d(dim),\n            nn.GELU(),\n        )\n        self.channel_interaction = nn.Sequential(\n            nn.AdaptiveAvgPool2d(1),\n            nn.Conv2d(dim, dim // 8, kernel_size=1),\n            nn.BatchNorm2d(dim // 8),\n            nn.GELU(),\n            nn.Conv2d(dim // 8, dim, kernel_size=1),\n        )\n        self.spatial_interaction = nn.Sequential(\n            nn.Conv2d(dim, dim // 16, kernel_size=1),\n            nn.BatchNorm2d(dim // 16),\n            nn.GELU(),\n            nn.Conv2d(dim // 16, 1, kernel_size=1),\n        )\n\n    def calculate_mask(self, H, W):\n        # The implementation builds on Swin Transformer code https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_transformer.py\n        # calculate attention mask for shift window\n        img_mask_0 = torch.zeros((1, H, W, 1))  # 1 H W 1 idx=0\n        img_mask_1 = torch.zeros((1, H, W, 1))  # 1 H W 1 idx=1\n        h_slices_0 = (\n            slice(0, -self.split_size[0]),\n            slice(-self.split_size[0], -self.shift_size[0]),\n            slice(-self.shift_size[0], None),\n        )\n        w_slices_0 = (\n            slice(0, -self.split_size[1]),\n            slice(-self.split_size[1], -self.shift_size[1]),\n            slice(-self.shift_size[1], None),\n        )\n\n        h_slices_1 = (\n            slice(0, -self.split_size[1]),\n            slice(-self.split_size[1], -self.shift_size[1]),\n            slice(-self.shift_size[1], None),\n        )\n        w_slices_1 = (\n            slice(0, -self.split_size[0]),\n            slice(-self.split_size[0], -self.shift_size[0]),\n            slice(-self.shift_size[0], None),\n        )\n        cnt = 0\n        for h in h_slices_0:\n            for w in w_slices_0:\n                img_mask_0[:, h, w, :] = cnt\n                cnt += 1\n        cnt = 0\n        for h in h_slices_1:\n            for w in w_slices_1:\n                img_mask_1[:, h, w, :] = cnt\n                cnt += 1\n\n        # calculate mask for window-0\n        img_mask_0 = img_mask_0.view(\n            1,\n            H // self.split_size[0],\n            self.split_size[0],\n            W // self.split_size[1],\n            self.split_size[1],\n            1,\n        )\n        img_mask_0 = (\n            img_mask_0.permute(0, 1, 3, 2, 4, 5)\n            .contiguous()\n            .view(-1, self.split_size[0], self.split_size[1], 1)\n        )  # nW, sw[0], sw[1], 1\n        mask_windows_0 = img_mask_0.view(-1, self.split_size[0] * self.split_size[1])\n        attn_mask_0 = mask_windows_0.unsqueeze(1) - mask_windows_0.unsqueeze(2)\n        attn_mask_0 = attn_mask_0.masked_fill(\n            attn_mask_0 != 0, float(-100.0)\n        ).masked_fill(attn_mask_0 == 0, float(0.0))\n\n        # calculate mask for window-1\n        img_mask_1 = img_mask_1.view(\n            1,\n            H // self.split_size[1],\n            self.split_size[1],\n            W // self.split_size[0],\n            self.split_size[0],\n            1,\n        )\n        img_mask_1 = (\n            img_mask_1.permute(0, 1, 3, 2, 4, 5)\n            .contiguous()\n            .view(-1, self.split_size[1], self.split_size[0], 1)\n        )  # nW, sw[1], sw[0], 1\n        mask_windows_1 = img_mask_1.view(-1, self.split_size[1] * self.split_size[0])\n        attn_mask_1 = mask_windows_1.unsqueeze(1) - mask_windows_1.unsqueeze(2)\n        attn_mask_1 = attn_mask_1.masked_fill(\n            attn_mask_1 != 0, float(-100.0)\n        ).masked_fill(attn_mask_1 == 0, float(0.0))\n\n        return attn_mask_0, attn_mask_1\n\n    def forward(self, x, H, W):\n        \"\"\"\n        Input: x: (B, H*W, C), H, W\n        Output: x: (B, H*W, C)\n        \"\"\"\n        B, L, C = x.shape\n        assert L == H * W, \"flatten img_tokens has wrong size\"\n\n        qkv = self.qkv(x).reshape(B, -1, 3, C).permute(2, 0, 1, 3)  # 3, B, HW, C\n        # V without partition\n        v = qkv[2].transpose(-2, -1).contiguous().view(B, C, H, W)\n\n        # image padding\n        max_split_size = max(self.split_size[0], self.split_size[1])\n        pad_l = pad_t = 0\n        pad_r = (max_split_size - W % max_split_size) % max_split_size\n        pad_b = (max_split_size - H % max_split_size) % max_split_size\n\n        qkv = qkv.reshape(3 * B, H, W, C).permute(0, 3, 1, 2)  # 3B C H W\n        qkv = (\n            F.pad(qkv, (pad_l, pad_r, pad_t, pad_b))\n            .reshape(3, B, C, -1)\n            .transpose(-2, -1)\n        )  # l r t b\n        _H = pad_b + H\n        _W = pad_r + W\n        _L = _H * _W\n\n        # window-0 and window-1 on split channels [C/2, C/2]; for square windows (e.g., 8x8), window-0 and window-1 can be merged\n        # shift in block: (0, 4, 8, ...), (2, 6, 10, ...), (0, 4, 8, ...), (2, 6, 10, ...), ...\n        if (self.rg_idx % 2 == 0 and self.b_idx > 0 and (self.b_idx - 2) % 4 == 0) or (\n            self.rg_idx % 2 != 0 and self.b_idx % 4 == 0\n        ):\n            qkv = qkv.view(3, B, _H, _W, C)\n            qkv_0 = torch.roll(\n                qkv[:, :, :, :, : C // 2],\n                shifts=(-self.shift_size[0], -self.shift_size[1]),\n                dims=(2, 3),\n            )\n            qkv_0 = qkv_0.view(3, B, _L, C // 2)\n            qkv_1 = torch.roll(\n                qkv[:, :, :, :, C // 2 :],\n                shifts=(-self.shift_size[1], -self.shift_size[0]),\n                dims=(2, 3),\n            )\n            qkv_1 = qkv_1.view(3, B, _L, C // 2)\n\n            if self.patches_resolution != _H or self.patches_resolution != _W:\n                mask_tmp = self.calculate_mask(_H, _W)\n                x1_shift = self.attns[0](qkv_0, _H, _W, mask=mask_tmp[0].to(x.device))\n                x2_shift = self.attns[1](qkv_1, _H, _W, mask=mask_tmp[1].to(x.device))\n            else:\n                x1_shift = self.attns[0](qkv_0, _H, _W, mask=self.attn_mask_0)\n                x2_shift = self.attns[1](qkv_1, _H, _W, mask=self.attn_mask_1)\n\n            x1 = torch.roll(\n                x1_shift, shifts=(self.shift_size[0], self.shift_size[1]), dims=(1, 2)\n            )\n            x2 = torch.roll(\n                x2_shift, shifts=(self.shift_size[1], self.shift_size[0]), dims=(1, 2)\n            )\n            x1 = x1[:, :H, :W, :].reshape(B, L, C // 2)\n            x2 = x2[:, :H, :W, :].reshape(B, L, C // 2)\n            # attention output\n            attened_x = torch.cat([x1, x2], dim=2)\n\n        else:\n            x1 = self.attns[0](qkv[:, :, :, : C // 2], _H, _W)[:, :H, :W, :].reshape(\n                B, L, C // 2\n            )\n            x2 = self.attns[1](qkv[:, :, :, C // 2 :], _H, _W)[:, :H, :W, :].reshape(\n                B, L, C // 2\n            )\n            # attention output\n            attened_x = torch.cat([x1, x2], dim=2)\n\n        # convolution output\n        conv_x = self.dwconv(v)\n\n        # Adaptive Interaction Module (AIM)\n        # C-Map (before sigmoid)\n        channel_map = (\n            self.channel_interaction(conv_x)\n            .permute(0, 2, 3, 1)\n            .contiguous()\n            .view(B, 1, C)\n        )\n        # S-Map (before sigmoid)\n        attention_reshape = attened_x.transpose(-2, -1).contiguous().view(B, C, H, W)\n        spatial_map = self.spatial_interaction(attention_reshape)\n\n        # C-I\n        attened_x = attened_x * torch.sigmoid(channel_map)\n        # S-I\n        conv_x = torch.sigmoid(spatial_map) * conv_x\n        conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(B, L, C)\n\n        x = attened_x + conv_x\n\n        x = self.proj(x)\n        x = self.proj_drop(x)\n\n        return x\n\n\nclass Adaptive_Channel_Attention(nn.Module):\n    # The implementation builds on XCiT code https://github.com/facebookresearch/xcit\n    \"\"\"Adaptive Channel Self-Attention\n    Args:\n        dim (int): Number of input channels.\n        num_heads (int): Number of attention heads. Default: 6\n        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True\n        qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set.\n        attn_drop (float): Attention dropout rate. Default: 0.0\n        drop_path (float): Stochastic depth rate. Default: 0.0\n    \"\"\"\n\n    def __init__(\n        self,\n        dim,\n        num_heads=8,\n        qkv_bias=False,\n        qk_scale=None,\n        attn_drop=0.0,\n        proj_drop=0.0,\n    ):\n        super().__init__()\n        self.num_heads = num_heads\n        self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1))\n\n        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)\n        self.attn_drop = nn.Dropout(attn_drop)\n        self.proj = nn.Linear(dim, dim)\n        self.proj_drop = nn.Dropout(proj_drop)\n\n        self.dwconv = nn.Sequential(\n            nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1, groups=dim),\n            nn.BatchNorm2d(dim),\n            nn.GELU(),\n        )\n        self.channel_interaction = nn.Sequential(\n            nn.AdaptiveAvgPool2d(1),\n            nn.Conv2d(dim, dim // 8, kernel_size=1),\n            nn.BatchNorm2d(dim // 8),\n            nn.GELU(),\n            nn.Conv2d(dim // 8, dim, kernel_size=1),\n        )\n        self.spatial_interaction = nn.Sequential(\n            nn.Conv2d(dim, dim // 16, kernel_size=1),\n            nn.BatchNorm2d(dim // 16),\n            nn.GELU(),\n            nn.Conv2d(dim // 16, 1, kernel_size=1),\n        )\n\n    def forward(self, x, H, W):\n        \"\"\"\n        Input: x: (B, H*W, C), H, W\n        Output: x: (B, H*W, C)\n        \"\"\"\n        B, N, C = x.shape\n        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)\n        qkv = qkv.permute(2, 0, 3, 1, 4)\n        q, k, v = qkv[0], qkv[1], qkv[2]\n\n        q = q.transpose(-2, -1)\n        k = k.transpose(-2, -1)\n        v = v.transpose(-2, -1)\n\n        v_ = v.reshape(B, C, N).contiguous().view(B, C, H, W)\n\n        q = torch.nn.functional.normalize(q, dim=-1)\n        k = torch.nn.functional.normalize(k, dim=-1)\n\n        attn = (q @ k.transpose(-2, -1)) * self.temperature\n        attn = attn.softmax(dim=-1)\n        attn = self.attn_drop(attn)\n\n        # attention output\n        attened_x = (attn @ v).permute(0, 3, 1, 2).reshape(B, N, C)\n\n        # convolution output\n        conv_x = self.dwconv(v_)\n\n        # Adaptive Interaction Module (AIM)\n        # C-Map (before sigmoid)\n        attention_reshape = attened_x.transpose(-2, -1).contiguous().view(B, C, H, W)\n        channel_map = self.channel_interaction(attention_reshape)\n        # S-Map (before sigmoid)\n        spatial_map = (\n            self.spatial_interaction(conv_x)\n            .permute(0, 2, 3, 1)\n            .contiguous()\n            .view(B, N, 1)\n        )\n\n        # S-I\n        attened_x = attened_x * torch.sigmoid(spatial_map)\n        # C-I\n        conv_x = conv_x * torch.sigmoid(channel_map)\n        conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(B, N, C)\n\n        x = attened_x + conv_x\n\n        x = self.proj(x)\n        x = self.proj_drop(x)\n\n        return x\n\n\nclass DATB(nn.Module):\n    def __init__(\n        self,\n        dim,\n        num_heads,\n        reso=64,\n        split_size=[2, 4],\n        shift_size=[1, 2],\n        expansion_factor=4.0,\n        qkv_bias=False,\n        qk_scale=None,\n        drop=0.0,\n        attn_drop=0.0,\n        drop_path=0.0,\n        act_layer=nn.GELU,\n        norm_layer=nn.LayerNorm,\n        rg_idx=0,\n        b_idx=0,\n    ):\n        super().__init__()\n\n        self.norm1 = norm_layer(dim)\n\n        if b_idx % 2 == 0:\n            # DSTB\n            self.attn = Adaptive_Spatial_Attention(\n                dim,\n                num_heads=num_heads,\n                reso=reso,\n                split_size=split_size,\n                shift_size=shift_size,\n                qkv_bias=qkv_bias,\n                qk_scale=qk_scale,\n                drop=drop,\n                attn_drop=attn_drop,\n                rg_idx=rg_idx,\n                b_idx=b_idx,\n            )\n        else:\n            # DCTB\n            self.attn = Adaptive_Channel_Attention(\n                dim,\n                num_heads=num_heads,\n                qkv_bias=qkv_bias,\n                qk_scale=qk_scale,\n                attn_drop=attn_drop,\n                proj_drop=drop,\n            )\n        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()\n\n        ffn_hidden_dim = int(dim * expansion_factor)\n        self.ffn = SGFN(\n            in_features=dim,\n            hidden_features=ffn_hidden_dim,\n            out_features=dim,\n            act_layer=act_layer,\n        )\n        self.norm2 = norm_layer(dim)\n\n    def forward(self, x, x_size):\n        \"\"\"\n        Input: x: (B, H*W, C), x_size: (H, W)\n        Output: x: (B, H*W, C)\n        \"\"\"\n        H, W = x_size\n        x = x + self.drop_path(self.attn(self.norm1(x), H, W))\n        x = x + self.drop_path(self.ffn(self.norm2(x), H, W))\n\n        return x\n\n\nclass ResidualGroup(nn.Module):\n    \"\"\"ResidualGroup\n    Args:\n        dim (int): Number of input channels.\n        reso (int): Input resolution.\n        num_heads (int): Number of attention heads.\n        split_size (tuple(int)): Height and Width of spatial window.\n        expansion_factor (float): Ratio of ffn hidden dim to embedding dim.\n        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True\n        qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. Default: None\n        drop (float): Dropout rate. Default: 0\n        attn_drop(float): Attention dropout rate. Default: 0\n        drop_paths (float | None): Stochastic depth rate.\n        act_layer (nn.Module): Activation layer. Default: nn.GELU\n        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm\n        depth (int): Number of dual aggregation Transformer blocks in residual group.\n        use_chk (bool): Whether to use checkpointing to save memory.\n        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'\n    \"\"\"\n\n    def __init__(\n        self,\n        dim,\n        reso,\n        num_heads,\n        split_size=[2, 4],\n        expansion_factor=4.0,\n        qkv_bias=False,\n        qk_scale=None,\n        drop=0.0,\n        attn_drop=0.0,\n        drop_paths=None,\n        act_layer=nn.GELU,\n        norm_layer=nn.LayerNorm,\n        depth=2,\n        use_chk=False,\n        resi_connection=\"1conv\",\n        rg_idx=0,\n    ):\n        super().__init__()\n        self.use_chk = use_chk\n        self.reso = reso\n\n        self.blocks = nn.ModuleList(\n            [\n                DATB(\n                    dim=dim,\n                    num_heads=num_heads,\n                    reso=reso,\n                    split_size=split_size,\n                    shift_size=[split_size[0] // 2, split_size[1] // 2],\n                    expansion_factor=expansion_factor,\n                    qkv_bias=qkv_bias,\n                    qk_scale=qk_scale,\n                    drop=drop,\n                    attn_drop=attn_drop,\n                    drop_path=drop_paths[i],\n                    act_layer=act_layer,\n                    norm_layer=norm_layer,\n                    rg_idx=rg_idx,\n                    b_idx=i,\n                )\n                for i in range(depth)\n            ]\n        )\n\n        if resi_connection == \"1conv\":\n            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)\n        elif resi_connection == \"3conv\":\n            self.conv = nn.Sequential(\n                nn.Conv2d(dim, dim // 4, 3, 1, 1),\n                nn.LeakyReLU(negative_slope=0.2, inplace=True),\n                nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),\n                nn.LeakyReLU(negative_slope=0.2, inplace=True),\n                nn.Conv2d(dim // 4, dim, 3, 1, 1),\n            )\n\n    def forward(self, x, x_size):\n        \"\"\"\n        Input: x: (B, H*W, C), x_size: (H, W)\n        Output: x: (B, H*W, C)\n        \"\"\"\n        H, W = x_size\n        res = x\n        for blk in self.blocks:\n            if self.use_chk:\n                x = checkpoint.checkpoint(blk, x, x_size)\n            else:\n                x = blk(x, x_size)\n        x = rearrange(x, \"b (h w) c -> b c h w\", h=H, w=W)\n        x = self.conv(x)\n        x = rearrange(x, \"b c h w -> b (h w) c\")\n        x = res + x\n\n        return x\n\n\nclass Upsample(nn.Sequential):\n    \"\"\"Upsample module.\n    Args:\n        scale (int): Scale factor. Supported scales: 2^n and 3.\n        num_feat (int): Channel number of intermediate features.\n    \"\"\"\n\n    def __init__(self, scale, num_feat):\n        m = []\n        if (scale & (scale - 1)) == 0:  # scale = 2^n\n            for _ in range(int(math.log(scale, 2))):\n                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))\n                m.append(nn.PixelShuffle(2))\n        elif scale == 3:\n            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))\n            m.append(nn.PixelShuffle(3))\n        else:\n            raise ValueError(\n                f\"scale {scale} is not supported. \" \"Supported scales: 2^n and 3.\"\n            )\n        super(Upsample, self).__init__(*m)\n\n\nclass UpsampleOneStep(nn.Sequential):\n    \"\"\"UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)\n       Used in lightweight SR to save parameters.\n\n    Args:\n        scale (int): Scale factor. Supported scales: 2^n and 3.\n        num_feat (int): Channel number of intermediate features.\n\n    \"\"\"\n\n    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):\n        self.num_feat = num_feat\n        self.input_resolution = input_resolution\n        m = []\n        m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1))\n        m.append(nn.PixelShuffle(scale))\n        super(UpsampleOneStep, self).__init__(*m)\n\n    def flops(self):\n        h, w = self.input_resolution\n        flops = h * w * self.num_feat * 3 * 9\n        return flops\n\n\nclass DAT(nn.Module):\n    \"\"\"Dual Aggregation Transformer\n    Args:\n        img_size (int): Input image size. Default: 64\n        in_chans (int): Number of input image channels. Default: 3\n        embed_dim (int): Patch embedding dimension. Default: 180\n        depths (tuple(int)): Depth of each residual group (number of DATB in each RG).\n        split_size (tuple(int)): Height and Width of spatial window.\n        num_heads (tuple(int)): Number of attention heads in different residual groups.\n        expansion_factor (float): Ratio of ffn hidden dim to embedding dim. Default: 4\n        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True\n        qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. Default: None\n        drop_rate (float): Dropout rate. Default: 0\n        attn_drop_rate (float): Attention dropout rate. Default: 0\n        drop_path_rate (float): Stochastic depth rate. Default: 0.1\n        act_layer (nn.Module): Activation layer. Default: nn.GELU\n        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm\n        use_chk (bool): Whether to use checkpointing to save memory.\n        upscale: Upscale factor. 2/3/4 for image SR\n        img_range: Image range. 1. or 255.\n        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'\n    \"\"\"\n\n    def __init__(self, state_dict):\n        super().__init__()\n\n        # defaults\n        img_size = 64\n        in_chans = 3\n        embed_dim = 180\n        split_size = [2, 4]\n        depth = [2, 2, 2, 2]\n        num_heads = [2, 2, 2, 2]\n        expansion_factor = 4.0\n        qkv_bias = True\n        qk_scale = None\n        drop_rate = 0.0\n        attn_drop_rate = 0.0\n        drop_path_rate = 0.1\n        act_layer = nn.GELU\n        norm_layer = nn.LayerNorm\n        use_chk = False\n        upscale = 2\n        img_range = 1.0\n        resi_connection = \"1conv\"\n        upsampler = \"pixelshuffle\"\n\n        self.model_arch = \"DAT\"\n        self.sub_type = \"SR\"\n        self.state = state_dict\n\n        state_keys = state_dict.keys()\n        if \"conv_before_upsample.0.weight\" in state_keys:\n            if \"conv_up1.weight\" in state_keys:\n                upsampler = \"nearest+conv\"\n            else:\n                upsampler = \"pixelshuffle\"\n                supports_fp16 = False\n        elif \"upsample.0.weight\" in state_keys:\n            upsampler = \"pixelshuffledirect\"\n        else:\n            upsampler = \"\"\n\n        num_feat = (\n            state_dict.get(\"conv_before_upsample.0.weight\", None).shape[1]\n            if state_dict.get(\"conv_before_upsample.weight\", None)\n            else 64\n        )\n\n        num_in_ch = state_dict[\"conv_first.weight\"].shape[1]\n        in_chans = num_in_ch\n        if \"conv_last.weight\" in state_keys:\n            num_out_ch = state_dict[\"conv_last.weight\"].shape[0]\n        else:\n            num_out_ch = num_in_ch\n\n        upscale = 1\n        if upsampler == \"nearest+conv\":\n            upsample_keys = [\n                x for x in state_keys if \"conv_up\" in x and \"bias\" not in x\n            ]\n\n            for upsample_key in upsample_keys:\n                upscale *= 2\n        elif upsampler == \"pixelshuffle\":\n            upsample_keys = [\n                x\n                for x in state_keys\n                if \"upsample\" in x and \"conv\" not in x and \"bias\" not in x\n            ]\n            for upsample_key in upsample_keys:\n                shape = state_dict[upsample_key].shape[0]\n                upscale *= math.sqrt(shape // num_feat)\n            upscale = int(upscale)\n        elif upsampler == \"pixelshuffledirect\":\n            upscale = int(\n                math.sqrt(state_dict[\"upsample.0.bias\"].shape[0] // num_out_ch)\n            )\n\n        max_layer_num = 0\n        max_block_num = 0\n        for key in state_keys:\n            result = re.match(r\"layers.(\\d*).blocks.(\\d*).norm1.weight\", key)\n            if result:\n                layer_num, block_num = result.groups()\n                max_layer_num = max(max_layer_num, int(layer_num))\n                max_block_num = max(max_block_num, int(block_num))\n\n        depth = [max_block_num + 1 for _ in range(max_layer_num + 1)]\n\n        if \"layers.0.blocks.1.attn.temperature\" in state_keys:\n            num_heads_num = state_dict[\"layers.0.blocks.1.attn.temperature\"].shape[0]\n            num_heads = [num_heads_num for _ in range(max_layer_num + 1)]\n        else:\n            num_heads = depth\n\n        embed_dim = state_dict[\"conv_first.weight\"].shape[0]\n        expansion_factor = float(\n            state_dict[\"layers.0.blocks.0.ffn.fc1.weight\"].shape[0] / embed_dim\n        )\n\n        # TODO: could actually count the layers, but this should do\n        if \"layers.0.conv.4.weight\" in state_keys:\n            resi_connection = \"3conv\"\n        else:\n            resi_connection = \"1conv\"\n\n        if \"layers.0.blocks.2.attn.attn_mask_0\" in state_keys:\n            attn_mask_0_x, attn_mask_0_y, attn_mask_0_z = state_dict[\n                \"layers.0.blocks.2.attn.attn_mask_0\"\n            ].shape\n\n            img_size = int(math.sqrt(attn_mask_0_x * attn_mask_0_y))\n\n        if \"layers.0.blocks.0.attn.attns.0.rpe_biases\" in state_keys:\n            split_sizes = (\n                state_dict[\"layers.0.blocks.0.attn.attns.0.rpe_biases\"][-1] + 1\n            )\n            split_size = [int(x) for x in split_sizes]\n\n        self.in_nc = num_in_ch\n        self.out_nc = num_out_ch\n        self.num_feat = num_feat\n        self.embed_dim = embed_dim\n        self.num_heads = num_heads\n        self.depth = depth\n        self.scale = upscale\n        self.upsampler = upsampler\n        self.img_size = img_size\n        self.img_range = img_range\n        self.expansion_factor = expansion_factor\n        self.resi_connection = resi_connection\n        self.split_size = split_size\n\n        self.supports_fp16 = False  # Too much weirdness to support this at the moment\n        self.supports_bfp16 = True\n        self.min_size_restriction = 16\n\n        num_in_ch = in_chans\n        num_out_ch = in_chans\n        num_feat = 64\n        self.img_range = img_range\n        if in_chans == 3:\n            rgb_mean = (0.4488, 0.4371, 0.4040)\n            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)\n        else:\n            self.mean = torch.zeros(1, 1, 1, 1)\n        self.upscale = upscale\n        self.upsampler = upsampler\n\n        # ------------------------- 1, Shallow Feature Extraction ------------------------- #\n        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)\n\n        # ------------------------- 2, Deep Feature Extraction ------------------------- #\n        self.num_layers = len(depth)\n        self.use_chk = use_chk\n        self.num_features = (\n            self.embed_dim\n        ) = embed_dim  # num_features for consistency with other models\n        heads = num_heads\n\n        self.before_RG = nn.Sequential(\n            Rearrange(\"b c h w -> b (h w) c\"), nn.LayerNorm(embed_dim)\n        )\n\n        curr_dim = embed_dim\n        dpr = [\n            x.item() for x in torch.linspace(0, drop_path_rate, np.sum(depth))\n        ]  # stochastic depth decay rule\n\n        self.layers = nn.ModuleList()\n        for i in range(self.num_layers):\n            layer = ResidualGroup(\n                dim=embed_dim,\n                num_heads=heads[i],\n                reso=img_size,\n                split_size=split_size,\n                expansion_factor=expansion_factor,\n                qkv_bias=qkv_bias,\n                qk_scale=qk_scale,\n                drop=drop_rate,\n                attn_drop=attn_drop_rate,\n                drop_paths=dpr[sum(depth[:i]) : sum(depth[: i + 1])],\n                act_layer=act_layer,\n                norm_layer=norm_layer,\n                depth=depth[i],\n                use_chk=use_chk,\n                resi_connection=resi_connection,\n                rg_idx=i,\n            )\n            self.layers.append(layer)\n\n        self.norm = norm_layer(curr_dim)\n        # build the last conv layer in deep feature extraction\n        if resi_connection == \"1conv\":\n            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)\n        elif resi_connection == \"3conv\":\n            # to save parameters and memory\n            self.conv_after_body = nn.Sequential(\n                nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),\n                nn.LeakyReLU(negative_slope=0.2, inplace=True),\n                nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),\n                nn.LeakyReLU(negative_slope=0.2, inplace=True),\n                nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1),\n            )\n\n        # ------------------------- 3, Reconstruction ------------------------- #\n        if self.upsampler == \"pixelshuffle\":\n            # for classical SR\n            self.conv_before_upsample = nn.Sequential(\n                nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)\n            )\n            self.upsample = Upsample(upscale, num_feat)\n            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)\n        elif self.upsampler == \"pixelshuffledirect\":\n            # for lightweight SR (to save parameters)\n            self.upsample = UpsampleOneStep(\n                upscale, embed_dim, num_out_ch, (img_size, img_size)\n            )\n\n        self.apply(self._init_weights)\n        self.load_state_dict(state_dict, strict=True)\n\n    def _init_weights(self, m):\n        if isinstance(m, nn.Linear):\n            trunc_normal_(m.weight, std=0.02)\n            if isinstance(m, nn.Linear) and m.bias is not None:\n                nn.init.constant_(m.bias, 0)\n        elif isinstance(\n            m, (nn.LayerNorm, nn.BatchNorm2d, nn.GroupNorm, nn.InstanceNorm2d)\n        ):\n            nn.init.constant_(m.bias, 0)\n            nn.init.constant_(m.weight, 1.0)\n\n    def forward_features(self, x):\n        _, _, H, W = x.shape\n        x_size = [H, W]\n        x = self.before_RG(x)\n        for layer in self.layers:\n            x = layer(x, x_size)\n        x = self.norm(x)\n        x = rearrange(x, \"b (h w) c -> b c h w\", h=H, w=W)\n\n        return x\n\n    def forward(self, x):\n        \"\"\"\n        Input: x: (B, C, H, W)\n        \"\"\"\n        self.mean = self.mean.type_as(x)\n        x = (x - self.mean) * self.img_range\n\n        if self.upsampler == \"pixelshuffle\":\n            # for image SR\n            x = self.conv_first(x)\n            x = self.conv_after_body(self.forward_features(x)) + x\n            x = self.conv_before_upsample(x)\n            x = self.conv_last(self.upsample(x))\n        elif self.upsampler == \"pixelshuffledirect\":\n            # for lightweight SR\n            x = self.conv_first(x)\n            x = self.conv_after_body(self.forward_features(x)) + x\n            x = self.upsample(x)\n\n        x = x / self.img_range + self.mean\n        return x\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/HAT.py",
    "content": "# pylint: skip-file\n# HAT from https://github.com/XPixelGroup/HAT/blob/main/hat/archs/hat_arch.py\nimport math\nimport re\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom einops import rearrange\n\nfrom .timm.helpers import to_2tuple\nfrom .timm.weight_init import trunc_normal_\n\n\ndef drop_path(x, drop_prob: float = 0.0, training: bool = False):\n    \"\"\"Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).\n    From: https://github.com/huggingface/pytorch-image-models/blob/main/timm/layers/drop.py\n    \"\"\"\n    if drop_prob == 0.0 or not training:\n        return x\n    keep_prob = 1 - drop_prob\n    shape = (x.shape[0],) + (1,) * (\n        x.ndim - 1\n    )  # work with diff dim tensors, not just 2D ConvNets\n    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)\n    random_tensor.floor_()  # binarize\n    output = x.div(keep_prob) * random_tensor\n    return output\n\n\nclass DropPath(nn.Module):\n    \"\"\"Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).\n    From: https://github.com/huggingface/pytorch-image-models/blob/main/timm/layers/drop.py\n    \"\"\"\n\n    def __init__(self, drop_prob=None):\n        super(DropPath, self).__init__()\n        self.drop_prob = drop_prob\n\n    def forward(self, x):\n        return drop_path(x, self.drop_prob, self.training)  # type: ignore\n\n\nclass ChannelAttention(nn.Module):\n    \"\"\"Channel attention used in RCAN.\n    Args:\n        num_feat (int): Channel number of intermediate features.\n        squeeze_factor (int): Channel squeeze factor. Default: 16.\n    \"\"\"\n\n    def __init__(self, num_feat, squeeze_factor=16):\n        super(ChannelAttention, self).__init__()\n        self.attention = nn.Sequential(\n            nn.AdaptiveAvgPool2d(1),\n            nn.Conv2d(num_feat, num_feat // squeeze_factor, 1, padding=0),\n            nn.ReLU(inplace=True),\n            nn.Conv2d(num_feat // squeeze_factor, num_feat, 1, padding=0),\n            nn.Sigmoid(),\n        )\n\n    def forward(self, x):\n        y = self.attention(x)\n        return x * y\n\n\nclass CAB(nn.Module):\n    def __init__(self, num_feat, compress_ratio=3, squeeze_factor=30):\n        super(CAB, self).__init__()\n\n        self.cab = nn.Sequential(\n            nn.Conv2d(num_feat, num_feat // compress_ratio, 3, 1, 1),\n            nn.GELU(),\n            nn.Conv2d(num_feat // compress_ratio, num_feat, 3, 1, 1),\n            ChannelAttention(num_feat, squeeze_factor),\n        )\n\n    def forward(self, x):\n        return self.cab(x)\n\n\nclass Mlp(nn.Module):\n    def __init__(\n        self,\n        in_features,\n        hidden_features=None,\n        out_features=None,\n        act_layer=nn.GELU,\n        drop=0.0,\n    ):\n        super().__init__()\n        out_features = out_features or in_features\n        hidden_features = hidden_features or in_features\n        self.fc1 = nn.Linear(in_features, hidden_features)\n        self.act = act_layer()\n        self.fc2 = nn.Linear(hidden_features, out_features)\n        self.drop = nn.Dropout(drop)\n\n    def forward(self, x):\n        x = self.fc1(x)\n        x = self.act(x)\n        x = self.drop(x)\n        x = self.fc2(x)\n        x = self.drop(x)\n        return x\n\n\ndef window_partition(x, window_size):\n    \"\"\"\n    Args:\n        x: (b, h, w, c)\n        window_size (int): window size\n    Returns:\n        windows: (num_windows*b, window_size, window_size, c)\n    \"\"\"\n    b, h, w, c = x.shape\n    x = x.view(b, h // window_size, window_size, w // window_size, window_size, c)\n    windows = (\n        x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, c)\n    )\n    return windows\n\n\ndef window_reverse(windows, window_size, h, w):\n    \"\"\"\n    Args:\n        windows: (num_windows*b, window_size, window_size, c)\n        window_size (int): Window size\n        h (int): Height of image\n        w (int): Width of image\n    Returns:\n        x: (b, h, w, c)\n    \"\"\"\n    b = int(windows.shape[0] / (h * w / window_size / window_size))\n    x = windows.view(\n        b, h // window_size, w // window_size, window_size, window_size, -1\n    )\n    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(b, h, w, -1)\n    return x\n\n\nclass WindowAttention(nn.Module):\n    r\"\"\"Window based multi-head self attention (W-MSA) module with relative position bias.\n    It supports both of shifted and non-shifted window.\n    Args:\n        dim (int): Number of input channels.\n        window_size (tuple[int]): The height and width of the window.\n        num_heads (int): Number of attention heads.\n        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True\n        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set\n        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0\n        proj_drop (float, optional): Dropout ratio of output. Default: 0.0\n    \"\"\"\n\n    def __init__(\n        self,\n        dim,\n        window_size,\n        num_heads,\n        qkv_bias=True,\n        qk_scale=None,\n        attn_drop=0.0,\n        proj_drop=0.0,\n    ):\n        super().__init__()\n        self.dim = dim\n        self.window_size = window_size  # Wh, Ww\n        self.num_heads = num_heads\n        head_dim = dim // num_heads\n        self.scale = qk_scale or head_dim**-0.5\n\n        # define a parameter table of relative position bias\n        self.relative_position_bias_table = nn.Parameter(  # type: ignore\n            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)\n        )  # 2*Wh-1 * 2*Ww-1, nH\n\n        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)\n        self.attn_drop = nn.Dropout(attn_drop)\n        self.proj = nn.Linear(dim, dim)\n\n        self.proj_drop = nn.Dropout(proj_drop)\n\n        trunc_normal_(self.relative_position_bias_table, std=0.02)\n        self.softmax = nn.Softmax(dim=-1)\n\n    def forward(self, x, rpi, mask=None):\n        \"\"\"\n        Args:\n            x: input features with shape of (num_windows*b, n, c)\n            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None\n        \"\"\"\n        b_, n, c = x.shape\n        qkv = (\n            self.qkv(x)\n            .reshape(b_, n, 3, self.num_heads, c // self.num_heads)\n            .permute(2, 0, 3, 1, 4)\n        )\n        q, k, v = (\n            qkv[0],\n            qkv[1],\n            qkv[2],\n        )  # make torchscript happy (cannot use tensor as tuple)\n\n        q = q * self.scale\n        attn = q @ k.transpose(-2, -1)\n\n        relative_position_bias = self.relative_position_bias_table[rpi.view(-1)].view(\n            self.window_size[0] * self.window_size[1],\n            self.window_size[0] * self.window_size[1],\n            -1,\n        )  # Wh*Ww,Wh*Ww,nH\n        relative_position_bias = relative_position_bias.permute(\n            2, 0, 1\n        ).contiguous()  # nH, Wh*Ww, Wh*Ww\n        attn = attn + relative_position_bias.unsqueeze(0)\n\n        if mask is not None:\n            nw = mask.shape[0]\n            attn = attn.view(b_ // nw, nw, self.num_heads, n, n) + mask.unsqueeze(\n                1\n            ).unsqueeze(0)\n            attn = attn.view(-1, self.num_heads, n, n)\n            attn = self.softmax(attn)\n        else:\n            attn = self.softmax(attn)\n\n        attn = self.attn_drop(attn)\n\n        x = (attn @ v).transpose(1, 2).reshape(b_, n, c)\n        x = self.proj(x)\n        x = self.proj_drop(x)\n        return x\n\n\nclass HAB(nn.Module):\n    r\"\"\"Hybrid Attention Block.\n    Args:\n        dim (int): Number of input channels.\n        input_resolution (tuple[int]): Input resolution.\n        num_heads (int): Number of attention heads.\n        window_size (int): Window size.\n        shift_size (int): Shift size for SW-MSA.\n        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.\n        drop (float, optional): Dropout rate. Default: 0.0\n        attn_drop (float, optional): Attention dropout rate. Default: 0.0\n        drop_path (float, optional): Stochastic depth rate. Default: 0.0\n        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU\n        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm\n    \"\"\"\n\n    def __init__(\n        self,\n        dim,\n        input_resolution,\n        num_heads,\n        window_size=7,\n        shift_size=0,\n        compress_ratio=3,\n        squeeze_factor=30,\n        conv_scale=0.01,\n        mlp_ratio=4.0,\n        qkv_bias=True,\n        qk_scale=None,\n        drop=0.0,\n        attn_drop=0.0,\n        drop_path=0.0,\n        act_layer=nn.GELU,\n        norm_layer=nn.LayerNorm,\n    ):\n        super().__init__()\n        self.dim = dim\n        self.input_resolution = input_resolution\n        self.num_heads = num_heads\n        self.window_size = window_size\n        self.shift_size = shift_size\n        self.mlp_ratio = mlp_ratio\n        if min(self.input_resolution) <= self.window_size:\n            # if window size is larger than input resolution, we don't partition windows\n            self.shift_size = 0\n            self.window_size = min(self.input_resolution)\n        assert (\n            0 <= self.shift_size < self.window_size\n        ), \"shift_size must in 0-window_size\"\n\n        self.norm1 = norm_layer(dim)\n        self.attn = WindowAttention(\n            dim,\n            window_size=to_2tuple(self.window_size),\n            num_heads=num_heads,\n            qkv_bias=qkv_bias,\n            qk_scale=qk_scale,\n            attn_drop=attn_drop,\n            proj_drop=drop,\n        )\n\n        self.conv_scale = conv_scale\n        self.conv_block = CAB(\n            num_feat=dim, compress_ratio=compress_ratio, squeeze_factor=squeeze_factor\n        )\n\n        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()\n        self.norm2 = norm_layer(dim)\n        mlp_hidden_dim = int(dim * mlp_ratio)\n        self.mlp = Mlp(\n            in_features=dim,\n            hidden_features=mlp_hidden_dim,\n            act_layer=act_layer,\n            drop=drop,\n        )\n\n    def forward(self, x, x_size, rpi_sa, attn_mask):\n        h, w = x_size\n        b, _, c = x.shape\n        # assert seq_len == h * w, \"input feature has wrong size\"\n\n        shortcut = x\n        x = self.norm1(x)\n        x = x.view(b, h, w, c)\n\n        # Conv_X\n        conv_x = self.conv_block(x.permute(0, 3, 1, 2))\n        conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(b, h * w, c)\n\n        # cyclic shift\n        if self.shift_size > 0:\n            shifted_x = torch.roll(\n                x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)\n            )\n            attn_mask = attn_mask\n        else:\n            shifted_x = x\n            attn_mask = None\n\n        # partition windows\n        x_windows = window_partition(\n            shifted_x, self.window_size\n        )  # nw*b, window_size, window_size, c\n        x_windows = x_windows.view(\n            -1, self.window_size * self.window_size, c\n        )  # nw*b, window_size*window_size, c\n\n        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size\n        attn_windows = self.attn(x_windows, rpi=rpi_sa, mask=attn_mask)\n\n        # merge windows\n        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, c)\n        shifted_x = window_reverse(attn_windows, self.window_size, h, w)  # b h' w' c\n\n        # reverse cyclic shift\n        if self.shift_size > 0:\n            attn_x = torch.roll(\n                shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)\n            )\n        else:\n            attn_x = shifted_x\n        attn_x = attn_x.view(b, h * w, c)\n\n        # FFN\n        x = shortcut + self.drop_path(attn_x) + conv_x * self.conv_scale\n        x = x + self.drop_path(self.mlp(self.norm2(x)))\n\n        return x\n\n\nclass PatchMerging(nn.Module):\n    r\"\"\"Patch Merging Layer.\n    Args:\n        input_resolution (tuple[int]): Resolution of input feature.\n        dim (int): Number of input channels.\n        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm\n    \"\"\"\n\n    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):\n        super().__init__()\n        self.input_resolution = input_resolution\n        self.dim = dim\n        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)\n        self.norm = norm_layer(4 * dim)\n\n    def forward(self, x):\n        \"\"\"\n        x: b, h*w, c\n        \"\"\"\n        h, w = self.input_resolution\n        b, seq_len, c = x.shape\n        assert seq_len == h * w, \"input feature has wrong size\"\n        assert h % 2 == 0 and w % 2 == 0, f\"x size ({h}*{w}) are not even.\"\n\n        x = x.view(b, h, w, c)\n\n        x0 = x[:, 0::2, 0::2, :]  # b h/2 w/2 c\n        x1 = x[:, 1::2, 0::2, :]  # b h/2 w/2 c\n        x2 = x[:, 0::2, 1::2, :]  # b h/2 w/2 c\n        x3 = x[:, 1::2, 1::2, :]  # b h/2 w/2 c\n        x = torch.cat([x0, x1, x2, x3], -1)  # b h/2 w/2 4*c\n        x = x.view(b, -1, 4 * c)  # b h/2*w/2 4*c\n\n        x = self.norm(x)\n        x = self.reduction(x)\n\n        return x\n\n\nclass OCAB(nn.Module):\n    # overlapping cross-attention block\n\n    def __init__(\n        self,\n        dim,\n        input_resolution,\n        window_size,\n        overlap_ratio,\n        num_heads,\n        qkv_bias=True,\n        qk_scale=None,\n        mlp_ratio=2,\n        norm_layer=nn.LayerNorm,\n    ):\n        super().__init__()\n        self.dim = dim\n        self.input_resolution = input_resolution\n        self.window_size = window_size\n        self.num_heads = num_heads\n        head_dim = dim // num_heads\n        self.scale = qk_scale or head_dim**-0.5\n        self.overlap_win_size = int(window_size * overlap_ratio) + window_size\n\n        self.norm1 = norm_layer(dim)\n        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)\n        self.unfold = nn.Unfold(\n            kernel_size=(self.overlap_win_size, self.overlap_win_size),\n            stride=window_size,\n            padding=(self.overlap_win_size - window_size) // 2,\n        )\n\n        # define a parameter table of relative position bias\n        self.relative_position_bias_table = nn.Parameter(  # type: ignore\n            torch.zeros(\n                (window_size + self.overlap_win_size - 1)\n                * (window_size + self.overlap_win_size - 1),\n                num_heads,\n            )\n        )  # 2*Wh-1 * 2*Ww-1, nH\n\n        trunc_normal_(self.relative_position_bias_table, std=0.02)\n        self.softmax = nn.Softmax(dim=-1)\n\n        self.proj = nn.Linear(dim, dim)\n\n        self.norm2 = norm_layer(dim)\n        mlp_hidden_dim = int(dim * mlp_ratio)\n        self.mlp = Mlp(\n            in_features=dim, hidden_features=mlp_hidden_dim, act_layer=nn.GELU\n        )\n\n    def forward(self, x, x_size, rpi):\n        h, w = x_size\n        b, _, c = x.shape\n\n        shortcut = x\n        x = self.norm1(x)\n        x = x.view(b, h, w, c)\n\n        qkv = self.qkv(x).reshape(b, h, w, 3, c).permute(3, 0, 4, 1, 2)  # 3, b, c, h, w\n        q = qkv[0].permute(0, 2, 3, 1)  # b, h, w, c\n        kv = torch.cat((qkv[1], qkv[2]), dim=1)  # b, 2*c, h, w\n\n        # partition windows\n        q_windows = window_partition(\n            q, self.window_size\n        )  # nw*b, window_size, window_size, c\n        q_windows = q_windows.view(\n            -1, self.window_size * self.window_size, c\n        )  # nw*b, window_size*window_size, c\n\n        kv_windows = self.unfold(kv)  # b, c*w*w, nw\n        kv_windows = rearrange(\n            kv_windows,\n            \"b (nc ch owh oww) nw -> nc (b nw) (owh oww) ch\",\n            nc=2,\n            ch=c,\n            owh=self.overlap_win_size,\n            oww=self.overlap_win_size,\n        ).contiguous()  # 2, nw*b, ow*ow, c\n        # Do the above rearrangement without the rearrange function\n        # kv_windows = kv_windows.view(\n        #     2, b, self.overlap_win_size, self.overlap_win_size, c, -1\n        # )\n        # kv_windows = kv_windows.permute(0, 5, 1, 2, 3, 4).contiguous()\n        # kv_windows = kv_windows.view(\n        #     2, -1, self.overlap_win_size * self.overlap_win_size, c\n        # )\n\n        k_windows, v_windows = kv_windows[0], kv_windows[1]  # nw*b, ow*ow, c\n\n        b_, nq, _ = q_windows.shape\n        _, n, _ = k_windows.shape\n        d = self.dim // self.num_heads\n        q = q_windows.reshape(b_, nq, self.num_heads, d).permute(\n            0, 2, 1, 3\n        )  # nw*b, nH, nq, d\n        k = k_windows.reshape(b_, n, self.num_heads, d).permute(\n            0, 2, 1, 3\n        )  # nw*b, nH, n, d\n        v = v_windows.reshape(b_, n, self.num_heads, d).permute(\n            0, 2, 1, 3\n        )  # nw*b, nH, n, d\n\n        q = q * self.scale\n        attn = q @ k.transpose(-2, -1)\n\n        relative_position_bias = self.relative_position_bias_table[rpi.view(-1)].view(\n            self.window_size * self.window_size,\n            self.overlap_win_size * self.overlap_win_size,\n            -1,\n        )  # ws*ws, wse*wse, nH\n        relative_position_bias = relative_position_bias.permute(\n            2, 0, 1\n        ).contiguous()  # nH, ws*ws, wse*wse\n        attn = attn + relative_position_bias.unsqueeze(0)\n\n        attn = self.softmax(attn)\n        attn_windows = (attn @ v).transpose(1, 2).reshape(b_, nq, self.dim)\n\n        # merge windows\n        attn_windows = attn_windows.view(\n            -1, self.window_size, self.window_size, self.dim\n        )\n        x = window_reverse(attn_windows, self.window_size, h, w)  # b h w c\n        x = x.view(b, h * w, self.dim)\n\n        x = self.proj(x) + shortcut\n\n        x = x + self.mlp(self.norm2(x))\n        return x\n\n\nclass AttenBlocks(nn.Module):\n    \"\"\"A series of attention blocks for one RHAG.\n    Args:\n        dim (int): Number of input channels.\n        input_resolution (tuple[int]): Input resolution.\n        depth (int): Number of blocks.\n        num_heads (int): Number of attention heads.\n        window_size (int): Local window size.\n        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.\n        drop (float, optional): Dropout rate. Default: 0.0\n        attn_drop (float, optional): Attention dropout rate. Default: 0.0\n        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0\n        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm\n        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None\n        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.\n    \"\"\"\n\n    def __init__(\n        self,\n        dim,\n        input_resolution,\n        depth,\n        num_heads,\n        window_size,\n        compress_ratio,\n        squeeze_factor,\n        conv_scale,\n        overlap_ratio,\n        mlp_ratio=4.0,\n        qkv_bias=True,\n        qk_scale=None,\n        drop=0.0,\n        attn_drop=0.0,\n        drop_path=0.0,\n        norm_layer=nn.LayerNorm,\n        downsample=None,\n        use_checkpoint=False,\n    ):\n        super().__init__()\n        self.dim = dim\n        self.input_resolution = input_resolution\n        self.depth = depth\n        self.use_checkpoint = use_checkpoint\n\n        # build blocks\n        self.blocks = nn.ModuleList(\n            [\n                HAB(\n                    dim=dim,\n                    input_resolution=input_resolution,\n                    num_heads=num_heads,\n                    window_size=window_size,\n                    shift_size=0 if (i % 2 == 0) else window_size // 2,\n                    compress_ratio=compress_ratio,\n                    squeeze_factor=squeeze_factor,\n                    conv_scale=conv_scale,\n                    mlp_ratio=mlp_ratio,\n                    qkv_bias=qkv_bias,\n                    qk_scale=qk_scale,\n                    drop=drop,\n                    attn_drop=attn_drop,\n                    drop_path=drop_path[i]\n                    if isinstance(drop_path, list)\n                    else drop_path,\n                    norm_layer=norm_layer,\n                )\n                for i in range(depth)\n            ]\n        )\n\n        # OCAB\n        self.overlap_attn = OCAB(\n            dim=dim,\n            input_resolution=input_resolution,\n            window_size=window_size,\n            overlap_ratio=overlap_ratio,\n            num_heads=num_heads,\n            qkv_bias=qkv_bias,\n            qk_scale=qk_scale,\n            mlp_ratio=mlp_ratio,  # type: ignore\n            norm_layer=norm_layer,\n        )\n\n        # patch merging layer\n        if downsample is not None:\n            self.downsample = downsample(\n                input_resolution, dim=dim, norm_layer=norm_layer\n            )\n        else:\n            self.downsample = None\n\n    def forward(self, x, x_size, params):\n        for blk in self.blocks:\n            x = blk(x, x_size, params[\"rpi_sa\"], params[\"attn_mask\"])\n\n        x = self.overlap_attn(x, x_size, params[\"rpi_oca\"])\n\n        if self.downsample is not None:\n            x = self.downsample(x)\n        return x\n\n\nclass RHAG(nn.Module):\n    \"\"\"Residual Hybrid Attention Group (RHAG).\n    Args:\n        dim (int): Number of input channels.\n        input_resolution (tuple[int]): Input resolution.\n        depth (int): Number of blocks.\n        num_heads (int): Number of attention heads.\n        window_size (int): Local window size.\n        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.\n        drop (float, optional): Dropout rate. Default: 0.0\n        attn_drop (float, optional): Attention dropout rate. Default: 0.0\n        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0\n        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm\n        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None\n        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.\n        img_size: Input image size.\n        patch_size: Patch size.\n        resi_connection: The convolutional block before residual connection.\n    \"\"\"\n\n    def __init__(\n        self,\n        dim,\n        input_resolution,\n        depth,\n        num_heads,\n        window_size,\n        compress_ratio,\n        squeeze_factor,\n        conv_scale,\n        overlap_ratio,\n        mlp_ratio=4.0,\n        qkv_bias=True,\n        qk_scale=None,\n        drop=0.0,\n        attn_drop=0.0,\n        drop_path=0.0,\n        norm_layer=nn.LayerNorm,\n        downsample=None,\n        use_checkpoint=False,\n        img_size=224,\n        patch_size=4,\n        resi_connection=\"1conv\",\n    ):\n        super(RHAG, self).__init__()\n\n        self.dim = dim\n        self.input_resolution = input_resolution\n\n        self.residual_group = AttenBlocks(\n            dim=dim,\n            input_resolution=input_resolution,\n            depth=depth,\n            num_heads=num_heads,\n            window_size=window_size,\n            compress_ratio=compress_ratio,\n            squeeze_factor=squeeze_factor,\n            conv_scale=conv_scale,\n            overlap_ratio=overlap_ratio,\n            mlp_ratio=mlp_ratio,\n            qkv_bias=qkv_bias,\n            qk_scale=qk_scale,\n            drop=drop,\n            attn_drop=attn_drop,\n            drop_path=drop_path,\n            norm_layer=norm_layer,\n            downsample=downsample,\n            use_checkpoint=use_checkpoint,\n        )\n\n        if resi_connection == \"1conv\":\n            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)\n        elif resi_connection == \"identity\":\n            self.conv = nn.Identity()\n\n        self.patch_embed = PatchEmbed(\n            img_size=img_size,\n            patch_size=patch_size,\n            in_chans=0,\n            embed_dim=dim,\n            norm_layer=None,\n        )\n\n        self.patch_unembed = PatchUnEmbed(\n            img_size=img_size,\n            patch_size=patch_size,\n            in_chans=0,\n            embed_dim=dim,\n            norm_layer=None,\n        )\n\n    def forward(self, x, x_size, params):\n        return (\n            self.patch_embed(\n                self.conv(\n                    self.patch_unembed(self.residual_group(x, x_size, params), x_size)\n                )\n            )\n            + x\n        )\n\n\nclass PatchEmbed(nn.Module):\n    r\"\"\"Image to Patch Embedding\n    Args:\n        img_size (int): Image size.  Default: 224.\n        patch_size (int): Patch token size. Default: 4.\n        in_chans (int): Number of input image channels. Default: 3.\n        embed_dim (int): Number of linear projection output channels. Default: 96.\n        norm_layer (nn.Module, optional): Normalization layer. Default: None\n    \"\"\"\n\n    def __init__(\n        self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None\n    ):\n        super().__init__()\n        img_size = to_2tuple(img_size)\n        patch_size = to_2tuple(patch_size)\n        patches_resolution = [\n            img_size[0] // patch_size[0],  # type: ignore\n            img_size[1] // patch_size[1],  # type: ignore\n        ]\n        self.img_size = img_size\n        self.patch_size = patch_size\n        self.patches_resolution = patches_resolution\n        self.num_patches = patches_resolution[0] * patches_resolution[1]\n\n        self.in_chans = in_chans\n        self.embed_dim = embed_dim\n\n        if norm_layer is not None:\n            self.norm = norm_layer(embed_dim)\n        else:\n            self.norm = None\n\n    def forward(self, x):\n        x = x.flatten(2).transpose(1, 2)  # b Ph*Pw c\n        if self.norm is not None:\n            x = self.norm(x)\n        return x\n\n\nclass PatchUnEmbed(nn.Module):\n    r\"\"\"Image to Patch Unembedding\n    Args:\n        img_size (int): Image size.  Default: 224.\n        patch_size (int): Patch token size. Default: 4.\n        in_chans (int): Number of input image channels. Default: 3.\n        embed_dim (int): Number of linear projection output channels. Default: 96.\n        norm_layer (nn.Module, optional): Normalization layer. Default: None\n    \"\"\"\n\n    def __init__(\n        self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None\n    ):\n        super().__init__()\n        img_size = to_2tuple(img_size)\n        patch_size = to_2tuple(patch_size)\n        patches_resolution = [\n            img_size[0] // patch_size[0],  # type: ignore\n            img_size[1] // patch_size[1],  # type: ignore\n        ]\n        self.img_size = img_size\n        self.patch_size = patch_size\n        self.patches_resolution = patches_resolution\n        self.num_patches = patches_resolution[0] * patches_resolution[1]\n\n        self.in_chans = in_chans\n        self.embed_dim = embed_dim\n\n    def forward(self, x, x_size):\n        x = (\n            x.transpose(1, 2)\n            .contiguous()\n            .view(x.shape[0], self.embed_dim, x_size[0], x_size[1])\n        )  # b Ph*Pw c\n        return x\n\n\nclass Upsample(nn.Sequential):\n    \"\"\"Upsample module.\n    Args:\n        scale (int): Scale factor. Supported scales: 2^n and 3.\n        num_feat (int): Channel number of intermediate features.\n    \"\"\"\n\n    def __init__(self, scale, num_feat):\n        m = []\n        if (scale & (scale - 1)) == 0:  # scale = 2^n\n            for _ in range(int(math.log(scale, 2))):\n                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))\n                m.append(nn.PixelShuffle(2))\n        elif scale == 3:\n            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))\n            m.append(nn.PixelShuffle(3))\n        else:\n            raise ValueError(\n                f\"scale {scale} is not supported. \" \"Supported scales: 2^n and 3.\"\n            )\n        super(Upsample, self).__init__(*m)\n\n\nclass HAT(nn.Module):\n    r\"\"\"Hybrid Attention Transformer\n        A PyTorch implementation of : `Activating More Pixels in Image Super-Resolution Transformer`.\n        Some codes are based on SwinIR.\n    Args:\n        img_size (int | tuple(int)): Input image size. Default 64\n        patch_size (int | tuple(int)): Patch size. Default: 1\n        in_chans (int): Number of input image channels. Default: 3\n        embed_dim (int): Patch embedding dimension. Default: 96\n        depths (tuple(int)): Depth of each Swin Transformer layer.\n        num_heads (tuple(int)): Number of attention heads in different layers.\n        window_size (int): Window size. Default: 7\n        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4\n        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True\n        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None\n        drop_rate (float): Dropout rate. Default: 0\n        attn_drop_rate (float): Attention dropout rate. Default: 0\n        drop_path_rate (float): Stochastic depth rate. Default: 0.1\n        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.\n        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False\n        patch_norm (bool): If True, add normalization after patch embedding. Default: True\n        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False\n        upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction\n        img_range: Image range. 1. or 255.\n        upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None\n        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'\n    \"\"\"\n\n    def __init__(\n        self,\n        state_dict,\n        **kwargs,\n    ):\n        super(HAT, self).__init__()\n\n        # Defaults\n        img_size = 64\n        patch_size = 1\n        in_chans = 3\n        embed_dim = 96\n        depths = (6, 6, 6, 6)\n        num_heads = (6, 6, 6, 6)\n        window_size = 7\n        compress_ratio = 3\n        squeeze_factor = 30\n        conv_scale = 0.01\n        overlap_ratio = 0.5\n        mlp_ratio = 4.0\n        qkv_bias = True\n        qk_scale = None\n        drop_rate = 0.0\n        attn_drop_rate = 0.0\n        drop_path_rate = 0.1\n        norm_layer = nn.LayerNorm\n        ape = False\n        patch_norm = True\n        use_checkpoint = False\n        upscale = 2\n        img_range = 1.0\n        upsampler = \"\"\n        resi_connection = \"1conv\"\n\n        self.state = state_dict\n        self.model_arch = \"HAT\"\n        self.sub_type = \"SR\"\n        self.supports_fp16 = False\n        self.support_bf16 = True\n        self.min_size_restriction = 16\n\n        state_keys = list(state_dict.keys())\n\n        num_feat = state_dict[\"conv_last.weight\"].shape[1]\n        in_chans = state_dict[\"conv_first.weight\"].shape[1]\n        num_out_ch = state_dict[\"conv_last.weight\"].shape[0]\n        embed_dim = state_dict[\"conv_first.weight\"].shape[0]\n\n        if \"conv_before_upsample.0.weight\" in state_keys:\n            if \"conv_up1.weight\" in state_keys:\n                upsampler = \"nearest+conv\"\n            else:\n                upsampler = \"pixelshuffle\"\n                supports_fp16 = False\n        elif \"upsample.0.weight\" in state_keys:\n            upsampler = \"pixelshuffledirect\"\n        else:\n            upsampler = \"\"\n        upscale = 1\n        if upsampler == \"nearest+conv\":\n            upsample_keys = [\n                x for x in state_keys if \"conv_up\" in x and \"bias\" not in x\n            ]\n\n            for upsample_key in upsample_keys:\n                upscale *= 2\n        elif upsampler == \"pixelshuffle\":\n            upsample_keys = [\n                x\n                for x in state_keys\n                if \"upsample\" in x and \"conv\" not in x and \"bias\" not in x\n            ]\n            for upsample_key in upsample_keys:\n                shape = self.state[upsample_key].shape[0]\n                upscale *= math.sqrt(shape // num_feat)\n            upscale = int(upscale)\n        elif upsampler == \"pixelshuffledirect\":\n            upscale = int(\n                math.sqrt(self.state[\"upsample.0.bias\"].shape[0] // num_out_ch)\n            )\n\n        max_layer_num = 0\n        max_block_num = 0\n        for key in state_keys:\n            result = re.match(\n                r\"layers.(\\d*).residual_group.blocks.(\\d*).conv_block.cab.0.weight\", key\n            )\n            if result:\n                layer_num, block_num = result.groups()\n                max_layer_num = max(max_layer_num, int(layer_num))\n                max_block_num = max(max_block_num, int(block_num))\n\n        depths = [max_block_num + 1 for _ in range(max_layer_num + 1)]\n\n        if (\n            \"layers.0.residual_group.blocks.0.attn.relative_position_bias_table\"\n            in state_keys\n        ):\n            num_heads_num = self.state[\n                \"layers.0.residual_group.blocks.0.attn.relative_position_bias_table\"\n            ].shape[-1]\n            num_heads = [num_heads_num for _ in range(max_layer_num + 1)]\n        else:\n            num_heads = depths\n\n        mlp_ratio = float(\n            self.state[\"layers.0.residual_group.blocks.0.mlp.fc1.bias\"].shape[0]\n            / embed_dim\n        )\n\n        # TODO: could actually count the layers, but this should do\n        if \"layers.0.conv.4.weight\" in state_keys:\n            resi_connection = \"3conv\"\n        else:\n            resi_connection = \"1conv\"\n\n        window_size = int(math.sqrt(self.state[\"relative_position_index_SA\"].shape[0]))\n\n        # Not sure if this is needed or used at all anywhere in HAT's config\n        if \"layers.0.residual_group.blocks.1.attn_mask\" in state_keys:\n            img_size = int(\n                math.sqrt(\n                    self.state[\"layers.0.residual_group.blocks.1.attn_mask\"].shape[0]\n                )\n                * window_size\n            )\n\n        self.window_size = window_size\n        self.shift_size = window_size // 2\n        self.overlap_ratio = overlap_ratio\n\n        self.in_nc = in_chans\n        self.out_nc = num_out_ch\n        self.num_feat = num_feat\n        self.embed_dim = embed_dim\n        self.num_heads = num_heads\n        self.depths = depths\n        self.window_size = window_size\n        self.mlp_ratio = mlp_ratio\n        self.scale = upscale\n        self.upsampler = upsampler\n        self.img_size = img_size\n        self.img_range = img_range\n        self.resi_connection = resi_connection\n\n        num_in_ch = in_chans\n        # num_out_ch = in_chans\n        # num_feat = 64\n        self.img_range = img_range\n        if in_chans == 3:\n            rgb_mean = (0.4488, 0.4371, 0.4040)\n            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)\n        else:\n            self.mean = torch.zeros(1, 1, 1, 1)\n        self.upscale = upscale\n        self.upsampler = upsampler\n\n        # relative position index\n        relative_position_index_SA = self.calculate_rpi_sa()\n        relative_position_index_OCA = self.calculate_rpi_oca()\n        self.register_buffer(\"relative_position_index_SA\", relative_position_index_SA)\n        self.register_buffer(\"relative_position_index_OCA\", relative_position_index_OCA)\n\n        # ------------------------- 1, shallow feature extraction ------------------------- #\n        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)\n\n        # ------------------------- 2, deep feature extraction ------------------------- #\n        self.num_layers = len(depths)\n        self.embed_dim = embed_dim\n        self.ape = ape\n        self.patch_norm = patch_norm\n        self.num_features = embed_dim\n        self.mlp_ratio = mlp_ratio\n\n        # split image into non-overlapping patches\n        self.patch_embed = PatchEmbed(\n            img_size=img_size,\n            patch_size=patch_size,\n            in_chans=embed_dim,\n            embed_dim=embed_dim,\n            norm_layer=norm_layer if self.patch_norm else None,\n        )\n        num_patches = self.patch_embed.num_patches\n        patches_resolution = self.patch_embed.patches_resolution\n        self.patches_resolution = patches_resolution\n\n        # merge non-overlapping patches into image\n        self.patch_unembed = PatchUnEmbed(\n            img_size=img_size,\n            patch_size=patch_size,\n            in_chans=embed_dim,\n            embed_dim=embed_dim,\n            norm_layer=norm_layer if self.patch_norm else None,\n        )\n\n        # absolute position embedding\n        if self.ape:\n            self.absolute_pos_embed = nn.Parameter(  # type: ignore[arg-type]\n                torch.zeros(1, num_patches, embed_dim)\n            )\n            trunc_normal_(self.absolute_pos_embed, std=0.02)\n\n        self.pos_drop = nn.Dropout(p=drop_rate)\n\n        # stochastic depth\n        dpr = [\n            x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))\n        ]  # stochastic depth decay rule\n\n        # build Residual Hybrid Attention Groups (RHAG)\n        self.layers = nn.ModuleList()\n        for i_layer in range(self.num_layers):\n            layer = RHAG(\n                dim=embed_dim,\n                input_resolution=(patches_resolution[0], patches_resolution[1]),\n                depth=depths[i_layer],\n                num_heads=num_heads[i_layer],\n                window_size=window_size,\n                compress_ratio=compress_ratio,\n                squeeze_factor=squeeze_factor,\n                conv_scale=conv_scale,\n                overlap_ratio=overlap_ratio,\n                mlp_ratio=self.mlp_ratio,\n                qkv_bias=qkv_bias,\n                qk_scale=qk_scale,\n                drop=drop_rate,\n                attn_drop=attn_drop_rate,\n                drop_path=dpr[\n                    sum(depths[:i_layer]) : sum(depths[: i_layer + 1])  # type: ignore\n                ],  # no impact on SR results\n                norm_layer=norm_layer,\n                downsample=None,\n                use_checkpoint=use_checkpoint,\n                img_size=img_size,\n                patch_size=patch_size,\n                resi_connection=resi_connection,\n            )\n            self.layers.append(layer)\n        self.norm = norm_layer(self.num_features)\n\n        # build the last conv layer in deep feature extraction\n        if resi_connection == \"1conv\":\n            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)\n        elif resi_connection == \"identity\":\n            self.conv_after_body = nn.Identity()\n\n        # ------------------------- 3, high quality image reconstruction ------------------------- #\n        if self.upsampler == \"pixelshuffle\":\n            # for classical SR\n            self.conv_before_upsample = nn.Sequential(\n                nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)\n            )\n            self.upsample = Upsample(upscale, num_feat)\n            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)\n\n        self.apply(self._init_weights)\n        self.load_state_dict(self.state, strict=False)\n\n    def _init_weights(self, m):\n        if isinstance(m, nn.Linear):\n            trunc_normal_(m.weight, std=0.02)\n            if isinstance(m, nn.Linear) and m.bias is not None:\n                nn.init.constant_(m.bias, 0)\n        elif isinstance(m, nn.LayerNorm):\n            nn.init.constant_(m.bias, 0)\n            nn.init.constant_(m.weight, 1.0)\n\n    def calculate_rpi_sa(self):\n        # calculate relative position index for SA\n        coords_h = torch.arange(self.window_size)\n        coords_w = torch.arange(self.window_size)\n        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww\n        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww\n        relative_coords = (\n            coords_flatten[:, :, None] - coords_flatten[:, None, :]\n        )  # 2, Wh*Ww, Wh*Ww\n        relative_coords = relative_coords.permute(\n            1, 2, 0\n        ).contiguous()  # Wh*Ww, Wh*Ww, 2\n        relative_coords[:, :, 0] += self.window_size - 1  # shift to start from 0\n        relative_coords[:, :, 1] += self.window_size - 1\n        relative_coords[:, :, 0] *= 2 * self.window_size - 1\n        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww\n        return relative_position_index\n\n    def calculate_rpi_oca(self):\n        # calculate relative position index for OCA\n        window_size_ori = self.window_size\n        window_size_ext = self.window_size + int(self.overlap_ratio * self.window_size)\n\n        coords_h = torch.arange(window_size_ori)\n        coords_w = torch.arange(window_size_ori)\n        coords_ori = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, ws, ws\n        coords_ori_flatten = torch.flatten(coords_ori, 1)  # 2, ws*ws\n\n        coords_h = torch.arange(window_size_ext)\n        coords_w = torch.arange(window_size_ext)\n        coords_ext = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, wse, wse\n        coords_ext_flatten = torch.flatten(coords_ext, 1)  # 2, wse*wse\n\n        relative_coords = (\n            coords_ext_flatten[:, None, :] - coords_ori_flatten[:, :, None]\n        )  # 2, ws*ws, wse*wse\n\n        relative_coords = relative_coords.permute(\n            1, 2, 0\n        ).contiguous()  # ws*ws, wse*wse, 2\n        relative_coords[:, :, 0] += (\n            window_size_ori - window_size_ext + 1\n        )  # shift to start from 0\n        relative_coords[:, :, 1] += window_size_ori - window_size_ext + 1\n\n        relative_coords[:, :, 0] *= window_size_ori + window_size_ext - 1\n        relative_position_index = relative_coords.sum(-1)\n        return relative_position_index\n\n    def calculate_mask(self, x_size):\n        # calculate attention mask for SW-MSA\n        h, w = x_size\n        img_mask = torch.zeros((1, h, w, 1))  # 1 h w 1\n        h_slices = (\n            slice(0, -self.window_size),\n            slice(-self.window_size, -self.shift_size),\n            slice(-self.shift_size, None),\n        )\n        w_slices = (\n            slice(0, -self.window_size),\n            slice(-self.window_size, -self.shift_size),\n            slice(-self.shift_size, None),\n        )\n        cnt = 0\n        for h in h_slices:\n            for w in w_slices:\n                img_mask[:, h, w, :] = cnt\n                cnt += 1\n\n        mask_windows = window_partition(\n            img_mask, self.window_size\n        )  # nw, window_size, window_size, 1\n        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)\n        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)\n        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(\n            attn_mask == 0, float(0.0)\n        )\n\n        return attn_mask\n\n    @torch.jit.ignore  # type: ignore\n    def no_weight_decay(self):\n        return {\"absolute_pos_embed\"}\n\n    @torch.jit.ignore  # type: ignore\n    def no_weight_decay_keywords(self):\n        return {\"relative_position_bias_table\"}\n\n    def check_image_size(self, x):\n        _, _, h, w = x.size()\n        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size\n        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size\n        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), \"reflect\")\n        return x\n\n    def forward_features(self, x):\n        x_size = (x.shape[2], x.shape[3])\n\n        # Calculate attention mask and relative position index in advance to speed up inference.\n        # The original code is very time-cosuming for large window size.\n        attn_mask = self.calculate_mask(x_size).to(x.device)\n        params = {\n            \"attn_mask\": attn_mask,\n            \"rpi_sa\": self.relative_position_index_SA,\n            \"rpi_oca\": self.relative_position_index_OCA,\n        }\n\n        x = self.patch_embed(x)\n        if self.ape:\n            x = x + self.absolute_pos_embed\n        x = self.pos_drop(x)\n\n        for layer in self.layers:\n            x = layer(x, x_size, params)\n\n        x = self.norm(x)  # b seq_len c\n        x = self.patch_unembed(x, x_size)\n\n        return x\n\n    def forward(self, x):\n        H, W = x.shape[2:]\n        self.mean = self.mean.type_as(x)\n        x = (x - self.mean) * self.img_range\n        x = self.check_image_size(x)\n\n        if self.upsampler == \"pixelshuffle\":\n            # for classical SR\n            x = self.conv_first(x)\n            x = self.conv_after_body(self.forward_features(x)) + x\n            x = self.conv_before_upsample(x)\n            x = self.conv_last(self.upsample(x))\n\n        x = x / self.img_range + self.mean\n\n        return x[:, :, : H * self.upscale, : W * self.upscale]\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/LICENSE-DAT",
    "content": "                                 Apache License\n                           Version 2.0, January 2004\n                        http://www.apache.org/licenses/\n\n   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n   1. Definitions.\n\n      \"License\" shall mean the terms and conditions for use, reproduction,\n      and distribution as defined by Sections 1 through 9 of this document.\n\n      \"Licensor\" shall mean the copyright owner or entity authorized by\n      the copyright owner that is granting the License.\n\n      \"Legal Entity\" shall mean the union of the acting entity and all\n      other entities that control, are controlled by, or are under common\n      control with that entity. For the purposes of this definition,\n      \"control\" means (i) the power, direct or indirect, to cause the\n      direction or management of such entity, whether by contract or\n      otherwise, or (ii) ownership of fifty percent (50%) or more of the\n      outstanding shares, or (iii) beneficial ownership of such entity.\n\n      \"You\" (or \"Your\") shall mean an individual or Legal Entity\n      exercising permissions granted by this License.\n\n      \"Source\" form shall mean the preferred form for making modifications,\n      including but not limited to software source code, documentation\n      source, and configuration files.\n\n      \"Object\" form shall mean any form resulting from mechanical\n      transformation or translation of a Source form, including but\n      not limited to compiled object code, generated documentation,\n      and conversions to other media types.\n\n      \"Work\" shall mean the work of authorship, whether in Source or\n      Object form, made available under the License, as indicated by a\n      copyright notice that is included in or attached to the work\n      (an example is provided in the Appendix below).\n\n      \"Derivative Works\" shall mean any work, whether in Source or Object\n      form, that is based on (or derived from) the Work and for which the\n      editorial revisions, annotations, elaborations, or other modifications\n      represent, as a whole, an original work of authorship. For the purposes\n      of this License, Derivative Works shall not include works that remain\n      separable from, or merely link (or bind by name) to the interfaces of,\n      the Work and Derivative Works thereof.\n\n      \"Contribution\" shall mean any work of authorship, including\n      the original version of the Work and any modifications or additions\n      to that Work or Derivative Works thereof, that is intentionally\n      submitted to Licensor for inclusion in the Work by the copyright owner\n      or by an individual or Legal Entity authorized to submit on behalf of\n      the copyright owner. For the purposes of this definition, \"submitted\"\n      means any form of electronic, verbal, or written communication sent\n      to the Licensor or its representatives, including but not limited to\n      communication on electronic mailing lists, source code control systems,\n      and issue tracking systems that are managed by, or on behalf of, the\n      Licensor for the purpose of discussing and improving the Work, but\n      excluding communication that is conspicuously marked or otherwise\n      designated in writing by the copyright owner as \"Not a Contribution.\"\n\n      \"Contributor\" shall mean Licensor and any individual or Legal Entity\n      on behalf of whom a Contribution has been received by Licensor and\n      subsequently incorporated within the Work.\n\n   2. Grant of Copyright License. Subject to the terms and conditions of\n      this License, each Contributor hereby grants to You a perpetual,\n      worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n      copyright license to reproduce, prepare Derivative Works of,\n      publicly display, publicly perform, sublicense, and distribute the\n      Work and such Derivative Works in Source or Object form.\n\n   3. Grant of Patent License. Subject to the terms and conditions of\n      this License, each Contributor hereby grants to You a perpetual,\n      worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n      (except as stated in this section) patent license to make, have made,\n      use, offer to sell, sell, import, and otherwise transfer the Work,\n      where such license applies only to those patent claims licensable\n      by such Contributor that are necessarily infringed by their\n      Contribution(s) alone or by combination of their Contribution(s)\n      with the Work to which such Contribution(s) was submitted. If You\n      institute patent litigation against any entity (including a\n      cross-claim or counterclaim in a lawsuit) alleging that the Work\n      or a Contribution incorporated within the Work constitutes direct\n      or contributory patent infringement, then any patent licenses\n      granted to You under this License for that Work shall terminate\n      as of the date such litigation is filed.\n\n   4. Redistribution. You may reproduce and distribute copies of the\n      Work or Derivative Works thereof in any medium, with or without\n      modifications, and in Source or Object form, provided that You\n      meet the following conditions:\n\n      (a) You must give any other recipients of the Work or\n          Derivative Works a copy of this License; and\n\n      (b) You must cause any modified files to carry prominent notices\n          stating that You changed the files; and\n\n      (c) You must retain, in the Source form of any Derivative Works\n          that You distribute, all copyright, patent, trademark, and\n          attribution notices from the Source form of the Work,\n          excluding those notices that do not pertain to any part of\n          the Derivative Works; and\n\n      (d) If the Work includes a \"NOTICE\" text file as part of its\n          distribution, then any Derivative Works that You distribute must\n          include a readable copy of the attribution notices contained\n          within such NOTICE file, excluding those notices that do not\n          pertain to any part of the Derivative Works, in at least one\n          of the following places: within a NOTICE text file distributed\n          as part of the Derivative Works; within the Source form or\n          documentation, if provided along with the Derivative Works; or,\n          within a display generated by the Derivative Works, if and\n          wherever such third-party notices normally appear. The contents\n          of the NOTICE file are for informational purposes only and\n          do not modify the License. You may add Your own attribution\n          notices within Derivative Works that You distribute, alongside\n          or as an addendum to the NOTICE text from the Work, provided\n          that such additional attribution notices cannot be construed\n          as modifying the License.\n\n      You may add Your own copyright statement to Your modifications and\n      may provide additional or different license terms and conditions\n      for use, reproduction, or distribution of Your modifications, or\n      for any such Derivative Works as a whole, provided Your use,\n      reproduction, and distribution of the Work otherwise complies with\n      the conditions stated in this License.\n\n   5. Submission of Contributions. Unless You explicitly state otherwise,\n      any Contribution intentionally submitted for inclusion in the Work\n      by You to the Licensor shall be under the terms and conditions of\n      this License, without any additional terms or conditions.\n      Notwithstanding the above, nothing herein shall supersede or modify\n      the terms of any separate license agreement you may have executed\n      with Licensor regarding such Contributions.\n\n   6. Trademarks. This License does not grant permission to use the trade\n      names, trademarks, service marks, or product names of the Licensor,\n      except as required for reasonable and customary use in describing the\n      origin of the Work and reproducing the content of the NOTICE file.\n\n   7. Disclaimer of Warranty. Unless required by applicable law or\n      agreed to in writing, Licensor provides the Work (and each\n      Contributor provides its Contributions) on an \"AS IS\" BASIS,\n      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n      implied, including, without limitation, any warranties or conditions\n      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A\n      PARTICULAR PURPOSE. You are solely responsible for determining the\n      appropriateness of using or redistributing the Work and assume any\n      risks associated with Your exercise of permissions under this License.\n\n   8. Limitation of Liability. In no event and under no legal theory,\n      whether in tort (including negligence), contract, or otherwise,\n      unless required by applicable law (such as deliberate and grossly\n      negligent acts) or agreed to in writing, shall any Contributor be\n      liable to You for damages, including any direct, indirect, special,\n      incidental, or consequential damages of any character arising as a\n      result of this License or out of the use or inability to use the\n      Work (including but not limited to damages for loss of goodwill,\n      work stoppage, computer failure or malfunction, or any and all\n      other commercial damages or losses), even if such Contributor\n      has been advised of the possibility of such damages.\n\n   9. Accepting Warranty or Additional Liability. While redistributing\n      the Work or Derivative Works thereof, You may choose to offer,\n      and charge a fee for, acceptance of support, warranty, indemnity,\n      or other liability obligations and/or rights consistent with this\n      License. However, in accepting such obligations, You may act only\n      on Your own behalf and on Your sole responsibility, not on behalf\n      of any other Contributor, and only if You agree to indemnify,\n      defend, and hold each Contributor harmless for any liability\n      incurred by, or claims asserted against, such Contributor by reason\n      of your accepting any such warranty or additional liability.\n\n   END OF TERMS AND CONDITIONS\n\n   APPENDIX: How to apply the Apache License to your work.\n\n      To apply the Apache License to your work, attach the following\n      boilerplate notice, with the fields enclosed by brackets \"[]\"\n      replaced with your own identifying information. (Don't include\n      the brackets!)  The text should be enclosed in the appropriate\n      comment syntax for the file format. We also recommend that a\n      file or class name and description of purpose be included on the\n      same \"printed page\" as the copyright notice for easier\n      identification within third-party archives.\n\n   Copyright [yyyy] [name of copyright owner]\n\n   Licensed under the Apache License, Version 2.0 (the \"License\");\n   you may not use this file except in compliance with the License.\n   You may obtain a copy of the License at\n\n       http://www.apache.org/licenses/LICENSE-2.0\n\n   Unless required by applicable law or agreed to in writing, software\n   distributed under the License is distributed on an \"AS IS\" BASIS,\n   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n   See the License for the specific language governing permissions and\n   limitations under the License.\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/LICENSE-ESRGAN",
    "content": "                                 Apache License\n                           Version 2.0, January 2004\n                        http://www.apache.org/licenses/\n\n   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n   1. Definitions.\n\n      \"License\" shall mean the terms and conditions for use, reproduction,\n      and distribution as defined by Sections 1 through 9 of this document.\n\n      \"Licensor\" shall mean the copyright owner or entity authorized by\n      the copyright owner that is granting the License.\n\n      \"Legal Entity\" shall mean the union of the acting entity and all\n      other entities that control, are controlled by, or are under common\n      control with that entity. For the purposes of this definition,\n      \"control\" means (i) the power, direct or indirect, to cause the\n      direction or management of such entity, whether by contract or\n      otherwise, or (ii) ownership of fifty percent (50%) or more of the\n      outstanding shares, or (iii) beneficial ownership of such entity.\n\n      \"You\" (or \"Your\") shall mean an individual or Legal Entity\n      exercising permissions granted by this License.\n\n      \"Source\" form shall mean the preferred form for making modifications,\n      including but not limited to software source code, documentation\n      source, and configuration files.\n\n      \"Object\" form shall mean any form resulting from mechanical\n      transformation or translation of a Source form, including but\n      not limited to compiled object code, generated documentation,\n      and conversions to other media types.\n\n      \"Work\" shall mean the work of authorship, whether in Source or\n      Object form, made available under the License, as indicated by a\n      copyright notice that is included in or attached to the work\n      (an example is provided in the Appendix below).\n\n      \"Derivative Works\" shall mean any work, whether in Source or Object\n      form, that is based on (or derived from) the Work and for which the\n      editorial revisions, annotations, elaborations, or other modifications\n      represent, as a whole, an original work of authorship. For the purposes\n      of this License, Derivative Works shall not include works that remain\n      separable from, or merely link (or bind by name) to the interfaces of,\n      the Work and Derivative Works thereof.\n\n      \"Contribution\" shall mean any work of authorship, including\n      the original version of the Work and any modifications or additions\n      to that Work or Derivative Works thereof, that is intentionally\n      submitted to Licensor for inclusion in the Work by the copyright owner\n      or by an individual or Legal Entity authorized to submit on behalf of\n      the copyright owner. For the purposes of this definition, \"submitted\"\n      means any form of electronic, verbal, or written communication sent\n      to the Licensor or its representatives, including but not limited to\n      communication on electronic mailing lists, source code control systems,\n      and issue tracking systems that are managed by, or on behalf of, the\n      Licensor for the purpose of discussing and improving the Work, but\n      excluding communication that is conspicuously marked or otherwise\n      designated in writing by the copyright owner as \"Not a Contribution.\"\n\n      \"Contributor\" shall mean Licensor and any individual or Legal Entity\n      on behalf of whom a Contribution has been received by Licensor and\n      subsequently incorporated within the Work.\n\n   2. Grant of Copyright License. Subject to the terms and conditions of\n      this License, each Contributor hereby grants to You a perpetual,\n      worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n      copyright license to reproduce, prepare Derivative Works of,\n      publicly display, publicly perform, sublicense, and distribute the\n      Work and such Derivative Works in Source or Object form.\n\n   3. Grant of Patent License. Subject to the terms and conditions of\n      this License, each Contributor hereby grants to You a perpetual,\n      worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n      (except as stated in this section) patent license to make, have made,\n      use, offer to sell, sell, import, and otherwise transfer the Work,\n      where such license applies only to those patent claims licensable\n      by such Contributor that are necessarily infringed by their\n      Contribution(s) alone or by combination of their Contribution(s)\n      with the Work to which such Contribution(s) was submitted. If You\n      institute patent litigation against any entity (including a\n      cross-claim or counterclaim in a lawsuit) alleging that the Work\n      or a Contribution incorporated within the Work constitutes direct\n      or contributory patent infringement, then any patent licenses\n      granted to You under this License for that Work shall terminate\n      as of the date such litigation is filed.\n\n   4. Redistribution. You may reproduce and distribute copies of the\n      Work or Derivative Works thereof in any medium, with or without\n      modifications, and in Source or Object form, provided that You\n      meet the following conditions:\n\n      (a) You must give any other recipients of the Work or\n          Derivative Works a copy of this License; and\n\n      (b) You must cause any modified files to carry prominent notices\n          stating that You changed the files; and\n\n      (c) You must retain, in the Source form of any Derivative Works\n          that You distribute, all copyright, patent, trademark, and\n          attribution notices from the Source form of the Work,\n          excluding those notices that do not pertain to any part of\n          the Derivative Works; and\n\n      (d) If the Work includes a \"NOTICE\" text file as part of its\n          distribution, then any Derivative Works that You distribute must\n          include a readable copy of the attribution notices contained\n          within such NOTICE file, excluding those notices that do not\n          pertain to any part of the Derivative Works, in at least one\n          of the following places: within a NOTICE text file distributed\n          as part of the Derivative Works; within the Source form or\n          documentation, if provided along with the Derivative Works; or,\n          within a display generated by the Derivative Works, if and\n          wherever such third-party notices normally appear. The contents\n          of the NOTICE file are for informational purposes only and\n          do not modify the License. You may add Your own attribution\n          notices within Derivative Works that You distribute, alongside\n          or as an addendum to the NOTICE text from the Work, provided\n          that such additional attribution notices cannot be construed\n          as modifying the License.\n\n      You may add Your own copyright statement to Your modifications and\n      may provide additional or different license terms and conditions\n      for use, reproduction, or distribution of Your modifications, or\n      for any such Derivative Works as a whole, provided Your use,\n      reproduction, and distribution of the Work otherwise complies with\n      the conditions stated in this License.\n\n   5. Submission of Contributions. Unless You explicitly state otherwise,\n      any Contribution intentionally submitted for inclusion in the Work\n      by You to the Licensor shall be under the terms and conditions of\n      this License, without any additional terms or conditions.\n      Notwithstanding the above, nothing herein shall supersede or modify\n      the terms of any separate license agreement you may have executed\n      with Licensor regarding such Contributions.\n\n   6. Trademarks. This License does not grant permission to use the trade\n      names, trademarks, service marks, or product names of the Licensor,\n      except as required for reasonable and customary use in describing the\n      origin of the Work and reproducing the content of the NOTICE file.\n\n   7. Disclaimer of Warranty. Unless required by applicable law or\n      agreed to in writing, Licensor provides the Work (and each\n      Contributor provides its Contributions) on an \"AS IS\" BASIS,\n      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n      implied, including, without limitation, any warranties or conditions\n      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A\n      PARTICULAR PURPOSE. You are solely responsible for determining the\n      appropriateness of using or redistributing the Work and assume any\n      risks associated with Your exercise of permissions under this License.\n\n   8. Limitation of Liability. In no event and under no legal theory,\n      whether in tort (including negligence), contract, or otherwise,\n      unless required by applicable law (such as deliberate and grossly\n      negligent acts) or agreed to in writing, shall any Contributor be\n      liable to You for damages, including any direct, indirect, special,\n      incidental, or consequential damages of any character arising as a\n      result of this License or out of the use or inability to use the\n      Work (including but not limited to damages for loss of goodwill,\n      work stoppage, computer failure or malfunction, or any and all\n      other commercial damages or losses), even if such Contributor\n      has been advised of the possibility of such damages.\n\n   9. Accepting Warranty or Additional Liability. While redistributing\n      the Work or Derivative Works thereof, You may choose to offer,\n      and charge a fee for, acceptance of support, warranty, indemnity,\n      or other liability obligations and/or rights consistent with this\n      License. However, in accepting such obligations, You may act only\n      on Your own behalf and on Your sole responsibility, not on behalf\n      of any other Contributor, and only if You agree to indemnify,\n      defend, and hold each Contributor harmless for any liability\n      incurred by, or claims asserted against, such Contributor by reason\n      of your accepting any such warranty or additional liability.\n\n   END OF TERMS AND CONDITIONS\n\n   APPENDIX: How to apply the Apache License to your work.\n\n      To apply the Apache License to your work, attach the following\n      boilerplate notice, with the fields enclosed by brackets \"[]\"\n      replaced with your own identifying information. (Don't include\n      the brackets!)  The text should be enclosed in the appropriate\n      comment syntax for the file format. We also recommend that a\n      file or class name and description of purpose be included on the\n      same \"printed page\" as the copyright notice for easier\n      identification within third-party archives.\n\n   Copyright [yyyy] [name of copyright owner]\n\n   Licensed under the Apache License, Version 2.0 (the \"License\");\n   you may not use this file except in compliance with the License.\n   You may obtain a copy of the License at\n\n       http://www.apache.org/licenses/LICENSE-2.0\n\n   Unless required by applicable law or agreed to in writing, software\n   distributed under the License is distributed on an \"AS IS\" BASIS,\n   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n   See the License for the specific language governing permissions and\n   limitations under the License.\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/LICENSE-HAT",
    "content": "MIT License\n\nCopyright (c) 2022 Xiangyu Chen\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/LICENSE-RealESRGAN",
    "content": "BSD 3-Clause License\n\nCopyright (c) 2021, Xintao Wang\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n1. Redistributions of source code must retain the above copyright notice, this\n   list of conditions and the following disclaimer.\n\n2. Redistributions in binary form must reproduce the above copyright notice,\n   this list of conditions and the following disclaimer in the documentation\n   and/or other materials provided with the distribution.\n\n3. Neither the name of the copyright holder nor the names of its\n   contributors may be used to endorse or promote products derived from\n   this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\nFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\nDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\nSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\nCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\nOR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/LICENSE-SCUNet",
    "content": "                                 Apache License\n                           Version 2.0, January 2004\n                        http://www.apache.org/licenses/\n\n   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n   1. Definitions.\n\n      \"License\" shall mean the terms and conditions for use, reproduction,\n      and distribution as defined by Sections 1 through 9 of this document.\n\n      \"Licensor\" shall mean the copyright owner or entity authorized by\n      the copyright owner that is granting the License.\n\n      \"Legal Entity\" shall mean the union of the acting entity and all\n      other entities that control, are controlled by, or are under common\n      control with that entity. For the purposes of this definition,\n      \"control\" means (i) the power, direct or indirect, to cause the\n      direction or management of such entity, whether by contract or\n      otherwise, or (ii) ownership of fifty percent (50%) or more of the\n      outstanding shares, or (iii) beneficial ownership of such entity.\n\n      \"You\" (or \"Your\") shall mean an individual or Legal Entity\n      exercising permissions granted by this License.\n\n      \"Source\" form shall mean the preferred form for making modifications,\n      including but not limited to software source code, documentation\n      source, and configuration files.\n\n      \"Object\" form shall mean any form resulting from mechanical\n      transformation or translation of a Source form, including but\n      not limited to compiled object code, generated documentation,\n      and conversions to other media types.\n\n      \"Work\" shall mean the work of authorship, whether in Source or\n      Object form, made available under the License, as indicated by a\n      copyright notice that is included in or attached to the work\n      (an example is provided in the Appendix below).\n\n      \"Derivative Works\" shall mean any work, whether in Source or Object\n      form, that is based on (or derived from) the Work and for which the\n      editorial revisions, annotations, elaborations, or other modifications\n      represent, as a whole, an original work of authorship. For the purposes\n      of this License, Derivative Works shall not include works that remain\n      separable from, or merely link (or bind by name) to the interfaces of,\n      the Work and Derivative Works thereof.\n\n      \"Contribution\" shall mean any work of authorship, including\n      the original version of the Work and any modifications or additions\n      to that Work or Derivative Works thereof, that is intentionally\n      submitted to Licensor for inclusion in the Work by the copyright owner\n      or by an individual or Legal Entity authorized to submit on behalf of\n      the copyright owner. For the purposes of this definition, \"submitted\"\n      means any form of electronic, verbal, or written communication sent\n      to the Licensor or its representatives, including but not limited to\n      communication on electronic mailing lists, source code control systems,\n      and issue tracking systems that are managed by, or on behalf of, the\n      Licensor for the purpose of discussing and improving the Work, but\n      excluding communication that is conspicuously marked or otherwise\n      designated in writing by the copyright owner as \"Not a Contribution.\"\n\n      \"Contributor\" shall mean Licensor and any individual or Legal Entity\n      on behalf of whom a Contribution has been received by Licensor and\n      subsequently incorporated within the Work.\n\n   2. Grant of Copyright License. Subject to the terms and conditions of\n      this License, each Contributor hereby grants to You a perpetual,\n      worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n      copyright license to reproduce, prepare Derivative Works of,\n      publicly display, publicly perform, sublicense, and distribute the\n      Work and such Derivative Works in Source or Object form.\n\n   3. Grant of Patent License. Subject to the terms and conditions of\n      this License, each Contributor hereby grants to You a perpetual,\n      worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n      (except as stated in this section) patent license to make, have made,\n      use, offer to sell, sell, import, and otherwise transfer the Work,\n      where such license applies only to those patent claims licensable\n      by such Contributor that are necessarily infringed by their\n      Contribution(s) alone or by combination of their Contribution(s)\n      with the Work to which such Contribution(s) was submitted. If You\n      institute patent litigation against any entity (including a\n      cross-claim or counterclaim in a lawsuit) alleging that the Work\n      or a Contribution incorporated within the Work constitutes direct\n      or contributory patent infringement, then any patent licenses\n      granted to You under this License for that Work shall terminate\n      as of the date such litigation is filed.\n\n   4. Redistribution. You may reproduce and distribute copies of the\n      Work or Derivative Works thereof in any medium, with or without\n      modifications, and in Source or Object form, provided that You\n      meet the following conditions:\n\n      (a) You must give any other recipients of the Work or\n          Derivative Works a copy of this License; and\n\n      (b) You must cause any modified files to carry prominent notices\n          stating that You changed the files; and\n\n      (c) You must retain, in the Source form of any Derivative Works\n          that You distribute, all copyright, patent, trademark, and\n          attribution notices from the Source form of the Work,\n          excluding those notices that do not pertain to any part of\n          the Derivative Works; and\n\n      (d) If the Work includes a \"NOTICE\" text file as part of its\n          distribution, then any Derivative Works that You distribute must\n          include a readable copy of the attribution notices contained\n          within such NOTICE file, excluding those notices that do not\n          pertain to any part of the Derivative Works, in at least one\n          of the following places: within a NOTICE text file distributed\n          as part of the Derivative Works; within the Source form or\n          documentation, if provided along with the Derivative Works; or,\n          within a display generated by the Derivative Works, if and\n          wherever such third-party notices normally appear. The contents\n          of the NOTICE file are for informational purposes only and\n          do not modify the License. You may add Your own attribution\n          notices within Derivative Works that You distribute, alongside\n          or as an addendum to the NOTICE text from the Work, provided\n          that such additional attribution notices cannot be construed\n          as modifying the License.\n\n      You may add Your own copyright statement to Your modifications and\n      may provide additional or different license terms and conditions\n      for use, reproduction, or distribution of Your modifications, or\n      for any such Derivative Works as a whole, provided Your use,\n      reproduction, and distribution of the Work otherwise complies with\n      the conditions stated in this License.\n\n   5. Submission of Contributions. Unless You explicitly state otherwise,\n      any Contribution intentionally submitted for inclusion in the Work\n      by You to the Licensor shall be under the terms and conditions of\n      this License, without any additional terms or conditions.\n      Notwithstanding the above, nothing herein shall supersede or modify\n      the terms of any separate license agreement you may have executed\n      with Licensor regarding such Contributions.\n\n   6. Trademarks. This License does not grant permission to use the trade\n      names, trademarks, service marks, or product names of the Licensor,\n      except as required for reasonable and customary use in describing the\n      origin of the Work and reproducing the content of the NOTICE file.\n\n   7. Disclaimer of Warranty. Unless required by applicable law or\n      agreed to in writing, Licensor provides the Work (and each\n      Contributor provides its Contributions) on an \"AS IS\" BASIS,\n      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n      implied, including, without limitation, any warranties or conditions\n      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A\n      PARTICULAR PURPOSE. You are solely responsible for determining the\n      appropriateness of using or redistributing the Work and assume any\n      risks associated with Your exercise of permissions under this License.\n\n   8. Limitation of Liability. In no event and under no legal theory,\n      whether in tort (including negligence), contract, or otherwise,\n      unless required by applicable law (such as deliberate and grossly\n      negligent acts) or agreed to in writing, shall any Contributor be\n      liable to You for damages, including any direct, indirect, special,\n      incidental, or consequential damages of any character arising as a\n      result of this License or out of the use or inability to use the\n      Work (including but not limited to damages for loss of goodwill,\n      work stoppage, computer failure or malfunction, or any and all\n      other commercial damages or losses), even if such Contributor\n      has been advised of the possibility of such damages.\n\n   9. Accepting Warranty or Additional Liability. While redistributing\n      the Work or Derivative Works thereof, You may choose to offer,\n      and charge a fee for, acceptance of support, warranty, indemnity,\n      or other liability obligations and/or rights consistent with this\n      License. However, in accepting such obligations, You may act only\n      on Your own behalf and on Your sole responsibility, not on behalf\n      of any other Contributor, and only if You agree to indemnify,\n      defend, and hold each Contributor harmless for any liability\n      incurred by, or claims asserted against, such Contributor by reason\n      of your accepting any such warranty or additional liability.\n\n   END OF TERMS AND CONDITIONS\n\n   APPENDIX: How to apply the Apache License to your work.\n\n      To apply the Apache License to your work, attach the following\n      boilerplate notice, with the fields enclosed by brackets \"[]\"\n      replaced with your own identifying information. (Don't include\n      the brackets!)  The text should be enclosed in the appropriate\n      comment syntax for the file format. We also recommend that a\n      file or class name and description of purpose be included on the\n      same \"printed page\" as the copyright notice for easier\n      identification within third-party archives.\n\n   Copyright 2022 Kai Zhang (cskaizhang@gmail.com, https://cszn.github.io/). All rights reserved.\n\n   Licensed under the Apache License, Version 2.0 (the \"License\");\n   you may not use this file except in compliance with the License.\n   You may obtain a copy of the License at\n\n       http://www.apache.org/licenses/LICENSE-2.0\n\n   Unless required by applicable law or agreed to in writing, software\n   distributed under the License is distributed on an \"AS IS\" BASIS,\n   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n   See the License for the specific language governing permissions and\n   limitations under the License.\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/LICENSE-SPSR",
    "content": "                                 Apache License\n                           Version 2.0, January 2004\n                        http://www.apache.org/licenses/\n\n   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n   1. Definitions.\n\n      \"License\" shall mean the terms and conditions for use, reproduction,\n      and distribution as defined by Sections 1 through 9 of this document.\n\n      \"Licensor\" shall mean the copyright owner or entity authorized by\n      the copyright owner that is granting the License.\n\n      \"Legal Entity\" shall mean the union of the acting entity and all\n      other entities that control, are controlled by, or are under common\n      control with that entity. For the purposes of this definition,\n      \"control\" means (i) the power, direct or indirect, to cause the\n      direction or management of such entity, whether by contract or\n      otherwise, or (ii) ownership of fifty percent (50%) or more of the\n      outstanding shares, or (iii) beneficial ownership of such entity.\n\n      \"You\" (or \"Your\") shall mean an individual or Legal Entity\n      exercising permissions granted by this License.\n\n      \"Source\" form shall mean the preferred form for making modifications,\n      including but not limited to software source code, documentation\n      source, and configuration files.\n\n      \"Object\" form shall mean any form resulting from mechanical\n      transformation or translation of a Source form, including but\n      not limited to compiled object code, generated documentation,\n      and conversions to other media types.\n\n      \"Work\" shall mean the work of authorship, whether in Source or\n      Object form, made available under the License, as indicated by a\n      copyright notice that is included in or attached to the work\n      (an example is provided in the Appendix below).\n\n      \"Derivative Works\" shall mean any work, whether in Source or Object\n      form, that is based on (or derived from) the Work and for which the\n      editorial revisions, annotations, elaborations, or other modifications\n      represent, as a whole, an original work of authorship. For the purposes\n      of this License, Derivative Works shall not include works that remain\n      separable from, or merely link (or bind by name) to the interfaces of,\n      the Work and Derivative Works thereof.\n\n      \"Contribution\" shall mean any work of authorship, including\n      the original version of the Work and any modifications or additions\n      to that Work or Derivative Works thereof, that is intentionally\n      submitted to Licensor for inclusion in the Work by the copyright owner\n      or by an individual or Legal Entity authorized to submit on behalf of\n      the copyright owner. For the purposes of this definition, \"submitted\"\n      means any form of electronic, verbal, or written communication sent\n      to the Licensor or its representatives, including but not limited to\n      communication on electronic mailing lists, source code control systems,\n      and issue tracking systems that are managed by, or on behalf of, the\n      Licensor for the purpose of discussing and improving the Work, but\n      excluding communication that is conspicuously marked or otherwise\n      designated in writing by the copyright owner as \"Not a Contribution.\"\n\n      \"Contributor\" shall mean Licensor and any individual or Legal Entity\n      on behalf of whom a Contribution has been received by Licensor and\n      subsequently incorporated within the Work.\n\n   2. Grant of Copyright License. Subject to the terms and conditions of\n      this License, each Contributor hereby grants to You a perpetual,\n      worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n      copyright license to reproduce, prepare Derivative Works of,\n      publicly display, publicly perform, sublicense, and distribute the\n      Work and such Derivative Works in Source or Object form.\n\n   3. Grant of Patent License. Subject to the terms and conditions of\n      this License, each Contributor hereby grants to You a perpetual,\n      worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n      (except as stated in this section) patent license to make, have made,\n      use, offer to sell, sell, import, and otherwise transfer the Work,\n      where such license applies only to those patent claims licensable\n      by such Contributor that are necessarily infringed by their\n      Contribution(s) alone or by combination of their Contribution(s)\n      with the Work to which such Contribution(s) was submitted. If You\n      institute patent litigation against any entity (including a\n      cross-claim or counterclaim in a lawsuit) alleging that the Work\n      or a Contribution incorporated within the Work constitutes direct\n      or contributory patent infringement, then any patent licenses\n      granted to You under this License for that Work shall terminate\n      as of the date such litigation is filed.\n\n   4. Redistribution. You may reproduce and distribute copies of the\n      Work or Derivative Works thereof in any medium, with or without\n      modifications, and in Source or Object form, provided that You\n      meet the following conditions:\n\n      (a) You must give any other recipients of the Work or\n          Derivative Works a copy of this License; and\n\n      (b) You must cause any modified files to carry prominent notices\n          stating that You changed the files; and\n\n      (c) You must retain, in the Source form of any Derivative Works\n          that You distribute, all copyright, patent, trademark, and\n          attribution notices from the Source form of the Work,\n          excluding those notices that do not pertain to any part of\n          the Derivative Works; and\n\n      (d) If the Work includes a \"NOTICE\" text file as part of its\n          distribution, then any Derivative Works that You distribute must\n          include a readable copy of the attribution notices contained\n          within such NOTICE file, excluding those notices that do not\n          pertain to any part of the Derivative Works, in at least one\n          of the following places: within a NOTICE text file distributed\n          as part of the Derivative Works; within the Source form or\n          documentation, if provided along with the Derivative Works; or,\n          within a display generated by the Derivative Works, if and\n          wherever such third-party notices normally appear. The contents\n          of the NOTICE file are for informational purposes only and\n          do not modify the License. You may add Your own attribution\n          notices within Derivative Works that You distribute, alongside\n          or as an addendum to the NOTICE text from the Work, provided\n          that such additional attribution notices cannot be construed\n          as modifying the License.\n\n      You may add Your own copyright statement to Your modifications and\n      may provide additional or different license terms and conditions\n      for use, reproduction, or distribution of Your modifications, or\n      for any such Derivative Works as a whole, provided Your use,\n      reproduction, and distribution of the Work otherwise complies with\n      the conditions stated in this License.\n\n   5. Submission of Contributions. Unless You explicitly state otherwise,\n      any Contribution intentionally submitted for inclusion in the Work\n      by You to the Licensor shall be under the terms and conditions of\n      this License, without any additional terms or conditions.\n      Notwithstanding the above, nothing herein shall supersede or modify\n      the terms of any separate license agreement you may have executed\n      with Licensor regarding such Contributions.\n\n   6. Trademarks. This License does not grant permission to use the trade\n      names, trademarks, service marks, or product names of the Licensor,\n      except as required for reasonable and customary use in describing the\n      origin of the Work and reproducing the content of the NOTICE file.\n\n   7. Disclaimer of Warranty. Unless required by applicable law or\n      agreed to in writing, Licensor provides the Work (and each\n      Contributor provides its Contributions) on an \"AS IS\" BASIS,\n      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n      implied, including, without limitation, any warranties or conditions\n      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A\n      PARTICULAR PURPOSE. You are solely responsible for determining the\n      appropriateness of using or redistributing the Work and assume any\n      risks associated with Your exercise of permissions under this License.\n\n   8. Limitation of Liability. In no event and under no legal theory,\n      whether in tort (including negligence), contract, or otherwise,\n      unless required by applicable law (such as deliberate and grossly\n      negligent acts) or agreed to in writing, shall any Contributor be\n      liable to You for damages, including any direct, indirect, special,\n      incidental, or consequential damages of any character arising as a\n      result of this License or out of the use or inability to use the\n      Work (including but not limited to damages for loss of goodwill,\n      work stoppage, computer failure or malfunction, or any and all\n      other commercial damages or losses), even if such Contributor\n      has been advised of the possibility of such damages.\n\n   9. Accepting Warranty or Additional Liability. While redistributing\n      the Work or Derivative Works thereof, You may choose to offer,\n      and charge a fee for, acceptance of support, warranty, indemnity,\n      or other liability obligations and/or rights consistent with this\n      License. However, in accepting such obligations, You may act only\n      on Your own behalf and on Your sole responsibility, not on behalf\n      of any other Contributor, and only if You agree to indemnify,\n      defend, and hold each Contributor harmless for any liability\n      incurred by, or claims asserted against, such Contributor by reason\n      of your accepting any such warranty or additional liability.\n\n   END OF TERMS AND CONDITIONS\n\n   APPENDIX: How to apply the Apache License to your work.\n\n      To apply the Apache License to your work, attach the following\n      boilerplate notice, with the fields enclosed by brackets \"[]\"\n      replaced with your own identifying information. (Don't include\n      the brackets!)  The text should be enclosed in the appropriate\n      comment syntax for the file format. We also recommend that a\n      file or class name and description of purpose be included on the\n      same \"printed page\" as the copyright notice for easier\n      identification within third-party archives.\n\n   Copyright 2018-2022 BasicSR Authors\n\n   Licensed under the Apache License, Version 2.0 (the \"License\");\n   you may not use this file except in compliance with the License.\n   You may obtain a copy of the License at\n\n       http://www.apache.org/licenses/LICENSE-2.0\n\n   Unless required by applicable law or agreed to in writing, software\n   distributed under the License is distributed on an \"AS IS\" BASIS,\n   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n   See the License for the specific language governing permissions and\n   limitations under the License.\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/LICENSE-SwiftSRGAN",
    "content": "Creative Commons Legal Code\n\nCC0 1.0 Universal\n\n    CREATIVE COMMONS CORPORATION IS NOT A LAW FIRM AND DOES NOT PROVIDE\n    LEGAL SERVICES. DISTRIBUTION OF THIS DOCUMENT DOES NOT CREATE AN\n    ATTORNEY-CLIENT RELATIONSHIP. CREATIVE COMMONS PROVIDES THIS\n    INFORMATION ON AN \"AS-IS\" BASIS. 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  },
  {
    "path": "ldm_patched/pfn/architecture/LICENSE-Swin2SR",
    "content": "                                 Apache License\n                           Version 2.0, January 2004\n                        http://www.apache.org/licenses/\n\n   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n   1. Definitions.\n\n      \"License\" shall mean the terms and conditions for use, reproduction,\n      and distribution as defined by Sections 1 through 9 of this document.\n\n      \"Licensor\" shall mean the copyright owner or entity authorized by\n      the copyright owner that is granting the License.\n\n      \"Legal Entity\" shall mean the union of the acting entity and all\n      other entities that control, are controlled by, or are under common\n      control with that entity. For the purposes of this definition,\n      \"control\" means (i) the power, direct or indirect, to cause the\n      direction or management of such entity, whether by contract or\n      otherwise, or (ii) ownership of fifty percent (50%) or more of the\n      outstanding shares, or (iii) beneficial ownership of such entity.\n\n      \"You\" (or \"Your\") shall mean an individual or Legal Entity\n      exercising permissions granted by this License.\n\n      \"Source\" form shall mean the preferred form for making modifications,\n      including but not limited to software source code, documentation\n      source, and configuration files.\n\n      \"Object\" form shall mean any form resulting from mechanical\n      transformation or translation of a Source form, including but\n      not limited to compiled object code, generated documentation,\n      and conversions to other media types.\n\n      \"Work\" shall mean the work of authorship, whether in Source or\n      Object form, made available under the License, as indicated by a\n      copyright notice that is included in or attached to the work\n      (an example is provided in the Appendix below).\n\n      \"Derivative Works\" shall mean any work, whether in Source or Object\n      form, that is based on (or derived from) the Work and for which the\n      editorial revisions, annotations, elaborations, or other modifications\n      represent, as a whole, an original work of authorship. For the purposes\n      of this License, Derivative Works shall not include works that remain\n      separable from, or merely link (or bind by name) to the interfaces of,\n      the Work and Derivative Works thereof.\n\n      \"Contribution\" shall mean any work of authorship, including\n      the original version of the Work and any modifications or additions\n      to that Work or Derivative Works thereof, that is intentionally\n      submitted to Licensor for inclusion in the Work by the copyright owner\n      or by an individual or Legal Entity authorized to submit on behalf of\n      the copyright owner. For the purposes of this definition, \"submitted\"\n      means any form of electronic, verbal, or written communication sent\n      to the Licensor or its representatives, including but not limited to\n      communication on electronic mailing lists, source code control systems,\n      and issue tracking systems that are managed by, or on behalf of, the\n      Licensor for the purpose of discussing and improving the Work, but\n      excluding communication that is conspicuously marked or otherwise\n      designated in writing by the copyright owner as \"Not a Contribution.\"\n\n      \"Contributor\" shall mean Licensor and any individual or Legal Entity\n      on behalf of whom a Contribution has been received by Licensor and\n      subsequently incorporated within the Work.\n\n   2. Grant of Copyright License. Subject to the terms and conditions of\n      this License, each Contributor hereby grants to You a perpetual,\n      worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n      copyright license to reproduce, prepare Derivative Works of,\n      publicly display, publicly perform, sublicense, and distribute the\n      Work and such Derivative Works in Source or Object form.\n\n   3. Grant of Patent License. Subject to the terms and conditions of\n      this License, each Contributor hereby grants to You a perpetual,\n      worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n      (except as stated in this section) patent license to make, have made,\n      use, offer to sell, sell, import, and otherwise transfer the Work,\n      where such license applies only to those patent claims licensable\n      by such Contributor that are necessarily infringed by their\n      Contribution(s) alone or by combination of their Contribution(s)\n      with the Work to which such Contribution(s) was submitted. If You\n      institute patent litigation against any entity (including a\n      cross-claim or counterclaim in a lawsuit) alleging that the Work\n      or a Contribution incorporated within the Work constitutes direct\n      or contributory patent infringement, then any patent licenses\n      granted to You under this License for that Work shall terminate\n      as of the date such litigation is filed.\n\n   4. Redistribution. You may reproduce and distribute copies of the\n      Work or Derivative Works thereof in any medium, with or without\n      modifications, and in Source or Object form, provided that You\n      meet the following conditions:\n\n      (a) You must give any other recipients of the Work or\n          Derivative Works a copy of this License; and\n\n      (b) You must cause any modified files to carry prominent notices\n          stating that You changed the files; and\n\n      (c) You must retain, in the Source form of any Derivative Works\n          that You distribute, all copyright, patent, trademark, and\n          attribution notices from the Source form of the Work,\n          excluding those notices that do not pertain to any part of\n          the Derivative Works; and\n\n      (d) If the Work includes a \"NOTICE\" text file as part of its\n          distribution, then any Derivative Works that You distribute must\n          include a readable copy of the attribution notices contained\n          within such NOTICE file, excluding those notices that do not\n          pertain to any part of the Derivative Works, in at least one\n          of the following places: within a NOTICE text file distributed\n          as part of the Derivative Works; within the Source form or\n          documentation, if provided along with the Derivative Works; or,\n          within a display generated by the Derivative Works, if and\n          wherever such third-party notices normally appear. The contents\n          of the NOTICE file are for informational purposes only and\n          do not modify the License. You may add Your own attribution\n          notices within Derivative Works that You distribute, alongside\n          or as an addendum to the NOTICE text from the Work, provided\n          that such additional attribution notices cannot be construed\n          as modifying the License.\n\n      You may add Your own copyright statement to Your modifications and\n      may provide additional or different license terms and conditions\n      for use, reproduction, or distribution of Your modifications, or\n      for any such Derivative Works as a whole, provided Your use,\n      reproduction, and distribution of the Work otherwise complies with\n      the conditions stated in this License.\n\n   5. Submission of Contributions. Unless You explicitly state otherwise,\n      any Contribution intentionally submitted for inclusion in the Work\n      by You to the Licensor shall be under the terms and conditions of\n      this License, without any additional terms or conditions.\n      Notwithstanding the above, nothing herein shall supersede or modify\n      the terms of any separate license agreement you may have executed\n      with Licensor regarding such Contributions.\n\n   6. Trademarks. This License does not grant permission to use the trade\n      names, trademarks, service marks, or product names of the Licensor,\n      except as required for reasonable and customary use in describing the\n      origin of the Work and reproducing the content of the NOTICE file.\n\n   7. Disclaimer of Warranty. Unless required by applicable law or\n      agreed to in writing, Licensor provides the Work (and each\n      Contributor provides its Contributions) on an \"AS IS\" BASIS,\n      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n      implied, including, without limitation, any warranties or conditions\n      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A\n      PARTICULAR PURPOSE. You are solely responsible for determining the\n      appropriateness of using or redistributing the Work and assume any\n      risks associated with Your exercise of permissions under this License.\n\n   8. Limitation of Liability. In no event and under no legal theory,\n      whether in tort (including negligence), contract, or otherwise,\n      unless required by applicable law (such as deliberate and grossly\n      negligent acts) or agreed to in writing, shall any Contributor be\n      liable to You for damages, including any direct, indirect, special,\n      incidental, or consequential damages of any character arising as a\n      result of this License or out of the use or inability to use the\n      Work (including but not limited to damages for loss of goodwill,\n      work stoppage, computer failure or malfunction, or any and all\n      other commercial damages or losses), even if such Contributor\n      has been advised of the possibility of such damages.\n\n   9. Accepting Warranty or Additional Liability. While redistributing\n      the Work or Derivative Works thereof, You may choose to offer,\n      and charge a fee for, acceptance of support, warranty, indemnity,\n      or other liability obligations and/or rights consistent with this\n      License. However, in accepting such obligations, You may act only\n      on Your own behalf and on Your sole responsibility, not on behalf\n      of any other Contributor, and only if You agree to indemnify,\n      defend, and hold each Contributor harmless for any liability\n      incurred by, or claims asserted against, such Contributor by reason\n      of your accepting any such warranty or additional liability.\n\n   END OF TERMS AND CONDITIONS\n\n   APPENDIX: How to apply the Apache License to your work.\n\n      To apply the Apache License to your work, attach the following\n      boilerplate notice, with the fields enclosed by brackets \"[]\"\n      replaced with your own identifying information. (Don't include\n      the brackets!)  The text should be enclosed in the appropriate\n      comment syntax for the file format. We also recommend that a\n      file or class name and description of purpose be included on the\n      same \"printed page\" as the copyright notice for easier\n      identification within third-party archives.\n\n   Copyright [2021] [SwinIR Authors]\n\n   Licensed under the Apache License, Version 2.0 (the \"License\");\n   you may not use this file except in compliance with the License.\n   You may obtain a copy of the License at\n\n       http://www.apache.org/licenses/LICENSE-2.0\n\n   Unless required by applicable law or agreed to in writing, software\n   distributed under the License is distributed on an \"AS IS\" BASIS,\n   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n   See the License for the specific language governing permissions and\n   limitations under the License.\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/LICENSE-SwinIR",
    "content": "                                 Apache License\n                           Version 2.0, January 2004\n                        http://www.apache.org/licenses/\n\n   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n   1. Definitions.\n\n      \"License\" shall mean the terms and conditions for use, reproduction,\n      and distribution as defined by Sections 1 through 9 of this document.\n\n      \"Licensor\" shall mean the copyright owner or entity authorized by\n      the copyright owner that is granting the License.\n\n      \"Legal Entity\" shall mean the union of the acting entity and all\n      other entities that control, are controlled by, or are under common\n      control with that entity. For the purposes of this definition,\n      \"control\" means (i) the power, direct or indirect, to cause the\n      direction or management of such entity, whether by contract or\n      otherwise, or (ii) ownership of fifty percent (50%) or more of the\n      outstanding shares, or (iii) beneficial ownership of such entity.\n\n      \"You\" (or \"Your\") shall mean an individual or Legal Entity\n      exercising permissions granted by this License.\n\n      \"Source\" form shall mean the preferred form for making modifications,\n      including but not limited to software source code, documentation\n      source, and configuration files.\n\n      \"Object\" form shall mean any form resulting from mechanical\n      transformation or translation of a Source form, including but\n      not limited to compiled object code, generated documentation,\n      and conversions to other media types.\n\n      \"Work\" shall mean the work of authorship, whether in Source or\n      Object form, made available under the License, as indicated by a\n      copyright notice that is included in or attached to the work\n      (an example is provided in the Appendix below).\n\n      \"Derivative Works\" shall mean any work, whether in Source or Object\n      form, that is based on (or derived from) the Work and for which the\n      editorial revisions, annotations, elaborations, or other modifications\n      represent, as a whole, an original work of authorship. For the purposes\n      of this License, Derivative Works shall not include works that remain\n      separable from, or merely link (or bind by name) to the interfaces of,\n      the Work and Derivative Works thereof.\n\n      \"Contribution\" shall mean any work of authorship, including\n      the original version of the Work and any modifications or additions\n      to that Work or Derivative Works thereof, that is intentionally\n      submitted to Licensor for inclusion in the Work by the copyright owner\n      or by an individual or Legal Entity authorized to submit on behalf of\n      the copyright owner. For the purposes of this definition, \"submitted\"\n      means any form of electronic, verbal, or written communication sent\n      to the Licensor or its representatives, including but not limited to\n      communication on electronic mailing lists, source code control systems,\n      and issue tracking systems that are managed by, or on behalf of, the\n      Licensor for the purpose of discussing and improving the Work, but\n      excluding communication that is conspicuously marked or otherwise\n      designated in writing by the copyright owner as \"Not a Contribution.\"\n\n      \"Contributor\" shall mean Licensor and any individual or Legal Entity\n      on behalf of whom a Contribution has been received by Licensor and\n      subsequently incorporated within the Work.\n\n   2. Grant of Copyright License. Subject to the terms and conditions of\n      this License, each Contributor hereby grants to You a perpetual,\n      worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n      copyright license to reproduce, prepare Derivative Works of,\n      publicly display, publicly perform, sublicense, and distribute the\n      Work and such Derivative Works in Source or Object form.\n\n   3. Grant of Patent License. Subject to the terms and conditions of\n      this License, each Contributor hereby grants to You a perpetual,\n      worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n      (except as stated in this section) patent license to make, have made,\n      use, offer to sell, sell, import, and otherwise transfer the Work,\n      where such license applies only to those patent claims licensable\n      by such Contributor that are necessarily infringed by their\n      Contribution(s) alone or by combination of their Contribution(s)\n      with the Work to which such Contribution(s) was submitted. If You\n      institute patent litigation against any entity (including a\n      cross-claim or counterclaim in a lawsuit) alleging that the Work\n      or a Contribution incorporated within the Work constitutes direct\n      or contributory patent infringement, then any patent licenses\n      granted to You under this License for that Work shall terminate\n      as of the date such litigation is filed.\n\n   4. Redistribution. You may reproduce and distribute copies of the\n      Work or Derivative Works thereof in any medium, with or without\n      modifications, and in Source or Object form, provided that You\n      meet the following conditions:\n\n      (a) You must give any other recipients of the Work or\n          Derivative Works a copy of this License; and\n\n      (b) You must cause any modified files to carry prominent notices\n          stating that You changed the files; and\n\n      (c) You must retain, in the Source form of any Derivative Works\n          that You distribute, all copyright, patent, trademark, and\n          attribution notices from the Source form of the Work,\n          excluding those notices that do not pertain to any part of\n          the Derivative Works; and\n\n      (d) If the Work includes a \"NOTICE\" text file as part of its\n          distribution, then any Derivative Works that You distribute must\n          include a readable copy of the attribution notices contained\n          within such NOTICE file, excluding those notices that do not\n          pertain to any part of the Derivative Works, in at least one\n          of the following places: within a NOTICE text file distributed\n          as part of the Derivative Works; within the Source form or\n          documentation, if provided along with the Derivative Works; or,\n          within a display generated by the Derivative Works, if and\n          wherever such third-party notices normally appear. The contents\n          of the NOTICE file are for informational purposes only and\n          do not modify the License. You may add Your own attribution\n          notices within Derivative Works that You distribute, alongside\n          or as an addendum to the NOTICE text from the Work, provided\n          that such additional attribution notices cannot be construed\n          as modifying the License.\n\n      You may add Your own copyright statement to Your modifications and\n      may provide additional or different license terms and conditions\n      for use, reproduction, or distribution of Your modifications, or\n      for any such Derivative Works as a whole, provided Your use,\n      reproduction, and distribution of the Work otherwise complies with\n      the conditions stated in this License.\n\n   5. Submission of Contributions. Unless You explicitly state otherwise,\n      any Contribution intentionally submitted for inclusion in the Work\n      by You to the Licensor shall be under the terms and conditions of\n      this License, without any additional terms or conditions.\n      Notwithstanding the above, nothing herein shall supersede or modify\n      the terms of any separate license agreement you may have executed\n      with Licensor regarding such Contributions.\n\n   6. Trademarks. This License does not grant permission to use the trade\n      names, trademarks, service marks, or product names of the Licensor,\n      except as required for reasonable and customary use in describing the\n      origin of the Work and reproducing the content of the NOTICE file.\n\n   7. Disclaimer of Warranty. Unless required by applicable law or\n      agreed to in writing, Licensor provides the Work (and each\n      Contributor provides its Contributions) on an \"AS IS\" BASIS,\n      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n      implied, including, without limitation, any warranties or conditions\n      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A\n      PARTICULAR PURPOSE. You are solely responsible for determining the\n      appropriateness of using or redistributing the Work and assume any\n      risks associated with Your exercise of permissions under this License.\n\n   8. Limitation of Liability. In no event and under no legal theory,\n      whether in tort (including negligence), contract, or otherwise,\n      unless required by applicable law (such as deliberate and grossly\n      negligent acts) or agreed to in writing, shall any Contributor be\n      liable to You for damages, including any direct, indirect, special,\n      incidental, or consequential damages of any character arising as a\n      result of this License or out of the use or inability to use the\n      Work (including but not limited to damages for loss of goodwill,\n      work stoppage, computer failure or malfunction, or any and all\n      other commercial damages or losses), even if such Contributor\n      has been advised of the possibility of such damages.\n\n   9. Accepting Warranty or Additional Liability. While redistributing\n      the Work or Derivative Works thereof, You may choose to offer,\n      and charge a fee for, acceptance of support, warranty, indemnity,\n      or other liability obligations and/or rights consistent with this\n      License. However, in accepting such obligations, You may act only\n      on Your own behalf and on Your sole responsibility, not on behalf\n      of any other Contributor, and only if You agree to indemnify,\n      defend, and hold each Contributor harmless for any liability\n      incurred by, or claims asserted against, such Contributor by reason\n      of your accepting any such warranty or additional liability.\n\n   END OF TERMS AND CONDITIONS\n\n   APPENDIX: How to apply the Apache License to your work.\n\n      To apply the Apache License to your work, attach the following\n      boilerplate notice, with the fields enclosed by brackets \"[]\"\n      replaced with your own identifying information. (Don't include\n      the brackets!)  The text should be enclosed in the appropriate\n      comment syntax for the file format. We also recommend that a\n      file or class name and description of purpose be included on the\n      same \"printed page\" as the copyright notice for easier\n      identification within third-party archives.\n\n   Copyright [2021] [SwinIR Authors]\n\n   Licensed under the Apache License, Version 2.0 (the \"License\");\n   you may not use this file except in compliance with the License.\n   You may obtain a copy of the License at\n\n       http://www.apache.org/licenses/LICENSE-2.0\n\n   Unless required by applicable law or agreed to in writing, software\n   distributed under the License is distributed on an \"AS IS\" BASIS,\n   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n   See the License for the specific language governing permissions and\n   limitations under the License.\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/LICENSE-lama",
    "content": "                                 Apache License\n                           Version 2.0, January 2004\n                        http://www.apache.org/licenses/\n\n   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n   1. Definitions.\n\n      \"License\" shall mean the terms and conditions for use, reproduction,\n      and distribution as defined by Sections 1 through 9 of this document.\n\n      \"Licensor\" shall mean the copyright owner or entity authorized by\n      the copyright owner that is granting the License.\n\n      \"Legal Entity\" shall mean the union of the acting entity and all\n      other entities that control, are controlled by, or are under common\n      control with that entity. For the purposes of this definition,\n      \"control\" means (i) the power, direct or indirect, to cause the\n      direction or management of such entity, whether by contract or\n      otherwise, or (ii) ownership of fifty percent (50%) or more of the\n      outstanding shares, or (iii) beneficial ownership of such entity.\n\n      \"You\" (or \"Your\") shall mean an individual or Legal Entity\n      exercising permissions granted by this License.\n\n      \"Source\" form shall mean the preferred form for making modifications,\n      including but not limited to software source code, documentation\n      source, and configuration files.\n\n      \"Object\" form shall mean any form resulting from mechanical\n      transformation or translation of a Source form, including but\n      not limited to compiled object code, generated documentation,\n      and conversions to other media types.\n\n      \"Work\" shall mean the work of authorship, whether in Source or\n      Object form, made available under the License, as indicated by a\n      copyright notice that is included in or attached to the work\n      (an example is provided in the Appendix below).\n\n      \"Derivative Works\" shall mean any work, whether in Source or Object\n      form, that is based on (or derived from) the Work and for which the\n      editorial revisions, annotations, elaborations, or other modifications\n      represent, as a whole, an original work of authorship. For the purposes\n      of this License, Derivative Works shall not include works that remain\n      separable from, or merely link (or bind by name) to the interfaces of,\n      the Work and Derivative Works thereof.\n\n      \"Contribution\" shall mean any work of authorship, including\n      the original version of the Work and any modifications or additions\n      to that Work or Derivative Works thereof, that is intentionally\n      submitted to Licensor for inclusion in the Work by the copyright owner\n      or by an individual or Legal Entity authorized to submit on behalf of\n      the copyright owner. For the purposes of this definition, \"submitted\"\n      means any form of electronic, verbal, or written communication sent\n      to the Licensor or its representatives, including but not limited to\n      communication on electronic mailing lists, source code control systems,\n      and issue tracking systems that are managed by, or on behalf of, the\n      Licensor for the purpose of discussing and improving the Work, but\n      excluding communication that is conspicuously marked or otherwise\n      designated in writing by the copyright owner as \"Not a Contribution.\"\n\n      \"Contributor\" shall mean Licensor and any individual or Legal Entity\n      on behalf of whom a Contribution has been received by Licensor and\n      subsequently incorporated within the Work.\n\n   2. Grant of Copyright License. Subject to the terms and conditions of\n      this License, each Contributor hereby grants to You a perpetual,\n      worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n      copyright license to reproduce, prepare Derivative Works of,\n      publicly display, publicly perform, sublicense, and distribute the\n      Work and such Derivative Works in Source or Object form.\n\n   3. Grant of Patent License. Subject to the terms and conditions of\n      this License, each Contributor hereby grants to You a perpetual,\n      worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n      (except as stated in this section) patent license to make, have made,\n      use, offer to sell, sell, import, and otherwise transfer the Work,\n      where such license applies only to those patent claims licensable\n      by such Contributor that are necessarily infringed by their\n      Contribution(s) alone or by combination of their Contribution(s)\n      with the Work to which such Contribution(s) was submitted. If You\n      institute patent litigation against any entity (including a\n      cross-claim or counterclaim in a lawsuit) alleging that the Work\n      or a Contribution incorporated within the Work constitutes direct\n      or contributory patent infringement, then any patent licenses\n      granted to You under this License for that Work shall terminate\n      as of the date such litigation is filed.\n\n   4. Redistribution. You may reproduce and distribute copies of the\n      Work or Derivative Works thereof in any medium, with or without\n      modifications, and in Source or Object form, provided that You\n      meet the following conditions:\n\n      (a) You must give any other recipients of the Work or\n          Derivative Works a copy of this License; and\n\n      (b) You must cause any modified files to carry prominent notices\n          stating that You changed the files; and\n\n      (c) You must retain, in the Source form of any Derivative Works\n          that You distribute, all copyright, patent, trademark, and\n          attribution notices from the Source form of the Work,\n          excluding those notices that do not pertain to any part of\n          the Derivative Works; and\n\n      (d) If the Work includes a \"NOTICE\" text file as part of its\n          distribution, then any Derivative Works that You distribute must\n          include a readable copy of the attribution notices contained\n          within such NOTICE file, excluding those notices that do not\n          pertain to any part of the Derivative Works, in at least one\n          of the following places: within a NOTICE text file distributed\n          as part of the Derivative Works; within the Source form or\n          documentation, if provided along with the Derivative Works; or,\n          within a display generated by the Derivative Works, if and\n          wherever such third-party notices normally appear. The contents\n          of the NOTICE file are for informational purposes only and\n          do not modify the License. You may add Your own attribution\n          notices within Derivative Works that You distribute, alongside\n          or as an addendum to the NOTICE text from the Work, provided\n          that such additional attribution notices cannot be construed\n          as modifying the License.\n\n      You may add Your own copyright statement to Your modifications and\n      may provide additional or different license terms and conditions\n      for use, reproduction, or distribution of Your modifications, or\n      for any such Derivative Works as a whole, provided Your use,\n      reproduction, and distribution of the Work otherwise complies with\n      the conditions stated in this License.\n\n   5. Submission of Contributions. Unless You explicitly state otherwise,\n      any Contribution intentionally submitted for inclusion in the Work\n      by You to the Licensor shall be under the terms and conditions of\n      this License, without any additional terms or conditions.\n      Notwithstanding the above, nothing herein shall supersede or modify\n      the terms of any separate license agreement you may have executed\n      with Licensor regarding such Contributions.\n\n   6. Trademarks. This License does not grant permission to use the trade\n      names, trademarks, service marks, or product names of the Licensor,\n      except as required for reasonable and customary use in describing the\n      origin of the Work and reproducing the content of the NOTICE file.\n\n   7. Disclaimer of Warranty. Unless required by applicable law or\n      agreed to in writing, Licensor provides the Work (and each\n      Contributor provides its Contributions) on an \"AS IS\" BASIS,\n      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n      implied, including, without limitation, any warranties or conditions\n      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A\n      PARTICULAR PURPOSE. You are solely responsible for determining the\n      appropriateness of using or redistributing the Work and assume any\n      risks associated with Your exercise of permissions under this License.\n\n   8. Limitation of Liability. In no event and under no legal theory,\n      whether in tort (including negligence), contract, or otherwise,\n      unless required by applicable law (such as deliberate and grossly\n      negligent acts) or agreed to in writing, shall any Contributor be\n      liable to You for damages, including any direct, indirect, special,\n      incidental, or consequential damages of any character arising as a\n      result of this License or out of the use or inability to use the\n      Work (including but not limited to damages for loss of goodwill,\n      work stoppage, computer failure or malfunction, or any and all\n      other commercial damages or losses), even if such Contributor\n      has been advised of the possibility of such damages.\n\n   9. Accepting Warranty or Additional Liability. While redistributing\n      the Work or Derivative Works thereof, You may choose to offer,\n      and charge a fee for, acceptance of support, warranty, indemnity,\n      or other liability obligations and/or rights consistent with this\n      License. However, in accepting such obligations, You may act only\n      on Your own behalf and on Your sole responsibility, not on behalf\n      of any other Contributor, and only if You agree to indemnify,\n      defend, and hold each Contributor harmless for any liability\n      incurred by, or claims asserted against, such Contributor by reason\n      of your accepting any such warranty or additional liability.\n\n   END OF TERMS AND CONDITIONS\n\n   APPENDIX: How to apply the Apache License to your work.\n\n      To apply the Apache License to your work, attach the following\n      boilerplate notice, with the fields enclosed by brackets \"[]\"\n      replaced with your own identifying information. (Don't include\n      the brackets!)  The text should be enclosed in the appropriate\n      comment syntax for the file format. We also recommend that a\n      file or class name and description of purpose be included on the\n      same \"printed page\" as the copyright notice for easier\n      identification within third-party archives.\n\n   Copyright [2021] Samsung Research\n\n   Licensed under the Apache License, Version 2.0 (the \"License\");\n   you may not use this file except in compliance with the License.\n   You may obtain a copy of the License at\n\n       http://www.apache.org/licenses/LICENSE-2.0\n\n   Unless required by applicable law or agreed to in writing, software\n   distributed under the License is distributed on an \"AS IS\" BASIS,\n   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n   See the License for the specific language governing permissions and\n   limitations under the License.\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/LaMa.py",
    "content": "# pylint: skip-file\n\"\"\"\nModel adapted from advimman's lama project: https://github.com/advimman/lama\n\"\"\"\n\n# Fast Fourier Convolution NeurIPS 2020\n# original implementation https://github.com/pkumivision/FFC/blob/main/model_zoo/ffc.py\n# paper https://proceedings.neurips.cc/paper/2020/file/2fd5d41ec6cfab47e32164d5624269b1-Paper.pdf\n\nfrom typing import List\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torchvision.transforms.functional import InterpolationMode, rotate\n\n\nclass LearnableSpatialTransformWrapper(nn.Module):\n    def __init__(self, impl, pad_coef=0.5, angle_init_range=80, train_angle=True):\n        super().__init__()\n        self.impl = impl\n        self.angle = torch.rand(1) * angle_init_range\n        if train_angle:\n            self.angle = nn.Parameter(self.angle, requires_grad=True)\n        self.pad_coef = pad_coef\n\n    def forward(self, x):\n        if torch.is_tensor(x):\n            return self.inverse_transform(self.impl(self.transform(x)), x)\n        elif isinstance(x, tuple):\n            x_trans = tuple(self.transform(elem) for elem in x)\n            y_trans = self.impl(x_trans)\n            return tuple(\n                self.inverse_transform(elem, orig_x) for elem, orig_x in zip(y_trans, x)\n            )\n        else:\n            raise ValueError(f\"Unexpected input type {type(x)}\")\n\n    def transform(self, x):\n        height, width = x.shape[2:]\n        pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef)\n        x_padded = F.pad(x, [pad_w, pad_w, pad_h, pad_h], mode=\"reflect\")\n        x_padded_rotated = rotate(\n            x_padded, self.angle.to(x_padded), InterpolationMode.BILINEAR, fill=0\n        )\n\n        return x_padded_rotated\n\n    def inverse_transform(self, y_padded_rotated, orig_x):\n        height, width = orig_x.shape[2:]\n        pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef)\n\n        y_padded = rotate(\n            y_padded_rotated,\n            -self.angle.to(y_padded_rotated),\n            InterpolationMode.BILINEAR,\n            fill=0,\n        )\n        y_height, y_width = y_padded.shape[2:]\n        y = y_padded[:, :, pad_h : y_height - pad_h, pad_w : y_width - pad_w]\n        return y\n\n\nclass SELayer(nn.Module):\n    def __init__(self, channel, reduction=16):\n        super(SELayer, self).__init__()\n        self.avg_pool = nn.AdaptiveAvgPool2d(1)\n        self.fc = nn.Sequential(\n            nn.Linear(channel, channel // reduction, bias=False),\n            nn.ReLU(inplace=True),\n            nn.Linear(channel // reduction, channel, bias=False),\n            nn.Sigmoid(),\n        )\n\n    def forward(self, x):\n        b, c, _, _ = x.size()\n        y = self.avg_pool(x).view(b, c)\n        y = self.fc(y).view(b, c, 1, 1)\n        res = x * y.expand_as(x)\n        return res\n\n\nclass FourierUnit(nn.Module):\n    def __init__(\n        self,\n        in_channels,\n        out_channels,\n        groups=1,\n        spatial_scale_factor=None,\n        spatial_scale_mode=\"bilinear\",\n        spectral_pos_encoding=False,\n        use_se=False,\n        se_kwargs=None,\n        ffc3d=False,\n        fft_norm=\"ortho\",\n    ):\n        # bn_layer not used\n        super(FourierUnit, self).__init__()\n        self.groups = groups\n\n        self.conv_layer = torch.nn.Conv2d(\n            in_channels=in_channels * 2 + (2 if spectral_pos_encoding else 0),\n            out_channels=out_channels * 2,\n            kernel_size=1,\n            stride=1,\n            padding=0,\n            groups=self.groups,\n            bias=False,\n        )\n        self.bn = torch.nn.BatchNorm2d(out_channels * 2)\n        self.relu = torch.nn.ReLU(inplace=True)\n\n        # squeeze and excitation block\n        self.use_se = use_se\n        if use_se:\n            if se_kwargs is None:\n                se_kwargs = {}\n            self.se = SELayer(self.conv_layer.in_channels, **se_kwargs)\n\n        self.spatial_scale_factor = spatial_scale_factor\n        self.spatial_scale_mode = spatial_scale_mode\n        self.spectral_pos_encoding = spectral_pos_encoding\n        self.ffc3d = ffc3d\n        self.fft_norm = fft_norm\n\n    def forward(self, x):\n        half_check = False\n        if x.type() == \"torch.cuda.HalfTensor\":\n            # half only works on gpu anyway\n            half_check = True\n\n        batch = x.shape[0]\n\n        if self.spatial_scale_factor is not None:\n            orig_size = x.shape[-2:]\n            x = F.interpolate(\n                x,\n                scale_factor=self.spatial_scale_factor,\n                mode=self.spatial_scale_mode,\n                align_corners=False,\n            )\n\n        # (batch, c, h, w/2+1, 2)\n        fft_dim = (-3, -2, -1) if self.ffc3d else (-2, -1)\n        if half_check == True:\n            ffted = torch.fft.rfftn(\n                x.float(), dim=fft_dim, norm=self.fft_norm\n            )  # .type(torch.cuda.HalfTensor)\n        else:\n            ffted = torch.fft.rfftn(x, dim=fft_dim, norm=self.fft_norm)\n\n        ffted = torch.stack((ffted.real, ffted.imag), dim=-1)\n        ffted = ffted.permute(0, 1, 4, 2, 3).contiguous()  # (batch, c, 2, h, w/2+1)\n        ffted = ffted.view(\n            (\n                batch,\n                -1,\n            )\n            + ffted.size()[3:]\n        )\n\n        if self.spectral_pos_encoding:\n            height, width = ffted.shape[-2:]\n            coords_vert = (\n                torch.linspace(0, 1, height)[None, None, :, None]\n                .expand(batch, 1, height, width)\n                .to(ffted)\n            )\n            coords_hor = (\n                torch.linspace(0, 1, width)[None, None, None, :]\n                .expand(batch, 1, height, width)\n                .to(ffted)\n            )\n            ffted = torch.cat((coords_vert, coords_hor, ffted), dim=1)\n\n        if self.use_se:\n            ffted = self.se(ffted)\n\n        if half_check == True:\n            ffted = self.conv_layer(ffted.half())  # (batch, c*2, h, w/2+1)\n        else:\n            ffted = self.conv_layer(\n                ffted\n            )  # .type(torch.cuda.FloatTensor)  # (batch, c*2, h, w/2+1)\n\n        ffted = self.relu(self.bn(ffted))\n        # forcing to be always float\n        ffted = ffted.float()\n\n        ffted = (\n            ffted.view(\n                (\n                    batch,\n                    -1,\n                    2,\n                )\n                + ffted.size()[2:]\n            )\n            .permute(0, 1, 3, 4, 2)\n            .contiguous()\n        )  # (batch,c, t, h, w/2+1, 2)\n\n        ffted = torch.complex(ffted[..., 0], ffted[..., 1])\n\n        ifft_shape_slice = x.shape[-3:] if self.ffc3d else x.shape[-2:]\n        output = torch.fft.irfftn(\n            ffted, s=ifft_shape_slice, dim=fft_dim, norm=self.fft_norm\n        )\n\n        if half_check == True:\n            output = output.half()\n\n        if self.spatial_scale_factor is not None:\n            output = F.interpolate(\n                output,\n                size=orig_size,\n                mode=self.spatial_scale_mode,\n                align_corners=False,\n            )\n\n        return output\n\n\nclass SpectralTransform(nn.Module):\n    def __init__(\n        self,\n        in_channels,\n        out_channels,\n        stride=1,\n        groups=1,\n        enable_lfu=True,\n        separable_fu=False,\n        **fu_kwargs,\n    ):\n        # bn_layer not used\n        super(SpectralTransform, self).__init__()\n        self.enable_lfu = enable_lfu\n        if stride == 2:\n            self.downsample = nn.AvgPool2d(kernel_size=(2, 2), stride=2)\n        else:\n            self.downsample = nn.Identity()\n\n        self.stride = stride\n        self.conv1 = nn.Sequential(\n            nn.Conv2d(\n                in_channels, out_channels // 2, kernel_size=1, groups=groups, bias=False\n            ),\n            nn.BatchNorm2d(out_channels // 2),\n            nn.ReLU(inplace=True),\n        )\n        fu_class = FourierUnit\n        self.fu = fu_class(out_channels // 2, out_channels // 2, groups, **fu_kwargs)\n        if self.enable_lfu:\n            self.lfu = fu_class(out_channels // 2, out_channels // 2, groups)\n        self.conv2 = torch.nn.Conv2d(\n            out_channels // 2, out_channels, kernel_size=1, groups=groups, bias=False\n        )\n\n    def forward(self, x):\n        x = self.downsample(x)\n        x = self.conv1(x)\n        output = self.fu(x)\n\n        if self.enable_lfu:\n            _, c, h, _ = x.shape\n            split_no = 2\n            split_s = h // split_no\n            xs = torch.cat(\n                torch.split(x[:, : c // 4], split_s, dim=-2), dim=1\n            ).contiguous()\n            xs = torch.cat(torch.split(xs, split_s, dim=-1), dim=1).contiguous()\n            xs = self.lfu(xs)\n            xs = xs.repeat(1, 1, split_no, split_no).contiguous()\n        else:\n            xs = 0\n\n        output = self.conv2(x + output + xs)\n\n        return output\n\n\nclass FFC(nn.Module):\n    def __init__(\n        self,\n        in_channels,\n        out_channels,\n        kernel_size,\n        ratio_gin,\n        ratio_gout,\n        stride=1,\n        padding=0,\n        dilation=1,\n        groups=1,\n        bias=False,\n        enable_lfu=True,\n        padding_type=\"reflect\",\n        gated=False,\n        **spectral_kwargs,\n    ):\n        super(FFC, self).__init__()\n\n        assert stride == 1 or stride == 2, \"Stride should be 1 or 2.\"\n        self.stride = stride\n\n        in_cg = int(in_channels * ratio_gin)\n        in_cl = in_channels - in_cg\n        out_cg = int(out_channels * ratio_gout)\n        out_cl = out_channels - out_cg\n        # groups_g = 1 if groups == 1 else int(groups * ratio_gout)\n        # groups_l = 1 if groups == 1 else groups - groups_g\n\n        self.ratio_gin = ratio_gin\n        self.ratio_gout = ratio_gout\n        self.global_in_num = in_cg\n\n        module = nn.Identity if in_cl == 0 or out_cl == 0 else nn.Conv2d\n        self.convl2l = module(\n            in_cl,\n            out_cl,\n            kernel_size,\n            stride,\n            padding,\n            dilation,\n            groups,\n            bias,\n            padding_mode=padding_type,\n        )\n        module = nn.Identity if in_cl == 0 or out_cg == 0 else nn.Conv2d\n        self.convl2g = module(\n            in_cl,\n            out_cg,\n            kernel_size,\n            stride,\n            padding,\n            dilation,\n            groups,\n            bias,\n            padding_mode=padding_type,\n        )\n        module = nn.Identity if in_cg == 0 or out_cl == 0 else nn.Conv2d\n        self.convg2l = module(\n            in_cg,\n            out_cl,\n            kernel_size,\n            stride,\n            padding,\n            dilation,\n            groups,\n            bias,\n            padding_mode=padding_type,\n        )\n        module = nn.Identity if in_cg == 0 or out_cg == 0 else SpectralTransform\n        self.convg2g = module(\n            in_cg,\n            out_cg,\n            stride,\n            1 if groups == 1 else groups // 2,\n            enable_lfu,\n            **spectral_kwargs,\n        )\n\n        self.gated = gated\n        module = (\n            nn.Identity if in_cg == 0 or out_cl == 0 or not self.gated else nn.Conv2d\n        )\n        self.gate = module(in_channels, 2, 1)\n\n    def forward(self, x):\n        x_l, x_g = x if type(x) is tuple else (x, 0)\n        out_xl, out_xg = 0, 0\n\n        if self.gated:\n            total_input_parts = [x_l]\n            if torch.is_tensor(x_g):\n                total_input_parts.append(x_g)\n            total_input = torch.cat(total_input_parts, dim=1)\n\n            gates = torch.sigmoid(self.gate(total_input))\n            g2l_gate, l2g_gate = gates.chunk(2, dim=1)\n        else:\n            g2l_gate, l2g_gate = 1, 1\n\n        if self.ratio_gout != 1:\n            out_xl = self.convl2l(x_l) + self.convg2l(x_g) * g2l_gate\n        if self.ratio_gout != 0:\n            out_xg = self.convl2g(x_l) * l2g_gate + self.convg2g(x_g)\n\n        return out_xl, out_xg\n\n\nclass FFC_BN_ACT(nn.Module):\n    def __init__(\n        self,\n        in_channels,\n        out_channels,\n        kernel_size,\n        ratio_gin,\n        ratio_gout,\n        stride=1,\n        padding=0,\n        dilation=1,\n        groups=1,\n        bias=False,\n        norm_layer=nn.BatchNorm2d,\n        activation_layer=nn.Identity,\n        padding_type=\"reflect\",\n        enable_lfu=True,\n        **kwargs,\n    ):\n        super(FFC_BN_ACT, self).__init__()\n        self.ffc = FFC(\n            in_channels,\n            out_channels,\n            kernel_size,\n            ratio_gin,\n            ratio_gout,\n            stride,\n            padding,\n            dilation,\n            groups,\n            bias,\n            enable_lfu,\n            padding_type=padding_type,\n            **kwargs,\n        )\n        lnorm = nn.Identity if ratio_gout == 1 else norm_layer\n        gnorm = nn.Identity if ratio_gout == 0 else norm_layer\n        global_channels = int(out_channels * ratio_gout)\n        self.bn_l = lnorm(out_channels - global_channels)\n        self.bn_g = gnorm(global_channels)\n\n        lact = nn.Identity if ratio_gout == 1 else activation_layer\n        gact = nn.Identity if ratio_gout == 0 else activation_layer\n        self.act_l = lact(inplace=True)\n        self.act_g = gact(inplace=True)\n\n    def forward(self, x):\n        x_l, x_g = self.ffc(x)\n        x_l = self.act_l(self.bn_l(x_l))\n        x_g = self.act_g(self.bn_g(x_g))\n        return x_l, x_g\n\n\nclass FFCResnetBlock(nn.Module):\n    def __init__(\n        self,\n        dim,\n        padding_type,\n        norm_layer,\n        activation_layer=nn.ReLU,\n        dilation=1,\n        spatial_transform_kwargs=None,\n        inline=False,\n        **conv_kwargs,\n    ):\n        super().__init__()\n        self.conv1 = FFC_BN_ACT(\n            dim,\n            dim,\n            kernel_size=3,\n            padding=dilation,\n            dilation=dilation,\n            norm_layer=norm_layer,\n            activation_layer=activation_layer,\n            padding_type=padding_type,\n            **conv_kwargs,\n        )\n        self.conv2 = FFC_BN_ACT(\n            dim,\n            dim,\n            kernel_size=3,\n            padding=dilation,\n            dilation=dilation,\n            norm_layer=norm_layer,\n            activation_layer=activation_layer,\n            padding_type=padding_type,\n            **conv_kwargs,\n        )\n        if spatial_transform_kwargs is not None:\n            self.conv1 = LearnableSpatialTransformWrapper(\n                self.conv1, **spatial_transform_kwargs\n            )\n            self.conv2 = LearnableSpatialTransformWrapper(\n                self.conv2, **spatial_transform_kwargs\n            )\n        self.inline = inline\n\n    def forward(self, x):\n        if self.inline:\n            x_l, x_g = (\n                x[:, : -self.conv1.ffc.global_in_num],\n                x[:, -self.conv1.ffc.global_in_num :],\n            )\n        else:\n            x_l, x_g = x if type(x) is tuple else (x, 0)\n\n        id_l, id_g = x_l, x_g\n\n        x_l, x_g = self.conv1((x_l, x_g))\n        x_l, x_g = self.conv2((x_l, x_g))\n\n        x_l, x_g = id_l + x_l, id_g + x_g\n        out = x_l, x_g\n        if self.inline:\n            out = torch.cat(out, dim=1)\n        return out\n\n\nclass ConcatTupleLayer(nn.Module):\n    def forward(self, x):\n        assert isinstance(x, tuple)\n        x_l, x_g = x\n        assert torch.is_tensor(x_l) or torch.is_tensor(x_g)\n        if not torch.is_tensor(x_g):\n            return x_l\n        return torch.cat(x, dim=1)\n\n\nclass FFCResNetGenerator(nn.Module):\n    def __init__(\n        self,\n        input_nc,\n        output_nc,\n        ngf=64,\n        n_downsampling=3,\n        n_blocks=18,\n        norm_layer=nn.BatchNorm2d,\n        padding_type=\"reflect\",\n        activation_layer=nn.ReLU,\n        up_norm_layer=nn.BatchNorm2d,\n        up_activation=nn.ReLU(True),\n        init_conv_kwargs={},\n        downsample_conv_kwargs={},\n        resnet_conv_kwargs={},\n        spatial_transform_layers=None,\n        spatial_transform_kwargs={},\n        max_features=1024,\n        out_ffc=False,\n        out_ffc_kwargs={},\n    ):\n        assert n_blocks >= 0\n        super().__init__()\n        \"\"\"\n        init_conv_kwargs = {'ratio_gin': 0, 'ratio_gout': 0, 'enable_lfu': False}\n        downsample_conv_kwargs = {'ratio_gin': '${generator.init_conv_kwargs.ratio_gout}', 'ratio_gout': '${generator.downsample_conv_kwargs.ratio_gin}', 'enable_lfu': False}\n        resnet_conv_kwargs = {'ratio_gin': 0.75, 'ratio_gout': '${generator.resnet_conv_kwargs.ratio_gin}', 'enable_lfu': False}\n        spatial_transform_kwargs = {}\n        out_ffc_kwargs = {}\n        \"\"\"\n        \"\"\"\n        print(input_nc, output_nc, ngf, n_downsampling, n_blocks, norm_layer,\n                padding_type, activation_layer,\n                up_norm_layer, up_activation,\n                spatial_transform_layers,\n                add_out_act, max_features, out_ffc, file=sys.stderr)\n\n        4 3 64 3 18 <class 'torch.nn.modules.batchnorm.BatchNorm2d'>\n        reflect <class 'torch.nn.modules.activation.ReLU'>\n        <class 'torch.nn.modules.batchnorm.BatchNorm2d'>\n        ReLU(inplace=True)\n        None sigmoid 1024 False\n        \"\"\"\n        init_conv_kwargs = {\"ratio_gin\": 0, \"ratio_gout\": 0, \"enable_lfu\": False}\n        downsample_conv_kwargs = {\"ratio_gin\": 0, \"ratio_gout\": 0, \"enable_lfu\": False}\n        resnet_conv_kwargs = {\n            \"ratio_gin\": 0.75,\n            \"ratio_gout\": 0.75,\n            \"enable_lfu\": False,\n        }\n        spatial_transform_kwargs = {}\n        out_ffc_kwargs = {}\n\n        model = [\n            nn.ReflectionPad2d(3),\n            FFC_BN_ACT(\n                input_nc,\n                ngf,\n                kernel_size=7,\n                padding=0,\n                norm_layer=norm_layer,\n                activation_layer=activation_layer,\n                **init_conv_kwargs,\n            ),\n        ]\n\n        ### downsample\n        for i in range(n_downsampling):\n            mult = 2**i\n            if i == n_downsampling - 1:\n                cur_conv_kwargs = dict(downsample_conv_kwargs)\n                cur_conv_kwargs[\"ratio_gout\"] = resnet_conv_kwargs.get(\"ratio_gin\", 0)\n            else:\n                cur_conv_kwargs = downsample_conv_kwargs\n            model += [\n                FFC_BN_ACT(\n                    min(max_features, ngf * mult),\n                    min(max_features, ngf * mult * 2),\n                    kernel_size=3,\n                    stride=2,\n                    padding=1,\n                    norm_layer=norm_layer,\n                    activation_layer=activation_layer,\n                    **cur_conv_kwargs,\n                )\n            ]\n\n        mult = 2**n_downsampling\n        feats_num_bottleneck = min(max_features, ngf * mult)\n\n        ### resnet blocks\n        for i in range(n_blocks):\n            cur_resblock = FFCResnetBlock(\n                feats_num_bottleneck,\n                padding_type=padding_type,\n                activation_layer=activation_layer,\n                norm_layer=norm_layer,\n                **resnet_conv_kwargs,\n            )\n            if spatial_transform_layers is not None and i in spatial_transform_layers:\n                cur_resblock = LearnableSpatialTransformWrapper(\n                    cur_resblock, **spatial_transform_kwargs\n                )\n            model += [cur_resblock]\n\n        model += [ConcatTupleLayer()]\n\n        ### upsample\n        for i in range(n_downsampling):\n            mult = 2 ** (n_downsampling - i)\n            model += [\n                nn.ConvTranspose2d(\n                    min(max_features, ngf * mult),\n                    min(max_features, int(ngf * mult / 2)),\n                    kernel_size=3,\n                    stride=2,\n                    padding=1,\n                    output_padding=1,\n                ),\n                up_norm_layer(min(max_features, int(ngf * mult / 2))),\n                up_activation,\n            ]\n\n        if out_ffc:\n            model += [\n                FFCResnetBlock(\n                    ngf,\n                    padding_type=padding_type,\n                    activation_layer=activation_layer,\n                    norm_layer=norm_layer,\n                    inline=True,\n                    **out_ffc_kwargs,\n                )\n            ]\n\n        model += [\n            nn.ReflectionPad2d(3),\n            nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0),\n        ]\n        model.append(nn.Sigmoid())\n        self.model = nn.Sequential(*model)\n\n    def forward(self, image, mask):\n        return self.model(torch.cat([image, mask], dim=1))\n\n\nclass LaMa(nn.Module):\n    def __init__(self, state_dict) -> None:\n        super(LaMa, self).__init__()\n        self.model_arch = \"LaMa\"\n        self.sub_type = \"Inpaint\"\n        self.in_nc = 4\n        self.out_nc = 3\n        self.scale = 1\n\n        self.min_size = None\n        self.pad_mod = 8\n        self.pad_to_square = False\n\n        self.model = FFCResNetGenerator(self.in_nc, self.out_nc)\n        self.state = {\n            k.replace(\"generator.model\", \"model.model\"): v\n            for k, v in state_dict.items()\n        }\n\n        self.supports_fp16 = False\n        self.support_bf16 = True\n\n        self.load_state_dict(self.state, strict=False)\n\n    def forward(self, img, mask):\n        masked_img = img * (1 - mask)\n        inpainted_mask = mask * self.model.forward(masked_img, mask)\n        result = inpainted_mask + (1 - mask) * img\n        return result\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/OmniSR/ChannelAttention.py",
    "content": "import math\n\nimport torch.nn as nn\n\n\nclass CA_layer(nn.Module):\n    def __init__(self, channel, reduction=16):\n        super(CA_layer, self).__init__()\n        # global average pooling\n        self.gap = nn.AdaptiveAvgPool2d(1)\n        self.fc = nn.Sequential(\n            nn.Conv2d(channel, channel // reduction, kernel_size=(1, 1), bias=False),\n            nn.GELU(),\n            nn.Conv2d(channel // reduction, channel, kernel_size=(1, 1), bias=False),\n            # nn.Sigmoid()\n        )\n\n    def forward(self, x):\n        y = self.fc(self.gap(x))\n        return x * y.expand_as(x)\n\n\nclass Simple_CA_layer(nn.Module):\n    def __init__(self, channel):\n        super(Simple_CA_layer, self).__init__()\n        self.gap = nn.AdaptiveAvgPool2d(1)\n        self.fc = nn.Conv2d(\n            in_channels=channel,\n            out_channels=channel,\n            kernel_size=1,\n            padding=0,\n            stride=1,\n            groups=1,\n            bias=True,\n        )\n\n    def forward(self, x):\n        return x * self.fc(self.gap(x))\n\n\nclass ECA_layer(nn.Module):\n    \"\"\"Constructs a ECA module.\n    Args:\n        channel: Number of channels of the input feature map\n        k_size: Adaptive selection of kernel size\n    \"\"\"\n\n    def __init__(self, channel):\n        super(ECA_layer, self).__init__()\n\n        b = 1\n        gamma = 2\n        k_size = int(abs(math.log(channel, 2) + b) / gamma)\n        k_size = k_size if k_size % 2 else k_size + 1\n        self.avg_pool = nn.AdaptiveAvgPool2d(1)\n        self.conv = nn.Conv1d(\n            1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False\n        )\n        # self.sigmoid = nn.Sigmoid()\n\n    def forward(self, x):\n        # x: input features with shape [b, c, h, w]\n        # b, c, h, w = x.size()\n\n        # feature descriptor on the global spatial information\n        y = self.avg_pool(x)\n\n        # Two different branches of ECA module\n        y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)\n\n        # Multi-scale information fusion\n        # y = self.sigmoid(y)\n\n        return x * y.expand_as(x)\n\n\nclass ECA_MaxPool_layer(nn.Module):\n    \"\"\"Constructs a ECA module.\n    Args:\n        channel: Number of channels of the input feature map\n        k_size: Adaptive selection of kernel size\n    \"\"\"\n\n    def __init__(self, channel):\n        super(ECA_MaxPool_layer, self).__init__()\n\n        b = 1\n        gamma = 2\n        k_size = int(abs(math.log(channel, 2) + b) / gamma)\n        k_size = k_size if k_size % 2 else k_size + 1\n        self.max_pool = nn.AdaptiveMaxPool2d(1)\n        self.conv = nn.Conv1d(\n            1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False\n        )\n        # self.sigmoid = nn.Sigmoid()\n\n    def forward(self, x):\n        # x: input features with shape [b, c, h, w]\n        # b, c, h, w = x.size()\n\n        # feature descriptor on the global spatial information\n        y = self.max_pool(x)\n\n        # Two different branches of ECA module\n        y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)\n\n        # Multi-scale information fusion\n        # y = self.sigmoid(y)\n\n        return x * y.expand_as(x)\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/OmniSR/LICENSE",
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  },
  {
    "path": "ldm_patched/pfn/architecture/OmniSR/OSA.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n#############################################################\n# File: OSA.py\n# Created Date: Tuesday April 28th 2022\n# Author: Chen Xuanhong\n# Email: chenxuanhongzju@outlook.com\n# Last Modified:  Sunday, 23rd April 2023 3:07:42 pm\n# Modified By: Chen Xuanhong\n# Copyright (c) 2020 Shanghai Jiao Tong University\n#############################################################\n\nimport torch\nimport torch.nn.functional as F\nfrom einops import rearrange, repeat\nfrom einops.layers.torch import Rearrange, Reduce\nfrom torch import einsum, nn\n\nfrom .layernorm import LayerNorm2d\n\n# helpers\n\n\ndef exists(val):\n    return val is not None\n\n\ndef default(val, d):\n    return val if exists(val) else d\n\n\ndef cast_tuple(val, length=1):\n    return val if isinstance(val, tuple) else ((val,) * length)\n\n\n# helper classes\n\n\nclass PreNormResidual(nn.Module):\n    def __init__(self, dim, fn):\n        super().__init__()\n        self.norm = nn.LayerNorm(dim)\n        self.fn = fn\n\n    def forward(self, x):\n        return self.fn(self.norm(x)) + x\n\n\nclass Conv_PreNormResidual(nn.Module):\n    def __init__(self, dim, fn):\n        super().__init__()\n        self.norm = LayerNorm2d(dim)\n        self.fn = fn\n\n    def forward(self, x):\n        return self.fn(self.norm(x)) + x\n\n\nclass FeedForward(nn.Module):\n    def __init__(self, dim, mult=2, dropout=0.0):\n        super().__init__()\n        inner_dim = int(dim * mult)\n        self.net = nn.Sequential(\n            nn.Linear(dim, inner_dim),\n            nn.GELU(),\n            nn.Dropout(dropout),\n            nn.Linear(inner_dim, dim),\n            nn.Dropout(dropout),\n        )\n\n    def forward(self, x):\n        return self.net(x)\n\n\nclass Conv_FeedForward(nn.Module):\n    def __init__(self, dim, mult=2, dropout=0.0):\n        super().__init__()\n        inner_dim = int(dim * mult)\n        self.net = nn.Sequential(\n            nn.Conv2d(dim, inner_dim, 1, 1, 0),\n            nn.GELU(),\n            nn.Dropout(dropout),\n            nn.Conv2d(inner_dim, dim, 1, 1, 0),\n            nn.Dropout(dropout),\n        )\n\n    def forward(self, x):\n        return self.net(x)\n\n\nclass Gated_Conv_FeedForward(nn.Module):\n    def __init__(self, dim, mult=1, bias=False, dropout=0.0):\n        super().__init__()\n\n        hidden_features = int(dim * mult)\n\n        self.project_in = nn.Conv2d(dim, hidden_features * 2, kernel_size=1, bias=bias)\n\n        self.dwconv = nn.Conv2d(\n            hidden_features * 2,\n            hidden_features * 2,\n            kernel_size=3,\n            stride=1,\n            padding=1,\n            groups=hidden_features * 2,\n            bias=bias,\n        )\n\n        self.project_out = nn.Conv2d(hidden_features, dim, kernel_size=1, bias=bias)\n\n    def forward(self, x):\n        x = self.project_in(x)\n        x1, x2 = self.dwconv(x).chunk(2, dim=1)\n        x = F.gelu(x1) * x2\n        x = self.project_out(x)\n        return x\n\n\n# MBConv\n\n\nclass SqueezeExcitation(nn.Module):\n    def __init__(self, dim, shrinkage_rate=0.25):\n        super().__init__()\n        hidden_dim = int(dim * shrinkage_rate)\n\n        self.gate = nn.Sequential(\n            Reduce(\"b c h w -> b c\", \"mean\"),\n            nn.Linear(dim, hidden_dim, bias=False),\n            nn.SiLU(),\n            nn.Linear(hidden_dim, dim, bias=False),\n            nn.Sigmoid(),\n            Rearrange(\"b c -> b c 1 1\"),\n        )\n\n    def forward(self, x):\n        return x * self.gate(x)\n\n\nclass MBConvResidual(nn.Module):\n    def __init__(self, fn, dropout=0.0):\n        super().__init__()\n        self.fn = fn\n        self.dropsample = Dropsample(dropout)\n\n    def forward(self, x):\n        out = self.fn(x)\n        out = self.dropsample(out)\n        return out + x\n\n\nclass Dropsample(nn.Module):\n    def __init__(self, prob=0):\n        super().__init__()\n        self.prob = prob\n\n    def forward(self, x):\n        device = x.device\n\n        if self.prob == 0.0 or (not self.training):\n            return x\n\n        keep_mask = (\n            torch.FloatTensor((x.shape[0], 1, 1, 1), device=device).uniform_()\n            > self.prob\n        )\n        return x * keep_mask / (1 - self.prob)\n\n\ndef MBConv(\n    dim_in, dim_out, *, downsample, expansion_rate=4, shrinkage_rate=0.25, dropout=0.0\n):\n    hidden_dim = int(expansion_rate * dim_out)\n    stride = 2 if downsample else 1\n\n    net = nn.Sequential(\n        nn.Conv2d(dim_in, hidden_dim, 1),\n        # nn.BatchNorm2d(hidden_dim),\n        nn.GELU(),\n        nn.Conv2d(\n            hidden_dim, hidden_dim, 3, stride=stride, padding=1, groups=hidden_dim\n        ),\n        # nn.BatchNorm2d(hidden_dim),\n        nn.GELU(),\n        SqueezeExcitation(hidden_dim, shrinkage_rate=shrinkage_rate),\n        nn.Conv2d(hidden_dim, dim_out, 1),\n        # nn.BatchNorm2d(dim_out)\n    )\n\n    if dim_in == dim_out and not downsample:\n        net = MBConvResidual(net, dropout=dropout)\n\n    return net\n\n\n# attention related classes\nclass Attention(nn.Module):\n    def __init__(\n        self,\n        dim,\n        dim_head=32,\n        dropout=0.0,\n        window_size=7,\n        with_pe=True,\n    ):\n        super().__init__()\n        assert (\n            dim % dim_head\n        ) == 0, \"dimension should be divisible by dimension per head\"\n\n        self.heads = dim // dim_head\n        self.scale = dim_head**-0.5\n        self.with_pe = with_pe\n\n        self.to_qkv = nn.Linear(dim, dim * 3, bias=False)\n\n        self.attend = nn.Sequential(nn.Softmax(dim=-1), nn.Dropout(dropout))\n\n        self.to_out = nn.Sequential(\n            nn.Linear(dim, dim, bias=False), nn.Dropout(dropout)\n        )\n\n        # relative positional bias\n        if self.with_pe:\n            self.rel_pos_bias = nn.Embedding((2 * window_size - 1) ** 2, self.heads)\n\n            pos = torch.arange(window_size)\n            grid = torch.stack(torch.meshgrid(pos, pos))\n            grid = rearrange(grid, \"c i j -> (i j) c\")\n            rel_pos = rearrange(grid, \"i ... -> i 1 ...\") - rearrange(\n                grid, \"j ... -> 1 j ...\"\n            )\n            rel_pos += window_size - 1\n            rel_pos_indices = (rel_pos * torch.tensor([2 * window_size - 1, 1])).sum(\n                dim=-1\n            )\n\n            self.register_buffer(\"rel_pos_indices\", rel_pos_indices, persistent=False)\n\n    def forward(self, x):\n        batch, height, width, window_height, window_width, _, device, h = (\n            *x.shape,\n            x.device,\n            self.heads,\n        )\n\n        # flatten\n\n        x = rearrange(x, \"b x y w1 w2 d -> (b x y) (w1 w2) d\")\n\n        # project for queries, keys, values\n\n        q, k, v = self.to_qkv(x).chunk(3, dim=-1)\n\n        # split heads\n\n        q, k, v = map(lambda t: rearrange(t, \"b n (h d ) -> b h n d\", h=h), (q, k, v))\n\n        # scale\n\n        q = q * self.scale\n\n        # sim\n\n        sim = einsum(\"b h i d, b h j d -> b h i j\", q, k)\n\n        # add positional bias\n        if self.with_pe:\n            bias = self.rel_pos_bias(self.rel_pos_indices)\n            sim = sim + rearrange(bias, \"i j h -> h i j\")\n\n        # attention\n\n        attn = self.attend(sim)\n\n        # aggregate\n\n        out = einsum(\"b h i j, b h j d -> b h i d\", attn, v)\n\n        # merge heads\n\n        out = rearrange(\n            out, \"b h (w1 w2) d -> b w1 w2 (h d)\", w1=window_height, w2=window_width\n        )\n\n        # combine heads out\n\n        out = self.to_out(out)\n        return rearrange(out, \"(b x y) ... -> b x y ...\", x=height, y=width)\n\n\nclass Block_Attention(nn.Module):\n    def __init__(\n        self,\n        dim,\n        dim_head=32,\n        bias=False,\n        dropout=0.0,\n        window_size=7,\n        with_pe=True,\n    ):\n        super().__init__()\n        assert (\n            dim % dim_head\n        ) == 0, \"dimension should be divisible by dimension per head\"\n\n        self.heads = dim // dim_head\n        self.ps = window_size\n        self.scale = dim_head**-0.5\n        self.with_pe = with_pe\n\n        self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias)\n        self.qkv_dwconv = nn.Conv2d(\n            dim * 3,\n            dim * 3,\n            kernel_size=3,\n            stride=1,\n            padding=1,\n            groups=dim * 3,\n            bias=bias,\n        )\n\n        self.attend = nn.Sequential(nn.Softmax(dim=-1), nn.Dropout(dropout))\n\n        self.to_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias)\n\n    def forward(self, x):\n        # project for queries, keys, values\n        b, c, h, w = x.shape\n\n        qkv = self.qkv_dwconv(self.qkv(x))\n        q, k, v = qkv.chunk(3, dim=1)\n\n        # split heads\n\n        q, k, v = map(\n            lambda t: rearrange(\n                t,\n                \"b (h d) (x w1) (y w2) -> (b x y) h (w1 w2) d\",\n                h=self.heads,\n                w1=self.ps,\n                w2=self.ps,\n            ),\n            (q, k, v),\n        )\n\n        # scale\n\n        q = q * self.scale\n\n        # sim\n\n        sim = einsum(\"b h i d, b h j d -> b h i j\", q, k)\n\n        # attention\n        attn = self.attend(sim)\n\n        # aggregate\n\n        out = einsum(\"b h i j, b h j d -> b h i d\", attn, v)\n\n        # merge heads\n        out = rearrange(\n            out,\n            \"(b x y) head (w1 w2) d -> b (head d) (x w1) (y w2)\",\n            x=h // self.ps,\n            y=w // self.ps,\n            head=self.heads,\n            w1=self.ps,\n            w2=self.ps,\n        )\n\n        out = self.to_out(out)\n        return out\n\n\nclass Channel_Attention(nn.Module):\n    def __init__(self, dim, heads, bias=False, dropout=0.0, window_size=7):\n        super(Channel_Attention, self).__init__()\n        self.heads = heads\n\n        self.temperature = nn.Parameter(torch.ones(heads, 1, 1))\n\n        self.ps = window_size\n\n        self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias)\n        self.qkv_dwconv = nn.Conv2d(\n            dim * 3,\n            dim * 3,\n            kernel_size=3,\n            stride=1,\n            padding=1,\n            groups=dim * 3,\n            bias=bias,\n        )\n        self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias)\n\n    def forward(self, x):\n        b, c, h, w = x.shape\n\n        qkv = self.qkv_dwconv(self.qkv(x))\n        qkv = qkv.chunk(3, dim=1)\n\n        q, k, v = map(\n            lambda t: rearrange(\n                t,\n                \"b (head d) (h ph) (w pw) -> b (h w) head d (ph pw)\",\n                ph=self.ps,\n                pw=self.ps,\n                head=self.heads,\n            ),\n            qkv,\n        )\n\n        q = F.normalize(q, dim=-1)\n        k = F.normalize(k, dim=-1)\n\n        attn = (q @ k.transpose(-2, -1)) * self.temperature\n        attn = attn.softmax(dim=-1)\n        out = attn @ v\n\n        out = rearrange(\n            out,\n            \"b (h w) head d (ph pw) -> b (head d) (h ph) (w pw)\",\n            h=h // self.ps,\n            w=w // self.ps,\n            ph=self.ps,\n            pw=self.ps,\n            head=self.heads,\n        )\n\n        out = self.project_out(out)\n\n        return out\n\n\nclass Channel_Attention_grid(nn.Module):\n    def __init__(self, dim, heads, bias=False, dropout=0.0, window_size=7):\n        super(Channel_Attention_grid, self).__init__()\n        self.heads = heads\n\n        self.temperature = nn.Parameter(torch.ones(heads, 1, 1))\n\n        self.ps = window_size\n\n        self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias)\n        self.qkv_dwconv = nn.Conv2d(\n            dim * 3,\n            dim * 3,\n            kernel_size=3,\n            stride=1,\n            padding=1,\n            groups=dim * 3,\n            bias=bias,\n        )\n        self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias)\n\n    def forward(self, x):\n        b, c, h, w = x.shape\n\n        qkv = self.qkv_dwconv(self.qkv(x))\n        qkv = qkv.chunk(3, dim=1)\n\n        q, k, v = map(\n            lambda t: rearrange(\n                t,\n                \"b (head d) (h ph) (w pw) -> b (ph pw) head d (h w)\",\n                ph=self.ps,\n                pw=self.ps,\n                head=self.heads,\n            ),\n            qkv,\n        )\n\n        q = F.normalize(q, dim=-1)\n        k = F.normalize(k, dim=-1)\n\n        attn = (q @ k.transpose(-2, -1)) * self.temperature\n        attn = attn.softmax(dim=-1)\n        out = attn @ v\n\n        out = rearrange(\n            out,\n            \"b (ph pw) head d (h w) -> b (head d) (h ph) (w pw)\",\n            h=h // self.ps,\n            w=w // self.ps,\n            ph=self.ps,\n            pw=self.ps,\n            head=self.heads,\n        )\n\n        out = self.project_out(out)\n\n        return out\n\n\nclass OSA_Block(nn.Module):\n    def __init__(\n        self,\n        channel_num=64,\n        bias=True,\n        ffn_bias=True,\n        window_size=8,\n        with_pe=False,\n        dropout=0.0,\n    ):\n        super(OSA_Block, self).__init__()\n\n        w = window_size\n\n        self.layer = nn.Sequential(\n            MBConv(\n                channel_num,\n                channel_num,\n                downsample=False,\n                expansion_rate=1,\n                shrinkage_rate=0.25,\n            ),\n            Rearrange(\n                \"b d (x w1) (y w2) -> b x y w1 w2 d\", w1=w, w2=w\n            ),  # block-like attention\n            PreNormResidual(\n                channel_num,\n                Attention(\n                    dim=channel_num,\n                    dim_head=channel_num // 4,\n                    dropout=dropout,\n                    window_size=window_size,\n                    with_pe=with_pe,\n                ),\n            ),\n            Rearrange(\"b x y w1 w2 d -> b d (x w1) (y w2)\"),\n            Conv_PreNormResidual(\n                channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout)\n            ),\n            # channel-like attention\n            Conv_PreNormResidual(\n                channel_num,\n                Channel_Attention(\n                    dim=channel_num, heads=4, dropout=dropout, window_size=window_size\n                ),\n            ),\n            Conv_PreNormResidual(\n                channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout)\n            ),\n            Rearrange(\n                \"b d (w1 x) (w2 y) -> b x y w1 w2 d\", w1=w, w2=w\n            ),  # grid-like attention\n            PreNormResidual(\n                channel_num,\n                Attention(\n                    dim=channel_num,\n                    dim_head=channel_num // 4,\n                    dropout=dropout,\n                    window_size=window_size,\n                    with_pe=with_pe,\n                ),\n            ),\n            Rearrange(\"b x y w1 w2 d -> b d (w1 x) (w2 y)\"),\n            Conv_PreNormResidual(\n                channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout)\n            ),\n            # channel-like attention\n            Conv_PreNormResidual(\n                channel_num,\n                Channel_Attention_grid(\n                    dim=channel_num, heads=4, dropout=dropout, window_size=window_size\n                ),\n            ),\n            Conv_PreNormResidual(\n                channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout)\n            ),\n        )\n\n    def forward(self, x):\n        out = self.layer(x)\n        return out\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/OmniSR/OSAG.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n#############################################################\n# File: OSAG.py\n# Created Date: Tuesday April 28th 2022\n# Author: Chen Xuanhong\n# Email: chenxuanhongzju@outlook.com\n# Last Modified:  Sunday, 23rd April 2023 3:08:49 pm\n# Modified By: Chen Xuanhong\n# Copyright (c) 2020 Shanghai Jiao Tong University\n#############################################################\n\n\nimport torch.nn as nn\n\nfrom .esa import ESA\nfrom .OSA import OSA_Block\n\n\nclass OSAG(nn.Module):\n    def __init__(\n        self,\n        channel_num=64,\n        bias=True,\n        block_num=4,\n        ffn_bias=False,\n        window_size=0,\n        pe=False,\n    ):\n        super(OSAG, self).__init__()\n\n        # print(\"window_size: %d\" % (window_size))\n        # print(\"with_pe\", pe)\n        # print(\"ffn_bias: %d\" % (ffn_bias))\n\n        # block_script_name = kwargs.get(\"block_script_name\", \"OSA\")\n        # block_class_name = kwargs.get(\"block_class_name\", \"OSA_Block\")\n\n        # script_name = \".\" + block_script_name\n        # package = __import__(script_name, fromlist=True)\n        block_class = OSA_Block  # getattr(package, block_class_name)\n        group_list = []\n        for _ in range(block_num):\n            temp_res = block_class(\n                channel_num,\n                bias,\n                ffn_bias=ffn_bias,\n                window_size=window_size,\n                with_pe=pe,\n            )\n            group_list.append(temp_res)\n        group_list.append(nn.Conv2d(channel_num, channel_num, 1, 1, 0, bias=bias))\n        self.residual_layer = nn.Sequential(*group_list)\n        esa_channel = max(channel_num // 4, 16)\n        self.esa = ESA(esa_channel, channel_num)\n\n    def forward(self, x):\n        out = self.residual_layer(x)\n        out = out + x\n        return self.esa(out)\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/OmniSR/OmniSR.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n#############################################################\n# File: OmniSR.py\n# Created Date: Tuesday April 28th 2022\n# Author: Chen Xuanhong\n# Email: chenxuanhongzju@outlook.com\n# Last Modified:  Sunday, 23rd April 2023 3:06:36 pm\n# Modified By: Chen Xuanhong\n# Copyright (c) 2020 Shanghai Jiao Tong University\n#############################################################\n\nimport math\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom .OSAG import OSAG\nfrom .pixelshuffle import pixelshuffle_block\n\n\nclass OmniSR(nn.Module):\n    def __init__(\n        self,\n        state_dict,\n        **kwargs,\n    ):\n        super(OmniSR, self).__init__()\n        self.state = state_dict\n\n        bias = True  # Fine to assume this for now\n        block_num = 1  # Fine to assume this for now\n        ffn_bias = True\n        pe = True\n\n        num_feat = state_dict[\"input.weight\"].shape[0] or 64\n        num_in_ch = state_dict[\"input.weight\"].shape[1] or 3\n        num_out_ch = num_in_ch  # we can just assume this for now. pixelshuffle smh\n\n        pixelshuffle_shape = state_dict[\"up.0.weight\"].shape[0]\n        up_scale = math.sqrt(pixelshuffle_shape / num_out_ch)\n        if up_scale - int(up_scale) > 0:\n            print(\n                \"out_nc is probably different than in_nc, scale calculation might be wrong\"\n            )\n        up_scale = int(up_scale)\n        res_num = 0\n        for key in state_dict.keys():\n            if \"residual_layer\" in key:\n                temp_res_num = int(key.split(\".\")[1])\n                if temp_res_num > res_num:\n                    res_num = temp_res_num\n        res_num = res_num + 1  # zero-indexed\n\n        residual_layer = []\n        self.res_num = res_num\n\n        if (\n            \"residual_layer.0.residual_layer.0.layer.2.fn.rel_pos_bias.weight\"\n            in state_dict.keys()\n        ):\n            rel_pos_bias_weight = state_dict[\n                \"residual_layer.0.residual_layer.0.layer.2.fn.rel_pos_bias.weight\"\n            ].shape[0]\n            self.window_size = int((math.sqrt(rel_pos_bias_weight) + 1) / 2)\n        else:\n            self.window_size = 8\n\n        self.up_scale = up_scale\n\n        for _ in range(res_num):\n            temp_res = OSAG(\n                channel_num=num_feat,\n                bias=bias,\n                block_num=block_num,\n                ffn_bias=ffn_bias,\n                window_size=self.window_size,\n                pe=pe,\n            )\n            residual_layer.append(temp_res)\n        self.residual_layer = nn.Sequential(*residual_layer)\n        self.input = nn.Conv2d(\n            in_channels=num_in_ch,\n            out_channels=num_feat,\n            kernel_size=3,\n            stride=1,\n            padding=1,\n            bias=bias,\n        )\n        self.output = nn.Conv2d(\n            in_channels=num_feat,\n            out_channels=num_feat,\n            kernel_size=3,\n            stride=1,\n            padding=1,\n            bias=bias,\n        )\n        self.up = pixelshuffle_block(num_feat, num_out_ch, up_scale, bias=bias)\n\n        # self.tail   = pixelshuffle_block(num_feat,num_out_ch,up_scale,bias=bias)\n\n        # for m in self.modules():\n        #     if isinstance(m, nn.Conv2d):\n        #         n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n        #         m.weight.data.normal_(0, sqrt(2. / n))\n\n        # chaiNNer specific stuff\n        self.model_arch = \"OmniSR\"\n        self.sub_type = \"SR\"\n        self.in_nc = num_in_ch\n        self.out_nc = num_out_ch\n        self.num_feat = num_feat\n        self.scale = up_scale\n\n        self.supports_fp16 = True  # TODO: Test this\n        self.supports_bfp16 = True\n        self.min_size_restriction = 16\n\n        self.load_state_dict(state_dict, strict=False)\n\n    def check_image_size(self, x):\n        _, _, h, w = x.size()\n        # import pdb; pdb.set_trace()\n        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size\n        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size\n        # x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')\n        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), \"constant\", 0)\n        return x\n\n    def forward(self, x):\n        H, W = x.shape[2:]\n        x = self.check_image_size(x)\n\n        residual = self.input(x)\n        out = self.residual_layer(residual)\n\n        # origin\n        out = torch.add(self.output(out), residual)\n        out = self.up(out)\n\n        out = out[:, :, : H * self.up_scale, : W * self.up_scale]\n        return out\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/OmniSR/esa.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n#############################################################\n# File: esa.py\n# Created Date: Tuesday April 28th 2022\n# Author: Chen Xuanhong\n# Email: chenxuanhongzju@outlook.com\n# Last Modified:  Thursday, 20th April 2023 9:28:06 am\n# Modified By: Chen Xuanhong\n# Copyright (c) 2020 Shanghai Jiao Tong University\n#############################################################\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom .layernorm import LayerNorm2d\n\n\ndef moment(x, dim=(2, 3), k=2):\n    assert len(x.size()) == 4\n    mean = torch.mean(x, dim=dim).unsqueeze(-1).unsqueeze(-1)\n    mk = (1 / (x.size(2) * x.size(3))) * torch.sum(torch.pow(x - mean, k), dim=dim)\n    return mk\n\n\nclass ESA(nn.Module):\n    \"\"\"\n    Modification of Enhanced Spatial Attention (ESA), which is proposed by\n    `Residual Feature Aggregation Network for Image Super-Resolution`\n    Note: `conv_max` and `conv3_` are NOT used here, so the corresponding codes\n    are deleted.\n    \"\"\"\n\n    def __init__(self, esa_channels, n_feats, conv=nn.Conv2d):\n        super(ESA, self).__init__()\n        f = esa_channels\n        self.conv1 = conv(n_feats, f, kernel_size=1)\n        self.conv_f = conv(f, f, kernel_size=1)\n        self.conv2 = conv(f, f, kernel_size=3, stride=2, padding=0)\n        self.conv3 = conv(f, f, kernel_size=3, padding=1)\n        self.conv4 = conv(f, n_feats, kernel_size=1)\n        self.sigmoid = nn.Sigmoid()\n        self.relu = nn.ReLU(inplace=True)\n\n    def forward(self, x):\n        c1_ = self.conv1(x)\n        c1 = self.conv2(c1_)\n        v_max = F.max_pool2d(c1, kernel_size=7, stride=3)\n        c3 = self.conv3(v_max)\n        c3 = F.interpolate(\n            c3, (x.size(2), x.size(3)), mode=\"bilinear\", align_corners=False\n        )\n        cf = self.conv_f(c1_)\n        c4 = self.conv4(c3 + cf)\n        m = self.sigmoid(c4)\n        return x * m\n\n\nclass LK_ESA(nn.Module):\n    def __init__(\n        self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True\n    ):\n        super(LK_ESA, self).__init__()\n        f = esa_channels\n        self.conv1 = conv(n_feats, f, kernel_size=1)\n        self.conv_f = conv(f, f, kernel_size=1)\n\n        kernel_size = 17\n        kernel_expand = kernel_expand\n        padding = kernel_size // 2\n\n        self.vec_conv = nn.Conv2d(\n            in_channels=f * kernel_expand,\n            out_channels=f * kernel_expand,\n            kernel_size=(1, kernel_size),\n            padding=(0, padding),\n            groups=2,\n            bias=bias,\n        )\n        self.vec_conv3x1 = nn.Conv2d(\n            in_channels=f * kernel_expand,\n            out_channels=f * kernel_expand,\n            kernel_size=(1, 3),\n            padding=(0, 1),\n            groups=2,\n            bias=bias,\n        )\n\n        self.hor_conv = nn.Conv2d(\n            in_channels=f * kernel_expand,\n            out_channels=f * kernel_expand,\n            kernel_size=(kernel_size, 1),\n            padding=(padding, 0),\n            groups=2,\n            bias=bias,\n        )\n        self.hor_conv1x3 = nn.Conv2d(\n            in_channels=f * kernel_expand,\n            out_channels=f * kernel_expand,\n            kernel_size=(3, 1),\n            padding=(1, 0),\n            groups=2,\n            bias=bias,\n        )\n\n        self.conv4 = conv(f, n_feats, kernel_size=1)\n        self.sigmoid = nn.Sigmoid()\n        self.relu = nn.ReLU(inplace=True)\n\n    def forward(self, x):\n        c1_ = self.conv1(x)\n\n        res = self.vec_conv(c1_) + self.vec_conv3x1(c1_)\n        res = self.hor_conv(res) + self.hor_conv1x3(res)\n\n        cf = self.conv_f(c1_)\n        c4 = self.conv4(res + cf)\n        m = self.sigmoid(c4)\n        return x * m\n\n\nclass LK_ESA_LN(nn.Module):\n    def __init__(\n        self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True\n    ):\n        super(LK_ESA_LN, self).__init__()\n        f = esa_channels\n        self.conv1 = conv(n_feats, f, kernel_size=1)\n        self.conv_f = conv(f, f, kernel_size=1)\n\n        kernel_size = 17\n        kernel_expand = kernel_expand\n        padding = kernel_size // 2\n\n        self.norm = LayerNorm2d(n_feats)\n\n        self.vec_conv = nn.Conv2d(\n            in_channels=f * kernel_expand,\n            out_channels=f * kernel_expand,\n            kernel_size=(1, kernel_size),\n            padding=(0, padding),\n            groups=2,\n            bias=bias,\n        )\n        self.vec_conv3x1 = nn.Conv2d(\n            in_channels=f * kernel_expand,\n            out_channels=f * kernel_expand,\n            kernel_size=(1, 3),\n            padding=(0, 1),\n            groups=2,\n            bias=bias,\n        )\n\n        self.hor_conv = nn.Conv2d(\n            in_channels=f * kernel_expand,\n            out_channels=f * kernel_expand,\n            kernel_size=(kernel_size, 1),\n            padding=(padding, 0),\n            groups=2,\n            bias=bias,\n        )\n        self.hor_conv1x3 = nn.Conv2d(\n            in_channels=f * kernel_expand,\n            out_channels=f * kernel_expand,\n            kernel_size=(3, 1),\n            padding=(1, 0),\n            groups=2,\n            bias=bias,\n        )\n\n        self.conv4 = conv(f, n_feats, kernel_size=1)\n        self.sigmoid = nn.Sigmoid()\n        self.relu = nn.ReLU(inplace=True)\n\n    def forward(self, x):\n        c1_ = self.norm(x)\n        c1_ = self.conv1(c1_)\n\n        res = self.vec_conv(c1_) + self.vec_conv3x1(c1_)\n        res = self.hor_conv(res) + self.hor_conv1x3(res)\n\n        cf = self.conv_f(c1_)\n        c4 = self.conv4(res + cf)\n        m = self.sigmoid(c4)\n        return x * m\n\n\nclass AdaGuidedFilter(nn.Module):\n    def __init__(\n        self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True\n    ):\n        super(AdaGuidedFilter, self).__init__()\n\n        self.gap = nn.AdaptiveAvgPool2d(1)\n        self.fc = nn.Conv2d(\n            in_channels=n_feats,\n            out_channels=1,\n            kernel_size=1,\n            padding=0,\n            stride=1,\n            groups=1,\n            bias=True,\n        )\n\n        self.r = 5\n\n    def box_filter(self, x, r):\n        channel = x.shape[1]\n        kernel_size = 2 * r + 1\n        weight = 1.0 / (kernel_size**2)\n        box_kernel = weight * torch.ones(\n            (channel, 1, kernel_size, kernel_size), dtype=torch.float32, device=x.device\n        )\n        output = F.conv2d(x, weight=box_kernel, stride=1, padding=r, groups=channel)\n        return output\n\n    def forward(self, x):\n        _, _, H, W = x.shape\n        N = self.box_filter(\n            torch.ones((1, 1, H, W), dtype=x.dtype, device=x.device), self.r\n        )\n\n        # epsilon = self.fc(self.gap(x))\n        # epsilon = torch.pow(epsilon, 2)\n        epsilon = 1e-2\n\n        mean_x = self.box_filter(x, self.r) / N\n        var_x = self.box_filter(x * x, self.r) / N - mean_x * mean_x\n\n        A = var_x / (var_x + epsilon)\n        b = (1 - A) * mean_x\n        m = A * x + b\n\n        # mean_A = self.box_filter(A, self.r) / N\n        # mean_b = self.box_filter(b, self.r) / N\n        # m = mean_A * x + mean_b\n        return x * m\n\n\nclass AdaConvGuidedFilter(nn.Module):\n    def __init__(\n        self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True\n    ):\n        super(AdaConvGuidedFilter, self).__init__()\n        f = esa_channels\n\n        self.conv_f = conv(f, f, kernel_size=1)\n\n        kernel_size = 17\n        kernel_expand = kernel_expand\n        padding = kernel_size // 2\n\n        self.vec_conv = nn.Conv2d(\n            in_channels=f,\n            out_channels=f,\n            kernel_size=(1, kernel_size),\n            padding=(0, padding),\n            groups=f,\n            bias=bias,\n        )\n\n        self.hor_conv = nn.Conv2d(\n            in_channels=f,\n            out_channels=f,\n            kernel_size=(kernel_size, 1),\n            padding=(padding, 0),\n            groups=f,\n            bias=bias,\n        )\n\n        self.gap = nn.AdaptiveAvgPool2d(1)\n        self.fc = nn.Conv2d(\n            in_channels=f,\n            out_channels=f,\n            kernel_size=1,\n            padding=0,\n            stride=1,\n            groups=1,\n            bias=True,\n        )\n\n    def forward(self, x):\n        y = self.vec_conv(x)\n        y = self.hor_conv(y)\n\n        sigma = torch.pow(y, 2)\n        epsilon = self.fc(self.gap(y))\n\n        weight = sigma / (sigma + epsilon)\n\n        m = weight * x + (1 - weight)\n\n        return x * m\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/OmniSR/layernorm.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n#############################################################\n# File: layernorm.py\n# Created Date: Tuesday April 28th 2022\n# Author: Chen Xuanhong\n# Email: chenxuanhongzju@outlook.com\n# Last Modified:  Thursday, 20th April 2023 9:28:20 am\n# Modified By: Chen Xuanhong\n# Copyright (c) 2020 Shanghai Jiao Tong University\n#############################################################\n\nimport torch\nimport torch.nn as nn\n\n\nclass LayerNormFunction(torch.autograd.Function):\n    @staticmethod\n    def forward(ctx, x, weight, bias, eps):\n        ctx.eps = eps\n        N, C, H, W = x.size()\n        mu = x.mean(1, keepdim=True)\n        var = (x - mu).pow(2).mean(1, keepdim=True)\n        y = (x - mu) / (var + eps).sqrt()\n        ctx.save_for_backward(y, var, weight)\n        y = weight.view(1, C, 1, 1) * y + bias.view(1, C, 1, 1)\n        return y\n\n    @staticmethod\n    def backward(ctx, grad_output):\n        eps = ctx.eps\n\n        N, C, H, W = grad_output.size()\n        y, var, weight = ctx.saved_variables\n        g = grad_output * weight.view(1, C, 1, 1)\n        mean_g = g.mean(dim=1, keepdim=True)\n\n        mean_gy = (g * y).mean(dim=1, keepdim=True)\n        gx = 1.0 / torch.sqrt(var + eps) * (g - y * mean_gy - mean_g)\n        return (\n            gx,\n            (grad_output * y).sum(dim=3).sum(dim=2).sum(dim=0),\n            grad_output.sum(dim=3).sum(dim=2).sum(dim=0),\n            None,\n        )\n\n\nclass LayerNorm2d(nn.Module):\n    def __init__(self, channels, eps=1e-6):\n        super(LayerNorm2d, self).__init__()\n        self.register_parameter(\"weight\", nn.Parameter(torch.ones(channels)))\n        self.register_parameter(\"bias\", nn.Parameter(torch.zeros(channels)))\n        self.eps = eps\n\n    def forward(self, x):\n        return LayerNormFunction.apply(x, self.weight, self.bias, self.eps)\n\n\nclass GRN(nn.Module):\n    \"\"\"GRN (Global Response Normalization) layer\"\"\"\n\n    def __init__(self, dim):\n        super().__init__()\n        self.gamma = nn.Parameter(torch.zeros(1, dim, 1, 1))\n        self.beta = nn.Parameter(torch.zeros(1, dim, 1, 1))\n\n    def forward(self, x):\n        Gx = torch.norm(x, p=2, dim=(2, 3), keepdim=True)\n        Nx = Gx / (Gx.mean(dim=1, keepdim=True) + 1e-6)\n        return self.gamma * (x * Nx) + self.beta + x\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/OmniSR/pixelshuffle.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n#############################################################\n# File: pixelshuffle.py\n# Created Date: Friday July 1st 2022\n# Author: Chen Xuanhong\n# Email: chenxuanhongzju@outlook.com\n# Last Modified:  Friday, 1st July 2022 10:18:39 am\n# Modified By: Chen Xuanhong\n# Copyright (c) 2022 Shanghai Jiao Tong University\n#############################################################\n\nimport torch.nn as nn\n\n\ndef pixelshuffle_block(\n    in_channels, out_channels, upscale_factor=2, kernel_size=3, bias=False\n):\n    \"\"\"\n    Upsample features according to `upscale_factor`.\n    \"\"\"\n    padding = kernel_size // 2\n    conv = nn.Conv2d(\n        in_channels,\n        out_channels * (upscale_factor**2),\n        kernel_size,\n        padding=1,\n        bias=bias,\n    )\n    pixel_shuffle = nn.PixelShuffle(upscale_factor)\n    return nn.Sequential(*[conv, pixel_shuffle])\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/RRDB.py",
    "content": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport functools\nimport math\nimport re\nfrom collections import OrderedDict\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom . import block as B\n\n\n# Borrowed from https://github.com/rlaphoenix/VSGAN/blob/master/vsgan/archs/esrgan.py\n# Which enhanced stuff that was already here\nclass RRDBNet(nn.Module):\n    def __init__(\n        self,\n        state_dict,\n        norm=None,\n        act: str = \"leakyrelu\",\n        upsampler: str = \"upconv\",\n        mode: B.ConvMode = \"CNA\",\n    ) -> None:\n        \"\"\"\n        ESRGAN - Enhanced Super-Resolution Generative Adversarial Networks.\n        By Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao,\n        and Chen Change Loy.\n        This is old-arch Residual in Residual Dense Block Network and is not\n        the newest revision that's available at github.com/xinntao/ESRGAN.\n        This is on purpose, the newest Network has severely limited the\n        potential use of the Network with no benefits.\n        This network supports model files from both new and old-arch.\n        Args:\n            norm: Normalization layer\n            act: Activation layer\n            upsampler: Upsample layer. upconv, pixel_shuffle\n            mode: Convolution mode\n        \"\"\"\n        super(RRDBNet, self).__init__()\n        self.model_arch = \"ESRGAN\"\n        self.sub_type = \"SR\"\n\n        self.state = state_dict\n        self.norm = norm\n        self.act = act\n        self.upsampler = upsampler\n        self.mode = mode\n\n        self.state_map = {\n            # currently supports old, new, and newer RRDBNet arch models\n            # ESRGAN, BSRGAN/RealSR, Real-ESRGAN\n            \"model.0.weight\": (\"conv_first.weight\",),\n            \"model.0.bias\": (\"conv_first.bias\",),\n            \"model.1.sub./NB/.weight\": (\"trunk_conv.weight\", \"conv_body.weight\"),\n            \"model.1.sub./NB/.bias\": (\"trunk_conv.bias\", \"conv_body.bias\"),\n            r\"model.1.sub.\\1.RDB\\2.conv\\3.0.\\4\": (\n                r\"RRDB_trunk\\.(\\d+)\\.RDB(\\d)\\.conv(\\d+)\\.(weight|bias)\",\n                r\"body\\.(\\d+)\\.rdb(\\d)\\.conv(\\d+)\\.(weight|bias)\",\n            ),\n        }\n        if \"params_ema\" in self.state:\n            self.state = self.state[\"params_ema\"]\n            # self.model_arch = \"RealESRGAN\"\n        self.num_blocks = self.get_num_blocks()\n        self.plus = any(\"conv1x1\" in k for k in self.state.keys())\n        if self.plus:\n            self.model_arch = \"ESRGAN+\"\n\n        self.state = self.new_to_old_arch(self.state)\n\n        self.key_arr = list(self.state.keys())\n\n        self.in_nc: int = self.state[self.key_arr[0]].shape[1]\n        self.out_nc: int = self.state[self.key_arr[-1]].shape[0]\n\n        self.scale: int = self.get_scale()\n        self.num_filters: int = self.state[self.key_arr[0]].shape[0]\n\n        c2x2 = False\n        if self.state[\"model.0.weight\"].shape[-2] == 2:\n            c2x2 = True\n            self.scale = round(math.sqrt(self.scale / 4))\n            self.model_arch = \"ESRGAN-2c2\"\n\n        self.supports_fp16 = True\n        self.supports_bfp16 = True\n        self.min_size_restriction = None\n\n        # Detect if pixelunshuffle was used (Real-ESRGAN)\n        if self.in_nc in (self.out_nc * 4, self.out_nc * 16) and self.out_nc in (\n            self.in_nc / 4,\n            self.in_nc / 16,\n        ):\n            self.shuffle_factor = int(math.sqrt(self.in_nc / self.out_nc))\n        else:\n            self.shuffle_factor = None\n\n        upsample_block = {\n            \"upconv\": B.upconv_block,\n            \"pixel_shuffle\": B.pixelshuffle_block,\n        }.get(self.upsampler)\n        if upsample_block is None:\n            raise NotImplementedError(f\"Upsample mode [{self.upsampler}] is not found\")\n\n        if self.scale == 3:\n            upsample_blocks = upsample_block(\n                in_nc=self.num_filters,\n                out_nc=self.num_filters,\n                upscale_factor=3,\n                act_type=self.act,\n                c2x2=c2x2,\n            )\n        else:\n            upsample_blocks = [\n                upsample_block(\n                    in_nc=self.num_filters,\n                    out_nc=self.num_filters,\n                    act_type=self.act,\n                    c2x2=c2x2,\n                )\n                for _ in range(int(math.log(self.scale, 2)))\n            ]\n\n        self.model = B.sequential(\n            # fea conv\n            B.conv_block(\n                in_nc=self.in_nc,\n                out_nc=self.num_filters,\n                kernel_size=3,\n                norm_type=None,\n                act_type=None,\n                c2x2=c2x2,\n            ),\n            B.ShortcutBlock(\n                B.sequential(\n                    # rrdb blocks\n                    *[\n                        B.RRDB(\n                            nf=self.num_filters,\n                            kernel_size=3,\n                            gc=32,\n                            stride=1,\n                            bias=True,\n                            pad_type=\"zero\",\n                            norm_type=self.norm,\n                            act_type=self.act,\n                            mode=\"CNA\",\n                            plus=self.plus,\n                            c2x2=c2x2,\n                        )\n                        for _ in range(self.num_blocks)\n                    ],\n                    # lr conv\n                    B.conv_block(\n                        in_nc=self.num_filters,\n                        out_nc=self.num_filters,\n                        kernel_size=3,\n                        norm_type=self.norm,\n                        act_type=None,\n                        mode=self.mode,\n                        c2x2=c2x2,\n                    ),\n                )\n            ),\n            *upsample_blocks,\n            # hr_conv0\n            B.conv_block(\n                in_nc=self.num_filters,\n                out_nc=self.num_filters,\n                kernel_size=3,\n                norm_type=None,\n                act_type=self.act,\n                c2x2=c2x2,\n            ),\n            # hr_conv1\n            B.conv_block(\n                in_nc=self.num_filters,\n                out_nc=self.out_nc,\n                kernel_size=3,\n                norm_type=None,\n                act_type=None,\n                c2x2=c2x2,\n            ),\n        )\n\n        # Adjust these properties for calculations outside of the model\n        if self.shuffle_factor:\n            self.in_nc //= self.shuffle_factor**2\n            self.scale //= self.shuffle_factor\n\n        self.load_state_dict(self.state, strict=False)\n\n    def new_to_old_arch(self, state):\n        \"\"\"Convert a new-arch model state dictionary to an old-arch dictionary.\"\"\"\n        if \"params_ema\" in state:\n            state = state[\"params_ema\"]\n\n        if \"conv_first.weight\" not in state:\n            # model is already old arch, this is a loose check, but should be sufficient\n            return state\n\n        # add nb to state keys\n        for kind in (\"weight\", \"bias\"):\n            self.state_map[f\"model.1.sub.{self.num_blocks}.{kind}\"] = self.state_map[\n                f\"model.1.sub./NB/.{kind}\"\n            ]\n            del self.state_map[f\"model.1.sub./NB/.{kind}\"]\n\n        old_state = OrderedDict()\n        for old_key, new_keys in self.state_map.items():\n            for new_key in new_keys:\n                if r\"\\1\" in old_key:\n                    for k, v in state.items():\n                        sub = re.sub(new_key, old_key, k)\n                        if sub != k:\n                            old_state[sub] = v\n                else:\n                    if new_key in state:\n                        old_state[old_key] = state[new_key]\n\n        # upconv layers\n        max_upconv = 0\n        for key in state.keys():\n            match = re.match(r\"(upconv|conv_up)(\\d)\\.(weight|bias)\", key)\n            if match is not None:\n                _, key_num, key_type = match.groups()\n                old_state[f\"model.{int(key_num) * 3}.{key_type}\"] = state[key]\n                max_upconv = max(max_upconv, int(key_num) * 3)\n\n        # final layers\n        for key in state.keys():\n            if key in (\"HRconv.weight\", \"conv_hr.weight\"):\n                old_state[f\"model.{max_upconv + 2}.weight\"] = state[key]\n            elif key in (\"HRconv.bias\", \"conv_hr.bias\"):\n                old_state[f\"model.{max_upconv + 2}.bias\"] = state[key]\n            elif key in (\"conv_last.weight\",):\n                old_state[f\"model.{max_upconv + 4}.weight\"] = state[key]\n            elif key in (\"conv_last.bias\",):\n                old_state[f\"model.{max_upconv + 4}.bias\"] = state[key]\n\n        # Sort by first numeric value of each layer\n        def compare(item1, item2):\n            parts1 = item1.split(\".\")\n            parts2 = item2.split(\".\")\n            int1 = int(parts1[1])\n            int2 = int(parts2[1])\n            return int1 - int2\n\n        sorted_keys = sorted(old_state.keys(), key=functools.cmp_to_key(compare))\n\n        # Rebuild the output dict in the right order\n        out_dict = OrderedDict((k, old_state[k]) for k in sorted_keys)\n\n        return out_dict\n\n    def get_scale(self, min_part: int = 6) -> int:\n        n = 0\n        for part in list(self.state):\n            parts = part.split(\".\")[1:]\n            if len(parts) == 2:\n                part_num = int(parts[0])\n                if part_num > min_part and parts[1] == \"weight\":\n                    n += 1\n        return 2**n\n\n    def get_num_blocks(self) -> int:\n        nbs = []\n        state_keys = self.state_map[r\"model.1.sub.\\1.RDB\\2.conv\\3.0.\\4\"] + (\n            r\"model\\.\\d+\\.sub\\.(\\d+)\\.RDB(\\d+)\\.conv(\\d+)\\.0\\.(weight|bias)\",\n        )\n        for state_key in state_keys:\n            for k in self.state:\n                m = re.search(state_key, k)\n                if m:\n                    nbs.append(int(m.group(1)))\n            if nbs:\n                break\n        return max(*nbs) + 1\n\n    def forward(self, x):\n        if self.shuffle_factor:\n            _, _, h, w = x.size()\n            mod_pad_h = (\n                self.shuffle_factor - h % self.shuffle_factor\n            ) % self.shuffle_factor\n            mod_pad_w = (\n                self.shuffle_factor - w % self.shuffle_factor\n            ) % self.shuffle_factor\n            x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), \"reflect\")\n            x = torch.pixel_unshuffle(x, downscale_factor=self.shuffle_factor)\n            x = self.model(x)\n            return x[:, :, : h * self.scale, : w * self.scale]\n        return self.model(x)\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/SCUNet.py",
    "content": "# pylint: skip-file\n# -----------------------------------------------------------------------------------\n# SCUNet: Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis, https://arxiv.org/abs/2203.13278\n# Zhang, Kai and Li, Yawei and Liang, Jingyun and Cao, Jiezhang and Zhang, Yulun and Tang, Hao and Timofte, Radu and Van Gool, Luc\n# -----------------------------------------------------------------------------------\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom einops import rearrange\nfrom einops.layers.torch import Rearrange\n\nfrom .timm.drop import DropPath\nfrom .timm.weight_init import trunc_normal_\n\n\n# Borrowed from https://github.com/cszn/SCUNet/blob/main/models/network_scunet.py\nclass WMSA(nn.Module):\n    \"\"\"Self-attention module in Swin Transformer\"\"\"\n\n    def __init__(self, input_dim, output_dim, head_dim, window_size, type):\n        super(WMSA, self).__init__()\n        self.input_dim = input_dim\n        self.output_dim = output_dim\n        self.head_dim = head_dim\n        self.scale = self.head_dim**-0.5\n        self.n_heads = input_dim // head_dim\n        self.window_size = window_size\n        self.type = type\n        self.embedding_layer = nn.Linear(self.input_dim, 3 * self.input_dim, bias=True)\n\n        self.relative_position_params = nn.Parameter(\n            torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads)\n        )\n        # TODO recover\n        # self.relative_position_params = nn.Parameter(torch.zeros(self.n_heads, 2 * window_size - 1, 2 * window_size -1))\n        self.relative_position_params = nn.Parameter(\n            torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads)\n        )\n\n        self.linear = nn.Linear(self.input_dim, self.output_dim)\n\n        trunc_normal_(self.relative_position_params, std=0.02)\n        self.relative_position_params = torch.nn.Parameter(\n            self.relative_position_params.view(\n                2 * window_size - 1, 2 * window_size - 1, self.n_heads\n            )\n            .transpose(1, 2)\n            .transpose(0, 1)\n        )\n\n    def generate_mask(self, h, w, p, shift):\n        \"\"\"generating the mask of SW-MSA\n        Args:\n            shift: shift parameters in CyclicShift.\n        Returns:\n            attn_mask: should be (1 1 w p p),\n        \"\"\"\n        # supporting square.\n        attn_mask = torch.zeros(\n            h,\n            w,\n            p,\n            p,\n            p,\n            p,\n            dtype=torch.bool,\n            device=self.relative_position_params.device,\n        )\n        if self.type == \"W\":\n            return attn_mask\n\n        s = p - shift\n        attn_mask[-1, :, :s, :, s:, :] = True\n        attn_mask[-1, :, s:, :, :s, :] = True\n        attn_mask[:, -1, :, :s, :, s:] = True\n        attn_mask[:, -1, :, s:, :, :s] = True\n        attn_mask = rearrange(\n            attn_mask, \"w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)\"\n        )\n        return attn_mask\n\n    def forward(self, x):\n        \"\"\"Forward pass of Window Multi-head Self-attention module.\n        Args:\n            x: input tensor with shape of [b h w c];\n            attn_mask: attention mask, fill -inf where the value is True;\n        Returns:\n            output: tensor shape [b h w c]\n        \"\"\"\n        if self.type != \"W\":\n            x = torch.roll(\n                x,\n                shifts=(-(self.window_size // 2), -(self.window_size // 2)),\n                dims=(1, 2),\n            )\n\n        x = rearrange(\n            x,\n            \"b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c\",\n            p1=self.window_size,\n            p2=self.window_size,\n        )\n        h_windows = x.size(1)\n        w_windows = x.size(2)\n        # square validation\n        # assert h_windows == w_windows\n\n        x = rearrange(\n            x,\n            \"b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c\",\n            p1=self.window_size,\n            p2=self.window_size,\n        )\n        qkv = self.embedding_layer(x)\n        q, k, v = rearrange(\n            qkv, \"b nw np (threeh c) -> threeh b nw np c\", c=self.head_dim\n        ).chunk(3, dim=0)\n        sim = torch.einsum(\"hbwpc,hbwqc->hbwpq\", q, k) * self.scale\n        # Adding learnable relative embedding\n        sim = sim + rearrange(self.relative_embedding(), \"h p q -> h 1 1 p q\")\n        # Using Attn Mask to distinguish different subwindows.\n        if self.type != \"W\":\n            attn_mask = self.generate_mask(\n                h_windows, w_windows, self.window_size, shift=self.window_size // 2\n            )\n            sim = sim.masked_fill_(attn_mask, float(\"-inf\"))\n\n        probs = nn.functional.softmax(sim, dim=-1)\n        output = torch.einsum(\"hbwij,hbwjc->hbwic\", probs, v)\n        output = rearrange(output, \"h b w p c -> b w p (h c)\")\n        output = self.linear(output)\n        output = rearrange(\n            output,\n            \"b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c\",\n            w1=h_windows,\n            p1=self.window_size,\n        )\n\n        if self.type != \"W\":\n            output = torch.roll(\n                output,\n                shifts=(self.window_size // 2, self.window_size // 2),\n                dims=(1, 2),\n            )\n\n        return output\n\n    def relative_embedding(self):\n        cord = torch.tensor(\n            np.array(\n                [\n                    [i, j]\n                    for i in range(self.window_size)\n                    for j in range(self.window_size)\n                ]\n            )\n        )\n        relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1\n        # negative is allowed\n        return self.relative_position_params[\n            :, relation[:, :, 0].long(), relation[:, :, 1].long()\n        ]\n\n\nclass Block(nn.Module):\n    def __init__(\n        self,\n        input_dim,\n        output_dim,\n        head_dim,\n        window_size,\n        drop_path,\n        type=\"W\",\n        input_resolution=None,\n    ):\n        \"\"\"SwinTransformer Block\"\"\"\n        super(Block, self).__init__()\n        self.input_dim = input_dim\n        self.output_dim = output_dim\n        assert type in [\"W\", \"SW\"]\n        self.type = type\n        if input_resolution <= window_size:\n            self.type = \"W\"\n\n        self.ln1 = nn.LayerNorm(input_dim)\n        self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type)\n        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()\n        self.ln2 = nn.LayerNorm(input_dim)\n        self.mlp = nn.Sequential(\n            nn.Linear(input_dim, 4 * input_dim),\n            nn.GELU(),\n            nn.Linear(4 * input_dim, output_dim),\n        )\n\n    def forward(self, x):\n        x = x + self.drop_path(self.msa(self.ln1(x)))\n        x = x + self.drop_path(self.mlp(self.ln2(x)))\n        return x\n\n\nclass ConvTransBlock(nn.Module):\n    def __init__(\n        self,\n        conv_dim,\n        trans_dim,\n        head_dim,\n        window_size,\n        drop_path,\n        type=\"W\",\n        input_resolution=None,\n    ):\n        \"\"\"SwinTransformer and Conv Block\"\"\"\n        super(ConvTransBlock, self).__init__()\n        self.conv_dim = conv_dim\n        self.trans_dim = trans_dim\n        self.head_dim = head_dim\n        self.window_size = window_size\n        self.drop_path = drop_path\n        self.type = type\n        self.input_resolution = input_resolution\n\n        assert self.type in [\"W\", \"SW\"]\n        if self.input_resolution <= self.window_size:\n            self.type = \"W\"\n\n        self.trans_block = Block(\n            self.trans_dim,\n            self.trans_dim,\n            self.head_dim,\n            self.window_size,\n            self.drop_path,\n            self.type,\n            self.input_resolution,\n        )\n        self.conv1_1 = nn.Conv2d(\n            self.conv_dim + self.trans_dim,\n            self.conv_dim + self.trans_dim,\n            1,\n            1,\n            0,\n            bias=True,\n        )\n        self.conv1_2 = nn.Conv2d(\n            self.conv_dim + self.trans_dim,\n            self.conv_dim + self.trans_dim,\n            1,\n            1,\n            0,\n            bias=True,\n        )\n\n        self.conv_block = nn.Sequential(\n            nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False),\n            nn.ReLU(True),\n            nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False),\n        )\n\n    def forward(self, x):\n        conv_x, trans_x = torch.split(\n            self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1\n        )\n        conv_x = self.conv_block(conv_x) + conv_x\n        trans_x = Rearrange(\"b c h w -> b h w c\")(trans_x)\n        trans_x = self.trans_block(trans_x)\n        trans_x = Rearrange(\"b h w c -> b c h w\")(trans_x)\n        res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1))\n        x = x + res\n\n        return x\n\n\nclass SCUNet(nn.Module):\n    def __init__(\n        self,\n        state_dict,\n        in_nc=3,\n        config=[4, 4, 4, 4, 4, 4, 4],\n        dim=64,\n        drop_path_rate=0.0,\n        input_resolution=256,\n    ):\n        super(SCUNet, self).__init__()\n        self.model_arch = \"SCUNet\"\n        self.sub_type = \"SR\"\n\n        self.num_filters: int = 0\n\n        self.state = state_dict\n        self.config = config\n        self.dim = dim\n        self.head_dim = 32\n        self.window_size = 8\n\n        self.in_nc = in_nc\n        self.out_nc = self.in_nc\n        self.scale = 1\n        self.supports_fp16 = True\n\n        # drop path rate for each layer\n        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))]\n\n        self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)]\n\n        begin = 0\n        self.m_down1 = [\n            ConvTransBlock(\n                dim // 2,\n                dim // 2,\n                self.head_dim,\n                self.window_size,\n                dpr[i + begin],\n                \"W\" if not i % 2 else \"SW\",\n                input_resolution,\n            )\n            for i in range(config[0])\n        ] + [nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)]\n\n        begin += config[0]\n        self.m_down2 = [\n            ConvTransBlock(\n                dim,\n                dim,\n                self.head_dim,\n                self.window_size,\n                dpr[i + begin],\n                \"W\" if not i % 2 else \"SW\",\n                input_resolution // 2,\n            )\n            for i in range(config[1])\n        ] + [nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)]\n\n        begin += config[1]\n        self.m_down3 = [\n            ConvTransBlock(\n                2 * dim,\n                2 * dim,\n                self.head_dim,\n                self.window_size,\n                dpr[i + begin],\n                \"W\" if not i % 2 else \"SW\",\n                input_resolution // 4,\n            )\n            for i in range(config[2])\n        ] + [nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)]\n\n        begin += config[2]\n        self.m_body = [\n            ConvTransBlock(\n                4 * dim,\n                4 * dim,\n                self.head_dim,\n                self.window_size,\n                dpr[i + begin],\n                \"W\" if not i % 2 else \"SW\",\n                input_resolution // 8,\n            )\n            for i in range(config[3])\n        ]\n\n        begin += config[3]\n        self.m_up3 = [\n            nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False),\n        ] + [\n            ConvTransBlock(\n                2 * dim,\n                2 * dim,\n                self.head_dim,\n                self.window_size,\n                dpr[i + begin],\n                \"W\" if not i % 2 else \"SW\",\n                input_resolution // 4,\n            )\n            for i in range(config[4])\n        ]\n\n        begin += config[4]\n        self.m_up2 = [\n            nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False),\n        ] + [\n            ConvTransBlock(\n                dim,\n                dim,\n                self.head_dim,\n                self.window_size,\n                dpr[i + begin],\n                \"W\" if not i % 2 else \"SW\",\n                input_resolution // 2,\n            )\n            for i in range(config[5])\n        ]\n\n        begin += config[5]\n        self.m_up1 = [\n            nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False),\n        ] + [\n            ConvTransBlock(\n                dim // 2,\n                dim // 2,\n                self.head_dim,\n                self.window_size,\n                dpr[i + begin],\n                \"W\" if not i % 2 else \"SW\",\n                input_resolution,\n            )\n            for i in range(config[6])\n        ]\n\n        self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)]\n\n        self.m_head = nn.Sequential(*self.m_head)\n        self.m_down1 = nn.Sequential(*self.m_down1)\n        self.m_down2 = nn.Sequential(*self.m_down2)\n        self.m_down3 = nn.Sequential(*self.m_down3)\n        self.m_body = nn.Sequential(*self.m_body)\n        self.m_up3 = nn.Sequential(*self.m_up3)\n        self.m_up2 = nn.Sequential(*self.m_up2)\n        self.m_up1 = nn.Sequential(*self.m_up1)\n        self.m_tail = nn.Sequential(*self.m_tail)\n        # self.apply(self._init_weights)\n        self.load_state_dict(state_dict, strict=True)\n\n    def check_image_size(self, x):\n        _, _, h, w = x.size()\n        mod_pad_h = (64 - h % 64) % 64\n        mod_pad_w = (64 - w % 64) % 64\n        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), \"reflect\")\n        return x\n\n    def forward(self, x0):\n        h, w = x0.size()[-2:]\n        x0 = self.check_image_size(x0)\n\n        x1 = self.m_head(x0)\n        x2 = self.m_down1(x1)\n        x3 = self.m_down2(x2)\n        x4 = self.m_down3(x3)\n        x = self.m_body(x4)\n        x = self.m_up3(x + x4)\n        x = self.m_up2(x + x3)\n        x = self.m_up1(x + x2)\n        x = self.m_tail(x + x1)\n\n        x = x[:, :, :h, :w]\n        return x\n\n    def _init_weights(self, m):\n        if isinstance(m, nn.Linear):\n            trunc_normal_(m.weight, std=0.02)\n            if m.bias is not None:\n                nn.init.constant_(m.bias, 0)\n        elif isinstance(m, nn.LayerNorm):\n            nn.init.constant_(m.bias, 0)\n            nn.init.constant_(m.weight, 1.0)\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/SPSR.py",
    "content": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport math\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom . import block as B\n\n\nclass Get_gradient_nopadding(nn.Module):\n    def __init__(self):\n        super(Get_gradient_nopadding, self).__init__()\n        kernel_v = [[0, -1, 0], [0, 0, 0], [0, 1, 0]]\n        kernel_h = [[0, 0, 0], [-1, 0, 1], [0, 0, 0]]\n        kernel_h = torch.FloatTensor(kernel_h).unsqueeze(0).unsqueeze(0)\n        kernel_v = torch.FloatTensor(kernel_v).unsqueeze(0).unsqueeze(0)\n        self.weight_h = nn.Parameter(data=kernel_h, requires_grad=False)  # type: ignore\n\n        self.weight_v = nn.Parameter(data=kernel_v, requires_grad=False)  # type: ignore\n\n    def forward(self, x):\n        x_list = []\n        for i in range(x.shape[1]):\n            x_i = x[:, i]\n            x_i_v = F.conv2d(x_i.unsqueeze(1), self.weight_v, padding=1)\n            x_i_h = F.conv2d(x_i.unsqueeze(1), self.weight_h, padding=1)\n            x_i = torch.sqrt(torch.pow(x_i_v, 2) + torch.pow(x_i_h, 2) + 1e-6)\n            x_list.append(x_i)\n\n        x = torch.cat(x_list, dim=1)\n\n        return x\n\n\nclass SPSRNet(nn.Module):\n    def __init__(\n        self,\n        state_dict,\n        norm=None,\n        act: str = \"leakyrelu\",\n        upsampler: str = \"upconv\",\n        mode: B.ConvMode = \"CNA\",\n    ):\n        super(SPSRNet, self).__init__()\n        self.model_arch = \"SPSR\"\n        self.sub_type = \"SR\"\n\n        self.state = state_dict\n        self.norm = norm\n        self.act = act\n        self.upsampler = upsampler\n        self.mode = mode\n\n        self.num_blocks = self.get_num_blocks()\n\n        self.in_nc: int = self.state[\"model.0.weight\"].shape[1]\n        self.out_nc: int = self.state[\"f_HR_conv1.0.bias\"].shape[0]\n\n        self.scale = self.get_scale(4)\n        self.num_filters: int = self.state[\"model.0.weight\"].shape[0]\n\n        self.supports_fp16 = True\n        self.supports_bfp16 = True\n        self.min_size_restriction = None\n\n        n_upscale = int(math.log(self.scale, 2))\n        if self.scale == 3:\n            n_upscale = 1\n\n        fea_conv = B.conv_block(\n            self.in_nc, self.num_filters, kernel_size=3, norm_type=None, act_type=None\n        )\n        rb_blocks = [\n            B.RRDB(\n                self.num_filters,\n                kernel_size=3,\n                gc=32,\n                stride=1,\n                bias=True,\n                pad_type=\"zero\",\n                norm_type=norm,\n                act_type=act,\n                mode=\"CNA\",\n            )\n            for _ in range(self.num_blocks)\n        ]\n        LR_conv = B.conv_block(\n            self.num_filters,\n            self.num_filters,\n            kernel_size=3,\n            norm_type=norm,\n            act_type=None,\n            mode=mode,\n        )\n\n        if upsampler == \"upconv\":\n            upsample_block = B.upconv_block\n        elif upsampler == \"pixelshuffle\":\n            upsample_block = B.pixelshuffle_block\n        else:\n            raise NotImplementedError(f\"upsample mode [{upsampler}] is not found\")\n        if self.scale == 3:\n            a_upsampler = upsample_block(\n                self.num_filters, self.num_filters, 3, act_type=act\n            )\n        else:\n            a_upsampler = [\n                upsample_block(self.num_filters, self.num_filters, act_type=act)\n                for _ in range(n_upscale)\n            ]\n        self.HR_conv0_new = B.conv_block(\n            self.num_filters,\n            self.num_filters,\n            kernel_size=3,\n            norm_type=None,\n            act_type=act,\n        )\n        self.HR_conv1_new = B.conv_block(\n            self.num_filters,\n            self.num_filters,\n            kernel_size=3,\n            norm_type=None,\n            act_type=None,\n        )\n\n        self.model = B.sequential(\n            fea_conv,\n            B.ShortcutBlockSPSR(B.sequential(*rb_blocks, LR_conv)),\n            *a_upsampler,\n            self.HR_conv0_new,\n        )\n\n        self.get_g_nopadding = Get_gradient_nopadding()\n\n        self.b_fea_conv = B.conv_block(\n            self.in_nc, self.num_filters, kernel_size=3, norm_type=None, act_type=None\n        )\n\n        self.b_concat_1 = B.conv_block(\n            2 * self.num_filters,\n            self.num_filters,\n            kernel_size=3,\n            norm_type=None,\n            act_type=None,\n        )\n        self.b_block_1 = B.RRDB(\n            self.num_filters * 2,\n            kernel_size=3,\n            gc=32,\n            stride=1,\n            bias=True,\n            pad_type=\"zero\",\n            norm_type=norm,\n            act_type=act,\n            mode=\"CNA\",\n        )\n\n        self.b_concat_2 = B.conv_block(\n            2 * self.num_filters,\n            self.num_filters,\n            kernel_size=3,\n            norm_type=None,\n            act_type=None,\n        )\n        self.b_block_2 = B.RRDB(\n            self.num_filters * 2,\n            kernel_size=3,\n            gc=32,\n            stride=1,\n            bias=True,\n            pad_type=\"zero\",\n            norm_type=norm,\n            act_type=act,\n            mode=\"CNA\",\n        )\n\n        self.b_concat_3 = B.conv_block(\n            2 * self.num_filters,\n            self.num_filters,\n            kernel_size=3,\n            norm_type=None,\n            act_type=None,\n        )\n        self.b_block_3 = B.RRDB(\n            self.num_filters * 2,\n            kernel_size=3,\n            gc=32,\n            stride=1,\n            bias=True,\n            pad_type=\"zero\",\n            norm_type=norm,\n            act_type=act,\n            mode=\"CNA\",\n        )\n\n        self.b_concat_4 = B.conv_block(\n            2 * self.num_filters,\n            self.num_filters,\n            kernel_size=3,\n            norm_type=None,\n            act_type=None,\n        )\n        self.b_block_4 = B.RRDB(\n            self.num_filters * 2,\n            kernel_size=3,\n            gc=32,\n            stride=1,\n            bias=True,\n            pad_type=\"zero\",\n            norm_type=norm,\n            act_type=act,\n            mode=\"CNA\",\n        )\n\n        self.b_LR_conv = B.conv_block(\n            self.num_filters,\n            self.num_filters,\n            kernel_size=3,\n            norm_type=norm,\n            act_type=None,\n            mode=mode,\n        )\n\n        if upsampler == \"upconv\":\n            upsample_block = B.upconv_block\n        elif upsampler == \"pixelshuffle\":\n            upsample_block = B.pixelshuffle_block\n        else:\n            raise NotImplementedError(f\"upsample mode [{upsampler}] is not found\")\n        if self.scale == 3:\n            b_upsampler = upsample_block(\n                self.num_filters, self.num_filters, 3, act_type=act\n            )\n        else:\n            b_upsampler = [\n                upsample_block(self.num_filters, self.num_filters, act_type=act)\n                for _ in range(n_upscale)\n            ]\n\n        b_HR_conv0 = B.conv_block(\n            self.num_filters,\n            self.num_filters,\n            kernel_size=3,\n            norm_type=None,\n            act_type=act,\n        )\n        b_HR_conv1 = B.conv_block(\n            self.num_filters,\n            self.num_filters,\n            kernel_size=3,\n            norm_type=None,\n            act_type=None,\n        )\n\n        self.b_module = B.sequential(*b_upsampler, b_HR_conv0, b_HR_conv1)\n\n        self.conv_w = B.conv_block(\n            self.num_filters, self.out_nc, kernel_size=1, norm_type=None, act_type=None\n        )\n\n        self.f_concat = B.conv_block(\n            self.num_filters * 2,\n            self.num_filters,\n            kernel_size=3,\n            norm_type=None,\n            act_type=None,\n        )\n\n        self.f_block = B.RRDB(\n            self.num_filters * 2,\n            kernel_size=3,\n            gc=32,\n            stride=1,\n            bias=True,\n            pad_type=\"zero\",\n            norm_type=norm,\n            act_type=act,\n            mode=\"CNA\",\n        )\n\n        self.f_HR_conv0 = B.conv_block(\n            self.num_filters,\n            self.num_filters,\n            kernel_size=3,\n            norm_type=None,\n            act_type=act,\n        )\n        self.f_HR_conv1 = B.conv_block(\n            self.num_filters, self.out_nc, kernel_size=3, norm_type=None, act_type=None\n        )\n\n        self.load_state_dict(self.state, strict=False)\n\n    def get_scale(self, min_part: int = 4) -> int:\n        n = 0\n        for part in list(self.state):\n            parts = part.split(\".\")\n            if len(parts) == 3:\n                part_num = int(parts[1])\n                if part_num > min_part and parts[0] == \"model\" and parts[2] == \"weight\":\n                    n += 1\n        return 2**n\n\n    def get_num_blocks(self) -> int:\n        nb = 0\n        for part in list(self.state):\n            parts = part.split(\".\")\n            n_parts = len(parts)\n            if n_parts == 5 and parts[2] == \"sub\":\n                nb = int(parts[3])\n        return nb\n\n    def forward(self, x):\n        x_grad = self.get_g_nopadding(x)\n        x = self.model[0](x)\n\n        x, block_list = self.model[1](x)\n\n        x_ori = x\n        for i in range(5):\n            x = block_list[i](x)\n        x_fea1 = x\n\n        for i in range(5):\n            x = block_list[i + 5](x)\n        x_fea2 = x\n\n        for i in range(5):\n            x = block_list[i + 10](x)\n        x_fea3 = x\n\n        for i in range(5):\n            x = block_list[i + 15](x)\n        x_fea4 = x\n\n        x = block_list[20:](x)\n        # short cut\n        x = x_ori + x\n        x = self.model[2:](x)\n        x = self.HR_conv1_new(x)\n\n        x_b_fea = self.b_fea_conv(x_grad)\n        x_cat_1 = torch.cat([x_b_fea, x_fea1], dim=1)\n\n        x_cat_1 = self.b_block_1(x_cat_1)\n        x_cat_1 = self.b_concat_1(x_cat_1)\n\n        x_cat_2 = torch.cat([x_cat_1, x_fea2], dim=1)\n\n        x_cat_2 = self.b_block_2(x_cat_2)\n        x_cat_2 = self.b_concat_2(x_cat_2)\n\n        x_cat_3 = torch.cat([x_cat_2, x_fea3], dim=1)\n\n        x_cat_3 = self.b_block_3(x_cat_3)\n        x_cat_3 = self.b_concat_3(x_cat_3)\n\n        x_cat_4 = torch.cat([x_cat_3, x_fea4], dim=1)\n\n        x_cat_4 = self.b_block_4(x_cat_4)\n        x_cat_4 = self.b_concat_4(x_cat_4)\n\n        x_cat_4 = self.b_LR_conv(x_cat_4)\n\n        # short cut\n        x_cat_4 = x_cat_4 + x_b_fea\n        x_branch = self.b_module(x_cat_4)\n\n        # x_out_branch = self.conv_w(x_branch)\n        ########\n        x_branch_d = x_branch\n        x_f_cat = torch.cat([x_branch_d, x], dim=1)\n        x_f_cat = self.f_block(x_f_cat)\n        x_out = self.f_concat(x_f_cat)\n        x_out = self.f_HR_conv0(x_out)\n        x_out = self.f_HR_conv1(x_out)\n\n        #########\n        # return x_out_branch, x_out, x_grad\n        return x_out\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/SRVGG.py",
    "content": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport math\n\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass SRVGGNetCompact(nn.Module):\n    \"\"\"A compact VGG-style network structure for super-resolution.\n    It is a compact network structure, which performs upsampling in the last layer and no convolution is\n    conducted on the HR feature space.\n    Args:\n        num_in_ch (int): Channel number of inputs. Default: 3.\n        num_out_ch (int): Channel number of outputs. Default: 3.\n        num_feat (int): Channel number of intermediate features. Default: 64.\n        num_conv (int): Number of convolution layers in the body network. Default: 16.\n        upscale (int): Upsampling factor. Default: 4.\n        act_type (str): Activation type, options: 'relu', 'prelu', 'leakyrelu'. Default: prelu.\n    \"\"\"\n\n    def __init__(\n        self,\n        state_dict,\n        act_type: str = \"prelu\",\n    ):\n        super(SRVGGNetCompact, self).__init__()\n        self.model_arch = \"SRVGG (RealESRGAN)\"\n        self.sub_type = \"SR\"\n\n        self.act_type = act_type\n\n        self.state = state_dict\n\n        if \"params\" in self.state:\n            self.state = self.state[\"params\"]\n\n        self.key_arr = list(self.state.keys())\n\n        self.in_nc = self.get_in_nc()\n        self.num_feat = self.get_num_feats()\n        self.num_conv = self.get_num_conv()\n        self.out_nc = self.in_nc  # :(\n        self.pixelshuffle_shape = None  # Defined in get_scale()\n        self.scale = self.get_scale()\n\n        self.supports_fp16 = True\n        self.supports_bfp16 = True\n        self.min_size_restriction = None\n\n        self.body = nn.ModuleList()\n        # the first conv\n        self.body.append(nn.Conv2d(self.in_nc, self.num_feat, 3, 1, 1))\n        # the first activation\n        if act_type == \"relu\":\n            activation = nn.ReLU(inplace=True)\n        elif act_type == \"prelu\":\n            activation = nn.PReLU(num_parameters=self.num_feat)\n        elif act_type == \"leakyrelu\":\n            activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)\n        self.body.append(activation)  # type: ignore\n\n        # the body structure\n        for _ in range(self.num_conv):\n            self.body.append(nn.Conv2d(self.num_feat, self.num_feat, 3, 1, 1))\n            # activation\n            if act_type == \"relu\":\n                activation = nn.ReLU(inplace=True)\n            elif act_type == \"prelu\":\n                activation = nn.PReLU(num_parameters=self.num_feat)\n            elif act_type == \"leakyrelu\":\n                activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)\n            self.body.append(activation)  # type: ignore\n\n        # the last conv\n        self.body.append(nn.Conv2d(self.num_feat, self.pixelshuffle_shape, 3, 1, 1))  # type: ignore\n        # upsample\n        self.upsampler = nn.PixelShuffle(self.scale)\n\n        self.load_state_dict(self.state, strict=False)\n\n    def get_num_conv(self) -> int:\n        return (int(self.key_arr[-1].split(\".\")[1]) - 2) // 2\n\n    def get_num_feats(self) -> int:\n        return self.state[self.key_arr[0]].shape[0]\n\n    def get_in_nc(self) -> int:\n        return self.state[self.key_arr[0]].shape[1]\n\n    def get_scale(self) -> int:\n        self.pixelshuffle_shape = self.state[self.key_arr[-1]].shape[0]\n        # Assume out_nc is the same as in_nc\n        # I cant think of a better way to do that\n        self.out_nc = self.in_nc\n        scale = math.sqrt(self.pixelshuffle_shape / self.out_nc)\n        if scale - int(scale) > 0:\n            print(\n                \"out_nc is probably different than in_nc, scale calculation might be wrong\"\n            )\n        scale = int(scale)\n        return scale\n\n    def forward(self, x):\n        out = x\n        for i in range(0, len(self.body)):\n            out = self.body[i](out)\n\n        out = self.upsampler(out)\n        # add the nearest upsampled image, so that the network learns the residual\n        base = F.interpolate(x, scale_factor=self.scale, mode=\"nearest\")\n        out += base\n        return out\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/SwiftSRGAN.py",
    "content": "# From https://github.com/Koushik0901/Swift-SRGAN/blob/master/swift-srgan/models.py\n\nimport torch\nfrom torch import nn\n\n\nclass SeperableConv2d(nn.Module):\n    def __init__(\n        self, in_channels, out_channels, kernel_size, stride=1, padding=1, bias=True\n    ):\n        super(SeperableConv2d, self).__init__()\n        self.depthwise = nn.Conv2d(\n            in_channels,\n            in_channels,\n            kernel_size=kernel_size,\n            stride=stride,\n            groups=in_channels,\n            bias=bias,\n            padding=padding,\n        )\n        self.pointwise = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=bias)\n\n    def forward(self, x):\n        return self.pointwise(self.depthwise(x))\n\n\nclass ConvBlock(nn.Module):\n    def __init__(\n        self,\n        in_channels,\n        out_channels,\n        use_act=True,\n        use_bn=True,\n        discriminator=False,\n        **kwargs,\n    ):\n        super(ConvBlock, self).__init__()\n\n        self.use_act = use_act\n        self.cnn = SeperableConv2d(in_channels, out_channels, **kwargs, bias=not use_bn)\n        self.bn = nn.BatchNorm2d(out_channels) if use_bn else nn.Identity()\n        self.act = (\n            nn.LeakyReLU(0.2, inplace=True)\n            if discriminator\n            else nn.PReLU(num_parameters=out_channels)\n        )\n\n    def forward(self, x):\n        return self.act(self.bn(self.cnn(x))) if self.use_act else self.bn(self.cnn(x))\n\n\nclass UpsampleBlock(nn.Module):\n    def __init__(self, in_channels, scale_factor):\n        super(UpsampleBlock, self).__init__()\n\n        self.conv = SeperableConv2d(\n            in_channels,\n            in_channels * scale_factor**2,\n            kernel_size=3,\n            stride=1,\n            padding=1,\n        )\n        self.ps = nn.PixelShuffle(\n            scale_factor\n        )  # (in_channels * 4, H, W) -> (in_channels, H*2, W*2)\n        self.act = nn.PReLU(num_parameters=in_channels)\n\n    def forward(self, x):\n        return self.act(self.ps(self.conv(x)))\n\n\nclass ResidualBlock(nn.Module):\n    def __init__(self, in_channels):\n        super(ResidualBlock, self).__init__()\n\n        self.block1 = ConvBlock(\n            in_channels, in_channels, kernel_size=3, stride=1, padding=1\n        )\n        self.block2 = ConvBlock(\n            in_channels, in_channels, kernel_size=3, stride=1, padding=1, use_act=False\n        )\n\n    def forward(self, x):\n        out = self.block1(x)\n        out = self.block2(out)\n        return out + x\n\n\nclass Generator(nn.Module):\n    \"\"\"Swift-SRGAN Generator\n    Args:\n        in_channels (int): number of input image channels.\n        num_channels (int): number of hidden channels.\n        num_blocks (int): number of residual blocks.\n        upscale_factor (int): factor to upscale the image [2x, 4x, 8x].\n    Returns:\n        torch.Tensor: super resolution image\n    \"\"\"\n\n    def __init__(\n        self,\n        state_dict,\n    ):\n        super(Generator, self).__init__()\n        self.model_arch = \"Swift-SRGAN\"\n        self.sub_type = \"SR\"\n        self.state = state_dict\n        if \"model\" in self.state:\n            self.state = self.state[\"model\"]\n\n        self.in_nc: int = self.state[\"initial.cnn.depthwise.weight\"].shape[0]\n        self.out_nc: int = self.state[\"final_conv.pointwise.weight\"].shape[0]\n        self.num_filters: int = self.state[\"initial.cnn.pointwise.weight\"].shape[0]\n        self.num_blocks = len(\n            set([x.split(\".\")[1] for x in self.state.keys() if \"residual\" in x])\n        )\n        self.scale: int = 2 ** len(\n            set([x.split(\".\")[1] for x in self.state.keys() if \"upsampler\" in x])\n        )\n\n        in_channels = self.in_nc\n        num_channels = self.num_filters\n        num_blocks = self.num_blocks\n        upscale_factor = self.scale\n\n        self.supports_fp16 = True\n        self.supports_bfp16 = True\n        self.min_size_restriction = None\n\n        self.initial = ConvBlock(\n            in_channels, num_channels, kernel_size=9, stride=1, padding=4, use_bn=False\n        )\n        self.residual = nn.Sequential(\n            *[ResidualBlock(num_channels) for _ in range(num_blocks)]\n        )\n        self.convblock = ConvBlock(\n            num_channels,\n            num_channels,\n            kernel_size=3,\n            stride=1,\n            padding=1,\n            use_act=False,\n        )\n        self.upsampler = nn.Sequential(\n            *[\n                UpsampleBlock(num_channels, scale_factor=2)\n                for _ in range(upscale_factor // 2)\n            ]\n        )\n        self.final_conv = SeperableConv2d(\n            num_channels, in_channels, kernel_size=9, stride=1, padding=4\n        )\n\n        self.load_state_dict(self.state, strict=False)\n\n    def forward(self, x):\n        initial = self.initial(x)\n        x = self.residual(initial)\n        x = self.convblock(x) + initial\n        x = self.upsampler(x)\n        return (torch.tanh(self.final_conv(x)) + 1) / 2\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/Swin2SR.py",
    "content": "# pylint: skip-file\n# -----------------------------------------------------------------------------------\n# Swin2SR: Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration, https://arxiv.org/abs/2209.11345\n# Written by Conde and Choi et al.\n# From: https://raw.githubusercontent.com/mv-lab/swin2sr/main/models/network_swin2sr.py\n# -----------------------------------------------------------------------------------\n\nimport math\nimport re\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.checkpoint as checkpoint\n\n# Originally from the timm package\nfrom .timm.drop import DropPath\nfrom .timm.helpers import to_2tuple\nfrom .timm.weight_init import trunc_normal_\n\n\nclass Mlp(nn.Module):\n    def __init__(\n        self,\n        in_features,\n        hidden_features=None,\n        out_features=None,\n        act_layer=nn.GELU,\n        drop=0.0,\n    ):\n        super().__init__()\n        out_features = out_features or in_features\n        hidden_features = hidden_features or in_features\n        self.fc1 = nn.Linear(in_features, hidden_features)\n        self.act = act_layer()\n        self.fc2 = nn.Linear(hidden_features, out_features)\n        self.drop = nn.Dropout(drop)\n\n    def forward(self, x):\n        x = self.fc1(x)\n        x = self.act(x)\n        x = self.drop(x)\n        x = self.fc2(x)\n        x = self.drop(x)\n        return x\n\n\ndef window_partition(x, window_size):\n    \"\"\"\n    Args:\n        x: (B, H, W, C)\n        window_size (int): window size\n    Returns:\n        windows: (num_windows*B, window_size, window_size, C)\n    \"\"\"\n    B, H, W, C = x.shape\n    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)\n    windows = (\n        x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)\n    )\n    return windows\n\n\ndef window_reverse(windows, window_size, H, W):\n    \"\"\"\n    Args:\n        windows: (num_windows*B, window_size, window_size, C)\n        window_size (int): Window size\n        H (int): Height of image\n        W (int): Width of image\n    Returns:\n        x: (B, H, W, C)\n    \"\"\"\n    B = int(windows.shape[0] / (H * W / window_size / window_size))\n    x = windows.view(\n        B, H // window_size, W // window_size, window_size, window_size, -1\n    )\n    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)\n    return x\n\n\nclass WindowAttention(nn.Module):\n    r\"\"\"Window based multi-head self attention (W-MSA) module with relative position bias.\n    It supports both of shifted and non-shifted window.\n    Args:\n        dim (int): Number of input channels.\n        window_size (tuple[int]): The height and width of the window.\n        num_heads (int): Number of attention heads.\n        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True\n        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0\n        proj_drop (float, optional): Dropout ratio of output. Default: 0.0\n        pretrained_window_size (tuple[int]): The height and width of the window in pre-training.\n    \"\"\"\n\n    def __init__(\n        self,\n        dim,\n        window_size,\n        num_heads,\n        qkv_bias=True,\n        attn_drop=0.0,\n        proj_drop=0.0,\n        pretrained_window_size=[0, 0],\n    ):\n        super().__init__()\n        self.dim = dim\n        self.window_size = window_size  # Wh, Ww\n        self.pretrained_window_size = pretrained_window_size\n        self.num_heads = num_heads\n\n        self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True)  # type: ignore\n\n        # mlp to generate continuous relative position bias\n        self.cpb_mlp = nn.Sequential(\n            nn.Linear(2, 512, bias=True),\n            nn.ReLU(inplace=True),\n            nn.Linear(512, num_heads, bias=False),\n        )\n\n        # get relative_coords_table\n        relative_coords_h = torch.arange(\n            -(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32\n        )\n        relative_coords_w = torch.arange(\n            -(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32\n        )\n        relative_coords_table = (\n            torch.stack(torch.meshgrid([relative_coords_h, relative_coords_w]))\n            .permute(1, 2, 0)\n            .contiguous()\n            .unsqueeze(0)\n        )  # 1, 2*Wh-1, 2*Ww-1, 2\n        if pretrained_window_size[0] > 0:\n            relative_coords_table[:, :, :, 0] /= pretrained_window_size[0] - 1\n            relative_coords_table[:, :, :, 1] /= pretrained_window_size[1] - 1\n        else:\n            relative_coords_table[:, :, :, 0] /= self.window_size[0] - 1\n            relative_coords_table[:, :, :, 1] /= self.window_size[1] - 1\n        relative_coords_table *= 8  # normalize to -8, 8\n        relative_coords_table = (\n            torch.sign(relative_coords_table)\n            * torch.log2(torch.abs(relative_coords_table) + 1.0)\n            / np.log2(8)\n        )\n\n        self.register_buffer(\"relative_coords_table\", relative_coords_table)\n\n        # get pair-wise relative position index for each token inside the window\n        coords_h = torch.arange(self.window_size[0])\n        coords_w = torch.arange(self.window_size[1])\n        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww\n        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww\n        relative_coords = (\n            coords_flatten[:, :, None] - coords_flatten[:, None, :]\n        )  # 2, Wh*Ww, Wh*Ww\n        relative_coords = relative_coords.permute(\n            1, 2, 0\n        ).contiguous()  # Wh*Ww, Wh*Ww, 2\n        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0\n        relative_coords[:, :, 1] += self.window_size[1] - 1\n        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1\n        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww\n        self.register_buffer(\"relative_position_index\", relative_position_index)\n\n        self.qkv = nn.Linear(dim, dim * 3, bias=False)\n        if qkv_bias:\n            self.q_bias = nn.Parameter(torch.zeros(dim))  # type: ignore\n            self.v_bias = nn.Parameter(torch.zeros(dim))  # type: ignore\n        else:\n            self.q_bias = None\n            self.v_bias = None\n        self.attn_drop = nn.Dropout(attn_drop)\n        self.proj = nn.Linear(dim, dim)\n        self.proj_drop = nn.Dropout(proj_drop)\n        self.softmax = nn.Softmax(dim=-1)\n\n    def forward(self, x, mask=None):\n        \"\"\"\n        Args:\n            x: input features with shape of (num_windows*B, N, C)\n            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None\n        \"\"\"\n        B_, N, C = x.shape\n        qkv_bias = None\n        if self.q_bias is not None:\n            qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))  # type: ignore\n        qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)\n        qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)\n        q, k, v = (\n            qkv[0],\n            qkv[1],\n            qkv[2],\n        )  # make torchscript happy (cannot use tensor as tuple)\n\n        # cosine attention\n        attn = F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1)\n        logit_scale = torch.clamp(\n            self.logit_scale,\n            max=torch.log(torch.tensor(1.0 / 0.01)).to(self.logit_scale.device),\n        ).exp()\n        attn = attn * logit_scale\n\n        relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(\n            -1, self.num_heads\n        )\n        relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view(  # type: ignore\n            self.window_size[0] * self.window_size[1],\n            self.window_size[0] * self.window_size[1],\n            -1,\n        )  # Wh*Ww,Wh*Ww,nH\n        relative_position_bias = relative_position_bias.permute(\n            2, 0, 1\n        ).contiguous()  # nH, Wh*Ww, Wh*Ww\n        relative_position_bias = 16 * torch.sigmoid(relative_position_bias)\n        attn = attn + relative_position_bias.unsqueeze(0)\n\n        if mask is not None:\n            nW = mask.shape[0]\n            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(\n                1\n            ).unsqueeze(0)\n            attn = attn.view(-1, self.num_heads, N, N)\n            attn = self.softmax(attn)\n        else:\n            attn = self.softmax(attn)\n\n        attn = self.attn_drop(attn)\n\n        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)\n        x = self.proj(x)\n        x = self.proj_drop(x)\n        return x\n\n    def extra_repr(self) -> str:\n        return (\n            f\"dim={self.dim}, window_size={self.window_size}, \"\n            f\"pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}\"\n        )\n\n    def flops(self, N):\n        # calculate flops for 1 window with token length of N\n        flops = 0\n        # qkv = self.qkv(x)\n        flops += N * self.dim * 3 * self.dim\n        # attn = (q @ k.transpose(-2, -1))\n        flops += self.num_heads * N * (self.dim // self.num_heads) * N\n        #  x = (attn @ v)\n        flops += self.num_heads * N * N * (self.dim // self.num_heads)\n        # x = self.proj(x)\n        flops += N * self.dim * self.dim\n        return flops\n\n\nclass SwinTransformerBlock(nn.Module):\n    r\"\"\"Swin Transformer Block.\n    Args:\n        dim (int): Number of input channels.\n        input_resolution (tuple[int]): Input resulotion.\n        num_heads (int): Number of attention heads.\n        window_size (int): Window size.\n        shift_size (int): Shift size for SW-MSA.\n        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n        drop (float, optional): Dropout rate. Default: 0.0\n        attn_drop (float, optional): Attention dropout rate. Default: 0.0\n        drop_path (float, optional): Stochastic depth rate. Default: 0.0\n        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU\n        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm\n        pretrained_window_size (int): Window size in pre-training.\n    \"\"\"\n\n    def __init__(\n        self,\n        dim,\n        input_resolution,\n        num_heads,\n        window_size=7,\n        shift_size=0,\n        mlp_ratio=4.0,\n        qkv_bias=True,\n        drop=0.0,\n        attn_drop=0.0,\n        drop_path=0.0,\n        act_layer=nn.GELU,\n        norm_layer=nn.LayerNorm,\n        pretrained_window_size=0,\n    ):\n        super().__init__()\n        self.dim = dim\n        self.input_resolution = input_resolution\n        self.num_heads = num_heads\n        self.window_size = window_size\n        self.shift_size = shift_size\n        self.mlp_ratio = mlp_ratio\n        if min(self.input_resolution) <= self.window_size:\n            # if window size is larger than input resolution, we don't partition windows\n            self.shift_size = 0\n            self.window_size = min(self.input_resolution)\n        assert (\n            0 <= self.shift_size < self.window_size\n        ), \"shift_size must in 0-window_size\"\n\n        self.norm1 = norm_layer(dim)\n        self.attn = WindowAttention(\n            dim,\n            window_size=to_2tuple(self.window_size),\n            num_heads=num_heads,\n            qkv_bias=qkv_bias,\n            attn_drop=attn_drop,\n            proj_drop=drop,\n            pretrained_window_size=to_2tuple(pretrained_window_size),\n        )\n\n        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()\n        self.norm2 = norm_layer(dim)\n        mlp_hidden_dim = int(dim * mlp_ratio)\n        self.mlp = Mlp(\n            in_features=dim,\n            hidden_features=mlp_hidden_dim,\n            act_layer=act_layer,\n            drop=drop,\n        )\n\n        if self.shift_size > 0:\n            attn_mask = self.calculate_mask(self.input_resolution)\n        else:\n            attn_mask = None\n\n        self.register_buffer(\"attn_mask\", attn_mask)\n\n    def calculate_mask(self, x_size):\n        # calculate attention mask for SW-MSA\n        H, W = x_size\n        img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1\n        h_slices = (\n            slice(0, -self.window_size),\n            slice(-self.window_size, -self.shift_size),\n            slice(-self.shift_size, None),\n        )\n        w_slices = (\n            slice(0, -self.window_size),\n            slice(-self.window_size, -self.shift_size),\n            slice(-self.shift_size, None),\n        )\n        cnt = 0\n        for h in h_slices:\n            for w in w_slices:\n                img_mask[:, h, w, :] = cnt\n                cnt += 1\n\n        mask_windows = window_partition(\n            img_mask, self.window_size\n        )  # nW, window_size, window_size, 1\n        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)\n        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)\n        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(\n            attn_mask == 0, float(0.0)\n        )\n\n        return attn_mask\n\n    def forward(self, x, x_size):\n        H, W = x_size\n        B, L, C = x.shape\n        # assert L == H * W, \"input feature has wrong size\"\n\n        shortcut = x\n        x = x.view(B, H, W, C)\n\n        # cyclic shift\n        if self.shift_size > 0:\n            shifted_x = torch.roll(\n                x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)\n            )\n        else:\n            shifted_x = x\n\n        # partition windows\n        x_windows = window_partition(\n            shifted_x, self.window_size\n        )  # nW*B, window_size, window_size, C\n        x_windows = x_windows.view(\n            -1, self.window_size * self.window_size, C\n        )  # nW*B, window_size*window_size, C\n\n        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size\n        if self.input_resolution == x_size:\n            attn_windows = self.attn(\n                x_windows, mask=self.attn_mask\n            )  # nW*B, window_size*window_size, C\n        else:\n            attn_windows = self.attn(\n                x_windows, mask=self.calculate_mask(x_size).to(x.device)\n            )\n\n        # merge windows\n        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)\n        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C\n\n        # reverse cyclic shift\n        if self.shift_size > 0:\n            x = torch.roll(\n                shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)\n            )\n        else:\n            x = shifted_x\n        x = x.view(B, H * W, C)\n        x = shortcut + self.drop_path(self.norm1(x))\n\n        # FFN\n        x = x + self.drop_path(self.norm2(self.mlp(x)))\n\n        return x\n\n    def extra_repr(self) -> str:\n        return (\n            f\"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, \"\n            f\"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}\"\n        )\n\n    def flops(self):\n        flops = 0\n        H, W = self.input_resolution\n        # norm1\n        flops += self.dim * H * W\n        # W-MSA/SW-MSA\n        nW = H * W / self.window_size / self.window_size\n        flops += nW * self.attn.flops(self.window_size * self.window_size)\n        # mlp\n        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio\n        # norm2\n        flops += self.dim * H * W\n        return flops\n\n\nclass PatchMerging(nn.Module):\n    r\"\"\"Patch Merging Layer.\n    Args:\n        input_resolution (tuple[int]): Resolution of input feature.\n        dim (int): Number of input channels.\n        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm\n    \"\"\"\n\n    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):\n        super().__init__()\n        self.input_resolution = input_resolution\n        self.dim = dim\n        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)\n        self.norm = norm_layer(2 * dim)\n\n    def forward(self, x):\n        \"\"\"\n        x: B, H*W, C\n        \"\"\"\n        H, W = self.input_resolution\n        B, L, C = x.shape\n        assert L == H * W, \"input feature has wrong size\"\n        assert H % 2 == 0 and W % 2 == 0, f\"x size ({H}*{W}) are not even.\"\n\n        x = x.view(B, H, W, C)\n\n        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C\n        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C\n        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C\n        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C\n        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C\n        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C\n\n        x = self.reduction(x)\n        x = self.norm(x)\n\n        return x\n\n    def extra_repr(self) -> str:\n        return f\"input_resolution={self.input_resolution}, dim={self.dim}\"\n\n    def flops(self):\n        H, W = self.input_resolution\n        flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim\n        flops += H * W * self.dim // 2\n        return flops\n\n\nclass BasicLayer(nn.Module):\n    \"\"\"A basic Swin Transformer layer for one stage.\n    Args:\n        dim (int): Number of input channels.\n        input_resolution (tuple[int]): Input resolution.\n        depth (int): Number of blocks.\n        num_heads (int): Number of attention heads.\n        window_size (int): Local window size.\n        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n        drop (float, optional): Dropout rate. Default: 0.0\n        attn_drop (float, optional): Attention dropout rate. Default: 0.0\n        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0\n        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm\n        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None\n        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.\n        pretrained_window_size (int): Local window size in pre-training.\n    \"\"\"\n\n    def __init__(\n        self,\n        dim,\n        input_resolution,\n        depth,\n        num_heads,\n        window_size,\n        mlp_ratio=4.0,\n        qkv_bias=True,\n        drop=0.0,\n        attn_drop=0.0,\n        drop_path=0.0,\n        norm_layer=nn.LayerNorm,\n        downsample=None,\n        use_checkpoint=False,\n        pretrained_window_size=0,\n    ):\n        super().__init__()\n        self.dim = dim\n        self.input_resolution = input_resolution\n        self.depth = depth\n        self.use_checkpoint = use_checkpoint\n\n        # build blocks\n        self.blocks = nn.ModuleList(\n            [\n                SwinTransformerBlock(\n                    dim=dim,\n                    input_resolution=input_resolution,\n                    num_heads=num_heads,\n                    window_size=window_size,\n                    shift_size=0 if (i % 2 == 0) else window_size // 2,\n                    mlp_ratio=mlp_ratio,\n                    qkv_bias=qkv_bias,\n                    drop=drop,\n                    attn_drop=attn_drop,\n                    drop_path=drop_path[i]\n                    if isinstance(drop_path, list)\n                    else drop_path,\n                    norm_layer=norm_layer,\n                    pretrained_window_size=pretrained_window_size,\n                )\n                for i in range(depth)\n            ]\n        )\n\n        # patch merging layer\n        if downsample is not None:\n            self.downsample = downsample(\n                input_resolution, dim=dim, norm_layer=norm_layer\n            )\n        else:\n            self.downsample = None\n\n    def forward(self, x, x_size):\n        for blk in self.blocks:\n            if self.use_checkpoint:\n                x = checkpoint.checkpoint(blk, x, x_size)\n            else:\n                x = blk(x, x_size)\n        if self.downsample is not None:\n            x = self.downsample(x)\n        return x\n\n    def extra_repr(self) -> str:\n        return f\"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}\"\n\n    def flops(self):\n        flops = 0\n        for blk in self.blocks:\n            flops += blk.flops()  # type: ignore\n        if self.downsample is not None:\n            flops += self.downsample.flops()\n        return flops\n\n    def _init_respostnorm(self):\n        for blk in self.blocks:\n            nn.init.constant_(blk.norm1.bias, 0)  # type: ignore\n            nn.init.constant_(blk.norm1.weight, 0)  # type: ignore\n            nn.init.constant_(blk.norm2.bias, 0)  # type: ignore\n            nn.init.constant_(blk.norm2.weight, 0)  # type: ignore\n\n\nclass PatchEmbed(nn.Module):\n    r\"\"\"Image to Patch Embedding\n    Args:\n        img_size (int): Image size.  Default: 224.\n        patch_size (int): Patch token size. Default: 4.\n        in_chans (int): Number of input image channels. Default: 3.\n        embed_dim (int): Number of linear projection output channels. Default: 96.\n        norm_layer (nn.Module, optional): Normalization layer. Default: None\n    \"\"\"\n\n    def __init__(\n        self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None\n    ):\n        super().__init__()\n        img_size = to_2tuple(img_size)\n        patch_size = to_2tuple(patch_size)\n        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]  # type: ignore\n        self.img_size = img_size\n        self.patch_size = patch_size\n        self.patches_resolution = patches_resolution\n        self.num_patches = patches_resolution[0] * patches_resolution[1]\n\n        self.in_chans = in_chans\n        self.embed_dim = embed_dim\n\n        self.proj = nn.Conv2d(\n            in_chans, embed_dim, kernel_size=patch_size, stride=patch_size  # type: ignore\n        )\n        if norm_layer is not None:\n            self.norm = norm_layer(embed_dim)\n        else:\n            self.norm = None\n\n    def forward(self, x):\n        B, C, H, W = x.shape\n        # FIXME look at relaxing size constraints\n        # assert H == self.img_size[0] and W == self.img_size[1],\n        #     f\"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]}).\"\n        x = self.proj(x).flatten(2).transpose(1, 2)  # B Ph*Pw C\n        if self.norm is not None:\n            x = self.norm(x)\n        return x\n\n    def flops(self):\n        Ho, Wo = self.patches_resolution\n        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])  # type: ignore\n        if self.norm is not None:\n            flops += Ho * Wo * self.embed_dim\n        return flops\n\n\nclass RSTB(nn.Module):\n    \"\"\"Residual Swin Transformer Block (RSTB).\n\n    Args:\n        dim (int): Number of input channels.\n        input_resolution (tuple[int]): Input resolution.\n        depth (int): Number of blocks.\n        num_heads (int): Number of attention heads.\n        window_size (int): Local window size.\n        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n        drop (float, optional): Dropout rate. Default: 0.0\n        attn_drop (float, optional): Attention dropout rate. Default: 0.0\n        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0\n        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm\n        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None\n        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.\n        img_size: Input image size.\n        patch_size: Patch size.\n        resi_connection: The convolutional block before residual connection.\n    \"\"\"\n\n    def __init__(\n        self,\n        dim,\n        input_resolution,\n        depth,\n        num_heads,\n        window_size,\n        mlp_ratio=4.0,\n        qkv_bias=True,\n        drop=0.0,\n        attn_drop=0.0,\n        drop_path=0.0,\n        norm_layer=nn.LayerNorm,\n        downsample=None,\n        use_checkpoint=False,\n        img_size=224,\n        patch_size=4,\n        resi_connection=\"1conv\",\n    ):\n        super(RSTB, self).__init__()\n\n        self.dim = dim\n        self.input_resolution = input_resolution\n\n        self.residual_group = BasicLayer(\n            dim=dim,\n            input_resolution=input_resolution,\n            depth=depth,\n            num_heads=num_heads,\n            window_size=window_size,\n            mlp_ratio=mlp_ratio,\n            qkv_bias=qkv_bias,\n            drop=drop,\n            attn_drop=attn_drop,\n            drop_path=drop_path,\n            norm_layer=norm_layer,\n            downsample=downsample,\n            use_checkpoint=use_checkpoint,\n        )\n\n        if resi_connection == \"1conv\":\n            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)\n        elif resi_connection == \"3conv\":\n            # to save parameters and memory\n            self.conv = nn.Sequential(\n                nn.Conv2d(dim, dim // 4, 3, 1, 1),\n                nn.LeakyReLU(negative_slope=0.2, inplace=True),\n                nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),\n                nn.LeakyReLU(negative_slope=0.2, inplace=True),\n                nn.Conv2d(dim // 4, dim, 3, 1, 1),\n            )\n\n        self.patch_embed = PatchEmbed(\n            img_size=img_size,\n            patch_size=patch_size,\n            in_chans=dim,\n            embed_dim=dim,\n            norm_layer=None,\n        )\n\n        self.patch_unembed = PatchUnEmbed(\n            img_size=img_size,\n            patch_size=patch_size,\n            in_chans=dim,\n            embed_dim=dim,\n            norm_layer=None,\n        )\n\n    def forward(self, x, x_size):\n        return (\n            self.patch_embed(\n                self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))\n            )\n            + x\n        )\n\n    def flops(self):\n        flops = 0\n        flops += self.residual_group.flops()\n        H, W = self.input_resolution\n        flops += H * W * self.dim * self.dim * 9\n        flops += self.patch_embed.flops()\n        flops += self.patch_unembed.flops()\n\n        return flops\n\n\nclass PatchUnEmbed(nn.Module):\n    r\"\"\"Image to Patch Unembedding\n\n    Args:\n        img_size (int): Image size.  Default: 224.\n        patch_size (int): Patch token size. Default: 4.\n        in_chans (int): Number of input image channels. Default: 3.\n        embed_dim (int): Number of linear projection output channels. Default: 96.\n        norm_layer (nn.Module, optional): Normalization layer. Default: None\n    \"\"\"\n\n    def __init__(\n        self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None\n    ):\n        super().__init__()\n        img_size = to_2tuple(img_size)\n        patch_size = to_2tuple(patch_size)\n        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]  # type: ignore\n        self.img_size = img_size\n        self.patch_size = patch_size\n        self.patches_resolution = patches_resolution\n        self.num_patches = patches_resolution[0] * patches_resolution[1]\n\n        self.in_chans = in_chans\n        self.embed_dim = embed_dim\n\n    def forward(self, x, x_size):\n        B, HW, C = x.shape\n        x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1])  # B Ph*Pw C\n        return x\n\n    def flops(self):\n        flops = 0\n        return flops\n\n\nclass Upsample(nn.Sequential):\n    \"\"\"Upsample module.\n\n    Args:\n        scale (int): Scale factor. Supported scales: 2^n and 3.\n        num_feat (int): Channel number of intermediate features.\n    \"\"\"\n\n    def __init__(self, scale, num_feat):\n        m = []\n        if (scale & (scale - 1)) == 0:  # scale = 2^n\n            for _ in range(int(math.log(scale, 2))):\n                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))\n                m.append(nn.PixelShuffle(2))\n        elif scale == 3:\n            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))\n            m.append(nn.PixelShuffle(3))\n        else:\n            raise ValueError(\n                f\"scale {scale} is not supported. \" \"Supported scales: 2^n and 3.\"\n            )\n        super(Upsample, self).__init__(*m)\n\n\nclass Upsample_hf(nn.Sequential):\n    \"\"\"Upsample module.\n\n    Args:\n        scale (int): Scale factor. Supported scales: 2^n and 3.\n        num_feat (int): Channel number of intermediate features.\n    \"\"\"\n\n    def __init__(self, scale, num_feat):\n        m = []\n        if (scale & (scale - 1)) == 0:  # scale = 2^n\n            for _ in range(int(math.log(scale, 2))):\n                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))\n                m.append(nn.PixelShuffle(2))\n        elif scale == 3:\n            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))\n            m.append(nn.PixelShuffle(3))\n        else:\n            raise ValueError(\n                f\"scale {scale} is not supported. \" \"Supported scales: 2^n and 3.\"\n            )\n        super(Upsample_hf, self).__init__(*m)\n\n\nclass UpsampleOneStep(nn.Sequential):\n    \"\"\"UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)\n       Used in lightweight SR to save parameters.\n\n    Args:\n        scale (int): Scale factor. Supported scales: 2^n and 3.\n        num_feat (int): Channel number of intermediate features.\n\n    \"\"\"\n\n    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):\n        self.num_feat = num_feat\n        self.input_resolution = input_resolution\n        m = []\n        m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1))\n        m.append(nn.PixelShuffle(scale))\n        super(UpsampleOneStep, self).__init__(*m)\n\n    def flops(self):\n        H, W = self.input_resolution  # type: ignore\n        flops = H * W * self.num_feat * 3 * 9\n        return flops\n\n\nclass Swin2SR(nn.Module):\n    r\"\"\"Swin2SR\n        A PyTorch impl of : `Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration`.\n\n    Args:\n        img_size (int | tuple(int)): Input image size. Default 64\n        patch_size (int | tuple(int)): Patch size. Default: 1\n        in_chans (int): Number of input image channels. Default: 3\n        embed_dim (int): Patch embedding dimension. Default: 96\n        depths (tuple(int)): Depth of each Swin Transformer layer.\n        num_heads (tuple(int)): Number of attention heads in different layers.\n        window_size (int): Window size. Default: 7\n        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4\n        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True\n        drop_rate (float): Dropout rate. Default: 0\n        attn_drop_rate (float): Attention dropout rate. Default: 0\n        drop_path_rate (float): Stochastic depth rate. Default: 0.1\n        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.\n        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False\n        patch_norm (bool): If True, add normalization after patch embedding. Default: True\n        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False\n        upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction\n        img_range: Image range. 1. or 255.\n        upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None\n        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'\n    \"\"\"\n\n    def __init__(\n        self,\n        state_dict,\n        **kwargs,\n    ):\n        super(Swin2SR, self).__init__()\n\n        # Defaults\n        img_size = 128\n        patch_size = 1\n        in_chans = 3\n        embed_dim = 96\n        depths = [6, 6, 6, 6]\n        num_heads = [6, 6, 6, 6]\n        window_size = 7\n        mlp_ratio = 4.0\n        qkv_bias = True\n        drop_rate = 0.0\n        attn_drop_rate = 0.0\n        drop_path_rate = 0.1\n        norm_layer = nn.LayerNorm\n        ape = False\n        patch_norm = True\n        use_checkpoint = False\n        upscale = 2\n        img_range = 1.0\n        upsampler = \"\"\n        resi_connection = \"1conv\"\n        num_in_ch = in_chans\n        num_out_ch = in_chans\n        num_feat = 64\n\n        self.model_arch = \"Swin2SR\"\n        self.sub_type = \"SR\"\n        self.state = state_dict\n        if \"params_ema\" in self.state:\n            self.state = self.state[\"params_ema\"]\n        elif \"params\" in self.state:\n            self.state = self.state[\"params\"]\n\n        state_keys = self.state.keys()\n\n        if \"conv_before_upsample.0.weight\" in state_keys:\n            if \"conv_aux.weight\" in state_keys:\n                upsampler = \"pixelshuffle_aux\"\n            elif \"conv_up1.weight\" in state_keys:\n                upsampler = \"nearest+conv\"\n            else:\n                upsampler = \"pixelshuffle\"\n                supports_fp16 = False\n        elif \"upsample.0.weight\" in state_keys:\n            upsampler = \"pixelshuffledirect\"\n        else:\n            upsampler = \"\"\n\n        num_feat = (\n            self.state.get(\"conv_before_upsample.0.weight\", None).shape[1]\n            if self.state.get(\"conv_before_upsample.weight\", None)\n            else 64\n        )\n\n        num_in_ch = self.state[\"conv_first.weight\"].shape[1]\n        in_chans = num_in_ch\n        if \"conv_last.weight\" in state_keys:\n            num_out_ch = self.state[\"conv_last.weight\"].shape[0]\n        else:\n            num_out_ch = num_in_ch\n\n        upscale = 1\n        if upsampler == \"nearest+conv\":\n            upsample_keys = [\n                x for x in state_keys if \"conv_up\" in x and \"bias\" not in x\n            ]\n\n            for upsample_key in upsample_keys:\n                upscale *= 2\n        elif upsampler == \"pixelshuffle\" or upsampler == \"pixelshuffle_aux\":\n            upsample_keys = [\n                x\n                for x in state_keys\n                if \"upsample\" in x and \"conv\" not in x and \"bias\" not in x\n            ]\n            for upsample_key in upsample_keys:\n                shape = self.state[upsample_key].shape[0]\n                upscale *= math.sqrt(shape // num_feat)\n            upscale = int(upscale)\n        elif upsampler == \"pixelshuffledirect\":\n            upscale = int(\n                math.sqrt(self.state[\"upsample.0.bias\"].shape[0] // num_out_ch)\n            )\n\n        max_layer_num = 0\n        max_block_num = 0\n        for key in state_keys:\n            result = re.match(\n                r\"layers.(\\d*).residual_group.blocks.(\\d*).norm1.weight\", key\n            )\n            if result:\n                layer_num, block_num = result.groups()\n                max_layer_num = max(max_layer_num, int(layer_num))\n                max_block_num = max(max_block_num, int(block_num))\n\n        depths = [max_block_num + 1 for _ in range(max_layer_num + 1)]\n\n        if (\n            \"layers.0.residual_group.blocks.0.attn.relative_position_bias_table\"\n            in state_keys\n        ):\n            num_heads_num = self.state[\n                \"layers.0.residual_group.blocks.0.attn.relative_position_bias_table\"\n            ].shape[-1]\n            num_heads = [num_heads_num for _ in range(max_layer_num + 1)]\n        else:\n            num_heads = depths\n\n        embed_dim = self.state[\"conv_first.weight\"].shape[0]\n\n        mlp_ratio = float(\n            self.state[\"layers.0.residual_group.blocks.0.mlp.fc1.bias\"].shape[0]\n            / embed_dim\n        )\n\n        # TODO: could actually count the layers, but this should do\n        if \"layers.0.conv.4.weight\" in state_keys:\n            resi_connection = \"3conv\"\n        else:\n            resi_connection = \"1conv\"\n\n        window_size = int(\n            math.sqrt(\n                self.state[\n                    \"layers.0.residual_group.blocks.0.attn.relative_position_index\"\n                ].shape[0]\n            )\n        )\n\n        if \"layers.0.residual_group.blocks.1.attn_mask\" in state_keys:\n            img_size = int(\n                math.sqrt(\n                    self.state[\"layers.0.residual_group.blocks.1.attn_mask\"].shape[0]\n                )\n                * window_size\n            )\n\n        # The JPEG models are the only ones with window-size 7, and they also use this range\n        img_range = 255.0 if window_size == 7 else 1.0\n\n        self.in_nc = num_in_ch\n        self.out_nc = num_out_ch\n        self.num_feat = num_feat\n        self.embed_dim = embed_dim\n        self.num_heads = num_heads\n        self.depths = depths\n        self.window_size = window_size\n        self.mlp_ratio = mlp_ratio\n        self.scale = upscale\n        self.upsampler = upsampler\n        self.img_size = img_size\n        self.img_range = img_range\n        self.resi_connection = resi_connection\n\n        self.supports_fp16 = False  # Too much weirdness to support this at the moment\n        self.supports_bfp16 = True\n        self.min_size_restriction = 16\n\n        ## END AUTO DETECTION\n\n        if in_chans == 3:\n            rgb_mean = (0.4488, 0.4371, 0.4040)\n            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)\n        else:\n            self.mean = torch.zeros(1, 1, 1, 1)\n        self.upscale = upscale\n        self.upsampler = upsampler\n        self.window_size = window_size\n\n        #####################################################################################################\n        ################################### 1, shallow feature extraction ###################################\n        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)\n\n        #####################################################################################################\n        ################################### 2, deep feature extraction ######################################\n        self.num_layers = len(depths)\n        self.embed_dim = embed_dim\n        self.ape = ape\n        self.patch_norm = patch_norm\n        self.num_features = embed_dim\n        self.mlp_ratio = mlp_ratio\n\n        # split image into non-overlapping patches\n        self.patch_embed = PatchEmbed(\n            img_size=img_size,\n            patch_size=patch_size,\n            in_chans=embed_dim,\n            embed_dim=embed_dim,\n            norm_layer=norm_layer if self.patch_norm else None,\n        )\n        num_patches = self.patch_embed.num_patches\n        patches_resolution = self.patch_embed.patches_resolution\n        self.patches_resolution = patches_resolution\n\n        # merge non-overlapping patches into image\n        self.patch_unembed = PatchUnEmbed(\n            img_size=img_size,\n            patch_size=patch_size,\n            in_chans=embed_dim,\n            embed_dim=embed_dim,\n            norm_layer=norm_layer if self.patch_norm else None,\n        )\n\n        # absolute position embedding\n        if self.ape:\n            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))  # type: ignore\n            trunc_normal_(self.absolute_pos_embed, std=0.02)\n\n        self.pos_drop = nn.Dropout(p=drop_rate)\n\n        # stochastic depth\n        dpr = [\n            x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))\n        ]  # stochastic depth decay rule\n\n        # build Residual Swin Transformer blocks (RSTB)\n        self.layers = nn.ModuleList()\n        for i_layer in range(self.num_layers):\n            layer = RSTB(\n                dim=embed_dim,\n                input_resolution=(patches_resolution[0], patches_resolution[1]),\n                depth=depths[i_layer],\n                num_heads=num_heads[i_layer],\n                window_size=window_size,\n                mlp_ratio=self.mlp_ratio,\n                qkv_bias=qkv_bias,\n                drop=drop_rate,\n                attn_drop=attn_drop_rate,\n                drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],  # type: ignore    # no impact on SR results\n                norm_layer=norm_layer,\n                downsample=None,\n                use_checkpoint=use_checkpoint,\n                img_size=img_size,\n                patch_size=patch_size,\n                resi_connection=resi_connection,\n            )\n            self.layers.append(layer)\n\n        if self.upsampler == \"pixelshuffle_hf\":\n            self.layers_hf = nn.ModuleList()\n            for i_layer in range(self.num_layers):\n                layer = RSTB(\n                    dim=embed_dim,\n                    input_resolution=(patches_resolution[0], patches_resolution[1]),\n                    depth=depths[i_layer],\n                    num_heads=num_heads[i_layer],\n                    window_size=window_size,\n                    mlp_ratio=self.mlp_ratio,\n                    qkv_bias=qkv_bias,\n                    drop=drop_rate,\n                    attn_drop=attn_drop_rate,\n                    drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],  # type: ignore    # no impact on SR results # type: ignore\n                    norm_layer=norm_layer,\n                    downsample=None,\n                    use_checkpoint=use_checkpoint,\n                    img_size=img_size,\n                    patch_size=patch_size,\n                    resi_connection=resi_connection,\n                )\n                self.layers_hf.append(layer)\n\n        self.norm = norm_layer(self.num_features)\n\n        # build the last conv layer in deep feature extraction\n        if resi_connection == \"1conv\":\n            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)\n        elif resi_connection == \"3conv\":\n            # to save parameters and memory\n            self.conv_after_body = nn.Sequential(\n                nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),\n                nn.LeakyReLU(negative_slope=0.2, inplace=True),\n                nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),\n                nn.LeakyReLU(negative_slope=0.2, inplace=True),\n                nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1),\n            )\n\n        #####################################################################################################\n        ################################ 3, high quality image reconstruction ################################\n        if self.upsampler == \"pixelshuffle\":\n            # for classical SR\n            self.conv_before_upsample = nn.Sequential(\n                nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)\n            )\n            self.upsample = Upsample(upscale, num_feat)\n            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)\n        elif self.upsampler == \"pixelshuffle_aux\":\n            self.conv_bicubic = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)\n            self.conv_before_upsample = nn.Sequential(\n                nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)\n            )\n            self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)\n            self.conv_after_aux = nn.Sequential(\n                nn.Conv2d(3, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)\n            )\n            self.upsample = Upsample(upscale, num_feat)\n            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)\n\n        elif self.upsampler == \"pixelshuffle_hf\":\n            self.conv_before_upsample = nn.Sequential(\n                nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)\n            )\n            self.upsample = Upsample(upscale, num_feat)\n            self.upsample_hf = Upsample_hf(upscale, num_feat)\n            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)\n            self.conv_first_hf = nn.Sequential(\n                nn.Conv2d(num_feat, embed_dim, 3, 1, 1), nn.LeakyReLU(inplace=True)\n            )\n            self.conv_after_body_hf = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)\n            self.conv_before_upsample_hf = nn.Sequential(\n                nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)\n            )\n            self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)\n\n        elif self.upsampler == \"pixelshuffledirect\":\n            # for lightweight SR (to save parameters)\n            self.upsample = UpsampleOneStep(\n                upscale,\n                embed_dim,\n                num_out_ch,\n                (patches_resolution[0], patches_resolution[1]),\n            )\n        elif self.upsampler == \"nearest+conv\":\n            # for real-world SR (less artifacts)\n            assert self.upscale == 4, \"only support x4 now.\"\n            self.conv_before_upsample = nn.Sequential(\n                nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)\n            )\n            self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)\n            self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)\n            self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)\n            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)\n            self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)\n        else:\n            # for image denoising and JPEG compression artifact reduction\n            self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)\n\n        self.apply(self._init_weights)\n\n        self.load_state_dict(state_dict)\n\n    def _init_weights(self, m):\n        if isinstance(m, nn.Linear):\n            trunc_normal_(m.weight, std=0.02)\n            if isinstance(m, nn.Linear) and m.bias is not None:\n                nn.init.constant_(m.bias, 0)\n        elif isinstance(m, nn.LayerNorm):\n            nn.init.constant_(m.bias, 0)\n            nn.init.constant_(m.weight, 1.0)\n\n    @torch.jit.ignore  # type: ignore\n    def no_weight_decay(self):\n        return {\"absolute_pos_embed\"}\n\n    @torch.jit.ignore  # type: ignore\n    def no_weight_decay_keywords(self):\n        return {\"relative_position_bias_table\"}\n\n    def check_image_size(self, x):\n        _, _, h, w = x.size()\n        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size\n        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size\n        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), \"reflect\")\n        return x\n\n    def forward_features(self, x):\n        x_size = (x.shape[2], x.shape[3])\n        x = self.patch_embed(x)\n        if self.ape:\n            x = x + self.absolute_pos_embed\n        x = self.pos_drop(x)\n\n        for layer in self.layers:\n            x = layer(x, x_size)\n\n        x = self.norm(x)  # B L C\n        x = self.patch_unembed(x, x_size)\n\n        return x\n\n    def forward_features_hf(self, x):\n        x_size = (x.shape[2], x.shape[3])\n        x = self.patch_embed(x)\n        if self.ape:\n            x = x + self.absolute_pos_embed\n        x = self.pos_drop(x)\n\n        for layer in self.layers_hf:\n            x = layer(x, x_size)\n\n        x = self.norm(x)  # B L C\n        x = self.patch_unembed(x, x_size)\n\n        return x\n\n    def forward(self, x):\n        H, W = x.shape[2:]\n        x = self.check_image_size(x)\n\n        self.mean = self.mean.type_as(x)\n        x = (x - self.mean) * self.img_range\n\n        if self.upsampler == \"pixelshuffle\":\n            # for classical SR\n            x = self.conv_first(x)\n            x = self.conv_after_body(self.forward_features(x)) + x\n            x = self.conv_before_upsample(x)\n            x = self.conv_last(self.upsample(x))\n        elif self.upsampler == \"pixelshuffle_aux\":\n            bicubic = F.interpolate(\n                x,\n                size=(H * self.upscale, W * self.upscale),\n                mode=\"bicubic\",\n                align_corners=False,\n            )\n            bicubic = self.conv_bicubic(bicubic)\n            x = self.conv_first(x)\n            x = self.conv_after_body(self.forward_features(x)) + x\n            x = self.conv_before_upsample(x)\n            aux = self.conv_aux(x)  # b, 3, LR_H, LR_W\n            x = self.conv_after_aux(aux)\n            x = (\n                self.upsample(x)[:, :, : H * self.upscale, : W * self.upscale]\n                + bicubic[:, :, : H * self.upscale, : W * self.upscale]\n            )\n            x = self.conv_last(x)\n            aux = aux / self.img_range + self.mean\n        elif self.upsampler == \"pixelshuffle_hf\":\n            # for classical SR with HF\n            x = self.conv_first(x)\n            x = self.conv_after_body(self.forward_features(x)) + x\n            x_before = self.conv_before_upsample(x)\n            x_out = self.conv_last(self.upsample(x_before))\n\n            x_hf = self.conv_first_hf(x_before)\n            x_hf = self.conv_after_body_hf(self.forward_features_hf(x_hf)) + x_hf\n            x_hf = self.conv_before_upsample_hf(x_hf)\n            x_hf = self.conv_last_hf(self.upsample_hf(x_hf))\n            x = x_out + x_hf\n            x_hf = x_hf / self.img_range + self.mean\n\n        elif self.upsampler == \"pixelshuffledirect\":\n            # for lightweight SR\n            x = self.conv_first(x)\n            x = self.conv_after_body(self.forward_features(x)) + x\n            x = self.upsample(x)\n        elif self.upsampler == \"nearest+conv\":\n            # for real-world SR\n            x = self.conv_first(x)\n            x = self.conv_after_body(self.forward_features(x)) + x\n            x = self.conv_before_upsample(x)\n            x = self.lrelu(\n                self.conv_up1(\n                    torch.nn.functional.interpolate(x, scale_factor=2, mode=\"nearest\")\n                )\n            )\n            x = self.lrelu(\n                self.conv_up2(\n                    torch.nn.functional.interpolate(x, scale_factor=2, mode=\"nearest\")\n                )\n            )\n            x = self.conv_last(self.lrelu(self.conv_hr(x)))\n        else:\n            # for image denoising and JPEG compression artifact reduction\n            x_first = self.conv_first(x)\n            res = self.conv_after_body(self.forward_features(x_first)) + x_first\n            x = x + self.conv_last(res)\n\n        x = x / self.img_range + self.mean\n        if self.upsampler == \"pixelshuffle_aux\":\n            # NOTE: I removed an \"aux\" output here. not sure what that was for\n            return x[:, :, : H * self.upscale, : W * self.upscale]  # type: ignore\n\n        elif self.upsampler == \"pixelshuffle_hf\":\n            x_out = x_out / self.img_range + self.mean  # type: ignore\n            return x_out[:, :, : H * self.upscale, : W * self.upscale], x[:, :, : H * self.upscale, : W * self.upscale], x_hf[:, :, : H * self.upscale, : W * self.upscale]  # type: ignore\n\n        else:\n            return x[:, :, : H * self.upscale, : W * self.upscale]\n\n    def flops(self):\n        flops = 0\n        H, W = self.patches_resolution\n        flops += H * W * 3 * self.embed_dim * 9\n        flops += self.patch_embed.flops()\n        for i, layer in enumerate(self.layers):\n            flops += layer.flops()  # type: ignore\n        flops += H * W * 3 * self.embed_dim * self.embed_dim\n        flops += self.upsample.flops()  # type: ignore\n        return flops\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/SwinIR.py",
    "content": "# pylint: skip-file\n# -----------------------------------------------------------------------------------\n# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257\n# Originally Written by Ze Liu, Modified by Jingyun Liang.\n# -----------------------------------------------------------------------------------\n\nimport math\nimport re\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.checkpoint as checkpoint\n\n# Originally from the timm package\nfrom .timm.drop import DropPath\nfrom .timm.helpers import to_2tuple\nfrom .timm.weight_init import trunc_normal_\n\n\nclass Mlp(nn.Module):\n    def __init__(\n        self,\n        in_features,\n        hidden_features=None,\n        out_features=None,\n        act_layer=nn.GELU,\n        drop=0.0,\n    ):\n        super().__init__()\n        out_features = out_features or in_features\n        hidden_features = hidden_features or in_features\n        self.fc1 = nn.Linear(in_features, hidden_features)\n        self.act = act_layer()\n        self.fc2 = nn.Linear(hidden_features, out_features)\n        self.drop = nn.Dropout(drop)\n\n    def forward(self, x):\n        x = self.fc1(x)\n        x = self.act(x)\n        x = self.drop(x)\n        x = self.fc2(x)\n        x = self.drop(x)\n        return x\n\n\ndef window_partition(x, window_size):\n    \"\"\"\n    Args:\n        x: (B, H, W, C)\n        window_size (int): window size\n\n    Returns:\n        windows: (num_windows*B, window_size, window_size, C)\n    \"\"\"\n    B, H, W, C = x.shape\n    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)\n    windows = (\n        x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)\n    )\n    return windows\n\n\ndef window_reverse(windows, window_size, H, W):\n    \"\"\"\n    Args:\n        windows: (num_windows*B, window_size, window_size, C)\n        window_size (int): Window size\n        H (int): Height of image\n        W (int): Width of image\n\n    Returns:\n        x: (B, H, W, C)\n    \"\"\"\n    B = int(windows.shape[0] / (H * W / window_size / window_size))\n    x = windows.view(\n        B, H // window_size, W // window_size, window_size, window_size, -1\n    )\n    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)\n    return x\n\n\nclass WindowAttention(nn.Module):\n    r\"\"\"Window based multi-head self attention (W-MSA) module with relative position bias.\n    It supports both of shifted and non-shifted window.\n\n    Args:\n        dim (int): Number of input channels.\n        window_size (tuple[int]): The height and width of the window.\n        num_heads (int): Number of attention heads.\n        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True\n        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set\n        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0\n        proj_drop (float, optional): Dropout ratio of output. Default: 0.0\n    \"\"\"\n\n    def __init__(\n        self,\n        dim,\n        window_size,\n        num_heads,\n        qkv_bias=True,\n        qk_scale=None,\n        attn_drop=0.0,\n        proj_drop=0.0,\n    ):\n        super().__init__()\n        self.dim = dim\n        self.window_size = window_size  # Wh, Ww\n        self.num_heads = num_heads\n        head_dim = dim // num_heads\n        self.scale = qk_scale or head_dim**-0.5\n\n        # define a parameter table of relative position bias\n        self.relative_position_bias_table = nn.Parameter(  # type: ignore\n            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)\n        )  # 2*Wh-1 * 2*Ww-1, nH\n\n        # get pair-wise relative position index for each token inside the window\n        coords_h = torch.arange(self.window_size[0])\n        coords_w = torch.arange(self.window_size[1])\n        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww\n        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww\n        relative_coords = (\n            coords_flatten[:, :, None] - coords_flatten[:, None, :]\n        )  # 2, Wh*Ww, Wh*Ww\n        relative_coords = relative_coords.permute(\n            1, 2, 0\n        ).contiguous()  # Wh*Ww, Wh*Ww, 2\n        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0\n        relative_coords[:, :, 1] += self.window_size[1] - 1\n        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1\n        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww\n        self.register_buffer(\"relative_position_index\", relative_position_index)\n\n        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)\n        self.attn_drop = nn.Dropout(attn_drop)\n        self.proj = nn.Linear(dim, dim)\n\n        self.proj_drop = nn.Dropout(proj_drop)\n\n        trunc_normal_(self.relative_position_bias_table, std=0.02)\n        self.softmax = nn.Softmax(dim=-1)\n\n    def forward(self, x, mask=None):\n        \"\"\"\n        Args:\n            x: input features with shape of (num_windows*B, N, C)\n            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None\n        \"\"\"\n        B_, N, C = x.shape\n        qkv = (\n            self.qkv(x)\n            .reshape(B_, N, 3, self.num_heads, C // self.num_heads)\n            .permute(2, 0, 3, 1, 4)\n        )\n        q, k, v = (\n            qkv[0],\n            qkv[1],\n            qkv[2],\n        )  # make torchscript happy (cannot use tensor as tuple)\n\n        q = q * self.scale\n        attn = q @ k.transpose(-2, -1)\n\n        relative_position_bias = self.relative_position_bias_table[\n            self.relative_position_index.view(-1)  # type: ignore\n        ].view(\n            self.window_size[0] * self.window_size[1],\n            self.window_size[0] * self.window_size[1],\n            -1,\n        )  # Wh*Ww,Wh*Ww,nH\n        relative_position_bias = relative_position_bias.permute(\n            2, 0, 1\n        ).contiguous()  # nH, Wh*Ww, Wh*Ww\n        attn = attn + relative_position_bias.unsqueeze(0)\n\n        if mask is not None:\n            nW = mask.shape[0]\n            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(\n                1\n            ).unsqueeze(0)\n            attn = attn.view(-1, self.num_heads, N, N)\n            attn = self.softmax(attn)\n        else:\n            attn = self.softmax(attn)\n\n        attn = self.attn_drop(attn)\n\n        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)\n        x = self.proj(x)\n        x = self.proj_drop(x)\n        return x\n\n    def extra_repr(self) -> str:\n        return f\"dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}\"\n\n    def flops(self, N):\n        # calculate flops for 1 window with token length of N\n        flops = 0\n        # qkv = self.qkv(x)\n        flops += N * self.dim * 3 * self.dim\n        # attn = (q @ k.transpose(-2, -1))\n        flops += self.num_heads * N * (self.dim // self.num_heads) * N\n        #  x = (attn @ v)\n        flops += self.num_heads * N * N * (self.dim // self.num_heads)\n        # x = self.proj(x)\n        flops += N * self.dim * self.dim\n        return flops\n\n\nclass SwinTransformerBlock(nn.Module):\n    r\"\"\"Swin Transformer Block.\n\n    Args:\n        dim (int): Number of input channels.\n        input_resolution (tuple[int]): Input resulotion.\n        num_heads (int): Number of attention heads.\n        window_size (int): Window size.\n        shift_size (int): Shift size for SW-MSA.\n        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.\n        drop (float, optional): Dropout rate. Default: 0.0\n        attn_drop (float, optional): Attention dropout rate. Default: 0.0\n        drop_path (float, optional): Stochastic depth rate. Default: 0.0\n        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU\n        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm\n    \"\"\"\n\n    def __init__(\n        self,\n        dim,\n        input_resolution,\n        num_heads,\n        window_size=7,\n        shift_size=0,\n        mlp_ratio=4.0,\n        qkv_bias=True,\n        qk_scale=None,\n        drop=0.0,\n        attn_drop=0.0,\n        drop_path=0.0,\n        act_layer=nn.GELU,\n        norm_layer=nn.LayerNorm,\n    ):\n        super().__init__()\n        self.dim = dim\n        self.input_resolution = input_resolution\n        self.num_heads = num_heads\n        self.window_size = window_size\n        self.shift_size = shift_size\n        self.mlp_ratio = mlp_ratio\n        if min(self.input_resolution) <= self.window_size:\n            # if window size is larger than input resolution, we don't partition windows\n            self.shift_size = 0\n            self.window_size = min(self.input_resolution)\n        assert (\n            0 <= self.shift_size < self.window_size\n        ), \"shift_size must in 0-window_size\"\n\n        self.norm1 = norm_layer(dim)\n        self.attn = WindowAttention(\n            dim,\n            window_size=to_2tuple(self.window_size),\n            num_heads=num_heads,\n            qkv_bias=qkv_bias,\n            qk_scale=qk_scale,\n            attn_drop=attn_drop,\n            proj_drop=drop,\n        )\n\n        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()\n        self.norm2 = norm_layer(dim)\n        mlp_hidden_dim = int(dim * mlp_ratio)\n        self.mlp = Mlp(\n            in_features=dim,\n            hidden_features=mlp_hidden_dim,\n            act_layer=act_layer,\n            drop=drop,\n        )\n\n        if self.shift_size > 0:\n            attn_mask = self.calculate_mask(self.input_resolution)\n        else:\n            attn_mask = None\n\n        self.register_buffer(\"attn_mask\", attn_mask)\n\n    def calculate_mask(self, x_size):\n        # calculate attention mask for SW-MSA\n        H, W = x_size\n        img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1\n        h_slices = (\n            slice(0, -self.window_size),\n            slice(-self.window_size, -self.shift_size),\n            slice(-self.shift_size, None),\n        )\n        w_slices = (\n            slice(0, -self.window_size),\n            slice(-self.window_size, -self.shift_size),\n            slice(-self.shift_size, None),\n        )\n        cnt = 0\n        for h in h_slices:\n            for w in w_slices:\n                img_mask[:, h, w, :] = cnt\n                cnt += 1\n\n        mask_windows = window_partition(\n            img_mask, self.window_size\n        )  # nW, window_size, window_size, 1\n        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)\n        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)\n        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(\n            attn_mask == 0, float(0.0)\n        )\n\n        return attn_mask\n\n    def forward(self, x, x_size):\n        H, W = x_size\n        B, L, C = x.shape\n        # assert L == H * W, \"input feature has wrong size\"\n\n        shortcut = x\n        x = self.norm1(x)\n        x = x.view(B, H, W, C)\n\n        # cyclic shift\n        if self.shift_size > 0:\n            shifted_x = torch.roll(\n                x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)\n            )\n        else:\n            shifted_x = x\n\n        # partition windows\n        x_windows = window_partition(\n            shifted_x, self.window_size\n        )  # nW*B, window_size, window_size, C\n        x_windows = x_windows.view(\n            -1, self.window_size * self.window_size, C\n        )  # nW*B, window_size*window_size, C\n\n        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size\n        if self.input_resolution == x_size:\n            attn_windows = self.attn(\n                x_windows, mask=self.attn_mask\n            )  # nW*B, window_size*window_size, C\n        else:\n            attn_windows = self.attn(\n                x_windows, mask=self.calculate_mask(x_size).to(x.device)\n            )\n\n        # merge windows\n        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)\n        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C\n\n        # reverse cyclic shift\n        if self.shift_size > 0:\n            x = torch.roll(\n                shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)\n            )\n        else:\n            x = shifted_x\n        x = x.view(B, H * W, C)\n\n        # FFN\n        x = shortcut + self.drop_path(x)\n        x = x + self.drop_path(self.mlp(self.norm2(x)))\n\n        return x\n\n    def extra_repr(self) -> str:\n        return (\n            f\"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, \"\n            f\"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}\"\n        )\n\n    def flops(self):\n        flops = 0\n        H, W = self.input_resolution\n        # norm1\n        flops += self.dim * H * W\n        # W-MSA/SW-MSA\n        nW = H * W / self.window_size / self.window_size\n        flops += nW * self.attn.flops(self.window_size * self.window_size)\n        # mlp\n        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio\n        # norm2\n        flops += self.dim * H * W\n        return flops\n\n\nclass PatchMerging(nn.Module):\n    r\"\"\"Patch Merging Layer.\n\n    Args:\n        input_resolution (tuple[int]): Resolution of input feature.\n        dim (int): Number of input channels.\n        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm\n    \"\"\"\n\n    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):\n        super().__init__()\n        self.input_resolution = input_resolution\n        self.dim = dim\n        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)\n        self.norm = norm_layer(4 * dim)\n\n    def forward(self, x):\n        \"\"\"\n        x: B, H*W, C\n        \"\"\"\n        H, W = self.input_resolution\n        B, L, C = x.shape\n        assert L == H * W, \"input feature has wrong size\"\n        assert H % 2 == 0 and W % 2 == 0, f\"x size ({H}*{W}) are not even.\"\n\n        x = x.view(B, H, W, C)\n\n        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C\n        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C\n        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C\n        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C\n        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C\n        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C\n\n        x = self.norm(x)\n        x = self.reduction(x)\n\n        return x\n\n    def extra_repr(self) -> str:\n        return f\"input_resolution={self.input_resolution}, dim={self.dim}\"\n\n    def flops(self):\n        H, W = self.input_resolution\n        flops = H * W * self.dim\n        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim\n        return flops\n\n\nclass BasicLayer(nn.Module):\n    \"\"\"A basic Swin Transformer layer for one stage.\n\n    Args:\n        dim (int): Number of input channels.\n        input_resolution (tuple[int]): Input resolution.\n        depth (int): Number of blocks.\n        num_heads (int): Number of attention heads.\n        window_size (int): Local window size.\n        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.\n        drop (float, optional): Dropout rate. Default: 0.0\n        attn_drop (float, optional): Attention dropout rate. Default: 0.0\n        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0\n        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm\n        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None\n        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.\n    \"\"\"\n\n    def __init__(\n        self,\n        dim,\n        input_resolution,\n        depth,\n        num_heads,\n        window_size,\n        mlp_ratio=4.0,\n        qkv_bias=True,\n        qk_scale=None,\n        drop=0.0,\n        attn_drop=0.0,\n        drop_path=0.0,\n        norm_layer=nn.LayerNorm,\n        downsample=None,\n        use_checkpoint=False,\n    ):\n        super().__init__()\n        self.dim = dim\n        self.input_resolution = input_resolution\n        self.depth = depth\n        self.use_checkpoint = use_checkpoint\n\n        # build blocks\n        self.blocks = nn.ModuleList(\n            [\n                SwinTransformerBlock(\n                    dim=dim,\n                    input_resolution=input_resolution,\n                    num_heads=num_heads,\n                    window_size=window_size,\n                    shift_size=0 if (i % 2 == 0) else window_size // 2,\n                    mlp_ratio=mlp_ratio,\n                    qkv_bias=qkv_bias,\n                    qk_scale=qk_scale,\n                    drop=drop,\n                    attn_drop=attn_drop,\n                    drop_path=drop_path[i]\n                    if isinstance(drop_path, list)\n                    else drop_path,\n                    norm_layer=norm_layer,\n                )\n                for i in range(depth)\n            ]\n        )\n\n        # patch merging layer\n        if downsample is not None:\n            self.downsample = downsample(\n                input_resolution, dim=dim, norm_layer=norm_layer\n            )\n        else:\n            self.downsample = None\n\n    def forward(self, x, x_size):\n        for blk in self.blocks:\n            if self.use_checkpoint:\n                x = checkpoint.checkpoint(blk, x, x_size)\n            else:\n                x = blk(x, x_size)\n        if self.downsample is not None:\n            x = self.downsample(x)\n        return x\n\n    def extra_repr(self) -> str:\n        return f\"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}\"\n\n    def flops(self):\n        flops = 0\n        for blk in self.blocks:\n            flops += blk.flops()  # type: ignore\n        if self.downsample is not None:\n            flops += self.downsample.flops()\n        return flops\n\n\nclass RSTB(nn.Module):\n    \"\"\"Residual Swin Transformer Block (RSTB).\n\n    Args:\n        dim (int): Number of input channels.\n        input_resolution (tuple[int]): Input resolution.\n        depth (int): Number of blocks.\n        num_heads (int): Number of attention heads.\n        window_size (int): Local window size.\n        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.\n        drop (float, optional): Dropout rate. Default: 0.0\n        attn_drop (float, optional): Attention dropout rate. Default: 0.0\n        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0\n        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm\n        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None\n        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.\n        img_size: Input image size.\n        patch_size: Patch size.\n        resi_connection: The convolutional block before residual connection.\n    \"\"\"\n\n    def __init__(\n        self,\n        dim,\n        input_resolution,\n        depth,\n        num_heads,\n        window_size,\n        mlp_ratio=4.0,\n        qkv_bias=True,\n        qk_scale=None,\n        drop=0.0,\n        attn_drop=0.0,\n        drop_path=0.0,\n        norm_layer=nn.LayerNorm,\n        downsample=None,\n        use_checkpoint=False,\n        img_size=224,\n        patch_size=4,\n        resi_connection=\"1conv\",\n    ):\n        super(RSTB, self).__init__()\n\n        self.dim = dim\n        self.input_resolution = input_resolution\n\n        self.residual_group = BasicLayer(\n            dim=dim,\n            input_resolution=input_resolution,\n            depth=depth,\n            num_heads=num_heads,\n            window_size=window_size,\n            mlp_ratio=mlp_ratio,\n            qkv_bias=qkv_bias,\n            qk_scale=qk_scale,\n            drop=drop,\n            attn_drop=attn_drop,\n            drop_path=drop_path,\n            norm_layer=norm_layer,\n            downsample=downsample,\n            use_checkpoint=use_checkpoint,\n        )\n\n        if resi_connection == \"1conv\":\n            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)\n        elif resi_connection == \"3conv\":\n            # to save parameters and memory\n            self.conv = nn.Sequential(\n                nn.Conv2d(dim, dim // 4, 3, 1, 1),\n                nn.LeakyReLU(negative_slope=0.2, inplace=True),\n                nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),\n                nn.LeakyReLU(negative_slope=0.2, inplace=True),\n                nn.Conv2d(dim // 4, dim, 3, 1, 1),\n            )\n\n        self.patch_embed = PatchEmbed(\n            img_size=img_size,\n            patch_size=patch_size,\n            in_chans=0,\n            embed_dim=dim,\n            norm_layer=None,\n        )\n\n        self.patch_unembed = PatchUnEmbed(\n            img_size=img_size,\n            patch_size=patch_size,\n            in_chans=0,\n            embed_dim=dim,\n            norm_layer=None,\n        )\n\n    def forward(self, x, x_size):\n        return (\n            self.patch_embed(\n                self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))\n            )\n            + x\n        )\n\n    def flops(self):\n        flops = 0\n        flops += self.residual_group.flops()\n        H, W = self.input_resolution\n        flops += H * W * self.dim * self.dim * 9\n        flops += self.patch_embed.flops()\n        flops += self.patch_unembed.flops()\n\n        return flops\n\n\nclass PatchEmbed(nn.Module):\n    r\"\"\"Image to Patch Embedding\n\n    Args:\n        img_size (int): Image size.  Default: 224.\n        patch_size (int): Patch token size. Default: 4.\n        in_chans (int): Number of input image channels. Default: 3.\n        embed_dim (int): Number of linear projection output channels. Default: 96.\n        norm_layer (nn.Module, optional): Normalization layer. Default: None\n    \"\"\"\n\n    def __init__(\n        self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None\n    ):\n        super().__init__()\n        img_size = to_2tuple(img_size)\n        patch_size = to_2tuple(patch_size)\n        patches_resolution = [\n            img_size[0] // patch_size[0],  # type: ignore\n            img_size[1] // patch_size[1],  # type: ignore\n        ]\n        self.img_size = img_size\n        self.patch_size = patch_size\n        self.patches_resolution = patches_resolution\n        self.num_patches = patches_resolution[0] * patches_resolution[1]\n\n        self.in_chans = in_chans\n        self.embed_dim = embed_dim\n\n        if norm_layer is not None:\n            self.norm = norm_layer(embed_dim)\n        else:\n            self.norm = None\n\n    def forward(self, x):\n        x = x.flatten(2).transpose(1, 2)  # B Ph*Pw C\n        if self.norm is not None:\n            x = self.norm(x)\n        return x\n\n    def flops(self):\n        flops = 0\n        H, W = self.img_size\n        if self.norm is not None:\n            flops += H * W * self.embed_dim  # type: ignore\n        return flops\n\n\nclass PatchUnEmbed(nn.Module):\n    r\"\"\"Image to Patch Unembedding\n\n    Args:\n        img_size (int): Image size.  Default: 224.\n        patch_size (int): Patch token size. Default: 4.\n        in_chans (int): Number of input image channels. Default: 3.\n        embed_dim (int): Number of linear projection output channels. Default: 96.\n        norm_layer (nn.Module, optional): Normalization layer. Default: None\n    \"\"\"\n\n    def __init__(\n        self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None\n    ):\n        super().__init__()\n        img_size = to_2tuple(img_size)\n        patch_size = to_2tuple(patch_size)\n        patches_resolution = [\n            img_size[0] // patch_size[0],  # type: ignore\n            img_size[1] // patch_size[1],  # type: ignore\n        ]\n        self.img_size = img_size\n        self.patch_size = patch_size\n        self.patches_resolution = patches_resolution\n        self.num_patches = patches_resolution[0] * patches_resolution[1]\n\n        self.in_chans = in_chans\n        self.embed_dim = embed_dim\n\n    def forward(self, x, x_size):\n        B, HW, C = x.shape\n        x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1])  # B Ph*Pw C\n        return x\n\n    def flops(self):\n        flops = 0\n        return flops\n\n\nclass Upsample(nn.Sequential):\n    \"\"\"Upsample module.\n\n    Args:\n        scale (int): Scale factor. Supported scales: 2^n and 3.\n        num_feat (int): Channel number of intermediate features.\n    \"\"\"\n\n    def __init__(self, scale, num_feat):\n        m = []\n        if (scale & (scale - 1)) == 0:  # scale = 2^n\n            for _ in range(int(math.log(scale, 2))):\n                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))\n                m.append(nn.PixelShuffle(2))\n        elif scale == 3:\n            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))\n            m.append(nn.PixelShuffle(3))\n        else:\n            raise ValueError(\n                f\"scale {scale} is not supported. \" \"Supported scales: 2^n and 3.\"\n            )\n        super(Upsample, self).__init__(*m)\n\n\nclass UpsampleOneStep(nn.Sequential):\n    \"\"\"UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)\n       Used in lightweight SR to save parameters.\n\n    Args:\n        scale (int): Scale factor. Supported scales: 2^n and 3.\n        num_feat (int): Channel number of intermediate features.\n\n    \"\"\"\n\n    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):\n        self.num_feat = num_feat\n        self.input_resolution = input_resolution\n        m = []\n        m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1))\n        m.append(nn.PixelShuffle(scale))\n        super(UpsampleOneStep, self).__init__(*m)\n\n    def flops(self):\n        H, W = self.input_resolution  # type: ignore\n        flops = H * W * self.num_feat * 3 * 9\n        return flops\n\n\nclass SwinIR(nn.Module):\n    r\"\"\"SwinIR\n        A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer.\n\n    Args:\n        img_size (int | tuple(int)): Input image size. Default 64\n        patch_size (int | tuple(int)): Patch size. Default: 1\n        in_chans (int): Number of input image channels. Default: 3\n        embed_dim (int): Patch embedding dimension. Default: 96\n        depths (tuple(int)): Depth of each Swin Transformer layer.\n        num_heads (tuple(int)): Number of attention heads in different layers.\n        window_size (int): Window size. Default: 7\n        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4\n        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True\n        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None\n        drop_rate (float): Dropout rate. Default: 0\n        attn_drop_rate (float): Attention dropout rate. Default: 0\n        drop_path_rate (float): Stochastic depth rate. Default: 0.1\n        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.\n        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False\n        patch_norm (bool): If True, add normalization after patch embedding. Default: True\n        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False\n        upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction\n        img_range: Image range. 1. or 255.\n        upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None\n        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'\n    \"\"\"\n\n    def __init__(\n        self,\n        state_dict,\n        **kwargs,\n    ):\n        super(SwinIR, self).__init__()\n\n        # Defaults\n        img_size = 64\n        patch_size = 1\n        in_chans = 3\n        embed_dim = 96\n        depths = [6, 6, 6, 6]\n        num_heads = [6, 6, 6, 6]\n        window_size = 7\n        mlp_ratio = 4.0\n        qkv_bias = True\n        qk_scale = None\n        drop_rate = 0.0\n        attn_drop_rate = 0.0\n        drop_path_rate = 0.1\n        norm_layer = nn.LayerNorm\n        ape = False\n        patch_norm = True\n        use_checkpoint = False\n        upscale = 2\n        img_range = 1.0\n        upsampler = \"\"\n        resi_connection = \"1conv\"\n        num_feat = 64\n        num_in_ch = in_chans\n        num_out_ch = in_chans\n        supports_fp16 = True\n        self.start_unshuffle = 1\n\n        self.model_arch = \"SwinIR\"\n        self.sub_type = \"SR\"\n        self.state = state_dict\n        if \"params_ema\" in self.state:\n            self.state = self.state[\"params_ema\"]\n        elif \"params\" in self.state:\n            self.state = self.state[\"params\"]\n\n        state_keys = self.state.keys()\n\n        if \"conv_before_upsample.0.weight\" in state_keys:\n            if \"conv_up1.weight\" in state_keys:\n                upsampler = \"nearest+conv\"\n            else:\n                upsampler = \"pixelshuffle\"\n                supports_fp16 = False\n        elif \"upsample.0.weight\" in state_keys:\n            upsampler = \"pixelshuffledirect\"\n        else:\n            upsampler = \"\"\n\n        num_feat = (\n            self.state.get(\"conv_before_upsample.0.weight\", None).shape[1]\n            if self.state.get(\"conv_before_upsample.weight\", None)\n            else 64\n        )\n\n        if \"conv_first.1.weight\" in self.state:\n            self.state[\"conv_first.weight\"] = self.state.pop(\"conv_first.1.weight\")\n            self.state[\"conv_first.bias\"] = self.state.pop(\"conv_first.1.bias\")\n            self.start_unshuffle = round(math.sqrt(self.state[\"conv_first.weight\"].shape[1] // 3))\n\n        num_in_ch = self.state[\"conv_first.weight\"].shape[1]\n        in_chans = num_in_ch\n        if \"conv_last.weight\" in state_keys:\n            num_out_ch = self.state[\"conv_last.weight\"].shape[0]\n        else:\n            num_out_ch = num_in_ch\n\n        upscale = 1\n        if upsampler == \"nearest+conv\":\n            upsample_keys = [\n                x for x in state_keys if \"conv_up\" in x and \"bias\" not in x\n            ]\n\n            for upsample_key in upsample_keys:\n                upscale *= 2\n        elif upsampler == \"pixelshuffle\":\n            upsample_keys = [\n                x\n                for x in state_keys\n                if \"upsample\" in x and \"conv\" not in x and \"bias\" not in x\n            ]\n            for upsample_key in upsample_keys:\n                shape = self.state[upsample_key].shape[0]\n                upscale *= math.sqrt(shape // num_feat)\n            upscale = int(upscale)\n        elif upsampler == \"pixelshuffledirect\":\n            upscale = int(\n                math.sqrt(self.state[\"upsample.0.bias\"].shape[0] // num_out_ch)\n            )\n\n        max_layer_num = 0\n        max_block_num = 0\n        for key in state_keys:\n            result = re.match(\n                r\"layers.(\\d*).residual_group.blocks.(\\d*).norm1.weight\", key\n            )\n            if result:\n                layer_num, block_num = result.groups()\n                max_layer_num = max(max_layer_num, int(layer_num))\n                max_block_num = max(max_block_num, int(block_num))\n\n        depths = [max_block_num + 1 for _ in range(max_layer_num + 1)]\n\n        if (\n            \"layers.0.residual_group.blocks.0.attn.relative_position_bias_table\"\n            in state_keys\n        ):\n            num_heads_num = self.state[\n                \"layers.0.residual_group.blocks.0.attn.relative_position_bias_table\"\n            ].shape[-1]\n            num_heads = [num_heads_num for _ in range(max_layer_num + 1)]\n        else:\n            num_heads = depths\n\n        embed_dim = self.state[\"conv_first.weight\"].shape[0]\n\n        mlp_ratio = float(\n            self.state[\"layers.0.residual_group.blocks.0.mlp.fc1.bias\"].shape[0]\n            / embed_dim\n        )\n\n        # TODO: could actually count the layers, but this should do\n        if \"layers.0.conv.4.weight\" in state_keys:\n            resi_connection = \"3conv\"\n        else:\n            resi_connection = \"1conv\"\n\n        window_size = int(\n            math.sqrt(\n                self.state[\n                    \"layers.0.residual_group.blocks.0.attn.relative_position_index\"\n                ].shape[0]\n            )\n        )\n\n        if \"layers.0.residual_group.blocks.1.attn_mask\" in state_keys:\n            img_size = int(\n                math.sqrt(\n                    self.state[\"layers.0.residual_group.blocks.1.attn_mask\"].shape[0]\n                )\n                * window_size\n            )\n\n        # The JPEG models are the only ones with window-size 7, and they also use this range\n        img_range = 255.0 if window_size == 7 else 1.0\n\n        self.in_nc = num_in_ch\n        self.out_nc = num_out_ch\n        self.num_feat = num_feat\n        self.embed_dim = embed_dim\n        self.num_heads = num_heads\n        self.depths = depths\n        self.window_size = window_size\n        self.mlp_ratio = mlp_ratio\n        self.scale = upscale / self.start_unshuffle\n        self.upsampler = upsampler\n        self.img_size = img_size\n        self.img_range = img_range\n        self.resi_connection = resi_connection\n\n        self.supports_fp16 = False  # Too much weirdness to support this at the moment\n        self.supports_bfp16 = True\n        self.min_size_restriction = 16\n\n        self.img_range = img_range\n        if in_chans == 3:\n            rgb_mean = (0.4488, 0.4371, 0.4040)\n            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)\n        else:\n            self.mean = torch.zeros(1, 1, 1, 1)\n        self.upscale = upscale\n        self.upsampler = upsampler\n        self.window_size = window_size\n\n        #####################################################################################################\n        ################################### 1, shallow feature extraction ###################################\n        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)\n\n        #####################################################################################################\n        ################################### 2, deep feature extraction ######################################\n        self.num_layers = len(depths)\n        self.embed_dim = embed_dim\n        self.ape = ape\n        self.patch_norm = patch_norm\n        self.num_features = embed_dim\n        self.mlp_ratio = mlp_ratio\n\n        # split image into non-overlapping patches\n        self.patch_embed = PatchEmbed(\n            img_size=img_size,\n            patch_size=patch_size,\n            in_chans=embed_dim,\n            embed_dim=embed_dim,\n            norm_layer=norm_layer if self.patch_norm else None,\n        )\n        num_patches = self.patch_embed.num_patches\n        patches_resolution = self.patch_embed.patches_resolution\n        self.patches_resolution = patches_resolution\n\n        # merge non-overlapping patches into image\n        self.patch_unembed = PatchUnEmbed(\n            img_size=img_size,\n            patch_size=patch_size,\n            in_chans=embed_dim,\n            embed_dim=embed_dim,\n            norm_layer=norm_layer if self.patch_norm else None,\n        )\n\n        # absolute position embedding\n        if self.ape:\n            self.absolute_pos_embed = nn.Parameter(  # type: ignore\n                torch.zeros(1, num_patches, embed_dim)\n            )\n            trunc_normal_(self.absolute_pos_embed, std=0.02)\n\n        self.pos_drop = nn.Dropout(p=drop_rate)\n\n        # stochastic depth\n        dpr = [\n            x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))\n        ]  # stochastic depth decay rule\n\n        # build Residual Swin Transformer blocks (RSTB)\n        self.layers = nn.ModuleList()\n        for i_layer in range(self.num_layers):\n            layer = RSTB(\n                dim=embed_dim,\n                input_resolution=(patches_resolution[0], patches_resolution[1]),\n                depth=depths[i_layer],\n                num_heads=num_heads[i_layer],\n                window_size=window_size,\n                mlp_ratio=self.mlp_ratio,\n                qkv_bias=qkv_bias,\n                qk_scale=qk_scale,\n                drop=drop_rate,\n                attn_drop=attn_drop_rate,\n                drop_path=dpr[\n                    sum(depths[:i_layer]) : sum(depths[: i_layer + 1])  # type: ignore\n                ],  # no impact on SR results\n                norm_layer=norm_layer,\n                downsample=None,\n                use_checkpoint=use_checkpoint,\n                img_size=img_size,\n                patch_size=patch_size,\n                resi_connection=resi_connection,\n            )\n            self.layers.append(layer)\n        self.norm = norm_layer(self.num_features)\n\n        # build the last conv layer in deep feature extraction\n        if resi_connection == \"1conv\":\n            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)\n        elif resi_connection == \"3conv\":\n            # to save parameters and memory\n            self.conv_after_body = nn.Sequential(\n                nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),\n                nn.LeakyReLU(negative_slope=0.2, inplace=True),\n                nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),\n                nn.LeakyReLU(negative_slope=0.2, inplace=True),\n                nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1),\n            )\n\n        #####################################################################################################\n        ################################ 3, high quality image reconstruction ################################\n        if self.upsampler == \"pixelshuffle\":\n            # for classical SR\n            self.conv_before_upsample = nn.Sequential(\n                nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)\n            )\n            self.upsample = Upsample(upscale, num_feat)\n            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)\n        elif self.upsampler == \"pixelshuffledirect\":\n            # for lightweight SR (to save parameters)\n            self.upsample = UpsampleOneStep(\n                upscale,\n                embed_dim,\n                num_out_ch,\n                (patches_resolution[0], patches_resolution[1]),\n            )\n        elif self.upsampler == \"nearest+conv\":\n            # for real-world SR (less artifacts)\n            self.conv_before_upsample = nn.Sequential(\n                nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)\n            )\n            self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)\n            if self.upscale == 4:\n                self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)\n            elif self.upscale == 8:\n                self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)\n                self.conv_up3 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)\n            self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)\n            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)\n            self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)\n        else:\n            # for image denoising and JPEG compression artifact reduction\n            self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)\n\n        self.apply(self._init_weights)\n        self.load_state_dict(self.state, strict=False)\n\n    def _init_weights(self, m):\n        if isinstance(m, nn.Linear):\n            trunc_normal_(m.weight, std=0.02)\n            if isinstance(m, nn.Linear) and m.bias is not None:\n                nn.init.constant_(m.bias, 0)\n        elif isinstance(m, nn.LayerNorm):\n            nn.init.constant_(m.bias, 0)\n            nn.init.constant_(m.weight, 1.0)\n\n    @torch.jit.ignore  # type: ignore\n    def no_weight_decay(self):\n        return {\"absolute_pos_embed\"}\n\n    @torch.jit.ignore  # type: ignore\n    def no_weight_decay_keywords(self):\n        return {\"relative_position_bias_table\"}\n\n    def check_image_size(self, x):\n        _, _, h, w = x.size()\n        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size\n        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size\n        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), \"reflect\")\n        return x\n\n    def forward_features(self, x):\n        x_size = (x.shape[2], x.shape[3])\n        x = self.patch_embed(x)\n        if self.ape:\n            x = x + self.absolute_pos_embed\n        x = self.pos_drop(x)\n\n        for layer in self.layers:\n            x = layer(x, x_size)\n\n        x = self.norm(x)  # B L C\n        x = self.patch_unembed(x, x_size)\n\n        return x\n\n    def forward(self, x):\n        H, W = x.shape[2:]\n        x = self.check_image_size(x)\n\n        self.mean = self.mean.type_as(x)\n        x = (x - self.mean) * self.img_range\n\n        if self.start_unshuffle > 1:\n            x = torch.nn.functional.pixel_unshuffle(x, self.start_unshuffle)\n\n        if self.upsampler == \"pixelshuffle\":\n            # for classical SR\n            x = self.conv_first(x)\n            x = self.conv_after_body(self.forward_features(x)) + x\n            x = self.conv_before_upsample(x)\n            x = self.conv_last(self.upsample(x))\n        elif self.upsampler == \"pixelshuffledirect\":\n            # for lightweight SR\n            x = self.conv_first(x)\n            x = self.conv_after_body(self.forward_features(x)) + x\n            x = self.upsample(x)\n        elif self.upsampler == \"nearest+conv\":\n            # for real-world SR\n            x = self.conv_first(x)\n            x = self.conv_after_body(self.forward_features(x)) + x\n            x = self.conv_before_upsample(x)\n            x = self.lrelu(\n                self.conv_up1(\n                    torch.nn.functional.interpolate(x, scale_factor=2, mode=\"nearest\")  # type: ignore\n                )\n            )\n            if self.upscale == 4:\n                x = self.lrelu(\n                    self.conv_up2(\n                        torch.nn.functional.interpolate(  # type: ignore\n                            x, scale_factor=2, mode=\"nearest\"\n                        )\n                    )\n                )\n            elif self.upscale == 8:\n                x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))\n                x = self.lrelu(self.conv_up3(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))\n            x = self.conv_last(self.lrelu(self.conv_hr(x)))\n        else:\n            # for image denoising and JPEG compression artifact reduction\n            x_first = self.conv_first(x)\n            res = self.conv_after_body(self.forward_features(x_first)) + x_first\n            x = x + self.conv_last(res)\n\n        x = x / self.img_range + self.mean\n\n        return x[:, :, : H * self.upscale, : W * self.upscale]\n\n    def flops(self):\n        flops = 0\n        H, W = self.patches_resolution\n        flops += H * W * 3 * self.embed_dim * 9\n        flops += self.patch_embed.flops()\n        for i, layer in enumerate(self.layers):\n            flops += layer.flops()  # type: ignore\n        flops += H * W * 3 * self.embed_dim * self.embed_dim\n        flops += self.upsample.flops()  # type: ignore\n        return flops\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/__init__.py",
    "content": ""
  },
  {
    "path": "ldm_patched/pfn/architecture/block.py",
    "content": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nfrom __future__ import annotations\n\nfrom collections import OrderedDict\ntry:\n    from typing import Literal\nexcept ImportError:\n    from typing_extensions import Literal\n\nimport torch\nimport torch.nn as nn\n\n####################\n# Basic blocks\n####################\n\n\ndef act(act_type: str, inplace=True, neg_slope=0.2, n_prelu=1):\n    # helper selecting activation\n    # neg_slope: for leakyrelu and init of prelu\n    # n_prelu: for p_relu num_parameters\n    act_type = act_type.lower()\n    if act_type == \"relu\":\n        layer = nn.ReLU(inplace)\n    elif act_type == \"leakyrelu\":\n        layer = nn.LeakyReLU(neg_slope, inplace)\n    elif act_type == \"prelu\":\n        layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope)\n    else:\n        raise NotImplementedError(\n            \"activation layer [{:s}] is not found\".format(act_type)\n        )\n    return layer\n\n\ndef norm(norm_type: str, nc: int):\n    # helper selecting normalization layer\n    norm_type = norm_type.lower()\n    if norm_type == \"batch\":\n        layer = nn.BatchNorm2d(nc, affine=True)\n    elif norm_type == \"instance\":\n        layer = nn.InstanceNorm2d(nc, affine=False)\n    else:\n        raise NotImplementedError(\n            \"normalization layer [{:s}] is not found\".format(norm_type)\n        )\n    return layer\n\n\ndef pad(pad_type: str, padding):\n    # helper selecting padding layer\n    # if padding is 'zero', do by conv layers\n    pad_type = pad_type.lower()\n    if padding == 0:\n        return None\n    if pad_type == \"reflect\":\n        layer = nn.ReflectionPad2d(padding)\n    elif pad_type == \"replicate\":\n        layer = nn.ReplicationPad2d(padding)\n    else:\n        raise NotImplementedError(\n            \"padding layer [{:s}] is not implemented\".format(pad_type)\n        )\n    return layer\n\n\ndef get_valid_padding(kernel_size, dilation):\n    kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1)\n    padding = (kernel_size - 1) // 2\n    return padding\n\n\nclass ConcatBlock(nn.Module):\n    # Concat the output of a submodule to its input\n    def __init__(self, submodule):\n        super(ConcatBlock, self).__init__()\n        self.sub = submodule\n\n    def forward(self, x):\n        output = torch.cat((x, self.sub(x)), dim=1)\n        return output\n\n    def __repr__(self):\n        tmpstr = \"Identity .. \\n|\"\n        modstr = self.sub.__repr__().replace(\"\\n\", \"\\n|\")\n        tmpstr = tmpstr + modstr\n        return tmpstr\n\n\nclass ShortcutBlock(nn.Module):\n    # Elementwise sum the output of a submodule to its input\n    def __init__(self, submodule):\n        super(ShortcutBlock, self).__init__()\n        self.sub = submodule\n\n    def forward(self, x):\n        output = x + self.sub(x)\n        return output\n\n    def __repr__(self):\n        tmpstr = \"Identity + \\n|\"\n        modstr = self.sub.__repr__().replace(\"\\n\", \"\\n|\")\n        tmpstr = tmpstr + modstr\n        return tmpstr\n\n\nclass ShortcutBlockSPSR(nn.Module):\n    # Elementwise sum the output of a submodule to its input\n    def __init__(self, submodule):\n        super(ShortcutBlockSPSR, self).__init__()\n        self.sub = submodule\n\n    def forward(self, x):\n        return x, self.sub\n\n    def __repr__(self):\n        tmpstr = \"Identity + \\n|\"\n        modstr = self.sub.__repr__().replace(\"\\n\", \"\\n|\")\n        tmpstr = tmpstr + modstr\n        return tmpstr\n\n\ndef sequential(*args):\n    # Flatten Sequential. It unwraps nn.Sequential.\n    if len(args) == 1:\n        if isinstance(args[0], OrderedDict):\n            raise NotImplementedError(\"sequential does not support OrderedDict input.\")\n        return args[0]  # No sequential is needed.\n    modules = []\n    for module in args:\n        if isinstance(module, nn.Sequential):\n            for submodule in module.children():\n                modules.append(submodule)\n        elif isinstance(module, nn.Module):\n            modules.append(module)\n    return nn.Sequential(*modules)\n\n\nConvMode = Literal[\"CNA\", \"NAC\", \"CNAC\"]\n\n\n# 2x2x2 Conv Block\ndef conv_block_2c2(\n    in_nc,\n    out_nc,\n    act_type=\"relu\",\n):\n    return sequential(\n        nn.Conv2d(in_nc, out_nc, kernel_size=2, padding=1),\n        nn.Conv2d(out_nc, out_nc, kernel_size=2, padding=0),\n        act(act_type) if act_type else None,\n    )\n\n\ndef conv_block(\n    in_nc: int,\n    out_nc: int,\n    kernel_size,\n    stride=1,\n    dilation=1,\n    groups=1,\n    bias=True,\n    pad_type=\"zero\",\n    norm_type: str | None = None,\n    act_type: str | None = \"relu\",\n    mode: ConvMode = \"CNA\",\n    c2x2=False,\n):\n    \"\"\"\n    Conv layer with padding, normalization, activation\n    mode: CNA --> Conv -> Norm -> Act\n        NAC --> Norm -> Act --> Conv (Identity Mappings in Deep Residual Networks, ECCV16)\n    \"\"\"\n\n    if c2x2:\n        return conv_block_2c2(in_nc, out_nc, act_type=act_type)\n\n    assert mode in (\"CNA\", \"NAC\", \"CNAC\"), \"Wrong conv mode [{:s}]\".format(mode)\n    padding = get_valid_padding(kernel_size, dilation)\n    p = pad(pad_type, padding) if pad_type and pad_type != \"zero\" else None\n    padding = padding if pad_type == \"zero\" else 0\n\n    c = nn.Conv2d(\n        in_nc,\n        out_nc,\n        kernel_size=kernel_size,\n        stride=stride,\n        padding=padding,\n        dilation=dilation,\n        bias=bias,\n        groups=groups,\n    )\n    a = act(act_type) if act_type else None\n    if mode in (\"CNA\", \"CNAC\"):\n        n = norm(norm_type, out_nc) if norm_type else None\n        return sequential(p, c, n, a)\n    elif mode == \"NAC\":\n        if norm_type is None and act_type is not None:\n            a = act(act_type, inplace=False)\n            # Important!\n            # input----ReLU(inplace)----Conv--+----output\n            #        |________________________|\n            # inplace ReLU will modify the input, therefore wrong output\n        n = norm(norm_type, in_nc) if norm_type else None\n        return sequential(n, a, p, c)\n    else:\n        assert False, f\"Invalid conv mode {mode}\"\n\n\n####################\n# Useful blocks\n####################\n\n\nclass ResNetBlock(nn.Module):\n    \"\"\"\n    ResNet Block, 3-3 style\n    with extra residual scaling used in EDSR\n    (Enhanced Deep Residual Networks for Single Image Super-Resolution, CVPRW 17)\n    \"\"\"\n\n    def __init__(\n        self,\n        in_nc,\n        mid_nc,\n        out_nc,\n        kernel_size=3,\n        stride=1,\n        dilation=1,\n        groups=1,\n        bias=True,\n        pad_type=\"zero\",\n        norm_type=None,\n        act_type=\"relu\",\n        mode: ConvMode = \"CNA\",\n        res_scale=1,\n    ):\n        super(ResNetBlock, self).__init__()\n        conv0 = conv_block(\n            in_nc,\n            mid_nc,\n            kernel_size,\n            stride,\n            dilation,\n            groups,\n            bias,\n            pad_type,\n            norm_type,\n            act_type,\n            mode,\n        )\n        if mode == \"CNA\":\n            act_type = None\n        if mode == \"CNAC\":  # Residual path: |-CNAC-|\n            act_type = None\n            norm_type = None\n        conv1 = conv_block(\n            mid_nc,\n            out_nc,\n            kernel_size,\n            stride,\n            dilation,\n            groups,\n            bias,\n            pad_type,\n            norm_type,\n            act_type,\n            mode,\n        )\n        # if in_nc != out_nc:\n        #     self.project = conv_block(in_nc, out_nc, 1, stride, dilation, 1, bias, pad_type, \\\n        #         None, None)\n        #     print('Need a projecter in ResNetBlock.')\n        # else:\n        #     self.project = lambda x:x\n        self.res = sequential(conv0, conv1)\n        self.res_scale = res_scale\n\n    def forward(self, x):\n        res = self.res(x).mul(self.res_scale)\n        return x + res\n\n\nclass RRDB(nn.Module):\n    \"\"\"\n    Residual in Residual Dense Block\n    (ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks)\n    \"\"\"\n\n    def __init__(\n        self,\n        nf,\n        kernel_size=3,\n        gc=32,\n        stride=1,\n        bias: bool = True,\n        pad_type=\"zero\",\n        norm_type=None,\n        act_type=\"leakyrelu\",\n        mode: ConvMode = \"CNA\",\n        _convtype=\"Conv2D\",\n        _spectral_norm=False,\n        plus=False,\n        c2x2=False,\n    ):\n        super(RRDB, self).__init__()\n        self.RDB1 = ResidualDenseBlock_5C(\n            nf,\n            kernel_size,\n            gc,\n            stride,\n            bias,\n            pad_type,\n            norm_type,\n            act_type,\n            mode,\n            plus=plus,\n            c2x2=c2x2,\n        )\n        self.RDB2 = ResidualDenseBlock_5C(\n            nf,\n            kernel_size,\n            gc,\n            stride,\n            bias,\n            pad_type,\n            norm_type,\n            act_type,\n            mode,\n            plus=plus,\n            c2x2=c2x2,\n        )\n        self.RDB3 = ResidualDenseBlock_5C(\n            nf,\n            kernel_size,\n            gc,\n            stride,\n            bias,\n            pad_type,\n            norm_type,\n            act_type,\n            mode,\n            plus=plus,\n            c2x2=c2x2,\n        )\n\n    def forward(self, x):\n        out = self.RDB1(x)\n        out = self.RDB2(out)\n        out = self.RDB3(out)\n        return out * 0.2 + x\n\n\nclass ResidualDenseBlock_5C(nn.Module):\n    \"\"\"\n    Residual Dense Block\n    style: 5 convs\n    The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18)\n    Modified options that can be used:\n        - \"Partial Convolution based Padding\" arXiv:1811.11718\n        - \"Spectral normalization\" arXiv:1802.05957\n        - \"ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN\" N. C.\n            {Rakotonirina} and A. {Rasoanaivo}\n\n    Args:\n        nf (int): Channel number of intermediate features (num_feat).\n        gc (int): Channels for each growth (num_grow_ch: growth channel,\n            i.e. intermediate channels).\n        convtype (str): the type of convolution to use. Default: 'Conv2D'\n        gaussian_noise (bool): enable the ESRGAN+ gaussian noise (no new\n            trainable parameters)\n        plus (bool): enable the additional residual paths from ESRGAN+\n            (adds trainable parameters)\n    \"\"\"\n\n    def __init__(\n        self,\n        nf=64,\n        kernel_size=3,\n        gc=32,\n        stride=1,\n        bias: bool = True,\n        pad_type=\"zero\",\n        norm_type=None,\n        act_type=\"leakyrelu\",\n        mode: ConvMode = \"CNA\",\n        plus=False,\n        c2x2=False,\n    ):\n        super(ResidualDenseBlock_5C, self).__init__()\n\n        ## +\n        self.conv1x1 = conv1x1(nf, gc) if plus else None\n        ## +\n\n        self.conv1 = conv_block(\n            nf,\n            gc,\n            kernel_size,\n            stride,\n            bias=bias,\n            pad_type=pad_type,\n            norm_type=norm_type,\n            act_type=act_type,\n            mode=mode,\n            c2x2=c2x2,\n        )\n        self.conv2 = conv_block(\n            nf + gc,\n            gc,\n            kernel_size,\n            stride,\n            bias=bias,\n            pad_type=pad_type,\n            norm_type=norm_type,\n            act_type=act_type,\n            mode=mode,\n            c2x2=c2x2,\n        )\n        self.conv3 = conv_block(\n            nf + 2 * gc,\n            gc,\n            kernel_size,\n            stride,\n            bias=bias,\n            pad_type=pad_type,\n            norm_type=norm_type,\n            act_type=act_type,\n            mode=mode,\n            c2x2=c2x2,\n        )\n        self.conv4 = conv_block(\n            nf + 3 * gc,\n            gc,\n            kernel_size,\n            stride,\n            bias=bias,\n            pad_type=pad_type,\n            norm_type=norm_type,\n            act_type=act_type,\n            mode=mode,\n            c2x2=c2x2,\n        )\n        if mode == \"CNA\":\n            last_act = None\n        else:\n            last_act = act_type\n        self.conv5 = conv_block(\n            nf + 4 * gc,\n            nf,\n            3,\n            stride,\n            bias=bias,\n            pad_type=pad_type,\n            norm_type=norm_type,\n            act_type=last_act,\n            mode=mode,\n            c2x2=c2x2,\n        )\n\n    def forward(self, x):\n        x1 = self.conv1(x)\n        x2 = self.conv2(torch.cat((x, x1), 1))\n        if self.conv1x1:\n            # pylint: disable=not-callable\n            x2 = x2 + self.conv1x1(x)  # +\n        x3 = self.conv3(torch.cat((x, x1, x2), 1))\n        x4 = self.conv4(torch.cat((x, x1, x2, x3), 1))\n        if self.conv1x1:\n            x4 = x4 + x2  # +\n        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))\n        return x5 * 0.2 + x\n\n\ndef conv1x1(in_planes, out_planes, stride=1):\n    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)\n\n\n####################\n# Upsampler\n####################\n\n\ndef pixelshuffle_block(\n    in_nc: int,\n    out_nc: int,\n    upscale_factor=2,\n    kernel_size=3,\n    stride=1,\n    bias=True,\n    pad_type=\"zero\",\n    norm_type: str | None = None,\n    act_type=\"relu\",\n):\n    \"\"\"\n    Pixel shuffle layer\n    (Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional\n    Neural Network, CVPR17)\n    \"\"\"\n    conv = conv_block(\n        in_nc,\n        out_nc * (upscale_factor**2),\n        kernel_size,\n        stride,\n        bias=bias,\n        pad_type=pad_type,\n        norm_type=None,\n        act_type=None,\n    )\n    pixel_shuffle = nn.PixelShuffle(upscale_factor)\n\n    n = norm(norm_type, out_nc) if norm_type else None\n    a = act(act_type) if act_type else None\n    return sequential(conv, pixel_shuffle, n, a)\n\n\ndef upconv_block(\n    in_nc: int,\n    out_nc: int,\n    upscale_factor=2,\n    kernel_size=3,\n    stride=1,\n    bias=True,\n    pad_type=\"zero\",\n    norm_type: str | None = None,\n    act_type=\"relu\",\n    mode=\"nearest\",\n    c2x2=False,\n):\n    # Up conv\n    # described in https://distill.pub/2016/deconv-checkerboard/\n    upsample = nn.Upsample(scale_factor=upscale_factor, mode=mode)\n    conv = conv_block(\n        in_nc,\n        out_nc,\n        kernel_size,\n        stride,\n        bias=bias,\n        pad_type=pad_type,\n        norm_type=norm_type,\n        act_type=act_type,\n        c2x2=c2x2,\n    )\n    return sequential(upsample, conv)\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/face/LICENSE-GFPGAN",
    "content": "Tencent is pleased to support the open source community by making GFPGAN available.\n\nCopyright (C) 2021 THL A29 Limited, a Tencent company.  All rights reserved.\n\nGFPGAN is licensed under the Apache License Version 2.0 except for the third-party components listed below.\n\n\nTerms of the Apache License Version 2.0:\n---------------------------------------------\nApache License\n\nVersion 2.0, January 2004\n\nhttp://www.apache.org/licenses/\n\nTERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n1. Definitions.\n\n“License” shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.\n\n“Licensor” shall mean the copyright owner or entity authorized by the copyright owner that is granting the License.\n\n“Legal Entity” shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. 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Limitation of Liability.\n\nEXCEPT AS PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL\nTHEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE\nSHALL ANY LICENSOR BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT,\nINDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF\nOR RELATED TO THIS LICENSE, THE USE OR INABILITY TO USE THE WORK\n(INCLUDING BUT NOT LIMITED TO LOSS OF GOODWILL, BUSINESS INTERRUPTION,\nLOST PROFITS OR DATA, COMPUTER FAILURE OR MALFUNCTION, OR ANY OTHER\nCOMMERCIAL DAMAGES OR LOSSES), EVEN IF THE LICENSOR HAS BEEN ADVISED OF\nTHE POSSIBILITY OF SUCH DAMAGES.\n\nMIT License\n\nCopyright (c) 2019 Kim Seonghyeon\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n\n\n\nOpen Source Software licensed under the BSD 3-Clause license:\n---------------------------------------------\n1. torchvision\nCopyright (c) Soumith Chintala 2016,\nAll rights reserved.\n\n2. torch\nCopyright (c) 2016-     Facebook, Inc            (Adam Paszke)\nCopyright (c) 2014-     Facebook, Inc            (Soumith Chintala)\nCopyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)\nCopyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)\nCopyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)\nCopyright (c) 2011-2013 NYU                      (Clement Farabet)\nCopyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)\nCopyright (c) 2006      Idiap Research Institute (Samy Bengio)\nCopyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)\n\n\nTerms of the BSD 3-Clause License:\n---------------------------------------------\nRedistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:\n\n1. 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We list these here.\n\nName: Numpydoc\nFiles: doc/sphinxext/numpydoc/*\nLicense: BSD-2-Clause\n  For details, see doc/sphinxext/LICENSE.txt\n\nName: scipy-sphinx-theme\nFiles: doc/scipy-sphinx-theme/*\nLicense: BSD-3-Clause AND PSF-2.0 AND Apache-2.0\n  For details, see doc/scipy-sphinx-theme/LICENSE.txt\n\nName: lapack-lite\nFiles: numpy/linalg/lapack_lite/*\nLicense: BSD-3-Clause\n  For details, see numpy/linalg/lapack_lite/LICENSE.txt\n\nName: tempita\nFiles: tools/npy_tempita/*\nLicense: MIT\n  For details, see tools/npy_tempita/license.txt\n\nName: dragon4\nFiles: numpy/core/src/multiarray/dragon4.c\nLicense: MIT\n  For license text, see numpy/core/src/multiarray/dragon4.c\n\n\n\nOpen Source Software licensed under the MIT license:\n---------------------------------------------\n1. facexlib\nCopyright (c) 2020 Xintao Wang\n\n2. opencv-python\nCopyright (c) Olli-Pekka Heinisuo\nPlease note that only files in cv2 package are used.\n\n\nTerms of the MIT License:\n---------------------------------------------\nPermission 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:\n\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n\nTHE 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.\n\n\n\nOpen Source Software licensed under the MIT license and Other Licenses of the Third-Party Components therein:\n---------------------------------------------\n1. tqdm\nCopyright (c) 2013 noamraph\n\n`tqdm` is a product of collaborative work.\nUnless otherwise stated, all authors (see commit logs) retain copyright\nfor their respective work, and release the work under the MIT licence\n(text below).\n\nExceptions or notable authors are listed below\nin reverse chronological order:\n\n* files: *\n  MPLv2.0 2015-2020 (c) Casper da Costa-Luis\n  [casperdcl](https://github.com/casperdcl).\n* files: tqdm/_tqdm.py\n  MIT 2016 (c) [PR #96] on behalf of Google Inc.\n* files: tqdm/_tqdm.py setup.py README.rst MANIFEST.in .gitignore\n  MIT 2013 (c) Noam Yorav-Raphael, original author.\n\n[PR #96]: https://github.com/tqdm/tqdm/pull/96\n\n\nMozilla Public Licence (MPL) v. 2.0 - Exhibit A\n-----------------------------------------------\n\nThis Source Code Form is subject to the terms of the\nMozilla Public License, v. 2.0.\nIf a copy of the MPL was not distributed with this file,\nYou can obtain one at https://mozilla.org/MPL/2.0/.\n\n\nMIT License (MIT)\n-----------------\n\nCopyright (c) 2013 noamraph\n\nPermission is hereby granted, free of charge, to any person obtaining a copy of\nthis software and associated documentation files (the \"Software\"), to deal in\nthe Software without restriction, including without limitation the rights to\nuse, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of\nthe Software, and to permit persons to whom the Software is furnished to do so,\nsubject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS\nFOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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  },
  {
    "path": "ldm_patched/pfn/architecture/face/LICENSE-RestoreFormer",
    "content": "Tencent is pleased to support the open source community by making GFPGAN available.\n\nCopyright (C) 2021 THL A29 Limited, a Tencent company.  All rights reserved.\n\nGFPGAN is licensed under the Apache License Version 2.0 except for the third-party components listed below.\n\n\nTerms of the Apache License Version 2.0:\n---------------------------------------------\nApache License\n\nVersion 2.0, January 2004\n\nhttp://www.apache.org/licenses/\n\nTERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n1. Definitions.\n\n“License” shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.\n\n“Licensor” shall mean the copyright owner or entity authorized by the copyright owner that is granting the License.\n\n“Legal Entity” shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. 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Here is the license of stylegan2-pytorch.\nThe official repository is https://github.com/NVlabs/stylegan2, and here is the NVIDIA license.\nDFDNet\nThe codes are largely modified from the repository DFDNet. Their license is Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.\n\nTerms of the Nvidia License:\n---------------------------------------------\n\n1. 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Limitation of Liability.\n\nEXCEPT AS PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL\nTHEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE\nSHALL ANY LICENSOR BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT,\nINDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF\nOR RELATED TO THIS LICENSE, THE USE OR INABILITY TO USE THE WORK\n(INCLUDING BUT NOT LIMITED TO LOSS OF GOODWILL, BUSINESS INTERRUPTION,\nLOST PROFITS OR DATA, COMPUTER FAILURE OR MALFUNCTION, OR ANY OTHER\nCOMMERCIAL DAMAGES OR LOSSES), EVEN IF THE LICENSOR HAS BEEN ADVISED OF\nTHE POSSIBILITY OF SUCH DAMAGES.\n\nMIT License\n\nCopyright (c) 2019 Kim Seonghyeon\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n\n\n\nOpen Source Software licensed under the BSD 3-Clause license:\n---------------------------------------------\n1. torchvision\nCopyright (c) Soumith Chintala 2016,\nAll rights reserved.\n\n2. torch\nCopyright (c) 2016-     Facebook, Inc            (Adam Paszke)\nCopyright (c) 2014-     Facebook, Inc            (Soumith Chintala)\nCopyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)\nCopyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)\nCopyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)\nCopyright (c) 2011-2013 NYU                      (Clement Farabet)\nCopyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)\nCopyright (c) 2006      Idiap Research Institute (Samy Bengio)\nCopyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)\n\n\nTerms of the BSD 3-Clause License:\n---------------------------------------------\nRedistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:\n\n1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.\n\n2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.\n\n3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\n\n\nOpen Source Software licensed under the BSD 3-Clause License and Other Licenses of the Third-Party Components therein:\n---------------------------------------------\n1. numpy\nCopyright (c) 2005-2020, NumPy Developers.\nAll rights reserved.\n\nA copy of BSD 3-Clause License is included in this file.\n\nThe NumPy repository and source distributions bundle several libraries that are\ncompatibly licensed.  We list these here.\n\nName: Numpydoc\nFiles: doc/sphinxext/numpydoc/*\nLicense: BSD-2-Clause\n  For details, see doc/sphinxext/LICENSE.txt\n\nName: scipy-sphinx-theme\nFiles: doc/scipy-sphinx-theme/*\nLicense: BSD-3-Clause AND PSF-2.0 AND Apache-2.0\n  For details, see doc/scipy-sphinx-theme/LICENSE.txt\n\nName: lapack-lite\nFiles: numpy/linalg/lapack_lite/*\nLicense: BSD-3-Clause\n  For details, see numpy/linalg/lapack_lite/LICENSE.txt\n\nName: tempita\nFiles: tools/npy_tempita/*\nLicense: MIT\n  For details, see tools/npy_tempita/license.txt\n\nName: dragon4\nFiles: numpy/core/src/multiarray/dragon4.c\nLicense: MIT\n  For license text, see numpy/core/src/multiarray/dragon4.c\n\n\n\nOpen Source Software licensed under the MIT license:\n---------------------------------------------\n1. facexlib\nCopyright (c) 2020 Xintao Wang\n\n2. opencv-python\nCopyright (c) Olli-Pekka Heinisuo\nPlease note that only files in cv2 package are used.\n\n\nTerms of the MIT License:\n---------------------------------------------\nPermission 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:\n\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n\nTHE 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.\n\n\n\nOpen Source Software licensed under the MIT license and Other Licenses of the Third-Party Components therein:\n---------------------------------------------\n1. tqdm\nCopyright (c) 2013 noamraph\n\n`tqdm` is a product of collaborative work.\nUnless otherwise stated, all authors (see commit logs) retain copyright\nfor their respective work, and release the work under the MIT licence\n(text below).\n\nExceptions or notable authors are listed below\nin reverse chronological order:\n\n* files: *\n  MPLv2.0 2015-2020 (c) Casper da Costa-Luis\n  [casperdcl](https://github.com/casperdcl).\n* files: tqdm/_tqdm.py\n  MIT 2016 (c) [PR #96] on behalf of Google Inc.\n* files: tqdm/_tqdm.py setup.py README.rst MANIFEST.in .gitignore\n  MIT 2013 (c) Noam Yorav-Raphael, original author.\n\n[PR #96]: https://github.com/tqdm/tqdm/pull/96\n\n\nMozilla Public Licence (MPL) v. 2.0 - Exhibit A\n-----------------------------------------------\n\nThis Source Code Form is subject to the terms of the\nMozilla Public License, v. 2.0.\nIf a copy of the MPL was not distributed with this file,\nYou can obtain one at https://mozilla.org/MPL/2.0/.\n\n\nMIT License (MIT)\n-----------------\n\nCopyright (c) 2013 noamraph\n\nPermission is hereby granted, free of charge, to any person obtaining a copy of\nthis software and associated documentation files (the \"Software\"), to deal in\nthe Software without restriction, including without limitation the rights to\nuse, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of\nthe Software, and to permit persons to whom the Software is furnished to do so,\nsubject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS\nFOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR\nCOPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER\nIN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN\nCONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
  },
  {
    "path": "ldm_patched/pfn/architecture/face/LICENSE-codeformer",
    "content": "S-Lab License 1.0\n\nCopyright 2022 S-Lab\n\nRedistribution and use for non-commercial purpose in source and\nbinary forms, with or without modification, are permitted provided\nthat the following conditions are met:\n\n1. Redistributions of source code must retain the above copyright\n   notice, this list of conditions and the following disclaimer.\n\n2. Redistributions in binary form must reproduce the above copyright\n   notice, this list of conditions and the following disclaimer in\n   the documentation and/or other materials provided with the\n   distribution.\n\n3. Neither the name of the copyright holder nor the names of its\n   contributors may be used to endorse or promote products derived\n   from this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\n\"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\nLIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR\nA PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT\nHOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,\nSPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT\nLIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,\nDATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY\nTHEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\nIn the event that redistribution and/or use for commercial purpose in\nsource or binary forms, with or without modification is required,\nplease contact the contributor(s) of the work.\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/face/arcface_arch.py",
    "content": "import torch.nn as nn\n\n\ndef conv3x3(inplanes, outplanes, stride=1):\n    \"\"\"A simple wrapper for 3x3 convolution with padding.\n\n    Args:\n        inplanes (int): Channel number of inputs.\n        outplanes (int): Channel number of outputs.\n        stride (int): Stride in convolution. Default: 1.\n    \"\"\"\n    return nn.Conv2d(\n        inplanes, outplanes, kernel_size=3, stride=stride, padding=1, bias=False\n    )\n\n\nclass BasicBlock(nn.Module):\n    \"\"\"Basic residual block used in the ResNetArcFace architecture.\n\n    Args:\n        inplanes (int): Channel number of inputs.\n        planes (int): Channel number of outputs.\n        stride (int): Stride in convolution. Default: 1.\n        downsample (nn.Module): The downsample module. Default: None.\n    \"\"\"\n\n    expansion = 1  # output channel expansion ratio\n\n    def __init__(self, inplanes, planes, stride=1, downsample=None):\n        super(BasicBlock, self).__init__()\n        self.conv1 = conv3x3(inplanes, planes, stride)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.relu = nn.ReLU(inplace=True)\n        self.conv2 = conv3x3(planes, planes)\n        self.bn2 = nn.BatchNorm2d(planes)\n        self.downsample = downsample\n        self.stride = stride\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        out += residual\n        out = self.relu(out)\n\n        return out\n\n\nclass IRBlock(nn.Module):\n    \"\"\"Improved residual block (IR Block) used in the ResNetArcFace architecture.\n\n    Args:\n        inplanes (int): Channel number of inputs.\n        planes (int): Channel number of outputs.\n        stride (int): Stride in convolution. Default: 1.\n        downsample (nn.Module): The downsample module. Default: None.\n        use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True.\n    \"\"\"\n\n    expansion = 1  # output channel expansion ratio\n\n    def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True):\n        super(IRBlock, self).__init__()\n        self.bn0 = nn.BatchNorm2d(inplanes)\n        self.conv1 = conv3x3(inplanes, inplanes)\n        self.bn1 = nn.BatchNorm2d(inplanes)\n        self.prelu = nn.PReLU()\n        self.conv2 = conv3x3(inplanes, planes, stride)\n        self.bn2 = nn.BatchNorm2d(planes)\n        self.downsample = downsample\n        self.stride = stride\n        self.use_se = use_se\n        if self.use_se:\n            self.se = SEBlock(planes)\n\n    def forward(self, x):\n        residual = x\n        out = self.bn0(x)\n        out = self.conv1(out)\n        out = self.bn1(out)\n        out = self.prelu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n        if self.use_se:\n            out = self.se(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        out += residual\n        out = self.prelu(out)\n\n        return out\n\n\nclass Bottleneck(nn.Module):\n    \"\"\"Bottleneck block used in the ResNetArcFace architecture.\n\n    Args:\n        inplanes (int): Channel number of inputs.\n        planes (int): Channel number of outputs.\n        stride (int): Stride in convolution. Default: 1.\n        downsample (nn.Module): The downsample module. Default: None.\n    \"\"\"\n\n    expansion = 4  # output channel expansion ratio\n\n    def __init__(self, inplanes, planes, stride=1, downsample=None):\n        super(Bottleneck, self).__init__()\n        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.conv2 = nn.Conv2d(\n            planes, planes, kernel_size=3, stride=stride, padding=1, bias=False\n        )\n        self.bn2 = nn.BatchNorm2d(planes)\n        self.conv3 = nn.Conv2d(\n            planes, planes * self.expansion, kernel_size=1, bias=False\n        )\n        self.bn3 = nn.BatchNorm2d(planes * self.expansion)\n        self.relu = nn.ReLU(inplace=True)\n        self.downsample = downsample\n        self.stride = stride\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n        out = self.relu(out)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        out += residual\n        out = self.relu(out)\n\n        return out\n\n\nclass SEBlock(nn.Module):\n    \"\"\"The squeeze-and-excitation block (SEBlock) used in the IRBlock.\n\n    Args:\n        channel (int): Channel number of inputs.\n        reduction (int): Channel reduction ration. Default: 16.\n    \"\"\"\n\n    def __init__(self, channel, reduction=16):\n        super(SEBlock, self).__init__()\n        self.avg_pool = nn.AdaptiveAvgPool2d(\n            1\n        )  # pool to 1x1 without spatial information\n        self.fc = nn.Sequential(\n            nn.Linear(channel, channel // reduction),\n            nn.PReLU(),\n            nn.Linear(channel // reduction, channel),\n            nn.Sigmoid(),\n        )\n\n    def forward(self, x):\n        b, c, _, _ = x.size()\n        y = self.avg_pool(x).view(b, c)\n        y = self.fc(y).view(b, c, 1, 1)\n        return x * y\n\n\nclass ResNetArcFace(nn.Module):\n    \"\"\"ArcFace with ResNet architectures.\n\n    Ref: ArcFace: Additive Angular Margin Loss for Deep Face Recognition.\n\n    Args:\n        block (str): Block used in the ArcFace architecture.\n        layers (tuple(int)): Block numbers in each layer.\n        use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True.\n    \"\"\"\n\n    def __init__(self, block, layers, use_se=True):\n        if block == \"IRBlock\":\n            block = IRBlock\n        self.inplanes = 64\n        self.use_se = use_se\n        super(ResNetArcFace, self).__init__()\n\n        self.conv1 = nn.Conv2d(1, 64, kernel_size=3, padding=1, bias=False)\n        self.bn1 = nn.BatchNorm2d(64)\n        self.prelu = nn.PReLU()\n        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)\n        self.layer1 = self._make_layer(block, 64, layers[0])\n        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)\n        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)\n        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)\n        self.bn4 = nn.BatchNorm2d(512)\n        self.dropout = nn.Dropout()\n        self.fc5 = nn.Linear(512 * 8 * 8, 512)\n        self.bn5 = nn.BatchNorm1d(512)\n\n        # initialization\n        for m in self.modules():\n            if isinstance(m, nn.Conv2d):\n                nn.init.xavier_normal_(m.weight)\n            elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):\n                nn.init.constant_(m.weight, 1)\n                nn.init.constant_(m.bias, 0)\n            elif isinstance(m, nn.Linear):\n                nn.init.xavier_normal_(m.weight)\n                nn.init.constant_(m.bias, 0)\n\n    def _make_layer(self, block, planes, num_blocks, stride=1):\n        downsample = None\n        if stride != 1 or self.inplanes != planes * block.expansion:\n            downsample = nn.Sequential(\n                nn.Conv2d(\n                    self.inplanes,\n                    planes * block.expansion,\n                    kernel_size=1,\n                    stride=stride,\n                    bias=False,\n                ),\n                nn.BatchNorm2d(planes * block.expansion),\n            )\n        layers = []\n        layers.append(\n            block(self.inplanes, planes, stride, downsample, use_se=self.use_se)\n        )\n        self.inplanes = planes\n        for _ in range(1, num_blocks):\n            layers.append(block(self.inplanes, planes, use_se=self.use_se))\n\n        return nn.Sequential(*layers)\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.prelu(x)\n        x = self.maxpool(x)\n\n        x = self.layer1(x)\n        x = self.layer2(x)\n        x = self.layer3(x)\n        x = self.layer4(x)\n        x = self.bn4(x)\n        x = self.dropout(x)\n        x = x.view(x.size(0), -1)\n        x = self.fc5(x)\n        x = self.bn5(x)\n\n        return x\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/face/codeformer.py",
    "content": "\"\"\"\nModified from https://github.com/sczhou/CodeFormer\nVQGAN code, adapted from the original created by the Unleashing Transformers authors:\nhttps://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py\nThis version of the arch specifically was gathered from an old version of GFPGAN. If this is a problem, please contact me.\n\"\"\"\nimport math\nfrom typing import Optional\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport logging as logger\nfrom torch import Tensor\n\n\nclass VectorQuantizer(nn.Module):\n    def __init__(self, codebook_size, emb_dim, beta):\n        super(VectorQuantizer, self).__init__()\n        self.codebook_size = codebook_size  # number of embeddings\n        self.emb_dim = emb_dim  # dimension of embedding\n        self.beta = beta  # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2\n        self.embedding = nn.Embedding(self.codebook_size, self.emb_dim)\n        self.embedding.weight.data.uniform_(\n            -1.0 / self.codebook_size, 1.0 / self.codebook_size\n        )\n\n    def forward(self, z):\n        # reshape z -> (batch, height, width, channel) and flatten\n        z = z.permute(0, 2, 3, 1).contiguous()\n        z_flattened = z.view(-1, self.emb_dim)\n\n        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z\n        d = (\n            (z_flattened**2).sum(dim=1, keepdim=True)\n            + (self.embedding.weight**2).sum(1)\n            - 2 * torch.matmul(z_flattened, self.embedding.weight.t())\n        )\n\n        mean_distance = torch.mean(d)\n        # find closest encodings\n        # min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1)\n        min_encoding_scores, min_encoding_indices = torch.topk(\n            d, 1, dim=1, largest=False\n        )\n        # [0-1], higher score, higher confidence\n        min_encoding_scores = torch.exp(-min_encoding_scores / 10)\n\n        min_encodings = torch.zeros(\n            min_encoding_indices.shape[0], self.codebook_size\n        ).to(z)\n        min_encodings.scatter_(1, min_encoding_indices, 1)\n\n        # get quantized latent vectors\n        z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape)\n        # compute loss for embedding\n        loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean(\n            (z_q - z.detach()) ** 2\n        )\n        # preserve gradients\n        z_q = z + (z_q - z).detach()\n\n        # perplexity\n        e_mean = torch.mean(min_encodings, dim=0)\n        perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10)))\n        # reshape back to match original input shape\n        z_q = z_q.permute(0, 3, 1, 2).contiguous()\n\n        return (\n            z_q,\n            loss,\n            {\n                \"perplexity\": perplexity,\n                \"min_encodings\": min_encodings,\n                \"min_encoding_indices\": min_encoding_indices,\n                \"min_encoding_scores\": min_encoding_scores,\n                \"mean_distance\": mean_distance,\n            },\n        )\n\n    def get_codebook_feat(self, indices, shape):\n        # input indices: batch*token_num -> (batch*token_num)*1\n        # shape: batch, height, width, channel\n        indices = indices.view(-1, 1)\n        min_encodings = torch.zeros(indices.shape[0], self.codebook_size).to(indices)\n        min_encodings.scatter_(1, indices, 1)\n        # get quantized latent vectors\n        z_q = torch.matmul(min_encodings.float(), self.embedding.weight)\n\n        if shape is not None:  # reshape back to match original input shape\n            z_q = z_q.view(shape).permute(0, 3, 1, 2).contiguous()\n\n        return z_q\n\n\nclass GumbelQuantizer(nn.Module):\n    def __init__(\n        self,\n        codebook_size,\n        emb_dim,\n        num_hiddens,\n        straight_through=False,\n        kl_weight=5e-4,\n        temp_init=1.0,\n    ):\n        super().__init__()\n        self.codebook_size = codebook_size  # number of embeddings\n        self.emb_dim = emb_dim  # dimension of embedding\n        self.straight_through = straight_through\n        self.temperature = temp_init\n        self.kl_weight = kl_weight\n        self.proj = nn.Conv2d(\n            num_hiddens, codebook_size, 1\n        )  # projects last encoder layer to quantized logits\n        self.embed = nn.Embedding(codebook_size, emb_dim)\n\n    def forward(self, z):\n        hard = self.straight_through if self.training else True\n\n        logits = self.proj(z)\n\n        soft_one_hot = F.gumbel_softmax(logits, tau=self.temperature, dim=1, hard=hard)\n\n        z_q = torch.einsum(\"b n h w, n d -> b d h w\", soft_one_hot, self.embed.weight)\n\n        # + kl divergence to the prior loss\n        qy = F.softmax(logits, dim=1)\n        diff = (\n            self.kl_weight\n            * torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean()\n        )\n        min_encoding_indices = soft_one_hot.argmax(dim=1)\n\n        return z_q, diff, {\"min_encoding_indices\": min_encoding_indices}\n\n\nclass Downsample(nn.Module):\n    def __init__(self, in_channels):\n        super().__init__()\n        self.conv = torch.nn.Conv2d(\n            in_channels, in_channels, kernel_size=3, stride=2, padding=0\n        )\n\n    def forward(self, x):\n        pad = (0, 1, 0, 1)\n        x = torch.nn.functional.pad(x, pad, mode=\"constant\", value=0)\n        x = self.conv(x)\n        return x\n\n\nclass Upsample(nn.Module):\n    def __init__(self, in_channels):\n        super().__init__()\n        self.conv = nn.Conv2d(\n            in_channels, in_channels, kernel_size=3, stride=1, padding=1\n        )\n\n    def forward(self, x):\n        x = F.interpolate(x, scale_factor=2.0, mode=\"nearest\")\n        x = self.conv(x)\n\n        return x\n\n\nclass AttnBlock(nn.Module):\n    def __init__(self, in_channels):\n        super().__init__()\n        self.in_channels = in_channels\n\n        self.norm = normalize(in_channels)\n        self.q = torch.nn.Conv2d(\n            in_channels, in_channels, kernel_size=1, stride=1, padding=0\n        )\n        self.k = torch.nn.Conv2d(\n            in_channels, in_channels, kernel_size=1, stride=1, padding=0\n        )\n        self.v = torch.nn.Conv2d(\n            in_channels, in_channels, kernel_size=1, stride=1, padding=0\n        )\n        self.proj_out = torch.nn.Conv2d(\n            in_channels, in_channels, kernel_size=1, stride=1, padding=0\n        )\n\n    def forward(self, x):\n        h_ = x\n        h_ = self.norm(h_)\n        q = self.q(h_)\n        k = self.k(h_)\n        v = self.v(h_)\n\n        # compute attention\n        b, c, h, w = q.shape\n        q = q.reshape(b, c, h * w)\n        q = q.permute(0, 2, 1)\n        k = k.reshape(b, c, h * w)\n        w_ = torch.bmm(q, k)\n        w_ = w_ * (int(c) ** (-0.5))\n        w_ = F.softmax(w_, dim=2)\n\n        # attend to values\n        v = v.reshape(b, c, h * w)\n        w_ = w_.permute(0, 2, 1)\n        h_ = torch.bmm(v, w_)\n        h_ = h_.reshape(b, c, h, w)\n\n        h_ = self.proj_out(h_)\n\n        return x + h_\n\n\nclass Encoder(nn.Module):\n    def __init__(\n        self,\n        in_channels,\n        nf,\n        out_channels,\n        ch_mult,\n        num_res_blocks,\n        resolution,\n        attn_resolutions,\n    ):\n        super().__init__()\n        self.nf = nf\n        self.num_resolutions = len(ch_mult)\n        self.num_res_blocks = num_res_blocks\n        self.resolution = resolution\n        self.attn_resolutions = attn_resolutions\n\n        curr_res = self.resolution\n        in_ch_mult = (1,) + tuple(ch_mult)\n\n        blocks = []\n        # initial convultion\n        blocks.append(nn.Conv2d(in_channels, nf, kernel_size=3, stride=1, padding=1))\n\n        # residual and downsampling blocks, with attention on smaller res (16x16)\n        for i in range(self.num_resolutions):\n            block_in_ch = nf * in_ch_mult[i]\n            block_out_ch = nf * ch_mult[i]\n            for _ in range(self.num_res_blocks):\n                blocks.append(ResBlock(block_in_ch, block_out_ch))\n                block_in_ch = block_out_ch\n                if curr_res in attn_resolutions:\n                    blocks.append(AttnBlock(block_in_ch))\n\n            if i != self.num_resolutions - 1:\n                blocks.append(Downsample(block_in_ch))\n                curr_res = curr_res // 2\n\n        # non-local attention block\n        blocks.append(ResBlock(block_in_ch, block_in_ch))  # type: ignore\n        blocks.append(AttnBlock(block_in_ch))  # type: ignore\n        blocks.append(ResBlock(block_in_ch, block_in_ch))  # type: ignore\n\n        # normalise and convert to latent size\n        blocks.append(normalize(block_in_ch))  # type: ignore\n        blocks.append(\n            nn.Conv2d(block_in_ch, out_channels, kernel_size=3, stride=1, padding=1)  # type: ignore\n        )\n        self.blocks = nn.ModuleList(blocks)\n\n    def forward(self, x):\n        for block in self.blocks:\n            x = block(x)\n\n        return x\n\n\nclass Generator(nn.Module):\n    def __init__(self, nf, ch_mult, res_blocks, img_size, attn_resolutions, emb_dim):\n        super().__init__()\n        self.nf = nf\n        self.ch_mult = ch_mult\n        self.num_resolutions = len(self.ch_mult)\n        self.num_res_blocks = res_blocks\n        self.resolution = img_size\n        self.attn_resolutions = attn_resolutions\n        self.in_channels = emb_dim\n        self.out_channels = 3\n        block_in_ch = self.nf * self.ch_mult[-1]\n        curr_res = self.resolution // 2 ** (self.num_resolutions - 1)\n\n        blocks = []\n        # initial conv\n        blocks.append(\n            nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1)\n        )\n\n        # non-local attention block\n        blocks.append(ResBlock(block_in_ch, block_in_ch))\n        blocks.append(AttnBlock(block_in_ch))\n        blocks.append(ResBlock(block_in_ch, block_in_ch))\n\n        for i in reversed(range(self.num_resolutions)):\n            block_out_ch = self.nf * self.ch_mult[i]\n\n            for _ in range(self.num_res_blocks):\n                blocks.append(ResBlock(block_in_ch, block_out_ch))\n                block_in_ch = block_out_ch\n\n                if curr_res in self.attn_resolutions:\n                    blocks.append(AttnBlock(block_in_ch))\n\n            if i != 0:\n                blocks.append(Upsample(block_in_ch))\n                curr_res = curr_res * 2\n\n        blocks.append(normalize(block_in_ch))\n        blocks.append(\n            nn.Conv2d(\n                block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1\n            )\n        )\n\n        self.blocks = nn.ModuleList(blocks)\n\n    def forward(self, x):\n        for block in self.blocks:\n            x = block(x)\n\n        return x\n\n\nclass VQAutoEncoder(nn.Module):\n    def __init__(\n        self,\n        img_size,\n        nf,\n        ch_mult,\n        quantizer=\"nearest\",\n        res_blocks=2,\n        attn_resolutions=[16],\n        codebook_size=1024,\n        emb_dim=256,\n        beta=0.25,\n        gumbel_straight_through=False,\n        gumbel_kl_weight=1e-8,\n        model_path=None,\n    ):\n        super().__init__()\n        self.in_channels = 3\n        self.nf = nf\n        self.n_blocks = res_blocks\n        self.codebook_size = codebook_size\n        self.embed_dim = emb_dim\n        self.ch_mult = ch_mult\n        self.resolution = img_size\n        self.attn_resolutions = attn_resolutions\n        self.quantizer_type = quantizer\n        self.encoder = Encoder(\n            self.in_channels,\n            self.nf,\n            self.embed_dim,\n            self.ch_mult,\n            self.n_blocks,\n            self.resolution,\n            self.attn_resolutions,\n        )\n        if self.quantizer_type == \"nearest\":\n            self.beta = beta  # 0.25\n            self.quantize = VectorQuantizer(\n                self.codebook_size, self.embed_dim, self.beta\n            )\n        elif self.quantizer_type == \"gumbel\":\n            self.gumbel_num_hiddens = emb_dim\n            self.straight_through = gumbel_straight_through\n            self.kl_weight = gumbel_kl_weight\n            self.quantize = GumbelQuantizer(\n                self.codebook_size,\n                self.embed_dim,\n                self.gumbel_num_hiddens,\n                self.straight_through,\n                self.kl_weight,\n            )\n        self.generator = Generator(\n            nf, ch_mult, res_blocks, img_size, attn_resolutions, emb_dim\n        )\n\n        if model_path is not None:\n            chkpt = torch.load(model_path, map_location=\"cpu\", weights_only=True)\n            if \"params_ema\" in chkpt:\n                self.load_state_dict(\n                    torch.load(model_path, map_location=\"cpu\", weights_only=True)[\"params_ema\"]\n                )\n                logger.info(f\"vqgan is loaded from: {model_path} [params_ema]\")\n            elif \"params\" in chkpt:\n                self.load_state_dict(\n                    torch.load(model_path, map_location=\"cpu\", weights_only=True)[\"params\"]\n                )\n                logger.info(f\"vqgan is loaded from: {model_path} [params]\")\n            else:\n                raise ValueError(\"Wrong params!\")\n\n    def forward(self, x):\n        x = self.encoder(x)\n        quant, codebook_loss, quant_stats = self.quantize(x)\n        x = self.generator(quant)\n        return x, codebook_loss, quant_stats\n\n\ndef calc_mean_std(feat, eps=1e-5):\n    \"\"\"Calculate mean and std for adaptive_instance_normalization.\n    Args:\n        feat (Tensor): 4D tensor.\n        eps (float): A small value added to the variance to avoid\n            divide-by-zero. Default: 1e-5.\n    \"\"\"\n    size = feat.size()\n    assert len(size) == 4, \"The input feature should be 4D tensor.\"\n    b, c = size[:2]\n    feat_var = feat.view(b, c, -1).var(dim=2) + eps\n    feat_std = feat_var.sqrt().view(b, c, 1, 1)\n    feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1)\n    return feat_mean, feat_std\n\n\ndef adaptive_instance_normalization(content_feat, style_feat):\n    \"\"\"Adaptive instance normalization.\n    Adjust the reference features to have the similar color and illuminations\n    as those in the degradate features.\n    Args:\n        content_feat (Tensor): The reference feature.\n        style_feat (Tensor): The degradate features.\n    \"\"\"\n    size = content_feat.size()\n    style_mean, style_std = calc_mean_std(style_feat)\n    content_mean, content_std = calc_mean_std(content_feat)\n    normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(\n        size\n    )\n    return normalized_feat * style_std.expand(size) + style_mean.expand(size)\n\n\nclass PositionEmbeddingSine(nn.Module):\n    \"\"\"\n    This is a more standard version of the position embedding, very similar to the one\n    used by the Attention is all you need paper, generalized to work on images.\n    \"\"\"\n\n    def __init__(\n        self, num_pos_feats=64, temperature=10000, normalize=False, scale=None\n    ):\n        super().__init__()\n        self.num_pos_feats = num_pos_feats\n        self.temperature = temperature\n        self.normalize = normalize\n        if scale is not None and normalize is False:\n            raise ValueError(\"normalize should be True if scale is passed\")\n        if scale is None:\n            scale = 2 * math.pi\n        self.scale = scale\n\n    def forward(self, x, mask=None):\n        if mask is None:\n            mask = torch.zeros(\n                (x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool\n            )\n        not_mask = ~mask  # pylint: disable=invalid-unary-operand-type\n        y_embed = not_mask.cumsum(1, dtype=torch.float32)\n        x_embed = not_mask.cumsum(2, dtype=torch.float32)\n        if self.normalize:\n            eps = 1e-6\n            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale\n            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale\n\n        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)\n        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)\n\n        pos_x = x_embed[:, :, :, None] / dim_t\n        pos_y = y_embed[:, :, :, None] / dim_t\n        pos_x = torch.stack(\n            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4\n        ).flatten(3)\n        pos_y = torch.stack(\n            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4\n        ).flatten(3)\n        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)\n        return pos\n\n\ndef _get_activation_fn(activation):\n    \"\"\"Return an activation function given a string\"\"\"\n    if activation == \"relu\":\n        return F.relu\n    if activation == \"gelu\":\n        return F.gelu\n    if activation == \"glu\":\n        return F.glu\n    raise RuntimeError(f\"activation should be relu/gelu, not {activation}.\")\n\n\nclass TransformerSALayer(nn.Module):\n    def __init__(\n        self, embed_dim, nhead=8, dim_mlp=2048, dropout=0.0, activation=\"gelu\"\n    ):\n        super().__init__()\n        self.self_attn = nn.MultiheadAttention(embed_dim, nhead, dropout=dropout)\n        # Implementation of Feedforward model - MLP\n        self.linear1 = nn.Linear(embed_dim, dim_mlp)\n        self.dropout = nn.Dropout(dropout)\n        self.linear2 = nn.Linear(dim_mlp, embed_dim)\n\n        self.norm1 = nn.LayerNorm(embed_dim)\n        self.norm2 = nn.LayerNorm(embed_dim)\n        self.dropout1 = nn.Dropout(dropout)\n        self.dropout2 = nn.Dropout(dropout)\n\n        self.activation = _get_activation_fn(activation)\n\n    def with_pos_embed(self, tensor, pos: Optional[Tensor]):\n        return tensor if pos is None else tensor + pos\n\n    def forward(\n        self,\n        tgt,\n        tgt_mask: Optional[Tensor] = None,\n        tgt_key_padding_mask: Optional[Tensor] = None,\n        query_pos: Optional[Tensor] = None,\n    ):\n        # self attention\n        tgt2 = self.norm1(tgt)\n        q = k = self.with_pos_embed(tgt2, query_pos)\n        tgt2 = self.self_attn(\n            q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask\n        )[0]\n        tgt = tgt + self.dropout1(tgt2)\n\n        # ffn\n        tgt2 = self.norm2(tgt)\n        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))\n        tgt = tgt + self.dropout2(tgt2)\n        return tgt\n\n\ndef normalize(in_channels):\n    return torch.nn.GroupNorm(\n        num_groups=32, num_channels=in_channels, eps=1e-6, affine=True\n    )\n\n\n@torch.jit.script  # type: ignore\ndef swish(x):\n    return x * torch.sigmoid(x)\n\n\nclass ResBlock(nn.Module):\n    def __init__(self, in_channels, out_channels=None):\n        super(ResBlock, self).__init__()\n        self.in_channels = in_channels\n        self.out_channels = in_channels if out_channels is None else out_channels\n        self.norm1 = normalize(in_channels)\n        self.conv1 = nn.Conv2d(\n            in_channels, out_channels, kernel_size=3, stride=1, padding=1  # type: ignore\n        )\n        self.norm2 = normalize(out_channels)\n        self.conv2 = nn.Conv2d(\n            out_channels, out_channels, kernel_size=3, stride=1, padding=1  # type: ignore\n        )\n        if self.in_channels != self.out_channels:\n            self.conv_out = nn.Conv2d(\n                in_channels, out_channels, kernel_size=1, stride=1, padding=0  # type: ignore\n            )\n\n    def forward(self, x_in):\n        x = x_in\n        x = self.norm1(x)\n        x = swish(x)\n        x = self.conv1(x)\n        x = self.norm2(x)\n        x = swish(x)\n        x = self.conv2(x)\n        if self.in_channels != self.out_channels:\n            x_in = self.conv_out(x_in)\n\n        return x + x_in\n\n\nclass Fuse_sft_block(nn.Module):\n    def __init__(self, in_ch, out_ch):\n        super().__init__()\n        self.encode_enc = ResBlock(2 * in_ch, out_ch)\n\n        self.scale = nn.Sequential(\n            nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),\n            nn.LeakyReLU(0.2, True),\n            nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1),\n        )\n\n        self.shift = nn.Sequential(\n            nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),\n            nn.LeakyReLU(0.2, True),\n            nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1),\n        )\n\n    def forward(self, enc_feat, dec_feat, w=1):\n        enc_feat = self.encode_enc(torch.cat([enc_feat, dec_feat], dim=1))\n        scale = self.scale(enc_feat)\n        shift = self.shift(enc_feat)\n        residual = w * (dec_feat * scale + shift)\n        out = dec_feat + residual\n        return out\n\n\nclass CodeFormer(VQAutoEncoder):\n    def __init__(self, state_dict):\n        dim_embd = 512\n        n_head = 8\n        n_layers = 9\n        codebook_size = 1024\n        latent_size = 256\n        connect_list = [\"32\", \"64\", \"128\", \"256\"]\n        fix_modules = [\"quantize\", \"generator\"]\n\n        # This is just a guess as I only have one model to look at\n        position_emb = state_dict[\"position_emb\"]\n        dim_embd = position_emb.shape[1]\n        latent_size = position_emb.shape[0]\n\n        try:\n            n_layers = len(\n                set([x.split(\".\")[1] for x in state_dict.keys() if \"ft_layers\" in x])\n            )\n        except:\n            pass\n\n        codebook_size = state_dict[\"quantize.embedding.weight\"].shape[0]\n\n        # This is also just another guess\n        n_head_exp = (\n            state_dict[\"ft_layers.0.self_attn.in_proj_weight\"].shape[0] // dim_embd\n        )\n        n_head = 2**n_head_exp\n\n        in_nc = state_dict[\"encoder.blocks.0.weight\"].shape[1]\n\n        self.model_arch = \"CodeFormer\"\n        self.sub_type = \"Face SR\"\n        self.scale = 8\n        self.in_nc = in_nc\n        self.out_nc = in_nc\n\n        self.state = state_dict\n\n        self.supports_fp16 = False\n        self.supports_bf16 = True\n        self.min_size_restriction = 16\n\n        super(CodeFormer, self).__init__(\n            512, 64, [1, 2, 2, 4, 4, 8], \"nearest\", 2, [16], codebook_size\n        )\n\n        if fix_modules is not None:\n            for module in fix_modules:\n                for param in getattr(self, module).parameters():\n                    param.requires_grad = False\n\n        self.connect_list = connect_list\n        self.n_layers = n_layers\n        self.dim_embd = dim_embd\n        self.dim_mlp = dim_embd * 2\n\n        self.position_emb = nn.Parameter(torch.zeros(latent_size, self.dim_embd))  # type: ignore\n        self.feat_emb = nn.Linear(256, self.dim_embd)\n\n        # transformer\n        self.ft_layers = nn.Sequential(\n            *[\n                TransformerSALayer(\n                    embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0\n                )\n                for _ in range(self.n_layers)\n            ]\n        )\n\n        # logits_predict head\n        self.idx_pred_layer = nn.Sequential(\n            nn.LayerNorm(dim_embd), nn.Linear(dim_embd, codebook_size, bias=False)\n        )\n\n        self.channels = {\n            \"16\": 512,\n            \"32\": 256,\n            \"64\": 256,\n            \"128\": 128,\n            \"256\": 128,\n            \"512\": 64,\n        }\n\n        # after second residual block for > 16, before attn layer for ==16\n        self.fuse_encoder_block = {\n            \"512\": 2,\n            \"256\": 5,\n            \"128\": 8,\n            \"64\": 11,\n            \"32\": 14,\n            \"16\": 18,\n        }\n        # after first residual block for > 16, before attn layer for ==16\n        self.fuse_generator_block = {\n            \"16\": 6,\n            \"32\": 9,\n            \"64\": 12,\n            \"128\": 15,\n            \"256\": 18,\n            \"512\": 21,\n        }\n\n        # fuse_convs_dict\n        self.fuse_convs_dict = nn.ModuleDict()\n        for f_size in self.connect_list:\n            in_ch = self.channels[f_size]\n            self.fuse_convs_dict[f_size] = Fuse_sft_block(in_ch, in_ch)\n\n        self.load_state_dict(state_dict)\n\n    def _init_weights(self, module):\n        if isinstance(module, (nn.Linear, nn.Embedding)):\n            module.weight.data.normal_(mean=0.0, std=0.02)\n            if isinstance(module, nn.Linear) and module.bias is not None:\n                module.bias.data.zero_()\n        elif isinstance(module, nn.LayerNorm):\n            module.bias.data.zero_()\n            module.weight.data.fill_(1.0)\n\n    def forward(self, x, weight=0.5, **kwargs):\n        detach_16 = True\n        code_only = False\n        adain = True\n        # ################### Encoder #####################\n        enc_feat_dict = {}\n        out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list]\n        for i, block in enumerate(self.encoder.blocks):\n            x = block(x)\n            if i in out_list:\n                enc_feat_dict[str(x.shape[-1])] = x.clone()\n\n        lq_feat = x\n        # ################# Transformer ###################\n        # quant_feat, codebook_loss, quant_stats = self.quantize(lq_feat)\n        pos_emb = self.position_emb.unsqueeze(1).repeat(1, x.shape[0], 1)\n        # BCHW -> BC(HW) -> (HW)BC\n        feat_emb = self.feat_emb(lq_feat.flatten(2).permute(2, 0, 1))\n        query_emb = feat_emb\n        # Transformer encoder\n        for layer in self.ft_layers:\n            query_emb = layer(query_emb, query_pos=pos_emb)\n\n        # output logits\n        logits = self.idx_pred_layer(query_emb)  # (hw)bn\n        logits = logits.permute(1, 0, 2)  # (hw)bn -> b(hw)n\n\n        if code_only:  # for training stage II\n            # logits doesn't need softmax before cross_entropy loss\n            return logits, lq_feat\n\n        # ################# Quantization ###################\n        # if self.training:\n        #     quant_feat = torch.einsum('btn,nc->btc', [soft_one_hot, self.quantize.embedding.weight])\n        #     # b(hw)c -> bc(hw) -> bchw\n        #     quant_feat = quant_feat.permute(0,2,1).view(lq_feat.shape)\n        # ------------\n        soft_one_hot = F.softmax(logits, dim=2)\n        _, top_idx = torch.topk(soft_one_hot, 1, dim=2)\n        quant_feat = self.quantize.get_codebook_feat(\n            top_idx, shape=[x.shape[0], 16, 16, 256]  # type: ignore\n        )\n        # preserve gradients\n        # quant_feat = lq_feat + (quant_feat - lq_feat).detach()\n\n        if detach_16:\n            quant_feat = quant_feat.detach()  # for training stage III\n        if adain:\n            quant_feat = adaptive_instance_normalization(quant_feat, lq_feat)\n\n        # ################## Generator ####################\n        x = quant_feat\n        fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list]\n\n        for i, block in enumerate(self.generator.blocks):\n            x = block(x)\n            if i in fuse_list:  # fuse after i-th block\n                f_size = str(x.shape[-1])\n                if weight > 0:\n                    x = self.fuse_convs_dict[f_size](\n                        enc_feat_dict[f_size].detach(), x, weight\n                    )\n        out = x\n        # logits doesn't need softmax before cross_entropy loss\n        # return out, logits, lq_feat\n        return out, logits\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/face/fused_act.py",
    "content": "# pylint: skip-file\n# type: ignore\n# modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_act.py # noqa:E501\n\nimport torch\nfrom torch import nn\nfrom torch.autograd import Function\n\nfused_act_ext = None\n\n\nclass FusedLeakyReLUFunctionBackward(Function):\n    @staticmethod\n    def forward(ctx, grad_output, out, negative_slope, scale):\n        ctx.save_for_backward(out)\n        ctx.negative_slope = negative_slope\n        ctx.scale = scale\n\n        empty = grad_output.new_empty(0)\n\n        grad_input = fused_act_ext.fused_bias_act(\n            grad_output, empty, out, 3, 1, negative_slope, scale\n        )\n\n        dim = [0]\n\n        if grad_input.ndim > 2:\n            dim += list(range(2, grad_input.ndim))\n\n        grad_bias = grad_input.sum(dim).detach()\n\n        return grad_input, grad_bias\n\n    @staticmethod\n    def backward(ctx, gradgrad_input, gradgrad_bias):\n        (out,) = ctx.saved_tensors\n        gradgrad_out = fused_act_ext.fused_bias_act(\n            gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale\n        )\n\n        return gradgrad_out, None, None, None\n\n\nclass FusedLeakyReLUFunction(Function):\n    @staticmethod\n    def forward(ctx, input, bias, negative_slope, scale):\n        empty = input.new_empty(0)\n        out = fused_act_ext.fused_bias_act(\n            input, bias, empty, 3, 0, negative_slope, scale\n        )\n        ctx.save_for_backward(out)\n        ctx.negative_slope = negative_slope\n        ctx.scale = scale\n\n        return out\n\n    @staticmethod\n    def backward(ctx, grad_output):\n        (out,) = ctx.saved_tensors\n\n        grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(\n            grad_output, out, ctx.negative_slope, ctx.scale\n        )\n\n        return grad_input, grad_bias, None, None\n\n\nclass FusedLeakyReLU(nn.Module):\n    def __init__(self, channel, negative_slope=0.2, scale=2**0.5):\n        super().__init__()\n\n        self.bias = nn.Parameter(torch.zeros(channel))\n        self.negative_slope = negative_slope\n        self.scale = scale\n\n    def forward(self, input):\n        return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)\n\n\ndef fused_leaky_relu(input, bias, negative_slope=0.2, scale=2**0.5):\n    return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/face/gfpgan_bilinear_arch.py",
    "content": "# pylint: skip-file\n# type: ignore\nimport math\nimport random\n\nimport torch\nfrom torch import nn\n\nfrom .gfpganv1_arch import ResUpBlock\nfrom .stylegan2_bilinear_arch import (\n    ConvLayer,\n    EqualConv2d,\n    EqualLinear,\n    ResBlock,\n    ScaledLeakyReLU,\n    StyleGAN2GeneratorBilinear,\n)\n\n\nclass StyleGAN2GeneratorBilinearSFT(StyleGAN2GeneratorBilinear):\n    \"\"\"StyleGAN2 Generator with SFT modulation (Spatial Feature Transform).\n    It is the bilinear version. It does not use the complicated UpFirDnSmooth function that is not friendly for\n    deployment. It can be easily converted to the clean version: StyleGAN2GeneratorCSFT.\n    Args:\n        out_size (int): The spatial size of outputs.\n        num_style_feat (int): Channel number of style features. Default: 512.\n        num_mlp (int): Layer number of MLP style layers. Default: 8.\n        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.\n        lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01.\n        narrow (float): The narrow ratio for channels. Default: 1.\n        sft_half (bool): Whether to apply SFT on half of the input channels. Default: False.\n    \"\"\"\n\n    def __init__(\n        self,\n        out_size,\n        num_style_feat=512,\n        num_mlp=8,\n        channel_multiplier=2,\n        lr_mlp=0.01,\n        narrow=1,\n        sft_half=False,\n    ):\n        super(StyleGAN2GeneratorBilinearSFT, self).__init__(\n            out_size,\n            num_style_feat=num_style_feat,\n            num_mlp=num_mlp,\n            channel_multiplier=channel_multiplier,\n            lr_mlp=lr_mlp,\n            narrow=narrow,\n        )\n        self.sft_half = sft_half\n\n    def forward(\n        self,\n        styles,\n        conditions,\n        input_is_latent=False,\n        noise=None,\n        randomize_noise=True,\n        truncation=1,\n        truncation_latent=None,\n        inject_index=None,\n        return_latents=False,\n    ):\n        \"\"\"Forward function for StyleGAN2GeneratorBilinearSFT.\n        Args:\n            styles (list[Tensor]): Sample codes of styles.\n            conditions (list[Tensor]): SFT conditions to generators.\n            input_is_latent (bool): Whether input is latent style. Default: False.\n            noise (Tensor | None): Input noise or None. Default: None.\n            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.\n            truncation (float): The truncation ratio. Default: 1.\n            truncation_latent (Tensor | None): The truncation latent tensor. Default: None.\n            inject_index (int | None): The injection index for mixing noise. Default: None.\n            return_latents (bool): Whether to return style latents. Default: False.\n        \"\"\"\n        # style codes -> latents with Style MLP layer\n        if not input_is_latent:\n            styles = [self.style_mlp(s) for s in styles]\n        # noises\n        if noise is None:\n            if randomize_noise:\n                noise = [None] * self.num_layers  # for each style conv layer\n            else:  # use the stored noise\n                noise = [\n                    getattr(self.noises, f\"noise{i}\") for i in range(self.num_layers)\n                ]\n        # style truncation\n        if truncation < 1:\n            style_truncation = []\n            for style in styles:\n                style_truncation.append(\n                    truncation_latent + truncation * (style - truncation_latent)\n                )\n            styles = style_truncation\n        # get style latents with injection\n        if len(styles) == 1:\n            inject_index = self.num_latent\n\n            if styles[0].ndim < 3:\n                # repeat latent code for all the layers\n                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)\n            else:  # used for encoder with different latent code for each layer\n                latent = styles[0]\n        elif len(styles) == 2:  # mixing noises\n            if inject_index is None:\n                inject_index = random.randint(1, self.num_latent - 1)\n            latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)\n            latent2 = (\n                styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)\n            )\n            latent = torch.cat([latent1, latent2], 1)\n\n        # main generation\n        out = self.constant_input(latent.shape[0])\n        out = self.style_conv1(out, latent[:, 0], noise=noise[0])\n        skip = self.to_rgb1(out, latent[:, 1])\n\n        i = 1\n        for conv1, conv2, noise1, noise2, to_rgb in zip(\n            self.style_convs[::2],\n            self.style_convs[1::2],\n            noise[1::2],\n            noise[2::2],\n            self.to_rgbs,\n        ):\n            out = conv1(out, latent[:, i], noise=noise1)\n\n            # the conditions may have fewer levels\n            if i < len(conditions):\n                # SFT part to combine the conditions\n                if self.sft_half:  # only apply SFT to half of the channels\n                    out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1)\n                    out_sft = out_sft * conditions[i - 1] + conditions[i]\n                    out = torch.cat([out_same, out_sft], dim=1)\n                else:  # apply SFT to all the channels\n                    out = out * conditions[i - 1] + conditions[i]\n\n            out = conv2(out, latent[:, i + 1], noise=noise2)\n            skip = to_rgb(out, latent[:, i + 2], skip)  # feature back to the rgb space\n            i += 2\n\n        image = skip\n\n        if return_latents:\n            return image, latent\n        else:\n            return image, None\n\n\nclass GFPGANBilinear(nn.Module):\n    \"\"\"The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT.\n    It is the bilinear version and it does not use the complicated UpFirDnSmooth function that is not friendly for\n    deployment. It can be easily converted to the clean version: GFPGANv1Clean.\n    Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior.\n    Args:\n        out_size (int): The spatial size of outputs.\n        num_style_feat (int): Channel number of style features. Default: 512.\n        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.\n        decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None.\n        fix_decoder (bool): Whether to fix the decoder. Default: True.\n        num_mlp (int): Layer number of MLP style layers. Default: 8.\n        lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01.\n        input_is_latent (bool): Whether input is latent style. Default: False.\n        different_w (bool): Whether to use different latent w for different layers. Default: False.\n        narrow (float): The narrow ratio for channels. Default: 1.\n        sft_half (bool): Whether to apply SFT on half of the input channels. Default: False.\n    \"\"\"\n\n    def __init__(\n        self,\n        out_size,\n        num_style_feat=512,\n        channel_multiplier=1,\n        decoder_load_path=None,\n        fix_decoder=True,\n        # for stylegan decoder\n        num_mlp=8,\n        lr_mlp=0.01,\n        input_is_latent=False,\n        different_w=False,\n        narrow=1,\n        sft_half=False,\n    ):\n        super(GFPGANBilinear, self).__init__()\n        self.input_is_latent = input_is_latent\n        self.different_w = different_w\n        self.num_style_feat = num_style_feat\n        self.min_size_restriction = 512\n\n        unet_narrow = narrow * 0.5  # by default, use a half of input channels\n        channels = {\n            \"4\": int(512 * unet_narrow),\n            \"8\": int(512 * unet_narrow),\n            \"16\": int(512 * unet_narrow),\n            \"32\": int(512 * unet_narrow),\n            \"64\": int(256 * channel_multiplier * unet_narrow),\n            \"128\": int(128 * channel_multiplier * unet_narrow),\n            \"256\": int(64 * channel_multiplier * unet_narrow),\n            \"512\": int(32 * channel_multiplier * unet_narrow),\n            \"1024\": int(16 * channel_multiplier * unet_narrow),\n        }\n\n        self.log_size = int(math.log(out_size, 2))\n        first_out_size = 2 ** (int(math.log(out_size, 2)))\n\n        self.conv_body_first = ConvLayer(\n            3, channels[f\"{first_out_size}\"], 1, bias=True, activate=True\n        )\n\n        # downsample\n        in_channels = channels[f\"{first_out_size}\"]\n        self.conv_body_down = nn.ModuleList()\n        for i in range(self.log_size, 2, -1):\n            out_channels = channels[f\"{2**(i - 1)}\"]\n            self.conv_body_down.append(ResBlock(in_channels, out_channels))\n            in_channels = out_channels\n\n        self.final_conv = ConvLayer(\n            in_channels, channels[\"4\"], 3, bias=True, activate=True\n        )\n\n        # upsample\n        in_channels = channels[\"4\"]\n        self.conv_body_up = nn.ModuleList()\n        for i in range(3, self.log_size + 1):\n            out_channels = channels[f\"{2**i}\"]\n            self.conv_body_up.append(ResUpBlock(in_channels, out_channels))\n            in_channels = out_channels\n\n        # to RGB\n        self.toRGB = nn.ModuleList()\n        for i in range(3, self.log_size + 1):\n            self.toRGB.append(\n                EqualConv2d(\n                    channels[f\"{2**i}\"],\n                    3,\n                    1,\n                    stride=1,\n                    padding=0,\n                    bias=True,\n                    bias_init_val=0,\n                )\n            )\n\n        if different_w:\n            linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat\n        else:\n            linear_out_channel = num_style_feat\n\n        self.final_linear = EqualLinear(\n            channels[\"4\"] * 4 * 4,\n            linear_out_channel,\n            bias=True,\n            bias_init_val=0,\n            lr_mul=1,\n            activation=None,\n        )\n\n        # the decoder: stylegan2 generator with SFT modulations\n        self.stylegan_decoder = StyleGAN2GeneratorBilinearSFT(\n            out_size=out_size,\n            num_style_feat=num_style_feat,\n            num_mlp=num_mlp,\n            channel_multiplier=channel_multiplier,\n            lr_mlp=lr_mlp,\n            narrow=narrow,\n            sft_half=sft_half,\n        )\n\n        # load pre-trained stylegan2 model if necessary\n        if decoder_load_path:\n            self.stylegan_decoder.load_state_dict(\n                torch.load(\n                    decoder_load_path, map_location=lambda storage, loc: storage, \n                weights_only=True)[\"params_ema\"]\n            )\n        # fix decoder without updating params\n        if fix_decoder:\n            for _, param in self.stylegan_decoder.named_parameters():\n                param.requires_grad = False\n\n        # for SFT modulations (scale and shift)\n        self.condition_scale = nn.ModuleList()\n        self.condition_shift = nn.ModuleList()\n        for i in range(3, self.log_size + 1):\n            out_channels = channels[f\"{2**i}\"]\n            if sft_half:\n                sft_out_channels = out_channels\n            else:\n                sft_out_channels = out_channels * 2\n            self.condition_scale.append(\n                nn.Sequential(\n                    EqualConv2d(\n                        out_channels,\n                        out_channels,\n                        3,\n                        stride=1,\n                        padding=1,\n                        bias=True,\n                        bias_init_val=0,\n                    ),\n                    ScaledLeakyReLU(0.2),\n                    EqualConv2d(\n                        out_channels,\n                        sft_out_channels,\n                        3,\n                        stride=1,\n                        padding=1,\n                        bias=True,\n                        bias_init_val=1,\n                    ),\n                )\n            )\n            self.condition_shift.append(\n                nn.Sequential(\n                    EqualConv2d(\n                        out_channels,\n                        out_channels,\n                        3,\n                        stride=1,\n                        padding=1,\n                        bias=True,\n                        bias_init_val=0,\n                    ),\n                    ScaledLeakyReLU(0.2),\n                    EqualConv2d(\n                        out_channels,\n                        sft_out_channels,\n                        3,\n                        stride=1,\n                        padding=1,\n                        bias=True,\n                        bias_init_val=0,\n                    ),\n                )\n            )\n\n    def forward(self, x, return_latents=False, return_rgb=True, randomize_noise=True):\n        \"\"\"Forward function for GFPGANBilinear.\n        Args:\n            x (Tensor): Input images.\n            return_latents (bool): Whether to return style latents. Default: False.\n            return_rgb (bool): Whether return intermediate rgb images. Default: True.\n            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.\n        \"\"\"\n        conditions = []\n        unet_skips = []\n        out_rgbs = []\n\n        # encoder\n        feat = self.conv_body_first(x)\n        for i in range(self.log_size - 2):\n            feat = self.conv_body_down[i](feat)\n            unet_skips.insert(0, feat)\n\n        feat = self.final_conv(feat)\n\n        # style code\n        style_code = self.final_linear(feat.view(feat.size(0), -1))\n        if self.different_w:\n            style_code = style_code.view(style_code.size(0), -1, self.num_style_feat)\n\n        # decode\n        for i in range(self.log_size - 2):\n            # add unet skip\n            feat = feat + unet_skips[i]\n            # ResUpLayer\n            feat = self.conv_body_up[i](feat)\n            # generate scale and shift for SFT layers\n            scale = self.condition_scale[i](feat)\n            conditions.append(scale.clone())\n            shift = self.condition_shift[i](feat)\n            conditions.append(shift.clone())\n            # generate rgb images\n            if return_rgb:\n                out_rgbs.append(self.toRGB[i](feat))\n\n        # decoder\n        image, _ = self.stylegan_decoder(\n            [style_code],\n            conditions,\n            return_latents=return_latents,\n            input_is_latent=self.input_is_latent,\n            randomize_noise=randomize_noise,\n        )\n\n        return image, out_rgbs\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/face/gfpganv1_arch.py",
    "content": "# pylint: skip-file\n# type: ignore\nimport math\nimport random\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom .fused_act import FusedLeakyReLU\nfrom .stylegan2_arch import (\n    ConvLayer,\n    EqualConv2d,\n    EqualLinear,\n    ResBlock,\n    ScaledLeakyReLU,\n    StyleGAN2Generator,\n)\n\n\nclass StyleGAN2GeneratorSFT(StyleGAN2Generator):\n    \"\"\"StyleGAN2 Generator with SFT modulation (Spatial Feature Transform).\n    Args:\n        out_size (int): The spatial size of outputs.\n        num_style_feat (int): Channel number of style features. Default: 512.\n        num_mlp (int): Layer number of MLP style layers. Default: 8.\n        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.\n        resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be\n            applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1).\n        lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01.\n        narrow (float): The narrow ratio for channels. Default: 1.\n        sft_half (bool): Whether to apply SFT on half of the input channels. Default: False.\n    \"\"\"\n\n    def __init__(\n        self,\n        out_size,\n        num_style_feat=512,\n        num_mlp=8,\n        channel_multiplier=2,\n        resample_kernel=(1, 3, 3, 1),\n        lr_mlp=0.01,\n        narrow=1,\n        sft_half=False,\n    ):\n        super(StyleGAN2GeneratorSFT, self).__init__(\n            out_size,\n            num_style_feat=num_style_feat,\n            num_mlp=num_mlp,\n            channel_multiplier=channel_multiplier,\n            resample_kernel=resample_kernel,\n            lr_mlp=lr_mlp,\n            narrow=narrow,\n        )\n        self.sft_half = sft_half\n\n    def forward(\n        self,\n        styles,\n        conditions,\n        input_is_latent=False,\n        noise=None,\n        randomize_noise=True,\n        truncation=1,\n        truncation_latent=None,\n        inject_index=None,\n        return_latents=False,\n    ):\n        \"\"\"Forward function for StyleGAN2GeneratorSFT.\n        Args:\n            styles (list[Tensor]): Sample codes of styles.\n            conditions (list[Tensor]): SFT conditions to generators.\n            input_is_latent (bool): Whether input is latent style. Default: False.\n            noise (Tensor | None): Input noise or None. Default: None.\n            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.\n            truncation (float): The truncation ratio. Default: 1.\n            truncation_latent (Tensor | None): The truncation latent tensor. Default: None.\n            inject_index (int | None): The injection index for mixing noise. Default: None.\n            return_latents (bool): Whether to return style latents. Default: False.\n        \"\"\"\n        # style codes -> latents with Style MLP layer\n        if not input_is_latent:\n            styles = [self.style_mlp(s) for s in styles]\n        # noises\n        if noise is None:\n            if randomize_noise:\n                noise = [None] * self.num_layers  # for each style conv layer\n            else:  # use the stored noise\n                noise = [\n                    getattr(self.noises, f\"noise{i}\") for i in range(self.num_layers)\n                ]\n        # style truncation\n        if truncation < 1:\n            style_truncation = []\n            for style in styles:\n                style_truncation.append(\n                    truncation_latent + truncation * (style - truncation_latent)\n                )\n            styles = style_truncation\n        # get style latents with injection\n        if len(styles) == 1:\n            inject_index = self.num_latent\n\n            if styles[0].ndim < 3:\n                # repeat latent code for all the layers\n                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)\n            else:  # used for encoder with different latent code for each layer\n                latent = styles[0]\n        elif len(styles) == 2:  # mixing noises\n            if inject_index is None:\n                inject_index = random.randint(1, self.num_latent - 1)\n            latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)\n            latent2 = (\n                styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)\n            )\n            latent = torch.cat([latent1, latent2], 1)\n\n        # main generation\n        out = self.constant_input(latent.shape[0])\n        out = self.style_conv1(out, latent[:, 0], noise=noise[0])\n        skip = self.to_rgb1(out, latent[:, 1])\n\n        i = 1\n        for conv1, conv2, noise1, noise2, to_rgb in zip(\n            self.style_convs[::2],\n            self.style_convs[1::2],\n            noise[1::2],\n            noise[2::2],\n            self.to_rgbs,\n        ):\n            out = conv1(out, latent[:, i], noise=noise1)\n\n            # the conditions may have fewer levels\n            if i < len(conditions):\n                # SFT part to combine the conditions\n                if self.sft_half:  # only apply SFT to half of the channels\n                    out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1)\n                    out_sft = out_sft * conditions[i - 1] + conditions[i]\n                    out = torch.cat([out_same, out_sft], dim=1)\n                else:  # apply SFT to all the channels\n                    out = out * conditions[i - 1] + conditions[i]\n\n            out = conv2(out, latent[:, i + 1], noise=noise2)\n            skip = to_rgb(out, latent[:, i + 2], skip)  # feature back to the rgb space\n            i += 2\n\n        image = skip\n\n        if return_latents:\n            return image, latent\n        else:\n            return image, None\n\n\nclass ConvUpLayer(nn.Module):\n    \"\"\"Convolutional upsampling layer. It uses bilinear upsampler + Conv.\n    Args:\n        in_channels (int): Channel number of the input.\n        out_channels (int): Channel number of the output.\n        kernel_size (int): Size of the convolving kernel.\n        stride (int): Stride of the convolution. Default: 1\n        padding (int): Zero-padding added to both sides of the input. Default: 0.\n        bias (bool): If ``True``, adds a learnable bias to the output. Default: ``True``.\n        bias_init_val (float): Bias initialized value. Default: 0.\n        activate (bool): Whether use activateion. Default: True.\n    \"\"\"\n\n    def __init__(\n        self,\n        in_channels,\n        out_channels,\n        kernel_size,\n        stride=1,\n        padding=0,\n        bias=True,\n        bias_init_val=0,\n        activate=True,\n    ):\n        super(ConvUpLayer, self).__init__()\n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.kernel_size = kernel_size\n        self.stride = stride\n        self.padding = padding\n        # self.scale is used to scale the convolution weights, which is related to the common initializations.\n        self.scale = 1 / math.sqrt(in_channels * kernel_size**2)\n\n        self.weight = nn.Parameter(\n            torch.randn(out_channels, in_channels, kernel_size, kernel_size)\n        )\n\n        if bias and not activate:\n            self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val))\n        else:\n            self.register_parameter(\"bias\", None)\n\n        # activation\n        if activate:\n            if bias:\n                self.activation = FusedLeakyReLU(out_channels)\n            else:\n                self.activation = ScaledLeakyReLU(0.2)\n        else:\n            self.activation = None\n\n    def forward(self, x):\n        # bilinear upsample\n        out = F.interpolate(x, scale_factor=2, mode=\"bilinear\", align_corners=False)\n        # conv\n        out = F.conv2d(\n            out,\n            self.weight * self.scale,\n            bias=self.bias,\n            stride=self.stride,\n            padding=self.padding,\n        )\n        # activation\n        if self.activation is not None:\n            out = self.activation(out)\n        return out\n\n\nclass ResUpBlock(nn.Module):\n    \"\"\"Residual block with upsampling.\n    Args:\n        in_channels (int): Channel number of the input.\n        out_channels (int): Channel number of the output.\n    \"\"\"\n\n    def __init__(self, in_channels, out_channels):\n        super(ResUpBlock, self).__init__()\n\n        self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True)\n        self.conv2 = ConvUpLayer(\n            in_channels, out_channels, 3, stride=1, padding=1, bias=True, activate=True\n        )\n        self.skip = ConvUpLayer(\n            in_channels, out_channels, 1, bias=False, activate=False\n        )\n\n    def forward(self, x):\n        out = self.conv1(x)\n        out = self.conv2(out)\n        skip = self.skip(x)\n        out = (out + skip) / math.sqrt(2)\n        return out\n\n\nclass GFPGANv1(nn.Module):\n    \"\"\"The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT.\n    Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior.\n    Args:\n        out_size (int): The spatial size of outputs.\n        num_style_feat (int): Channel number of style features. Default: 512.\n        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.\n        resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be\n            applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1).\n        decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None.\n        fix_decoder (bool): Whether to fix the decoder. Default: True.\n        num_mlp (int): Layer number of MLP style layers. Default: 8.\n        lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01.\n        input_is_latent (bool): Whether input is latent style. Default: False.\n        different_w (bool): Whether to use different latent w for different layers. Default: False.\n        narrow (float): The narrow ratio for channels. Default: 1.\n        sft_half (bool): Whether to apply SFT on half of the input channels. Default: False.\n    \"\"\"\n\n    def __init__(\n        self,\n        out_size,\n        num_style_feat=512,\n        channel_multiplier=1,\n        resample_kernel=(1, 3, 3, 1),\n        decoder_load_path=None,\n        fix_decoder=True,\n        # for stylegan decoder\n        num_mlp=8,\n        lr_mlp=0.01,\n        input_is_latent=False,\n        different_w=False,\n        narrow=1,\n        sft_half=False,\n    ):\n        super(GFPGANv1, self).__init__()\n        self.input_is_latent = input_is_latent\n        self.different_w = different_w\n        self.num_style_feat = num_style_feat\n\n        unet_narrow = narrow * 0.5  # by default, use a half of input channels\n        channels = {\n            \"4\": int(512 * unet_narrow),\n            \"8\": int(512 * unet_narrow),\n            \"16\": int(512 * unet_narrow),\n            \"32\": int(512 * unet_narrow),\n            \"64\": int(256 * channel_multiplier * unet_narrow),\n            \"128\": int(128 * channel_multiplier * unet_narrow),\n            \"256\": int(64 * channel_multiplier * unet_narrow),\n            \"512\": int(32 * channel_multiplier * unet_narrow),\n            \"1024\": int(16 * channel_multiplier * unet_narrow),\n        }\n\n        self.log_size = int(math.log(out_size, 2))\n        first_out_size = 2 ** (int(math.log(out_size, 2)))\n\n        self.conv_body_first = ConvLayer(\n            3, channels[f\"{first_out_size}\"], 1, bias=True, activate=True\n        )\n\n        # downsample\n        in_channels = channels[f\"{first_out_size}\"]\n        self.conv_body_down = nn.ModuleList()\n        for i in range(self.log_size, 2, -1):\n            out_channels = channels[f\"{2**(i - 1)}\"]\n            self.conv_body_down.append(\n                ResBlock(in_channels, out_channels, resample_kernel)\n            )\n            in_channels = out_channels\n\n        self.final_conv = ConvLayer(\n            in_channels, channels[\"4\"], 3, bias=True, activate=True\n        )\n\n        # upsample\n        in_channels = channels[\"4\"]\n        self.conv_body_up = nn.ModuleList()\n        for i in range(3, self.log_size + 1):\n            out_channels = channels[f\"{2**i}\"]\n            self.conv_body_up.append(ResUpBlock(in_channels, out_channels))\n            in_channels = out_channels\n\n        # to RGB\n        self.toRGB = nn.ModuleList()\n        for i in range(3, self.log_size + 1):\n            self.toRGB.append(\n                EqualConv2d(\n                    channels[f\"{2**i}\"],\n                    3,\n                    1,\n                    stride=1,\n                    padding=0,\n                    bias=True,\n                    bias_init_val=0,\n                )\n            )\n\n        if different_w:\n            linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat\n        else:\n            linear_out_channel = num_style_feat\n\n        self.final_linear = EqualLinear(\n            channels[\"4\"] * 4 * 4,\n            linear_out_channel,\n            bias=True,\n            bias_init_val=0,\n            lr_mul=1,\n            activation=None,\n        )\n\n        # the decoder: stylegan2 generator with SFT modulations\n        self.stylegan_decoder = StyleGAN2GeneratorSFT(\n            out_size=out_size,\n            num_style_feat=num_style_feat,\n            num_mlp=num_mlp,\n            channel_multiplier=channel_multiplier,\n            resample_kernel=resample_kernel,\n            lr_mlp=lr_mlp,\n            narrow=narrow,\n            sft_half=sft_half,\n        )\n\n        # load pre-trained stylegan2 model if necessary\n        if decoder_load_path:\n            self.stylegan_decoder.load_state_dict(\n                torch.load(\n                    decoder_load_path, map_location=lambda storage, loc: storage, \n                weights_only=True)[\"params_ema\"]\n            )\n        # fix decoder without updating params\n        if fix_decoder:\n            for _, param in self.stylegan_decoder.named_parameters():\n                param.requires_grad = False\n\n        # for SFT modulations (scale and shift)\n        self.condition_scale = nn.ModuleList()\n        self.condition_shift = nn.ModuleList()\n        for i in range(3, self.log_size + 1):\n            out_channels = channels[f\"{2**i}\"]\n            if sft_half:\n                sft_out_channels = out_channels\n            else:\n                sft_out_channels = out_channels * 2\n            self.condition_scale.append(\n                nn.Sequential(\n                    EqualConv2d(\n                        out_channels,\n                        out_channels,\n                        3,\n                        stride=1,\n                        padding=1,\n                        bias=True,\n                        bias_init_val=0,\n                    ),\n                    ScaledLeakyReLU(0.2),\n                    EqualConv2d(\n                        out_channels,\n                        sft_out_channels,\n                        3,\n                        stride=1,\n                        padding=1,\n                        bias=True,\n                        bias_init_val=1,\n                    ),\n                )\n            )\n            self.condition_shift.append(\n                nn.Sequential(\n                    EqualConv2d(\n                        out_channels,\n                        out_channels,\n                        3,\n                        stride=1,\n                        padding=1,\n                        bias=True,\n                        bias_init_val=0,\n                    ),\n                    ScaledLeakyReLU(0.2),\n                    EqualConv2d(\n                        out_channels,\n                        sft_out_channels,\n                        3,\n                        stride=1,\n                        padding=1,\n                        bias=True,\n                        bias_init_val=0,\n                    ),\n                )\n            )\n\n    def forward(\n        self, x, return_latents=False, return_rgb=True, randomize_noise=True, **kwargs\n    ):\n        \"\"\"Forward function for GFPGANv1.\n        Args:\n            x (Tensor): Input images.\n            return_latents (bool): Whether to return style latents. Default: False.\n            return_rgb (bool): Whether return intermediate rgb images. Default: True.\n            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.\n        \"\"\"\n        conditions = []\n        unet_skips = []\n        out_rgbs = []\n\n        # encoder\n        feat = self.conv_body_first(x)\n        for i in range(self.log_size - 2):\n            feat = self.conv_body_down[i](feat)\n            unet_skips.insert(0, feat)\n\n        feat = self.final_conv(feat)\n\n        # style code\n        style_code = self.final_linear(feat.view(feat.size(0), -1))\n        if self.different_w:\n            style_code = style_code.view(style_code.size(0), -1, self.num_style_feat)\n\n        # decode\n        for i in range(self.log_size - 2):\n            # add unet skip\n            feat = feat + unet_skips[i]\n            # ResUpLayer\n            feat = self.conv_body_up[i](feat)\n            # generate scale and shift for SFT layers\n            scale = self.condition_scale[i](feat)\n            conditions.append(scale.clone())\n            shift = self.condition_shift[i](feat)\n            conditions.append(shift.clone())\n            # generate rgb images\n            if return_rgb:\n                out_rgbs.append(self.toRGB[i](feat))\n\n        # decoder\n        image, _ = self.stylegan_decoder(\n            [style_code],\n            conditions,\n            return_latents=return_latents,\n            input_is_latent=self.input_is_latent,\n            randomize_noise=randomize_noise,\n        )\n\n        return image, out_rgbs\n\n\nclass FacialComponentDiscriminator(nn.Module):\n    \"\"\"Facial component (eyes, mouth, noise) discriminator used in GFPGAN.\"\"\"\n\n    def __init__(self):\n        super(FacialComponentDiscriminator, self).__init__()\n        # It now uses a VGG-style architectrue with fixed model size\n        self.conv1 = ConvLayer(\n            3,\n            64,\n            3,\n            downsample=False,\n            resample_kernel=(1, 3, 3, 1),\n            bias=True,\n            activate=True,\n        )\n        self.conv2 = ConvLayer(\n            64,\n            128,\n            3,\n            downsample=True,\n            resample_kernel=(1, 3, 3, 1),\n            bias=True,\n            activate=True,\n        )\n        self.conv3 = ConvLayer(\n            128,\n            128,\n            3,\n            downsample=False,\n            resample_kernel=(1, 3, 3, 1),\n            bias=True,\n            activate=True,\n        )\n        self.conv4 = ConvLayer(\n            128,\n            256,\n            3,\n            downsample=True,\n            resample_kernel=(1, 3, 3, 1),\n            bias=True,\n            activate=True,\n        )\n        self.conv5 = ConvLayer(\n            256,\n            256,\n            3,\n            downsample=False,\n            resample_kernel=(1, 3, 3, 1),\n            bias=True,\n            activate=True,\n        )\n        self.final_conv = ConvLayer(256, 1, 3, bias=True, activate=False)\n\n    def forward(self, x, return_feats=False, **kwargs):\n        \"\"\"Forward function for FacialComponentDiscriminator.\n        Args:\n            x (Tensor): Input images.\n            return_feats (bool): Whether to return intermediate features. Default: False.\n        \"\"\"\n        feat = self.conv1(x)\n        feat = self.conv3(self.conv2(feat))\n        rlt_feats = []\n        if return_feats:\n            rlt_feats.append(feat.clone())\n        feat = self.conv5(self.conv4(feat))\n        if return_feats:\n            rlt_feats.append(feat.clone())\n        out = self.final_conv(feat)\n\n        if return_feats:\n            return out, rlt_feats\n        else:\n            return out, None\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/face/gfpganv1_clean_arch.py",
    "content": "# pylint: skip-file\n# type: ignore\nimport math\nimport random\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom .stylegan2_clean_arch import StyleGAN2GeneratorClean\n\n\nclass StyleGAN2GeneratorCSFT(StyleGAN2GeneratorClean):\n    \"\"\"StyleGAN2 Generator with SFT modulation (Spatial Feature Transform).\n    It is the clean version without custom compiled CUDA extensions used in StyleGAN2.\n    Args:\n        out_size (int): The spatial size of outputs.\n        num_style_feat (int): Channel number of style features. Default: 512.\n        num_mlp (int): Layer number of MLP style layers. Default: 8.\n        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.\n        narrow (float): The narrow ratio for channels. Default: 1.\n        sft_half (bool): Whether to apply SFT on half of the input channels. Default: False.\n    \"\"\"\n\n    def __init__(\n        self,\n        out_size,\n        num_style_feat=512,\n        num_mlp=8,\n        channel_multiplier=2,\n        narrow=1,\n        sft_half=False,\n    ):\n        super(StyleGAN2GeneratorCSFT, self).__init__(\n            out_size,\n            num_style_feat=num_style_feat,\n            num_mlp=num_mlp,\n            channel_multiplier=channel_multiplier,\n            narrow=narrow,\n        )\n        self.sft_half = sft_half\n\n    def forward(\n        self,\n        styles,\n        conditions,\n        input_is_latent=False,\n        noise=None,\n        randomize_noise=True,\n        truncation=1,\n        truncation_latent=None,\n        inject_index=None,\n        return_latents=False,\n    ):\n        \"\"\"Forward function for StyleGAN2GeneratorCSFT.\n        Args:\n            styles (list[Tensor]): Sample codes of styles.\n            conditions (list[Tensor]): SFT conditions to generators.\n            input_is_latent (bool): Whether input is latent style. Default: False.\n            noise (Tensor | None): Input noise or None. Default: None.\n            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.\n            truncation (float): The truncation ratio. Default: 1.\n            truncation_latent (Tensor | None): The truncation latent tensor. Default: None.\n            inject_index (int | None): The injection index for mixing noise. Default: None.\n            return_latents (bool): Whether to return style latents. Default: False.\n        \"\"\"\n        # style codes -> latents with Style MLP layer\n        if not input_is_latent:\n            styles = [self.style_mlp(s) for s in styles]\n        # noises\n        if noise is None:\n            if randomize_noise:\n                noise = [None] * self.num_layers  # for each style conv layer\n            else:  # use the stored noise\n                noise = [\n                    getattr(self.noises, f\"noise{i}\") for i in range(self.num_layers)\n                ]\n        # style truncation\n        if truncation < 1:\n            style_truncation = []\n            for style in styles:\n                style_truncation.append(\n                    truncation_latent + truncation * (style - truncation_latent)\n                )\n            styles = style_truncation\n        # get style latents with injection\n        if len(styles) == 1:\n            inject_index = self.num_latent\n\n            if styles[0].ndim < 3:\n                # repeat latent code for all the layers\n                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)\n            else:  # used for encoder with different latent code for each layer\n                latent = styles[0]\n        elif len(styles) == 2:  # mixing noises\n            if inject_index is None:\n                inject_index = random.randint(1, self.num_latent - 1)\n            latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)\n            latent2 = (\n                styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)\n            )\n            latent = torch.cat([latent1, latent2], 1)\n\n        # main generation\n        out = self.constant_input(latent.shape[0])\n        out = self.style_conv1(out, latent[:, 0], noise=noise[0])\n        skip = self.to_rgb1(out, latent[:, 1])\n\n        i = 1\n        for conv1, conv2, noise1, noise2, to_rgb in zip(\n            self.style_convs[::2],\n            self.style_convs[1::2],\n            noise[1::2],\n            noise[2::2],\n            self.to_rgbs,\n        ):\n            out = conv1(out, latent[:, i], noise=noise1)\n\n            # the conditions may have fewer levels\n            if i < len(conditions):\n                # SFT part to combine the conditions\n                if self.sft_half:  # only apply SFT to half of the channels\n                    out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1)\n                    out_sft = out_sft * conditions[i - 1] + conditions[i]\n                    out = torch.cat([out_same, out_sft], dim=1)\n                else:  # apply SFT to all the channels\n                    out = out * conditions[i - 1] + conditions[i]\n\n            out = conv2(out, latent[:, i + 1], noise=noise2)\n            skip = to_rgb(out, latent[:, i + 2], skip)  # feature back to the rgb space\n            i += 2\n\n        image = skip\n\n        if return_latents:\n            return image, latent\n        else:\n            return image, None\n\n\nclass ResBlock(nn.Module):\n    \"\"\"Residual block with bilinear upsampling/downsampling.\n    Args:\n        in_channels (int): Channel number of the input.\n        out_channels (int): Channel number of the output.\n        mode (str): Upsampling/downsampling mode. Options: down | up. Default: down.\n    \"\"\"\n\n    def __init__(self, in_channels, out_channels, mode=\"down\"):\n        super(ResBlock, self).__init__()\n\n        self.conv1 = nn.Conv2d(in_channels, in_channels, 3, 1, 1)\n        self.conv2 = nn.Conv2d(in_channels, out_channels, 3, 1, 1)\n        self.skip = nn.Conv2d(in_channels, out_channels, 1, bias=False)\n        if mode == \"down\":\n            self.scale_factor = 0.5\n        elif mode == \"up\":\n            self.scale_factor = 2\n\n    def forward(self, x):\n        out = F.leaky_relu_(self.conv1(x), negative_slope=0.2)\n        # upsample/downsample\n        out = F.interpolate(\n            out, scale_factor=self.scale_factor, mode=\"bilinear\", align_corners=False\n        )\n        out = F.leaky_relu_(self.conv2(out), negative_slope=0.2)\n        # skip\n        x = F.interpolate(\n            x, scale_factor=self.scale_factor, mode=\"bilinear\", align_corners=False\n        )\n        skip = self.skip(x)\n        out = out + skip\n        return out\n\n\nclass GFPGANv1Clean(nn.Module):\n    \"\"\"The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT.\n    It is the clean version without custom compiled CUDA extensions used in StyleGAN2.\n    Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior.\n    Args:\n        out_size (int): The spatial size of outputs.\n        num_style_feat (int): Channel number of style features. Default: 512.\n        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.\n        decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None.\n        fix_decoder (bool): Whether to fix the decoder. Default: True.\n        num_mlp (int): Layer number of MLP style layers. Default: 8.\n        input_is_latent (bool): Whether input is latent style. Default: False.\n        different_w (bool): Whether to use different latent w for different layers. Default: False.\n        narrow (float): The narrow ratio for channels. Default: 1.\n        sft_half (bool): Whether to apply SFT on half of the input channels. Default: False.\n    \"\"\"\n\n    def __init__(\n        self,\n        state_dict,\n    ):\n        super(GFPGANv1Clean, self).__init__()\n\n        out_size = 512\n        num_style_feat = 512\n        channel_multiplier = 2\n        decoder_load_path = None\n        fix_decoder = False\n        num_mlp = 8\n        input_is_latent = True\n        different_w = True\n        narrow = 1\n        sft_half = True\n\n        self.model_arch = \"GFPGAN\"\n        self.sub_type = \"Face SR\"\n        self.scale = 8\n        self.in_nc = 3\n        self.out_nc = 3\n        self.state = state_dict\n\n        self.supports_fp16 = False\n        self.supports_bf16 = True\n        self.min_size_restriction = 512\n\n        self.input_is_latent = input_is_latent\n        self.different_w = different_w\n        self.num_style_feat = num_style_feat\n\n        unet_narrow = narrow * 0.5  # by default, use a half of input channels\n        channels = {\n            \"4\": int(512 * unet_narrow),\n            \"8\": int(512 * unet_narrow),\n            \"16\": int(512 * unet_narrow),\n            \"32\": int(512 * unet_narrow),\n            \"64\": int(256 * channel_multiplier * unet_narrow),\n            \"128\": int(128 * channel_multiplier * unet_narrow),\n            \"256\": int(64 * channel_multiplier * unet_narrow),\n            \"512\": int(32 * channel_multiplier * unet_narrow),\n            \"1024\": int(16 * channel_multiplier * unet_narrow),\n        }\n\n        self.log_size = int(math.log(out_size, 2))\n        first_out_size = 2 ** (int(math.log(out_size, 2)))\n\n        self.conv_body_first = nn.Conv2d(3, channels[f\"{first_out_size}\"], 1)\n\n        # downsample\n        in_channels = channels[f\"{first_out_size}\"]\n        self.conv_body_down = nn.ModuleList()\n        for i in range(self.log_size, 2, -1):\n            out_channels = channels[f\"{2**(i - 1)}\"]\n            self.conv_body_down.append(ResBlock(in_channels, out_channels, mode=\"down\"))\n            in_channels = out_channels\n\n        self.final_conv = nn.Conv2d(in_channels, channels[\"4\"], 3, 1, 1)\n\n        # upsample\n        in_channels = channels[\"4\"]\n        self.conv_body_up = nn.ModuleList()\n        for i in range(3, self.log_size + 1):\n            out_channels = channels[f\"{2**i}\"]\n            self.conv_body_up.append(ResBlock(in_channels, out_channels, mode=\"up\"))\n            in_channels = out_channels\n\n        # to RGB\n        self.toRGB = nn.ModuleList()\n        for i in range(3, self.log_size + 1):\n            self.toRGB.append(nn.Conv2d(channels[f\"{2**i}\"], 3, 1))\n\n        if different_w:\n            linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat\n        else:\n            linear_out_channel = num_style_feat\n\n        self.final_linear = nn.Linear(channels[\"4\"] * 4 * 4, linear_out_channel)\n\n        # the decoder: stylegan2 generator with SFT modulations\n        self.stylegan_decoder = StyleGAN2GeneratorCSFT(\n            out_size=out_size,\n            num_style_feat=num_style_feat,\n            num_mlp=num_mlp,\n            channel_multiplier=channel_multiplier,\n            narrow=narrow,\n            sft_half=sft_half,\n        )\n\n        # load pre-trained stylegan2 model if necessary\n        if decoder_load_path:\n            self.stylegan_decoder.load_state_dict(\n                torch.load(\n                    decoder_load_path, map_location=lambda storage, loc: storage, \n                weights_only=True)[\"params_ema\"]\n            )\n        # fix decoder without updating params\n        if fix_decoder:\n            for _, param in self.stylegan_decoder.named_parameters():\n                param.requires_grad = False\n\n        # for SFT modulations (scale and shift)\n        self.condition_scale = nn.ModuleList()\n        self.condition_shift = nn.ModuleList()\n        for i in range(3, self.log_size + 1):\n            out_channels = channels[f\"{2**i}\"]\n            if sft_half:\n                sft_out_channels = out_channels\n            else:\n                sft_out_channels = out_channels * 2\n            self.condition_scale.append(\n                nn.Sequential(\n                    nn.Conv2d(out_channels, out_channels, 3, 1, 1),\n                    nn.LeakyReLU(0.2, True),\n                    nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1),\n                )\n            )\n            self.condition_shift.append(\n                nn.Sequential(\n                    nn.Conv2d(out_channels, out_channels, 3, 1, 1),\n                    nn.LeakyReLU(0.2, True),\n                    nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1),\n                )\n            )\n        self.load_state_dict(state_dict)\n\n    def forward(\n        self, x, return_latents=False, return_rgb=True, randomize_noise=True, **kwargs\n    ):\n        \"\"\"Forward function for GFPGANv1Clean.\n        Args:\n            x (Tensor): Input images.\n            return_latents (bool): Whether to return style latents. Default: False.\n            return_rgb (bool): Whether return intermediate rgb images. Default: True.\n            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.\n        \"\"\"\n        conditions = []\n        unet_skips = []\n        out_rgbs = []\n\n        # encoder\n        feat = F.leaky_relu_(self.conv_body_first(x), negative_slope=0.2)\n        for i in range(self.log_size - 2):\n            feat = self.conv_body_down[i](feat)\n            unet_skips.insert(0, feat)\n        feat = F.leaky_relu_(self.final_conv(feat), negative_slope=0.2)\n\n        # style code\n        style_code = self.final_linear(feat.view(feat.size(0), -1))\n        if self.different_w:\n            style_code = style_code.view(style_code.size(0), -1, self.num_style_feat)\n\n        # decode\n        for i in range(self.log_size - 2):\n            # add unet skip\n            feat = feat + unet_skips[i]\n            # ResUpLayer\n            feat = self.conv_body_up[i](feat)\n            # generate scale and shift for SFT layers\n            scale = self.condition_scale[i](feat)\n            conditions.append(scale.clone())\n            shift = self.condition_shift[i](feat)\n            conditions.append(shift.clone())\n            # generate rgb images\n            if return_rgb:\n                out_rgbs.append(self.toRGB[i](feat))\n\n        # decoder\n        image, _ = self.stylegan_decoder(\n            [style_code],\n            conditions,\n            return_latents=return_latents,\n            input_is_latent=self.input_is_latent,\n            randomize_noise=randomize_noise,\n        )\n\n        return image, out_rgbs\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/face/restoreformer_arch.py",
    "content": "# pylint: skip-file\n# type: ignore\n\"\"\"Modified from https://github.com/wzhouxiff/RestoreFormer\n\"\"\"\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass VectorQuantizer(nn.Module):\n    \"\"\"\n    see https://github.com/MishaLaskin/vqvae/blob/d761a999e2267766400dc646d82d3ac3657771d4/models/quantizer.py\n    ____________________________________________\n    Discretization bottleneck part of the VQ-VAE.\n    Inputs:\n    - n_e : number of embeddings\n    - e_dim : dimension of embedding\n    - beta : commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2\n    _____________________________________________\n    \"\"\"\n\n    def __init__(self, n_e, e_dim, beta):\n        super(VectorQuantizer, self).__init__()\n        self.n_e = n_e\n        self.e_dim = e_dim\n        self.beta = beta\n\n        self.embedding = nn.Embedding(self.n_e, self.e_dim)\n        self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)\n\n    def forward(self, z):\n        \"\"\"\n        Inputs the output of the encoder network z and maps it to a discrete\n        one-hot vector that is the index of the closest embedding vector e_j\n        z (continuous) -> z_q (discrete)\n        z.shape = (batch, channel, height, width)\n        quantization pipeline:\n            1. get encoder input (B,C,H,W)\n            2. flatten input to (B*H*W,C)\n        \"\"\"\n        # reshape z -> (batch, height, width, channel) and flatten\n        z = z.permute(0, 2, 3, 1).contiguous()\n        z_flattened = z.view(-1, self.e_dim)\n        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z\n\n        d = (\n            torch.sum(z_flattened**2, dim=1, keepdim=True)\n            + torch.sum(self.embedding.weight**2, dim=1)\n            - 2 * torch.matmul(z_flattened, self.embedding.weight.t())\n        )\n\n        # could possible replace this here\n        # #\\start...\n        # find closest encodings\n\n        min_value, min_encoding_indices = torch.min(d, dim=1)\n\n        min_encoding_indices = min_encoding_indices.unsqueeze(1)\n\n        min_encodings = torch.zeros(min_encoding_indices.shape[0], self.n_e).to(z)\n        min_encodings.scatter_(1, min_encoding_indices, 1)\n\n        # dtype min encodings: torch.float32\n        # min_encodings shape: torch.Size([2048, 512])\n        # min_encoding_indices.shape: torch.Size([2048, 1])\n\n        # get quantized latent vectors\n        z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape)\n        # .........\\end\n\n        # with:\n        # .........\\start\n        # min_encoding_indices = torch.argmin(d, dim=1)\n        # z_q = self.embedding(min_encoding_indices)\n        # ......\\end......... (TODO)\n\n        # compute loss for embedding\n        loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean(\n            (z_q - z.detach()) ** 2\n        )\n\n        # preserve gradients\n        z_q = z + (z_q - z).detach()\n\n        # perplexity\n\n        e_mean = torch.mean(min_encodings, dim=0)\n        perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10)))\n\n        # reshape back to match original input shape\n        z_q = z_q.permute(0, 3, 1, 2).contiguous()\n\n        return z_q, loss, (perplexity, min_encodings, min_encoding_indices, d)\n\n    def get_codebook_entry(self, indices, shape):\n        # shape specifying (batch, height, width, channel)\n        # TODO: check for more easy handling with nn.Embedding\n        min_encodings = torch.zeros(indices.shape[0], self.n_e).to(indices)\n        min_encodings.scatter_(1, indices[:, None], 1)\n\n        # get quantized latent vectors\n        z_q = torch.matmul(min_encodings.float(), self.embedding.weight)\n\n        if shape is not None:\n            z_q = z_q.view(shape)\n\n            # reshape back to match original input shape\n            z_q = z_q.permute(0, 3, 1, 2).contiguous()\n\n        return z_q\n\n\n# pytorch_diffusion + derived encoder decoder\ndef nonlinearity(x):\n    # swish\n    return x * torch.sigmoid(x)\n\n\ndef Normalize(in_channels):\n    return torch.nn.GroupNorm(\n        num_groups=32, num_channels=in_channels, eps=1e-6, affine=True\n    )\n\n\nclass Upsample(nn.Module):\n    def __init__(self, in_channels, with_conv):\n        super().__init__()\n        self.with_conv = with_conv\n        if self.with_conv:\n            self.conv = torch.nn.Conv2d(\n                in_channels, in_channels, kernel_size=3, stride=1, padding=1\n            )\n\n    def forward(self, x):\n        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode=\"nearest\")\n        if self.with_conv:\n            x = self.conv(x)\n        return x\n\n\nclass Downsample(nn.Module):\n    def __init__(self, in_channels, with_conv):\n        super().__init__()\n        self.with_conv = with_conv\n        if self.with_conv:\n            # no asymmetric padding in torch conv, must do it ourselves\n            self.conv = torch.nn.Conv2d(\n                in_channels, in_channels, kernel_size=3, stride=2, padding=0\n            )\n\n    def forward(self, x):\n        if self.with_conv:\n            pad = (0, 1, 0, 1)\n            x = torch.nn.functional.pad(x, pad, mode=\"constant\", value=0)\n            x = self.conv(x)\n        else:\n            x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)\n        return x\n\n\nclass ResnetBlock(nn.Module):\n    def __init__(\n        self,\n        *,\n        in_channels,\n        out_channels=None,\n        conv_shortcut=False,\n        dropout,\n        temb_channels=512\n    ):\n        super().__init__()\n        self.in_channels = in_channels\n        out_channels = in_channels if out_channels is None else out_channels\n        self.out_channels = out_channels\n        self.use_conv_shortcut = conv_shortcut\n\n        self.norm1 = Normalize(in_channels)\n        self.conv1 = torch.nn.Conv2d(\n            in_channels, out_channels, kernel_size=3, stride=1, padding=1\n        )\n        if temb_channels > 0:\n            self.temb_proj = torch.nn.Linear(temb_channels, out_channels)\n        self.norm2 = Normalize(out_channels)\n        self.dropout = torch.nn.Dropout(dropout)\n        self.conv2 = torch.nn.Conv2d(\n            out_channels, out_channels, kernel_size=3, stride=1, padding=1\n        )\n        if self.in_channels != self.out_channels:\n            if self.use_conv_shortcut:\n                self.conv_shortcut = torch.nn.Conv2d(\n                    in_channels, out_channels, kernel_size=3, stride=1, padding=1\n                )\n            else:\n                self.nin_shortcut = torch.nn.Conv2d(\n                    in_channels, out_channels, kernel_size=1, stride=1, padding=0\n                )\n\n    def forward(self, x, temb):\n        h = x\n        h = self.norm1(h)\n        h = nonlinearity(h)\n        h = self.conv1(h)\n\n        if temb is not None:\n            h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None]\n\n        h = self.norm2(h)\n        h = nonlinearity(h)\n        h = self.dropout(h)\n        h = self.conv2(h)\n\n        if self.in_channels != self.out_channels:\n            if self.use_conv_shortcut:\n                x = self.conv_shortcut(x)\n            else:\n                x = self.nin_shortcut(x)\n\n        return x + h\n\n\nclass MultiHeadAttnBlock(nn.Module):\n    def __init__(self, in_channels, head_size=1):\n        super().__init__()\n        self.in_channels = in_channels\n        self.head_size = head_size\n        self.att_size = in_channels // head_size\n        assert (\n            in_channels % head_size == 0\n        ), \"The size of head should be divided by the number of channels.\"\n\n        self.norm1 = Normalize(in_channels)\n        self.norm2 = Normalize(in_channels)\n\n        self.q = torch.nn.Conv2d(\n            in_channels, in_channels, kernel_size=1, stride=1, padding=0\n        )\n        self.k = torch.nn.Conv2d(\n            in_channels, in_channels, kernel_size=1, stride=1, padding=0\n        )\n        self.v = torch.nn.Conv2d(\n            in_channels, in_channels, kernel_size=1, stride=1, padding=0\n        )\n        self.proj_out = torch.nn.Conv2d(\n            in_channels, in_channels, kernel_size=1, stride=1, padding=0\n        )\n        self.num = 0\n\n    def forward(self, x, y=None):\n        h_ = x\n        h_ = self.norm1(h_)\n        if y is None:\n            y = h_\n        else:\n            y = self.norm2(y)\n\n        q = self.q(y)\n        k = self.k(h_)\n        v = self.v(h_)\n\n        # compute attention\n        b, c, h, w = q.shape\n        q = q.reshape(b, self.head_size, self.att_size, h * w)\n        q = q.permute(0, 3, 1, 2)  # b, hw, head, att\n\n        k = k.reshape(b, self.head_size, self.att_size, h * w)\n        k = k.permute(0, 3, 1, 2)\n\n        v = v.reshape(b, self.head_size, self.att_size, h * w)\n        v = v.permute(0, 3, 1, 2)\n\n        q = q.transpose(1, 2)\n        v = v.transpose(1, 2)\n        k = k.transpose(1, 2).transpose(2, 3)\n\n        scale = int(self.att_size) ** (-0.5)\n        q.mul_(scale)\n        w_ = torch.matmul(q, k)\n        w_ = F.softmax(w_, dim=3)\n\n        w_ = w_.matmul(v)\n\n        w_ = w_.transpose(1, 2).contiguous()  # [b, h*w, head, att]\n        w_ = w_.view(b, h, w, -1)\n        w_ = w_.permute(0, 3, 1, 2)\n\n        w_ = self.proj_out(w_)\n\n        return x + w_\n\n\nclass MultiHeadEncoder(nn.Module):\n    def __init__(\n        self,\n        ch,\n        out_ch,\n        ch_mult=(1, 2, 4, 8),\n        num_res_blocks=2,\n        attn_resolutions=(16,),\n        dropout=0.0,\n        resamp_with_conv=True,\n        in_channels=3,\n        resolution=512,\n        z_channels=256,\n        double_z=True,\n        enable_mid=True,\n        head_size=1,\n        **ignore_kwargs\n    ):\n        super().__init__()\n        self.ch = ch\n        self.temb_ch = 0\n        self.num_resolutions = len(ch_mult)\n        self.num_res_blocks = num_res_blocks\n        self.resolution = resolution\n        self.in_channels = in_channels\n        self.enable_mid = enable_mid\n\n        # downsampling\n        self.conv_in = torch.nn.Conv2d(\n            in_channels, self.ch, kernel_size=3, stride=1, padding=1\n        )\n\n        curr_res = resolution\n        in_ch_mult = (1,) + tuple(ch_mult)\n        self.down = nn.ModuleList()\n        for i_level in range(self.num_resolutions):\n            block = nn.ModuleList()\n            attn = nn.ModuleList()\n            block_in = ch * in_ch_mult[i_level]\n            block_out = ch * ch_mult[i_level]\n            for i_block in range(self.num_res_blocks):\n                block.append(\n                    ResnetBlock(\n                        in_channels=block_in,\n                        out_channels=block_out,\n                        temb_channels=self.temb_ch,\n                        dropout=dropout,\n                    )\n                )\n                block_in = block_out\n                if curr_res in attn_resolutions:\n                    attn.append(MultiHeadAttnBlock(block_in, head_size))\n            down = nn.Module()\n            down.block = block\n            down.attn = attn\n            if i_level != self.num_resolutions - 1:\n                down.downsample = Downsample(block_in, resamp_with_conv)\n                curr_res = curr_res // 2\n            self.down.append(down)\n\n        # middle\n        if self.enable_mid:\n            self.mid = nn.Module()\n            self.mid.block_1 = ResnetBlock(\n                in_channels=block_in,\n                out_channels=block_in,\n                temb_channels=self.temb_ch,\n                dropout=dropout,\n            )\n            self.mid.attn_1 = MultiHeadAttnBlock(block_in, head_size)\n            self.mid.block_2 = ResnetBlock(\n                in_channels=block_in,\n                out_channels=block_in,\n                temb_channels=self.temb_ch,\n                dropout=dropout,\n            )\n\n        # end\n        self.norm_out = Normalize(block_in)\n        self.conv_out = torch.nn.Conv2d(\n            block_in,\n            2 * z_channels if double_z else z_channels,\n            kernel_size=3,\n            stride=1,\n            padding=1,\n        )\n\n    def forward(self, x):\n        hs = {}\n        # timestep embedding\n        temb = None\n\n        # downsampling\n        h = self.conv_in(x)\n        hs[\"in\"] = h\n        for i_level in range(self.num_resolutions):\n            for i_block in range(self.num_res_blocks):\n                h = self.down[i_level].block[i_block](h, temb)\n                if len(self.down[i_level].attn) > 0:\n                    h = self.down[i_level].attn[i_block](h)\n\n            if i_level != self.num_resolutions - 1:\n                # hs.append(h)\n                hs[\"block_\" + str(i_level)] = h\n                h = self.down[i_level].downsample(h)\n\n        # middle\n        # h = hs[-1]\n        if self.enable_mid:\n            h = self.mid.block_1(h, temb)\n            hs[\"block_\" + str(i_level) + \"_atten\"] = h\n            h = self.mid.attn_1(h)\n            h = self.mid.block_2(h, temb)\n            hs[\"mid_atten\"] = h\n\n        # end\n        h = self.norm_out(h)\n        h = nonlinearity(h)\n        h = self.conv_out(h)\n        # hs.append(h)\n        hs[\"out\"] = h\n\n        return hs\n\n\nclass MultiHeadDecoder(nn.Module):\n    def __init__(\n        self,\n        ch,\n        out_ch,\n        ch_mult=(1, 2, 4, 8),\n        num_res_blocks=2,\n        attn_resolutions=(16,),\n        dropout=0.0,\n        resamp_with_conv=True,\n        in_channels=3,\n        resolution=512,\n        z_channels=256,\n        give_pre_end=False,\n        enable_mid=True,\n        head_size=1,\n        **ignorekwargs\n    ):\n        super().__init__()\n        self.ch = ch\n        self.temb_ch = 0\n        self.num_resolutions = len(ch_mult)\n        self.num_res_blocks = num_res_blocks\n        self.resolution = resolution\n        self.in_channels = in_channels\n        self.give_pre_end = give_pre_end\n        self.enable_mid = enable_mid\n\n        # compute in_ch_mult, block_in and curr_res at lowest res\n        block_in = ch * ch_mult[self.num_resolutions - 1]\n        curr_res = resolution // 2 ** (self.num_resolutions - 1)\n        self.z_shape = (1, z_channels, curr_res, curr_res)\n        print(\n            \"Working with z of shape {} = {} dimensions.\".format(\n                self.z_shape, np.prod(self.z_shape)\n            )\n        )\n\n        # z to block_in\n        self.conv_in = torch.nn.Conv2d(\n            z_channels, block_in, kernel_size=3, stride=1, padding=1\n        )\n\n        # middle\n        if self.enable_mid:\n            self.mid = nn.Module()\n            self.mid.block_1 = ResnetBlock(\n                in_channels=block_in,\n                out_channels=block_in,\n                temb_channels=self.temb_ch,\n                dropout=dropout,\n            )\n            self.mid.attn_1 = MultiHeadAttnBlock(block_in, head_size)\n            self.mid.block_2 = ResnetBlock(\n                in_channels=block_in,\n                out_channels=block_in,\n                temb_channels=self.temb_ch,\n                dropout=dropout,\n            )\n\n        # upsampling\n        self.up = nn.ModuleList()\n        for i_level in reversed(range(self.num_resolutions)):\n            block = nn.ModuleList()\n            attn = nn.ModuleList()\n            block_out = ch * ch_mult[i_level]\n            for i_block in range(self.num_res_blocks + 1):\n                block.append(\n                    ResnetBlock(\n                        in_channels=block_in,\n                        out_channels=block_out,\n                        temb_channels=self.temb_ch,\n                        dropout=dropout,\n                    )\n                )\n                block_in = block_out\n                if curr_res in attn_resolutions:\n                    attn.append(MultiHeadAttnBlock(block_in, head_size))\n            up = nn.Module()\n            up.block = block\n            up.attn = attn\n            if i_level != 0:\n                up.upsample = Upsample(block_in, resamp_with_conv)\n                curr_res = curr_res * 2\n            self.up.insert(0, up)  # prepend to get consistent order\n\n        # end\n        self.norm_out = Normalize(block_in)\n        self.conv_out = torch.nn.Conv2d(\n            block_in, out_ch, kernel_size=3, stride=1, padding=1\n        )\n\n    def forward(self, z):\n        # assert z.shape[1:] == self.z_shape[1:]\n        self.last_z_shape = z.shape\n\n        # timestep embedding\n        temb = None\n\n        # z to block_in\n        h = self.conv_in(z)\n\n        # middle\n        if self.enable_mid:\n            h = self.mid.block_1(h, temb)\n            h = self.mid.attn_1(h)\n            h = self.mid.block_2(h, temb)\n\n        # upsampling\n        for i_level in reversed(range(self.num_resolutions)):\n            for i_block in range(self.num_res_blocks + 1):\n                h = self.up[i_level].block[i_block](h, temb)\n                if len(self.up[i_level].attn) > 0:\n                    h = self.up[i_level].attn[i_block](h)\n            if i_level != 0:\n                h = self.up[i_level].upsample(h)\n\n        # end\n        if self.give_pre_end:\n            return h\n\n        h = self.norm_out(h)\n        h = nonlinearity(h)\n        h = self.conv_out(h)\n        return h\n\n\nclass MultiHeadDecoderTransformer(nn.Module):\n    def __init__(\n        self,\n        ch,\n        out_ch,\n        ch_mult=(1, 2, 4, 8),\n        num_res_blocks=2,\n        attn_resolutions=(16,),\n        dropout=0.0,\n        resamp_with_conv=True,\n        in_channels=3,\n        resolution=512,\n        z_channels=256,\n        give_pre_end=False,\n        enable_mid=True,\n        head_size=1,\n        **ignorekwargs\n    ):\n        super().__init__()\n        self.ch = ch\n        self.temb_ch = 0\n        self.num_resolutions = len(ch_mult)\n        self.num_res_blocks = num_res_blocks\n        self.resolution = resolution\n        self.in_channels = in_channels\n        self.give_pre_end = give_pre_end\n        self.enable_mid = enable_mid\n\n        # compute in_ch_mult, block_in and curr_res at lowest res\n        block_in = ch * ch_mult[self.num_resolutions - 1]\n        curr_res = resolution // 2 ** (self.num_resolutions - 1)\n        self.z_shape = (1, z_channels, curr_res, curr_res)\n        print(\n            \"Working with z of shape {} = {} dimensions.\".format(\n                self.z_shape, np.prod(self.z_shape)\n            )\n        )\n\n        # z to block_in\n        self.conv_in = torch.nn.Conv2d(\n            z_channels, block_in, kernel_size=3, stride=1, padding=1\n        )\n\n        # middle\n        if self.enable_mid:\n            self.mid = nn.Module()\n            self.mid.block_1 = ResnetBlock(\n                in_channels=block_in,\n                out_channels=block_in,\n                temb_channels=self.temb_ch,\n                dropout=dropout,\n            )\n            self.mid.attn_1 = MultiHeadAttnBlock(block_in, head_size)\n            self.mid.block_2 = ResnetBlock(\n                in_channels=block_in,\n                out_channels=block_in,\n                temb_channels=self.temb_ch,\n                dropout=dropout,\n            )\n\n        # upsampling\n        self.up = nn.ModuleList()\n        for i_level in reversed(range(self.num_resolutions)):\n            block = nn.ModuleList()\n            attn = nn.ModuleList()\n            block_out = ch * ch_mult[i_level]\n            for i_block in range(self.num_res_blocks + 1):\n                block.append(\n                    ResnetBlock(\n                        in_channels=block_in,\n                        out_channels=block_out,\n                        temb_channels=self.temb_ch,\n                        dropout=dropout,\n                    )\n                )\n                block_in = block_out\n                if curr_res in attn_resolutions:\n                    attn.append(MultiHeadAttnBlock(block_in, head_size))\n            up = nn.Module()\n            up.block = block\n            up.attn = attn\n            if i_level != 0:\n                up.upsample = Upsample(block_in, resamp_with_conv)\n                curr_res = curr_res * 2\n            self.up.insert(0, up)  # prepend to get consistent order\n\n        # end\n        self.norm_out = Normalize(block_in)\n        self.conv_out = torch.nn.Conv2d(\n            block_in, out_ch, kernel_size=3, stride=1, padding=1\n        )\n\n    def forward(self, z, hs):\n        # assert z.shape[1:] == self.z_shape[1:]\n        # self.last_z_shape = z.shape\n\n        # timestep embedding\n        temb = None\n\n        # z to block_in\n        h = self.conv_in(z)\n\n        # middle\n        if self.enable_mid:\n            h = self.mid.block_1(h, temb)\n            h = self.mid.attn_1(h, hs[\"mid_atten\"])\n            h = self.mid.block_2(h, temb)\n\n        # upsampling\n        for i_level in reversed(range(self.num_resolutions)):\n            for i_block in range(self.num_res_blocks + 1):\n                h = self.up[i_level].block[i_block](h, temb)\n                if len(self.up[i_level].attn) > 0:\n                    h = self.up[i_level].attn[i_block](\n                        h, hs[\"block_\" + str(i_level) + \"_atten\"]\n                    )\n                    # hfeature = h.clone()\n            if i_level != 0:\n                h = self.up[i_level].upsample(h)\n\n        # end\n        if self.give_pre_end:\n            return h\n\n        h = self.norm_out(h)\n        h = nonlinearity(h)\n        h = self.conv_out(h)\n        return h\n\n\nclass RestoreFormer(nn.Module):\n    def __init__(\n        self,\n        state_dict,\n    ):\n        super(RestoreFormer, self).__init__()\n\n        n_embed = 1024\n        embed_dim = 256\n        ch = 64\n        out_ch = 3\n        ch_mult = (1, 2, 2, 4, 4, 8)\n        num_res_blocks = 2\n        attn_resolutions = (16,)\n        dropout = 0.0\n        in_channels = 3\n        resolution = 512\n        z_channels = 256\n        double_z = False\n        enable_mid = True\n        fix_decoder = False\n        fix_codebook = True\n        fix_encoder = False\n        head_size = 8\n\n        self.model_arch = \"RestoreFormer\"\n        self.sub_type = \"Face SR\"\n        self.scale = 8\n        self.in_nc = 3\n        self.out_nc = out_ch\n        self.state = state_dict\n\n        self.supports_fp16 = False\n        self.supports_bf16 = True\n        self.min_size_restriction = 16\n\n        self.encoder = MultiHeadEncoder(\n            ch=ch,\n            out_ch=out_ch,\n            ch_mult=ch_mult,\n            num_res_blocks=num_res_blocks,\n            attn_resolutions=attn_resolutions,\n            dropout=dropout,\n            in_channels=in_channels,\n            resolution=resolution,\n            z_channels=z_channels,\n            double_z=double_z,\n            enable_mid=enable_mid,\n            head_size=head_size,\n        )\n        self.decoder = MultiHeadDecoderTransformer(\n            ch=ch,\n            out_ch=out_ch,\n            ch_mult=ch_mult,\n            num_res_blocks=num_res_blocks,\n            attn_resolutions=attn_resolutions,\n            dropout=dropout,\n            in_channels=in_channels,\n            resolution=resolution,\n            z_channels=z_channels,\n            enable_mid=enable_mid,\n            head_size=head_size,\n        )\n\n        self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25)\n\n        self.quant_conv = torch.nn.Conv2d(z_channels, embed_dim, 1)\n        self.post_quant_conv = torch.nn.Conv2d(embed_dim, z_channels, 1)\n\n        if fix_decoder:\n            for _, param in self.decoder.named_parameters():\n                param.requires_grad = False\n            for _, param in self.post_quant_conv.named_parameters():\n                param.requires_grad = False\n            for _, param in self.quantize.named_parameters():\n                param.requires_grad = False\n        elif fix_codebook:\n            for _, param in self.quantize.named_parameters():\n                param.requires_grad = False\n\n        if fix_encoder:\n            for _, param in self.encoder.named_parameters():\n                param.requires_grad = False\n\n        self.load_state_dict(state_dict)\n\n    def encode(self, x):\n        hs = self.encoder(x)\n        h = self.quant_conv(hs[\"out\"])\n        quant, emb_loss, info = self.quantize(h)\n        return quant, emb_loss, info, hs\n\n    def decode(self, quant, hs):\n        quant = self.post_quant_conv(quant)\n        dec = self.decoder(quant, hs)\n\n        return dec\n\n    def forward(self, input, **kwargs):\n        quant, diff, info, hs = self.encode(input)\n        dec = self.decode(quant, hs)\n\n        return dec, None\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/face/stylegan2_arch.py",
    "content": "# pylint: skip-file\n# type: ignore\nimport math\nimport random\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom .fused_act import FusedLeakyReLU, fused_leaky_relu\nfrom .upfirdn2d import upfirdn2d\n\n\nclass NormStyleCode(nn.Module):\n    def forward(self, x):\n        \"\"\"Normalize the style codes.\n\n        Args:\n            x (Tensor): Style codes with shape (b, c).\n\n        Returns:\n            Tensor: Normalized tensor.\n        \"\"\"\n        return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8)\n\n\ndef make_resample_kernel(k):\n    \"\"\"Make resampling kernel for UpFirDn.\n\n    Args:\n        k (list[int]): A list indicating the 1D resample kernel magnitude.\n\n    Returns:\n        Tensor: 2D resampled kernel.\n    \"\"\"\n    k = torch.tensor(k, dtype=torch.float32)\n    if k.ndim == 1:\n        k = k[None, :] * k[:, None]  # to 2D kernel, outer product\n    # normalize\n    k /= k.sum()\n    return k\n\n\nclass UpFirDnUpsample(nn.Module):\n    \"\"\"Upsample, FIR filter, and downsample (upsampole version).\n\n    References:\n    1. https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.upfirdn.html  # noqa: E501\n    2. http://www.ece.northwestern.edu/local-apps/matlabhelp/toolbox/signal/upfirdn.html  # noqa: E501\n\n    Args:\n        resample_kernel (list[int]): A list indicating the 1D resample kernel\n            magnitude.\n        factor (int): Upsampling scale factor. Default: 2.\n    \"\"\"\n\n    def __init__(self, resample_kernel, factor=2):\n        super(UpFirDnUpsample, self).__init__()\n        self.kernel = make_resample_kernel(resample_kernel) * (factor**2)\n        self.factor = factor\n\n        pad = self.kernel.shape[0] - factor\n        self.pad = ((pad + 1) // 2 + factor - 1, pad // 2)\n\n    def forward(self, x):\n        out = upfirdn2d(x, self.kernel.type_as(x), up=self.factor, down=1, pad=self.pad)\n        return out\n\n    def __repr__(self):\n        return f\"{self.__class__.__name__}(factor={self.factor})\"\n\n\nclass UpFirDnDownsample(nn.Module):\n    \"\"\"Upsample, FIR filter, and downsample (downsampole version).\n\n    Args:\n        resample_kernel (list[int]): A list indicating the 1D resample kernel\n            magnitude.\n        factor (int): Downsampling scale factor. Default: 2.\n    \"\"\"\n\n    def __init__(self, resample_kernel, factor=2):\n        super(UpFirDnDownsample, self).__init__()\n        self.kernel = make_resample_kernel(resample_kernel)\n        self.factor = factor\n\n        pad = self.kernel.shape[0] - factor\n        self.pad = ((pad + 1) // 2, pad // 2)\n\n    def forward(self, x):\n        out = upfirdn2d(x, self.kernel.type_as(x), up=1, down=self.factor, pad=self.pad)\n        return out\n\n    def __repr__(self):\n        return f\"{self.__class__.__name__}(factor={self.factor})\"\n\n\nclass UpFirDnSmooth(nn.Module):\n    \"\"\"Upsample, FIR filter, and downsample (smooth version).\n\n    Args:\n        resample_kernel (list[int]): A list indicating the 1D resample kernel\n            magnitude.\n        upsample_factor (int): Upsampling scale factor. Default: 1.\n        downsample_factor (int): Downsampling scale factor. Default: 1.\n        kernel_size (int): Kernel size: Default: 1.\n    \"\"\"\n\n    def __init__(\n        self, resample_kernel, upsample_factor=1, downsample_factor=1, kernel_size=1\n    ):\n        super(UpFirDnSmooth, self).__init__()\n        self.upsample_factor = upsample_factor\n        self.downsample_factor = downsample_factor\n        self.kernel = make_resample_kernel(resample_kernel)\n        if upsample_factor > 1:\n            self.kernel = self.kernel * (upsample_factor**2)\n\n        if upsample_factor > 1:\n            pad = (self.kernel.shape[0] - upsample_factor) - (kernel_size - 1)\n            self.pad = ((pad + 1) // 2 + upsample_factor - 1, pad // 2 + 1)\n        elif downsample_factor > 1:\n            pad = (self.kernel.shape[0] - downsample_factor) + (kernel_size - 1)\n            self.pad = ((pad + 1) // 2, pad // 2)\n        else:\n            raise NotImplementedError\n\n    def forward(self, x):\n        out = upfirdn2d(x, self.kernel.type_as(x), up=1, down=1, pad=self.pad)\n        return out\n\n    def __repr__(self):\n        return (\n            f\"{self.__class__.__name__}(upsample_factor={self.upsample_factor}\"\n            f\", downsample_factor={self.downsample_factor})\"\n        )\n\n\nclass EqualLinear(nn.Module):\n    \"\"\"Equalized Linear as StyleGAN2.\n\n    Args:\n        in_channels (int): Size of each sample.\n        out_channels (int): Size of each output sample.\n        bias (bool): If set to ``False``, the layer will not learn an additive\n            bias. Default: ``True``.\n        bias_init_val (float): Bias initialized value. Default: 0.\n        lr_mul (float): Learning rate multiplier. Default: 1.\n        activation (None | str): The activation after ``linear`` operation.\n            Supported: 'fused_lrelu', None. Default: None.\n    \"\"\"\n\n    def __init__(\n        self,\n        in_channels,\n        out_channels,\n        bias=True,\n        bias_init_val=0,\n        lr_mul=1,\n        activation=None,\n    ):\n        super(EqualLinear, self).__init__()\n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.lr_mul = lr_mul\n        self.activation = activation\n        if self.activation not in [\"fused_lrelu\", None]:\n            raise ValueError(\n                f\"Wrong activation value in EqualLinear: {activation}\"\n                \"Supported ones are: ['fused_lrelu', None].\"\n            )\n        self.scale = (1 / math.sqrt(in_channels)) * lr_mul\n\n        self.weight = nn.Parameter(torch.randn(out_channels, in_channels).div_(lr_mul))\n        if bias:\n            self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val))\n        else:\n            self.register_parameter(\"bias\", None)\n\n    def forward(self, x):\n        if self.bias is None:\n            bias = None\n        else:\n            bias = self.bias * self.lr_mul\n        if self.activation == \"fused_lrelu\":\n            out = F.linear(x, self.weight * self.scale)\n            out = fused_leaky_relu(out, bias)\n        else:\n            out = F.linear(x, self.weight * self.scale, bias=bias)\n        return out\n\n    def __repr__(self):\n        return (\n            f\"{self.__class__.__name__}(in_channels={self.in_channels}, \"\n            f\"out_channels={self.out_channels}, bias={self.bias is not None})\"\n        )\n\n\nclass ModulatedConv2d(nn.Module):\n    \"\"\"Modulated Conv2d used in StyleGAN2.\n\n    There is no bias in ModulatedConv2d.\n\n    Args:\n        in_channels (int): Channel number of the input.\n        out_channels (int): Channel number of the output.\n        kernel_size (int): Size of the convolving kernel.\n        num_style_feat (int): Channel number of style features.\n        demodulate (bool): Whether to demodulate in the conv layer.\n            Default: True.\n        sample_mode (str | None): Indicating 'upsample', 'downsample' or None.\n            Default: None.\n        resample_kernel (list[int]): A list indicating the 1D resample kernel\n            magnitude. Default: (1, 3, 3, 1).\n        eps (float): A value added to the denominator for numerical stability.\n            Default: 1e-8.\n    \"\"\"\n\n    def __init__(\n        self,\n        in_channels,\n        out_channels,\n        kernel_size,\n        num_style_feat,\n        demodulate=True,\n        sample_mode=None,\n        resample_kernel=(1, 3, 3, 1),\n        eps=1e-8,\n    ):\n        super(ModulatedConv2d, self).__init__()\n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.kernel_size = kernel_size\n        self.demodulate = demodulate\n        self.sample_mode = sample_mode\n        self.eps = eps\n\n        if self.sample_mode == \"upsample\":\n            self.smooth = UpFirDnSmooth(\n                resample_kernel,\n                upsample_factor=2,\n                downsample_factor=1,\n                kernel_size=kernel_size,\n            )\n        elif self.sample_mode == \"downsample\":\n            self.smooth = UpFirDnSmooth(\n                resample_kernel,\n                upsample_factor=1,\n                downsample_factor=2,\n                kernel_size=kernel_size,\n            )\n        elif self.sample_mode is None:\n            pass\n        else:\n            raise ValueError(\n                f\"Wrong sample mode {self.sample_mode}, \"\n                \"supported ones are ['upsample', 'downsample', None].\"\n            )\n\n        self.scale = 1 / math.sqrt(in_channels * kernel_size**2)\n        # modulation inside each modulated conv\n        self.modulation = EqualLinear(\n            num_style_feat,\n            in_channels,\n            bias=True,\n            bias_init_val=1,\n            lr_mul=1,\n            activation=None,\n        )\n\n        self.weight = nn.Parameter(\n            torch.randn(1, out_channels, in_channels, kernel_size, kernel_size)\n        )\n        self.padding = kernel_size // 2\n\n    def forward(self, x, style):\n        \"\"\"Forward function.\n\n        Args:\n            x (Tensor): Tensor with shape (b, c, h, w).\n            style (Tensor): Tensor with shape (b, num_style_feat).\n\n        Returns:\n            Tensor: Modulated tensor after convolution.\n        \"\"\"\n        b, c, h, w = x.shape  # c = c_in\n        # weight modulation\n        style = self.modulation(style).view(b, 1, c, 1, 1)\n        # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1)\n        weight = self.scale * self.weight * style  # (b, c_out, c_in, k, k)\n\n        if self.demodulate:\n            demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps)\n            weight = weight * demod.view(b, self.out_channels, 1, 1, 1)\n\n        weight = weight.view(\n            b * self.out_channels, c, self.kernel_size, self.kernel_size\n        )\n\n        if self.sample_mode == \"upsample\":\n            x = x.view(1, b * c, h, w)\n            weight = weight.view(\n                b, self.out_channels, c, self.kernel_size, self.kernel_size\n            )\n            weight = weight.transpose(1, 2).reshape(\n                b * c, self.out_channels, self.kernel_size, self.kernel_size\n            )\n            out = F.conv_transpose2d(x, weight, padding=0, stride=2, groups=b)\n            out = out.view(b, self.out_channels, *out.shape[2:4])\n            out = self.smooth(out)\n        elif self.sample_mode == \"downsample\":\n            x = self.smooth(x)\n            x = x.view(1, b * c, *x.shape[2:4])\n            out = F.conv2d(x, weight, padding=0, stride=2, groups=b)\n            out = out.view(b, self.out_channels, *out.shape[2:4])\n        else:\n            x = x.view(1, b * c, h, w)\n            # weight: (b*c_out, c_in, k, k), groups=b\n            out = F.conv2d(x, weight, padding=self.padding, groups=b)\n            out = out.view(b, self.out_channels, *out.shape[2:4])\n\n        return out\n\n    def __repr__(self):\n        return (\n            f\"{self.__class__.__name__}(in_channels={self.in_channels}, \"\n            f\"out_channels={self.out_channels}, \"\n            f\"kernel_size={self.kernel_size}, \"\n            f\"demodulate={self.demodulate}, sample_mode={self.sample_mode})\"\n        )\n\n\nclass StyleConv(nn.Module):\n    \"\"\"Style conv.\n\n    Args:\n        in_channels (int): Channel number of the input.\n        out_channels (int): Channel number of the output.\n        kernel_size (int): Size of the convolving kernel.\n        num_style_feat (int): Channel number of style features.\n        demodulate (bool): Whether demodulate in the conv layer. Default: True.\n        sample_mode (str | None): Indicating 'upsample', 'downsample' or None.\n            Default: None.\n        resample_kernel (list[int]): A list indicating the 1D resample kernel\n            magnitude. Default: (1, 3, 3, 1).\n    \"\"\"\n\n    def __init__(\n        self,\n        in_channels,\n        out_channels,\n        kernel_size,\n        num_style_feat,\n        demodulate=True,\n        sample_mode=None,\n        resample_kernel=(1, 3, 3, 1),\n    ):\n        super(StyleConv, self).__init__()\n        self.modulated_conv = ModulatedConv2d(\n            in_channels,\n            out_channels,\n            kernel_size,\n            num_style_feat,\n            demodulate=demodulate,\n            sample_mode=sample_mode,\n            resample_kernel=resample_kernel,\n        )\n        self.weight = nn.Parameter(torch.zeros(1))  # for noise injection\n        self.activate = FusedLeakyReLU(out_channels)\n\n    def forward(self, x, style, noise=None):\n        # modulate\n        out = self.modulated_conv(x, style)\n        # noise injection\n        if noise is None:\n            b, _, h, w = out.shape\n            noise = out.new_empty(b, 1, h, w).normal_()\n        out = out + self.weight * noise\n        # activation (with bias)\n        out = self.activate(out)\n        return out\n\n\nclass ToRGB(nn.Module):\n    \"\"\"To RGB from features.\n\n    Args:\n        in_channels (int): Channel number of input.\n        num_style_feat (int): Channel number of style features.\n        upsample (bool): Whether to upsample. Default: True.\n        resample_kernel (list[int]): A list indicating the 1D resample kernel\n            magnitude. Default: (1, 3, 3, 1).\n    \"\"\"\n\n    def __init__(\n        self, in_channels, num_style_feat, upsample=True, resample_kernel=(1, 3, 3, 1)\n    ):\n        super(ToRGB, self).__init__()\n        if upsample:\n            self.upsample = UpFirDnUpsample(resample_kernel, factor=2)\n        else:\n            self.upsample = None\n        self.modulated_conv = ModulatedConv2d(\n            in_channels,\n            3,\n            kernel_size=1,\n            num_style_feat=num_style_feat,\n            demodulate=False,\n            sample_mode=None,\n        )\n        self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))\n\n    def forward(self, x, style, skip=None):\n        \"\"\"Forward function.\n\n        Args:\n            x (Tensor): Feature tensor with shape (b, c, h, w).\n            style (Tensor): Tensor with shape (b, num_style_feat).\n            skip (Tensor): Base/skip tensor. Default: None.\n\n        Returns:\n            Tensor: RGB images.\n        \"\"\"\n        out = self.modulated_conv(x, style)\n        out = out + self.bias\n        if skip is not None:\n            if self.upsample:\n                skip = self.upsample(skip)\n            out = out + skip\n        return out\n\n\nclass ConstantInput(nn.Module):\n    \"\"\"Constant input.\n\n    Args:\n        num_channel (int): Channel number of constant input.\n        size (int): Spatial size of constant input.\n    \"\"\"\n\n    def __init__(self, num_channel, size):\n        super(ConstantInput, self).__init__()\n        self.weight = nn.Parameter(torch.randn(1, num_channel, size, size))\n\n    def forward(self, batch):\n        out = self.weight.repeat(batch, 1, 1, 1)\n        return out\n\n\nclass StyleGAN2Generator(nn.Module):\n    \"\"\"StyleGAN2 Generator.\n\n    Args:\n        out_size (int): The spatial size of outputs.\n        num_style_feat (int): Channel number of style features. Default: 512.\n        num_mlp (int): Layer number of MLP style layers. Default: 8.\n        channel_multiplier (int): Channel multiplier for large networks of\n            StyleGAN2. Default: 2.\n        resample_kernel (list[int]): A list indicating the 1D resample kernel\n            magnitude. A cross production will be applied to extent 1D resample\n            kernel to 2D resample kernel. Default: (1, 3, 3, 1).\n        lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01.\n        narrow (float): Narrow ratio for channels. Default: 1.0.\n    \"\"\"\n\n    def __init__(\n        self,\n        out_size,\n        num_style_feat=512,\n        num_mlp=8,\n        channel_multiplier=2,\n        resample_kernel=(1, 3, 3, 1),\n        lr_mlp=0.01,\n        narrow=1,\n    ):\n        super(StyleGAN2Generator, self).__init__()\n        # Style MLP layers\n        self.num_style_feat = num_style_feat\n        style_mlp_layers = [NormStyleCode()]\n        for i in range(num_mlp):\n            style_mlp_layers.append(\n                EqualLinear(\n                    num_style_feat,\n                    num_style_feat,\n                    bias=True,\n                    bias_init_val=0,\n                    lr_mul=lr_mlp,\n                    activation=\"fused_lrelu\",\n                )\n            )\n        self.style_mlp = nn.Sequential(*style_mlp_layers)\n\n        channels = {\n            \"4\": int(512 * narrow),\n            \"8\": int(512 * narrow),\n            \"16\": int(512 * narrow),\n            \"32\": int(512 * narrow),\n            \"64\": int(256 * channel_multiplier * narrow),\n            \"128\": int(128 * channel_multiplier * narrow),\n            \"256\": int(64 * channel_multiplier * narrow),\n            \"512\": int(32 * channel_multiplier * narrow),\n            \"1024\": int(16 * channel_multiplier * narrow),\n        }\n        self.channels = channels\n\n        self.constant_input = ConstantInput(channels[\"4\"], size=4)\n        self.style_conv1 = StyleConv(\n            channels[\"4\"],\n            channels[\"4\"],\n            kernel_size=3,\n            num_style_feat=num_style_feat,\n            demodulate=True,\n            sample_mode=None,\n            resample_kernel=resample_kernel,\n        )\n        self.to_rgb1 = ToRGB(\n            channels[\"4\"],\n            num_style_feat,\n            upsample=False,\n            resample_kernel=resample_kernel,\n        )\n\n        self.log_size = int(math.log(out_size, 2))\n        self.num_layers = (self.log_size - 2) * 2 + 1\n        self.num_latent = self.log_size * 2 - 2\n\n        self.style_convs = nn.ModuleList()\n        self.to_rgbs = nn.ModuleList()\n        self.noises = nn.Module()\n\n        in_channels = channels[\"4\"]\n        # noise\n        for layer_idx in range(self.num_layers):\n            resolution = 2 ** ((layer_idx + 5) // 2)\n            shape = [1, 1, resolution, resolution]\n            self.noises.register_buffer(f\"noise{layer_idx}\", torch.randn(*shape))\n        # style convs and to_rgbs\n        for i in range(3, self.log_size + 1):\n            out_channels = channels[f\"{2**i}\"]\n            self.style_convs.append(\n                StyleConv(\n                    in_channels,\n                    out_channels,\n                    kernel_size=3,\n                    num_style_feat=num_style_feat,\n                    demodulate=True,\n                    sample_mode=\"upsample\",\n                    resample_kernel=resample_kernel,\n                )\n            )\n            self.style_convs.append(\n                StyleConv(\n                    out_channels,\n                    out_channels,\n                    kernel_size=3,\n                    num_style_feat=num_style_feat,\n                    demodulate=True,\n                    sample_mode=None,\n                    resample_kernel=resample_kernel,\n                )\n            )\n            self.to_rgbs.append(\n                ToRGB(\n                    out_channels,\n                    num_style_feat,\n                    upsample=True,\n                    resample_kernel=resample_kernel,\n                )\n            )\n            in_channels = out_channels\n\n    def make_noise(self):\n        \"\"\"Make noise for noise injection.\"\"\"\n        device = self.constant_input.weight.device\n        noises = [torch.randn(1, 1, 4, 4, device=device)]\n\n        for i in range(3, self.log_size + 1):\n            for _ in range(2):\n                noises.append(torch.randn(1, 1, 2**i, 2**i, device=device))\n\n        return noises\n\n    def get_latent(self, x):\n        return self.style_mlp(x)\n\n    def mean_latent(self, num_latent):\n        latent_in = torch.randn(\n            num_latent, self.num_style_feat, device=self.constant_input.weight.device\n        )\n        latent = self.style_mlp(latent_in).mean(0, keepdim=True)\n        return latent\n\n    def forward(\n        self,\n        styles,\n        input_is_latent=False,\n        noise=None,\n        randomize_noise=True,\n        truncation=1,\n        truncation_latent=None,\n        inject_index=None,\n        return_latents=False,\n    ):\n        \"\"\"Forward function for StyleGAN2Generator.\n\n        Args:\n            styles (list[Tensor]): Sample codes of styles.\n            input_is_latent (bool): Whether input is latent style.\n                Default: False.\n            noise (Tensor | None): Input noise or None. Default: None.\n            randomize_noise (bool): Randomize noise, used when 'noise' is\n                False. Default: True.\n            truncation (float): TODO. Default: 1.\n            truncation_latent (Tensor | None): TODO. Default: None.\n            inject_index (int | None): The injection index for mixing noise.\n                Default: None.\n            return_latents (bool): Whether to return style latents.\n                Default: False.\n        \"\"\"\n        # style codes -> latents with Style MLP layer\n        if not input_is_latent:\n            styles = [self.style_mlp(s) for s in styles]\n        # noises\n        if noise is None:\n            if randomize_noise:\n                noise = [None] * self.num_layers  # for each style conv layer\n            else:  # use the stored noise\n                noise = [\n                    getattr(self.noises, f\"noise{i}\") for i in range(self.num_layers)\n                ]\n        # style truncation\n        if truncation < 1:\n            style_truncation = []\n            for style in styles:\n                style_truncation.append(\n                    truncation_latent + truncation * (style - truncation_latent)\n                )\n            styles = style_truncation\n        # get style latent with injection\n        if len(styles) == 1:\n            inject_index = self.num_latent\n\n            if styles[0].ndim < 3:\n                # repeat latent code for all the layers\n                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)\n            else:  # used for encoder with different latent code for each layer\n                latent = styles[0]\n        elif len(styles) == 2:  # mixing noises\n            if inject_index is None:\n                inject_index = random.randint(1, self.num_latent - 1)\n            latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)\n            latent2 = (\n                styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)\n            )\n            latent = torch.cat([latent1, latent2], 1)\n\n        # main generation\n        out = self.constant_input(latent.shape[0])\n        out = self.style_conv1(out, latent[:, 0], noise=noise[0])\n        skip = self.to_rgb1(out, latent[:, 1])\n\n        i = 1\n        for conv1, conv2, noise1, noise2, to_rgb in zip(\n            self.style_convs[::2],\n            self.style_convs[1::2],\n            noise[1::2],\n            noise[2::2],\n            self.to_rgbs,\n        ):\n            out = conv1(out, latent[:, i], noise=noise1)\n            out = conv2(out, latent[:, i + 1], noise=noise2)\n            skip = to_rgb(out, latent[:, i + 2], skip)\n            i += 2\n\n        image = skip\n\n        if return_latents:\n            return image, latent\n        else:\n            return image, None\n\n\nclass ScaledLeakyReLU(nn.Module):\n    \"\"\"Scaled LeakyReLU.\n\n    Args:\n        negative_slope (float): Negative slope. Default: 0.2.\n    \"\"\"\n\n    def __init__(self, negative_slope=0.2):\n        super(ScaledLeakyReLU, self).__init__()\n        self.negative_slope = negative_slope\n\n    def forward(self, x):\n        out = F.leaky_relu(x, negative_slope=self.negative_slope)\n        return out * math.sqrt(2)\n\n\nclass EqualConv2d(nn.Module):\n    \"\"\"Equalized Linear as StyleGAN2.\n\n    Args:\n        in_channels (int): Channel number of the input.\n        out_channels (int): Channel number of the output.\n        kernel_size (int): Size of the convolving kernel.\n        stride (int): Stride of the convolution. Default: 1\n        padding (int): Zero-padding added to both sides of the input.\n            Default: 0.\n        bias (bool): If ``True``, adds a learnable bias to the output.\n            Default: ``True``.\n        bias_init_val (float): Bias initialized value. Default: 0.\n    \"\"\"\n\n    def __init__(\n        self,\n        in_channels,\n        out_channels,\n        kernel_size,\n        stride=1,\n        padding=0,\n        bias=True,\n        bias_init_val=0,\n    ):\n        super(EqualConv2d, self).__init__()\n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.kernel_size = kernel_size\n        self.stride = stride\n        self.padding = padding\n        self.scale = 1 / math.sqrt(in_channels * kernel_size**2)\n\n        self.weight = nn.Parameter(\n            torch.randn(out_channels, in_channels, kernel_size, kernel_size)\n        )\n        if bias:\n            self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val))\n        else:\n            self.register_parameter(\"bias\", None)\n\n    def forward(self, x):\n        out = F.conv2d(\n            x,\n            self.weight * self.scale,\n            bias=self.bias,\n            stride=self.stride,\n            padding=self.padding,\n        )\n\n        return out\n\n    def __repr__(self):\n        return (\n            f\"{self.__class__.__name__}(in_channels={self.in_channels}, \"\n            f\"out_channels={self.out_channels}, \"\n            f\"kernel_size={self.kernel_size},\"\n            f\" stride={self.stride}, padding={self.padding}, \"\n            f\"bias={self.bias is not None})\"\n        )\n\n\nclass ConvLayer(nn.Sequential):\n    \"\"\"Conv Layer used in StyleGAN2 Discriminator.\n\n    Args:\n        in_channels (int): Channel number of the input.\n        out_channels (int): Channel number of the output.\n        kernel_size (int): Kernel size.\n        downsample (bool): Whether downsample by a factor of 2.\n            Default: False.\n        resample_kernel (list[int]): A list indicating the 1D resample\n            kernel magnitude. A cross production will be applied to\n            extent 1D resample kernel to 2D resample kernel.\n            Default: (1, 3, 3, 1).\n        bias (bool): Whether with bias. Default: True.\n        activate (bool): Whether use activateion. Default: True.\n    \"\"\"\n\n    def __init__(\n        self,\n        in_channels,\n        out_channels,\n        kernel_size,\n        downsample=False,\n        resample_kernel=(1, 3, 3, 1),\n        bias=True,\n        activate=True,\n    ):\n        layers = []\n        # downsample\n        if downsample:\n            layers.append(\n                UpFirDnSmooth(\n                    resample_kernel,\n                    upsample_factor=1,\n                    downsample_factor=2,\n                    kernel_size=kernel_size,\n                )\n            )\n            stride = 2\n            self.padding = 0\n        else:\n            stride = 1\n            self.padding = kernel_size // 2\n        # conv\n        layers.append(\n            EqualConv2d(\n                in_channels,\n                out_channels,\n                kernel_size,\n                stride=stride,\n                padding=self.padding,\n                bias=bias and not activate,\n            )\n        )\n        # activation\n        if activate:\n            if bias:\n                layers.append(FusedLeakyReLU(out_channels))\n            else:\n                layers.append(ScaledLeakyReLU(0.2))\n\n        super(ConvLayer, self).__init__(*layers)\n\n\nclass ResBlock(nn.Module):\n    \"\"\"Residual block used in StyleGAN2 Discriminator.\n\n    Args:\n        in_channels (int): Channel number of the input.\n        out_channels (int): Channel number of the output.\n        resample_kernel (list[int]): A list indicating the 1D resample\n            kernel magnitude. A cross production will be applied to\n            extent 1D resample kernel to 2D resample kernel.\n            Default: (1, 3, 3, 1).\n    \"\"\"\n\n    def __init__(self, in_channels, out_channels, resample_kernel=(1, 3, 3, 1)):\n        super(ResBlock, self).__init__()\n\n        self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True)\n        self.conv2 = ConvLayer(\n            in_channels,\n            out_channels,\n            3,\n            downsample=True,\n            resample_kernel=resample_kernel,\n            bias=True,\n            activate=True,\n        )\n        self.skip = ConvLayer(\n            in_channels,\n            out_channels,\n            1,\n            downsample=True,\n            resample_kernel=resample_kernel,\n            bias=False,\n            activate=False,\n        )\n\n    def forward(self, x):\n        out = self.conv1(x)\n        out = self.conv2(out)\n        skip = self.skip(x)\n        out = (out + skip) / math.sqrt(2)\n        return out\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/face/stylegan2_bilinear_arch.py",
    "content": "# pylint: skip-file\n# type: ignore\nimport math\nimport random\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom .fused_act import FusedLeakyReLU, fused_leaky_relu\n\n\nclass NormStyleCode(nn.Module):\n    def forward(self, x):\n        \"\"\"Normalize the style codes.\n        Args:\n            x (Tensor): Style codes with shape (b, c).\n        Returns:\n            Tensor: Normalized tensor.\n        \"\"\"\n        return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8)\n\n\nclass EqualLinear(nn.Module):\n    \"\"\"Equalized Linear as StyleGAN2.\n    Args:\n        in_channels (int): Size of each sample.\n        out_channels (int): Size of each output sample.\n        bias (bool): If set to ``False``, the layer will not learn an additive\n            bias. Default: ``True``.\n        bias_init_val (float): Bias initialized value. Default: 0.\n        lr_mul (float): Learning rate multiplier. Default: 1.\n        activation (None | str): The activation after ``linear`` operation.\n            Supported: 'fused_lrelu', None. Default: None.\n    \"\"\"\n\n    def __init__(\n        self,\n        in_channels,\n        out_channels,\n        bias=True,\n        bias_init_val=0,\n        lr_mul=1,\n        activation=None,\n    ):\n        super(EqualLinear, self).__init__()\n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.lr_mul = lr_mul\n        self.activation = activation\n        if self.activation not in [\"fused_lrelu\", None]:\n            raise ValueError(\n                f\"Wrong activation value in EqualLinear: {activation}\"\n                \"Supported ones are: ['fused_lrelu', None].\"\n            )\n        self.scale = (1 / math.sqrt(in_channels)) * lr_mul\n\n        self.weight = nn.Parameter(torch.randn(out_channels, in_channels).div_(lr_mul))\n        if bias:\n            self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val))\n        else:\n            self.register_parameter(\"bias\", None)\n\n    def forward(self, x):\n        if self.bias is None:\n            bias = None\n        else:\n            bias = self.bias * self.lr_mul\n        if self.activation == \"fused_lrelu\":\n            out = F.linear(x, self.weight * self.scale)\n            out = fused_leaky_relu(out, bias)\n        else:\n            out = F.linear(x, self.weight * self.scale, bias=bias)\n        return out\n\n    def __repr__(self):\n        return (\n            f\"{self.__class__.__name__}(in_channels={self.in_channels}, \"\n            f\"out_channels={self.out_channels}, bias={self.bias is not None})\"\n        )\n\n\nclass ModulatedConv2d(nn.Module):\n    \"\"\"Modulated Conv2d used in StyleGAN2.\n    There is no bias in ModulatedConv2d.\n    Args:\n        in_channels (int): Channel number of the input.\n        out_channels (int): Channel number of the output.\n        kernel_size (int): Size of the convolving kernel.\n        num_style_feat (int): Channel number of style features.\n        demodulate (bool): Whether to demodulate in the conv layer.\n            Default: True.\n        sample_mode (str | None): Indicating 'upsample', 'downsample' or None.\n            Default: None.\n        eps (float): A value added to the denominator for numerical stability.\n            Default: 1e-8.\n    \"\"\"\n\n    def __init__(\n        self,\n        in_channels,\n        out_channels,\n        kernel_size,\n        num_style_feat,\n        demodulate=True,\n        sample_mode=None,\n        eps=1e-8,\n        interpolation_mode=\"bilinear\",\n    ):\n        super(ModulatedConv2d, self).__init__()\n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.kernel_size = kernel_size\n        self.demodulate = demodulate\n        self.sample_mode = sample_mode\n        self.eps = eps\n        self.interpolation_mode = interpolation_mode\n        if self.interpolation_mode == \"nearest\":\n            self.align_corners = None\n        else:\n            self.align_corners = False\n\n        self.scale = 1 / math.sqrt(in_channels * kernel_size**2)\n        # modulation inside each modulated conv\n        self.modulation = EqualLinear(\n            num_style_feat,\n            in_channels,\n            bias=True,\n            bias_init_val=1,\n            lr_mul=1,\n            activation=None,\n        )\n\n        self.weight = nn.Parameter(\n            torch.randn(1, out_channels, in_channels, kernel_size, kernel_size)\n        )\n        self.padding = kernel_size // 2\n\n    def forward(self, x, style):\n        \"\"\"Forward function.\n        Args:\n            x (Tensor): Tensor with shape (b, c, h, w).\n            style (Tensor): Tensor with shape (b, num_style_feat).\n        Returns:\n            Tensor: Modulated tensor after convolution.\n        \"\"\"\n        b, c, h, w = x.shape  # c = c_in\n        # weight modulation\n        style = self.modulation(style).view(b, 1, c, 1, 1)\n        # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1)\n        weight = self.scale * self.weight * style  # (b, c_out, c_in, k, k)\n\n        if self.demodulate:\n            demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps)\n            weight = weight * demod.view(b, self.out_channels, 1, 1, 1)\n\n        weight = weight.view(\n            b * self.out_channels, c, self.kernel_size, self.kernel_size\n        )\n\n        if self.sample_mode == \"upsample\":\n            x = F.interpolate(\n                x,\n                scale_factor=2,\n                mode=self.interpolation_mode,\n                align_corners=self.align_corners,\n            )\n        elif self.sample_mode == \"downsample\":\n            x = F.interpolate(\n                x,\n                scale_factor=0.5,\n                mode=self.interpolation_mode,\n                align_corners=self.align_corners,\n            )\n\n        b, c, h, w = x.shape\n        x = x.view(1, b * c, h, w)\n        # weight: (b*c_out, c_in, k, k), groups=b\n        out = F.conv2d(x, weight, padding=self.padding, groups=b)\n        out = out.view(b, self.out_channels, *out.shape[2:4])\n\n        return out\n\n    def __repr__(self):\n        return (\n            f\"{self.__class__.__name__}(in_channels={self.in_channels}, \"\n            f\"out_channels={self.out_channels}, \"\n            f\"kernel_size={self.kernel_size}, \"\n            f\"demodulate={self.demodulate}, sample_mode={self.sample_mode})\"\n        )\n\n\nclass StyleConv(nn.Module):\n    \"\"\"Style conv.\n    Args:\n        in_channels (int): Channel number of the input.\n        out_channels (int): Channel number of the output.\n        kernel_size (int): Size of the convolving kernel.\n        num_style_feat (int): Channel number of style features.\n        demodulate (bool): Whether demodulate in the conv layer. Default: True.\n        sample_mode (str | None): Indicating 'upsample', 'downsample' or None.\n            Default: None.\n    \"\"\"\n\n    def __init__(\n        self,\n        in_channels,\n        out_channels,\n        kernel_size,\n        num_style_feat,\n        demodulate=True,\n        sample_mode=None,\n        interpolation_mode=\"bilinear\",\n    ):\n        super(StyleConv, self).__init__()\n        self.modulated_conv = ModulatedConv2d(\n            in_channels,\n            out_channels,\n            kernel_size,\n            num_style_feat,\n            demodulate=demodulate,\n            sample_mode=sample_mode,\n            interpolation_mode=interpolation_mode,\n        )\n        self.weight = nn.Parameter(torch.zeros(1))  # for noise injection\n        self.activate = FusedLeakyReLU(out_channels)\n\n    def forward(self, x, style, noise=None):\n        # modulate\n        out = self.modulated_conv(x, style)\n        # noise injection\n        if noise is None:\n            b, _, h, w = out.shape\n            noise = out.new_empty(b, 1, h, w).normal_()\n        out = out + self.weight * noise\n        # activation (with bias)\n        out = self.activate(out)\n        return out\n\n\nclass ToRGB(nn.Module):\n    \"\"\"To RGB from features.\n    Args:\n        in_channels (int): Channel number of input.\n        num_style_feat (int): Channel number of style features.\n        upsample (bool): Whether to upsample. Default: True.\n    \"\"\"\n\n    def __init__(\n        self, in_channels, num_style_feat, upsample=True, interpolation_mode=\"bilinear\"\n    ):\n        super(ToRGB, self).__init__()\n        self.upsample = upsample\n        self.interpolation_mode = interpolation_mode\n        if self.interpolation_mode == \"nearest\":\n            self.align_corners = None\n        else:\n            self.align_corners = False\n        self.modulated_conv = ModulatedConv2d(\n            in_channels,\n            3,\n            kernel_size=1,\n            num_style_feat=num_style_feat,\n            demodulate=False,\n            sample_mode=None,\n            interpolation_mode=interpolation_mode,\n        )\n        self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))\n\n    def forward(self, x, style, skip=None):\n        \"\"\"Forward function.\n        Args:\n            x (Tensor): Feature tensor with shape (b, c, h, w).\n            style (Tensor): Tensor with shape (b, num_style_feat).\n            skip (Tensor): Base/skip tensor. Default: None.\n        Returns:\n            Tensor: RGB images.\n        \"\"\"\n        out = self.modulated_conv(x, style)\n        out = out + self.bias\n        if skip is not None:\n            if self.upsample:\n                skip = F.interpolate(\n                    skip,\n                    scale_factor=2,\n                    mode=self.interpolation_mode,\n                    align_corners=self.align_corners,\n                )\n            out = out + skip\n        return out\n\n\nclass ConstantInput(nn.Module):\n    \"\"\"Constant input.\n    Args:\n        num_channel (int): Channel number of constant input.\n        size (int): Spatial size of constant input.\n    \"\"\"\n\n    def __init__(self, num_channel, size):\n        super(ConstantInput, self).__init__()\n        self.weight = nn.Parameter(torch.randn(1, num_channel, size, size))\n\n    def forward(self, batch):\n        out = self.weight.repeat(batch, 1, 1, 1)\n        return out\n\n\nclass StyleGAN2GeneratorBilinear(nn.Module):\n    \"\"\"StyleGAN2 Generator.\n    Args:\n        out_size (int): The spatial size of outputs.\n        num_style_feat (int): Channel number of style features. Default: 512.\n        num_mlp (int): Layer number of MLP style layers. Default: 8.\n        channel_multiplier (int): Channel multiplier for large networks of\n            StyleGAN2. Default: 2.\n        lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01.\n        narrow (float): Narrow ratio for channels. Default: 1.0.\n    \"\"\"\n\n    def __init__(\n        self,\n        out_size,\n        num_style_feat=512,\n        num_mlp=8,\n        channel_multiplier=2,\n        lr_mlp=0.01,\n        narrow=1,\n        interpolation_mode=\"bilinear\",\n    ):\n        super(StyleGAN2GeneratorBilinear, self).__init__()\n        # Style MLP layers\n        self.num_style_feat = num_style_feat\n        style_mlp_layers = [NormStyleCode()]\n        for i in range(num_mlp):\n            style_mlp_layers.append(\n                EqualLinear(\n                    num_style_feat,\n                    num_style_feat,\n                    bias=True,\n                    bias_init_val=0,\n                    lr_mul=lr_mlp,\n                    activation=\"fused_lrelu\",\n                )\n            )\n        self.style_mlp = nn.Sequential(*style_mlp_layers)\n\n        channels = {\n            \"4\": int(512 * narrow),\n            \"8\": int(512 * narrow),\n            \"16\": int(512 * narrow),\n            \"32\": int(512 * narrow),\n            \"64\": int(256 * channel_multiplier * narrow),\n            \"128\": int(128 * channel_multiplier * narrow),\n            \"256\": int(64 * channel_multiplier * narrow),\n            \"512\": int(32 * channel_multiplier * narrow),\n            \"1024\": int(16 * channel_multiplier * narrow),\n        }\n        self.channels = channels\n\n        self.constant_input = ConstantInput(channels[\"4\"], size=4)\n        self.style_conv1 = StyleConv(\n            channels[\"4\"],\n            channels[\"4\"],\n            kernel_size=3,\n            num_style_feat=num_style_feat,\n            demodulate=True,\n            sample_mode=None,\n            interpolation_mode=interpolation_mode,\n        )\n        self.to_rgb1 = ToRGB(\n            channels[\"4\"],\n            num_style_feat,\n            upsample=False,\n            interpolation_mode=interpolation_mode,\n        )\n\n        self.log_size = int(math.log(out_size, 2))\n        self.num_layers = (self.log_size - 2) * 2 + 1\n        self.num_latent = self.log_size * 2 - 2\n\n        self.style_convs = nn.ModuleList()\n        self.to_rgbs = nn.ModuleList()\n        self.noises = nn.Module()\n\n        in_channels = channels[\"4\"]\n        # noise\n        for layer_idx in range(self.num_layers):\n            resolution = 2 ** ((layer_idx + 5) // 2)\n            shape = [1, 1, resolution, resolution]\n            self.noises.register_buffer(f\"noise{layer_idx}\", torch.randn(*shape))\n        # style convs and to_rgbs\n        for i in range(3, self.log_size + 1):\n            out_channels = channels[f\"{2**i}\"]\n            self.style_convs.append(\n                StyleConv(\n                    in_channels,\n                    out_channels,\n                    kernel_size=3,\n                    num_style_feat=num_style_feat,\n                    demodulate=True,\n                    sample_mode=\"upsample\",\n                    interpolation_mode=interpolation_mode,\n                )\n            )\n            self.style_convs.append(\n                StyleConv(\n                    out_channels,\n                    out_channels,\n                    kernel_size=3,\n                    num_style_feat=num_style_feat,\n                    demodulate=True,\n                    sample_mode=None,\n                    interpolation_mode=interpolation_mode,\n                )\n            )\n            self.to_rgbs.append(\n                ToRGB(\n                    out_channels,\n                    num_style_feat,\n                    upsample=True,\n                    interpolation_mode=interpolation_mode,\n                )\n            )\n            in_channels = out_channels\n\n    def make_noise(self):\n        \"\"\"Make noise for noise injection.\"\"\"\n        device = self.constant_input.weight.device\n        noises = [torch.randn(1, 1, 4, 4, device=device)]\n\n        for i in range(3, self.log_size + 1):\n            for _ in range(2):\n                noises.append(torch.randn(1, 1, 2**i, 2**i, device=device))\n\n        return noises\n\n    def get_latent(self, x):\n        return self.style_mlp(x)\n\n    def mean_latent(self, num_latent):\n        latent_in = torch.randn(\n            num_latent, self.num_style_feat, device=self.constant_input.weight.device\n        )\n        latent = self.style_mlp(latent_in).mean(0, keepdim=True)\n        return latent\n\n    def forward(\n        self,\n        styles,\n        input_is_latent=False,\n        noise=None,\n        randomize_noise=True,\n        truncation=1,\n        truncation_latent=None,\n        inject_index=None,\n        return_latents=False,\n    ):\n        \"\"\"Forward function for StyleGAN2Generator.\n        Args:\n            styles (list[Tensor]): Sample codes of styles.\n            input_is_latent (bool): Whether input is latent style.\n                Default: False.\n            noise (Tensor | None): Input noise or None. Default: None.\n            randomize_noise (bool): Randomize noise, used when 'noise' is\n                False. Default: True.\n            truncation (float): TODO. Default: 1.\n            truncation_latent (Tensor | None): TODO. Default: None.\n            inject_index (int | None): The injection index for mixing noise.\n                Default: None.\n            return_latents (bool): Whether to return style latents.\n                Default: False.\n        \"\"\"\n        # style codes -> latents with Style MLP layer\n        if not input_is_latent:\n            styles = [self.style_mlp(s) for s in styles]\n        # noises\n        if noise is None:\n            if randomize_noise:\n                noise = [None] * self.num_layers  # for each style conv layer\n            else:  # use the stored noise\n                noise = [\n                    getattr(self.noises, f\"noise{i}\") for i in range(self.num_layers)\n                ]\n        # style truncation\n        if truncation < 1:\n            style_truncation = []\n            for style in styles:\n                style_truncation.append(\n                    truncation_latent + truncation * (style - truncation_latent)\n                )\n            styles = style_truncation\n        # get style latent with injection\n        if len(styles) == 1:\n            inject_index = self.num_latent\n\n            if styles[0].ndim < 3:\n                # repeat latent code for all the layers\n                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)\n            else:  # used for encoder with different latent code for each layer\n                latent = styles[0]\n        elif len(styles) == 2:  # mixing noises\n            if inject_index is None:\n                inject_index = random.randint(1, self.num_latent - 1)\n            latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)\n            latent2 = (\n                styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)\n            )\n            latent = torch.cat([latent1, latent2], 1)\n\n        # main generation\n        out = self.constant_input(latent.shape[0])\n        out = self.style_conv1(out, latent[:, 0], noise=noise[0])\n        skip = self.to_rgb1(out, latent[:, 1])\n\n        i = 1\n        for conv1, conv2, noise1, noise2, to_rgb in zip(\n            self.style_convs[::2],\n            self.style_convs[1::2],\n            noise[1::2],\n            noise[2::2],\n            self.to_rgbs,\n        ):\n            out = conv1(out, latent[:, i], noise=noise1)\n            out = conv2(out, latent[:, i + 1], noise=noise2)\n            skip = to_rgb(out, latent[:, i + 2], skip)\n            i += 2\n\n        image = skip\n\n        if return_latents:\n            return image, latent\n        else:\n            return image, None\n\n\nclass ScaledLeakyReLU(nn.Module):\n    \"\"\"Scaled LeakyReLU.\n    Args:\n        negative_slope (float): Negative slope. Default: 0.2.\n    \"\"\"\n\n    def __init__(self, negative_slope=0.2):\n        super(ScaledLeakyReLU, self).__init__()\n        self.negative_slope = negative_slope\n\n    def forward(self, x):\n        out = F.leaky_relu(x, negative_slope=self.negative_slope)\n        return out * math.sqrt(2)\n\n\nclass EqualConv2d(nn.Module):\n    \"\"\"Equalized Linear as StyleGAN2.\n    Args:\n        in_channels (int): Channel number of the input.\n        out_channels (int): Channel number of the output.\n        kernel_size (int): Size of the convolving kernel.\n        stride (int): Stride of the convolution. Default: 1\n        padding (int): Zero-padding added to both sides of the input.\n            Default: 0.\n        bias (bool): If ``True``, adds a learnable bias to the output.\n            Default: ``True``.\n        bias_init_val (float): Bias initialized value. Default: 0.\n    \"\"\"\n\n    def __init__(\n        self,\n        in_channels,\n        out_channels,\n        kernel_size,\n        stride=1,\n        padding=0,\n        bias=True,\n        bias_init_val=0,\n    ):\n        super(EqualConv2d, self).__init__()\n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.kernel_size = kernel_size\n        self.stride = stride\n        self.padding = padding\n        self.scale = 1 / math.sqrt(in_channels * kernel_size**2)\n\n        self.weight = nn.Parameter(\n            torch.randn(out_channels, in_channels, kernel_size, kernel_size)\n        )\n        if bias:\n            self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val))\n        else:\n            self.register_parameter(\"bias\", None)\n\n    def forward(self, x):\n        out = F.conv2d(\n            x,\n            self.weight * self.scale,\n            bias=self.bias,\n            stride=self.stride,\n            padding=self.padding,\n        )\n\n        return out\n\n    def __repr__(self):\n        return (\n            f\"{self.__class__.__name__}(in_channels={self.in_channels}, \"\n            f\"out_channels={self.out_channels}, \"\n            f\"kernel_size={self.kernel_size},\"\n            f\" stride={self.stride}, padding={self.padding}, \"\n            f\"bias={self.bias is not None})\"\n        )\n\n\nclass ConvLayer(nn.Sequential):\n    \"\"\"Conv Layer used in StyleGAN2 Discriminator.\n    Args:\n        in_channels (int): Channel number of the input.\n        out_channels (int): Channel number of the output.\n        kernel_size (int): Kernel size.\n        downsample (bool): Whether downsample by a factor of 2.\n            Default: False.\n        bias (bool): Whether with bias. Default: True.\n        activate (bool): Whether use activateion. Default: True.\n    \"\"\"\n\n    def __init__(\n        self,\n        in_channels,\n        out_channels,\n        kernel_size,\n        downsample=False,\n        bias=True,\n        activate=True,\n        interpolation_mode=\"bilinear\",\n    ):\n        layers = []\n        self.interpolation_mode = interpolation_mode\n        # downsample\n        if downsample:\n            if self.interpolation_mode == \"nearest\":\n                self.align_corners = None\n            else:\n                self.align_corners = False\n\n            layers.append(\n                torch.nn.Upsample(\n                    scale_factor=0.5,\n                    mode=interpolation_mode,\n                    align_corners=self.align_corners,\n                )\n            )\n        stride = 1\n        self.padding = kernel_size // 2\n        # conv\n        layers.append(\n            EqualConv2d(\n                in_channels,\n                out_channels,\n                kernel_size,\n                stride=stride,\n                padding=self.padding,\n                bias=bias and not activate,\n            )\n        )\n        # activation\n        if activate:\n            if bias:\n                layers.append(FusedLeakyReLU(out_channels))\n            else:\n                layers.append(ScaledLeakyReLU(0.2))\n\n        super(ConvLayer, self).__init__(*layers)\n\n\nclass ResBlock(nn.Module):\n    \"\"\"Residual block used in StyleGAN2 Discriminator.\n    Args:\n        in_channels (int): Channel number of the input.\n        out_channels (int): Channel number of the output.\n    \"\"\"\n\n    def __init__(self, in_channels, out_channels, interpolation_mode=\"bilinear\"):\n        super(ResBlock, self).__init__()\n\n        self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True)\n        self.conv2 = ConvLayer(\n            in_channels,\n            out_channels,\n            3,\n            downsample=True,\n            interpolation_mode=interpolation_mode,\n            bias=True,\n            activate=True,\n        )\n        self.skip = ConvLayer(\n            in_channels,\n            out_channels,\n            1,\n            downsample=True,\n            interpolation_mode=interpolation_mode,\n            bias=False,\n            activate=False,\n        )\n\n    def forward(self, x):\n        out = self.conv1(x)\n        out = self.conv2(out)\n        skip = self.skip(x)\n        out = (out + skip) / math.sqrt(2)\n        return out\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/face/stylegan2_clean_arch.py",
    "content": "# pylint: skip-file\n# type: ignore\nimport math\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom torch.nn import init\nfrom torch.nn.modules.batchnorm import _BatchNorm\n\n\n@torch.no_grad()\ndef default_init_weights(module_list, scale=1, bias_fill=0, **kwargs):\n    \"\"\"Initialize network weights.\n    Args:\n        module_list (list[nn.Module] | nn.Module): Modules to be initialized.\n        scale (float): Scale initialized weights, especially for residual\n            blocks. Default: 1.\n        bias_fill (float): The value to fill bias. Default: 0\n        kwargs (dict): Other arguments for initialization function.\n    \"\"\"\n    if not isinstance(module_list, list):\n        module_list = [module_list]\n    for module in module_list:\n        for m in module.modules():\n            if isinstance(m, nn.Conv2d):\n                init.kaiming_normal_(m.weight, **kwargs)\n                m.weight.data *= scale\n                if m.bias is not None:\n                    m.bias.data.fill_(bias_fill)\n            elif isinstance(m, nn.Linear):\n                init.kaiming_normal_(m.weight, **kwargs)\n                m.weight.data *= scale\n                if m.bias is not None:\n                    m.bias.data.fill_(bias_fill)\n            elif isinstance(m, _BatchNorm):\n                init.constant_(m.weight, 1)\n                if m.bias is not None:\n                    m.bias.data.fill_(bias_fill)\n\n\nclass NormStyleCode(nn.Module):\n    def forward(self, x):\n        \"\"\"Normalize the style codes.\n        Args:\n            x (Tensor): Style codes with shape (b, c).\n        Returns:\n            Tensor: Normalized tensor.\n        \"\"\"\n        return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8)\n\n\nclass ModulatedConv2d(nn.Module):\n    \"\"\"Modulated Conv2d used in StyleGAN2.\n    There is no bias in ModulatedConv2d.\n    Args:\n        in_channels (int): Channel number of the input.\n        out_channels (int): Channel number of the output.\n        kernel_size (int): Size of the convolving kernel.\n        num_style_feat (int): Channel number of style features.\n        demodulate (bool): Whether to demodulate in the conv layer. Default: True.\n        sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None.\n        eps (float): A value added to the denominator for numerical stability. Default: 1e-8.\n    \"\"\"\n\n    def __init__(\n        self,\n        in_channels,\n        out_channels,\n        kernel_size,\n        num_style_feat,\n        demodulate=True,\n        sample_mode=None,\n        eps=1e-8,\n    ):\n        super(ModulatedConv2d, self).__init__()\n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.kernel_size = kernel_size\n        self.demodulate = demodulate\n        self.sample_mode = sample_mode\n        self.eps = eps\n\n        # modulation inside each modulated conv\n        self.modulation = nn.Linear(num_style_feat, in_channels, bias=True)\n        # initialization\n        default_init_weights(\n            self.modulation,\n            scale=1,\n            bias_fill=1,\n            a=0,\n            mode=\"fan_in\",\n            nonlinearity=\"linear\",\n        )\n\n        self.weight = nn.Parameter(\n            torch.randn(1, out_channels, in_channels, kernel_size, kernel_size)\n            / math.sqrt(in_channels * kernel_size**2)\n        )\n        self.padding = kernel_size // 2\n\n    def forward(self, x, style):\n        \"\"\"Forward function.\n        Args:\n            x (Tensor): Tensor with shape (b, c, h, w).\n            style (Tensor): Tensor with shape (b, num_style_feat).\n        Returns:\n            Tensor: Modulated tensor after convolution.\n        \"\"\"\n        b, c, h, w = x.shape  # c = c_in\n        # weight modulation\n        style = self.modulation(style).view(b, 1, c, 1, 1)\n        # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1)\n        weight = self.weight * style  # (b, c_out, c_in, k, k)\n\n        if self.demodulate:\n            demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps)\n            weight = weight * demod.view(b, self.out_channels, 1, 1, 1)\n\n        weight = weight.view(\n            b * self.out_channels, c, self.kernel_size, self.kernel_size\n        )\n\n        # upsample or downsample if necessary\n        if self.sample_mode == \"upsample\":\n            x = F.interpolate(x, scale_factor=2, mode=\"bilinear\", align_corners=False)\n        elif self.sample_mode == \"downsample\":\n            x = F.interpolate(x, scale_factor=0.5, mode=\"bilinear\", align_corners=False)\n\n        b, c, h, w = x.shape\n        x = x.view(1, b * c, h, w)\n        # weight: (b*c_out, c_in, k, k), groups=b\n        out = F.conv2d(x, weight, padding=self.padding, groups=b)\n        out = out.view(b, self.out_channels, *out.shape[2:4])\n\n        return out\n\n    def __repr__(self):\n        return (\n            f\"{self.__class__.__name__}(in_channels={self.in_channels}, out_channels={self.out_channels}, \"\n            f\"kernel_size={self.kernel_size}, demodulate={self.demodulate}, sample_mode={self.sample_mode})\"\n        )\n\n\nclass StyleConv(nn.Module):\n    \"\"\"Style conv used in StyleGAN2.\n    Args:\n        in_channels (int): Channel number of the input.\n        out_channels (int): Channel number of the output.\n        kernel_size (int): Size of the convolving kernel.\n        num_style_feat (int): Channel number of style features.\n        demodulate (bool): Whether demodulate in the conv layer. Default: True.\n        sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None.\n    \"\"\"\n\n    def __init__(\n        self,\n        in_channels,\n        out_channels,\n        kernel_size,\n        num_style_feat,\n        demodulate=True,\n        sample_mode=None,\n    ):\n        super(StyleConv, self).__init__()\n        self.modulated_conv = ModulatedConv2d(\n            in_channels,\n            out_channels,\n            kernel_size,\n            num_style_feat,\n            demodulate=demodulate,\n            sample_mode=sample_mode,\n        )\n        self.weight = nn.Parameter(torch.zeros(1))  # for noise injection\n        self.bias = nn.Parameter(torch.zeros(1, out_channels, 1, 1))\n        self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True)\n\n    def forward(self, x, style, noise=None):\n        # modulate\n        out = self.modulated_conv(x, style) * 2**0.5  # for conversion\n        # noise injection\n        if noise is None:\n            b, _, h, w = out.shape\n            noise = out.new_empty(b, 1, h, w).normal_()\n        out = out + self.weight * noise\n        # add bias\n        out = out + self.bias\n        # activation\n        out = self.activate(out)\n        return out\n\n\nclass ToRGB(nn.Module):\n    \"\"\"To RGB (image space) from features.\n    Args:\n        in_channels (int): Channel number of input.\n        num_style_feat (int): Channel number of style features.\n        upsample (bool): Whether to upsample. Default: True.\n    \"\"\"\n\n    def __init__(self, in_channels, num_style_feat, upsample=True):\n        super(ToRGB, self).__init__()\n        self.upsample = upsample\n        self.modulated_conv = ModulatedConv2d(\n            in_channels,\n            3,\n            kernel_size=1,\n            num_style_feat=num_style_feat,\n            demodulate=False,\n            sample_mode=None,\n        )\n        self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))\n\n    def forward(self, x, style, skip=None):\n        \"\"\"Forward function.\n        Args:\n            x (Tensor): Feature tensor with shape (b, c, h, w).\n            style (Tensor): Tensor with shape (b, num_style_feat).\n            skip (Tensor): Base/skip tensor. Default: None.\n        Returns:\n            Tensor: RGB images.\n        \"\"\"\n        out = self.modulated_conv(x, style)\n        out = out + self.bias\n        if skip is not None:\n            if self.upsample:\n                skip = F.interpolate(\n                    skip, scale_factor=2, mode=\"bilinear\", align_corners=False\n                )\n            out = out + skip\n        return out\n\n\nclass ConstantInput(nn.Module):\n    \"\"\"Constant input.\n    Args:\n        num_channel (int): Channel number of constant input.\n        size (int): Spatial size of constant input.\n    \"\"\"\n\n    def __init__(self, num_channel, size):\n        super(ConstantInput, self).__init__()\n        self.weight = nn.Parameter(torch.randn(1, num_channel, size, size))\n\n    def forward(self, batch):\n        out = self.weight.repeat(batch, 1, 1, 1)\n        return out\n\n\nclass StyleGAN2GeneratorClean(nn.Module):\n    \"\"\"Clean version of StyleGAN2 Generator.\n    Args:\n        out_size (int): The spatial size of outputs.\n        num_style_feat (int): Channel number of style features. Default: 512.\n        num_mlp (int): Layer number of MLP style layers. Default: 8.\n        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.\n        narrow (float): Narrow ratio for channels. Default: 1.0.\n    \"\"\"\n\n    def __init__(\n        self, out_size, num_style_feat=512, num_mlp=8, channel_multiplier=2, narrow=1\n    ):\n        super(StyleGAN2GeneratorClean, self).__init__()\n        # Style MLP layers\n        self.num_style_feat = num_style_feat\n        style_mlp_layers = [NormStyleCode()]\n        for i in range(num_mlp):\n            style_mlp_layers.extend(\n                [\n                    nn.Linear(num_style_feat, num_style_feat, bias=True),\n                    nn.LeakyReLU(negative_slope=0.2, inplace=True),\n                ]\n            )\n        self.style_mlp = nn.Sequential(*style_mlp_layers)\n        # initialization\n        default_init_weights(\n            self.style_mlp,\n            scale=1,\n            bias_fill=0,\n            a=0.2,\n            mode=\"fan_in\",\n            nonlinearity=\"leaky_relu\",\n        )\n\n        # channel list\n        channels = {\n            \"4\": int(512 * narrow),\n            \"8\": int(512 * narrow),\n            \"16\": int(512 * narrow),\n            \"32\": int(512 * narrow),\n            \"64\": int(256 * channel_multiplier * narrow),\n            \"128\": int(128 * channel_multiplier * narrow),\n            \"256\": int(64 * channel_multiplier * narrow),\n            \"512\": int(32 * channel_multiplier * narrow),\n            \"1024\": int(16 * channel_multiplier * narrow),\n        }\n        self.channels = channels\n\n        self.constant_input = ConstantInput(channels[\"4\"], size=4)\n        self.style_conv1 = StyleConv(\n            channels[\"4\"],\n            channels[\"4\"],\n            kernel_size=3,\n            num_style_feat=num_style_feat,\n            demodulate=True,\n            sample_mode=None,\n        )\n        self.to_rgb1 = ToRGB(channels[\"4\"], num_style_feat, upsample=False)\n\n        self.log_size = int(math.log(out_size, 2))\n        self.num_layers = (self.log_size - 2) * 2 + 1\n        self.num_latent = self.log_size * 2 - 2\n\n        self.style_convs = nn.ModuleList()\n        self.to_rgbs = nn.ModuleList()\n        self.noises = nn.Module()\n\n        in_channels = channels[\"4\"]\n        # noise\n        for layer_idx in range(self.num_layers):\n            resolution = 2 ** ((layer_idx + 5) // 2)\n            shape = [1, 1, resolution, resolution]\n            self.noises.register_buffer(f\"noise{layer_idx}\", torch.randn(*shape))\n        # style convs and to_rgbs\n        for i in range(3, self.log_size + 1):\n            out_channels = channels[f\"{2**i}\"]\n            self.style_convs.append(\n                StyleConv(\n                    in_channels,\n                    out_channels,\n                    kernel_size=3,\n                    num_style_feat=num_style_feat,\n                    demodulate=True,\n                    sample_mode=\"upsample\",\n                )\n            )\n            self.style_convs.append(\n                StyleConv(\n                    out_channels,\n                    out_channels,\n                    kernel_size=3,\n                    num_style_feat=num_style_feat,\n                    demodulate=True,\n                    sample_mode=None,\n                )\n            )\n            self.to_rgbs.append(ToRGB(out_channels, num_style_feat, upsample=True))\n            in_channels = out_channels\n\n    def make_noise(self):\n        \"\"\"Make noise for noise injection.\"\"\"\n        device = self.constant_input.weight.device\n        noises = [torch.randn(1, 1, 4, 4, device=device)]\n\n        for i in range(3, self.log_size + 1):\n            for _ in range(2):\n                noises.append(torch.randn(1, 1, 2**i, 2**i, device=device))\n\n        return noises\n\n    def get_latent(self, x):\n        return self.style_mlp(x)\n\n    def mean_latent(self, num_latent):\n        latent_in = torch.randn(\n            num_latent, self.num_style_feat, device=self.constant_input.weight.device\n        )\n        latent = self.style_mlp(latent_in).mean(0, keepdim=True)\n        return latent\n\n    def forward(\n        self,\n        styles,\n        input_is_latent=False,\n        noise=None,\n        randomize_noise=True,\n        truncation=1,\n        truncation_latent=None,\n        inject_index=None,\n        return_latents=False,\n    ):\n        \"\"\"Forward function for StyleGAN2GeneratorClean.\n        Args:\n            styles (list[Tensor]): Sample codes of styles.\n            input_is_latent (bool): Whether input is latent style. Default: False.\n            noise (Tensor | None): Input noise or None. Default: None.\n            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.\n            truncation (float): The truncation ratio. Default: 1.\n            truncation_latent (Tensor | None): The truncation latent tensor. Default: None.\n            inject_index (int | None): The injection index for mixing noise. Default: None.\n            return_latents (bool): Whether to return style latents. Default: False.\n        \"\"\"\n        # style codes -> latents with Style MLP layer\n        if not input_is_latent:\n            styles = [self.style_mlp(s) for s in styles]\n        # noises\n        if noise is None:\n            if randomize_noise:\n                noise = [None] * self.num_layers  # for each style conv layer\n            else:  # use the stored noise\n                noise = [\n                    getattr(self.noises, f\"noise{i}\") for i in range(self.num_layers)\n                ]\n        # style truncation\n        if truncation < 1:\n            style_truncation = []\n            for style in styles:\n                style_truncation.append(\n                    truncation_latent + truncation * (style - truncation_latent)\n                )\n            styles = style_truncation\n        # get style latents with injection\n        if len(styles) == 1:\n            inject_index = self.num_latent\n\n            if styles[0].ndim < 3:\n                # repeat latent code for all the layers\n                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)\n            else:  # used for encoder with different latent code for each layer\n                latent = styles[0]\n        elif len(styles) == 2:  # mixing noises\n            if inject_index is None:\n                inject_index = random.randint(1, self.num_latent - 1)\n            latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)\n            latent2 = (\n                styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)\n            )\n            latent = torch.cat([latent1, latent2], 1)\n\n        # main generation\n        out = self.constant_input(latent.shape[0])\n        out = self.style_conv1(out, latent[:, 0], noise=noise[0])\n        skip = self.to_rgb1(out, latent[:, 1])\n\n        i = 1\n        for conv1, conv2, noise1, noise2, to_rgb in zip(\n            self.style_convs[::2],\n            self.style_convs[1::2],\n            noise[1::2],\n            noise[2::2],\n            self.to_rgbs,\n        ):\n            out = conv1(out, latent[:, i], noise=noise1)\n            out = conv2(out, latent[:, i + 1], noise=noise2)\n            skip = to_rgb(out, latent[:, i + 2], skip)  # feature back to the rgb space\n            i += 2\n\n        image = skip\n\n        if return_latents:\n            return image, latent\n        else:\n            return image, None\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/face/upfirdn2d.py",
    "content": "# pylint: skip-file\n# type: ignore\n# modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d.py  # noqa:E501\n\nimport os\n\nimport torch\nfrom torch.autograd import Function\nfrom torch.nn import functional as F\n\nupfirdn2d_ext = None\n\n\nclass UpFirDn2dBackward(Function):\n    @staticmethod\n    def forward(\n        ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size\n    ):\n        up_x, up_y = up\n        down_x, down_y = down\n        g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad\n\n        grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1)\n\n        grad_input = upfirdn2d_ext.upfirdn2d(\n            grad_output,\n            grad_kernel,\n            down_x,\n            down_y,\n            up_x,\n            up_y,\n            g_pad_x0,\n            g_pad_x1,\n            g_pad_y0,\n            g_pad_y1,\n        )\n        grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3])\n\n        ctx.save_for_backward(kernel)\n\n        pad_x0, pad_x1, pad_y0, pad_y1 = pad\n\n        ctx.up_x = up_x\n        ctx.up_y = up_y\n        ctx.down_x = down_x\n        ctx.down_y = down_y\n        ctx.pad_x0 = pad_x0\n        ctx.pad_x1 = pad_x1\n        ctx.pad_y0 = pad_y0\n        ctx.pad_y1 = pad_y1\n        ctx.in_size = in_size\n        ctx.out_size = out_size\n\n        return grad_input\n\n    @staticmethod\n    def backward(ctx, gradgrad_input):\n        (kernel,) = ctx.saved_tensors\n\n        gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1)\n\n        gradgrad_out = upfirdn2d_ext.upfirdn2d(\n            gradgrad_input,\n            kernel,\n            ctx.up_x,\n            ctx.up_y,\n            ctx.down_x,\n            ctx.down_y,\n            ctx.pad_x0,\n            ctx.pad_x1,\n            ctx.pad_y0,\n            ctx.pad_y1,\n        )\n        # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0],\n        #                                  ctx.out_size[1], ctx.in_size[3])\n        gradgrad_out = gradgrad_out.view(\n            ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1]\n        )\n\n        return gradgrad_out, None, None, None, None, None, None, None, None\n\n\nclass UpFirDn2d(Function):\n    @staticmethod\n    def forward(ctx, input, kernel, up, down, pad):\n        up_x, up_y = up\n        down_x, down_y = down\n        pad_x0, pad_x1, pad_y0, pad_y1 = pad\n\n        kernel_h, kernel_w = kernel.shape\n        _, channel, in_h, in_w = input.shape\n        ctx.in_size = input.shape\n\n        input = input.reshape(-1, in_h, in_w, 1)\n\n        ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1]))\n\n        out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1\n        out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1\n        ctx.out_size = (out_h, out_w)\n\n        ctx.up = (up_x, up_y)\n        ctx.down = (down_x, down_y)\n        ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1)\n\n        g_pad_x0 = kernel_w - pad_x0 - 1\n        g_pad_y0 = kernel_h - pad_y0 - 1\n        g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1\n        g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1\n\n        ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1)\n\n        out = upfirdn2d_ext.upfirdn2d(\n            input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1\n        )\n        # out = out.view(major, out_h, out_w, minor)\n        out = out.view(-1, channel, out_h, out_w)\n\n        return out\n\n    @staticmethod\n    def backward(ctx, grad_output):\n        kernel, grad_kernel = ctx.saved_tensors\n\n        grad_input = UpFirDn2dBackward.apply(\n            grad_output,\n            kernel,\n            grad_kernel,\n            ctx.up,\n            ctx.down,\n            ctx.pad,\n            ctx.g_pad,\n            ctx.in_size,\n            ctx.out_size,\n        )\n\n        return grad_input, None, None, None, None\n\n\ndef upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):\n    if input.device.type == \"cpu\":\n        out = upfirdn2d_native(\n            input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1]\n        )\n    else:\n        out = UpFirDn2d.apply(\n            input, kernel, (up, up), (down, down), (pad[0], pad[1], pad[0], pad[1])\n        )\n\n    return out\n\n\ndef upfirdn2d_native(\n    input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1\n):\n    _, channel, in_h, in_w = input.shape\n    input = input.reshape(-1, in_h, in_w, 1)\n\n    _, in_h, in_w, minor = input.shape\n    kernel_h, kernel_w = kernel.shape\n\n    out = input.view(-1, in_h, 1, in_w, 1, minor)\n    out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])\n    out = out.view(-1, in_h * up_y, in_w * up_x, minor)\n\n    out = F.pad(\n        out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)]\n    )\n    out = out[\n        :,\n        max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0),\n        max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0),\n        :,\n    ]\n\n    out = out.permute(0, 3, 1, 2)\n    out = out.reshape(\n        [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1]\n    )\n    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)\n    out = F.conv2d(out, w)\n    out = out.reshape(\n        -1,\n        minor,\n        in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,\n        in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,\n    )\n    out = out.permute(0, 2, 3, 1)\n    out = out[:, ::down_y, ::down_x, :]\n\n    out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1\n    out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1\n\n    return out.view(-1, channel, out_h, out_w)\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/timm/LICENSE",
    "content": "                                 Apache License\n                           Version 2.0, January 2004\n                        http://www.apache.org/licenses/\n\n   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n   1. Definitions.\n\n      \"License\" shall mean the terms and conditions for use, reproduction,\n      and distribution as defined by Sections 1 through 9 of this document.\n\n      \"Licensor\" shall mean the copyright owner or entity authorized by\n      the copyright owner that is granting the License.\n\n      \"Legal Entity\" shall mean the union of the acting entity and all\n      other entities that control, are controlled by, or are under common\n      control with that entity. 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The text should be enclosed in the appropriate\n      comment syntax for the file format. We also recommend that a\n      file or class name and description of purpose be included on the\n      same \"printed page\" as the copyright notice for easier\n      identification within third-party archives.\n\n   Copyright 2019 Ross Wightman\n\n   Licensed under the Apache License, Version 2.0 (the \"License\");\n   you may not use this file except in compliance with the License.\n   You may obtain a copy of the License at\n\n       http://www.apache.org/licenses/LICENSE-2.0\n\n   Unless required by applicable law or agreed to in writing, software\n   distributed under the License is distributed on an \"AS IS\" BASIS,\n   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n   See the License for the specific language governing permissions and\n   limitations under the License."
  },
  {
    "path": "ldm_patched/pfn/architecture/timm/drop.py",
    "content": "\"\"\" DropBlock, DropPath\n\nPyTorch implementations of DropBlock and DropPath (Stochastic Depth) regularization layers.\n\nPapers:\nDropBlock: A regularization method for convolutional networks (https://arxiv.org/abs/1810.12890)\n\nDeep Networks with Stochastic Depth (https://arxiv.org/abs/1603.09382)\n\nCode:\nDropBlock impl inspired by two Tensorflow impl that I liked:\n - https://github.com/tensorflow/tpu/blob/master/models/official/resnet/resnet_model.py#L74\n - https://github.com/clovaai/assembled-cnn/blob/master/nets/blocks.py\n\nHacked together by / Copyright 2020 Ross Wightman\n\"\"\"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\ndef drop_block_2d(\n    x,\n    drop_prob: float = 0.1,\n    block_size: int = 7,\n    gamma_scale: float = 1.0,\n    with_noise: bool = False,\n    inplace: bool = False,\n    batchwise: bool = False,\n):\n    \"\"\"DropBlock. See https://arxiv.org/pdf/1810.12890.pdf\n\n    DropBlock with an experimental gaussian noise option. This layer has been tested on a few training\n    runs with success, but needs further validation and possibly optimization for lower runtime impact.\n    \"\"\"\n    _, C, H, W = x.shape\n    total_size = W * H\n    clipped_block_size = min(block_size, min(W, H))\n    # seed_drop_rate, the gamma parameter\n    gamma = (\n        gamma_scale\n        * drop_prob\n        * total_size\n        / clipped_block_size**2\n        / ((W - block_size + 1) * (H - block_size + 1))\n    )\n\n    # Forces the block to be inside the feature map.\n    w_i, h_i = torch.meshgrid(\n        torch.arange(W).to(x.device), torch.arange(H).to(x.device)\n    )\n    valid_block = (\n        (w_i >= clipped_block_size // 2) & (w_i < W - (clipped_block_size - 1) // 2)\n    ) & ((h_i >= clipped_block_size // 2) & (h_i < H - (clipped_block_size - 1) // 2))\n    valid_block = torch.reshape(valid_block, (1, 1, H, W)).to(dtype=x.dtype)\n\n    if batchwise:\n        # one mask for whole batch, quite a bit faster\n        uniform_noise = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device)\n    else:\n        uniform_noise = torch.rand_like(x)\n    block_mask = ((2 - gamma - valid_block + uniform_noise) >= 1).to(dtype=x.dtype)\n    block_mask = -F.max_pool2d(\n        -block_mask,\n        kernel_size=clipped_block_size,  # block_size,\n        stride=1,\n        padding=clipped_block_size // 2,\n    )\n\n    if with_noise:\n        normal_noise = (\n            torch.randn((1, C, H, W), dtype=x.dtype, device=x.device)\n            if batchwise\n            else torch.randn_like(x)\n        )\n        if inplace:\n            x.mul_(block_mask).add_(normal_noise * (1 - block_mask))\n        else:\n            x = x * block_mask + normal_noise * (1 - block_mask)\n    else:\n        normalize_scale = (\n            block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7)\n        ).to(x.dtype)\n        if inplace:\n            x.mul_(block_mask * normalize_scale)\n        else:\n            x = x * block_mask * normalize_scale\n    return x\n\n\ndef drop_block_fast_2d(\n    x: torch.Tensor,\n    drop_prob: float = 0.1,\n    block_size: int = 7,\n    gamma_scale: float = 1.0,\n    with_noise: bool = False,\n    inplace: bool = False,\n):\n    \"\"\"DropBlock. See https://arxiv.org/pdf/1810.12890.pdf\n\n    DropBlock with an experimental gaussian noise option. Simplied from above without concern for valid\n    block mask at edges.\n    \"\"\"\n    _, _, H, W = x.shape\n    total_size = W * H\n    clipped_block_size = min(block_size, min(W, H))\n    gamma = (\n        gamma_scale\n        * drop_prob\n        * total_size\n        / clipped_block_size**2\n        / ((W - block_size + 1) * (H - block_size + 1))\n    )\n\n    block_mask = torch.empty_like(x).bernoulli_(gamma)\n    block_mask = F.max_pool2d(\n        block_mask.to(x.dtype),\n        kernel_size=clipped_block_size,\n        stride=1,\n        padding=clipped_block_size // 2,\n    )\n\n    if with_noise:\n        normal_noise = torch.empty_like(x).normal_()\n        if inplace:\n            x.mul_(1.0 - block_mask).add_(normal_noise * block_mask)\n        else:\n            x = x * (1.0 - block_mask) + normal_noise * block_mask\n    else:\n        block_mask = 1 - block_mask\n        normalize_scale = (\n            block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-6)\n        ).to(dtype=x.dtype)\n        if inplace:\n            x.mul_(block_mask * normalize_scale)\n        else:\n            x = x * block_mask * normalize_scale\n    return x\n\n\nclass DropBlock2d(nn.Module):\n    \"\"\"DropBlock. See https://arxiv.org/pdf/1810.12890.pdf\"\"\"\n\n    def __init__(\n        self,\n        drop_prob: float = 0.1,\n        block_size: int = 7,\n        gamma_scale: float = 1.0,\n        with_noise: bool = False,\n        inplace: bool = False,\n        batchwise: bool = False,\n        fast: bool = True,\n    ):\n        super(DropBlock2d, self).__init__()\n        self.drop_prob = drop_prob\n        self.gamma_scale = gamma_scale\n        self.block_size = block_size\n        self.with_noise = with_noise\n        self.inplace = inplace\n        self.batchwise = batchwise\n        self.fast = fast  # FIXME finish comparisons of fast vs not\n\n    def forward(self, x):\n        if not self.training or not self.drop_prob:\n            return x\n        if self.fast:\n            return drop_block_fast_2d(\n                x,\n                self.drop_prob,\n                self.block_size,\n                self.gamma_scale,\n                self.with_noise,\n                self.inplace,\n            )\n        else:\n            return drop_block_2d(\n                x,\n                self.drop_prob,\n                self.block_size,\n                self.gamma_scale,\n                self.with_noise,\n                self.inplace,\n                self.batchwise,\n            )\n\n\ndef drop_path(\n    x, drop_prob: float = 0.0, training: bool = False, scale_by_keep: bool = True\n):\n    \"\"\"Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).\n\n    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,\n    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...\n    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for\n    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use\n    'survival rate' as the argument.\n\n    \"\"\"\n    if drop_prob == 0.0 or not training:\n        return x\n    keep_prob = 1 - drop_prob\n    shape = (x.shape[0],) + (1,) * (\n        x.ndim - 1\n    )  # work with diff dim tensors, not just 2D ConvNets\n    random_tensor = x.new_empty(shape).bernoulli_(keep_prob)\n    if keep_prob > 0.0 and scale_by_keep:\n        random_tensor.div_(keep_prob)\n    return x * random_tensor\n\n\nclass DropPath(nn.Module):\n    \"\"\"Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).\"\"\"\n\n    def __init__(self, drop_prob: float = 0.0, scale_by_keep: bool = True):\n        super(DropPath, self).__init__()\n        self.drop_prob = drop_prob\n        self.scale_by_keep = scale_by_keep\n\n    def forward(self, x):\n        return drop_path(x, self.drop_prob, self.training, self.scale_by_keep)\n\n    def extra_repr(self):\n        return f\"drop_prob={round(self.drop_prob,3):0.3f}\"\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/timm/helpers.py",
    "content": "\"\"\" Layer/Module Helpers\nHacked together by / Copyright 2020 Ross Wightman\n\"\"\"\nimport collections.abc\nfrom itertools import repeat\n\n\n# From PyTorch internals\ndef _ntuple(n):\n    def parse(x):\n        if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):\n            return x\n        return tuple(repeat(x, n))\n\n    return parse\n\n\nto_1tuple = _ntuple(1)\nto_2tuple = _ntuple(2)\nto_3tuple = _ntuple(3)\nto_4tuple = _ntuple(4)\nto_ntuple = _ntuple\n\n\ndef make_divisible(v, divisor=8, min_value=None, round_limit=0.9):\n    min_value = min_value or divisor\n    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)\n    # Make sure that round down does not go down by more than 10%.\n    if new_v < round_limit * v:\n        new_v += divisor\n    return new_v\n"
  },
  {
    "path": "ldm_patched/pfn/architecture/timm/weight_init.py",
    "content": "import math\nimport warnings\n\nimport torch\nfrom torch.nn.init import _calculate_fan_in_and_fan_out\n\n\ndef _no_grad_trunc_normal_(tensor, mean, std, a, b):\n    # Cut & paste from PyTorch official master until it's in a few official releases - RW\n    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf\n    def norm_cdf(x):\n        # Computes standard normal cumulative distribution function\n        return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0\n\n    if (mean < a - 2 * std) or (mean > b + 2 * std):\n        warnings.warn(\n            \"mean is more than 2 std from [a, b] in nn.init.trunc_normal_. \"\n            \"The distribution of values may be incorrect.\",\n            stacklevel=2,\n        )\n\n    with torch.no_grad():\n        # Values are generated by using a truncated uniform distribution and\n        # then using the inverse CDF for the normal distribution.\n        # Get upper and lower cdf values\n        l = norm_cdf((a - mean) / std)\n        u = norm_cdf((b - mean) / std)\n\n        # Uniformly fill tensor with values from [l, u], then translate to\n        # [2l-1, 2u-1].\n        tensor.uniform_(2 * l - 1, 2 * u - 1)\n\n        # Use inverse cdf transform for normal distribution to get truncated\n        # standard normal\n        tensor.erfinv_()\n\n        # Transform to proper mean, std\n        tensor.mul_(std * math.sqrt(2.0))\n        tensor.add_(mean)\n\n        # Clamp to ensure it's in the proper range\n        tensor.clamp_(min=a, max=b)\n        return tensor\n\n\ndef trunc_normal_(\n    tensor: torch.Tensor, mean=0.0, std=1.0, a=-2.0, b=2.0\n) -> torch.Tensor:\n    r\"\"\"Fills the input Tensor with values drawn from a truncated\n    normal distribution. The values are effectively drawn from the\n    normal distribution :math:`\\mathcal{N}(\\text{mean}, \\text{std}^2)`\n    with values outside :math:`[a, b]` redrawn until they are within\n    the bounds. The method used for generating the random values works\n    best when :math:`a \\leq \\text{mean} \\leq b`.\n\n    NOTE: this impl is similar to the PyTorch trunc_normal_, the bounds [a, b] are\n    applied while sampling the normal with mean/std applied, therefore a, b args\n    should be adjusted to match the range of mean, std args.\n\n    Args:\n        tensor: an n-dimensional `torch.Tensor`\n        mean: the mean of the normal distribution\n        std: the standard deviation of the normal distribution\n        a: the minimum cutoff value\n        b: the maximum cutoff value\n    Examples:\n        >>> w = torch.empty(3, 5)\n        >>> nn.init.trunc_normal_(w)\n    \"\"\"\n    return _no_grad_trunc_normal_(tensor, mean, std, a, b)\n\n\ndef trunc_normal_tf_(\n    tensor: torch.Tensor, mean=0.0, std=1.0, a=-2.0, b=2.0\n) -> torch.Tensor:\n    r\"\"\"Fills the input Tensor with values drawn from a truncated\n    normal distribution. The values are effectively drawn from the\n    normal distribution :math:`\\mathcal{N}(\\text{mean}, \\text{std}^2)`\n    with values outside :math:`[a, b]` redrawn until they are within\n    the bounds. The method used for generating the random values works\n    best when :math:`a \\leq \\text{mean} \\leq b`.\n\n    NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the\n    bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0\n    and the result is subsquently scaled and shifted by the mean and std args.\n\n    Args:\n        tensor: an n-dimensional `torch.Tensor`\n        mean: the mean of the normal distribution\n        std: the standard deviation of the normal distribution\n        a: the minimum cutoff value\n        b: the maximum cutoff value\n    Examples:\n        >>> w = torch.empty(3, 5)\n        >>> nn.init.trunc_normal_(w)\n    \"\"\"\n    _no_grad_trunc_normal_(tensor, 0, 1.0, a, b)\n    with torch.no_grad():\n        tensor.mul_(std).add_(mean)\n    return tensor\n\n\ndef variance_scaling_(tensor, scale=1.0, mode=\"fan_in\", distribution=\"normal\"):\n    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)\n    if mode == \"fan_in\":\n        denom = fan_in\n    elif mode == \"fan_out\":\n        denom = fan_out\n    elif mode == \"fan_avg\":\n        denom = (fan_in + fan_out) / 2\n\n    variance = scale / denom  # type: ignore\n\n    if distribution == \"truncated_normal\":\n        # constant is stddev of standard normal truncated to (-2, 2)\n        trunc_normal_tf_(tensor, std=math.sqrt(variance) / 0.87962566103423978)\n    elif distribution == \"normal\":\n        tensor.normal_(std=math.sqrt(variance))\n    elif distribution == \"uniform\":\n        bound = math.sqrt(3 * variance)\n        # pylint: disable=invalid-unary-operand-type\n        tensor.uniform_(-bound, bound)\n    else:\n        raise ValueError(f\"invalid distribution {distribution}\")\n\n\ndef lecun_normal_(tensor):\n    variance_scaling_(tensor, mode=\"fan_in\", distribution=\"truncated_normal\")\n"
  },
  {
    "path": "ldm_patched/pfn/model_loading.py",
    "content": "import logging as logger\n\nfrom .architecture.DAT import DAT\nfrom .architecture.face.codeformer import CodeFormer\nfrom .architecture.face.gfpganv1_clean_arch import GFPGANv1Clean\nfrom .architecture.face.restoreformer_arch import RestoreFormer\nfrom .architecture.HAT import HAT\nfrom .architecture.LaMa import LaMa\nfrom .architecture.OmniSR.OmniSR import OmniSR\nfrom .architecture.RRDB import RRDBNet as ESRGAN\nfrom .architecture.SCUNet import SCUNet\nfrom .architecture.SPSR import SPSRNet as SPSR\nfrom .architecture.SRVGG import SRVGGNetCompact as RealESRGANv2\nfrom .architecture.SwiftSRGAN import Generator as SwiftSRGAN\nfrom .architecture.Swin2SR import Swin2SR\nfrom .architecture.SwinIR import SwinIR\nfrom .types import PyTorchModel\n\n\nclass UnsupportedModel(Exception):\n    pass\n\n\ndef load_state_dict(state_dict) -> PyTorchModel:\n    logger.debug(f\"Loading state dict into pytorch model arch\")\n\n    state_dict_keys = list(state_dict.keys())\n\n    if \"params_ema\" in state_dict_keys:\n        state_dict = state_dict[\"params_ema\"]\n    elif \"params-ema\" in state_dict_keys:\n        state_dict = state_dict[\"params-ema\"]\n    elif \"params\" in state_dict_keys:\n        state_dict = state_dict[\"params\"]\n\n    state_dict_keys = list(state_dict.keys())\n    # SRVGGNet Real-ESRGAN (v2)\n    if \"body.0.weight\" in state_dict_keys and \"body.1.weight\" in state_dict_keys:\n        model = RealESRGANv2(state_dict)\n    # SPSR (ESRGAN with lots of extra layers)\n    elif \"f_HR_conv1.0.weight\" in state_dict:\n        model = SPSR(state_dict)\n    # Swift-SRGAN\n    elif (\n        \"model\" in state_dict_keys\n        and \"initial.cnn.depthwise.weight\" in state_dict[\"model\"].keys()\n    ):\n        model = SwiftSRGAN(state_dict)\n    # SwinIR, Swin2SR, HAT\n    elif \"layers.0.residual_group.blocks.0.norm1.weight\" in state_dict_keys:\n        if (\n            \"layers.0.residual_group.blocks.0.conv_block.cab.0.weight\"\n            in state_dict_keys\n        ):\n            model = HAT(state_dict)\n        elif \"patch_embed.proj.weight\" in state_dict_keys:\n            model = Swin2SR(state_dict)\n        else:\n            model = SwinIR(state_dict)\n    # GFPGAN\n    elif (\n        \"toRGB.0.weight\" in state_dict_keys\n        and \"stylegan_decoder.style_mlp.1.weight\" in state_dict_keys\n    ):\n        model = GFPGANv1Clean(state_dict)\n    # RestoreFormer\n    elif (\n        \"encoder.conv_in.weight\" in state_dict_keys\n        and \"encoder.down.0.block.0.norm1.weight\" in state_dict_keys\n    ):\n        model = RestoreFormer(state_dict)\n    elif (\n        \"encoder.blocks.0.weight\" in state_dict_keys\n        and \"quantize.embedding.weight\" in state_dict_keys\n    ):\n        model = CodeFormer(state_dict)\n    # LaMa\n    elif (\n        \"model.model.1.bn_l.running_mean\" in state_dict_keys\n        or \"generator.model.1.bn_l.running_mean\" in state_dict_keys\n    ):\n        model = LaMa(state_dict)\n    # Omni-SR\n    elif \"residual_layer.0.residual_layer.0.layer.0.fn.0.weight\" in state_dict_keys:\n        model = OmniSR(state_dict)\n    # SCUNet\n    elif \"m_head.0.weight\" in state_dict_keys and \"m_tail.0.weight\" in state_dict_keys:\n        model = SCUNet(state_dict)\n    # DAT\n    elif \"layers.0.blocks.2.attn.attn_mask_0\" in state_dict_keys:\n        model = DAT(state_dict)\n    # Regular ESRGAN, \"new-arch\" ESRGAN, Real-ESRGAN v1\n    else:\n        try:\n            model = ESRGAN(state_dict)\n        except:\n            # pylint: disable=raise-missing-from\n            raise UnsupportedModel\n    return model\n"
  },
  {
    "path": "ldm_patched/pfn/types.py",
    "content": "from typing import Union\n\nfrom .architecture.DAT import DAT\nfrom .architecture.face.codeformer import CodeFormer\nfrom .architecture.face.gfpganv1_clean_arch import GFPGANv1Clean\nfrom .architecture.face.restoreformer_arch import RestoreFormer\nfrom .architecture.HAT import HAT\nfrom .architecture.LaMa import LaMa\nfrom .architecture.OmniSR.OmniSR import OmniSR\nfrom .architecture.RRDB import RRDBNet as ESRGAN\nfrom .architecture.SCUNet import SCUNet\nfrom .architecture.SPSR import SPSRNet as SPSR\nfrom .architecture.SRVGG import SRVGGNetCompact as RealESRGANv2\nfrom .architecture.SwiftSRGAN import Generator as SwiftSRGAN\nfrom .architecture.Swin2SR import Swin2SR\nfrom .architecture.SwinIR import SwinIR\n\nPyTorchSRModels = (\n    RealESRGANv2,\n    SPSR,\n    SwiftSRGAN,\n    ESRGAN,\n    SwinIR,\n    Swin2SR,\n    HAT,\n    OmniSR,\n    SCUNet,\n    DAT,\n)\nPyTorchSRModel = Union[\n    RealESRGANv2,\n    SPSR,\n    SwiftSRGAN,\n    ESRGAN,\n    SwinIR,\n    Swin2SR,\n    HAT,\n    OmniSR,\n    SCUNet,\n    DAT,\n]\n\n\ndef is_pytorch_sr_model(model: object):\n    return isinstance(model, PyTorchSRModels)\n\n\nPyTorchFaceModels = (GFPGANv1Clean, RestoreFormer, CodeFormer)\nPyTorchFaceModel = Union[GFPGANv1Clean, RestoreFormer, CodeFormer]\n\n\ndef is_pytorch_face_model(model: object):\n    return isinstance(model, PyTorchFaceModels)\n\n\nPyTorchInpaintModels = (LaMa,)\nPyTorchInpaintModel = Union[LaMa]\n\n\ndef is_pytorch_inpaint_model(model: object):\n    return isinstance(model, PyTorchInpaintModels)\n\n\nPyTorchModels = (*PyTorchSRModels, *PyTorchFaceModels, *PyTorchInpaintModels)\nPyTorchModel = Union[PyTorchSRModel, PyTorchFaceModel, PyTorchInpaintModel]\n\n\ndef is_pytorch_model(model: object):\n    return isinstance(model, PyTorchModels)\n"
  },
  {
    "path": "ldm_patched/t2ia/adapter.py",
    "content": "#taken from https://github.com/TencentARC/T2I-Adapter\nimport torch\nimport torch.nn as nn\nfrom collections import OrderedDict\n\n\ndef conv_nd(dims, *args, **kwargs):\n    \"\"\"\n    Create a 1D, 2D, or 3D convolution module.\n    \"\"\"\n    if dims == 1:\n        return nn.Conv1d(*args, **kwargs)\n    elif dims == 2:\n        return nn.Conv2d(*args, **kwargs)\n    elif dims == 3:\n        return nn.Conv3d(*args, **kwargs)\n    raise ValueError(f\"unsupported dimensions: {dims}\")\n\n\ndef avg_pool_nd(dims, *args, **kwargs):\n    \"\"\"\n    Create a 1D, 2D, or 3D average pooling module.\n    \"\"\"\n    if dims == 1:\n        return nn.AvgPool1d(*args, **kwargs)\n    elif dims == 2:\n        return nn.AvgPool2d(*args, **kwargs)\n    elif dims == 3:\n        return nn.AvgPool3d(*args, **kwargs)\n    raise ValueError(f\"unsupported dimensions: {dims}\")\n\n\nclass Downsample(nn.Module):\n    \"\"\"\n    A downsampling layer with an optional convolution.\n    :param channels: channels in the inputs and outputs.\n    :param use_conv: a bool determining if a convolution is applied.\n    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then\n                 downsampling occurs in the inner-two dimensions.\n    \"\"\"\n\n    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):\n        super().__init__()\n        self.channels = channels\n        self.out_channels = out_channels or channels\n        self.use_conv = use_conv\n        self.dims = dims\n        stride = 2 if dims != 3 else (1, 2, 2)\n        if use_conv:\n            self.op = conv_nd(\n                dims, self.channels, self.out_channels, 3, stride=stride, padding=padding\n            )\n        else:\n            assert self.channels == self.out_channels\n            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)\n\n    def forward(self, x):\n        assert x.shape[1] == self.channels\n        if not self.use_conv:\n            padding = [x.shape[2] % 2, x.shape[3] % 2]\n            self.op.padding = padding\n\n        x = self.op(x)\n        return x\n\n\nclass ResnetBlock(nn.Module):\n    def __init__(self, in_c, out_c, down, ksize=3, sk=False, use_conv=True):\n        super().__init__()\n        ps = ksize // 2\n        if in_c != out_c or sk == False:\n            self.in_conv = nn.Conv2d(in_c, out_c, ksize, 1, ps)\n        else:\n            # print('n_in')\n            self.in_conv = None\n        self.block1 = nn.Conv2d(out_c, out_c, 3, 1, 1)\n        self.act = nn.ReLU()\n        self.block2 = nn.Conv2d(out_c, out_c, ksize, 1, ps)\n        if sk == False:\n            self.skep = nn.Conv2d(in_c, out_c, ksize, 1, ps)\n        else:\n            self.skep = None\n\n        self.down = down\n        if self.down == True:\n            self.down_opt = Downsample(in_c, use_conv=use_conv)\n\n    def forward(self, x):\n        if self.down == True:\n            x = self.down_opt(x)\n        if self.in_conv is not None:  # edit\n            x = self.in_conv(x)\n\n        h = self.block1(x)\n        h = self.act(h)\n        h = self.block2(h)\n        if self.skep is not None:\n            return h + self.skep(x)\n        else:\n            return h + x\n\n\nclass Adapter(nn.Module):\n    def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64, ksize=3, sk=False, use_conv=True, xl=True):\n        super(Adapter, self).__init__()\n        self.unshuffle_amount = 8\n        resblock_no_downsample = []\n        resblock_downsample = [3, 2, 1]\n        self.xl = xl\n        if self.xl:\n            self.unshuffle_amount = 16\n            resblock_no_downsample = [1]\n            resblock_downsample = [2]\n\n        self.input_channels = cin // (self.unshuffle_amount * self.unshuffle_amount)\n        self.unshuffle = nn.PixelUnshuffle(self.unshuffle_amount)\n        self.channels = channels\n        self.nums_rb = nums_rb\n        self.body = []\n        for i in range(len(channels)):\n            for j in range(nums_rb):\n                if (i in resblock_downsample) and (j == 0):\n                    self.body.append(\n                        ResnetBlock(channels[i - 1], channels[i], down=True, ksize=ksize, sk=sk, use_conv=use_conv))\n                elif (i in resblock_no_downsample) and (j == 0):\n                    self.body.append(\n                        ResnetBlock(channels[i - 1], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv))\n                else:\n                    self.body.append(\n                        ResnetBlock(channels[i], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv))\n        self.body = nn.ModuleList(self.body)\n        self.conv_in = nn.Conv2d(cin, channels[0], 3, 1, 1)\n\n    def forward(self, x):\n        # unshuffle\n        x = self.unshuffle(x)\n        # extract features\n        features = []\n        x = self.conv_in(x)\n        for i in range(len(self.channels)):\n            for j in range(self.nums_rb):\n                idx = i * self.nums_rb + j\n                x = self.body[idx](x)\n            if self.xl:\n                features.append(None)\n                if i == 0:\n                    features.append(None)\n                    features.append(None)\n                if i == 2:\n                    features.append(None)\n            else:\n                features.append(None)\n                features.append(None)\n            features.append(x)\n\n        return features\n\n\nclass LayerNorm(nn.LayerNorm):\n    \"\"\"Subclass torch's LayerNorm to handle fp16.\"\"\"\n\n    def forward(self, x: torch.Tensor):\n        orig_type = x.dtype\n        ret = super().forward(x.type(torch.float32))\n        return ret.type(orig_type)\n\n\nclass QuickGELU(nn.Module):\n\n    def forward(self, x: torch.Tensor):\n        return x * torch.sigmoid(1.702 * x)\n\n\nclass ResidualAttentionBlock(nn.Module):\n\n    def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):\n        super().__init__()\n\n        self.attn = nn.MultiheadAttention(d_model, n_head)\n        self.ln_1 = LayerNorm(d_model)\n        self.mlp = nn.Sequential(\n            OrderedDict([(\"c_fc\", nn.Linear(d_model, d_model * 4)), (\"gelu\", QuickGELU()),\n                         (\"c_proj\", nn.Linear(d_model * 4, d_model))]))\n        self.ln_2 = LayerNorm(d_model)\n        self.attn_mask = attn_mask\n\n    def attention(self, x: torch.Tensor):\n        self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None\n        return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]\n\n    def forward(self, x: torch.Tensor):\n        x = x + self.attention(self.ln_1(x))\n        x = x + self.mlp(self.ln_2(x))\n        return x\n\n\nclass StyleAdapter(nn.Module):\n\n    def __init__(self, width=1024, context_dim=768, num_head=8, n_layes=3, num_token=4):\n        super().__init__()\n\n        scale = width ** -0.5\n        self.transformer_layes = nn.Sequential(*[ResidualAttentionBlock(width, num_head) for _ in range(n_layes)])\n        self.num_token = num_token\n        self.style_embedding = nn.Parameter(torch.randn(1, num_token, width) * scale)\n        self.ln_post = LayerNorm(width)\n        self.ln_pre = LayerNorm(width)\n        self.proj = nn.Parameter(scale * torch.randn(width, context_dim))\n\n    def forward(self, x):\n        # x shape [N, HW+1, C]\n        style_embedding = self.style_embedding + torch.zeros(\n            (x.shape[0], self.num_token, self.style_embedding.shape[-1]), device=x.device)\n        x = torch.cat([x, style_embedding], dim=1)\n        x = self.ln_pre(x)\n        x = x.permute(1, 0, 2)  # NLD -> LND\n        x = self.transformer_layes(x)\n        x = x.permute(1, 0, 2)  # LND -> NLD\n\n        x = self.ln_post(x[:, -self.num_token:, :])\n        x = x @ self.proj\n\n        return x\n\n\nclass ResnetBlock_light(nn.Module):\n    def __init__(self, in_c):\n        super().__init__()\n        self.block1 = nn.Conv2d(in_c, in_c, 3, 1, 1)\n        self.act = nn.ReLU()\n        self.block2 = nn.Conv2d(in_c, in_c, 3, 1, 1)\n\n    def forward(self, x):\n        h = self.block1(x)\n        h = self.act(h)\n        h = self.block2(h)\n\n        return h + x\n\n\nclass extractor(nn.Module):\n    def __init__(self, in_c, inter_c, out_c, nums_rb, down=False):\n        super().__init__()\n        self.in_conv = nn.Conv2d(in_c, inter_c, 1, 1, 0)\n        self.body = []\n        for _ in range(nums_rb):\n            self.body.append(ResnetBlock_light(inter_c))\n        self.body = nn.Sequential(*self.body)\n        self.out_conv = nn.Conv2d(inter_c, out_c, 1, 1, 0)\n        self.down = down\n        if self.down == True:\n            self.down_opt = Downsample(in_c, use_conv=False)\n\n    def forward(self, x):\n        if self.down == True:\n            x = self.down_opt(x)\n        x = self.in_conv(x)\n        x = self.body(x)\n        x = self.out_conv(x)\n\n        return x\n\n\nclass Adapter_light(nn.Module):\n    def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64):\n        super(Adapter_light, self).__init__()\n        self.unshuffle_amount = 8\n        self.unshuffle = nn.PixelUnshuffle(self.unshuffle_amount)\n        self.input_channels = cin // (self.unshuffle_amount * self.unshuffle_amount)\n        self.channels = channels\n        self.nums_rb = nums_rb\n        self.body = []\n        self.xl = False\n\n        for i in range(len(channels)):\n            if i == 0:\n                self.body.append(extractor(in_c=cin, inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=False))\n            else:\n                self.body.append(extractor(in_c=channels[i-1], inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=True))\n        self.body = nn.ModuleList(self.body)\n\n    def forward(self, x):\n        # unshuffle\n        x = self.unshuffle(x)\n        # extract features\n        features = []\n        for i in range(len(self.channels)):\n            x = self.body[i](x)\n            features.append(None)\n            features.append(None)\n            features.append(x)\n\n        return features\n"
  },
  {
    "path": "ldm_patched/taesd/taesd.py",
    "content": "#!/usr/bin/env python3\n\"\"\"\nTiny AutoEncoder for Stable Diffusion\n(DNN for encoding / decoding SD's latent space)\n\"\"\"\nimport torch\nimport torch.nn as nn\n\nimport ldm_patched.modules.utils\nimport ldm_patched.modules.ops\n\ndef conv(n_in, n_out, **kwargs):\n    return ldm_patched.modules.ops.disable_weight_init.Conv2d(n_in, n_out, 3, padding=1, **kwargs)\n\nclass Clamp(nn.Module):\n    def forward(self, x):\n        return torch.tanh(x / 3) * 3\n\nclass Block(nn.Module):\n    def __init__(self, n_in, n_out):\n        super().__init__()\n        self.conv = nn.Sequential(conv(n_in, n_out), nn.ReLU(), conv(n_out, n_out), nn.ReLU(), conv(n_out, n_out))\n        self.skip = ldm_patched.modules.ops.disable_weight_init.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity()\n        self.fuse = nn.ReLU()\n    def forward(self, x):\n        return self.fuse(self.conv(x) + self.skip(x))\n\ndef Encoder():\n    return nn.Sequential(\n        conv(3, 64), Block(64, 64),\n        conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),\n        conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),\n        conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),\n        conv(64, 4),\n    )\n\ndef Decoder():\n    return nn.Sequential(\n        Clamp(), conv(4, 64), nn.ReLU(),\n        Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),\n        Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),\n        Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),\n        Block(64, 64), conv(64, 3),\n    )\n\nclass TAESD(nn.Module):\n    latent_magnitude = 3\n    latent_shift = 0.5\n\n    def __init__(self, encoder_path=None, decoder_path=None):\n        \"\"\"Initialize pretrained TAESD on the given device from the given checkpoints.\"\"\"\n        super().__init__()\n        self.taesd_encoder = Encoder()\n        self.taesd_decoder = Decoder()\n        self.vae_scale = torch.nn.Parameter(torch.tensor(1.0))\n        if encoder_path is not None:\n            self.taesd_encoder.load_state_dict(ldm_patched.modules.utils.load_torch_file(encoder_path, safe_load=True))\n        if decoder_path is not None:\n            self.taesd_decoder.load_state_dict(ldm_patched.modules.utils.load_torch_file(decoder_path, safe_load=True))\n\n    @staticmethod\n    def scale_latents(x):\n        \"\"\"raw latents -> [0, 1]\"\"\"\n        return x.div(2 * TAESD.latent_magnitude).add(TAESD.latent_shift).clamp(0, 1)\n\n    @staticmethod\n    def unscale_latents(x):\n        \"\"\"[0, 1] -> raw latents\"\"\"\n        return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude)\n\n    def decode(self, x):\n        x_sample = self.taesd_decoder(x * self.vae_scale)\n        x_sample = x_sample.sub(0.5).mul(2)\n        return x_sample\n\n    def encode(self, x):\n        return self.taesd_encoder(x * 0.5 + 0.5) / self.vae_scale\n"
  },
  {
    "path": "ldm_patched/unipc/uni_pc.py",
    "content": "#code taken from: https://github.com/wl-zhao/UniPC and modified\n\nimport torch\nimport torch.nn.functional as F\nimport math\n\nfrom tqdm.auto import trange, tqdm\n\n\nclass NoiseScheduleVP:\n    def __init__(\n            self,\n            schedule='discrete',\n            betas=None,\n            alphas_cumprod=None,\n            continuous_beta_0=0.1,\n            continuous_beta_1=20.,\n        ):\n        \"\"\"Create a wrapper class for the forward SDE (VP type).\n\n        ***\n        Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t.\n                We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images.\n        ***\n\n        The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ).\n        We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper).\n        Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have:\n\n            log_alpha_t = self.marginal_log_mean_coeff(t)\n            sigma_t = self.marginal_std(t)\n            lambda_t = self.marginal_lambda(t)\n\n        Moreover, as lambda(t) is an invertible function, we also support its inverse function:\n\n            t = self.inverse_lambda(lambda_t)\n\n        ===============================================================\n\n        We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]).\n\n        1. For discrete-time DPMs:\n\n            For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by:\n                t_i = (i + 1) / N\n            e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1.\n            We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3.\n\n            Args:\n                betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details)\n                alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details)\n\n            Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`.\n\n            **Important**:  Please pay special attention for the args for `alphas_cumprod`:\n                The `alphas_cumprod` is the \\hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that\n                    q_{t_n | 0}(x_{t_n} | x_0) = N ( \\sqrt{\\hat{alpha_n}} * x_0, (1 - \\hat{alpha_n}) * I ).\n                Therefore, the notation \\hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have\n                    alpha_{t_n} = \\sqrt{\\hat{alpha_n}},\n                and\n                    log(alpha_{t_n}) = 0.5 * log(\\hat{alpha_n}).\n\n\n        2. For continuous-time DPMs:\n\n            We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise\n            schedule are the default settings in DDPM and improved-DDPM:\n\n            Args:\n                beta_min: A `float` number. The smallest beta for the linear schedule.\n                beta_max: A `float` number. The largest beta for the linear schedule.\n                cosine_s: A `float` number. The hyperparameter in the cosine schedule.\n                cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule.\n                T: A `float` number. The ending time of the forward process.\n\n        ===============================================================\n\n        Args:\n            schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs,\n                    'linear' or 'cosine' for continuous-time DPMs.\n        Returns:\n            A wrapper object of the forward SDE (VP type).\n        \n        ===============================================================\n\n        Example:\n\n        # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1):\n        >>> ns = NoiseScheduleVP('discrete', betas=betas)\n\n        # For discrete-time DPMs, given alphas_cumprod (the \\hat{alpha_n} array for n = 0, 1, ..., N - 1):\n        >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)\n\n        # For continuous-time DPMs (VPSDE), linear schedule:\n        >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.)\n\n        \"\"\"\n\n        if schedule not in ['discrete', 'linear', 'cosine']:\n            raise ValueError(\"Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'\".format(schedule))\n\n        self.schedule = schedule\n        if schedule == 'discrete':\n            if betas is not None:\n                log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0)\n            else:\n                assert alphas_cumprod is not None\n                log_alphas = 0.5 * torch.log(alphas_cumprod)\n            self.total_N = len(log_alphas)\n            self.T = 1.\n            self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1))\n            self.log_alpha_array = log_alphas.reshape((1, -1,))\n        else:\n            self.total_N = 1000\n            self.beta_0 = continuous_beta_0\n            self.beta_1 = continuous_beta_1\n            self.cosine_s = 0.008\n            self.cosine_beta_max = 999.\n            self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s\n            self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.))\n            self.schedule = schedule\n            if schedule == 'cosine':\n                # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T.\n                # Note that T = 0.9946 may be not the optimal setting. However, we find it works well.\n                self.T = 0.9946\n            else:\n                self.T = 1.\n\n    def marginal_log_mean_coeff(self, t):\n        \"\"\"\n        Compute log(alpha_t) of a given continuous-time label t in [0, T].\n        \"\"\"\n        if self.schedule == 'discrete':\n            return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), self.log_alpha_array.to(t.device)).reshape((-1))\n        elif self.schedule == 'linear':\n            return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0\n        elif self.schedule == 'cosine':\n            log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.))\n            log_alpha_t =  log_alpha_fn(t) - self.cosine_log_alpha_0\n            return log_alpha_t\n\n    def marginal_alpha(self, t):\n        \"\"\"\n        Compute alpha_t of a given continuous-time label t in [0, T].\n        \"\"\"\n        return torch.exp(self.marginal_log_mean_coeff(t))\n\n    def marginal_std(self, t):\n        \"\"\"\n        Compute sigma_t of a given continuous-time label t in [0, T].\n        \"\"\"\n        return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t)))\n\n    def marginal_lambda(self, t):\n        \"\"\"\n        Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].\n        \"\"\"\n        log_mean_coeff = self.marginal_log_mean_coeff(t)\n        log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff))\n        return log_mean_coeff - log_std\n\n    def inverse_lambda(self, lamb):\n        \"\"\"\n        Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t.\n        \"\"\"\n        if self.schedule == 'linear':\n            tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))\n            Delta = self.beta_0**2 + tmp\n            return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0)\n        elif self.schedule == 'discrete':\n            log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb)\n            t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]), torch.flip(self.t_array.to(lamb.device), [1]))\n            return t.reshape((-1,))\n        else:\n            log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))\n            t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s\n            t = t_fn(log_alpha)\n            return t\n\n\ndef model_wrapper(\n    model,\n    noise_schedule,\n    model_type=\"noise\",\n    model_kwargs={},\n    guidance_type=\"uncond\",\n    condition=None,\n    unconditional_condition=None,\n    guidance_scale=1.,\n    classifier_fn=None,\n    classifier_kwargs={},\n):\n    \"\"\"Create a wrapper function for the noise prediction model.\n\n    DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to\n    firstly wrap the model function to a noise prediction model that accepts the continuous time as the input.\n\n    We support four types of the diffusion model by setting `model_type`:\n\n        1. \"noise\": noise prediction model. (Trained by predicting noise).\n\n        2. \"x_start\": data prediction model. (Trained by predicting the data x_0 at time 0).\n\n        3. \"v\": velocity prediction model. (Trained by predicting the velocity).\n            The \"v\" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2].\n\n            [1] Salimans, Tim, and Jonathan Ho. \"Progressive distillation for fast sampling of diffusion models.\"\n                arXiv preprint arXiv:2202.00512 (2022).\n            [2] Ho, Jonathan, et al. \"Imagen Video: High Definition Video Generation with Diffusion Models.\"\n                arXiv preprint arXiv:2210.02303 (2022).\n    \n        4. \"score\": marginal score function. (Trained by denoising score matching).\n            Note that the score function and the noise prediction model follows a simple relationship:\n            ```\n                noise(x_t, t) = -sigma_t * score(x_t, t)\n            ```\n\n    We support three types of guided sampling by DPMs by setting `guidance_type`:\n        1. \"uncond\": unconditional sampling by DPMs.\n            The input `model` has the following format:\n            ``\n                model(x, t_input, **model_kwargs) -> noise | x_start | v | score\n            ``\n\n        2. \"classifier\": classifier guidance sampling [3] by DPMs and another classifier.\n            The input `model` has the following format:\n            ``\n                model(x, t_input, **model_kwargs) -> noise | x_start | v | score\n            `` \n\n            The input `classifier_fn` has the following format:\n            ``\n                classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond)\n            ``\n\n            [3] P. Dhariwal and A. Q. Nichol, \"Diffusion models beat GANs on image synthesis,\"\n                in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794.\n\n        3. \"classifier-free\": classifier-free guidance sampling by conditional DPMs.\n            The input `model` has the following format:\n            ``\n                model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score\n            `` \n            And if cond == `unconditional_condition`, the model output is the unconditional DPM output.\n\n            [4] Ho, Jonathan, and Tim Salimans. \"Classifier-free diffusion guidance.\"\n                arXiv preprint arXiv:2207.12598 (2022).\n        \n\n    The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999)\n    or continuous-time labels (i.e. epsilon to T).\n\n    We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise:\n    ``\n        def model_fn(x, t_continuous) -> noise:\n            t_input = get_model_input_time(t_continuous)\n            return noise_pred(model, x, t_input, **model_kwargs)         \n    ``\n    where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver.\n\n    ===============================================================\n\n    Args:\n        model: A diffusion model with the corresponding format described above.\n        noise_schedule: A noise schedule object, such as NoiseScheduleVP.\n        model_type: A `str`. The parameterization type of the diffusion model.\n                    \"noise\" or \"x_start\" or \"v\" or \"score\".\n        model_kwargs: A `dict`. A dict for the other inputs of the model function.\n        guidance_type: A `str`. The type of the guidance for sampling.\n                    \"uncond\" or \"classifier\" or \"classifier-free\".\n        condition: A pytorch tensor. The condition for the guided sampling.\n                    Only used for \"classifier\" or \"classifier-free\" guidance type.\n        unconditional_condition: A pytorch tensor. The condition for the unconditional sampling.\n                    Only used for \"classifier-free\" guidance type.\n        guidance_scale: A `float`. The scale for the guided sampling.\n        classifier_fn: A classifier function. Only used for the classifier guidance.\n        classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function.\n    Returns:\n        A noise prediction model that accepts the noised data and the continuous time as the inputs.\n    \"\"\"\n\n    def get_model_input_time(t_continuous):\n        \"\"\"\n        Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time.\n        For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N].\n        For continuous-time DPMs, we just use `t_continuous`.\n        \"\"\"\n        if noise_schedule.schedule == 'discrete':\n            return (t_continuous - 1. / noise_schedule.total_N) * 1000.\n        else:\n            return t_continuous\n\n    def noise_pred_fn(x, t_continuous, cond=None):\n        if t_continuous.reshape((-1,)).shape[0] == 1:\n            t_continuous = t_continuous.expand((x.shape[0]))\n        t_input = get_model_input_time(t_continuous)\n        output = model(x, t_input, **model_kwargs)\n        if model_type == \"noise\":\n            return output\n        elif model_type == \"x_start\":\n            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)\n            dims = x.dim()\n            return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims)\n        elif model_type == \"v\":\n            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)\n            dims = x.dim()\n            return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x\n        elif model_type == \"score\":\n            sigma_t = noise_schedule.marginal_std(t_continuous)\n            dims = x.dim()\n            return -expand_dims(sigma_t, dims) * output\n\n    def cond_grad_fn(x, t_input):\n        \"\"\"\n        Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t).\n        \"\"\"\n        with torch.enable_grad():\n            x_in = x.detach().requires_grad_(True)\n            log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs)\n            return torch.autograd.grad(log_prob.sum(), x_in)[0]\n\n    def model_fn(x, t_continuous):\n        \"\"\"\n        The noise predicition model function that is used for DPM-Solver.\n        \"\"\"\n        if t_continuous.reshape((-1,)).shape[0] == 1:\n            t_continuous = t_continuous.expand((x.shape[0]))\n        if guidance_type == \"uncond\":\n            return noise_pred_fn(x, t_continuous)\n        elif guidance_type == \"classifier\":\n            assert classifier_fn is not None\n            t_input = get_model_input_time(t_continuous)\n            cond_grad = cond_grad_fn(x, t_input)\n            sigma_t = noise_schedule.marginal_std(t_continuous)\n            noise = noise_pred_fn(x, t_continuous)\n            return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad\n        elif guidance_type == \"classifier-free\":\n            if guidance_scale == 1. or unconditional_condition is None:\n                return noise_pred_fn(x, t_continuous, cond=condition)\n            else:\n                x_in = torch.cat([x] * 2)\n                t_in = torch.cat([t_continuous] * 2)\n                c_in = torch.cat([unconditional_condition, condition])\n                noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2)\n                return noise_uncond + guidance_scale * (noise - noise_uncond)\n\n    assert model_type in [\"noise\", \"x_start\", \"v\"]\n    assert guidance_type in [\"uncond\", \"classifier\", \"classifier-free\"]\n    return model_fn\n\n\nclass UniPC:\n    def __init__(\n        self,\n        model_fn,\n        noise_schedule,\n        predict_x0=True,\n        thresholding=False,\n        max_val=1.,\n        variant='bh1',\n        noise_mask=None,\n        masked_image=None,\n        noise=None,\n    ):\n        \"\"\"Construct a UniPC. \n\n        We support both data_prediction and noise_prediction.\n        \"\"\"\n        self.model = model_fn\n        self.noise_schedule = noise_schedule\n        self.variant = variant\n        self.predict_x0 = predict_x0\n        self.thresholding = thresholding\n        self.max_val = max_val\n        self.noise_mask = noise_mask\n        self.masked_image = masked_image\n        self.noise = noise\n\n    def dynamic_thresholding_fn(self, x0, t=None):\n        \"\"\"\n        The dynamic thresholding method. \n        \"\"\"\n        dims = x0.dim()\n        p = self.dynamic_thresholding_ratio\n        s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)\n        s = expand_dims(torch.maximum(s, self.thresholding_max_val * torch.ones_like(s).to(s.device)), dims)\n        x0 = torch.clamp(x0, -s, s) / s\n        return x0\n\n    def noise_prediction_fn(self, x, t):\n        \"\"\"\n        Return the noise prediction model.\n        \"\"\"\n        if self.noise_mask is not None:\n            return self.model(x, t) * self.noise_mask\n        else:\n            return self.model(x, t)\n\n    def data_prediction_fn(self, x, t):\n        \"\"\"\n        Return the data prediction model (with thresholding).\n        \"\"\"\n        noise = self.noise_prediction_fn(x, t)\n        dims = x.dim()\n        alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t)\n        x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims)\n        if self.thresholding:\n            p = 0.995   # A hyperparameter in the paper of \"Imagen\" [1].\n            s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)\n            s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims)\n            x0 = torch.clamp(x0, -s, s) / s\n        if self.noise_mask is not None:\n            x0 = x0 * self.noise_mask + (1. - self.noise_mask) * self.masked_image\n        return x0\n\n    def model_fn(self, x, t):\n        \"\"\"\n        Convert the model to the noise prediction model or the data prediction model. \n        \"\"\"\n        if self.predict_x0:\n            return self.data_prediction_fn(x, t)\n        else:\n            return self.noise_prediction_fn(x, t)\n\n    def get_time_steps(self, skip_type, t_T, t_0, N, device):\n        \"\"\"Compute the intermediate time steps for sampling.\n        \"\"\"\n        if skip_type == 'logSNR':\n            lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device))\n            lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device))\n            logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device)\n            return self.noise_schedule.inverse_lambda(logSNR_steps)\n        elif skip_type == 'time_uniform':\n            return torch.linspace(t_T, t_0, N + 1).to(device)\n        elif skip_type == 'time_quadratic':\n            t_order = 2\n            t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device)\n            return t\n        else:\n            raise ValueError(\"Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'\".format(skip_type))\n\n    def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device):\n        \"\"\"\n        Get the order of each step for sampling by the singlestep DPM-Solver.\n        \"\"\"\n        if order == 3:\n            K = steps // 3 + 1\n            if steps % 3 == 0:\n                orders = [3,] * (K - 2) + [2, 1]\n            elif steps % 3 == 1:\n                orders = [3,] * (K - 1) + [1]\n            else:\n                orders = [3,] * (K - 1) + [2]\n        elif order == 2:\n            if steps % 2 == 0:\n                K = steps // 2\n                orders = [2,] * K\n            else:\n                K = steps // 2 + 1\n                orders = [2,] * (K - 1) + [1]\n        elif order == 1:\n            K = steps\n            orders = [1,] * steps\n        else:\n            raise ValueError(\"'order' must be '1' or '2' or '3'.\")\n        if skip_type == 'logSNR':\n            # To reproduce the results in DPM-Solver paper\n            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device)\n        else:\n            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[torch.cumsum(torch.tensor([0,] + orders), 0).to(device)]\n        return timesteps_outer, orders\n\n    def denoise_to_zero_fn(self, x, s):\n        \"\"\"\n        Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization. \n        \"\"\"\n        return self.data_prediction_fn(x, s)\n\n    def multistep_uni_pc_update(self, x, model_prev_list, t_prev_list, t, order, **kwargs):\n        if len(t.shape) == 0:\n            t = t.view(-1)\n        if 'bh' in self.variant:\n            return self.multistep_uni_pc_bh_update(x, model_prev_list, t_prev_list, t, order, **kwargs)\n        else:\n            assert self.variant == 'vary_coeff'\n            return self.multistep_uni_pc_vary_update(x, model_prev_list, t_prev_list, t, order, **kwargs)\n\n    def multistep_uni_pc_vary_update(self, x, model_prev_list, t_prev_list, t, order, use_corrector=True):\n        print(f'using unified predictor-corrector with order {order} (solver type: vary coeff)')\n        ns = self.noise_schedule\n        assert order <= len(model_prev_list)\n\n        # first compute rks\n        t_prev_0 = t_prev_list[-1]\n        lambda_prev_0 = ns.marginal_lambda(t_prev_0)\n        lambda_t = ns.marginal_lambda(t)\n        model_prev_0 = model_prev_list[-1]\n        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)\n        log_alpha_t = ns.marginal_log_mean_coeff(t)\n        alpha_t = torch.exp(log_alpha_t)\n\n        h = lambda_t - lambda_prev_0\n\n        rks = []\n        D1s = []\n        for i in range(1, order):\n            t_prev_i = t_prev_list[-(i + 1)]\n            model_prev_i = model_prev_list[-(i + 1)]\n            lambda_prev_i = ns.marginal_lambda(t_prev_i)\n            rk = (lambda_prev_i - lambda_prev_0) / h\n            rks.append(rk)\n            D1s.append((model_prev_i - model_prev_0) / rk)\n\n        rks.append(1.)\n        rks = torch.tensor(rks, device=x.device)\n\n        K = len(rks)\n        # build C matrix\n        C = []\n\n        col = torch.ones_like(rks)\n        for k in range(1, K + 1):\n            C.append(col)\n            col = col * rks / (k + 1) \n        C = torch.stack(C, dim=1)\n\n        if len(D1s) > 0:\n            D1s = torch.stack(D1s, dim=1) # (B, K)\n            C_inv_p = torch.linalg.inv(C[:-1, :-1])\n            A_p = C_inv_p\n\n        if use_corrector:\n            print('using corrector')\n            C_inv = torch.linalg.inv(C)\n            A_c = C_inv\n\n        hh = -h if self.predict_x0 else h\n        h_phi_1 = torch.expm1(hh)\n        h_phi_ks = []\n        factorial_k = 1\n        h_phi_k = h_phi_1\n        for k in range(1, K + 2):\n            h_phi_ks.append(h_phi_k)\n            h_phi_k = h_phi_k / hh - 1 / factorial_k\n            factorial_k *= (k + 1)\n\n        model_t = None\n        if self.predict_x0:\n            x_t_ = (\n                sigma_t / sigma_prev_0 * x\n                - alpha_t * h_phi_1 * model_prev_0\n            )\n            # now predictor\n            x_t = x_t_\n            if len(D1s) > 0:\n                # compute the residuals for predictor\n                for k in range(K - 1):\n                    x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k])\n            # now corrector\n            if use_corrector:\n                model_t = self.model_fn(x_t, t)\n                D1_t = (model_t - model_prev_0)\n                x_t = x_t_\n                k = 0\n                for k in range(K - 1):\n                    x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1])\n                x_t = x_t - alpha_t * h_phi_ks[K] * (D1_t * A_c[k][-1])\n        else:\n            log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)\n            x_t_ = (\n                (torch.exp(log_alpha_t - log_alpha_prev_0)) * x\n                - (sigma_t * h_phi_1) * model_prev_0\n            )\n            # now predictor\n            x_t = x_t_\n            if len(D1s) > 0:\n                # compute the residuals for predictor\n                for k in range(K - 1):\n                    x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k])\n            # now corrector\n            if use_corrector:\n                model_t = self.model_fn(x_t, t)\n                D1_t = (model_t - model_prev_0)\n                x_t = x_t_\n                k = 0\n                for k in range(K - 1):\n                    x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1])\n                x_t = x_t - sigma_t * h_phi_ks[K] * (D1_t * A_c[k][-1])\n        return x_t, model_t\n\n    def multistep_uni_pc_bh_update(self, x, model_prev_list, t_prev_list, t, order, x_t=None, use_corrector=True):\n        # print(f'using unified predictor-corrector with order {order} (solver type: B(h))')\n        ns = self.noise_schedule\n        assert order <= len(model_prev_list)\n        dims = x.dim()\n\n        # first compute rks\n        t_prev_0 = t_prev_list[-1]\n        lambda_prev_0 = ns.marginal_lambda(t_prev_0)\n        lambda_t = ns.marginal_lambda(t)\n        model_prev_0 = model_prev_list[-1]\n        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)\n        log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)\n        alpha_t = torch.exp(log_alpha_t)\n\n        h = lambda_t - lambda_prev_0\n\n        rks = []\n        D1s = []\n        for i in range(1, order):\n            t_prev_i = t_prev_list[-(i + 1)]\n            model_prev_i = model_prev_list[-(i + 1)]\n            lambda_prev_i = ns.marginal_lambda(t_prev_i)\n            rk = ((lambda_prev_i - lambda_prev_0) / h)[0]\n            rks.append(rk)\n            D1s.append((model_prev_i - model_prev_0) / rk)\n\n        rks.append(1.)\n        rks = torch.tensor(rks, device=x.device)\n\n        R = []\n        b = []\n\n        hh = -h[0] if self.predict_x0 else h[0]\n        h_phi_1 = torch.expm1(hh) # h\\phi_1(h) = e^h - 1\n        h_phi_k = h_phi_1 / hh - 1\n\n        factorial_i = 1\n\n        if self.variant == 'bh1':\n            B_h = hh\n        elif self.variant == 'bh2':\n            B_h = torch.expm1(hh)\n        else:\n            raise NotImplementedError()\n            \n        for i in range(1, order + 1):\n            R.append(torch.pow(rks, i - 1))\n            b.append(h_phi_k * factorial_i / B_h)\n            factorial_i *= (i + 1)\n            h_phi_k = h_phi_k / hh - 1 / factorial_i \n\n        R = torch.stack(R)\n        b = torch.tensor(b, device=x.device)\n\n        # now predictor\n        use_predictor = len(D1s) > 0 and x_t is None\n        if len(D1s) > 0:\n            D1s = torch.stack(D1s, dim=1) # (B, K)\n            if x_t is None:\n                # for order 2, we use a simplified version\n                if order == 2:\n                    rhos_p = torch.tensor([0.5], device=b.device)\n                else:\n                    rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1])\n        else:\n            D1s = None\n\n        if use_corrector:\n            # print('using corrector')\n            # for order 1, we use a simplified version\n            if order == 1:\n                rhos_c = torch.tensor([0.5], device=b.device)\n            else:\n                rhos_c = torch.linalg.solve(R, b)\n\n        model_t = None\n        if self.predict_x0:\n            x_t_ = (\n                expand_dims(sigma_t / sigma_prev_0, dims) * x\n                - expand_dims(alpha_t * h_phi_1, dims)* model_prev_0\n            )\n\n            if x_t is None:\n                if use_predictor:\n                    pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s)\n                else:\n                    pred_res = 0\n                x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * pred_res\n\n            if use_corrector:\n                model_t = self.model_fn(x_t, t)\n                if D1s is not None:\n                    corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s)\n                else:\n                    corr_res = 0\n                D1_t = (model_t - model_prev_0)\n                x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t)\n        else:\n            x_t_ = (\n                expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x\n                - expand_dims(sigma_t * h_phi_1, dims) * model_prev_0\n            )\n            if x_t is None:\n                if use_predictor:\n                    pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s)\n                else:\n                    pred_res = 0\n                x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * pred_res\n\n            if use_corrector:\n                model_t = self.model_fn(x_t, t)\n                if D1s is not None:\n                    corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s)\n                else:\n                    corr_res = 0\n                D1_t = (model_t - model_prev_0)\n                x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t)\n        return x_t, model_t\n\n\n    def sample(self, x, timesteps, t_start=None, t_end=None, order=3, skip_type='time_uniform',\n        method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver',\n        atol=0.0078, rtol=0.05, corrector=False, callback=None, disable_pbar=False\n    ):\n        # t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end\n        # t_T = self.noise_schedule.T if t_start is None else t_start\n        device = x.device\n        steps = len(timesteps) - 1\n        if method == 'multistep':\n            assert steps >= order\n            # timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)\n            assert timesteps.shape[0] - 1 == steps\n            # with torch.no_grad():\n            for step_index in trange(steps, disable=disable_pbar):\n                if self.noise_mask is not None:\n                    x = x * self.noise_mask + (1. - self.noise_mask) * (self.masked_image * self.noise_schedule.marginal_alpha(timesteps[step_index]) + self.noise * self.noise_schedule.marginal_std(timesteps[step_index]))\n                if step_index == 0:\n                    vec_t = timesteps[0].expand((x.shape[0]))\n                    model_prev_list = [self.model_fn(x, vec_t)]\n                    t_prev_list = [vec_t]\n                elif step_index < order:\n                    init_order = step_index\n                # Init the first `order` values by lower order multistep DPM-Solver.\n                # for init_order in range(1, order):\n                    vec_t = timesteps[init_order].expand(x.shape[0])\n                    x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, init_order, use_corrector=True)\n                    if model_x is None:\n                        model_x = self.model_fn(x, vec_t)\n                    model_prev_list.append(model_x)\n                    t_prev_list.append(vec_t)\n                else:\n                    extra_final_step = 0\n                    if step_index == (steps - 1):\n                        extra_final_step = 1\n                    for step in range(step_index, step_index + 1 + extra_final_step):\n                        vec_t = timesteps[step].expand(x.shape[0])\n                        if lower_order_final:\n                            step_order = min(order, steps + 1 - step)\n                        else:\n                            step_order = order\n                        # print('this step order:', step_order)\n                        if step == steps:\n                            # print('do not run corrector at the last step')\n                            use_corrector = False\n                        else:\n                            use_corrector = True\n                        x, model_x =  self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, step_order, use_corrector=use_corrector)\n                        for i in range(order - 1):\n                            t_prev_list[i] = t_prev_list[i + 1]\n                            model_prev_list[i] = model_prev_list[i + 1]\n                        t_prev_list[-1] = vec_t\n                        # We do not need to evaluate the final model value.\n                        if step < steps:\n                            if model_x is None:\n                                model_x = self.model_fn(x, vec_t)\n                            model_prev_list[-1] = model_x\n                if callback is not None:\n                    callback(step_index, model_prev_list[-1], x, steps)\n        else:\n            raise NotImplementedError()\n        # if denoise_to_zero:\n        #     x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0)\n        return x\n\n\n#############################################################\n# other utility functions\n#############################################################\n\ndef interpolate_fn(x, xp, yp):\n    \"\"\"\n    A piecewise linear function y = f(x), using xp and yp as keypoints.\n    We implement f(x) in a differentiable way (i.e. applicable for autograd).\n    The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.)\n\n    Args:\n        x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver).\n        xp: PyTorch tensor with shape [C, K], where K is the number of keypoints.\n        yp: PyTorch tensor with shape [C, K].\n    Returns:\n        The function values f(x), with shape [N, C].\n    \"\"\"\n    N, K = x.shape[0], xp.shape[1]\n    all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2)\n    sorted_all_x, x_indices = torch.sort(all_x, dim=2)\n    x_idx = torch.argmin(x_indices, dim=2)\n    cand_start_idx = x_idx - 1\n    start_idx = torch.where(\n        torch.eq(x_idx, 0),\n        torch.tensor(1, device=x.device),\n        torch.where(\n            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,\n        ),\n    )\n    end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1)\n    start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2)\n    end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2)\n    start_idx2 = torch.where(\n        torch.eq(x_idx, 0),\n        torch.tensor(0, device=x.device),\n        torch.where(\n            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,\n        ),\n    )\n    y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1)\n    start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2)\n    end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2)\n    cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x)\n    return cand\n\n\ndef expand_dims(v, dims):\n    \"\"\"\n    Expand the tensor `v` to the dim `dims`.\n\n    Args:\n        `v`: a PyTorch tensor with shape [N].\n        `dim`: a `int`.\n    Returns:\n        a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`.\n    \"\"\"\n    return v[(...,) + (None,)*(dims - 1)]\n\n\nclass SigmaConvert:\n    schedule = \"\"\n    def marginal_log_mean_coeff(self, sigma):\n        return 0.5 * torch.log(1 / ((sigma * sigma) + 1))\n\n    def marginal_alpha(self, t):\n        return torch.exp(self.marginal_log_mean_coeff(t))\n\n    def marginal_std(self, t):\n        return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t)))\n\n    def marginal_lambda(self, t):\n        \"\"\"\n        Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].\n        \"\"\"\n        log_mean_coeff = self.marginal_log_mean_coeff(t)\n        log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff))\n        return log_mean_coeff - log_std\n\ndef predict_eps_sigma(model, input, sigma_in, **kwargs):\n    sigma = sigma_in.view(sigma_in.shape[:1] + (1,) * (input.ndim - 1))\n    input = input * ((sigma ** 2 + 1.0) ** 0.5)\n    return  (input - model(input, sigma_in, **kwargs)) / sigma\n\n\ndef sample_unipc(model, noise, image, sigmas, max_denoise, extra_args=None, callback=None, disable=False, noise_mask=None, variant='bh1'):\n        timesteps = sigmas.clone()\n        if sigmas[-1] == 0:\n            timesteps = sigmas[:]\n            timesteps[-1] = 0.001\n        else:\n            timesteps = sigmas.clone()\n        ns = SigmaConvert()\n\n        if image is not None:\n            img = image * ns.marginal_alpha(timesteps[0])\n            if max_denoise:\n                noise_mult = 1.0\n            else:\n                noise_mult = ns.marginal_std(timesteps[0])\n            img += noise * noise_mult\n        else:\n            img = noise\n\n        model_type = \"noise\"\n\n        model_fn = model_wrapper(\n            lambda input, sigma, **kwargs: predict_eps_sigma(model, input, sigma, **kwargs),\n            ns,\n            model_type=model_type,\n            guidance_type=\"uncond\",\n            model_kwargs=extra_args,\n        )\n\n        order = min(3, len(timesteps) - 2)\n        uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, noise_mask=noise_mask, masked_image=image, noise=noise, variant=variant)\n        x = uni_pc.sample(img, timesteps=timesteps, skip_type=\"time_uniform\", method=\"multistep\", order=order, lower_order_final=True, callback=callback, disable_pbar=disable)\n        x /= ns.marginal_alpha(timesteps[-1])\n        return x\n"
  },
  {
    "path": "ldm_patched/utils/latent_visualization.py",
    "content": "import torch\nfrom PIL import Image\nimport struct\nimport numpy as np\nfrom ldm_patched.modules.args_parser import args, LatentPreviewMethod\nfrom ldm_patched.taesd.taesd import TAESD\nimport ldm_patched.utils.path_utils\nimport ldm_patched.modules.utils\n\nMAX_PREVIEW_RESOLUTION = 512\n\nclass LatentPreviewer:\n    def decode_latent_to_preview(self, x0):\n        pass\n\n    def decode_latent_to_preview_image(self, preview_format, x0):\n        preview_image = self.decode_latent_to_preview(x0)\n        return (\"JPEG\", preview_image, MAX_PREVIEW_RESOLUTION)\n\nclass TAESDPreviewerImpl(LatentPreviewer):\n    def __init__(self, taesd):\n        self.taesd = taesd\n\n    def decode_latent_to_preview(self, x0):\n        x_sample = self.taesd.decode(x0[:1])[0].detach()\n        x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)\n        x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)\n        x_sample = x_sample.astype(np.uint8)\n\n        preview_image = Image.fromarray(x_sample)\n        return preview_image\n\n\nclass Latent2RGBPreviewer(LatentPreviewer):\n    def __init__(self, latent_rgb_factors):\n        self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device=\"cpu\")\n\n    def decode_latent_to_preview(self, x0):\n        latent_image = x0[0].permute(1, 2, 0).cpu() @ self.latent_rgb_factors\n\n        latents_ubyte = (((latent_image + 1) / 2)\n                            .clamp(0, 1)  # change scale from -1..1 to 0..1\n                            .mul(0xFF)  # to 0..255\n                            .byte()).cpu()\n\n        return Image.fromarray(latents_ubyte.numpy())\n\n\ndef get_previewer(device, latent_format):\n    previewer = None\n    method = args.preview_option\n    if method != LatentPreviewMethod.NoPreviews:\n        # TODO previewer methods\n        taesd_decoder_path = None\n        if latent_format.taesd_decoder_name is not None:\n            taesd_decoder_path = next(\n                (fn for fn in ldm_patched.utils.path_utils.get_filename_list(\"vae_approx\")\n                    if fn.startswith(latent_format.taesd_decoder_name)),\n                \"\"\n            )\n            taesd_decoder_path = ldm_patched.utils.path_utils.get_full_path(\"vae_approx\", taesd_decoder_path)\n\n        if method == LatentPreviewMethod.Auto:\n            method = LatentPreviewMethod.Latent2RGB\n            if taesd_decoder_path:\n                method = LatentPreviewMethod.TAESD\n\n        if method == LatentPreviewMethod.TAESD:\n            if taesd_decoder_path:\n                taesd = TAESD(None, taesd_decoder_path).to(device)\n                previewer = TAESDPreviewerImpl(taesd)\n            else:\n                print(\"Warning: TAESD previews enabled, but could not find models/vae_approx/{}\".format(latent_format.taesd_decoder_name))\n\n        if previewer is None:\n            if latent_format.latent_rgb_factors is not None:\n                previewer = Latent2RGBPreviewer(latent_format.latent_rgb_factors)\n    return previewer\n\ndef prepare_callback(model, steps, x0_output_dict=None):\n    preview_format = \"JPEG\"\n    if preview_format not in [\"JPEG\", \"PNG\"]:\n        preview_format = \"JPEG\"\n\n    previewer = get_previewer(model.load_device, model.model.latent_format)\n\n    pbar = ldm_patched.modules.utils.ProgressBar(steps)\n    def callback(step, x0, x, total_steps):\n        if x0_output_dict is not None:\n            x0_output_dict[\"x0\"] = x0\n\n        preview_bytes = None\n        if previewer:\n            preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0)\n        pbar.update_absolute(step + 1, total_steps, preview_bytes)\n    return callback\n\n"
  },
  {
    "path": "ldm_patched/utils/path_utils.py",
    "content": "import os\nimport time\n\nsupported_pt_extensions = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors'])\n\nfolder_names_and_paths = {}\n\nbase_path = os.getcwd()\nmodels_dir = os.path.join(base_path, \"models\")\nfolder_names_and_paths[\"checkpoints\"] = ([os.path.join(models_dir, \"checkpoints\")], supported_pt_extensions)\nfolder_names_and_paths[\"configs\"] = ([os.path.join(models_dir, \"configs\")], [\".yaml\"])\n\nfolder_names_and_paths[\"loras\"] = ([os.path.join(models_dir, \"loras\")], supported_pt_extensions)\nfolder_names_and_paths[\"vae\"] = ([os.path.join(models_dir, \"vae\")], supported_pt_extensions)\nfolder_names_and_paths[\"clip\"] = ([os.path.join(models_dir, \"clip\")], supported_pt_extensions)\nfolder_names_and_paths[\"unet\"] = ([os.path.join(models_dir, \"unet\")], supported_pt_extensions)\nfolder_names_and_paths[\"clip_vision\"] = ([os.path.join(models_dir, \"clip_vision\")], supported_pt_extensions)\nfolder_names_and_paths[\"style_models\"] = ([os.path.join(models_dir, \"style_models\")], supported_pt_extensions)\nfolder_names_and_paths[\"embeddings\"] = ([os.path.join(models_dir, \"embeddings\")], supported_pt_extensions)\nfolder_names_and_paths[\"diffusers\"] = ([os.path.join(models_dir, \"diffusers\")], [\"folder\"])\nfolder_names_and_paths[\"vae_approx\"] = ([os.path.join(models_dir, \"vae_approx\")], supported_pt_extensions)\n\nfolder_names_and_paths[\"controlnet\"] = ([os.path.join(models_dir, \"controlnet\"), os.path.join(models_dir, \"t2i_adapter\")], supported_pt_extensions)\nfolder_names_and_paths[\"gligen\"] = ([os.path.join(models_dir, \"gligen\")], supported_pt_extensions)\n\nfolder_names_and_paths[\"upscale_models\"] = ([os.path.join(models_dir, \"upscale_models\")], supported_pt_extensions)\n\nfolder_names_and_paths[\"custom_nodes\"] = ([os.path.join(base_path, \"custom_nodes\")], [])\n\nfolder_names_and_paths[\"hypernetworks\"] = ([os.path.join(models_dir, \"hypernetworks\")], supported_pt_extensions)\n\nfolder_names_and_paths[\"photomaker\"] = ([os.path.join(models_dir, \"photomaker\")], supported_pt_extensions)\n\nfolder_names_and_paths[\"classifiers\"] = ([os.path.join(models_dir, \"classifiers\")], {\"\"})\n\noutput_directory = os.path.join(os.getcwd(), \"output\")\ntemp_directory = os.path.join(os.getcwd(), \"temp\")\ninput_directory = os.path.join(os.getcwd(), \"input\")\nuser_directory = os.path.join(os.getcwd(), \"user\")\n\nfilename_list_cache = {}\n\nif not os.path.exists(input_directory):\n    try:\n        pass  # os.makedirs(input_directory)\n    except:\n        print(\"Failed to create input directory\")\n\ndef set_output_directory(output_dir):\n    global output_directory\n    output_directory = output_dir\n\ndef set_temp_directory(temp_dir):\n    global temp_directory\n    temp_directory = temp_dir\n\ndef set_input_directory(input_dir):\n    global input_directory\n    input_directory = input_dir\n\ndef get_output_directory():\n    global output_directory\n    return output_directory\n\ndef get_temp_directory():\n    global temp_directory\n    return temp_directory\n\ndef get_input_directory():\n    global input_directory\n    return input_directory\n\n\n#NOTE: used in http server so don't put folders that should not be accessed remotely\ndef get_directory_by_type(type_name):\n    if type_name == \"output\":\n        return get_output_directory()\n    if type_name == \"temp\":\n        return get_temp_directory()\n    if type_name == \"input\":\n        return get_input_directory()\n    return None\n\n\n# determine base_dir rely on annotation if name is 'filename.ext [annotation]' format\n# otherwise use default_path as base_dir\ndef annotated_filepath(name):\n    if name.endswith(\"[output]\"):\n        base_dir = get_output_directory()\n        name = name[:-9]\n    elif name.endswith(\"[input]\"):\n        base_dir = get_input_directory()\n        name = name[:-8]\n    elif name.endswith(\"[temp]\"):\n        base_dir = get_temp_directory()\n        name = name[:-7]\n    else:\n        return name, None\n\n    return name, base_dir\n\n\ndef get_annotated_filepath(name, default_dir=None):\n    name, base_dir = annotated_filepath(name)\n\n    if base_dir is None:\n        if default_dir is not None:\n            base_dir = default_dir\n        else:\n            base_dir = get_input_directory()  # fallback path\n\n    return os.path.join(base_dir, name)\n\n\ndef exists_annotated_filepath(name):\n    name, base_dir = annotated_filepath(name)\n\n    if base_dir is None:\n        base_dir = get_input_directory()  # fallback path\n\n    filepath = os.path.join(base_dir, name)\n    return os.path.exists(filepath)\n\n\ndef add_model_folder_path(folder_name, full_folder_path):\n    global folder_names_and_paths\n    if folder_name in folder_names_and_paths:\n        folder_names_and_paths[folder_name][0].append(full_folder_path)\n    else:\n        folder_names_and_paths[folder_name] = ([full_folder_path], set())\n\ndef get_folder_paths(folder_name):\n    return folder_names_and_paths[folder_name][0][:]\n\ndef recursive_search(directory, excluded_dir_names=None):\n    if not os.path.isdir(directory):\n        return [], {}\n\n    if excluded_dir_names is None:\n        excluded_dir_names = []\n\n    result = []\n    dirs = {}\n\n    # Attempt to add the initial directory to dirs with error handling\n    try:\n        dirs[directory] = os.path.getmtime(directory)\n    except FileNotFoundError:\n        print(f\"Warning: Unable to access {directory}. Skipping this path.\")\n        \n    for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True):\n        subdirs[:] = [d for d in subdirs if d not in excluded_dir_names]\n        for file_name in filenames:\n            relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory)\n            result.append(relative_path)\n        \n        for d in subdirs:\n            path = os.path.join(dirpath, d)\n            try:\n                dirs[path] = os.path.getmtime(path)\n            except FileNotFoundError:\n                print(f\"Warning: Unable to access {path}. Skipping this path.\")\n                continue\n    return result, dirs\n\ndef filter_files_extensions(files, extensions):\n    return sorted(list(filter(lambda a: os.path.splitext(a)[-1].lower() in extensions or len(extensions) == 0, files)))\n\n\n\ndef get_full_path(folder_name, filename):\n    global folder_names_and_paths\n    if folder_name not in folder_names_and_paths:\n        return None\n    folders = folder_names_and_paths[folder_name]\n    filename = os.path.relpath(os.path.join(\"/\", filename), \"/\")\n    for x in folders[0]:\n        full_path = os.path.join(x, filename)\n        if os.path.isfile(full_path):\n            return full_path\n\n    return None\n\ndef get_filename_list_(folder_name):\n    global folder_names_and_paths\n    output_list = set()\n    folders = folder_names_and_paths[folder_name]\n    output_folders = {}\n    for x in folders[0]:\n        files, folders_all = recursive_search(x, excluded_dir_names=[\".git\"])\n        output_list.update(filter_files_extensions(files, folders[1]))\n        output_folders = {**output_folders, **folders_all}\n\n    return (sorted(list(output_list)), output_folders, time.perf_counter())\n\ndef cached_filename_list_(folder_name):\n    global filename_list_cache\n    global folder_names_and_paths\n    if folder_name not in filename_list_cache:\n        return None\n    out = filename_list_cache[folder_name]\n\n    for x in out[1]:\n        time_modified = out[1][x]\n        folder = x\n        if os.path.getmtime(folder) != time_modified:\n            return None\n\n    folders = folder_names_and_paths[folder_name]\n    for x in folders[0]:\n        if os.path.isdir(x):\n            if x not in out[1]:\n                return None\n\n    return out\n\ndef get_filename_list(folder_name):\n    out = cached_filename_list_(folder_name)\n    if out is None:\n        out = get_filename_list_(folder_name)\n        global filename_list_cache\n        filename_list_cache[folder_name] = out\n    return list(out[0])\n\ndef get_save_image_path(filename_prefix, output_dir, image_width=0, image_height=0):\n    def map_filename(filename):\n        prefix_len = len(os.path.basename(filename_prefix))\n        prefix = filename[:prefix_len + 1]\n        try:\n            digits = int(filename[prefix_len + 1:].split('_')[0])\n        except:\n            digits = 0\n        return (digits, prefix)\n\n    def compute_vars(input, image_width, image_height):\n        input = input.replace(\"%width%\", str(image_width))\n        input = input.replace(\"%height%\", str(image_height))\n        return input\n\n    filename_prefix = compute_vars(filename_prefix, image_width, image_height)\n\n    subfolder = os.path.dirname(os.path.normpath(filename_prefix))\n    filename = os.path.basename(os.path.normpath(filename_prefix))\n\n    full_output_folder = os.path.join(output_dir, subfolder)\n\n    if os.path.commonpath((output_dir, os.path.abspath(full_output_folder))) != output_dir:\n        err = \"**** ERROR: Saving image outside the output folder is not allowed.\" + \\\n              \"\\n full_output_folder: \" + os.path.abspath(full_output_folder) + \\\n              \"\\n         output_dir: \" + output_dir + \\\n              \"\\n         commonpath: \" + os.path.commonpath((output_dir, os.path.abspath(full_output_folder))) \n        print(err)\n        raise Exception(err)\n\n    try:\n        counter = max(filter(lambda a: a[1][:-1] == filename and a[1][-1] == \"_\", map(map_filename, os.listdir(full_output_folder))))[0] + 1\n    except ValueError:\n        counter = 1\n    except FileNotFoundError:\n        os.makedirs(full_output_folder, exist_ok=True)\n        counter = 1\n    return full_output_folder, filename, counter, subfolder, filename_prefix\n"
  },
  {
    "path": "models/checkpoints/put_checkpoints_here",
    "content": ""
  },
  {
    "path": "models/clip/put_clip_or_text_encoder_models_here",
    "content": ""
  },
  {
    "path": "models/clip_vision/put_clip_vision_models_here",
    "content": ""
  },
  {
    "path": "models/clip_vision/wd-v1-4-moat-tagger-v2.csv",
    "content": "tag_id,name,category,count\n9999999,general,9,807858\n9999998,sensitive,9,3771700\n9999997,questionable,9,769899\n9999996,explicit,9,560281\n470575,1girl,0,4225150\n212816,solo,0,3515897\n13197,long_hair,0,2982517\n8601,breasts,0,2323580\n469576,looking_at_viewer,0,2089971\n3389,blush,0,2040471\n1815,smile,0,1903619\n15080,short_hair,0,1568265\n11906,open_mouth,0,1565950\n16751,bangs,0,1516840\n10959,blue_eyes,0,1225129\n566835,multiple_girls,0,1120328\n429,skirt,0,1100620\n87788,blonde_hair,0,1098200\n403247,large_breasts,0,1083979\n412368,simple_background,0,1074818\n16867,brown_hair,0,1072209\n12590,shirt,0,1001030\n13200,black_hair,0,981413\n380350,hair_ornament,0,939495\n8526,red_eyes,0,897316\n1882,thighhighs,0,890813\n5735,gloves,0,886283\n383159,long_sleeves,0,883900\n540830,1boy,0,881194\n2373,hat,0,879697\n515193,white_background,0,874291\n2241,dress,0,838290\n4563,bow,0,795194\n464575,ribbon,0,793922\n9294,navel,0,786293\n375387,holding,0,732899\n1821,2girls,0,729721\n6126,animal_ears,0,722334\n4607,cleavage,0,693321\n658573,hair_between_eyes,0,692014\n376054,bare_shoulders,0,656975\n1709,twintails,0,648384\n16578,brown_eyes,0,645874\n16613,jewelry,0,644654\n667868,medium_breasts,0,642525\n12289,sitting,0,630993\n417660,very_long_hair,0,622920\n572080,closed_mouth,0,618062\n464906,underwear,0,610036\n8889,nipples,0,591774\n16509,school_uniform,0,585679\n10960,green_eyes,0,584783\n10953,blue_hair,0,564360\n15675,standing,0,551783\n15654,purple_eyes,0,536623\n466499,collarbone,0,520875\n391,panties,0,506334\n3843,jacket,0,493731\n15674,tail,0,487490\n1681,monochrome,0,478584\n444,swimsuit,0,467619\n608813,full_body,0,463008\n465619,closed_eyes,0,455512\n464561,hair_ribbon,0,449452\n89189,yellow_eyes,0,447582\n376766,white_shirt,0,435867\n547463,upper_body,0,434670\n2355,ponytail,0,431021\n11449,weapon,0,430315\n11429,pink_hair,0,427100\n16442,purple_hair,0,426385\n8101,ass,0,423113\n4334,braid,0,417832\n464559,flower,0,411874\n63,comic,0,411524\n3522,ahoge,0,408654\n16581,white_hair,0,407226\n472154,short_sleeves,0,389311\n384553,:d,0,387533\n622137,hetero,0,384576\n374844,hair_bow,0,381335\n513837,greyscale,0,377967\n16580,grey_hair,0,375103\n1300281,male_focus,0,371503\n2750,heart,0,361897\n2363,pantyhose,0,356177\n484168,sidelocks,0,354421\n6539,bikini,0,349809\n3870,thighs,0,348316\n2365,nude,0,341086\n5403,red_hair,0,338682\n390728,multicolored_hair,0,336863\n660909,cowboy_shot,0,336374\n4569,sweat,0,334084\n383282,pleated_skirt,0,332323\n2376,hairband,0,329485\n13804,earrings,0,328755\n465265,small_breasts,0,325542\n5827,boots,0,320845\n13879,outdoors,0,320641\n301022,lying,0,312125\n4352,censored,0,310189\n194013,frills,0,305699\n664375,parted_lips,0,304757\n387884,detached_sleeves,0,297463\n461042,one_eye_closed,0,294831\n1575,food,0,294463\n1707,japanese_clothes,0,289163\n8388,green_hair,0,286703\n568656,multiple_boys,0,286561\n375669,open_clothes,0,286373\n2866,wings,0,284183\n384774,necktie,0,281254\n2785,horns,0,279479\n406,sky,0,279456\n4190,penis,0,276603\n8672,shoes,0,273266\n6532,glasses,0,264431\n3985,shorts,0,263445\n11826,barefoot,0,260159\n6054,teeth,0,259880\n4378,pussy,0,254400\n268819,serafuku,0,250901\n400314,sleeveless,0,247486\n931006,solo_focus,0,246572\n431446,alternate_costume,0,245996\n4025,choker,0,245304\n435324,day,0,243849\n10863,tongue,0,243841\n15522,pointy_ears,0,237263\n474820,black_gloves,0,236325\n1731,socks,0,235479\n2646,hairclip,0,224901\n10228,elbow_gloves,0,224390\n1793,fang,0,220644\n9843,midriff,0,217384\n2177,striped,0,215308\n581144,puffy_sleeves,0,214965\n2772,shiny,0,213040\n478849,collared_shirt,0,210998\n9864,belt,0,210075\n410129,looking_back,0,208023\n10926,pants,0,206605\n401228,sword,0,203029\n657955,artist_name,0,200583\n455880,black_thighhighs,0,199445\n464549,cloud,0,197453\n375171,indoors,0,197331\n1407561,virtual_youtuber,0,190790\n72,cat_ears,0,189097\n6010,tears,0,187990\n474821,white_gloves,0,187659\n539367,hand_up,0,184840\n411263,signature,0,183546\n400123,hair_flower,0,183132\n71730,dark_skin,0,182935\n399836,3girls,0,181979\n16252,spread_legs,0,179394\n1842,cum,0,177804\n5948,hood,0,176872\n449194,2boys,0,176291\n2357,sex,0,176106\n502710,tongue_out,0,174092\n9168,miniskirt,0,173815\n394970,wide_sleeves,0,172751\n537684,blunt_bangs,0,172693\n401481,on_back,0,172347\n375020,fingerless_gloves,0,171771\n11628,bowtie,0,171509\n374628,black_skirt,0,171449\n426781,medium_hair,0,170863\n16750,pink_eyes,0,168674\n5576,armpits,0,168191\n389378,sailor_collar,0,166004\n445,kimono,0,163145\n632214,grey_background,0,159811\n1573,water,0,158702\n4068,necklace,0,156154\n375229,off_shoulder,0,153621\n467104,stomach,0,153138\n4333,bag,0,151509\n454933,hair_bun,0,151363\n1747,chibi,0,151305\n473983,clothes_lift,0,150801\n1254363,twitter_username,0,150476\n234192,from_behind,0,150284\n656624,star_(symbol),0,150164\n3198,scarf,0,149539\n6346,cape,0,149374\n102962,nail_polish,0,148880\n659098,black_footwear,0,148127\n670071,holding_weapon,0,147624\n3796,bra,0,147612\n14620,white_dress,0,147542\n87676,orange_hair,0,147142\n261,yuri,0,146254\n125238,sweatdrop,0,145506\n4659,armor,0,143723\n465583,rabbit_ears,0,143630\n4019,mole,0,143178\n374791,white_panties,0,142500\n399541,hair_over_one_eye,0,141370\n384884,grin,0,140525\n6107,blurry,0,140047\n393959,huge_breasts,0,138103\n641577,looking_at_another,0,137202\n14599,:o,0,137129\n4152,uniform,0,136675\n13199,black_eyes,0,136390\n273,apron,0,136126\n510962,character_name,0,135131\n6176,vest,0,133100\n39127,black_dress,0,132376\n682673,mosaic_censoring,0,131028\n475187,arm_up,0,130488\n10484,vaginal,0,130137\n470807,red_bow,0,129853\n9882,high_heels,0,129652\n524070,shiny_hair,0,128887\n52138,twin_braids,0,127920\n399827,arms_up,0,126645\n378032,flat_chest,0,126144\n381629,side_ponytail,0,126049\n4052,collar,0,125695\n11904,bracelet,0,125557\n2060,feet,0,125245\n515329,covered_nipples,0,124425\n511141,from_side,0,122971\n560958,dated,0,122085\n413564,two-tone_hair,0,121190\n89368,aqua_eyes,0,120894\n3477,sweater,0,120879\n440465,speech_bubble,0,120449\n375002,white_thighhighs,0,119709\n590165,english_text,0,118831\n6295,leotard,0,118770\n379615,open_jacket,0,116898\n1515536,official_alternate_costume,0,116756\n390401,red_ribbon,0,116423\n633894,dark-skinned_female,0,115383\n652604,two_side_up,0,114391\n464586,tree,0,113682\n464553,cup,0,113550\n490655,blue_sky,0,113376\n1931,sketch,0,112175\n684639,puffy_short_sleeves,0,111946\n5501,lips,0,111075\n428173,blue_skirt,0,110413\n10644,zettai_ryouiki,0,109096\n719985,streaked_hair,0,108228\n5569,coat,0,108099\n547860,black_jacket,0,107631\n395321,crop_top,0,107314\n754325,groin,0,107206\n427008,fingernails,0,106798\n4867,wet,0,105408\n1445905,v-shaped_eyebrows,0,104747\n389404,cat_tail,0,104720\n438623,neckerchief,0,103697\n95405,orange_eyes,0,103149\n1486996,animal_ear_fluff,0,102828\n431755,head_tilt,0,102674\n451371,see-through,0,102344\n391631,gradient,0,101686\n451155,hand_on_hip,0,100931\n589,gun,0,100672\n4311,legs,0,100492\n494251,one-piece_swimsuit,0,100304\n494744,shiny_skin,0,100275\n479939,sleeves_past_wrists,0,100013\n551772,parted_bangs,0,99655\n579466,looking_to_the_side,0,99629\n464572,pillow,0,99176\n412078,wrist_cuffs,0,98658\n89228,grey_eyes,0,98579\n10923,torn_clothes,0,98539\n464535,book,0,98180\n465277,plaid,0,97928\n457408,black_pantyhose,0,97171\n59,maid,0,97157\n537200,symbol-shaped_pupils,0,96639\n481846,hands_up,0,95746\n379475,sash,0,95553\n539584,fur_trim,0,95386\n3314,kneehighs,0,95270\n463173,maid_headdress,0,95208\n7820,military,0,94582\n16718,black_panties,0,94548\n358,cosplay,0,94464\n475418,bare_arms,0,93864\n13132,petals,0,93775\n12552,pubic_hair,0,93487\n524961,black_shirt,0,93152\n2335,fox_ears,0,92753\n128,loli,0,92684\n531371,gradient_background,0,92533\n394174,short_shorts,0,92401\n449676,ascot,0,90602\n1594634,clothing_cutout,0,90293\n822149,completely_nude,0,90178\n375372,dutch_angle,0,89835\n416202,eyelashes,0,88539\n492223,bar_censor,0,88359\n1277433,mole_under_eye,0,88173\n1128907,pokemon_(creature),0,88046\n542846,no_humans,0,87715\n469714,bare_legs,0,87348\n2813,window,0,87096\n7577,open_shirt,0,86869\n464579,sparkle,0,86798\n8641,dress_shirt,0,86540\n10447,kneeling,0,86034\n407186,sleeveless_shirt,0,85905\n389813,single_braid,0,85125\n2687,v,0,84806\n1390441,black_headwear,0,84355\n382397,strapless,0,84204\n412555,4girls,0,84095\n6536,bell,0,83798\n5126,hug,0,83531\n390035,no_bra,0,83268\n7926,saliva,0,83232\n468554,double_bun,0,83167\n474500,black_ribbon,0,82872\n5032,uncensored,0,82813\n94007,aqua_hair,0,82429\n9344,bodysuit,0,81762\n2508,blood,0,80986\n2585,bed,0,80885\n202817,hoodie,0,80505\n387214,military_uniform,0,80184\n6028,sideboob,0,80142\n1247160,black_bow,0,79946\n1269639,covered_navel,0,79696\n464584,tattoo,0,79445\n391128,gradient_hair,0,79377\n444539,skindentation,0,79260\n467863,neck_ribbon,0,79084\n13853,pussy_juice,0,79048\n1736,profile,0,78791\n9312,makeup,0,78404\n443395,thigh_strap,0,78303\n427348,leaning_forward,0,78279\n513475,multiple_views,0,78169\n3918,4koma,0,77795\n411323,capelet,0,77781\n1797,mask,0,77477\n465719,muscular,0,76977\n2217,anus,0,76396\n1595,no_panties,0,76376\n10422,witch_hat,0,76061\n600177,detached_collar,0,75584\n28200,toes,0,75467\n5565,:3,0,75433\n675314,copyright_name,0,75245\n442167,alternate_hairstyle,0,74848\n626,underboob,0,74757\n3472,night,0,74665\n395400,buttons,0,74509\n565513,floating_hair,0,74507\n5832,fruit,0,74401\n568880,sleeveless_dress,0,74380\n460262,depth_of_field,0,74158\n553142,blurry_background,0,73931\n1393877,feet_out_of_frame,0,73883\n381555,headband,0,73755\n529256,fake_animal_ears,0,73059\n402217,^_^,0,72948\n377140,blue_dress,0,72944\n4075,cameltoe,0,72587\n465950,cum_in_pussy,0,72511\n51528,fox_tail,0,72338\n457597,swept_bangs,0,72173\n13176,shadow,0,71857\n374620,black_bikini,0,71747\n503552,red_skirt,0,71298\n420531,nose_blush,0,71248\n4528,bottomless,0,71247\n4320,glowing,0,71120\n1231230,side-tie_bikini_bottom,0,71041\n460802,rose,0,70153\n478565,bed_sheet,0,69319\n1094664,colored_skin,0,69055\n4009,turtleneck,0,68964\n464808,holding_hands,0,68738\n458933,facial_hair,0,68495\n464546,chain,0,68464\n442865,headgear,0,68404\n464534,bird,0,68263\n4108,pov,0,67888\n377844,siblings,0,67882\n2279,headphones,0,67786\n11325,ocean,0,67516\n567316,6+girls,0,67320\n479563,low_twintails,0,67147\n3449,heterochromia,0,67128\n394222,arm_support,0,66756\n10701,animal,0,66577\n546821,halterneck,0,66448\n374938,frown,0,66197\n4244,leaf,0,65930\n258190,beret,0,65845\n1345229,white_headwear,0,65590\n1915,umbrella,0,65482\n490999,on_bed,0,65478\n653206,one_side_up,0,65367\n144876,embarrassed,0,65057\n395448,thigh_boots,0,64869\n446950,fangs,0,64492\n1875867,upper_teeth_only,0,64490\n8714,watermark,0,64472\n444002,from_above,0,64438\n377124,back,0,64206\n436054,highleg,0,64134\n580545,blue_background,0,63957\n1328533,ass_visible_through_thighs,0,63880\n421663,wavy_hair,0,63724\n469652,garter_straps,0,63656\n1382794,black_choker,0,63389\n4010,halo,0,63200\n546609,blue_bow,0,62796\n3988,scar,0,62791\n426936,white_bikini,0,62532\n447393,on_side,0,62264\n418899,plaid_skirt,0,62245\n2904,chair,0,61608\n484666,transparent_background,0,61514\n541727,wariza,0,61484\n481508,facial_mark,0,61342\n10231,mouth_hold,0,61142\n452032,looking_away,0,61139\n400120,traditional_media,0,61046\n2799,beach,0,61036\n2091,bandages,0,61017\n1723,parody,0,61010\n663669,female_pubic_hair,0,61006\n419938,expressionless,0,60732\n1303252,brown_footwear,0,60667\n499624,blush_stickers,0,60546\n8830,shirt_lift,0,60141\n419309,thick_thighs,0,60024\n463179,no_shoes,0,59911\n698161,holding_sword,0,59858\n600250,hair_tubes,0,59809\n375404,chinese_clothes,0,59795\n8091,drill_hair,0,59557\n572767,grabbing,0,59427\n475775,arms_behind_back,0,59272\n375476,soles,0,59215\n3920,obi,0,58917\n572731,heart-shaped_pupils,0,58894\n4207,eating,0,58688\n467856,clothes_pull,0,58462\n415974,looking_down,0,58352\n2993,phone,0,58200\n612641,black_shorts,0,57955\n403649,thigh_gap,0,57892\n514890,black_pants,0,57867\n436576,short_dress,0,57816\n3593,topless,0,57655\n4831,piercing,0,57372\n2770,pantyshot,0,57314\n534835,hair_intakes,0,57252\n2270,eyepatch,0,57130\n511136,border,0,56981\n8831,skirt_lift,0,56906\n476621,floral_print,0,56816\n460404,stuffed_toy,0,56698\n466226,bound,0,56676\n2689,formal,0,56558\n709734,playboy_bunny,0,56534\n647551,flying_sweatdrops,0,56366\n375176,crossed_arms,0,56301\n574407,wavy_mouth,0,56126\n425206,magical_girl,0,56021\n3234,erection,0,55814\n387991,abs,0,55656\n2726,moon,0,55466\n688777,half-closed_eyes,0,55390\n413672,leg_up,0,55163\n420366,from_below,0,54898\n389777,red_dress,0,54821\n477028,cleavage_cutout,0,54599\n3986,sandals,0,54528\n3209,table,0,54497\n13227,happy,0,54420\n356542,sunlight,0,54218\n1850,oral,0,54046\n1976,cover,0,53985\n465810,squatting,0,53344\n1801537,single_hair_bun,0,53328\n2716,cat,0,53145\n419387,testicles,0,53002\n584749,pink_background,0,52864\n5524,sunglasses,0,52501\n488167,scrunchie,0,52459\n1288957,white_footwear,0,52395\n563478,dark-skinned_male,0,52274\n547073,underwear_only,0,52043\n461293,cum_on_body,0,51426\n413179,trembling,0,51424\n445388,bob_cut,0,51373\n3949,ring,0,51372\n9354,bdsm,0,51131\n221,school_swimsuit,0,51107\n582201,mob_cap,0,50771\n385639,wolf_ears,0,50613\n6059,blazer,0,50589\n468534,light_brown_hair,0,50353\n580232,white_jacket,0,50297\n459291,standing_on_one_leg,0,50267\n3875,sleeping,0,50236\n450107,thick_eyebrows,0,50035\n1556,backpack,0,49896\n520398,white_skirt,0,49388\n8068,demon_girl,0,49382\n492544,frilled_dress,0,49309\n643561,eyes_visible_through_hair,0,49304\n15399,breast_grab,0,49237\n6128,cardigan,0,49158\n431235,knee_boots,0,48944\n7952,suspenders,0,48876\n464588,hat_ribbon,0,48794\n465525,crossed_legs,0,48759\n319,lingerie,0,48692\n379915,stuffed_animal,0,48687\n5831,katana,0,48654\n589398,hood_down,0,48528\n466990,;d,0,48266\n487156,3boys,0,48154\n15737,bat_wings,0,48120\n391680,horse_ears,0,48063\n3649,helmet,0,47863\n487205,cloudy_sky,0,47782\n3239,cellphone,0,47781\n464903,crying,0,47537\n9114,antenna_hair,0,47482\n900563,own_hands_together,0,47389\n7455,tank_top,0,47356\n10833,bottle,0,47321\n10229,suit,0,47203\n6364,grass,0,47129\n516477,outstretched_arms,0,47051\n4526,cross,0,46975\n464539,bug,0,46589\n609507,holding_food,0,46523\n4474,fire,0,46465\n419429,frilled_skirt,0,46415\n429999,tiara,0,46411\n1836990,aged_down,0,46406\n464574,polka_dot,0,46378\n14452,feathers,0,46364\n510254,breasts_out,0,46228\n1936,crossover,0,46213\n4188,crown,0,46184\n539837,high_ponytail,0,46117\n375459,looking_up,0,46055\n580738,black_hairband,0,46027\n9714,bent_over,0,46026\n3592,undressing,0,45997\n397327,blue_shirt,0,45944\n701697,white_bow,0,45887\n421662,5girls,0,45886\n462569,straddling,0,45739\n575982,light_smile,0,45730\n464565,knife,0,45555\n634316,pectorals,0,45480\n1337464,x_hair_ornament,0,45361\n464573,plant,0,45164\n14814,couple,0,45140\n219401,denim,0,45131\n432696,on_stomach,0,44802\n487562,wing_collar,0,44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3024\n427674,epaulettes,0,23020\n376923,black_wings,0,23007\n464538,bubble,0,22982\n388067,demon_wings,0,22937\n4925,thong,0,22871\n617615,legs_apart,0,22854\n4648,teacup,0,22847\n1569,condom,0,22825\n442816,veins,0,22823\n465382,crossdressing,0,22797\n1258089,ribbon-trimmed_sleeves,0,22795\n1251593,holding_phone,0,22789\n7450,gym_uniform,0,22731\n475720,short_ponytail,0,22714\n529447,arm_behind_head,0,22709\n1916,cake,0,22701\n602223,out_of_frame,0,22689\n2169,innertube,0,22668\n583857,oni_horns,0,22662\n669624,contrapposto,0,22656\n10376,naughty_face,0,22568\n593297,green_background,0,22526\n633528,alternate_breast_size,0,22467\n643898,purple_background,0,22460\n1373029,black-framed_eyewear,0,22424\n1810,rape,0,22416\n6441,beads,0,22362\n654772,knee_up,0,22316\n610698,hat_ornament,0,22311\n1636487,one-hour_drawing_challenge,0,22279\n570718,fur_collar,0,22266\n617356,blue_shorts,0,22246\n713987,outside_border,0,22190\n728008,thighband_pantyhose,0,22190\n8198,meme,0,22166\n10402,bowl,0,22146\n2056,toenails,0,22138\n3354,cumdrip,0,22082\n1242758,blue_flower,0,22045\n416676,denim_shorts,0,22042\n394305,curly_hair,0,22035\n457726,track_jacket,0,22026\n1388800,black_sailor_collar,0,21962\n1388933,light_blush,0,21928\n374849,school_bag,0,21924\n508016,pocket,0,21923\n10543,spread_pussy,0,21902\n403286,toned,0,21876\n514515,pink_shirt,0,21867\n5875,doggystyle,0,21840\n688711,white_sleeves,0,21801\n412202,:q,0,21781\n1278062,hand_in_own_hair,0,21700\n1292999,spoken_ellipsis,0,21670\n375986,empty_eyes,0,21646\n610272,purple_skirt,0,21645\n572906,crying_with_eyes_open,0,21630\n583299,goggles_on_head,0,21629\n166757,green_dress,0,21584\n479955,4boys,0,21539\n168390,bulge,0,21519\n466881,sun_hat,0,21508\n377078,cum_in_mouth,0,21505\n452086,lolita_fashion,0,21497\n427050,shiny_clothes,0,21494\n9872,pauldrons,0,21475\n397215,outline,0,21438\n2378,buruma,0,21384\n670088,hand_on_another's_head,0,21384\n389,futanari,0,21376\n1592077,topless_male,0,21362\n1441886,under-rim_eyewear,0,21290\n579793,frilled_apron,0,21156\n560247,white_pupils,0,21124\n464578,skull,0,21119\n492380,jitome,0,21103\n684644,gold_trim,0,21037\n398273,long_legs,0,21023\n2489,sunset,0,21004\n464566,monster,0,20950\n1258262,frilled_shirt_collar,0,20942\n559163,emphasis_lines,0,20888\n426491,hands_on_hips,0,20853\n1119509,high-waist_skirt,0,20812\n7585,new_year,0,20801\n106450,shield,0,20770\n580379,aged_up,0,20754\n1670660,animal_hands,0,20736\n554098,mole_on_breast,0,20679\n4145,spear,0,20661\n538475,asymmetrical_hair,0,20605\n472916,female_masturbation,0,20559\n534168,v_arms,0,20548\n483919,single_earring,0,20521\n6439,running,0,20477\n4039,dog,0,20400\n384087,angel_wings,0,20387\n391713,long_skirt,0,20326\n1874884,breasts_squeezed_together,0,20292\n442474,competition_swimsuit,0,20256\n11882,watch,0,20197\n380572,dog_tail,0,20182\n546229,black_belt,0,20142\n1233478,black_serafuku,0,20119\n10031,faceless_male,0,20106\n652293,legs_together,0,20102\n10538,ice,0,20076\n391297,white_skin,0,20066\n1293269,blue_footwear,0,20063\n434996,o_o,0,20055\n454379,=_=,0,20055\n18013,ass_grab,0,20045\n509884,impossible_clothes,0,19994\n1247162,purple_bow,0,19993\n493843,head_rest,0,19941\n494245,red_scarf,0,19940\n9831,teddy_bear,0,19932\n399655,striped_bikini,0,19911\n537668,poke_ball,0,19839\n613885,brown_skirt,0,19829\n442316,pouch,0,19819\n1559,minigirl,0,19805\n452445,+_+,0,19786\n1396724,white_sailor_collar,0,19749\n1515358,blue_theme,0,19669\n16554,plump,0,19626\n2467,ghost,0,19612\n2336,cigarette,0,19600\n538012,hat_removed,0,19598\n394759,hand_in_pocket,0,19584\n474995,brown_pantyhose,0,19528\n520850,bespectacled,0,19521\n494842,braided_ponytail,0,19474\n417888,age_difference,0,19463\n690177,tress_ribbon,0,19460\n648056,paw_pose,0,19458\n375606,open_coat,0,19416\n465871,rabbit,0,19393\n8565,sarashi,0,19391\n494869,black_coat,0,19379\n658682,knees_together_feet_apart,0,19360\n15571,shawl,0,19349\n1255562,folded_ponytail,0,19273\n553367,turret,0,19264\n1271922,black_vest,0,19257\n1835738,blue_one-piece_swimsuit,0,19186\n2268,popsicle,0,19177\n462546,hand_between_legs,0,19129\n15916,hair_down,0,19087\n483081,high_collar,0,19049\n578153,drinking_straw,0,19046\n502548,yellow_shirt,0,18994\n477354,sheathed,0,18974\n375883,dragon_girl,0,18958\n399,yukata,0,18933\n620577,brown_jacket,0,18918\n1811,twins,0,18785\n382969,bow_(weapon),0,18780\n15200,robe,0,18714\n3661,oni,0,18708\n462209,thought_bubble,0,18690\n451370,upside-down,0,18665\n2967,jeans,0,18614\n417866,serious,0,18609\n401289,yellow_ribbon,0,18572\n466335,object_insertion,0,18543\n417883,striped_shirt,0,18515\n506510,military_hat,0,18514\n464562,injury,0,18443\n405124,smirk,0,18439\n398889,circlet,0,18423\n600222,light_rays,0,18405\n3603,lollipop,0,18324\n16721,blue_panties,0,18317\n11285,@_@,0,18293\n473529,;),0,18288\n452112,tabard,0,18277\n380289,missionary,0,18275\n1303583,blue_necktie,0,18271\n534968,mini_hat,0,18270\n717047,page_number,0,18270\n1386163,patreon_username,0,18260\n394368,bouncing_breasts,0,18250\n446472,red_rose,0,18244\n1814875,eyebrows_hidden_by_hair,0,18221\n467008,hoop_earrings,0,18199\n400,pajamas,0,18122\n572346,wide-eyed,0,18110\n1822,threesome,0,18100\n1611666,dakimakura_(medium),0,18057\n467285,crystal,0,18054\n4033,lantern,0,18052\n1369802,blurry_foreground,0,18022\n1314750,white_hairband,0,17984\n427398,butt_crack,0,17982\n458932,sideburns,0,17937\n1517199,mature_female,0,17906\n712723,striped_bow,0,17905\n1045681,white_shorts,0,17904\n493026,tentacle_hair,0,17900\n392585,contemporary,0,17864\n374921,sports_bra,0,17840\n5705,ofuda,0,17806\n561624,water_drop,0,17796\n1320204,black_bowtie,0,17724\n15770,top_hat,0,17717\n379945,hairpin,0,17711\n475744,breastplate,0,17707\n1256698,partially_fingerless_gloves,0,17688\n502047,blood_on_face,0,17681\n423620,:t,0,17653\n472727,wooden_floor,0,17584\n538768,height_difference,0,17567\n11135,rock,0,17531\n508272,belt_buckle,0,17522\n387213,handbag,0,17511\n448882,|_|,0,17504\n447403,jumping,0,17482\n461117,snowing,0,17469\n544306,colored_eyelashes,0,17455\n3478,mirror,0,17445\n252271,mug,0,17428\n1283885,crossed_bangs,0,17403\n4357,chocolate,0,17384\n513807,hug_from_behind,0,17364\n872616,white_kimono,0,17362\n461492,green_jacket,0,17340\n456193,outstretched_hand,0,17340\n677856,star_hair_ornament,0,17329\n464536,bouquet,0,17310\n4909,chopsticks,0,17307\n381092,paper,0,17304\n527682,object_hug,0,17298\n394992,shoulder_bag,0,17284\n610013,pink_lips,0,17263\n6207,sportswear,0,17239\n7532,stairs,0,17184\n643286,full-face_blush,0,17181\n1349206,cropped_torso,0,17141\n1532466,colored_inner_hair,0,17112\n1648,oekaki,0,17056\n667171,holding_umbrella,0,17024\n662799,animal_hood,0,17005\n460907,one_eye_covered,0,17000\n464567,mountain,0,16918\n507245,glint,0,16875\n409364,covering_breasts,0,16833\n469668,raised_eyebrows,0,16829\n8807,city,0,16812\n1631677,mature_male,0,16767\n1671,underwater,0,16731\n2074,vibrator,0,16722\n2312,valentine,0,16712\n9434,wedding_dress,0,16709\n456688,multicolored_eyes,0,16704\n548722,enmaided,0,16697\n553015,open_book,0,16653\n498828,adjusting_clothes,0,16652\n1405752,meme_attire,0,16650\n1582,miko,0,16636\n594859,backless_outfit,0,16613\n379632,panties_aside,0,16594\n701042,bandaged_arm,0,16573\n472407,turtleneck_sweater,0,16443\n16609,bloomers,0,16435\n486,doll,0,16425\n411759,cum_on_hair,0,16414\n7426,sign,0,16400\n458728,waving,0,16390\n1249693,borrowed_character,0,16369\n7783,pout,0,16369\n5953,snake,0,16363\n478640,hair_bell,0,16356\n7486,fighting_stance,0,16351\n11858,forehead_mark,0,16335\n397690,motion_blur,0,16322\n376102,realistic,0,16156\n1226391,yellow_flower,0,16135\n8705,ice_cream,0,16133\n464582,strawberry,0,16122\n442898,skirt_hold,0,16109\n379820,aircraft,0,16077\n472197,white_pants,0,16042\n1243463,orange_bow,0,16034\n560,card,0,16015\n617355,grey_jacket,0,16004\n2809,spoon,0,15980\n433182,!?,0,15963\n499629,headpiece,0,15949\n397574,panties_around_one_leg,0,15937\n2205,mouse_ears,0,15932\n428330,brother_and_sister,0,15870\n1802,leash,0,15849\n456585,pink_bra,0,15847\n401340,nose,0,15816\n375144,nurse_cap,0,15808\n456370,hands_in_pockets,0,15808\n570942,front-tie_bikini_top,0,15724\n471090,hair_tie,0,15710\n1260354,text_focus,0,15704\n8418,fence,0,15687\n687736,short_eyebrows,0,15675\n1339995,green_headwear,0,15662\n13207,bandana,0,15650\n643257,white_leotard,0,15646\n667463,visor_cap,0,15637\n2319,fantasy,0,15635\n1441883,round_eyewear,0,15633\n594664,green_shirt,0,15616\n16139,mustache,0,15597\n1332796,round_teeth,0,15590\n1335533,holding_staff,0,15576\n421198,center_opening,0,15520\n10549,clitoris,0,15483\n15224,sand,0,15473\n446622,hime_cut,0,15432\n3429,drink,0,15413\n509379,on_floor,0,15395\n1352777,eighth_note,0,15359\n406736,covering_mouth,0,15357\n464544,can,0,15347\n165438,anklet,0,15332\n1081309,mouth_mask,0,15326\n378561,blue_skin,0,15320\n417996,overflow,0,15310\n645753,orange_background,0,15299\n1755,dildo,0,15284\n464569,palm_tree,0,15266\n10440,door,0,15240\n1252945,scar_across_eye,0,15238\n16738,geta,0,15231\n1226390,purple_flower,0,15227\n3313,dragon,0,15216\n660726,rabbit_girl,0,15211\n375266,top-down_bottom-up,0,15186\n645579,fake_tail,0,15181\n513625,asymmetrical_bangs,0,15171\n548703,sitting_on_person,0,15168\n435262,stubble,0,15155\n1681088,furry_female,0,15151\n588674,furrowed_brow,0,15142\n457114,dragon_tail,0,15125\n15126,interracial,0,15118\n13027,fork,0,15080\n465444,antennae,0,15070\n384441,alternate_color,0,15070\n676924,anchor_symbol,0,15035\n383337,sailor_hat,0,15022\n1569657,arrow_(projectile),0,14974\n1262298,off-shoulder_dress,0,14966\n1757,music,0,14950\n501706,card_(medium),0,14946\n477439,white_wings,0,14916\n2367,blindfold,0,14860\n487236,long_dress,0,14814\n444411,licking_lips,0,14807\n1310938,white_one-piece_swimsuit,0,14803\n1569656,arrow_(symbol),0,14797\n553797,sharp_fingernails,0,14786\n407647,adjusting_hair,0,14768\n478262,double_v,0,14763\n527256,green_ribbon,0,14760\n662424,frog_hair_ornament,0,14727\n460642,spread_arms,0,14713\n549355,arms_at_sides,0,14706\n394528,winter_uniform,0,14666\n2562,camera,0,14662\n5228,wand,0,14618\n432198,arched_back,0,14618\n498413,jacket_on_shoulders,0,14614\n1509969,hip_focus,0,14602\n421492,veiny_penis,0,14598\n1516547,brown_headwear,0,14574\n681331,heart_hair_ornament,0,14555\n594129,head_out_of_frame,0,14554\n8891,zoom_layer,0,14541\n7508,straw_hat,0,14516\n461529,apple,0,14510\n524661,folding_fan,0,14476\n5267,labcoat,0,14458\n397045,railing,0,14457\n494337,purple_ribbon,0,14443\n9260,blanket,0,14441\n5160,gag,0,14424\n387050,sun,0,14422\n297980,hanging_breasts,0,14410\n1358345,pink_footwear,0,14406\n1835743,black_one-piece_swimsuit,0,14389\n541716,yokozuwari,0,14316\n450779,emblem,0,14316\n464583,sunflower,0,14308\n448621,arm_warmers,0,14308\n4318,pasties,0,14304\n465048,dog_girl,0,14302\n392031,groping,0,14294\n622688,muscular_female,0,14294\n2614,shota,0,14290\n10801,android,0,14284\n640898,alternate_hair_length,0,14279\n457574,horizon,0,14277\n403785,lowleg,0,14275\n390589,branch,0,14230\n660814,dot_nose,0,14212\n526693,holding_clothes,0,14179\n5621,smoking,0,14176\n414971,purple_shirt,0,14154\n3714,cannon,0,14150\n1304046,black_bodysuit,0,14119\n1363594,red_hairband,0,14114\n472943,black_sclera,0,14107\n1505129,chinese_text,0,14085\n8104,angel,0,14083\n1349592,blue_kimono,0,14078\n9044,leg_lift,0,14074\n526218,bandeau,0,14074\n420598,highleg_panties,0,14066\n9351,gothic_lolita,0,14051\n646549,red_cape,0,14046\n1351963,ass_focus,0,14019\n380423,goatee,0,14013\n460323,pink_bikini,0,13999\n393578,beanie,0,13998\n397380,name_tag,0,13995\n445092,wince,0,13987\n656161,hand_on_own_cheek,0,13981\n453244,wolf_girl,0,13969\n578430,floral_background,0,13951\n1256471,off-shoulder_shirt,0,13946\n451332,bun_cover,0,13942\n407056,unbuttoned,0,13931\n15395,clock,0,13912\n594404,hat_flower,0,13902\n529493,jack-o'-lantern,0,13886\n560473,partially_visible_vulva,0,13886\n578,lactation,0,13869\n1263229,hand_on_headwear,0,13839\n550131,large_areolae,0,13796\n1343866,black_sleeves,0,13790\n1077303,underbust,0,13739\n408040,hitodama,0,13722\n1359441,collared_dress,0,13716\n562882,blue_pants,0,13705\n442577,ahegao,0,13675\n516350,white_coat,0,13658\n547289,drawstring,0,13624\n524779,microskirt,0,13600\n133767,swimsuit_under_clothes,0,13598\n541903,purple_nails,0,13596\n505450,gift_box,0,13585\n552306,criss-cross_halter,0,13571\n451767,food_on_face,0,13549\n409425,bookshelf,0,13542\n4974,gangbang,0,13540\n6032,tabi,0,13538\n484456,back-to-back,0,13531\n8822,eyeball,0,13531\n2898,basket,0,13522\n560167,character_doll,0,13507\n381163,petticoat,0,13506\n15987,logo,0,13506\n376830,untied,0,13497\n390591,nipple_slip,0,13485\n667849,bandaid_on_face,0,13474\n444095,tail_ornament,0,13468\n1256690,suspender_skirt,0,13451\n4816,gakuran,0,13430\n462124,large_penis,0,13430\n609887,blue_gloves,0,13421\n643824,grabbing_from_behind,0,13400\n448279,cabbie_hat,0,13375\n1312862,blue_bowtie,0,13370\n670983,hand_on_another's_shoulder,0,13361\n305065,saliva_trail,0,13305\n453768,fairy_wings,0,13293\n509,pocky,0,13291\n572684,white_scarf,0,13274\n409592,argyle,0,13257\n455932,size_difference,0,13250\n518422,covered_mouth,0,13250\n663804,armored_dress,0,13203\n537093,lace-up_boots,0,13197\n1354790,yellow_ascot,0,13188\n621064,red_vest,0,13166\n519572,toenail_polish,0,13148\n14859,pointless_censoring,0,13141\n639717,taut_clothes,0,13141\n1332478,shrug_(clothing),0,13135\n550405,holding_flower,0,13117\n420,onsen,0,13111\n1764,guitar,0,13111\n11030,silhouette,0,13100\n457877,leaning_back,0,13073\n558436,silent_comic,0,13061\n588702,vambraces,0,13060\n530083,kneepits,0,13036\n460911,tiles,0,13010\n375026,headdress,0,12999\n10713,unzipped,0,12965\n469042,2koma,0,12953\n1329246,center_frills,0,12943\n535691,butterfly_hair_ornament,0,12934\n375285,winter_clothes,0,12896\n232297,gohei,0,12889\n493832,military_vehicle,0,12879\n149791,drinking,0,12869\n583983,black_cape,0,12865\n713203,crotch_seam,0,12849\n522051,on_head,0,12835\n1602539,vision_(genshin_impact),0,12775\n455615,cow_print,0,12766\n481511,red_lips,0,12763\n5252,cow_ears,0,12756\n399930,highleg_swimsuit,0,12754\n474819,brown_thighhighs,0,12740\n392133,heart_censor,0,12731\n2586,witch,0,12722\n1601823,retro_artstyle,0,12713\n454489,no_nose,0,12712\n4308,pen,0,12700\n1799095,female_child,0,12686\n413878,electricity,0,12677\n470562,spaghetti_strap,0,12671\n406034,arm_grab,0,12648\n43263,anchor,0,12642\n23249,everyone,0,12603\n559022,shirt_tucked_in,0,12589\n2276,pool,0,12575\n11173,sundress,0,12575\n16704,glass,0,12539\n1804525,bridal_garter,0,12500\n571873,checkered_clothes,0,12495\n450702,naval_uniform,0,12444\n378454,wind_lift,0,12417\n1241714,korean_text,0,12403\n742715,adjusting_eyewear,0,12401\n818365,selfie,0,12344\n1571375,poke_ball_(basic),0,12343\n416906,bangle,0,12329\n379970,bound_wrists,0,12313\n10280,flag,0,12313\n488169,cleft_of_venus,0,12302\n482679,facing_away,0,12302\n11270,mittens,0,12284\n464560,frog,0,12276\n843399,double-breasted,0,12249\n435550,headpat,0,12235\n423612,wedding_ring,0,12233\n84427,reading,0,12185\n3468,classroom,0,12185\n469224,blue_thighhighs,0,12162\n720637,chromatic_aberration,0,12156\n517935,purple_bikini,0,12141\n280408,salute,0,12131\n542666,flipped_hair,0,12106\n297241,smug,0,12103\n1447826,notice_lines,0,12078\n1372775,pinafore_dress,0,12076\n1328010,fur-trimmed_jacket,0,12052\n2880,scythe,0,12042\n450337,ankle_boots,0,12038\n2181,cheerleader,0,12038\n644684,chestnut_mouth,0,12015\n693118,absurdly_long_hair,0,12009\n1574,magic,0,11985\n246,nun,0,11976\n659072,muneate,0,11975\n1781,car,0,11969\n458819,talking,0,11958\n668671,holding_knife,0,11922\n607626,hair_stick,0,11915\n481383,wristwatch,0,11893\n230,waitress,0,11884\n1374209,food-themed_hair_ornament,0,11861\n3242,airplane,0,11859\n1307840,stud_earrings,0,11851\n624352,strap_gap,0,11840\n643027,light_blue_hair,0,11820\n376451,inverted_nipples,0,11812\n411783,potted_plant,0,11776\n1297465,pulled_by_self,0,11774\n1667541,pom_pom_(cheerleading),0,11740\n2780,beachball,0,11717\n3880,light,0,11690\n1609981,uneven_legwear,0,11671\n680569,foot_focus,0,11656\n665407,hand_to_own_mouth,0,11651\n1053124,sideways_glance,0,11642\n390064,gagged,0,11622\n492202,flat_cap,0,11622\n458,nurse,0,11587\n1315939,brown_belt,0,11579\n416331,animal_costume,0,11554\n495530,bikini_under_clothes,0,11545\n397948,road,0,11538\n2802,earmuffs,0,11535\n684289,spoken_question_mark,0,11533\n661340,holding_poke_ball,0,11521\n408359,bare_back,0,11507\n8318,nipple_tweak,0,11485\n536,bukkake,0,11472\n656170,hands_on_own_chest,0,11451\n1230896,horn_ornament,0,11447\n3992,bucket,0,11441\n1416387,kita_high_school_uniform,0,11437\n1113504,hair_behind_ear,0,11436\n152,ninja,0,11416\n516299,style_parody,0,11403\n391568,assault_rifle,0,11397\n1387418,back_bow,0,11386\n704492,armored_boots,0,11381\n376460,fishnet_pantyhose,0,11378\n1278425,rigging,0,11372\n374936,lamp,0,11370\n414765,hair_over_eyes,0,11368\n471181,eyeliner,0,11367\n12457,tatami,0,11350\n543958,bags_under_eyes,0,11328\n446289,cow_horns,0,11318\n2565,battle,0,11310\n6312,femdom,0,11272\n1301111,white_sweater,0,11258\n4572,candle,0,11252\n393028,controller,0,11245\n4436,bench,0,11240\n476771,bush,0,11232\n42918,scales,0,11213\n407744,happy_sex,0,11210\n440780,sailor_shirt,0,11194\n646445,mask_on_head,0,11186\n5214,overalls,0,11182\n1327458,steaming_body,0,11182\n1685196,hakama_short_skirt,0,11165\n626241,triangular_headpiece,0,11147\n411560,sexually_suggestive,0,11144\n11883,cityscape,0,11138\n688713,snake_hair_ornament,0,11138\n7580,mecha_musume,0,11109\n465707,biting,0,11105\n461439,tube_top,0,11104\n1328421,crown_braid,0,11099\n613947,toeless_legwear,0,11091\n484631,naked_shirt,0,11046\n15131,riding,0,11036\n468449,brown_dress,0,11032\n605892,pov_hands,0,11004\n441419,:>,0,11000\n473301,bow_bra,0,10993\n544390,>:),0,10964\n498201,mini_crown,0,10958\n1516549,pink_headwear,0,10944\n479176,bird_wings,0,10934\n452205,stuffed_bunny,0,10882\n374409,bad_anatomy,0,10880\n331704,bodystocking,0,10879\n15272,bridal_veil,0,10876\n1862786,reaching_towards_viewer,0,10864\n572672,japanese_armor,0,10861\n482857,joints,0,10857\n2231,fairy,0,10857\n568920,+++,0,10851\n699839,halloween_costume,0,10845\n1372733,blue_leotard,0,10838\n404958,pilot_suit,0,10829\n464543,cable,0,10826\n705211,fake_horns,0,10826\n412037,open_fly,0,10811\n843857,red_kimono,0,10769\n393840,purple_panties,0,10765\n526025,pleated_dress,0,10761\n374533,creature,0,10746\n6158,orgasm,0,10743\n506,dark,0,10736\n558230,ice_wings,0,10733\n532943,covering_crotch,0,10708\n5104,shade,0,10696\n211409,wet_shirt,0,10693\n6318,space,0,10690\n682963,claw_pose,0,10690\n7495,sarong,0,10662\n1308012,red_hakama,0,10624\n669033,triangle_mouth,0,10620\n1669227,hugging_own_legs,0,10590\n1257079,print_kimono,0,10568\n535186,breast_pocket,0,10565\n386389,bracer,0,10564\n390180,bathing,0,10559\n185924,key,0,10559\n1389122,black_collar,0,10551\n480899,5boys,0,10529\n394881,school_desk,0,10512\n394136,red_panties,0,10506\n1801550,single_side_bun,0,10504\n463399,randoseru,0,10500\n1303436,red_choker,0,10497\n464570,peach,0,10486\n389456,holster,0,10474\n599873,air_bubble,0,10456\n493887,food_in_mouth,0,10448\n576561,alternate_eye_color,0,10424\n484924,heart_hands,0,10415\n16149,sack,0,10410\n547893,indian_style,0,10398\n525747,lolita_hairband,0,10392\n1611664,oil-paper_umbrella,0,10388\n695937,white_outline,0,10369\n414957,confetti,0,10366\n4965,dagger,0,10358\n648077,heart_of_string,0,10355\n421188,against_wall,0,10350\n603647,frilled_shirt,0,10347\n1685910,painting_(medium),0,10330\n1693074,own_hands_clasped,0,10329\n613597,print_legwear,0,10327\n656166,hands_on_own_face,0,10322\n507378,yellow_bikini,0,10320\n580906,low_wings,0,10316\n466164,laughing,0,10311\n669725,holding_bag,0,10306\n459290,snowflakes,0,10304\n469422,paw_print,0,10283\n670635,cross-laced_clothes,0,10276\n394722,wet_hair,0,10266\n528129,one_breast_out,0,10265\n614988,pink_jacket,0,10262\n466325,autumn_leaves,0,10259\n394150,blue_rose,0,10234\n377998,petite,0,10222\n5626,antlers,0,10214\n610524,star-shaped_pupils,0,10212\n408438,hair_up,0,10209\n1262171,holding_bottle,0,10182\n614271,lace-trimmed_legwear,0,10161\n551848,polka_dot_background,0,10147\n1795966,cone_hair_bun,0,10144\n8180,drunk,0,10135\n461736,alternate_hair_color,0,10128\n15020,hibiscus,0,10120\n1447858,feather_hair_ornament,0,10080\n464545,carrot,0,10075\n502136,whisker_markings,0,10073\n468789,paw_gloves,0,10067\n434704,string,0,10059\n653480,yellow_jacket,0,10056\n2559,incest,0,10052\n1681089,furry_male,0,10041\n411770,cum_in_ass,0,10031\n4358,plugsuit,0,10025\n372,bath,0,10024\n464674,stretching,0,10003\n288161,office_lady,0,9994\n15771,raccoon_ears,0,9993\n1335363,arm_tattoo,0,9987\n4026,pumpkin,0,9981\n602796,star_print,0,9977\n1285325,wrist_scrunchie,0,9952\n1297657,zipper_pull_tab,0,9949\n1320887,anal_object_insertion,0,9943\n474598,tate_eboshi,0,9933\n473213,red_thighhighs,0,9930\n656167,hand_on_another's_face,0,9907\n450266,wine_glass,0,9903\n617956,brown_shirt,0,9902\n1314823,black_sweater,0,9902\n9586,habit,0,9879\n469551,facepaint,0,9873\n496617,marker_(medium),0,9871\n594366,bat_(animal),0,9870\n1250483,holding_fan,0,9859\n409720,scabbard,0,9851\n1222476,crescent_hair_ornament,0,9841\n412048,ribbon_choker,0,9807\n3444,computer,0,9803\n4960,parasol,0,9779\n638006,thighlet,0,9776\n576287,blood_on_clothes,0,9756\n592643,navel_cutout,0,9752\n616593,purple_gloves,0,9741\n13810,torii,0,9738\n1481917,sakuragaoka_high_school_uniform,0,9728\n375110,haori,0,9727\n544230,asymmetrical_wings,0,9708\n9481,teapot,0,9705\n456270,tasuki,0,9704\n720760,holding_tray,0,9683\n485597,grey_dress,0,9677\n251010,tiger_ears,0,9659\n4787,axe,0,9654\n158473,string_panties,0,9651\n1369967,watercraft,0,9647\n1410771,artist_logo,0,9625\n14607,tight,0,9619\n414233,cum_on_clothes,0,9606\n488078,purple_jacket,0,9606\n375461,belly,0,9587\n15202,nipple_piercing,0,9585\n6182,shibari,0,9571\n461639,happy_new_year,0,9568\n613998,mismatched_legwear,0,9561\n477517,chinese_zodiac,0,9552\n9504,magatama,0,9538\n3748,slippers,0,9521\n1799094,male_child,0,9515\n555246,black_border,0,9514\n426594,lace-trimmed_panties,0,9497\n871328,black_kimono,0,9495\n4939,precum,0,9450\n672705,pink_kimono,0,9438\n408248,transparent,0,9427\n125420,bald,0,9406\n428808,wagashi,0,9402\n1316394,hip_vent,0,9399\n614827,tachi-e,0,9376\n1260880,fang_out,0,9362\n2414,vampire,0,9361\n1393212,yellow_neckerchief,0,9349\n547199,bikini_skirt,0,9343\n675233,open_cardigan,0,9334\n472448,highleg_bikini,0,9302\n499324,red_shorts,0,9295\n666816,hand_on_own_head,0,9293\n1516550,purple_headwear,0,9284\n723097,after_vaginal,0,9283\n419116,tied_shirt,0,9268\n448185,garrison_cap,0,9268\n1310698,curled_horns,0,9264\n1749079,sample_watermark,0,9235\n589024,arm_strap,0,9233\n394083,knee_pads,0,9225\n1339640,midriff_peek,0,9216\n483988,condom_wrapper,0,9209\n881062,grabbing_own_breast,0,9198\n377888,torn_pantyhose,0,9190\n1244676,one-piece_tan,0,9186\n400382,mouse_tail,0,9163\n678558,hand_on_own_chin,0,9161\n1276712,layered_sleeves,0,9154\n3473,giant,0,9154\n500472,evil_smile,0,9152\n4964,nosebleed,0,9148\n1355714,crescent_hat_ornament,0,9140\n9983,dancing,0,9125\n456515,tiger_print,0,9101\n474210,leg_ribbon,0,9099\n396079,looking_afar,0,9094\n686321,half_gloves,0,9094\n397156,presenting,0,9077\n390148,green_skin,0,9064\n504750,princess_carry,0,9062\n492982,layered_dress,0,9050\n480027,purple_thighhighs,0,9049\n615659,blue_vest,0,9031\n376270,pantyhose_pull,0,9024\n388908,raglan_sleeves,0,9022\n200140,magic_circle,0,9019\n375526,short_kimono,0,9014\n500740,very_short_hair,0,9011\n578891,shoulder_blades,0,9004\n515909,asymmetrical_clothes,0,8994\n473248,blue_bra,0,8975\n578643,striped_background,0,8966\n562575,green_bikini,0,8960\n785234,shoulder_cutout,0,8944\n11019,scared,0,8937\n461530,balloon,0,8936\n618117,legwear_under_shorts,0,8925\n444507,deep_skin,0,8924\n75228,aura,0,8909\n2246,wall,0,8904\n451190,layered_skirt,0,8899\n1451201,draph,0,8887\n411966,used_condom,0,8885\n460555,hair_censor,0,8876\n1618371,body_fur,0,8864\n493064,over_shoulder,0,8857\n454128,head_wreath,0,8850\n547384,tail_raised,0,8841\n468991,kicking,0,8840\n395583,mother_and_daughter,0,8838\n533391,open_hand,0,8832\n520839,sound_effects,0,8828\n461159,reference_sheet,0,8828\n1635846,tracen_school_uniform,0,8818\n541599,mechanical_arms,0,8817\n470019,one_knee,0,8804\n449932,w,0,8800\n166945,yellow_dress,0,8796\n11870,manly,0,8789\n498546,male_underwear,0,8784\n397155,huge_ass,0,8779\n513423,graphite_(medium),0,8775\n1672,tea,0,8754\n521120,backless_dress,0,8747\n1303251,grey_pants,0,8746\n1318267,public_indecency,0,8718\n538586,hat_feather,0,8714\n1233477,crop_top_overhang,0,8702\n14461,open_kimono,0,8696\n596449,pigeon-toed,0,8687\n8577,paintbrush,0,8685\n1302741,red_ascot,0,8670\n1420938,holding_polearm,0,8660\n5857,leather,0,8653\n473218,lace-trimmed_bra,0,8648\n1462380,disposable_cup,0,8626\n669790,futa_with_female,0,8624\n15258,torpedo,0,8616\n574178,o-ring_top,0,8610\n522720,naked_towel,0,8608\n572821,star_earrings,0,8595\n574501,ringed_eyes,0,8594\n1740,mermaid,0,8590\n567329,limited_palette,0,8586\n547394,o-ring_bikini,0,8586\n1257811,on_couch,0,8586\n1253894,holding_microphone,0,8580\n516029,shimenawa,0,8576\n728807,animification,0,8571\n558829,animal_on_head,0,8562\n1379635,mmf_threesome,0,8547\n1256688,bandaged_leg,0,8535\n462974,squiggle,0,8526\n501583,in_container,0,8526\n416486,photo_(object),0,8523\n1388797,blue_neckerchief,0,8514\n469978,glowing_eye,0,8491\n12724,field,0,8487\n484616,maple_leaf,0,8481\n507586,yellow_skirt,0,8480\n3102,greaves,0,8472\n1575551,gap_(touhou),0,8465\n484880,architecture,0,8439\n10369,bedroom,0,8419\n383117,breast_sucking,0,8415\n388762,sleeveless_turtleneck,0,8392\n513428,watercolor_(medium),0,8390\n1617412,bare_pectorals,0,8389\n646363,company_name,0,8382\n384905,symmetrical_docking,0,8373\n421507,leg_grab,0,8371\n1349338,red_leotard,0,8367\n563862,gameplay_mechanics,0,8356\n380303,bound_arms,0,8353\n593165,out-of-frame_censoring,0,8346\n1409552,ooarai_school_uniform,0,8335\n430765,sunbeam,0,8334\n13126,lineart,0,8325\n1081137,imminent_penetration,0,8323\n617541,brown_pants,0,8310\n10704,bamboo,0,8306\n516432,imminent_kiss,0,8301\n8433,thumbs_up,0,8295\n440369,multiple_persona,0,8287\n726933,clothes_around_waist,0,8264\n447310,green_nails,0,8240\n576693,tile_floor,0,8235\n726825,shoulder_tattoo,0,8188\n811495,undercut,0,8166\n718360,polka_dot_bow,0,8151\n13851,halftone,0,8144\n3356,peeing,0,8144\n396470,arm_cannon,0,8124\n545704,blue_hairband,0,8107\n412080,sleeve_cuffs,0,8091\n443796,under_covers,0,8086\n4065,explosion,0,8083\n459097,cowbell,0,8081\n639869,white_cape,0,8076\n481547,holding_hair,0,8074\n7834,naked_apron,0,8071\n514087,cross_necklace,0,8065\n4633,singing,0,8060\n465393,pee,0,8051\n546667,print_shirt,0,8035\n5199,leggings,0,8021\n7929,shackles,0,8009\n4429,pencil,0,8008\n530616,checkered_floor,0,8005\n412376,stitches,0,7998\n1379926,black_hoodie,0,7998\n637674,slingshot_swimsuit,0,7995\n1301235,pubic_tattoo,0,7995\n12391,double_penetration,0,7977\n1447595,foot_out_of_frame,0,7969\n2570,idol,0,7969\n108777,bra_lift,0,7966\n374955,ruins,0,7962\n2100,summer,0,7947\n6203,vines,0,7946\n9331,falling,0,7937\n443371,raccoon_tail,0,7937\n515552,striped_dress,0,7913\n8301,black_cat,0,7906\n16791,strap,0,7905\n411657,torn_thighhighs,0,7901\n1396655,white_capelet,0,7899\n460204,hair_over_breasts,0,7896\n1441862,purple_footwear,0,7895\n491150,^o^,0,7888\n491645,head_fins,0,7886\n530962,arm_under_breasts,0,7879\n11651,annoyed,0,7846\n2566,mushroom,0,7841\n2215,house,0,7822\n463475,iron_cross,0,7811\n1271756,black_scarf,0,7811\n1488686,tilted_headwear,0,7803\n1429562,two-tone_dress,0,7795\n682686,spoken_exclamation_mark,0,7795\n186201,skirt_pull,0,7793\n581500,plaid_vest,0,7793\n30851,3koma,0,7783\n1441878,eyewear_removed,0,7776\n662950,name_connection,0,7769\n3898,tank,0,7761\n1320888,vaginal_object_insertion,0,7760\n491943,dark_persona,0,7756\n53988,fox_mask,0,7754\n457806,artist_self-insert,0,7751\n399391,spiked_bracelet,0,7746\n1257081,scar_on_cheek,0,7741\n610074,platform_footwear,0,7726\n430066,id_card,0,7720\n1364407,genderswap_(ftm),0,7710\n1393342,erune,0,7710\n1501454,tinted_eyewear,0,7706\n496894,face-to-face,0,7693\n11555,bathroom,0,7684\n5157,sad,0,7681\n403904,french_kiss,0,7681\n16170,television,0,7676\n384906,asymmetrical_docking,0,7672\n400657,one-eyed,0,7665\n388149,ranguage,0,7639\n715545,duel_monster,0,7629\n650433,invisible_chair,0,7628\n421031,huge_weapon,0,7615\n1278539,long_sideburns,0,7615\n268105,pants_pull,0,7606\n532949,trigger_discipline,0,7603\n395294,head_scarf,0,7603\n724764,hand_on_own_thigh,0,7601\n395929,bikini_pull,0,7592\n502731,hachimaki,0,7590\n471436,doughnut,0,7587\n720163,partially_unbuttoned,0,7566\n698860,hand_on_own_knee,0,7563\n1644111,official_alternate_hairstyle,0,7560\n392495,tiptoes,0,7543\n1470809,red_horns,0,7539\n1441860,grey_footwear,0,7531\n238471,babydoll,0,7528\n1370047,white_choker,0,7528\n458210,bikini_armor,0,7519\n408887,shoulder_pads,0,7517\n419695,sakazuki,0,7507\n745997,holding_animal,0,7496\n474306,headphones_around_neck,0,7496\n465836,cutoffs,0,7481\n404056,yukkuri_shiteitte_ne,0,7470\n376034,white_eyes,0,7469\n838605,animal_focus,0,7469\n1392820,black_neckerchief,0,7468\n1328011,fur-trimmed_sleeves,0,7459\n525152,white_fur,0,7451\n1450619,wide_shot,0,7448\n414159,x-ray,0,7447\n665137,sandwiched,0,7438\n485014,white_rose,0,7434\n3138,bread,0,7434\n658791,necktie_between_breasts,0,7425\n1554924,m_legs,0,7415\n2998,cunnilingus,0,7414\n4924,camouflage,0,7408\n560670,narrow_waist,0,7400\n490069,disembodied_limb,0,7396\n462735,old,0,7394\n390530,grey_skin,0,7379\n500,fundoshi,0,7377\n537886,shorts_under_skirt,0,7375\n575497,starry_background,0,7371\n434799,0_0,0,7365\n1383260,purple_kimono,0,7360\n406340,oversized_clothes,0,7355\n474715,crescent_moon,0,7354\n1701063,holding_another's_wrist,0,7353\n584250,summer_uniform,0,7346\n498950,orange_skirt,0,7335\n401680,sweater_dress,0,7335\n665739,holding_gift,0,7327\n612000,constricted_pupils,0,7324\n464580,split,0,7321\n629455,striped_necktie,0,7315\n392034,dripping,0,7314\n538023,on_ground,0,7311\n529429,covered_eyes,0,7294\n403779,navel_piercing,0,7290\n420828,sliding_doors,0,7286\n703196,leaf_hair_ornament,0,7283\n420298,big_hair,0,7279\n385088,earphones,0,7265\n10403,futon,0,7263\n6202,winter,0,7255\n732590,interspecies,0,7250\n1363856,star_hat_ornament,0,7249\n472069,cross-section,0,7243\n279773,body_writing,0,7234\n715714,voice_actor_connection,0,7232\n1379636,ffm_threesome,0,7231\n1267724,symbol_in_eye,0,7230\n473742,two_tails,0,7216\n661337,aqua_nails,0,7214\n510068,splashing,0,7209\n467794,cow_girl,0,7208\n1456682,armpit_crease,0,7206\n534254,brick_wall,0,7205\n415106,neck_ring,0,7199\n142348,wardrobe_malfunction,0,7198\n590917,leaf_on_head,0,7197\n10802,bonnet,0,7196\n478328,rolling_eyes,0,7194\n491802,standing_sex,0,7190\n1468069,multiple_others,0,7172\n5861,bishounen,0,7162\n478321,checkered_background,0,7160\n595503,facial_tattoo,0,7160\n412952,arm_garter,0,7155\n374961,no_pupils,0,7144\n581653,smoking_pipe,0,7135\n3971,handcuffs,0,7116\n684956,frilled_bow,0,7114\n666125,holding_fruit,0,7112\n416892,;o,0,7110\n462594,bobby_socks,0,7106\n558653,casual_one-piece_swimsuit,0,7099\n1515359,pink_theme,0,7096\n710372,orange_shirt,0,7095\n108765,broom_riding,0,7094\n588956,untied_bikini,0,7093\n4940,foreskin,0,7082\n1264677,anchor_hair_ornament,0,7082\n391251,breast_rest,0,7080\n438708,frilled_panties,0,7062\n569780,pink_rose,0,7060\n464551,cookie,0,7055\n1238881,yellow_necktie,0,7052\n606614,shoes_removed,0,7051\n606732,soaking_feet,0,7044\n447060,spiked_collar,0,7038\n1622419,straight-on,0,7034\n1269638,handheld_game_console,0,7006\n390893,water_bottle,0,7005\n551141,arm_ribbon,0,7002\n395658,christmas_tree,0,7002\n388207,yin_yang,0,7000\n402799,tiger_tail,0,6999\n501739,folded,0,6990\n2918,fireworks,0,6979\n490606,afterimage,0,6977\n492645,gyaru,0,6975\n493164,forehead_jewel,0,6972\n596854,blue_scarf,0,6969\n1400566,toned_male,0,6962\n423161,object_on_head,0,6956\n490261,partially_colored,0,6915\n511662,arm_guards,0,6904\n483436,fish_tail,0,6898\n392008,chalkboard,0,6879\n410002,sheet_grab,0,6869\n562151,short_over_long_sleeves,0,6864\n1269062,mitakihara_school_uniform,0,6848\n1441887,rimless_eyewear,0,6847\n439130,anniversary,0,6846\n452549,!!,0,6845\n1508967,purple_theme,0,6844\n3509,syringe,0,6844\n1228172,asymmetrical_gloves,0,6843\n511640,single_shoe,0,6836\n1551197,1990s_(style),0,6832\n2298,watermelon,0,6825\n3531,bridge,0,6818\n487745,sitting_on_lap,0,6811\n2576,fat,0,6804\n148527,stool,0,6804\n5006,rice,0,6803\n12286,badge,0,6793\n526607,print_bikini,0,6792\n54490,monitor,0,6790\n673732,goat_horns,0,6781\n390703,purple_skin,0,6770\n543207,east_asian_architecture,0,6768\n436870,:/,0,6764\n1491182,vehicle_focus,0,6762\n498466,implied_sex,0,6760\n385431,submachine_gun,0,6752\n396065,green_panties,0,6749\n16507,lion_ears,0,6743\n663521,shoe_soles,0,6742\n1447648,sailor_senshi_uniform,0,6742\n1261156,green_vest,0,6736\n559585,playing_instrument,0,6736\n1637638,heart_brooch,0,6717\n1328025,fur-trimmed_coat,0,6714\n381114,red_skin,0,6712\n546385,heart_earrings,0,6708\n1505172,engrish_text,0,6700\n675356,multicolored_background,0,6696\n614744,red_pants,0,6695\n643811,micro_shorts,0,6693\n1898,hammer,0,6666\n1330221,fur-trimmed_gloves,0,6666\n714822,red_theme,0,6657\n1313352,brown_sweater,0,6654\n416507,torn_shirt,0,6653\n418177,ripples,0,6653\n606748,chibi_inset,0,6650\n606296,dappled_sunlight,0,6636\n1248463,brown_coat,0,6636\n1326379,red_capelet,0,6625\n604394,pointing_at_viewer,0,6623\n676529,licking_penis,0,6622\n724584,hooded_cloak,0,6622\n1600,beer,0,6619\n376671,scroll,0,6603\n1661326,content_rating,0,6602\n378581,brothers,0,6595\n1708,sake,0,6594\n3904,rainbow,0,6593\n1733109,jaggy_lines,0,6576\n1490428,fringe_trim,0,6558\n464558,egg,0,6557\n585852,skirt_removed,0,6555\n374606,giantess,0,6555\n706995,erection_under_clothes,0,6541\n1314745,blue_choker,0,6538\n1392012,white_bodysuit,0,6532\n399604,fucked_silly,0,6527\n8988,sniper_rifle,0,6526\n622607,official_style,0,6521\n1397428,white_tank_top,0,6513\n413865,planet,0,6511\n1382120,holding_instrument,0,6509\n483418,mini_top_hat,0,6506\n509903,tying_hair,0,6502\n1354336,grey_shorts,0,6501\n375594,pink_thighhighs,0,6498\n1375840,rabbit_hair_ornament,0,6488\n69696,undershirt,0,6482\n439891,d:,0,6477\n1542129,skin-covered_horns,0,6470\n8696,bathtub,0,6469\n608355,karakasa_obake,0,6453\n473284,frilled_bra,0,6450\n11231,feeding,0,6449\n1422110,ears_through_headwear,0,6435\n933253,on_chair,0,6435\n1251101,off-shoulder_sweater,0,6427\n482927,belt_pouch,0,6405\n501783,miqo'te,0,6396\n1431068,holding_stuffed_toy,0,6394\n4547,lowleg_panties,0,6386\n3577,motorcycle,0,6373\n391915,print_panties,0,6371\n468075,jiangshi,0,6364\n2409,error,0,6362\n394743,cushion,0,6357\n1732562,bikini_bottom_only,0,6351\n466142,fishnet_thighhighs,0,6345\n1260411,holding_plate,0,6341\n464547,cherry,0,6336\n16710,sepia,0,6336\n1304069,yellow_bowtie,0,6334\n6941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n1316985,shark_tail,0,5523\n410239,superhero,0,5520\n41161,street,0,5515\n460438,damaged,0,5505\n407260,polka_dot_panties,0,5502\n663579,holding_spoon,0,5500\n480577,:|,0,5494\n3516,river,0,5492\n8671,female_ejaculation,0,5488\n432914,flat_color,0,5483\n449907,topknot,0,5483\n1318935,aqua_necktie,0,5482\n660963,diamond_(shape),0,5468\n665765,animal_collar,0,5465\n438648,stand_(jojo),0,5463\n389066,castle,0,5462\n5473,tape,0,5460\n172313,time_paradox,0,5460\n393283,orb,0,5456\n2282,footjob,0,5450\n556987,sparkling_eyes,0,5450\n482590,=3,0,5445\n11458,baseball_bat,0,5437\n12438,grapes,0,5437\n15117,police_uniform,0,5434\n3767,laptop,0,5430\n605308,between_fingers,0,5424\n495048,lily_(flower),0,5422\n490010,extra_arms,0,5422\n1410613,patreon_logo,0,5421\n474502,explosive,0,5420\n302968,wet_panties,0,5417\n473214,red_bra,0,5416\n547348,extra_eyes,0,5410\n511691,sweater_lift,0,5404\n821623,female_pervert,0,5400\n1251292,fur-trimmed_cape,0,5393\n634063,flag_print,0,5382\n1631734,single_mechanical_arm,0,5382\n511642,single_sock,0,5372\n652562,book_stack,0,5372\n6413,uterus,0,5372\n1398817,thighhighs_under_boots,0,5356\n1303018,blue_coat,0,5353\n613923,argyle_legwear,0,5348\n1719049,split-color_hair,0,5347\n564515,vertical-striped_thighhighs,0,5345\n1303914,pink_choker,0,5343\n413872,breast_envy,0,5329\n1416880,yellow_footwear,0,5328\n1408307,black_blindfold,0,5327\n6188,dressing,0,5323\n582261,unmoving_pattern,0,5319\n399834,forehead_protector,0,5312\n614881,open_vest,0,5307\n623331,company_connection,0,5304\n399073,waistcoat,0,5304\n727102,hand_on_own_stomach,0,5303\n1492952,super_crown,0,5298\n1320446,facing_another,0,5296\n491474,gym_shirt,0,5296\n1290625,single_bare_shoulder,0,5293\n501384,long_braid,0,5282\n413907,;p,0,5280\n3288,penguin,0,5278\n626213,diagonal_stripes,0,5277\n665797,hand_on_own_ass,0,5270\n381455,shopping_bag,0,5251\n568318,bokeh,0,5237\n577266,chain-link_fence,0,5236\n660644,heart_cutout,0,5234\n407913,bursting_breasts,0,5231\n403477,electric_guitar,0,5230\n474204,frilled_thighhighs,0,5228\n526806,tengu-geta,0,5227\n416268,v-neck,0,5223\n2806,exhibitionism,0,5222\n456272,dog_tags,0,5216\n710056,fox_shadow_puppet,0,5215\n9127,dango,0,5212\n471020,covering_face,0,5210\n1243442,competition_school_swimsuit,0,5209\n10322,albino,0,5205\n582827,adjusting_headwear,0,5204\n425624,charm_(object),0,5199\n4491,monocle,0,5192\n9449,internal_cumshot,0,5189\n2083,meat,0,5184\n388072,costume_switch,0,5183\n434225,purple_lips,0,5180\n1398298,fur-trimmed_dress,0,5174\n415545,twilight,0,5171\n405409,tail_ribbon,0,5170\n5814,autumn,0,5169\n1418952,colored_tips,0,5163\n55663,danmaku,0,5161\n375456,zombie,0,5161\n374357,yandere,0,5160\n492671,moaning,0,5160\n632123,clothes_removed,0,5159\n1258734,looking_ahead,0,5157\n405981,breast_suppress,0,5155\n656330,hands_on_own_cheeks,0,5141\n1548634,braided_bangs,0,5130\n4440,wolf,0,5123\n379597,shorts_pull,0,5123\n1008745,sanpaku,0,5121\n471598,tail_bow,0,5119\n1441881,holding_eyewear,0,5118\n540619,holding_hat,0,5118\n1406853,serval_print,0,5115\n1618887,infection_monitor_(arknights),0,5114\n432947,no_socks,0,5102\n485638,blood_splatter,0,5101\n1782390,traditional_bowtie,0,5099\n442229,flame,0,5094\n400587,reaching,0,5094\n450547,water_gun,0,5082\n448399,dougi,0,5074\n436889,stomach_bulge,0,5063\n485218,trading_card,0,5058\n406885,thong_bikini,0,5056\n374989,quiver,0,5054\n651810,holding_card,0,5051\n481812,teardrop,0,5044\n587579,floating_object,0,5042\n388210,gold,0,5036\n666921,holding_fork,0,5027\n7818,anal_beads,0,5024\n1356896,armpit_peek,0,5023\n486149,military_jacket,0,5019\n423823,ship,0,5013\n2688,knight,0,5010\n439147,cum_on_tongue,0,4999\n2364,onigiri,0,4999\n424779,thigh_holster,0,4992\n449465,firing,0,4985\n603226,heart_print,0,4974\n10367,prosthesis,0,4974\n456933,microphone_stand,0,4972\n578856,pink_sweater,0,4971\n578948,closed_umbrella,0,4969\n398857,mind_control,0,4963\n655244,cross_earrings,0,4960\n398955,flower_field,0,4958\n431532,fake_screenshot,0,4953\n438177,father_and_daughter,0,4953\n455753,leg_warmers,0,4938\n10495,cowboy_hat,0,4938\n615718,green_gloves,0,4935\n9469,noodles,0,4928\n11067,jumpsuit,0,4928\n646772,solid_circle_eyes,0,4920\n10045,family,0,4917\n11062,fox,0,4913\n494896,hydrangea,0,4913\n383172,train_interior,0,4907\n468509,paper_lantern,0,4903\n409832,robot_joints,0,4893\n1268860,chest_jewel,0,4891\n502421,glaring,0,4887\n1345874,weapon_on_back,0,4887\n715118,ear_covers,0,4883\n784749,leotard_under_clothes,0,4883\n405861,rose_petals,0,4880\n417653,convenient_leg,0,4879\n553870,sleeveless_jacket,0,4875\n533114,turn_pale,0,4870\n410885,skyscraper,0,4869\n449502,dimples_of_venus,0,4869\n547349,heart_ahoge,0,4867\n398219,breast_lift,0,4865\n1269470,chest_tattoo,0,4864\n8475,bicycle,0,4861\n640872,pov_crotch,0,4861\n1304062,yellow_hairband,0,4850\n1347239,holding_wand,0,4848\n508932,side-by-side,0,4841\n398888,vase,0,4840\n1168235,halter_dress,0,4833\n1587249,oripathy_lesion_(arknights),0,4833\n459964,unsheathing,0,4830\n3152,lance,0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405636,locker,0,4189\n1312163,purple_necktie,0,4185\n395984,dress_pull,0,4182\n408969,no_pussy,0,4179\n3093,bear,0,4179\n1515362,yellow_theme,0,4178\n441477,defeat,0,4173\n609001,jacket_removed,0,4173\n1444169,yellow_headwear,0,4168\n640090,red_eyeshadow,0,4164\n427529,fat_man,0,4163\n507242,monster_boy,0,4162\n492418,orange_dress,0,4154\n483830,imagining,0,4153\n13099,hot,0,4152\n596219,sagging_breasts,0,4149\n431758,locked_arms,0,4147\n618610,yellow_gloves,0,4145\n568650,grey_pantyhose,0,4143\n416506,torn_pants,0,4143\n1258092,ribbon-trimmed_legwear,0,4139\n483292,gradient_eyes,0,4134\n723993,bird_tail,0,4133\n1492981,bikini_bottom_aside,0,4129\n579607,towel_around_neck,0,4122\n1313101,green_bowtie,0,4121\n391942,long_tongue,0,4121\n434827,soccer_uniform,0,4119\n1276067,floppy_ears,0,4117\n415277,recording,0,4113\n683424,cat_hair_ornament,0,4111\n539090,imminent_rape,0,4106\n151645,evil_grin,0,4104\n797417,ear_bow,0,4100\n395355,fusion,0,4099\n380931,pirate_hat,0,4097\n3997,landscape,0,4096\n1320211,orange_bowtie,0,4094\n613646,on_desk,0,4091\n4365,tsundere,0,4090\n882518,blue_butterfly,0,4083\n2606,milk,0,4080\n1428577,ryouou_school_uniform,0,4078\n524381,ear_ornament,0,4074\n616376,assisted_exposure,0,4073\n1619425,crescent_pin,0,4071\n589477,argyle_background,0,4066\n10332,crow,0,4066\n400542,road_sign,0,4062\n636788,frilled_pillow,0,4057\n1350141,cat_cutout,0,4054\n596179,anal_tail,0,4052\n6453,death,0,4050\n667200,hand_on_another's_chin,0,4045\n486797,unsheathed,0,4035\n1928,tiger,0,4028\n1389971,nintendo_switch,0,4027\n633106,mask_removed,0,4024\n526090,checkered_skirt,0,4024\n648045,sideways_mouth,0,4023\n686687,hatching_(texture),0,4023\n640153,muted_color,0,4023\n1270233,1koma,0,4022\n1537333,interface_headset,0,4021\n537720,track_pants,0,4021\n485999,coffee_mug,0,4013\n482913,black_suit,0,4012\n657475,back_cutout,0,4011\n426587,yellow_panties,0,4004\n9618,library,0,4003\n477117,raised_eyebrow,0,4003\n468460,torn_skirt,0,4000\n634270,bat_print,0,3999\n1429011,holding_candy,0,3996\n98920,starfish,0,3990\n1505313,romaji_text,0,3990\n3045,alien,0,3987\n1350220,cat_lingerie,0,3983\n1346561,holding_cigarette,0,3981\n609685,two-tone_skin,0,3980\n494236,taking_picture,0,3967\n466061,album_cover,0,3963\n465851,defloration,0,3962\n427758,kiseru,0,3959\n3291,teacher,0,3955\n1403308,virgin_killer_sweater,0,3952\n661261,planted_sword,0,3951\n560881,soap_bubbles,0,3949\n460488,feet_up,0,3949\n509953,holding_paper,0,3947\n677008,school_chair,0,3940\n1235097,blood_on_weapon,0,3938\n586284,heart_background,0,3935\n1405351,mountainous_horizon,0,3929\n16128,you_gonna_get_raped,0,3928\n403588,pink_skin,0,3928\n415839,armchair,0,3927\n4855,school,0,3926\n216327,shy,0,3923\n1245131,bikini_tan,0,3923\n1291919,holding_shield,0,3922\n502180,blue_wings,0,3921\n605808,snout,0,3920\n1303985,blue_buruma,0,3918\n491861,sleeves_pushed_up,0,3916\n376585,stick,0,3912\n516128,backboob,0,3909\n1413412,obijime,0,3907\n725289,multicolored_nails,0,3907\n599652,spoken_blush,0,3906\n1393858,white_camisole,0,3903\n12336,costume,0,3902\n1291748,print_bow,0,3901\n1551196,1980s_(style),0,3900\n4119,suitcase,0,3895\n631144,halftone_background,0,3894\n668478,holding_pen,0,3891\n1289945,orange_jacket,0,3887\n460085,mechanical_wings,0,3885\n584023,shark_girl,0,3880\n473300,mandarin_orange,0,3876\n551333,uchiwa,0,3874\n605145,soul_gem,0,3874\n1415843,white_ascot,0,3872\n521411,husband_and_wife,0,3872\n397397,cum_pool,0,3871\n614707,no_legwear,0,3869\n491550,lanyard,0,3865\n419496,male_masturbation,0,3864\n1390882,blue_sleeves,0,3858\n1307533,carrot_hair_ornament,0,3855\n582680,unaligned_breasts,0,3852\n380538,bubble_blowing,0,3850\n473410,dragon_wings,0,3846\n1298903,half-closed_eye,0,3837\n649373,command_spell,0,3834\n6381,clover,0,3832\n473818,tissue_box,0,3832\n406620,pouring,0,3831\n390579,colorful,0,3830\n390,demon,0,3827\n1574241,dolphin_shorts,0,3827\n382729,striped_pantyhose,0,3822\n1350193,musical_note_hair_ornament,0,3814\n584126,pointing_up,0,3814\n426470,silk,0,3812\n494868,black_cloak,0,3809\n1253174,employee_uniform,0,3807\n673956,falling_leaves,0,3805\n1342157,fur-trimmed_boots,0,3804\n493515,game_console,0,3803\n466779,belt_collar,0,3803\n399131,barcode,0,3799\n589889,v_over_eye,0,3798\n616844,blood_from_mouth,0,3797\n385804,lifebuoy,0,3793\n1256300,multicolored_skirt,0,3787\n375424,the_pose,0,3783\n675901,frilled_hat,0,3782\n394046,power_armor,0,3780\n411038,:>=,0,3776\n639592,heart_necklace,0,3776\n571473,bandaid_on_knee,0,3770\n1399133,grey_cardigan,0,3770\n464548,clone,0,3768\n1497310,button_gap,0,3767\n432388,leather_jacket,0,3762\n410903,black_skin,0,3761\n406397,against_glass,0,3754\n891436,food_focus,0,3750\n1161088,2021,0,3749\n1246561,brown_scarf,0,3748\n383694,jar,0,3747\n429161,paizuri_under_clothes,0,3746\n473292,blue_socks,0,3744\n706041,cross-shaped_pupils,0,3735\n684169,ambiguous_gender,0,3732\n1312340,red_rope,0,3730\n631542,pointing_at_self,0,3729\n7801,lamia,0,3720\n560091,cat_hood,0,3720\n394795,baggy_pants,0,3718\n382106,fisheye,0,3712\n6376,lake,0,3712\n1555383,fur-trimmed_headwear,0,3711\n1471843,waist_bow,0,3710\n677023,underboob_cutout,0,3709\n1338623,purple_hairband,0,3708\n376168,puddle,0,3707\n414773,aiming,0,3706\n506307,thong_leotard,0,3699\n716981,holding_own_arm,0,3697\n638022,see-through_silhouette,0,3695\n504035,film_grain,0,3687\n11028,swimming,0,3684\n423074,photo_background,0,3682\n422775,dark_nipples,0,3681\n525461,smokestack,0,3681\n1516548,orange_headwear,0,3680\n570382,hands_in_hair,0,3675\n11828,flexible,0,3672\n1344144,metal_collar,0,3672\n1622516,earth_(planet),0,3661\n2191,shop,0,3659\n544274,backwards_hat,0,3658\n1454257,2others,0,3657\n400545,red_moon,0,3653\n379878,thorns,0,3647\n375225,flaccid,0,3644\n538953,zouri,0,3640\n411896,no_nipples,0,3638\n512713,aqua_bow,0,3638\n476596,sode,0,3637\n458486,mother_and_son,0,3636\n1280405,sleeveless_kimono,0,3635\n553771,cheek-to-cheek,0,3632\n1397752,two-tone_shirt,0,3626\n1316028,american_flag_legwear,0,3623\n662240,mini_wings,0,3622\n472867,arm_hug,0,3622\n222138,shrine,0,3622\n386432,leaning,0,3620\n565064,framed,0,3619\n1766885,demon_slayer_uniform,0,3619\n508193,novelty_censor,0,3618\n1304117,yellow_sweater,0,3618\n404134,spread_ass,0,3614\n672073,brown_fur,0,3613\n392612,pentagram,0,3612\n1161089,2022,0,3598\n702916,imminent_vaginal,0,3589\n456376,pince-nez,0,3589\n466449,spider_lily,0,3589\n652486,v-fin,0,3586\n460863,spider_web,0,3579\n1262158,holding_broom,0,3577\n390314,nude_cover,0,3577\n46079,robot_ears,0,3572\n553007,strapless_bikini,0,3572\n443901,shide,0,3568\n618153,pink_scarf,0,3568\n567636,painting_(object),0,3567\n476371,showering,0,3565\n500976,netorare,0,3564\n423625,bikini_lift,0,3564\n2476,hose,0,3563\n2288,octopus,0,3561\n1468959,horror_(theme),0,3561\n1409116,red_sailor_collar,0,3559\n538327,black_flower,0,3556\n1417504,nanamori_school_uniform,0,3556\n85824,pole,0,3553\n461531,banana,0,3553\n451769,toe_scrunch,0,3552\n1875949,lower_teeth_only,0,3550\n390569,cat_paws,0,3549\n902927,collared_jacket,0,3549\n9799,kunai,0,3548\n1281555,aran_sweater,0,3547\n1392011,purple_bodysuit,0,3546\n1356361,grey_vest,0,3545\n540428,arm_around_shoulder,0,3543\n605759,skirt_suit,0,3542\n522913,multiple_wings,0,3541\n5980,rooftop,0,3536\n1399176,two-tone_fur,0,3536\n399479,fog,0,3529\n3375,chicken,0,3529\n546453,cat_boy,0,3529\n1498357,single_leg_pantyhose,0,3525\n1312906,rei_no_himo,0,3525\n405756,hypnosis,0,3518\n1282899,improvised_gag,0,3516\n586174,neon_trim,0,3516\n578557,digital_media_player,0,3514\n411148,remote_control,0,3514\n376648,fishing_rod,0,3510\n481454,copyright,0,3508\n1477193,lightning_bolt_symbol,0,3506\n1468130,paradis_military_uniform,0,3506\n1345564,partially_unzipped,0,3505\n1770616,blue_gemstone,0,3502\n1730,what,0,3501\n1335474,sleeves_past_elbows,0,3501\n395305,wizard_hat,0,3495\n1330707,cake_slice,0,3493\n621745,brand_name_imitation,0,3491\n1385010,kissing_cheek,0,3491\n382125,rapier,0,3490\n541609,open_door,0,3489\n623390,glowing_weapon,0,3489\n660422,leaning_on_person,0,3488\n6424,cage,0,3488\n481427,steepled_fingers,0,3485\n589286,purple_rose,0,3482\n638728,print_gloves,0,3476\n667663,elbow_rest,0,3474\n1391985,grey_hoodie,0,3473\n1409115,green_sailor_collar,0,3470\n1328598,holding_bowl,0,3469\n1328649,orange_flower,0,3466\n516646,macaron,0,3460\n375919,demon_boy,0,3458\n717483,flower-shaped_pupils,0,3455\n718139,food_print,0,3452\n581054,keyboard_(computer),0,3449\n566804,single_sleeve,0,3449\n2709,needle,0,3448\n468016,;q,0,3442\n9275,butt_plug,0,3440\n383907,swimsuit_aside,0,3436\n380328,pompadour,0,3434\n7646,slave,0,3432\n643102,loose_necktie,0,3431\n525997,print_dress,0,3426\n485061,hair_tucking,0,3425\n524552,shiny_pokemon,0,3424\n1684874,laevatein_(touhou),0,3423\n576406,folded_fan,0,3420\n400245,opaque_glasses,0,3419\n410131,perky_breasts,0,3418\n11912,wrench,0,3418\n4434,violin,0,3418\n378993,energy_sword,0,3413\n411511,adjusting_swimsuit,0,3412\n1419007,stirrup_legwear,0,3406\n460475,naked_sweater,0,3406\n652354,too_many,0,3405\n664058,orange_ribbon,0,3405\n754200,oppai_loli,0,3403\n378543,egg_vibrator,0,3402\n384248,trefoil,0,3402\n379189,floor,0,3400\n477228,drum,0,3399\n483981,american_flag,0,3396\n12570,armpit_hair,0,3394\n8255,dancer,0,3393\n560040,framed_breasts,0,3390\n382440,pantylines,0,3389\n4768,sheep,0,3387\n441110,office_chair,0,3384\n1315028,lower_body,0,3378\n11366,policewoman,0,3376\n513303,afloat,0,3376\n1312614,blue_hakama,0,3375\n4191,sushi,0,3368\n531372,mouse_girl,0,3367\n9510,cream,0,3364\n575181,o-ring_bottom,0,3363\n207834,mascara,0,3351\n458795,suction_cups,0,3350\n10769,transformation,0,3346\n383173,train,0,3336\n634393,heart_choker,0,3335\n1411341,hooded_capelet,0,3331\n446070,lion_tail,0,3327\n1257056,holding_towel,0,3322\n554588,suit_jacket,0,3321\n482172,contrail,0,3320\n507625,calligraphy_brush,0,3319\n548668,excalibur_(fate/stay_night),0,3316\n1560432,reverse_outfit,0,3315\n1483713,multiple_rings,0,3314\n723098,after_anal,0,3310\n262264,tight_pants,0,3305\n1330167,american_flag_dress,0,3305\n554092,food_on_body,0,3304\n711562,striped_tail,0,3303\n1247747,hair_strand,0,3302\n458711,hand_under_clothes,0,3301\n639641,bra_removed,0,3298\n449248,scope,0,3298\n543339,bandaids_on_nipples,0,3297\n514260,left-handed,0,3297\n422830,paper_bag,0,3297\n611884,uneven_eyes,0,3293\n463490,spacecraft,0,3288\n395198,test_tube,0,3287\n4470,duel,0,3286\n667460,real_life_insert,0,3284\n1219136,hand_on_own_arm,0,3284\n2073,hairpods,0,3283\n675353,frilled_gloves,0,3283\n1421763,blue_capelet,0,3283\n147971,stylus,0,3283\n430704,medical_eyepatch,0,3283\n5171,map,0,3282\n1413751,print_bowtie,0,3281\n450123,staring,0,3275\n479792,circle,0,3273\n1416797,pink_cardigan,0,3272\n408052,gym_shorts,0,3270\n1374290,covered_collarbone,0,3266\n522010,character_profile,0,3264\n1303383,blue_scrunchie,0,3262\n1305030,green_leotard,0,3255\n684190,gradient_sky,0,3255\n1582181,string_of_fate,0,3252\n8295,concept_art,0,3249\n49941,lemon,0,3248\n375251,egyptian,0,3247\n10359,middle_finger,0,3242\n151042,envelope,0,3238\n393891,no_shirt,0,3235\n1636976,footwear_bow,0,3234\n750849,holding_mask,0,3232\n10983,shotgun,0,3231\n1504889,rudder_footwear,0,3230\n386503,binoculars,0,3222\n1233722,maid_bikini,0,3221\n380805,flashing,0,3221\n411223,father_and_son,0,3221\n626303,mismatched_gloves,0,3218\n163224,fins,0,3217\n1475826,holding_pokemon,0,3217\n392360,stage,0,3215\n619121,hair_beads,0,3214\n459009,penis_on_face,0,3213\n641554,tomoe_(symbol),0,3211\n687681,partially_undressed,0,3209\n521852,pussy_juice_trail,0,3206\n758409,taur,0,3206\n403195,squirrel_ears,0,3206\n1335364,stomach_tattoo,0,3205\n687202,pussy_juice_stain,0,3203\n1238711,pink_shorts,0,3202\n16144,rubber_duck,0,3201\n674492,dress_bow,0,3200\n5567,pizza,0,3200\n2553,basketball,0,3198\n455038,capri_pants,0,3198\n724241,holding_camera,0,3197\n1641895,gloved_handjob,0,3196\n1612103,milestone_celebration,0,3195\n394619,furisode,0,3192\n638426,arm_belt,0,3190\n399418,shore,0,3188\n1227744,multiple_crossover,0,3186\n419379,bandage_over_one_eye,0,3185\n1337096,white_vest,0,3185\n508748,horn_bow,0,3183\n548829,chewing_gum,0,3183\n613840,happy_halloween,0,3183\n619633,baozi,0,3180\n1387105,pink_leotard,0,3179\n1512867,multiple_hair_bows,0,3177\n1229697,drop_shadow,0,3174\n447030,candy_apple,0,3174\n396737,striped_bra,0,3170\n409293,open_dress,0,3169\n459215,finger_gun,0,3169\n481814,background_text,0,3169\n1422082,digimon_(creature),0,3167\n234,gothic,0,3165\n471771,pumps,0,3164\n535308,body_markings,0,3164\n540545,rod_of_remorse,0,3162\n562136,lip_biting,0,3161\n379713,bad_feet,0,3158\n413820,deep_penetration,0,3156\n447189,spitroast,0,3155\n467674,sake_bottle,0,3154\n549346,blue_pantyhose,0,3153\n535048,humanization,0,3152\n1035339,red_headband,0,3151\n387248,banner,0,3148\n10469,duck,0,3147\n1454264,heart-shaped_eyewear,0,3141\n1425516,uranohoshi_school_uniform,0,3140\n560677,arm_rest,0,3139\n1311981,meiji_schoolgirl_uniform,0,3136\n3759,parfait,0,3135\n510576,swim_trunks,0,3131\n411566,nightcap,0,3126\n538859,kindergarten_uniform,0,3125\n1559152,reverse_bunnysuit,0,3125\n492468,plaid_dress,0,3117\n822828,reverse_suspended_congress,0,3114\n684686,grabbing_another's_ass,0,3111\n1410709,ejaculating_while_penetrated,0,3110\n375882,cuts,0,3109\n1408536,holding_lollipop,0,3106\n379595,breast_slip,0,3103\n1400488,pink_neckerchief,0,3103\n536524,tablet_pc,0,3103\n1436828,obiage,0,3100\n425142,submerged,0,3097\n1319842,forked_eyebrows,0,3096\n588209,irrumatio,0,3095\n1582503,scar_on_chest,0,3095\n585211,energy_gun,0,3094\n1341593,bandaged_hand,0,3090\n493348,christmas_ornaments,0,3089\n769705,holding_another's_arm,0,3088\n666708,w_arms,0,3085\n551537,lying_on_person,0,3082\n590746,goggles_around_neck,0,3082\n420283,leotard_aside,0,3081\n502536,paint_splatter,0,3079\n10692,sandwich,0,3076\n7641,piggyback,0,3073\n420128,upright_straddle,0,3072\n601423,yellow_scarf,0,3072\n568804,anus_peek,0,3070\n1326150,purple_vest,0,3069\n1415656,grey_sailor_collar,0,3068\n473217,green_bra,0,3066\n481604,penis_awe,0,3059\n545136,coat_on_shoulders,0,3058\n676352,pokephilia,0,3056\n1590671,chest_harness,0,3056\n1394528,single_sidelock,0,3055\n10919,lineup,0,3054\n15667,riding_crop,0,3052\n1217551,black_armor,0,3052\n1358847,polos_crown,0,3051\n7631,harpy,0,3050\n599594,prosthetic_arm,0,3048\n438208,throwing,0,3047\n666377,hair_spread_out,0,3045\n592316,colored_pubic_hair,0,3044\n15004,toy,0,3043\n484752,clothes_grab,0,3043\n398608,hair_twirling,0,3042\n424585,viewfinder,0,3040\n511645,single_kneehigh,0,3040\n403196,squirrel_tail,0,3040\n378782,hiding,0,3039\n1613301,obliques,0,3038\n488003,leg_hair,0,3035\n1299132,holding_box,0,3034\n54471,midair,0,3034\n1312165,orange_necktie,0,3030\n1627141,gae_bolg_(fate),0,3028\n464585,tomato,0,3027\n1670357,furry_with_non-furry,0,3027\n626701,bird_ears,0,3025\n1417279,holding_underwear,0,3024\n891880,lion_girl,0,3023\n669594,bat_hair_ornament,0,3022\n1339523,bone_hair_ornament,0,3013\n486697,frilled_swimsuit,0,3012\n676138,asymmetrical_footwear,0,3012\n1409831,kuromorimine_military_uniform,0,3012\n1839475,chips_(food),0,3011\n682128,hands_on_another's_shoulders,0,3002\n646029,egasumi,0,2995\n1408498,japari_symbol,0,2995\n1328271,layered_bikini,0,2991\n656168,hands_on_another's_face,0,2989\n573688,reindeer_antlers,0,2983\n1373025,blue-framed_eyewear,0,2980\n464577,shooting_star,0,2980\n380157,shovel,0,2977\n990162,heart_in_eye,0,2975\n464576,seagull,0,2972\n583009,arm_around_waist,0,2970\n385637,earbuds,0,2968\n466613,slime_girl,0,2964\n7994,fighting,0,2963\n1481015,hands_in_opposite_sleeves,0,2961\n702200,sailor_bikini,0,2961\n474988,speaker,0,2960\n688378,multi-tied_hair,0,2960\n1332539,pubic_hair_peek,0,2956\n513153,magazine_(weapon),0,2956\n390685,darkness,0,2953\n659834,spoken_squiggle,0,2947\n493576,feather_boa,0,2942\n461372,mobile_suit,0,2942\n720478,lifting_person,0,2941\n539465,tiger_girl,0,2941\n378767,bleeding,0,2938\n567734,shikishi,0,2938\n440347,leopard_print,0,2938\n347508,miniboy,0,2936\n1258923,star_in_eye,0,2934\n378810,grinding,0,2930\n557508,loose_belt,0,2930\n1371098,purple_umbrella,0,2930\n600421,after_fellatio,0,2930\n12721,gas_mask,0,2928\n489904,arms_around_neck,0,2926\n1455730,raccoon_girl,0,2926\n470690,pancake,0,2924\n1291177,orange_bodysuit,0,2921\n378151,megaphone,0,2920\n467500,beer_can,0,2920\n11436,deepthroat,0,2919\n506137,jacket_around_waist,0,2918\n4550,grenade,0,2914\n1327681,red_sleeves,0,2914\n461788,scowl,0,2912\n1336138,red_belt,0,2911\n1382349,goggles_on_headwear,0,2904\n488365,letterman_jacket,0,2903\n1277869,horned_headwear,0,2898\n471357,treble_clef,0,2897\n1308608,bag_charm,0,2897\n612379,bubble_tea,0,2897\n447984,vegetable,0,2896\n375460,ceiling,0,2895\n405671,soda_can,0,2894\n692860,alternate_legwear,0,2892\n526914,licking_finger,0,2891\n559692,clothed_pokemon,0,2891\n707150,finger_on_trigger,0,2889\n5597,newspaper,0,2886\n399829,magazine_cover,0,2883\n446690,gloom_(expression),0,2882\n584478,leaf_print,0,2882\n375231,shirt_tug,0,2881\n1301494,spoken_interrobang,0,2880\n1455114,hair_tie_in_mouth,0,2880\n4262,surreal,0,2880\n1235204,bike_shorts_under_skirt,0,2878\n546810,orange_sky,0,2875\n487143,bamboo_forest,0,2872\n399543,flip-flops,0,2871\n2230,guro,0,2869\n588534,in_box,0,2866\n460940,lily_pad,0,2865\n644588,long_eyelashes,0,2865\n713253,cross_hair_ornament,0,2865\n541137,kanzashi,0,2862\n380265,skates,0,2861\n1385298,animal_ear_headphones,0,2861\n1384365,orange_hairband,0,2855\n481088,constellation,0,2848\n1397450,plantar_flexion,0,2847\n531896,2020,0,2845\n13976,frying_pan,0,2844\n2377,piano,0,2842\n424961,remote_control_vibrator,0,2842\n546133,ghost_tail,0,2841\n1291900,yellow_fur,0,2839\n577161,in_tree,0,2836\n442883,city_lights,0,2835\n483264,yellow_rose,0,2833\n486613,extra,0,2832\n12733,thinking,0,2832\n480578,hagoromo,0,2832\n1273311,vertical-striped_dress,0,2831\n633428,bunny_print,0,2829\n1423707,holding_scythe,0,2829\n847095,sparkle_background,0,2828\n13273,stained_glass,0,2828\n828858,hand_over_own_mouth,0,2825\n1253185,snowflake_hair_ornament,0,2824\n1298076,winged_arms,0,2822\n7526,crotch_rope,0,2818\n629659,food_on_head,0,2818\n9335,5koma,0,2817\n462213,peeing_self,0,2816\n377929,sheep_ears,0,2813\n507697,purple_pantyhose,0,2812\n1327007,holding_axe,0,2811\n1837805,traditional_youkai,0,2811\n2571,mochi,0,2810\n14111,candy_cane,0,2809\n1304169,au_ra,0,2806\n476448,kote,0,2801\n389804,quad_tails,0,2800\n1335455,watson_cross,0,2799\n663513,brown_bag,0,2798\n1853327,bar_(place),0,2796\n722453,thumb_ring,0,2795\n1252645,orange_nails,0,2794\n471821,debris,0,2793\n1794320,bow-shaped_hair,0,2792\n1400482,earclip,0,2790\n549590,uneven_gloves,0,2786\n379306,public_nudity,0,2785\n1552754,breast_curtains,0,2785\n572410,ankle_ribbon,0,2782\n1259682,ribbed_dress,0,2780\n530963,arms_under_breasts,0,2779\n390154,left-to-right_manga,0,2779\n1307725,green_scarf,0,2779\n1298625,open-chest_sweater,0,2778\n585305,ear_ribbon,0,2775\n458232,flats,0,2774\n9921,panda,0,2773\n426621,grey_panties,0,2773\n468765,pith_helmet,0,2773\n16978,writing,0,2772\n513195,bird_on_head,0,2771\n1379954,diffraction_spikes,0,2771\n590943,neck_ruff,0,2770\n662265,object_namesake,0,2769\n1408401,white_scrunchie,0,2769\n1373356,yellow_kimono,0,2767\n511736,heart_in_mouth,0,2767\n475209,button_badge,0,2763\n447780,peeking,0,2761\n4807,spill,0,2758\n1234394,see-through_shirt,0,2755\n662500,caterpillar_tracks,0,2755\n618149,yugake,0,2754\n1315142,pink_scrunchie,0,2754\n4773,puppet,0,2754\n404857,cupcake,0,2753\n501057,breastless_clothes,0,2752\n2641,paint,0,2751\n402497,lotus,0,2750\n634400,light_green_hair,0,2744\n380355,chained,0,2743\n467080,thank_you,0,2742\n633519,american_flag_bikini,0,2740\n1400511,fur-trimmed_hood,0,2739\n1205954,looking_at_penis,0,2738\n413033,gaping,0,2738\n25477,chemise,0,2737\n548543,arm_hair,0,2736\n1874313,covering_own_eyes,0,2735\n484394,four-leaf_clover,0,2735\n649327,wide_ponytail,0,2734\n457141,lace_panties,0,2733\n1344905,holding_dagger,0,2733\n1215711,numbered,0,2733\n610726,shared_clothes,0,2731\n1714873,chest_sarashi,0,2729\n406406,shoe_dangle,0,2727\n652707,domino_mask,0,2727\n4177,lotion,0,2724\n279898,shell_casing,0,2722\n409789,rubber_boots,0,2718\n478568,medal,0,2716\n10624,exercise,0,2713\n2833,collage,0,2711\n1384369,green_hairband,0,2709\n8808,hallway,0,2704\n666648,mitsudomoe_(shape),0,2704\n378,cheese,0,2702\n406895,tunic,0,2702\n743282,layered_clothes,0,2701\n494802,on_shoulder,0,2701\n464876,bento,0,2699\n602690,stuffed_cat,0,2699\n1770615,red_gemstone,0,2695\n1373278,black_ascot,0,2693\n619493,ears_down,0,2691\n395226,shoulder_carry,0,2688\n1441861,orange_footwear,0,2687\n1350604,cat_ear_panties,0,2687\n1435434,two-tone_skirt,0,2685\n1474162,gekkoukan_high_school_uniform,0,2684\n503705,paw_shoes,0,2682\n397148,huge_nipples,0,2680\n433752,yunomi,0,2679\n558702,pinching,0,2676\n1383796,rabbit_hood,0,2675\n15996,moonlight,0,2673\n2490,pirate,0,2673\n656171,hand_on_another's_chest,0,2673\n405984,snot,0,2672\n9002,stethoscope,0,2671\n530091,corruption,0,2670\n535299,red_sclera,0,2668\n1316609,mole_on_thigh,0,2667\n724362,single_detached_sleeve,0,2664\n417408,keyhole,0,2663\n619992,world_war_ii,0,2662\n626084,bangs_pinned_back,0,2662\n478424,striker_unit,0,2662\n1322674,latin_cross,0,2660\n1474402,qing_guanmao,0,2656\n631299,pillarboxed,0,2655\n12650,subtitled,0,2654\n644051,aiming_at_viewer,0,2652\n399594,urethra,0,2652\n2626,volleyball,0,2652\n640241,helmet_removed,0,2652\n709557,torn_bodysuit,0,2652\n1183480,blue_belt,0,2651\n1471518,horseshoe_ornament,0,2650\n5272,dice,0,2649\n429439,fetal_position,0,2649\n587679,vibrator_under_clothes,0,2648\n11981,kyuubi,0,2644\n465450,trash_can,0,2644\n1242915,rose_print,0,2642\n10731,chainsaw,0,2641\n1312757,bra_peek,0,2641\n420964,hip_bones,0,2640\n3237,goldfish,0,2639\n484121,leg_lock,0,2639\n1390670,breast_tattoo,0,2639\n505313,torn_sleeves,0,2635\n470159,jealous,0,2634\n1383010,purple_choker,0,2634\n1291568,pink_apron,0,2634\n473427,green_thighhighs,0,2632\n1330014,year_of_the_tiger,0,2632\n494260,purple_wings,0,2630\n407041,profanity,0,2629\n375792,kogal,0,2629\n2848,wedding,0,2628\n447824,doorway,0,2627\n1449257,gem_uniform_(houseki_no_kuni),0,2625\n350,ramen,0,2624\n1280149,crescent_earrings,0,2624\n528555,flat_ass,0,2623\n397935,ladder,0,2623\n378452,hand_in_panties,0,2622\n650514,wet_swimsuit,0,2618\n8874,pudding,0,2617\n555933,sword_of_hisou,0,2617\n1441888,over-rim_eyewear,0,2615\n519703,alternate_universe,0,2615\n413035,soccer_ball,0,2614\n515648,coattails,0,2613\n1261354,feather_trim,0,2613\n414783,padlock,0,2612\n1229660,multiple_horns,0,2612\n11316,abstract,0,2611\n1262166,seamed_legwear,0,2605\n15201,soldier,0,2605\n1468295,quarter_note,0,2603\n617590,wa_maid,0,2602\n449458,bullpup,0,2602\n474982,white_robe,0,2602\n667185,burn_scar,0,2600\n1467259,diagonal_bangs,0,2598\n10309,fashion,0,2597\n663385,crossed_ankles,0,2597\n1554926,wide_spread_legs,0,2594\n605757,pant_suit,0,2593\n673352,flaming_eye,0,2591\n675804,nervous_smile,0,2591\n8796,electric_fan,0,2586\n10631,paddle,0,2584\n619600,zombie_pose,0,2584\n1297470,lifted_by_another,0,2583\n400527,skirt_tug,0,2582\n449624,bass_guitar,0,2582\n12326,owl,0,2578\n695265,clitoral_stimulation,0,2577\n1355655,grey_coat,0,2576\n1178138,snowflake_print,0,2576\n1901,orgy,0,2575\n682862,hand_on_another's_hip,0,2573\n1434382,spade_(shape),0,2572\n1444529,linea_alba,0,2572\n469445,sleepwear,0,2572\n534387,heart_pillow,0,2567\n1438791,holding_pom_poms,0,2566\n1515727,strap_between_breasts,0,2565\n11243,shower_head,0,2564\n1397369,year_of_the_ox,0,2563\n1392007,pink_bodysuit,0,2562\n547698,sunburst,0,2561\n32298,duffel_bag,0,2558\n540097,turtle_shell,0,2557\n615041,finger_to_cheek,0,2553\n619145,orange_gloves,0,2552\n609122,multiple_belts,0,2552\n595006,green_sweater,0,2550\n1431325,mismatched_bikini,0,2548\n1395493,eyepatch_bikini,0,2548\n375886,sheep_girl,0,2547\n1376523,weibo_username,0,2544\n1161083,2018,0,2544\n670773,happy_valentine,0,2543\n69844,pig,0,2542\n1518440,mouth_drool,0,2542\n14861,chef_hat,0,2541\n649248,red_pupils,0,2541\n1304208,black_cardigan,0,2539\n622606,anime_coloring,0,2537\n1396558,frilled_hair_tubes,0,2537\n10340,shark,0,2537\n13958,locker_room,0,2535\n525182,santa_bikini,0,2535\n4031,negligee,0,2533\n1344732,striped_jacket,0,2533\n821841,2019,0,2532\n476787,talons,0,2531\n380173,seashell,0,2528\n422957,come_hither,0,2527\n1410787,st._gloriana's_school_uniform,0,2527\n9554,g-string,0,2526\n722842,frilled_kimono,0,2525\n1161081,2016,0,2522\n1416804,orange_scrunchie,0,2521\n1283223,shibari_over_clothes,0,2520\n1515354,brown_theme,0,2518\n1201870,prone_bone,0,2517\n428664,embers,0,2517\n1582500,scar_on_arm,0,2515\n578713,giving,0,2514\n656916,animal_penis,0,2514\n1336997,green_coat,0,2514\n527684,doll_hug,0,2510\n1338640,yellow_scrunchie,0,2508\n10168,suspension,0,2506\n475709,bandaid_on_pussy,0,2505\n1314965,horizontal_pupils,0,2504\n381372,cold,0,2502\n602427,wrestling_outfit,0,2502\n499060,ainu_clothes,0,2500\n586749,mismatched_footwear,0,2499\n1530508,scar_on_nose,0,2498\n1515361,white_theme,0,2496\n632927,alternate_headwear,0,2496\n1723482,flame-tipped_tail,0,2496\n550123,sitting_on_face,0,2495\n11433,nipple_rings,0,2494\n228096,spacesuit,0,2492\n677406,multiple_earrings,0,2492\n471929,cum_on_stomach,0,2492\n835764,untied_panties,0,2491\n640801,clothes_in_mouth,0,2490\n4011,blade,0,2489\n390993,sigh,0,2487\n9101,cigar,0,2487\n404436,ribbed_shirt,0,2487\n642455,shirt_removed,0,2487\n14074,raincoat,0,2487\n1437532,two-tone_jacket,0,2486\n551584,updo,0,2485\n398742,pastry,0,2484\n402204,holding_panties,0,2483\n495857,arm_around_neck,0,2483\n725441,black_headband,0,2482\n1393295,orange_choker,0,2481\n515468,head-mounted_display,0,2479\n556999,two-footed_footjob,0,2477\n379432,lights,0,2477\n399308,walk-in,0,2476\n405623,dog_boy,0,2474\n633092,arm_held_back,0,2473\n374959,still_life,0,2472\n607667,checkered_scarf,0,2472\n1393855,holding_flag,0,2471\n685091,hand_on_another's_back,0,2471\n388452,sweets,0,2470\n376473,mallet,0,2470\n393166,bloom,0,2470\n10736,spandex,0,2468\n1319452,hand_on_another's_arm,0,2467\n612542,bow_bikini,0,2462\n1538888,kitauji_high_school_uniform,0,2459\n9511,spotlight,0,2458\n533035,undersized_clothes,0,2458\n1451957,naoetsu_high_school_uniform,0,2457\n663171,sun_symbol,0,2456\n495002,skirt_around_one_leg,0,2455\n527607,magazine_(object),0,2455\n461542,red_sash,0,2454\n480444,shouji,0,2452\n883683,white_feathers,0,2450\n523426,hand_grab,0,2450\n10412,shaved_ice,0,2449\n374334,open_shorts,0,2448\n540150,puckered_lips,0,2446\n446993,open_collar,0,2445\n692418,suspender_shorts,0,2443\n472424,skull_and_crossbones,0,2442\n470441,plastic_bag,0,2441\n410525,typo,0,2441\n473040,tombstone,0,2439\n374379,hairy,0,2438\n542457,2015,0,2435\n11943,zero_suit,0,2433\n433704,super_saiyan,0,2428\n1409556,ooarai_military_uniform,0,2427\n409526,thigh_sex,0,2426\n816551,jackal_ears,0,2425\n1515357,orange_theme,0,2424\n1403936,bodysuit_under_clothes,0,2424\n1827216,cube_hair_ornament,0,2422\n375083,tuxedo,0,2421\n2316,carpet,0,2421\n701781,calendar_(medium),0,2420\n637971,heart_tattoo,0,2420\n468923,screaming,0,2419\n13265,cum_on_boy,0,2417\n11717,mohawk,0,2417\n647581,look-alike,0,2416\n1322421,bandaid_on_cheek,0,2416\n3917,toothbrush,0,2415\n486062,on_lap,0,2414\n570082,swirl_lollipop,0,2414\n572679,rubbing_eyes,0,2414\n462454,small_penis,0,2412\n1141250,black_fur,0,2412\n469020,rice_bowl,0,2410\n523488,flower_pot,0,2410\n6339,dusk,0,2409\n1274212,green_cape,0,2409\n617471,female_pov,0,2408\n375037,striped_socks,0,2408\n1257075,number_tattoo,0,2406\n1434570,hadanugi_dousa,0,2405\n691977,egyptian_clothes,0,2405\n7867,orc,0,2401\n418007,o3o,0,2397\n662701,brown_ribbon,0,2397\n514768,grey_sky,0,2395\n505860,tally,0,2394\n390347,flask,0,2391\n1161082,2017,0,2391\n242,princess,0,2389\n862221,club_(weapon),0,2388\n404541,hexagram,0,2388\n1791866,holding_smoking_pipe,0,2388\n645791,patterned_background,0,2386\n445826,phimosis,0,2386\n601821,nape,0,2386\n423600,bit_gag,0,2385\n512534,squirrel_girl,0,2383\n569544,condom_on_penis,0,2382\n1673151,uneven_sleeves,0,2382\n1835734,red_one-piece_swimsuit,0,2380\n1410099,purple_sleeves,0,2380\n1437301,black_bag,0,2379\n375272,evening_gown,0,2371\n573757,beach_towel,0,2370\n643796,power_symbol,0,2368\n510070,:i,0,2368\n407322,smell,0,2367\n587435,side_cutout,0,2366\n580695,big_belly,0,2366\n13691,bomber_jacket,0,2365\n1304570,cat_ear_headphones,0,2364\n556491,convenient_arm,0,2363\n669792,futa_with_male,0,2362\n453104,cameo,0,2357\n400905,talisman,0,2355\n623327,creator_connection,0,2354\n1513962,tam_o'_shanter,0,2353\n396063,asphyxiation,0,2352\n459289,blue_lips,0,2352\n1268355,carrot_necklace,0,2352\n528325,red_pantyhose,0,2347\n413782,d-pad,0,2340\n297185,drumsticks,0,2339\n493298,police_hat,0,2339\n419192,monkey_tail,0,2339\n456655,broken_glass,0,2339\n440796,triangle,0,2338\n479397,white_headband,0,2336\n3874,sink,0,2335\n474252,path,0,2335\n382047,sunrise,0,2335\n1484339,diagonal-striped_bow,0,2333\n455175,smiley_face,0,2331\n676653,mouse_(computer),0,2331\n1300235,purple_pants,0,2329\n513424,millipen_(medium),0,2329\n613439,roman_numeral,0,2328\n1227710,tail_through_clothes,0,2327\n1695986,tied_up_(nonsexual),0,2326\n622943,cover_image,0,2326\n484578,disembodied_head,0,2324\n517255,plum_blossoms,0,2324\n1373030,pink-framed_eyewear,0,2319\n776463,enpera,0,2318\n395568,cyberpunk,0,2316\n1551114,covered_abs,0,2316\n390096,hill,0,2314\n494832,faux_traditional_media,0,2311\n480812,steam_censor,0,2309\n699975,shoulder_spikes,0,2309\n1441884,rectangular_eyewear,0,2307\n673610,green_choker,0,2307\n526974,coffee_cup,0,2304\n377197,desert,0,2303\n3054,curry,0,2301\n1631337,shimakaze_(kancolle)_(cosplay),0,2301\n12396,ice_cube,0,2299\n425654,beak,0,2297\n1505132,russian_text,0,2297\n398959,trick_or_treat,0,2295\n1407477,black_scrunchie,0,2295\n380573,quill,0,2294\n589610,spider_web_print,0,2294\n16614,diadem,0,2294\n4381,ufo,0,2291\n426146,fur_coat,0,2290\n1787327,shuuchiin_academy_school_uniform,0,2290\n631055,tree_shade,0,2288\n502177,bird_girl,0,2288\n1396985,holding_sheath,0,2287\n578084,black_tail,0,2287\n393321,scratches,0,2287\n466898,strangling,0,2286\n700189,finger_in_another's_mouth,0,2286\n665350,microdress,0,2285\n189096,anal_fingering,0,2284\n634002,matching_outfit,0,2284\n488880,fourth_wall,0,2284\n426384,clitoral_hood,0,2282\n8371,hamster,0,2282\n1343573,holding_arrow,0,2279\n856710,neck_tattoo,0,2279\n2909,cyclops,0,2273\n498627,short_sword,0,2272\n410605,shared_scarf,0,2270\n581705,sitting_on_desk,0,2270\n410379,praying,0,2268\n13606,cervix,0,2268\n506562,tri_tails,0,2264\n222208,eraser,0,2262\n1321704,string_of_flags,0,2262\n160066,driving,0,2261\n11486,jellyfish,0,2261\n419919,diving_mask,0,2261\n1528617,retrofit_(azur_lane),0,2261\n435180,bad_hands,0,2260\n1853,tribadism,0,2257\n666137,aqua_bikini,0,2257\n462978,??,0,2257\n575698,torn_cape,0,2255\n613216,multicolored_legwear,0,2254\n1312065,chinese_knot,0,2254\n1320347,octarian,0,2254\n401614,tennis_uniform,0,2253\n554120,dirty_face,0,2251\n1637933,evolutionary_line,0,2251\n506895,ankle_cuffs,0,2250\n1392360,black_camisole,0,2248\n1533601,sideless_outfit,0,2246\n673694,large_tail,0,2246\n1262143,phone_screen,0,2246\n5871,restaurant,0,2244\n539295,feather_hair,0,2244\n16335,spatula,0,2243\n453895,mechanical_parts,0,2243\n613940,white_cat,0,2242\n1793456,cooperative_fellatio,0,2241\n8721,breast_bondage,0,2241\n1376935,blue_ascot,0,2240\n490993,colored_tongue,0,2240\n1430633,two-tone_bikini,0,2240\n399776,torch,0,2240\n400119,melting,0,2239\n381796,satchel,0,2239\n1524489,looking_at_object,0,2238\n534340,calendar_(object),0,2238\n384086,jet,0,2234\n854287,ribbed_legwear,0,2231\n543351,crossed_bandaids,0,2231\n548910,chain_necklace,0,2230\n109323,cliff,0,2230\n1252969,white_nails,0,2230\n1342770,strappy_heels,0,2227\n844443,red_umbrella,0,2225\n564233,hand_on_hilt,0,2221\n1514281,shark_hair_ornament,0,2221\n539390,insignia,0,2220\n410599,crotchless_panties,0,2217\n423171,prayer_beads,0,2214\n1286664,kabedon,0,2214\n490692,shading_eyes,0,2213\n434965,studded_belt,0,2213\n1321301,purple_shorts,0,2212\n1391834,feather-trimmed_sleeves,0,2212\n1570687,starter_pokemon_trio,0,2212\n640820,spoken_anger_vein,0,2211\n457376,milk_bottle,0,2211\n1276335,fur-trimmed_legwear,0,2210\n550560,oni_mask,0,2210\n703219,multicolored_wings,0,2205\n615392,hair_horns,0,2205\n14927,dessert,0,2204\n1577661,dynamax_band,0,2204\n3139,samurai,0,2203\n493584,striped_pants,0,2203\n379208,smelling,0,2202\n466731,ear_tag,0,2200\n1443349,blue_cardigan,0,2200\n144057,harem_outfit,0,2200\n381887,caught,0,2199\n1513986,creature_and_personification,0,2198\n712510,bare_hips,0,2197\n1452471,hikarizaka_private_high_school_uniform,0,2196\n539585,sleeping_upright,0,2196\n379758,cave,0,2196\n479466,fine_art_parody,0,2195\n444189,arabian_clothes,0,2195\n1402052,white_cloak,0,2195\n1304064,black_apron,0,2195\n455722,curtain_grab,0,2192\n381727,open_skirt,0,2191\n491230,horseback_riding,0,2190\n1422860,two-tone_swimsuit,0,2190\n427930,liquid,0,2189\n1447394,whistle_around_neck,0,2188\n1447450,d-pad_hair_ornament,0,2187\n638777,ritual_baton,0,2186\n87501,wire,0,2185\n480182,keyboard_(instrument),0,2185\n950553,yoga_pants,0,2185\n687728,hands_on_own_head,0,2184\n664440,lace-trimmed_dress,0,2183\n543455,bolt_action,0,2182\n458230,strapless_bra,0,2181\n423438,racket,0,2181\n458132,winged_hat,0,2180\n514239,holding_condom,0,2180\n482447,tailcoat,0,2180\n1098217,drawing_tablet,0,2177\n1399795,no_eyewear,0,2173\n540767,abstract_background,0,2172\n1297466,pulled_by_another,0,2171\n488260,purple_cape,0,2171\n501554,sweater_around_waist,0,2168\n595850,hooded_cape,0,2167\n513765,broken_horn,0,2164\n633435,magical_musket,0,2162\n1243347,mega_pokemon,0,2161\n8279,church,0,2160\n572264,gold_chain,0,2160\n1344680,holding_doll,0,2159\n379555,graffiti,0,2159\n519892,naked_coat,0,2158\n486210,tie_clip,0,2158\n433619,dreadlocks,0,2157\n1076181,flower_(symbol),0,2157\n10861,town,0,2156\n9032,attack,0,2155\n519804,black_lips,0,2153\n839099,green_lips,0,2152\n482936,long_pointy_ears,0,2150\n1609053,twitching_penis,0,2149\n438849,pill,0,2149\n478965,light_bulb,0,2148\n457115,when_you_see_it,0,2147\n453756,;3,0,2147\n405207,arch,0,2145\n644564,butterfly_print,0,2145\n683211,carrying_over_shoulder,0,2141\n467696,black_rose,0,2139\n389161,french_fries,0,2139\n427771,omelet,0,2138\n637281,mechanical_legs,0,2137\n1446770,instant_loss,0,2136\n2281,festival,0,2135\n1280102,single_hair_intake,0,2135\n1411065,anzio_school_uniform,0,2135\n403072,rocket_launcher,0,2131\n1338775,patterned_clothing,0,2126\n425236,pulling,0,2124\n1315557,grey_bow,0,2123\n553947,>_o,0,2122\n644284,holding_sign,0,2121\n530881,spilling,0,2120\n721674,purple_scarf,0,2119\n713394,hand_on_another's_thigh,0,2118\n1448342,two-tone_gloves,0,2118\n7728,tankini,0,2116\n442279,vial,0,2115\n1411023,st._gloriana's_military_uniform,0,2114\n560724,belly_chain,0,2113\n635980,impossible_leotard,0,2112\n430139,single_vertical_stripe,0,2112\n658525,white_cardigan,0,2112\n514196,cat_print,0,2111\n481778,stage_lights,0,2110\n562075,plaid_bikini,0,2108\n489623,glowstick,0,2106\n403183,nail,0,2105\n5058,surfboard,0,2105\n588453,laurel_crown,0,2105\n543193,spread_wings,0,2104\n396962,sweatband,0,2101\n465814,video_camera,0,2100\n9534,trumpet,0,2099\n643426,reverse_upright_straddle,0,2099\n540105,fleeing,0,2098\n682638,flower_earrings,0,2097\n1300390,bandaged_head,0,2097\n421313,product_placement,0,2096\n422126,moss,0,2096\n396859,budget_sarashi,0,2096\n420749,turban,0,2095\n586819,energy_wings,0,2094\n1438722,plunging_neckline,0,2092\n1669073,official_alternate_hair_length,0,2092\n497966,penis_grab,0,2091\n551641,thigh_ribbon,0,2091\n5983,briefs,0,2090\n485269,clothes_hanger,0,2089\n423281,mini-hakkero,0,2089\n1605926,see-through_legwear,0,2088\n475076,cat_hat,0,2087\n472623,hammer_and_sickle,0,2087\n437149,tongue_piercing,0,2087\n421506,head_grab,0,2086\n1353605,standing_on_liquid,0,2085\n1726780,egg_(food),0,2085\n1281566,spiked_armlet,0,2085\n417809,denim_skirt,0,2084\n561087,covering_nipples,0,2083\n1420004,blur_censor,0,2082\n1594627,monocle_hair_ornament,0,2081\n648389,spread_fingers,0,2079\n448292,porkpie_hat,0,2078\n1350232,orange_shorts,0,2074\n667237,oversized_animal,0,2073\n661191,aqua_skirt,0,2072\n669534,clothes_down,0,2070\n435549,veranda,0,2069\n442051,dust,0,2069\n1769934,finger_in_own_mouth,0,2068\n541927,manga_(object),0,2068\n402726,spanked,0,2067\n482791,tusks,0,2067\n1338653,fur_scarf,0,2066\n521817,black_feathers,0,2065\n700045,stray_pubic_hair,0,2065\n1289190,vertical-striped_skirt,0,2063\n1269637,hands_on_headwear,0,2061\n488487,ankle_socks,0,2061\n389909,potato_chips,0,2061\n631042,pool_ladder,0,2058\n1307801,feather_earrings,0,2057\n460050,sleep_molestation,0,2055\n473703,car_interior,0,2050\n405548,tail_bell,0,2050\n519626,on_table,0,2050\n12284,gown,0,2049\n1394292,blue_serafuku,0,2048\n527651,naked_sheet,0,2048\n879036,false_smile,0,2048\n6267,whale,0,2048\n3993,mop,0,2043\n1277599,ribbon-trimmed_skirt,0,2043\n491714,santa_dress,0,2042\n1257581,bowl_hat,0,2042\n699964,pocky_day,0,2042\n448198,shako_cap,0,2041\n589583,kappougi,0,2040\n1370858,mechanical_horns,0,2039\n1453261,low_twin_braids,0,2038\n378768,dreaming,0,2037\n1782786,median_furrow,0,2033\n447322,korean_clothes,0,2031\n381083,sketchbook,0,2031\n1327736,purple_coat,0,2030\n653262,back_tattoo,0,2029\n439517,nose_bubble,0,2026\n2130,tanuki,0,2026\n541092,tegaki,0,2026\n419258,child_on_child,0,2023\n466441,sweet_potato,0,2023\n104991,have_to_pee,0,2022\n1254442,scissor_blade,0,2022\n513425,nib_pen_(medium),0,2022\n287085,boxing_gloves,0,2017\n1389873,orange_kimono,0,2017\n8235,shuriken,0,2016\n1465099,black_sports_bra,0,2016\n477241,birthday_cake,0,2014\n615345,bunny_pose,0,2014\n1349485,clover_hair_ornament,0,2012\n784913,wife_and_wife,0,2012\n581551,covered_face,0,2012\n10884,crotch_rub,0,2010\n385780,high_contrast,0,2005\n1343930,black_hakama,0,2005\n1391600,borrowed_garments,0,2004\n711978,clothed_animal,0,2003\n453979,gusset,0,2002\n1282702,wooden_bucket,0,2002\n460720,wind_chime,0,2002\n555866,pillow_grab,0,2002\n1233843,impossible_bodysuit,0,2002\n686220,pink_sleeves,0,2002\n1279634,mating_press,0,2000\n576093,hands_on_lap,0,1999\n511515,grimace,0,1998\n5114,turtle,0,1997\n495234,inflatable_toy,0,1997\n396726,wreath,0,1997\n6327,afro,0,1996\n551169,transparent_umbrella,0,1993\n1721304,dyed_bangs,0,1992\n585,taiyaki,0,1991\n661899,glowing_sword,0,1991\n521722,nengajou,0,1991\n118742,team_rocket,0,1991\n454425,juice_box,0,1989\n493037,drawer,0,1989\n380358,setsubun,0,1989\n1743,baseball,0,1984\n450205,prehensile_hair,0,1984\n580340,picture_(object),0,1983\n408804,bad_proportions,0,1982\n1618015,slime_(creature),0,1982\n549041,vocaloid_append,0,1982\n1231742,three-dimensional_maneuver_gear,0,1982\n702226,holding_cat,0,1981\n717296,hand_in_another's_hair,0,1981\n416114,symmetry,0,1979\n470701,cube,0,1978\n559145,school_hat,0,1977\n409034,bite_mark,0,1976\n403942,earpiece,0,1974\n4510,crepe,0,1973\n416356,corpse,0,1973\n662965,tokkuri,0,1972\n461237,skewer,0,1972\n379744,counter,0,1972\n1468298,beamed_sixteenth_notes,0,1971\n43623,scepter,0,1971\n10638,boxers,0,1970\n2560,wrestling,0,1970\n1360724,brown_cape,0,1970\n175633,comb,0,1969\n1372686,shark_hood,0,1968\n1427258,holding_sack,0,1967\n1463294,holding_controller,0,1967\n1374594,grey-framed_eyewear,0,1964\n1343802,holding_basket,0,1962\n516012,clothed_masturbation,0,1962\n516930,hanfu,0,1959\n535055,warship,0,1959\n549629,ankle_lace-up,0,1955\n699142,diamond-shaped_pupils,0,1954\n589659,in_bucket,0,1952\n1770614,green_gemstone,0,1951\n1204400,playing_with_own_hair,0,1951\n558703,cheek_pinching,0,1949\n488,flute,0,1949\n408254,tripping,0,1948\n494375,shared_umbrella,0,1948\n416493,red_sky,0,1948\n646637,cocktail_glass,0,1948\n421443,spinning,0,1947\n419280,between_thighs,0,1947\n1240137,swim_briefs,0,1947\n558384,on_grass,0,1944\n1667144,mod3_(girls'_frontline),0,1944\n552818,parted_hair,0,1942\n1637,failure,0,1941\n473206,grey_socks,0,1940\n378083,barrel,0,1940\n666929,light_trail,0,1939\n1392029,multicolored_bodysuit,0,1937\n502382,hyur,0,1937\n422427,sports_bikini,0,1935\n465416,sleeveless_sweater,0,1935\n1456557,hair_through_headwear,0,1931\n608539,column_lineup,0,1930\n574719,multicolored_swimsuit,0,1930\n657424,candlestand,0,1928\n383903,vending_machine,0,1927\n498946,turnaround,0,1926\n1330708,purple_sweater,0,1926\n422821,safety_pin,0,1925\n414746,spider_girl,0,1925\n1334708,holding_paintbrush,0,1925\n499,toast,0,1924\n393832,impregnation,0,1924\n374542,daisy,0,1923\n477184,large_hat,0,1923\n498823,burning,0,1923\n550621,head_back,0,1922\n600284,cheek_poking,0,1921\n605455,volleyball_uniform,0,1920\n1227460,hands_on_another's_head,0,1919\n514943,torn_shorts,0,1919\n5001,squid,0,1915\n1691759,censored_nipples,0,1914\n4864,lion,0,1913\n1435804,bisexual_female,0,1913\n616023,aqua_dress,0,1913\n1442709,fewer_digits,0,1913\n377200,balcony,0,1912\n604468,eye_mask,0,1912\n565089,caustics,0,1911\n1258088,ribbon-trimmed_clothes,0,1911\n1499947,looking_over_eyewear,0,1911\n1314744,yellow_choker,0,1910\n541224,heart_hands_duo,0,1910\n1582499,scar_on_forehead,0,1910\n1401067,face_to_breasts,0,1909\n688904,overskirt,0,1909\n447307,stone,0,1909\n16006,union_jack,0,1907\n478305,striped_sleeves,0,1906\n577309,multiple_condoms,0,1906\n484182,nose_piercing,0,1905\n1399079,holding_drink,0,1905\n381921,energy_ball,0,1905\n7737,soap,0,1904\n452530,ok_sign,0,1904\n1039733,licking_nipple,0,1902\n1606642,white_male_underwear,0,1902\n880368,humanoid_robot,0,1901\n459931,prostitution,0,1900\n1713028,two-tone_bowtie,0,1900\n5707,chaps,0,1899\n379983,under_table,0,1898\n418677,gate,0,1896\n374503,strawberry_shortcake,0,1895\n481420,blood_stain,0,1893\n453886,cosmetics,0,1892\n380490,alarm_clock,0,1889\n1405888,harvin,0,1889\n1296384,multicolored_gloves,0,1888\n530877,lace_bra,0,1887\n490349,head_bump,0,1884\n2834,69,0,1879\n16150,keychain,0,1879\n412179,polka_dot_bra,0,1876\n598410,ceiling_light,0,1875\n468817,flower_wreath,0,1875\n523897,fried_egg,0,1872\n1344665,yellow_vest,0,1871\n1791862,industrial_pipe,0,1870\n1383063,clover_print,0,1870\n536151,snake_tail,0,1870\n486179,naked_cape,0,1869\n419890,rug,0,1869\n4651,dominatrix,0,1867\n492858,lemon_slice,0,1863\n412890,tissue,0,1862\n420546,spear_the_gungnir,0,1860\n380154,squirrel,0,1860\n585851,bound_ankles,0,1859\n484124,deer_ears,0,1859\n1261917,looking_at_phone,0,1853\n1686527,jack-o'_challenge,0,1853\n1739,shrimp,0,1852\n1412053,magatama_necklace,0,1852\n476158,traffic_light,0,1851\n1700602,thick_arms,0,1851\n433424,if_they_mated,0,1850\n657469,rainbow_order,0,1849\n1926,chart,0,1847\n1408089,star_choker,0,1847\n1404865,brown_sailor_collar,0,1846\n378821,watering_can,0,1845\n1306104,orange_scarf,0,1845\n400230,akeome,0,1845\n4313,pointer,0,1844\n1614785,finger_to_own_chin,0,1843\n8806,train_station,0,1842\n1386147,holding_hammer,0,1840\n617893,sleeping_on_person,0,1840\n413859,footprints,0,1837\n1331430,bandaid_on_arm,0,1837\n1506804,pearl_(gemstone),0,1834\n546293,happy_tears,0,1833\n708792,checkered_necktie,0,1833\n473239,yellow_bra,0,1832\n5494,harem,0,1832\n711883,checkered_kimono,0,1832\n1552758,breast_curtain,0,1832\n406727,against_tree,0,1831\n1440529,two-tone_ribbon,0,1831\n1345684,holding_syringe,0,1830\n7827,whisk,0,1829\n664405,plaid_necktie,0,1829\n706581,condom_packet_strip,0,1828\n440956,fluffy,0,1828\n8781,bomb,0,1827\n521350,color_guide,0,1826\n15412,refrigerator,0,1825\n377878,hairdressing,0,1824\n1371311,aqua_bowtie,0,1824\n460146,hitachi_magic_wand,0,1823\n546937,cracked_skin,0,1821\n1307800,skull_earrings,0,1819\n493288,chasing,0,1818\n394047,circle_cut,0,1817\n1289525,cross_scar,0,1817\n408365,tape_gag,0,1816\n155336,poncho,0,1816\n612699,dirty_clothes,0,1815\n1332003,champagne_flute,0,1815\n1342774,rabbit_house_uniform,0,1815\n1551,drill,0,1814\n1403984,brown_capelet,0,1814\n443220,you're_doing_it_wrong,0,1813\n538158,shared_food,0,1811\n1414514,purple_sailor_collar,0,1811\n428250,plectrum,0,1809\n1793464,cheating_(relationship),0,1808\n528616,drone,0,1808\n1805482,non-humanoid_robot,0,1807\n566340,loose_clothes,0,1807\n1806377,homurahara_academy_school_uniform,0,1806\n3946,faucet,0,1806\n388868,m4_carbine,0,1804\n470667,board_game,0,1803\n514885,after_kiss,0,1802\n612635,sidesaddle,0,1801\n3537,sticker,0,1801\n379794,cafe,0,1800\n638838,in_food,0,1799\n15222,alley,0,1797\n585021,spoken_sweatdrop,0,1797\n621654,pale_color,0,1796\n419998,:x,0,1796\n512237,identity_censor,0,1795\n664066,aqua_ribbon,0,1794\n548122,strawberry_print,0,1791\n382271,destruction,0,1791\n690509,purple_sky,0,1790\n400296,gatling_gun,0,1789\n83990,tennis_racket,0,1789\n10415,railroad_tracks,0,1789\n876848,bandage_on_face,0,1786\n1641903,eye_focus,0,1785\n1489840,white_horns,0,1785\n478617,stripper_pole,0,1785\n1336190,drill_locks,0,1785\n396286,cockpit,0,1784\n716395,dixie_cup_hat,0,1784\n188238,crossbow,0,1783\n1793517,legwear_garter,0,1781\n472,monkey,0,1781\n3342,beltbra,0,1779\n674367,starry_sky_print,0,1778\n390436,birdcage,0,1777\n1076157,futanari_masturbation,0,1776\n478170,anti-materiel_rifle,0,1776\n1231015,character_print,0,1775\n14562,holly,0,1775\n911862,mask_pull,0,1775\n590039,animal_feet,0,1774\n719573,doyagao,0,1773\n10911,pond,0,1773\n1286826,cloud_print,0,1771\n471577,red_socks,0,1771\n511657,alternate_form,0,1770\n375862,butler,0,1769\n521377,winged_helmet,0,1769\n961827,pink_hoodie,0,1767\n148824,dove,0,1766\n1356833,frilled_capelet,0,1764\n610944,mixed_media,0,1762\n433470,fanny_pack,0,1762\n403432,hooves,0,1761\n11395,swing,0,1761\n1343849,fellatio_gesture,0,1761\n1287705,song_name,0,1760\n380828,open_bra,0,1757\n653053,strapless_shirt,0,1757\n390242,variations,0,1756\n1333731,hands_on_own_thighs,0,1756\n1329387,deviantart_username,0,1756\n1448570,gold_earrings,0,1756\n1373028,yellow-framed_eyewear,0,1753\n478938,impossible_dress,0,1751\n380329,briefcase,0,1750\n1344462,blue_apron,0,1749\n16992,see-through_dress,0,1749\n2684,fishing,0,1747\n412950,rubble,0,1747\n1365001,black_robe,0,1746\n6389,tickling,0,1746\n13185,spitting,0,1745\n228746,spider,0,1745\n655271,naked_jacket,0,1745\n444545,pocky_kiss,0,1744\n629987,radio_antenna,0,1744\n440406,buck_teeth,0,1744\n501689,body_hair,0,1743\n585543,pussy_juice_puddle,0,1742\n4144,mace,0,1742\n1253396,character_hair_ornament,0,1742\n533490,adjusting_gloves,0,1741\n3532,ink,0,1740\n670532,holding_pencil,0,1739\n1438761,high-waist_pants,0,1739\n2431,missile,0,1738\n8309,straitjacket,0,1738\n473259,artificial_vagina,0,1738\n638540,multicolored_bikini,0,1737\n1597364,chain_leash,0,1736\n461218,selfcest,0,1734\n675346,frilled_ribbon,0,1732\n1328016,o-ring_choker,0,1730\n440374,leg_belt,0,1729\n4948,mimikaki,0,1728\n466439,sparks,0,1726\n1521615,skin_fangs,0,1726\n1510922,other_focus,0,1725\n15614,tube,0,1724\n430228,pole_dancing,0,1724\n1617800,see-through_leotard,0,1724\n593715,bird_on_hand,0,1722\n391066,roller_skates,0,1722\n1416546,red_hoodie,0,1722\n1258390,holding_leash,0,1721\n406767,super_robot,0,1721\n633809,eastern_dragon,0,1720\n1353409,fine_fabric_emphasis,0,1719\n555276,surprise_kiss,0,1717\n819855,puffy_shorts,0,1716\n400313,beam_rifle,0,1716\n557675,no_wings,0,1715\n414749,helm,0,1715\n1308909,barcode_tattoo,0,1714\n477329,brushing_hair,0,1711\n429442,patch,0,1711\n1472233,high-waist_shorts,0,1711\n464556,dolphin,0,1710\n1476489,pokemon_on_head,0,1709\n389904,cabinet,0,1706\n1696268,cowboy_western,0,1706\n570969,red_armor,0,1706\n1675931,kalashnikov_rifle,0,1705\n521422,skull_print,0,1705\n688139,implied_futanari,0,1703\n15124,pain,0,1701\n376636,confused,0,1701\n550124,sitting_in_tree,0,1701\n487161,breasts_on_head,0,1700\n547292,reflective_floor,0,1700\n475729,through_clothes,0,1699\n77315,worried,0,1698\n394155,instrument_case,0,1698\n603830,on_person,0,1698\n507655,freediving,0,1695\n1637843,crescent_facial_mark,0,1695\n443879,gorget,0,1695\n1372792,fox_boy,0,1694\n524875,nail_art,0,1693\n473417,yellow_thighhighs,0,1692\n1415469,torpedo_tubes,0,1692\n641695,aerial_fireworks,0,1691\n1253523,\\||/,0,1690\n1113198,latex_bodysuit,0,1690\n909672,star_halo,0,1689\n1416333,cherry_blossom_print,0,1687\n11960,tanabata,0,1686\n1427966,yellow_cardigan,0,1686\n450823,x_x,0,1686\n445622,leopard_ears,0,1686\n1489808,poke_ball_symbol,0,1685\n499937,lip_piercing,0,1685\n1233871,circle_name,0,1684\n423915,wooden_sword,0,1684\n468068,spread_toes,0,1684\n859661,red_cloak,0,1683\n468635,coral,0,1682\n520227,cowlick,0,1682\n481514,white_suit,0,1682\n645771,seigaiha,0,1680\n431012,tangzhuang,0,1679\n1410984,kuromorimine_school_uniform,0,1679\n1559079,light_areolae,0,1678\n11745,breast_smother,0,1677\n396795,menu,0,1676\n1378575,monsterification,0,1676\n1284171,after_paizuri,0,1676\n1319620,wing_hair_ornament,0,1676\n1531697,mismatched_pupils,0,1673\n679014,greatsword,0,1673\n377843,cousins,0,1671\n399148,voyakiloid,0,1671\n1383226,ofuda_on_clothes,0,1671\n614243,plaid_background,0,1670\n695921,white_sash,0,1670\n10429,shinsengumi,0,1670\n1441680,holstered_weapon,0,1670\n723924,very_long_fingernails,0,1670\n1819565,jirai_kei,0,1670\n5873,confession,0,1669\n670645,tactical_clothes,0,1668\n495132,autobot,0,1667\n459174,dress_tug,0,1666\n1345660,leaning_on_object,0,1666\n420651,blinds,0,1665\n396872,ribbon_bondage,0,1665\n516894,tokusatsu,0,1664\n469082,milk_carton,0,1663\n392117,insect_wings,0,1662\n1499389,patchwork_skin,0,1660\n526470,thighhighs_pull,0,1658\n385733,baseball_uniform,0,1658\n622860,spring_(season),0,1658\n494944,flexing,0,1658\n1426293,yasogami_school_uniform,0,1656\n1174935,hand_on_own_leg,0,1654\n1262479,plaid_bowtie,0,1653\n542055,polka_dot_dress,0,1652\n567592,untucked_shirt,0,1649\n610495,bondage_outfit,0,1647\n418420,stuck,0,1647\n1418114,green_hoodie,0,1647\n413866,breasts_on_glass,0,1646\n586859,beckoning,0,1646\n1641153,icho_private_high_school_uniform,0,1646\n476416,biker_clothes,0,1645\n1323958,pink_vest,0,1645\n10293,marker,0,1645\n1346997,holding_whip,0,1644\n1275036,german_clothes,0,1644\n1268348,pawpads,0,1644\n442232,scarlet_devil_mansion,0,1643\n663528,wooden_wall,0,1643\n667138,hikimayu,0,1643\n475212,very_long_sleeves,0,1640\n1307726,magatama_earrings,0,1639\n1684797,grabbing_another's_chin,0,1636\n418987,park_bench,0,1636\n379603,hair_brush,0,1634\n379164,messy,0,1632\n1411720,medium_dress,0,1631\n549281,in_the_face,0,1631\n1242767,star_necklace,0,1631\n427833,palms,0,1631\n1328688,moon_(ornament),0,1630\n437999,full_nelson,0,1629\n444634,torn,0,1629\n403915,wheel,0,1628\n2232,tarot,0,1627\n478821,teasing,0,1627\n539771,incoming_gift,0,1627\n6303,naginata,0,1627\n1623670,diamond_button,0,1626\n1373026,green-framed_eyewear,0,1623\n610198,leotard_pull,0,1622\n660019,color_connection,0,1620\n462556,cuddling,0,1620\n1496652,gold_hairband,0,1619\n599839,horned_helmet,0,1618\n1434344,pencil_dress,0,1618\n1402835,scene_reference,0,1617\n464552,cucumber,0,1616\n1530483,elite_ii_(arknights),0,1616\n8989,viera,0,1612\n540139,different_reflection,0,1612\n1088997,bear_hair_ornament,0,1612\n667586,single_pauldron,0,1612\n815986,fortissimo,0,1612\n1400382,barbell_piercing,0,1611\n544708,fish_girl,0,1611\n454517,unconscious,0,1610\n574422,claw_(weapon),0,1609\n379311,chandelier,0,1608\n377993,petting,0,1608\n450208,chocolate_bar,0,1607\n713479,2014,0,1607\n476495,dowsing_rod,0,1607\n469396,role_reversal,0,1605\n375381,hair_in_mouth,0,1602\n565136,projected_inset,0,1601\n664546,taut_dress,0,1599\n692799,choko_(cup),0,1598\n613128,embellished_costume,0,1596\n1379633,fff_threesome,0,1595\n457418,fighter_jet,0,1595\n442863,heckler_&_koch,0,1594\n393579,mechanical_pencil,0,1593\n514889,polo_shirt,0,1593\n508957,health_bar,0,1593\n684621,puffy_detached_sleeves,0,1593\n700119,cropped_vest,0,1592\n421489,ushanka,0,1591\n403478,suppressor,0,1590\n411198,leg_hold,0,1589\n1396796,green_neckerchief,0,1589\n1260991,pink_pants,0,1588\n669791,futa_with_futa,0,1588\n723472,stiletto_heels,0,1588\n1274842,two-handed,0,1587\n412925,cupless_bra,0,1587\n642312,handsfree_ejaculation,0,1586\n1598810,nontraditional_playboy_bunny,0,1586\n1403286,black_mask,0,1586\n412357,lounge_chair,0,1585\n664263,bikini_top_removed,0,1585\n430449,spine,0,1585\n442852,old_woman,0,1585\n1439570,tall_female,0,1585\n406711,strap-on,0,1584\n1342607,holding_lantern,0,1584\n512488,crotch_plate,0,1583\n679286,faux_figurine,0,1582\n12083,graveyard,0,1582\n668887,shallow_water,0,1580\n1362227,holding_stick,0,1580\n469368,striped_gloves,0,1579\n683983,poke_ball_print,0,1579\n1515352,aqua_theme,0,1578\n388093,hakurei_reimu_(cosplay),0,1578\n600210,perineum,0,1578\n452130,neon_lights,0,1578\n492105,groin_tendon,0,1577\n395436,reindeer_costume,0,1577\n721109,visible_air,0,1577\n592444,mars_symbol,0,1576\n399999,whipped_cream,0,1575\n452157,hagoita,0,1575\n759492,wolf_boy,0,1575\n538746,lipstick_tube,0,1574\n542357,red_cross,0,1573\n405026,large_insertion,0,1573\n410111,bowing,0,1570\n480324,staff_(music),0,1570\n495020,crazy_smile,0,1570\n397693,shikigami,0,1569\n703456,sorcerer's_sutra_scroll,0,1569\n1426454,holding_clipboard,0,1568\n1505130,german_text,0,1567\n391475,fireflies,0,1567\n29555,office,0,1567\n451262,notepad,0,1566\n1440623,jacket_partially_removed,0,1566\n1514129,sunburst_background,0,1565\n545758,wisteria,0,1563\n550778,checkerboard_cookie,0,1563\n1517305,presenting_armpit,0,1563\n582361,head_between_breasts,0,1562\n1374895,spiked_tail,0,1561\n660754,aqua_shirt,0,1560\n537783,bamboo_broom,0,1559\n1781164,gem_(symbol),0,1559\n1296314,single_gauntlet,0,1558\n15578,eagle,0,1557\n1240006,bishamonten's_pagoda,0,1554\n485203,video_game,0,1553\n585862,reach-around,0,1553\n617315,pantyhose_under_shorts,0,1552\n1438860,black_sash,0,1551\n699609,frilled_cuffs,0,1550\n16279,air_conditioner,0,1549\n657500,fish_hair_ornament,0,1548\n892083,green_apron,0,1545\n2812,ramune,0,1545\n1314693,grey_scarf,0,1545\n1505167,simplified_chinese_text,0,1544\n473599,fingers,0,1543\n9781,pacifier,0,1543\n585009,sword_over_shoulder,0,1542\n399875,beach_chair,0,1541\n1296346,stationary_restraints,0,1540\n501344,fallen_down,0,1539\n1274924,hand_on_another's_ass,0,1539\n671484,stuffed_shark,0,1539\n632854,naked_hoodie,0,1539\n1557024,mouth_veil,0,1539\n8775,mahjong,0,1538\n1464685,collared_cape,0,1537\n394989,hexagon,0,1537\n476602,kusazuri,0,1537\n590941,santa_boots,0,1536\n378038,pentacle,0,1535\n693272,tan_background,0,1535\n1373024,white-framed_eyewear,0,1534\n1775923,circled_9,0,1534\n467745,japanese_flag,0,1533\n453530,power_suit,0,1532\n1665195,reference_inset,0,1531\n1781330,nijigasaki_academy_school_uniform,0,1531\n607070,caressing_testicles,0,1529\n1247633,narrowed_eyes,0,1529\n1791857,painting_(action),0,1528\n633053,hooded_sweater,0,1527\n1835746,pink_one-piece_swimsuit,0,1526\n440361,lipstick_mark,0,1526\n650139,clothed_female_nude_female,0,1526\n1258091,ribbon-trimmed_collar,0,1526\n524796,age_progression,0,1525\n1558733,tulip_hat,0,1525\n3489,dinosaur,0,1523\n598269,kissing_forehead,0,1523\n1328687,earth_(ornament),0,1523\n420294,yellow_skin,0,1522\n1333530,grey_fur,0,1521\n714422,sports_car,0,1520\n664350,tail_censor,0,1520\n9587,love_letter,0,1520\n724196,thigh_pouch,0,1520\n1265860,pink_blood,0,1519\n482956,caution_tape,0,1518\n415621,blueberry,0,1518\n712801,pussy_piercing,0,1517\n472861,ovum,0,1517\n478228,title,0,1515\n503126,ribbon_in_mouth,0,1514\n374573,centaur,0,1514\n10562,truck,0,1513\n628220,claw_ring,0,1511\n541729,rotational_symmetry,0,1510\n1333955,side-tie_leotard,0,1510\n1298471,blood_from_eyes,0,1509\n1424925,holding_needle,0,1509\n1320208,grey_necktie,0,1509\n1452545,kibito_high_school_uniform,0,1509\n656250,arm_between_breasts,0,1506\n629486,used_tissue,0,1504\n1346802,spiked_shell,0,1503\n1678267,palette_(object),0,1502\n485953,puffy_pants,0,1502\n1446284,looking_at_animal,0,1502\n615984,bendy_straw,0,1502\n1560,mascot,0,1500\n1424211,sakugawa_school_uniform,0,1500\n16695,magnifying_glass,0,1499\n1482386,tiger_boy,0,1499\n1923,truth,0,1498\n528496,holding_shoes,0,1498\n415310,overgrown,0,1498\n712381,thigh_cutout,0,1498\n1298573,sanshoku_dango,0,1496\n416448,yagasuri,0,1495\n1672391,viewer_holding_leash,0,1495\n1450243,weibo_logo,0,1494\n635465,twitching,0,1493\n452581,vibrator_under_panties,0,1491\n414700,buttjob,0,1491\n607798,arm_wrap,0,1491\n1859633,a_certain_high_school_uniform,0,1490\n362945,garden,0,1488\n399302,drying,0,1487\n593466,flame_print,0,1487\n1179281,bear_girl,0,1487\n1323483,fangs_out,0,1486\n1396969,holding_innertube,0,1485\n453719,plant_girl,0,1484\n1390886,brown_kimono,0,1484\n180889,morning,0,1483\n16135,cotton_candy,0,1483\n376147,hanging,0,1482\n460511,hand_puppet,0,1481\n437614,boned_meat,0,1481\n714136,hand_on_another's_stomach,0,1481\n1234797,foot_up,0,1478\n397906,party_hat,0,1478\n709214,alternate_skin_color,0,1478\n520939,wiping_tears,0,1477\n655903,fake_facial_hair,0,1477\n1373018,striped_kimono,0,1477\n10125,halberd,0,1477\n461720,buzz_cut,0,1477\n1637102,doughnut_hair_bun,0,1476\n465754,spanking,0,1476\n544204,bowl_cut,0,1476\n705551,pixiv_id,0,1476\n475794,column,0,1475\n699449,raimon,0,1474\n8918,inflation,0,1473\n516011,playstation_portable,0,1472\n492764,heart_tail,0,1472\n14637,tight_shirt,0,1471\n394771,utility_belt,0,1471\n544443,penis_out,0,1470\n387935,spirit,0,1470\n421056,spiked_club,0,1468\n466411,invisible_penis,0,1467\n1568979,plaid_headwear,0,1467\n617246,leather_belt,0,1467\n1325039,oral_invitation,0,1467\n434522,gym_storeroom,0,1465\n1747766,lapels,0,1464\n625484,glitch,0,1464\n405444,helicopter,0,1463\n533100,sitting_on_object,0,1462\n1609998,after_ejaculation,0,1462\n1411201,kiyosumi_school_uniform,0,1462\n1305007,chest_belt,0,1461\n590857,okamisty,0,1461\n670994,chocolate_on_body,0,1460\n1240596,mole_on_neck,0,1460\n389933,harem_pants,0,1459\n376486,skyline,0,1459\n1495030,red-tinted_eyewear,0,1459\n628115,open_window,0,1458\n14860,chef,0,1458\n480571,mechanization,0,1458\n662800,fake_antlers,0,1458\n9590,jersey,0,1458\n637922,2013,0,1457\n656303,shirt_in_mouth,0,1456\n1441520,horns_through_headwear,0,1456\n398229,people,0,1455\n381954,untying,0,1454\n483333,pavement,0,1454\n392935,tree_stump,0,1453\n410544,handkerchief,0,1452\n1257314,idol_clothes,0,1452\n622601,window_shade,0,1451\n493565,timestamp,0,1451\n418781,pet_play,0,1450\n399061,camcorder,0,1449\n595751,ass_cutout,0,1449\n1605421,halloween_bucket,0,1449\n580394,dirndl,0,1447\n1227348,back-seamed_legwear,0,1445\n642015,expressive_hair,0,1445\n472201,baguette,0,1444\n439406,shiba_inu,0,1444\n1378645,constellation_print,0,1444\n535,molestation,0,1442\n1373313,excessive_pubic_hair,0,1442\n11754,neck,0,1442\n1317604,panty_straps,0,1442\n1476167,holding_handheld_game_console,0,1442\n556841,sitting_on_stairs,0,1441\n504308,redesign,0,1440\n1549885,crane_(machine),0,1439\n974780,stomach_cutout,0,1439\n1267456,holding_leaf,0,1438\n389048,donation_box,0,1437\n523847,omurice,0,1436\n426749,2012,0,1435\n1378370,black_buruma,0,1433\n470025,open_pants,0,1432\n1279772,yellow_wings,0,1432\n473475,orange_thighhighs,0,1431\n547320,excessive_cum,0,1431\n478325,2010,0,1431\n399246,stadium,0,1430\n658254,holding_helmet,0,1430\n1507608,h&k_hk416,0,1430\n1279653,holding_brush,0,1429\n451782,brushing_teeth,0,1427\n461401,treasure_chest,0,1427\n494781,makizushi,0,1427\n1441880,hand_on_eyewear,0,1427\n565996,goat_ears,0,1427\n414107,gamepad,0,1426\n1602480,index_fingers_together,0,1425\n405498,panda_ears,0,1424\n162717,triforce,0,1424\n431374,curtsey,0,1423\n479141,raised_fist,0,1422\n526532,oversized_shirt,0,1422\n1397563,fur-trimmed_skirt,0,1422\n629378,ruffling_hair,0,1422\n1321158,brown_necktie,0,1422\n1442004,eyewear_on_headwear,0,1421\n380988,goblin,0,1417\n1374287,ribbed_bodysuit,0,1417\n673730,palm_leaf,0,1416\n1518970,pectoral_grab,0,1416\n646260,pointless_condom,0,1414\n421385,kanji,0,1414\n381861,\\o/,0,1411\n1414389,pink_sailor_collar,0,1409\n8234,radio,0,1408\n545201,micro_panties,0,1407\n587990,bodice,0,1407\n500820,stone_lantern,0,1406\n1422826,sangvis_ferri,0,1406\n384435,onmyouji,0,1405\n1285391,turtleneck_dress,0,1405\n1667891,pom_pom_hair_ornament,0,1404\n467946,coca-cola,0,1403\n12086,globe,0,1403\n1163924,blue_fur,0,1403\n539536,blue_headband,0,1401\n494162,monkey_ears,0,1399\n1478255,dangle_earrings,0,1397\n665766,carrying_under_arm,0,1396\n501337,39,0,1396\n594236,expressive_clothes,0,1394\n662045,lotion_bottle,0,1394\n534991,pillbox_hat,0,1394\n379633,fertilization,0,1393\n460870,spell_card,0,1392\n536429,cupping_hands,0,1392\n400656,red_hood,0,1392\n431361,baseball_mitt,0,1391\n1598686,male_playboy_bunny,0,1389\n1835740,purple_one-piece_swimsuit,0,1388\n3197,takoyaki,0,1387\n508969,cow_boy,0,1387\n380,priest,0,1386\n1413926,orange_sailor_collar,0,1386\n497917,thrusters,0,1386\n525748,dakimakura_(object),0,1384\n420884,countdown,0,1384\n1613542,electrokinesis,0,1384\n569173,spread_pussy_under_clothes,0,1384\n644441,fiery_hair,0,1383\n480430,bear_print,0,1381\n433011,polka_dot_swimsuit,0,1381\n1436311,u.a._school_uniform,0,1380\n617594,alternate_weapon,0,1379\n396034,propeller,0,1378\n497295,happi,0,1378\n586366,prosthetic_leg,0,1378\n592445,venus_symbol,0,1377\n547081,incoming_attack,0,1377\n610123,forehead-to-forehead,0,1376\n632009,blonde_pubic_hair,0,1376\n683919,ajirogasa,0,1376\n1448374,reflective_water,0,1375\n12254,tribal,0,1374\n450649,shin_guards,0,1374\n663848,naked_kimono,0,1374\n593015,finger_to_face,0,1373\n560134,u_u,0,1373\n11353,twincest,0,1370\n593552,plaid_pants,0,1370\n1415445,nanairogaoka_middle_school_uniform,0,1370\n1230150,leg_between_thighs,0,1369\n549142,yes-no_pillow,0,1369\n401422,screen,0,1369\n1324575,tapir_tail,0,1369\n16095,glomp,0,1368\n389416,impaled,0,1368\n488375,rainbow_gradient,0,1368\n578012,talking_on_phone,0,1367\n638293,novel_cover,0,1367\n1670355,furry_with_furry,0,1367\n646459,striped_shorts,0,1366\n1332978,short_jumpsuit,0,1366\n16601,overcoat,0,1365\n5323,kettle,0,1364\n1624910,sidepec,0,1364\n473249,print_bra,0,1363\n1409636,furrification,0,1363\n723185,stone_floor,0,1362\n389374,cat_teaser,0,1361\n395712,christmas_lights,0,1361\n384969,intravenous_drip,0,1360\n424974,no_testicles,0,1358\n481054,grey_bra,0,1358\n1454017,debt,0,1358\n991822,blue_eyeshadow,0,1356\n14704,nike,0,1355\n419304,crosswalk,0,1355\n659938,poker_chip,0,1355\n494704,2011,0,1355\n572582,masturbation_through_clothes,0,1354\n1344701,grey_border,0,1354\n649700,full-package_futanari,0,1353\n8411,sheet_music,0,1353\n683789,wooden_table,0,1351\n1288040,incoming_food,0,1351\n1249322,hauchiwa,0,1349\n1282898,cloth_gag,0,1349\n394997,sweatpants,0,1349\n1515355,black_theme,0,1348\n1455250,chest_strap,0,1348\n449943,stab,0,1347\n389647,clock_tower,0,1346\n1665347,full-body_tattoo,0,1345\n1249520,drawing_bow,0,1344\n8617,potion,0,1344\n680046,carrying_person,0,1344\n563716,italian_flag,0,1342\n374948,school_briefcase,0,1341\n1373027,brown-framed_eyewear,0,1341\n1268086,pink_bag,0,1341\n593696,leather_gloves,0,1340\n903375,flip_phone,0,1339\n635016,broken_chain,0,1339\n548895,frilled_socks,0,1338\n594785,blowing_kiss,0,1338\n405,soccer,0,1337\n468165,mixing_bowl,0,1337\n395851,log,0,1336\n1454154,black_pubic_hair,0,1336\n375527,kimono_skirt,0,1336\n562826,slapping,0,1334\n4577,snorkel,0,1334\n587202,shoelaces,0,1333\n9355,humiliation,0,1333\n2839,lube,0,1333\n1608327,mixed-language_text,0,1333\n626479,orange_slice,0,1333\n585203,veiny_breasts,0,1333\n1761435,onee-loli,0,1332\n500152,breathing_fire,0,1332\n559601,cheering,0,1331\n505317,lily_of_the_valley,0,1331\n1287296,diving_mask_on_head,0,1330\n473312,camellia,0,1329\n3408,origami,0,1329\n410102,yes,0,1328\n621930,mandarin_collar,0,1328\n1276269,ribbed_sleeves,0,1327\n1406765,jaguar_ears,0,1327\n408186,frottage,0,1326\n565797,tropical_drink,0,1326\n705858,partially_underwater_shot,0,1326\n624870,imminent_fellatio,0,1326\n1584543,pokedex_number,0,1326\n600401,wooden_fence,0,1325\n1317361,multicolored_bow,0,1325\n1396461,brown_hairband,0,1323\n1387512,orange_ascot,0,1323\n413721,latex_gloves,0,1322\n457946,stone_wall,0,1321\n1402473,blue_sash,0,1320\n1578780,diagonal-striped_necktie,0,1320\n496388,._.,0,1317\n1354736,green_flower,0,1316\n9396,april_fools,0,1316\n568512,bird_on_shoulder,0,1315\n1719547,sailor_moon_redraw_challenge_(meme),0,1314\n11756,cup_ramen,0,1313\n573617,food-themed_clothes,0,1313\n564677,playstation_controller,0,1313\n61654,tail_grab,0,1313\n486968,red_bandana,0,1313\n639779,mechanical_tail,0,1312\n393602,severed_head,0,1311\n58241,platform_heels,0,1311\n482510,plaid_panties,0,1309\n722706,jojo_pose,0,1309\n1793460,cooperative_paizuri,0,1308\n525108,hands_on_feet,0,1307\n8803,cleaning,0,1307\n843166,yellow_hoodie,0,1307\n609486,single_strap,0,1307\n539990,tail_between_legs,0,1306\n1365638,bandaid_on_hand,0,1306\n473470,wrist_guards,0,1305\n9628,scooter,0,1304\n1314748,pink_collar,0,1304\n1422416,twitter_logo,0,1304\n1780,cow,0,1303\n1409793,grey_kimono,0,1303\n546044,sleeveless_hoodie,0,1302\n698497,gloves_removed,0,1301\n563461,full-length_zipper,0,1301\n1262922,head_on_another's_shoulder,0,1301\n1383769,dot_mouth,0,1300\n395433,traditional_clothes,0,1300\n668383,lace-trimmed_sleeves,0,1300\n1259138,mole_on_ass,0,1300\n1410489,shower_(place),0,1299\n477993,bad_perspective,0,1298\n391175,stove,0,1298\n424623,unitard,0,1298\n825568,shortstack,0,1298\n392839,torn_panties,0,1297\n661926,ankle_strap,0,1296\n1574672,two-sided_cape,0,1295\n16550,stream,0,1294\n12727,phonograph,0,1293\n10399,x,0,1293\n15305,steampunk,0,1293\n488193,slap_mark,0,1293\n543247,eyebrow_piercing,0,1293\n416904,cat_on_head,0,1292\n612104,respirator,0,1292\n963327,yordle,0,1292\n592457,nippleless_clothes,0,1291\n389021,deer,0,1290\n388798,hatsune_miku_(cosplay),0,1289\n1606641,black_male_underwear,0,1288\n595244,hamaya,0,1286\n444567,tail_wrap,0,1286\n524686,hand_net,0,1285\n511641,single_pantsleg,0,1285\n1476395,pokemon_on_shoulder,0,1284\n380213,sponge,0,1283\n1504221,purple_tail,0,1283\n1318470,yellow_pupils,0,1282\n1601080,animal_ear_legwear,0,1282\n431872,balancing,0,1281\n629555,desk_lamp,0,1280\n1475076,blue_horns,0,1278\n686531,undone_necktie,0,1277\n477355,chainmail,0,1277\n695246,hakama_pants,0,1276\n1414657,purple_capelet,0,1275\n531186,gold_armor,0,1274\n473618,clitoris_piercing,0,1274\n433212,bookmark,0,1274\n15307,werewolf,0,1274\n374909,whispering,0,1272\n518423,black_leggings,0,1271\n637282,pants_rolled_up,0,1269\n690442,boots_removed,0,1269\n992674,emoji,0,1269\n695232,mismatched_pubic_hair,0,1268\n492677,bolo_tie,0,1267\n534114,cocktail_dress,0,1267\n1507443,sidelighting,0,1266\n410474,picnic_basket,0,1265\n1554055,fiery_horns,0,1265\n11623,bad_end,0,1263\n1238376,multicolored_shirt,0,1263\n8500,eggplant,0,1263\n1325030,new_school_swimsuit,0,1263\n399269,tablecloth,0,1262\n1400049,on_bench,0,1262\n685325,shiny_legwear,0,1261\n1586950,sobu_high_school_uniform,0,1261\n628271,turning_head,0,1260\n1392009,green_bodysuit,0,1260\n457562,fake_mustache,0,1259\n1565319,power_suit_(metroid),0,1258\n652288,alphes_(style),0,1258\n1713829,mithra_(ff11),0,1257\n382610,skateboard,0,1257\n1542756,turtleneck_leotard,0,1256\n1411229,orange_neckerchief,0,1256\n392251,fireplace,0,1256\n1394090,see-through_skirt,0,1256\n551591,deerstalker,0,1255\n415735,sideways,0,1254\n414232,cum_on_hands,0,1254\n726190,breast_conscious,0,1254\n613598,polka_dot_legwear,0,1254\n418106,dark_penis,0,1253\n4654,flustered,0,1253\n666658,grey_bikini,0,1252\n711344,hand_on_own_shoulder,0,1252\n1237065,sunflower_hair_ornament,0,1251\n1304010,pink_pupils,0,1250\n702474,ribbed_leotard,0,1250\n3334,kigurumi,0,1249\n798294,club_(shape),0,1249\n1429613,tomoeda_elementary_school_uniform,0,1249\n543408,real_world_location,0,1249\n1492501,shinda_sekai_sensen_uniform,0,1249\n4930,doctor,0,1248\n565279,german_flag,0,1248\n616223,no_gloves,0,1248\n1501189,stitched_face,0,1247\n652468,brown_bikini,0,1246\n674500,large_testicles,0,1246\n1762322,greyscale_with_colored_background,0,1246\n507478,aqua_panties,0,1245\n726943,pink_coat,0,1245\n383146,panties_on_head,0,1245\n1274419,spoken_character,0,1244\n519211,ryona,0,1244\n447287,high_kick,0,1243\n9348,wheelchair,0,1243\n1413210,blue_collar,0,1243\n1824446,lycoris_uniform,0,1243\n1349438,animal_bag,0,1242\n491661,hat_tip,0,1242\n496346,impossible_swimsuit,0,1241\n643516,pyrokinesis,0,1241\n632853,fake_wings,0,1240\n603398,lace-trimmed_skirt,0,1239\n1781886,stroking_own_chin,0,1239\n383835,runes,0,1239\n1492898,green_sleeves,0,1239\n443587,sunscreen,0,1238\n427020,stepped_on,0,1238\n575113,kimono_pull,0,1238\n651163,kourindou_tengu_costume,0,1238\n583212,fingering_through_clothes,0,1237\n492714,curry_rice,0,1237\n495137,tulip,0,1236\n330013,pie,0,1236\n488259,skull_mask,0,1236\n11216,soup,0,1235\n11603,paper_airplane,0,1235\n557999,wiping_face,0,1235\n1771225,lord_camelot_(fate),0,1235\n522405,kanabou,0,1234\n684234,perpendicular_paizuri,0,1234\n548540,puckered_anus,0,1234\n588173,sex_machine,0,1233\n420441,teamwork,0,1232\n439204,friends,0,1231\n629461,duffel_coat,0,1231\n176537,bartender,0,1231\n416557,ammunition_belt,0,1230\n179157,tent,0,1230\n510652,flashback,0,1230\n541435,cellphone_picture,0,1227\n465875,age_regression,0,1227\n600052,butterfly_on_hand,0,1227\n518510,dust_cloud,0,1227\n1372592,bath_yukata,0,1227\n1283900,single_fingerless_glove,0,1226\n491621,cheek_bulge,0,1225\n609657,animal_hug,0,1225\n1501551,sakuramon,0,1225\n330372,sausage,0,1224\n603173,molten_rock,0,1223\n3419,shinai,0,1222\n15057,nearly_naked_apron,0,1221\n468159,sparkler,0,1220\n1403859,3d_background,0,1220\n498369,naked_bandage,0,1220\n549321,cum_on_penis,0,1219\n1271959,short_sidetail,0,1218\n434471,angel_and_devil,0,1217\n475249,large_wings,0,1217\n710257,odd_one_out,0,1217\n688960,holding_pillow,0,1216\n1835593,ornate_ring,0,1216\n509739,burnt_clothes,0,1215\n452607,2009,0,1215\n1377225,bruise_on_face,0,1215\n1856853,asticassia_school_uniform,0,1215\n1369179,orange_fur,0,1214\n1461344,papakha,0,1214\n164246,teruterubouzu,0,1212\n408976,bat_ears,0,1212\n9574,sick,0,1211\n11998,open_robe,0,1211\n1410988,pravda_school_uniform,0,1211\n1585295,athletic_leotard,0,1209\n11273,harp,0,1208\n1330230,black_wristband,0,1208\n13916,tempura,0,1206\n577080,hand_on_lap,0,1206\n1388990,two-tone_legwear,0,1205\n995808,penis_size_difference,0,1205\n476199,striped_sweater,0,1205\n2660,lettuce,0,1204\n570296,giving_up_the_ghost,0,1204\n411779,ankh,0,1204\n451184,holographic_interface,0,1203\n547391,winged_footwear,0,1203\n468977,split_screen,0,1203\n660817,opening_door,0,1203\n537130,arm_blade,0,1202\n513421,acrylic_paint_(medium),0,1202\n822829,suspended_congress,0,1201\n374515,hawaiian_shirt,0,1201\n578544,leg_armor,0,1201\n1419859,fur-trimmed_cloak,0,1201\n1281997,asymmetrical_horns,0,1201\n473050,crate,0,1200\n405415,milking_machine,0,1199\n10246,wig,0,1199\n1361800,bow_earrings,0,1198\n518732,anilingus,0,1197\n735235,eye_of_horus,0,1197\n652086,gathers,0,1196\n271102,ladybug,0,1196\n5632,laser,0,1195\n628425,tiered_tray,0,1195\n10428,wading_pool,0,1194\n494464,uvula,0,1194\n700361,watermelon_bar,0,1194\n656386,hands_on_another's_cheeks,0,1194\n1392006,yellow_bodysuit,0,1193\n685266,sandals_removed,0,1192\n472922,inset,0,1191\n1627136,excalibur_morgan_(fate),0,1191\n389703,ema,0,1190\n1342945,behind_another,0,1190\n8260,ferris_wheel,0,1189\n636872,lizard_tail,0,1189\n251194,gym,0,1188\n15187,machine,0,1186\n5497,fountain,0,1186\n515008,cum_on_self,0,1186\n1292445,torn_scarf,0,1186\n421282,pasta,0,1184\n3493,voyeurism,0,1183\n891632,artificial_eye,0,1183\n671651,hair_ears,0,1183\n1349107,candy_hair_ornament,0,1183\n1090326,bird_mask,0,1182\n1355967,on_vehicle,0,1181\n437001,living_clothes,0,1181\n664061,grey_ribbon,0,1179\n426371,through_wall,0,1179\n1534682,aqua_headwear,0,1179\n542830,chest_of_drawers,0,1178\n658981,open_belt,0,1178\n629694,leopard_tail,0,1178\n1853184,kamiyama_high_school_uniform_(hyouka),0,1178\n6259,park,0,1177\n324075,ballerina,0,1177\n373823,ketchup,0,1175\n1251057,ginkgo_leaf,0,1175\n9706,snail,0,1174\n1360843,neckwear_grab,0,1174\n419773,iphone,0,1174\n12011,potato,0,1173\n456884,brown_panties,0,1172\n511422,newhalf,0,1172\n487203,overcast,0,1172\n1354455,year_of_the_rat,0,1172\n1561175,champion_uniform,0,1172\n456589,leather_boots,0,1171\n374749,heartbeat,0,1171\n541262,disgust,0,1170\n1514953,cropped_shoulders,0,1170\n1504868,eyebrow_cut,0,1170\n504744,load_bearing_vest,0,1169\n1230967,rook_(chess),0,1169\n433522,cheek_squash,0,1168\n648546,belt_boots,0,1168\n1457585,hooded_cardigan,0,1168\n582353,lace-trimmed_gloves,0,1167\n383854,native_american,0,1167\n653049,red_eyeliner,0,1166\n252960,tengu,0,1166\n1494963,orange-tinted_eyewear,0,1166\n379968,breast_expansion,0,1166\n419074,hitting,0,1165\n514538,hands_on_ass,0,1164\n708149,blue_armor,0,1164\n674293,gift_bag,0,1164\n1241321,striped_horns,0,1164\n442124,orange_panties,0,1163\n1394304,honeycomb_(pattern),0,1163\n662440,konohagakure_symbol,0,1163\n667190,plate_armor,0,1163\n1023384,white_serafuku,0,1161\n1506275,riding_pokemon,0,1161\n535323,art_brush,0,1160\n1605653,utensil_in_mouth,0,1160\n494932,hickey,0,1159\n626246,crystal_hair,0,1158\n574252,mismatched_sleeves,0,1157\n1384370,two-tone_hairband,0,1156\n660778,knees_apart_feet_together,0,1154\n412641,steering_wheel,0,1154\n383675,bus_stop,0,1153\n1235654,gradient_clothes,0,1153\n393169,torn_jeans,0,1153\n721796,kesa,0,1153\n16114,chalk,0,1152\n554447,dark_aura,0,1152\n1086828,bow_(music),0,1152\n530749,orange_pantyhose,0,1151\n404788,wrestling_ring,0,1151\n381519,vibrator_in_thighhighs,0,1150\n522500,dark_green_hair,0,1150\n408474,flashlight,0,1150\n456305,pink_pantyhose,0,1150\n494923,futa_on_male,0,1150\n703702,hooded_track_jacket,0,1150\n1399177,brown_cloak,0,1150\n1719544,they_had_lots_of_sex_afterwards_(meme),0,1150\n553041,flower_necklace,0,1149\n211997,battle_axe,0,1149\n1379986,alpaca_ears,0,1149\n501457,lalafell,0,1148\n1405177,purple_belt,0,1146\n1469769,grey_sleeves,0,1146\n3533,laundry,0,1145\n1845182,guiding_hand,0,1145\n483079,shards,0,1145\n1452744,collared_coat,0,1145\n1294981,digitigrade,0,1145\n1349162,holding_balloon,0,1144\n68505,bikesuit,0,1143\n1400652,torpedo_launcher,0,1143\n493245,theft,0,1142\n509961,battle_rifle,0,1142\n724856,low_neckline,0,1142\n11370,island,0,1142\n1465616,eyewear_strap,0,1142\n1682706,phoenix_crown,0,1142\n419288,cum_on_feet,0,1141\n469760,oven_mitts,0,1141\n641957,bishop_(chess),0,1141\n1589331,off-shoulder_bikini,0,1141\n478077,ready_to_draw,0,1140\n374636,unicorn,0,1140\n781851,user_interface,0,1139\n1509564,holding_game_controller,0,1139\n494838,soda_bottle,0,1139\n5525,chimney,0,1138\n1932,ipod,0,1138\n616555,uchikake,0,1138\n1479378,silver_trim,0,1138\n596634,gradient_legwear,0,1138\n659417,mechanical_ears,0,1136\n1440480,holding_water_gun,0,1136\n397626,guitar_case,0,1136\n1473511,petals_on_liquid,0,1136\n68500,ruler,0,1134\n687207,round_window,0,1134\n62962,buruma_pull,0,1133\n1810603,back_focus,0,1133\n2370,cactus,0,1132\n611089,implied_yuri,0,1131\n441185,ballet_slippers,0,1131\n424682,horse_penis,0,1131\n599606,fuuin_no_tsue,0,1129\n10326,archery,0,1129\n615286,pinching_sleeves,0,1129\n1306233,triangle_print,0,1129\n615369,blue_sclera,0,1129\n10023,toilet_use,0,1128\n1259665,bow_choker,0,1128\n601192,mechanical_hands,0,1128\n1476659,french_text,0,1127\n543093,motherly,0,1127\n533771,kitchen_knife,0,1127\n165653,shirt_grab,0,1127\n1314077,dice_hair_ornament,0,1126\n529177,ootachi,0,1126\n482051,drum_set,0,1125\n563204,dumbbell,0,1125\n526083,brown_socks,0,1124\n682286,title_parody,0,1124\n632801,blue_tongue,0,1124\n451182,grimoire,0,1123\n393428,vaulting_horse,0,1123\n1853211,single_hair_ring,0,1122\n623242,light_censor,0,1121\n1350077,mask_around_neck,0,1121\n4861,lighter,0,1120\n406035,legs_over_head,0,1120\n5884,champagne,0,1120\n482391,bad_food,0,1119\n432965,red_oni,0,1119\n484724,pouty_lips,0,1119\n688664,adjusting_legwear,0,1117\n1262324,shared_bathing,0,1117\n1339894,fishnet_top,0,1117\n1345473,ribbon-trimmed_dress,0,1117\n12413,coffin,0,1117\n1467809,3others,0,1116\n1334628,anchor_print,0,1116\n10938,reindeer,0,1115\n1820926,sleeveless_turtleneck_leotard,0,1114\n1277344,soap_censor,0,1113\n644571,flock,0,1113\n375275,climbing,0,1113\n522628,upshorts,0,1113\n396796,luggage,0,1113\n685558,cat_day,0,1113\n16355,sundae,0,1112\n607136,split_ponytail,0,1112\n471850,cum_in_clothes,0,1111\n422468,satin,0,1111\n642029,queen_(chess),0,1111\n516468,fake_cover,0,1110\n877384,hooded_robe,0,1110\n1764814,compass_rose_halo,0,1110\n705059,fish_print,0,1109\n1260944,heart-shaped_lock,0,1109\n398497,mouth_pull,0,1108\n473421,pink_socks,0,1108\n388012,legs_folded,0,1108\n472508,drum_(container),0,1108\n653485,torn_leotard,0,1108\n842878,liquid_hair,0,1108\n5563,comparison,0,1107\n1399082,heart_button,0,1107\n473988,cooler,0,1105\n707712,consensual_tentacles,0,1105\n413758,ammunition,0,1105\n1346569,ass_support,0,1105\n618005,pennant,0,1104\n1425103,body_freckles,0,1104\n652996,salaryman,0,1104\n10157,honey,0,1103\n723350,weight_conscious,0,1102\n657530,spoken_object,0,1101\n634911,photo_inset,0,1101\n1835748,striped_one-piece_swimsuit,0,1100\n724478,pink_wings,0,1100\n550954,drawing_sword,0,1100\n1262645,yellow_apron,0,1100\n557859,puppet_strings,0,1100\n668848,multiple_monochrome,0,1099\n421120,shell_bikini,0,1099\n519382,decepticon,0,1099\n463031,anal_hair,0,1098\n684567,looking_at_mirror,0,1098\n499809,butterfly_net,0,1097\n375377,boar,0,1097\n1290693,looking_at_breasts,0,1096\n416752,strap_lift,0,1096\n536575,cheek_press,0,1095\n1467811,6+others,0,1095\n1607725,stuffed_winged_unicorn,0,1095\n652002,no_lineart,0,1094\n1409106,head_on_pillow,0,1094\n399923,washing,0,1094\n497039,test_plugsuit,0,1094\n374998,abuse,0,1093\n1411111,keizoku_military_uniform,0,1093\n566734,tanzaku,0,1092\n458295,rising_sun_flag,0,1092\n572545,wall_of_text,0,1092\n1349919,brown_bowtie,0,1092\n1402459,blue_cloak,0,1092\n1231611,arrow_through_heart,0,1092\n1438305,holding_baseball_bat,0,1091\n547746,patterned,0,1091\n689483,plaid_jacket,0,1091\n414165,cum_on_pussy,0,1090\n575592,glowing_wings,0,1090\n403486,party_popper,0,1090\n661553,grey_belt,0,1089\n462342,after_rape,0,1089\n1247470,chocolate_on_breasts,0,1089\n665720,candy_wrapper,0,1089\n614276,bow_legwear,0,1088\n27649,phallic_symbol,0,1088\n1475838,blue_overalls,0,1088\n496608,master_sword,0,1088\n541640,button_eyes,0,1088\n643517,purple_fire,0,1088\n533033,covering_ass,0,1087\n482734,bicorne,0,1087\n1257558,round_image,0,1087\n575714,c:,0,1087\n388391,screwdriver,0,1086\n419287,swim_cap,0,1085\n436891,combat_boots,0,1085\n15731,clothes,0,1085\n476893,torn_swimsuit,0,1085\n507338,fishnet_gloves,0,1085\n317,bus,0,1084\n598531,dropping,0,1084\n499376,wrinkled_skin,0,1084\n483095,birthmark,0,1084\n1334561,loose_bowtie,0,1083\n1193565,jimiko,0,1082\n1102042,strawberry_hair_ornament,0,1081\n386413,clog_sandals,0,1081\n1411060,saunders_military_uniform,0,1080\n4051,duster,0,1079\n474653,cutting_board,0,1079\n411563,forked_tongue,0,1079\n1361077,mole_above_mouth,0,1078\n608993,uncommon_stimulation,0,1078\n1336227,red_bag,0,1078\n1499492,k/da_(league_of_legends),0,1078\n411498,tearing_clothes,0,1077\n2795,picnic,0,1077\n7702,hairjob,0,1076\n13242,hanetsuki,0,1076\n512928,white_tail,0,1076\n485392,denim_jacket,0,1076\n716813,sword_behind_back,0,1075\n1353739,borrowed_design,0,1075\n663596,aestus_estus,0,1075\n549266,grocery_bag,0,1074\n1258750,mechanical_eye,0,1074\n11435,spreader_bar,0,1073\n457698,comforting,0,1073\n8722,detective,0,1073\n634879,fume,0,1073\n1271601,bunny-shaped_pupils,0,1073\n701116,weighing_scale,0,1072\n417929,tennis_ball,0,1072\n1326209,yellow_sash,0,1072\n665044,holding_own_tail,0,1072\n1367101,holding_scissors,0,1072\n1708252,licking_another's_face,0,1071\n490582,onbashira,0,1071\n1881200,ashford_academy_school_uniform,0,1070\n1327857,red_apron,0,1070\n597826,santa_gloves,0,1070\n1483316,single_horizontal_stripe,0,1069\n479250,tricorne,0,1069\n699876,imminent_anal,0,1069\n473049,doily,0,1068\n1299138,back-print_panties,0,1068\n1750472,galaxy_expedition_team_survey_corps_uniform,0,1068\n555432,head_down,0,1067\n1335609,cross_print,0,1067\n1379921,camouflage_jacket,0,1067\n1391097,grey_neckerchief,0,1067\n1512007,pill_earrings,0,1067\n705129,holding_jacket,0,1065\n547495,morning_glory,0,1065\n1270497,pearl_bracelet,0,1064\n699336,sharp_toenails,0,1064\n1505137,thai_text,0,1064\n1255353,loungewear,0,1064\n502383,elezen,0,1064\n365028,stealth_sex,0,1063\n1579902,nata_(tool),0,1062\n547034,guided_breast_grab,0,1061\n540293,vampire_costume,0,1061\n602058,shoulder_strap,0,1060\n659959,scarf_over_mouth,0,1058\n489995,bulletproof_vest,0,1058\n1274530,angora_rabbit,0,1058\n374852,wakizashi,0,1057\n637714,holding_legs,0,1055\n1445390,aria_company_uniform,0,1055\n4760,campfire,0,1055\n390867,soda,0,1055\n378755,beans,0,1055\n1346040,pink_hakama,0,1055\n1352539,squidbeak_splatoon,0,1055\n204274,upshirt,0,1053\n1440630,pink_capelet,0,1053\n1283735,grey_nails,0,1052\n10639,yarn,0,1052\n378487,telescope,0,1050\n1248044,tooth_necklace,0,1050\n564526,vertical-striped_pantyhose,0,1050\n463235,training_bra,0,1049\n759865,accidental_exposure,0,1049\n49867,summer_festival,0,1049\n479155,necktie_grab,0,1049\n598441,yin_yang_orb,0,1049\n1421701,smokestack_hair_ornament,0,1049\n462605,snack,0,1048\n481999,motorcycle_helmet,0,1048\n612924,\"don't_say_\"\"lazy\"\"\",0,1048\n527131,canvas_(object),0,1047\n1510188,dildo_riding,0,1046\n490687,prehensile_tail,0,1046\n610305,hand_to_head,0,1046\n12725,headless,0,1046\n1697967,tassel_hair_ornament,0,1046\n1530487,off-shoulder_jacket,0,1046\n437409,hospital_bed,0,1045\n1265402,heart_balloon,0,1045\n646372,asa_no_ha_(pattern),0,1044\n1381675,holding_stylus,0,1044\n432413,lute_(instrument),0,1044\n682869,extra_mouth,0,1044\n507520,goat_girl,0,1044\n1399019,multicolored_fur,0,1044\n375033,bored,0,1043\n1583922,pointy_footwear,0,1043\n1408058,brown_leotard,0,1043\n1601,clapping,0,1041\n466838,walkie-talkie,0,1040\n550352,hand_on_forehead,0,1039\n989449,paw_print_background,0,1039\n1559228,2000s_(style),0,1038\n439288,basketball_uniform,0,1038\n1326377,black_corset,0,1038\n378166,star_of_david,0,1038\n1468127,survey_corps_(emblem),0,1038\n1557673,pendant_choker,0,1037\n591683,under_kotatsu,0,1037\n5254,thermometer,0,1035\n180296,wetsuit,0,1035\n581056,multiple_braids,0,1035\n375906,hot_dog,0,1035\n573761,trash_bag,0,1035\n1825415,shuujin_academy_school_uniform,0,1035\n10673,vore,0,1034\n374312,pilot,0,1034\n430316,messenger_bag,0,1034\n456535,spit_take,0,1033\n1450741,oohashi_high_school_uniform,0,1032\n658794,poke_ball_theme,0,1032\n515827,frog_print,0,1032\n1344962,egg_hair_ornament,0,1032\n510057,sand_sculpture,0,1031\n433234,erect_clitoris,0,1031\n470625,torn_gloves,0,1031\n558698,sig_sauer,0,1030\n712047,belly_grab,0,1030\n572583,radiation_symbol,0,1029\n542338,snake_hair,0,1029\n643402,shorts_under_dress,0,1028\n760104,hand_on_another's_waist,0,1027\n1768621,heart_o-ring,0,1027\n1401971,backless_leotard,0,1027\n1326818,fur-trimmed_kimono,0,1026\n1610148,poster_(medium),0,1025\n465863,cum_on_legs,0,1025\n428847,ugly_man,0,1024\n398109,ice_skates,0,1023\n578777,outstretched_leg,0,1022\n1575195,crocodilian_tail,0,1022\n1514232,breast_focus,0,1021\n1469130,multiple_straps,0,1021\n618406,yellow_pants,0,1020\n9213,diaper,0,1020\n643912,tail_piercing,0,1020\n1372846,pink_pajamas,0,1018\n269258,chat_log,0,1018\n615253,applying_makeup,0,1017\n9017,kaijuu,0,1017\n1556082,print_headwear,0,1017\n1258484,key_necklace,0,1017\n6160,cocktail,0,1016\n398803,cowboy_boots,0,1016\n1243775,colored_shadow,0,1016\n1330451,multicolored_cape,0,1016\n7877,nintendo_ds,0,1015\n1409572,yellow_leotard,0,1015\n600996,socks_removed,0,1014\n501095,berry,0,1014\n649302,hydrokinesis,0,1014\n572593,noh_mask,0,1014\n1090981,tantou,0,1013\n5231,tonfa,0,1013\n627286,covering_one_eye,0,1013\n671725,purple_eyeshadow,0,1013\n1716159,human_scabbard,0,1013\n407500,easel,0,1012\n1465373,triangle_earrings,0,1012\n1293722,bandaged_neck,0,1011\n394852,hair_flip,0,1010\n1295529,frilled_shorts,0,1010\n669112,broken_weapon,0,1010\n542449,furigana,0,1008\n698204,multiple_piercings,0,1008\n465806,voice_actor,0,1007\n482205,crayon,0,1007\n1386020,st._gloriana's_(emblem),0,1007\n714474,thighhighs_over_pantyhose,0,1006\n385968,player_2,0,1006\n604753,panzerkampfwagen_iv,0,1006\n533783,reins,0,1005\n504063,ninja_mask,0,1005\n1373454,instagram_username,0,1005\n4910,corn,0,1004\n389154,screw,0,1004\n425258,naked_overalls,0,1003\n438516,zabuton,0,1003\n6481,tools,0,1003\n713580,torn_jacket,0,1003\n1604448,duel_academy_uniform_(yu-gi-oh!_gx),0,1002\n1250999,red_apple,0,1002\n1327189,glowing_horns,0,1002\n1734694,musou_isshin_(genshin_impact),0,1002\n1242015,vanishing_point,0,1001\n714848,rectangular_mouth,0,1001\n552812,in_cup,0,1000\n508103,tentacles_under_clothes,0,1000\n1302681,orange_pants,0,1000\n406104,psychic,0,1000\n383197,toilet_paper,0,999\n434844,folding_chair,0,999\n222856,good_end,0,998\n317438,war,0,998\n1311812,green_hakama,0,998\n586561,penises_touching,0,997\n1505168,traditional_chinese_text,0,997\n375718,daruma_doll,0,997\n1502082,brown_sweater_vest,0,997\n539907,calico,0,996\n1324009,lactation_through_clothes,0,995\n5248,snowball,0,995\n458547,wood,0,995\n1881201,eden_academy_school_uniform,0,995\n727101,overall_shorts,0,994\n3114,thread,0,994\n452907,chewing,0,993\n542208,blank_stare,0,993\n1411474,bc_freedom_military_uniform,0,993\n682863,hand_on_another's_leg,0,991\n489621,corded_phone,0,991\n1516826,drinking_straw_in_mouth,0,991\n1341736,hanten_(clothes),0,989\n1423058,facing_to_the_side,0,989\n416845,toast_in_mouth,0,988\n642887,wiping_sweat,0,988\n601148,huge_bow,0,988\n1353525,from_outside,0,987\n665502,saiyan_armor,0,987\n1353702,hands_on_own_ass,0,987\n576788,drying_hair,0,986\n8664,hoop,0,986\n483367,pornography,0,986\n385619,facepalm,0,986\n620608,no_tail,0,986\n469426,tiger_stripes,0,985\n689166,king_(chess),0,984\n412915,pet_bowl,0,983\n1770613,purple_gemstone,0,983\n687072,dark_areolae,0,983\n8787,cd,0,982\n527949,troll_face,0,982\n1538197,square_4koma,0,982\n1238314,transparent_wings,0,981\n1582502,scar_on_stomach,0,981\n614593,white_snake,0,981\n633878,pursed_lips,0,980\n663918,holding_fishing_rod,0,980\n1360081,purple_scrunchie,0,980\n1193485,dudou,0,979\n1304063,yellow_bag,0,979\n1411064,anzio_military_uniform,0,979\n383664,pier,0,978\n668886,animal_on_lap,0,977\n666509,>o<,0,977\n2374,shopping,0,977\n1366557,ink_tank_(splatoon),0,977\n400507,sailor_senshi,0,976\n487760,under_tree,0,975\n1636694,sleeve_garter,0,975\n1415933,fur-trimmed_shorts,0,975\n1498023,adapted_turret,0,975\n1430264,coin_hair_ornament,0,975\n460344,ear_biting,0,974\n1492155,eyewear_hang,0,974\n565669,telstar,0,973\n430138,double_vertical_stripe,0,973\n662488,palms_together,0,973\n417908,white_tiger,0,973\n538324,manga_cover,0,972\n560409,streamers,0,972\n467246,dotted_line,0,972\n1397657,cat_ear_legwear,0,972\n1335718,vibrator_cord,0,971\n10497,rocket,0,971\n593433,rice_on_face,0,971\n543900,hat_over_one_eye,0,971\n43071,blind,0,971\n590886,bird_legs,0,970\n1476481,multicolored_horns,0,969\n1509772,industrial_piercing,0,969\n16929,barbed_wire,0,968\n1663181,alice_(alice_in_wonderland)_(cosplay),0,968\n406420,popcorn,0,968\n11381,frogtie,0,967\n11835,ballet,0,967\n589292,slashing,0,966\n1443767,team_rocket_uniform,0,966\n1339824,cropped_hoodie,0,966\n504393,looking_outside,0,965\n1609648,cum_on_pectorals,0,965\n1527638,bubble_tea_challenge,0,965\n1793017,griffin_&_kryuger_military_uniform,0,965\n608070,boxer_briefs,0,964\n1515353,grey_theme,0,963\n1638312,footwear_ribbon,0,963\n551459,kine,0,963\n463437,pencil_case,0,963\n677487,brown_wings,0,962\n1484368,white_bag,0,961\n475500,kissing_hand,0,961\n583243,telekinesis,0,961\n1237688,open_bodysuit,0,959\n74341,floating_island,0,959\n1595384,blank_censor,0,959\n1303272,print_jacket,0,959\n1295636,shark_costume,0,959\n456565,flying_kick,0,958\n1457953,sparse_pubic_hair,0,958\n1440492,green_capelet,0,958\n1347286,yellow_coat,0,958\n445618,wet_dress,0,958\n643256,tentacle_pit,0,958\n1324856,virgin_killer_outfit,0,958\n583869,character_censor,0,957\n393237,bandolier,0,957\n1396368,frilled_ascot,0,956\n4638,drugs,0,956\n669932,wrist_wrap,0,956\n1582504,scar_on_neck,0,956\n568887,single_boot,0,956\n569329,catholic,0,956\n478362,kepi,0,956\n1720557,swimsuit_cover-up,0,956\n593295,red_border,0,955\n494539,texture,0,955\n471805,pastel_colors,0,954\n548034,keystone,0,954\n514359,naked_scarf,0,953\n8173,bokken,0,952\n1478006,holding_vegetable,0,952\n1282931,raimon_soccer_uniform,0,952\n394999,spooning,0,951\n80452,unzipping,0,951\n665405,white_umbrella,0,951\n1629910,star_brooch,0,951\n500760,flower_ornament,0,951\n1373023,purple-framed_eyewear,0,950\n476104,saber_(weapon),0,950\n567635,portrait_(object),0,950\n565478,skirt_basket,0,949\n1070114,single_stripe,0,949\n447171,lowleg_pants,0,949\n1575185,polka_dot_headwear,0,949\n1342773,fleur_de_lapin_uniform,0,949\n1337534,boobplate,0,948\n464554,dandelion,0,948\n649117,multiple_swords,0,946\n1677790,blood_on_knife,0,946\n1845191,glaive_(polearm),0,946\n4640,hanbok,0,946\n1484444,yellow_butterfly,0,945\n497027,pointy_breasts,0,945\n54102,noose,0,943\n16756,aquarium,0,943\n1426213,multiple_riders,0,943\n381183,brick,0,943\n507308,voile,0,943\n13597,triple_penetration,0,942\n1298917,brown_apron,0,942\n1592986,rabbit_boy,0,942\n477804,rainbow_hair,0,942\n16833,sidewalk,0,941\n1681601,mash_kyrielight_(dangerous_beast)_(cosplay),0,941\n1506800,diamond_(gemstone),0,940\n1382643,flaming_weapon,0,940\n1271930,nanodesu_(phrase),0,940\n1406788,otter_ears,0,940\n3614,stain,0,939\n1350036,crystal_earrings,0,939\n1349615,red_fur,0,939\n1448822,brown_hoodie,0,939\n468645,child_drawing,0,938\n255403,cleaver,0,938\n382573,akanbe,0,937\n1243923,backpack_removed,0,937\n405411,team_9,0,937\n753278,analog_clock,0,936\n603414,space_helmet,0,936\n645622,sleeveless_coat,0,934\n460553,no_eyebrows,0,934\n507379,yellow_belt,0,934\n600871,ar-15,0,934\n468827,pushing,0,933\n519098,yarn_ball,0,933\n660791,fur_cape,0,933\n9535,icing,0,932\n12002,foam,0,932\n605380,vertical-striped_bikini,0,932\n538478,haniwa_(statue),0,932\n474917,peace_symbol,0,931\n396687,hourglass,0,931\n540308,baggy_clothes,0,931\n1201168,undressing_another,0,931\n568529,barefoot_sandals,0,931\n1309935,notched_ear,0,931\n12456,dvd_cover,0,930\n1386653,falchion_(fire_emblem),0,930\n8697,porch,0,929\n453294,houndstooth,0,929\n1407009,japari_bun,0,929\n413107,puffy_cheeks,0,928\n1265662,lace-trimmed_hairband,0,928\n393568,amulet,0,927\n1423578,brown_collar,0,926\n417078,bayonet,0,926\n632074,owl_ears,0,925\n548331,bamboo_steamer,0,924\n394157,papers,0,924\n545481,hand_on_leg,0,924\n584716,camouflage_pants,0,924\n1350589,bandaid_on_forehead,0,924\n1478379,dress_flower,0,924\n540095,bilingual,0,923\n4219,henshin,0,923\n1553320,two-tone_headwear,0,923\n487790,studded_bracelet,0,923\n1430735,black_garter_belt,0,922\n1363208,blue_bag,0,922\n712473,mummy_costume,0,921\n1426299,tokisadame_school_uniform,0,921\n1258824,print_shorts,0,921\n1493305,heel_up,0,920\n471824,searchlight,0,919\n1509089,between_pectorals,0,919\n1427498,holding_pizza,0,918\n673693,kouhaku_nawa,0,918\n1058783,aviator_sunglasses,0,918\n1375732,snap-fit_buckle,0,918\n693639,striped_hoodie,0,918\n1393881,green_bag,0,917\n191357,loose_shirt,0,917\n617955,polka_dot_skirt,0,917\n1312469,purple_hakama,0,916\n409179,smoking_gun,0,915\n657589,crotch_cutout,0,915\n1759071,cetacean_tail,0,915\n1301010,orange_sweater,0,914\n408039,crystal_ball,0,914\n415025,convenience_store,0,914\n390528,seaweed,0,914\n564002,guided_penetration,0,913\n1154025,ankle_wrap,0,913\n517244,anglerfish,0,913\n585400,inverted_cross,0,912\n15147,concert,0,912\n1289607,nursing_handjob,0,912\n1607304,linear_hatching,0,911\n377994,playing,0,911\n448692,saddle,0,911\n643356,dress_removed,0,911\n10014,washing_machine,0,910\n239,valkyrie,0,909\n1518587,striped_headwear,0,909\n710509,antique_firearm,0,908\n1365439,jaguar_print,0,908\n652982,visor_(armor),0,907\n9716,strawberry_panties,0,906\n438209,checkered_flag,0,906\n1378073,ears_visible_through_hair,0,905\n617340,strapless_swimsuit,0,905\n537085,objectification,0,905\n182494,audience,0,904\n1450864,head_chain,0,904\n663896,hand_on_another's_neck,0,903\n465140,breastfeeding,0,903\n1403674,pink_camisole,0,903\n670641,clothes_between_breasts,0,902\n664838,green_wings,0,902\n5119,pinwheel,0,902\n521714,cursive,0,902\n1274515,double_w,0,902\n663895,hand_on_own_neck,0,901\n474428,blowing,0,901\n692139,penguin_hood,0,901\n690466,monster_energy,0,901\n15469,coconut,0,900\n1334692,side_drill,0,900\n589458,<o>_<o>,0,900\n515225,sperm_cell,0,900\n498585,cute_&_girly_(idolmaster),0,899\n5302,elvaan,0,899\n8482,waiter,0,899\n374917,prison_clothes,0,899\n657147,fur_boots,0,899\n478497,sleep_mask,0,899\n1424580,oda_uri,0,899\n462190,public_use,0,898\n399273,adidas,0,898\n696698,gold_bikini,0,897\n593473,coke-bottle_glasses,0,896\n4047,pickaxe,0,894\n491766,painterly,0,894\n599986,cutting_hair,0,893\n457725,traffic_cone,0,893\n585968,heads-up_display,0,893\n609352,themed_object,0,892\n550434,side_slit_shorts,0,892\n1508825,pouring_onto_self,0,892\n504098,m1911,0,891\n562229,food_stand,0,891\n1258067,hands_on_own_stomach,0,891\n1494889,lion_boy,0,891\n2944,airship,0,890\n600288,tail_feathers,0,890\n502629,bullet_hole,0,889\n471358,bass_clef,0,889\n720344,round-bottom_flask,0,888\n10079,double_dildo,0,888\n577453,lace_gloves,0,888\n15251,undead,0,888\n427033,hologram,0,887\n1323466,brown_nails,0,887\n395164,napkin,0,886\n433870,broken_heart,0,886\n539119,ultra_ball,0,886\n2430,recorder,0,885\n564894,united_states,0,885\n1490333,yellow_sleeves,0,885\n401062,x3,0,885\n1456713,cross_choker,0,884\n607245,cropped_arms,0,883\n579303,tail_ring,0,883\n4281,hole,0,882\n1303240,polka_dot_scrunchie,0,882\n495021,rider_belt,0,882\n570043,pine_tree,0,881\n661864,pink_belt,0,880\n652399,araki_hirohiko_(style),0,880\n1393886,multicolored_kimono,0,879\n1414615,mole_on_stomach,0,879\n514585,plaid_bra,0,879\n581487,hishaku,0,879\n9656,crazy,0,878\n559663,unamused,0,878\n1451576,checkered_sash,0,878\n1437142,purple_hoodie,0,878\n699281,oversized_food,0,877\n500653,mahjong_tile,0,877\n1433794,holding_saucer,0,876\n585602,jeweled_branch_of_hourai,0,876\n1326378,black_umbrella,0,876\n406042,exhausted,0,876\n430683,sling,0,876\n15227,screentones,0,875\n1480168,white_sports_bra,0,875\n668837,ghost_costume,0,875\n395011,tube_dress,0,874\n463435,parka,0,874\n416210,dirty_feet,0,874\n497015,wringing_clothes,0,873\n1582498,scar_on_leg,0,873\n378541,gills,0,872\n468583,melon_bread,0,872\n430376,bear_costume,0,872\n381280,lighthouse,0,872\n716956,puff_and_slash_sleeves,0,872\n607934,tentacles_on_male,0,871\n1593160,unusually_open_eyes,0,871\n1328259,multiple_moles,0,870\n401136,kimono_lift,0,869\n1292517,glowing_butterfly,0,869\n388245,cum_in_nose,0,868\n473178,\\n/,0,868\n405504,apron_lift,0,867\n672588,cardigan_vest,0,867\n663869,looking_through_legs,0,867\n681979,double_\\m/,0,867\n460270,sparrow,0,865\n138127,art_nouveau,0,865\n1404921,pink_ascot,0,864\n10268,net,0,864\n564789,romper,0,864\n545590,easter_egg,0,864\n1881202,st._chronica_academy_school_uniform,0,864\n1826631,tracen_training_uniform,0,864\n607513,arms_around_waist,0,863\n645655,wall_clock,0,863\n376621,wa_lolita,0,863\n1336108,crime_prevention_buzzer,0,863\n635240,star_pasties,0,862\n418811,acoustic_guitar,0,861\n1786594,tokyo-3_middle_school_uniform,0,860\n637908,adapted_uniform,0,860\n389325,grave,0,859\n388208,orange_hoodie,0,859\n399680,arachne,0,859\n549347,green_pantyhose,0,859\n3912,p90,0,858\n1874891,object_through_head,0,858\n423080,hat_with_ears,0,857\n546085,brown_bra,0,856\n1300914,leash_pull,0,856\n1470700,black_undershirt,0,855\n1374880,bralines,0,855\n461078,squinting,0,854\n697415,storefront,0,854\n389926,panty_lift,0,853\n1292892,cracked_wall,0,853\n16792,golf_club,0,852\n799867,futasub,0,852\n1504031,white_butterfly,0,852\n428710,buster_sword,0,852\n1371471,anchor_necklace,0,852\n513996,lyrics,0,851\n531189,foliage,0,851\n405020,wheelbarrow,0,851\n1235655,gradient_dress,0,850\n1822470,wataboushi,0,849\n390026,chalice,0,849\n594313,shoulder_holster,0,849\n1384374,grey_hairband,0,849\n529750,glowing_hair,0,849\n1360271,grey_cape,0,849\n472653,stained_panties,0,849\n10056,grill,0,848\n413290,hand_under_shirt,0,848\n1411208,purple_neckerchief,0,848\n1270535,curtained_hair,0,848\n1613886,animal_ear_headwear,0,848\n164255,nudist,0,846\n1243627,penis_peek,0,846\n438748,breast_poke,0,845\n533029,dragging,0,845\n1715534,baton_(conducting),0,845\n468850,tall,0,844\n582333,ojou-sama_pose,0,844\n664894,aqua_gloves,0,844\n457370,masochism,0,844\n570496,struggling,0,844\n675461,rolling_suitcase,0,844\n561654,animal_skull,0,844\n16918,tutu,0,843\n1448672,tsab_ground_military_uniform,0,843\n499780,holding_breath,0,843\n477179,tire,0,842\n1317436,satin_panties,0,842\n1398131,hooded_bodysuit,0,842\n690260,vibrator_on_nipple,0,841\n482315,breast_padding,0,841\n1298677,armpit_cutout,0,841\n1372787,multi-strapped_panties,0,841\n469235,amplifier,0,840\n400231,kotoyoro,0,840\n1208724,string_bra,0,840\n1337502,heart_lock_(kantai_collection),0,840\n393062,skirt_flip,0,839\n626440,season_connection,0,839\n688469,spiked_choker,0,839\n381750,thermos,0,838\n8409,spaghetti,0,838\n1344738,snowflake_background,0,838\n600460,group_picture,0,837\n496226,multiple_legs,0,837\n8698,windowsill,0,837\n1229971,shoulder_cannon,0,837\n1411084,pravda_military_uniform,0,837\n1411077,chi-hatan_military_uniform,0,837\n439167,urethral_insertion,0,836\n46728,excited,0,836\n386926,polar_bear,0,836\n742744,bean_bag_chair,0,835\n555321,hand_gesture,0,835\n1468128,training_corps_(emblem),0,835\n1575514,yuigaoka_school_uniform,0,835\n4517,gymnastics,0,834\n594978,naked_tabard,0,834\n702958,holding_ribbon,0,834\n469710,energy_drink,0,834\n419383,wallet,0,833\n1835765,evangelion_(mecha),0,833\n609535,witch_(madoka_magica),0,833\n1642271,yurigaoka_girls_academy_school_uniform,0,833\n1396676,dressing_another,0,832\n557780,alternate_wings,0,831\n1376753,crescent_print,0,831\n680577,color_trace,0,831\n1316299,multicolored_scarf,0,831\n720999,aqua_jacket,0,831\n383546,stole,0,831\n1242619,fake_nails,0,830\n509088,penis_in_panties,0,829\n646377,kikumon,0,829\n1375970,hair_flowing_over,0,829\n664951,coat_removed,0,829\n571951,flaming_sword,0,828\n1355541,lattice,0,828\n488521,canopy_bed,0,828\n1328348,group_name,0,828\n614256,bunny_hat,0,827\n411205,pigeon,0,827\n1441859,aqua_footwear,0,826\n505302,bar_stool,0,826\n679205,catchphrase,0,826\n1545323,multicolored_headwear,0,826\n1452498,grey_capelet,0,826\n1518971,pectoral_press,0,826\n550074,in_water,0,825\n1345310,collared_vest,0,825\n1407027,jaguar_tail,0,825\n397136,trigram,0,824\n228097,astronaut,0,824\n1377986,riyo_(lyomsnpmp)_(style),0,824\n1475691,hanasakigawa_school_uniform,0,824\n653176,circle_skirt,0,823\n1392086,grey_bodysuit,0,823\n1247424,brick_floor,0,823\n1529528,yellow_raincoat,0,823\n403070,grenade_launcher,0,823\n409820,cat_costume,0,822\n620885,latex_legwear,0,822\n440354,yo-yo,0,822\n440482,leg_wrap,0,822\n556519,electrical_outlet,0,822\n562990,bath_stool,0,821\n1358423,multicolored_footwear,0,821\n400267,dumpling,0,821\n1281944,anchor_choker,0,821\n511246,blank_speech_bubble,0,820\n1397788,brown_choker,0,820\n472542,beam_saber,0,820\n487949,arm_above_head,0,819\n455483,shorts_around_one_leg,0,819\n394402,wheat,0,818\n15450,onion,0,818\n586471,rounded_corners,0,818\n1605157,colored_nipples,0,818\n476906,spray_can,0,817\n1501670,double_fox_shadow_puppet,0,817\n533099,sitting_on_rock,0,816\n507276,paper_crane,0,816\n628065,eyepatch_removed,0,816\n1324692,pink_fur,0,816\n1232450,sparkle_print,0,816\n1417853,heart_collar,0,816\n1734,karaoke,0,815\n5364,ganguro,0,815\n1409664,floating_scarf,0,815\n1406324,no_blindfold,0,815\n544688,bunching_hair,0,814\n11414,nipple_clamps,0,814\n678345,maneki-neko,0,814\n574527,surcoat,0,814\n473342,imperial_japanese_army,0,814\n1549436,cutout_above_navel,0,814\n1281282,taimanin_suit,0,814\n1327704,nejiri_hachimaki,0,813\n1371202,yellow_cape,0,812\n622973,chicken_(food),0,812\n261984,snow_bunny,0,812\n1092127,condom_belt,0,812\n1281553,german_flag_bikini,0,811\n121795,inline_skates,0,810\n488001,tape_measure,0,810\n10727,bib,0,809\n504316,hands_on_hilt,0,809\n330159,green_tea,0,809\n1318429,pendant_watch,0,809\n15619,sand_castle,0,809\n839892,no_mole,0,809\n14739,hammock,0,808\n393203,handstand,0,808\n1287846,ammunition_pouch,0,808\n1453635,cartoon_bone,0,808\n1161750,boy_sandwich,0,807\n1251054,okobo,0,807\n484561,arm_cuffs,0,806\n488430,seat,0,805\n535613,upturned_eyes,0,805\n1827382,st._theresa's_girls_academy_school_uniform,0,805\n721903,fur_cloak,0,805\n642323,teacher_and_student,0,804\n704182,character_signature,0,804\n713277,no_eyepatch,0,804\n682665,super_soaker,0,804\n1474354,holding_ladle,0,803\n1483518,orange_sleeves,0,803\n413459,inflatable_raft,0,803\n462239,chinese_new_year,0,803\n483045,kinchaku,0,802\n519941,finger_biting,0,802\n399166,cursor,0,802\n684797,hands_on_another's_hips,0,802\n1617036,pokemon_move,0,802\n2477,lightsaber,0,801\n498775,orange_skin,0,801\n9166,windmill,0,801\n1390981,ribbon_braid,0,801\n1296117,sideways_hat,0,801\n1113314,thick_lips,0,800\n553675,ehoumaki,0,800\n1152233,star_tattoo,0,800\n1467949,single_ear_cover,0,800\n1835737,green_one-piece_swimsuit,0,799\n531424,multiple_heads,0,799\n695119,blood_in_hair,0,798\n593299,pink_border,0,798\n1319348,holding_bucket,0,797\n1257023,dog_penis,0,797\n509238,dialogue_box,0,797\n603240,vertical-striped_panties,0,797\n622330,oversized_limbs,0,797\n548294,nib_pen_(object),0,796\n596588,jacket_pull,0,796\n558489,white_armor,0,796\n394916,2008,0,796\n613430,bagged_fish,0,795\n662803,honeycomb_background,0,795\n433742,frozen,0,795\n475494,dissolving,0,795\n1505371,star_guardian_(league_of_legends),0,795\n714122,ball_and_chain_restraint,0,794\n1322883,flag_background,0,794\n659790,eldritch_abomination,0,794\n1648109,hololive_idol_uniform,0,794\n659364,licking_armpit,0,793\n1272545,orange_leotard,0,793\n1320993,print_necktie,0,793\n7947,beltskirt,0,792\n475951,ornament,0,792\n591348,breast_cutout,0,792\n614915,pink_cape,0,792\n1713082,neck_tassel,0,792\n549921,swinging,0,791\n822043,mundane_utility,0,791\n705927,frilled_leotard,0,790\n1569456,pizza_slice,0,790\n1299125,elbows_on_table,0,789\n7700,weightlifting,0,789\n410619,toe_ring,0,788\n701996,nipple_bar,0,788\n1728479,kousaka_kirino's_school_uniform,0,788\n1393397,orange_goggles,0,788\n6352,elephant,0,787\n1293918,bicycle_basket,0,787\n3499,kappa,0,787\n5946,boxcutter,0,787\n592592,personality_switch,0,787\n547300,misunderstanding,0,787\n1544416,clothes_between_thighs,0,787\n721466,pointy_nose,0,787\n1648167,x-shaped_pupils,0,787\n452532,two-handed_handjob,0,786\n392867,melon,0,786\n702627,ass-to-ass,0,786\n548516,pinstripe_shirt,0,786\n741418,card_parody,0,786\n504065,cephalopod_eyes,0,785\n1391806,two-tone_bow,0,785\n1390068,joy-con,0,785\n5466,mummy,0,784\n398573,cartridge,0,783\n619281,blue_border,0,783\n383898,bullying,0,783\n472408,sneezing,0,783\n4899,stats,0,783\n1365868,holding_envelope,0,783\n496190,kabuto_(helmet),0,783\n1396770,animal_on_arm,0,782\n5547,pet,0,782\n1522400,tented_shirt,0,782\n582033,clothes_tug,0,782\n411403,tongs,0,782\n1488405,mole_on_cheek,0,781\n444001,legband,0,781\n379013,note,0,781\n1409157,flower_tattoo,0,781\n553040,flower_bracelet,0,780\n1577478,super_saiyan_1,0,780\n1398925,speaking_tube_headset,0,780\n468311,tail_hug,0,779\n1495160,mismatched_eyebrows,0,779\n1860041,multiple_drawing_challenge,0,779\n1481466,purple_horns,0,779\n1426179,luna_nova_school_uniform,0,779\n573016,hands_on_shoulders,0,778\n1432417,brown_tail,0,778\n419647,stakes_of_purgatory,0,778\n492703,long_neck,0,777\n723519,chin_strap,0,777\n393452,leaf_umbrella,0,777\n715181,post-apocalypse,0,777\n550385,linked_piercing,0,776\n15836,butter,0,776\n379180,zora,0,776\n1376893,pink_eyeshadow,0,776\n499526,green_socks,0,775\n2730,massage,0,775\n1464853,blue-tinted_eyewear,0,775\n658977,wooden_chair,0,774\n397923,group_hug,0,774\n657414,panties_under_buruma,0,774\n382030,beetle,0,774\n529455,tight_dress,0,774\n1347574,mole_on_body,0,774\n284608,tea_set,0,773\n453888,hand_mirror,0,773\n416380,gokkun,0,773\n399607,hair_dryer,0,773\n643401,dragon_boy,0,773\n463449,sugar_cube,0,772\n501887,levitation,0,772\n467444,kerchief,0,772\n1427360,gold_choker,0,772\n646311,polka_dot_shirt,0,771\n446404,cheek_pull,0,771\n1283338,american_flag_print,0,771\n1411059,saunders_school_uniform,0,771\n501282,lipgloss,0,770\n1352247,variable_fighter,0,770\n375406,clown,0,770\n507447,family_crest,0,770\n416825,omikuji,0,769\n524801,hakurei_shrine,0,769\n641133,aqua_bra,0,769\n214260,keep_out,0,769\n1378265,z-ring,0,769\n435855,messy_room,0,768\n1339258,logo_parody,0,768\n1252372,sitting_on_bench,0,768\n1441858,lap_pillow_invitation,0,768\n474442,plume,0,768\n1136370,sextuplets,0,768\n1303976,print_sarong,0,767\n534139,popped_button,0,767\n1433477,floating_cape,0,767\n1245350,implied_fellatio,0,767\n168769,band_uniform,0,766\n712902,pumpkin_hair_ornament,0,765\n7839,beam,0,765\n375496,kagami_mochi,0,765\n716637,argyle_sweater,0,765\n380200,2007,0,765\n1571287,berry_(pokemon),0,765\n7461,lizard,0,764\n587390,hand_on_headphones,0,764\n1680143,pegasus_knight_uniform_(fire_emblem),0,764\n447159,fishing_line,0,764\n605773,head_on_hand,0,763\n317012,zeon,0,763\n646375,sayagata,0,763\n635045,solid_eyes,0,762\n1340897,blue_feathers,0,762\n1373241,meka_(overwatch),0,762\n1594642,holomyth,0,762\n6527,cart,0,761\n449699,grand_piano,0,761\n460701,pagoda,0,761\n15902,jetpack,0,761\n1008938,clothes_theft,0,760\n1315295,holding_another's_hair,0,760\n683907,jingasa,0,760\n2145,juice,0,759\n691041,yellow_sky,0,759\n499998,sitting_backwards,0,758\n1336500,pointing_at_another,0,758\n606330,condom_box,0,758\n1552258,fish_boy,0,758\n960973,cardigan_around_waist,0,758\n623986,green_sclera,0,758\n5099,pegasus,0,757\n746897,pussy_juice_drip_through_clothes,0,757\n1289376,bishamonten's_spear,0,757\n1539213,flower_over_eye,0,756\n511500,head_on_chest,0,756\n1421046,holding_cane,0,756\n509654,forced_orgasm,0,755\n1548224,butterfly_brooch,0,755\n382343,adjusting_panties,0,754\n457036,steak,0,754\n1412249,green_scrunchie,0,754\n5817,sauna,0,753\n443238,wardrobe_error,0,752\n3438,japan,0,751\n5402,crowbar,0,751\n1510994,sweaty_clothes,0,751\n1398336,year_of_the_dog,0,751\n527344,body_armor,0,751\n701994,pov_across_table,0,750\n14011,height_chart,0,749\n460435,ivy,0,749\n421692,game_boy,0,749\n426382,bear_tail,0,749\n778226,greek_clothes,0,749\n1039471,prostration,0,748\n1255173,shoujo_kitou-chuu,0,748\n1386173,pixiv_username,0,748\n1366556,splattershot_(splatoon),0,748\n9745,hungry,0,747\n385480,buruma_aside,0,747\n399404,skull_necklace,0,747\n467744,mount_fuji,0,747\n1611404,food-themed_earrings,0,747\n452085,hospital_gown,0,746\n658126,cream_on_face,0,746\n396015,gunblade,0,746\n646003,curled_fingers,0,746\n484040,winding_key,0,745\n9347,puppy,0,745\n1059619,kissing_penis,0,745\n375353,soviet,0,745\n475528,ankle_grab,0,744\n8921,white_day,0,744\n5102,wizard,0,744\n397561,radish,0,744\n471296,infinity,0,744\n693309,sleeve_grab,0,743\n531095,book_hug,0,742\n1375741,extra_faces,0,742\n3742,ferret,0,741\n436459,acorn,0,741\n583137,ear_protection,0,741\n1239921,pink_sky,0,740\n1353065,falling_feathers,0,740\n1496708,hitodama_print,0,740\n1257082,clock_eyes,0,740\n729551,kiwi_(fruit),0,739\n1193090,military_helmet,0,739\n1343008,orange_vest,0,738\n395496,cloth,0,738\n384700,dock,0,738\n541741,strapless_bottom,0,738\n563180,nintendo_3ds,0,738\n386212,diving,0,737\n630151,bikini_shorts,0,737\n1429582,dress_swimsuit,0,737\n9476,minotaur,0,736\n381360,wrong_feet,0,736\n1469257,single_epaulette,0,736\n563949,french_flag,0,735\n1424344,sling_bikini_top,0,735\n458787,testicle_grab,0,734\n450248,sickle,0,733\n429978,between_toes,0,733\n120759,mat,0,733\n474597,mitre,0,733\n1657712,veiny_arms,0,733\n576346,adjusting_necktie,0,732\n1305509,box_of_chocolates,0,732\n596273,behind_back,0,732\n1489407,martial_arts_belt,0,732\n1595590,pectoral_focus,0,732\n393640,aurora,0,731\n554570,jockstrap,0,731\n478042,track_uniform,0,731\n11347,army,0,730\n404973,galaxy,0,730\n1520977,print_mug,0,730\n487783,leather_pants,0,729\n375625,model_kit,0,729\n1344687,holding_letter,0,729\n1344492,on_motorcycle,0,729\n1001710,shell_necklace,0,729\n1303975,blue_sarong,0,728\n489578,whiskey,0,728\n467700,salad,0,728\n548098,mast,0,728\n485599,silver_dress,0,728\n1377852,hair_on_horn,0,728\n642272,above_clouds,0,727\n1427943,yellow_tank_top,0,727\n12070,battleship,0,727\n611065,lace_choker,0,727\n445428,guard_rail,0,726\n564925,polka_dot_ribbon,0,726\n1286244,rei_no_pool,0,726\n490067,holding_head,0,726\n630542,butterfly_sitting,0,725\n1379634,mmm_threesome,0,725\n1392344,white_sarong,0,724\n524545,box_art,0,724\n630404,pumpkin_hat,0,724\n287045,hanami,0,724\n1262145,hands_on_ground,0,724\n1562798,rhodes_island_logo,0,724\n495149,throwing_knife,0,723\n1404665,aqua_neckerchief,0,723\n411775,clothesline,0,723\n483853,bulletin_board,0,722\n665892,cross-laced_legwear,0,722\n1411110,keizoku_school_uniform,0,722\n441997,track_and_field,0,721\n877388,long_tail,0,721\n621690,no_mask,0,721\n678477,shared_speech_bubble,0,720\n434547,oonusa,0,720\n1601728,gladiator_sandals,0,720\n1325214,seal_(animal),0,719\n1660195,long_earlobes,0,719\n474347,affectionate,0,718\n1495060,pants_tucked_in,0,718\n1386603,orange_cape,0,717\n401696,hook,0,717\n552418,detached_hair,0,717\n1282902,solid_circle_pupils,0,716\n571097,crane_(animal),0,716\n486022,vacuum_cleaner,0,716\n409759,cleave_gag,0,716\n1657724,just_the_tip,0,716\n1597931,pancake_stack,0,716\n1419059,blue_tank_top,0,716\n82088,sewing,0,715\n449709,uneven_twintails,0,715\n1070070,fishnet_bodysuit,0,715\n1481865,brown_corset,0,715\n420279,sock_pull,0,714\n648669,lowleg_skirt,0,714\n12500,pendulum,0,714\n1441951,gold_footwear,0,714\n490118,pool_of_blood,0,713\n468109,tengu_mask,0,713\n523961,hair_between_breasts,0,713\n562139,glove_biting,0,713\n1419480,maid_day,0,713\n1260902,folded_hair,0,712\n1494953,pink-tinted_eyewear,0,712\n4410,dragonfly,0,711\n487992,inkwell,0,711\n1326625,implied_yaoi,0,711\n1273445,miracle_mallet,0,711\n547020,fidgeting,0,710\n707514,stone_stairs,0,710\n1423439,livestream,0,710\n1417085,double_horizontal_stripe,0,709\n109159,nyan,0,708\n119610,triplets,0,708\n561371,qr_code,0,708\n1329132,cherry_hair_ornament,0,708\n500191,rabbit_costume,0,707\n1247324,implied_fingering,0,707\n1466798,holding_money,0,706\n1324059,holding_jewelry,0,706\n663412,holding_own_foot,0,706\n1338731,holding_skull,0,706\n533238,milky_way,0,706\n1450600,very_wide_shot,0,706\n607013,stitched_mouth,0,706\n809507,eye_black,0,706\n675626,in_palm,0,705\n537098,watching_television,0,705\n376977,parrot,0,705\n479840,severed_limb,0,705\n1416325,multicolored_hairband,0,705\n576440,naked_cloak,0,704\n638337,multicolored_stripes,0,704\n572327,splatter,0,704\n467754,foot_hold,0,703\n876691,cum_in_container,0,703\n54531,blood_bag,0,703\n532082,knight_(chess),0,703\n1398335,year_of_the_pig,0,703\n512129,symbolism,0,702\n1386018,ooarai_(emblem),0,702\n1627128,caliburn_(fate),0,700\n1076064,covering_one_breast,0,700\n384589,lyre,0,700\n1247797,bike_shorts_under_shorts,0,700\n1532157,green_eyeshadow,0,700\n1515529,shindan_maker,0,700\n465272,kyuudou,0,699\n378206,joystick,0,699\n615409,bag_of_chips,0,699\n1426900,white_bird,0,699\n1375854,dream_soul,0,699\n393506,shrimp_tempura,0,698\n4374,meta,0,698\n1505135,italian_text,0,698\n971148,underbutt,0,698\n605322,checkered_shirt,0,698\n1353857,blood_on_arm,0,698\n1258093,ribbon-trimmed_headwear,0,697\n694148,candlelight,0,697\n1531641,light_blue_background,0,697\n580277,green_tail,0,696\n470859,purple_socks,0,696\n632045,no_jacket,0,696\n476447,kurokote,0,695\n613159,homu,0,695\n1470859,black_garter_straps,0,694\n502186,broken_window,0,694\n478554,contrast,0,694\n1258486,musical_note_print,0,694\n381761,cola,0,694\n1574670,two-sided_dress,0,694\n649072,grimoire_of_alice,0,693\n394720,afterglow,0,693\n407128,crumbs,0,693\n477060,hair_lift,0,693\n1386162,facebook_username,0,693\n411331,long_toenails,0,692\n525804,self_hug,0,692\n552753,perfume_bottle,0,692\n1401831,purple_ascot,0,691\n643172,shotgun_shell,0,691\n502652,ink_(medium),0,690\n624228,sky_print,0,690\n535487,hand_over_eye,0,690\n1631697,alternate_pectoral_size,0,689\n1263595,soap_bottle,0,688\n505038,duel_disk,0,688\n434881,baby_bottle,0,688\n1238293,grey_wings,0,687\n1749113,arthropod_limbs,0,687\n1470190,frilled_sailor_collar,0,687\n427948,tying,0,686\n397500,electric_plug,0,686\n389771,syrup,0,686\n391741,fingersmile,0,686\n2685,kickboard,0,685\n1252120,roundel,0,685\n1578028,heart_on_chest,0,685\n1370485,crescent_rose,0,685\n10240,hardhat,0,684\n433080,koi,0,684\n664262,bikini_bottom_removed,0,684\n662225,floating_book,0,683\n3513,goat,0,683\n1417605,blue_shawl,0,683\n4854,cast,0,682\n437278,masu,0,682\n168887,age_comparison,0,682\n572094,h&k_ump,0,681\n14528,bathrobe,0,680\n518347,yamakasa,0,680\n1494956,purple-tinted_eyewear,0,680\n1318748,floating_weapon,0,680\n492292,behind-the-head_headphones,0,679\n513429,watercolor_pencil_(medium),0,679\n725442,blue_bandana,0,679\n1345296,skeleton_print,0,679\n1332216,propeller_hair_ornament,0,679\n1370713,pink_umbrella,0,678\n417910,duckling,0,678\n433244,first_aid_kit,0,677\n446984,gun_to_head,0,677\n504867,digital_dissolve,0,677\n1253889,joestar_birthmark,0,677\n719987,ghost_pose,0,677\n1289258,bokura_wa_ima_no_naka_de,0,676\n389367,pineapple,0,675\n1349482,igote,0,675\n618480,sitting_on_shoulder,0,675\n1230297,single_wrist_cuff,0,675\n1513506,patchwork_clothes,0,675\n1353604,walking_on_liquid,0,674\n529805,shamoji,0,674\n547795,breaking,0,674\n410219,scylla,0,674\n720294,weasel_ears,0,674\n974485,hoodie_lift,0,674\n516577,groom,0,673\n1867868,motosu_school_uniform,0,673\n374958,cyrillic,0,672\n622480,food_art,0,671\n451855,studded_collar,0,671\n1556083,camouflage_headwear,0,671\n510646,blue_pupils,0,670\n489491,platform_boots,0,670\n14645,marshmallow,0,670\n403,chikan,0,669\n1344657,cat_bag,0,669\n473987,tooth,0,669\n1253133,spade_hair_ornament,0,669\n403466,medallion,0,668\n1240072,hair_color_connection,0,668\n381685,pig_ears,0,668\n565798,crazy_straw,0,668\n1574664,two-sided_skirt,0,668\n616835,calligraphy_brush_(medium),0,668\n1455458,brown_flower,0,668\n191640,ak-47,0,667\n378288,blob,0,667\n542882,against_railing,0,667\n1268234,alternate_hair_ornament,0,667\n1274150,print_sleeves,0,667\n449449,beretta_92,0,666\n556112,red_tail,0,666\n571640,jacket_over_swimsuit,0,666\n381749,tamagoyaki,0,666\n657138,cat_mask,0,666\n646318,necktie_removed,0,666\n413830,wakamezake,0,665\n537572,teeth_hold,0,665\n496893,checkered_dress,0,665\n1429073,holding_beachball,0,664\n1433301,two-tone_leotard,0,664\n1331283,bridal_legwear,0,664\n1494960,yellow-tinted_eyewear,0,664\n510173,easter,0,664\n690728,missile_pod,0,663\n396000,bowler_hat,0,663\n561682,clownfish,0,663\n412041,biwa_lute,0,663\n4947,groceries,0,662\n1243501,gibson_les_paul,0,662\n1322934,gesugao,0,662\n1578455,cumulonimbus_cloud,0,662\n589932,negative_space,0,661\n423622,pelt,0,661\n1609367,tail_around_leg,0,661\n1391188,hand_tattoo,0,661\n1684976,craft_essence_(fate),0,661\n590202,pinstripe_suit,0,660\n1453787,golden_arms,0,660\n1799877,tracen_swimsuit,0,660\n1401249,grey_leotard,0,659\n727812,loaded_interior,0,659\n3927,gao,0,659\n1252918,sitting_on_table,0,659\n1317359,floating_clothes,0,659\n673705,hand_rest,0,659\n1440820,usekh_collar,0,659\n1328313,shell_hair_ornament,0,658\n1325319,wrist_bow,0,658\n1402775,white_mask,0,658\n1391449,cropped_sweater,0,658\n551086,protecting,0,657\n1333942,exposed_pocket,0,657\n609075,red_mask,0,656\n1231690,slim_legs,0,655\n1425321,hogwarts_school_uniform,0,655\n457678,animal_slippers,0,655\n1249968,little_red_riding_hood_(grimm)_(cosplay),0,655\n1435997,eye_trail,0,655\n528408,soft_serve,0,654\n613600,checkered_legwear,0,654\n1435395,grey_tank_top,0,653\n1583008,side-tie_peek,0,653\n1861772,crisis_management_form_(machimazo),0,653\n647108,flying_teardrops,0,652\n666041,multiple_torii,0,652\n546978,two-finger_salute,0,652\n529223,cellphone_charm,0,652\n1297290,split_theme,0,652\n1460120,fur-trimmed_footwear,0,652\n1327283,cracked_floor,0,651\n563545,braiding_hair,0,650\n791840,blue_umbrella,0,650\n1328442,green_belt,0,649\n658222,stuffed_penguin,0,649\n1497365,pov_doorway,0,649\n728040,flat_chest_grab,0,648\n524807,unfastened,0,647\n172018,nail_bat,0,647\n11665,seatbelt,0,646\n723518,arms_between_legs,0,645\n522469,centauroid,0,642\n562074,plaid_ribbon,0,641\n679788,wet_towel,0,641\n1782264,sticker_on_face,0,641\n1714871,midriff_sarashi,0,640\n1684568,paint_splatter_on_face,0,640\n464635,cattail,0,640\n707905,object_on_breast,0,640\n1329330,bunny_day,0,640\n1881204,starlight_academy_school_uniform,0,639\n655737,pants_under_skirt,0,636\n1442516,paw_print_pattern,0,636\n518157,peony_(flower),0,635\n1427573,brown_sleeves,0,635\n551767,pastry_bag,0,633\n1012946,breasts_on_table,0,633\n449872,walther,0,632\n1535711,cross_tie,0,632\n543243,chrysanthemum,0,631\n1407354,brown_neckerchief,0,629\n1468297,sixteenth_note,0,629\n647474,stuffed_dog,0,628\n1325576,four-leaf_clover_hair_ornament,0,628\n1397372,year_of_the_rooster,0,628\n549834,person_on_head,0,628\n1588824,lifebuoy_ornament,0,628\n492648,yellow_socks,0,626\n651505,animal_on_hand,0,625\n1414486,red_mittens,0,625\n1291060,rabbit_on_head,0,623\n1672268,qingxin_flower,0,618\n385430,hatsune_miku,4,78616\n12239,hakurei_reimu,4,67710\n12242,kirisame_marisa,4,62327\n12248,remilia_scarlet,4,46894\n12249,flandre_scarlet,4,43434\n12314,izayoi_sakuya,4,42465\n1301082,admiral_(kancolle),4,34812\n1478495,artoria_pendragon_(fate),4,33041\n9123,alice_margatroid,4,32516\n388200,kochiya_sanae,4,32504\n12247,patchouli_knowledge,4,32120\n12244,konpaku_youmu,4,31146\n1720,cirno,4,30904\n12246,yakumo_yukari,4,29607\n427142,komeiji_koishi,4,27406\n12308,shameimaru_aya,4,25978\n12307,fujiwara_no_mokou,4,24550\n12304,reisen_udongein_inaba,4,24375\n11374,hong_meiling,4,23457\n592924,akemi_homura,4,23123\n427143,komeiji_satori,4,22757\n592925,kaname_madoka,4,22080\n12245,saigyouji_yuyuko,4,21758\n1631074,kaga_(kancolle),4,20549\n395218,inubashiri_momiji,4,20497\n12250,yakumo_ran,4,18043\n390427,kagamine_rin,4,17416\n602257,konpaku_youmu_(ghost),4,17065\n384842,moriya_suwako,4,17047\n2082,rumia,4,16841\n593109,miki_sayaka,4,16686\n427145,kaenbyou_rin,4,16675\n401582,kazami_yuuka,4,16551\n1275729,shimakaze_(kancolle),4,16285\n427184,reiuji_utsuho,4,16263\n6,saber,4,15967\n1275718,hibiki_(kancolle),4,15954\n474,chen,4,15887\n12302,kamishirasawa_keine,4,15388\n473327,tatara_kogasa,4,15022\n383392,kawashiro_nitori,4,14973\n1414209,mash_kyrielight,4,14919\n415326,hinanawi_tenshi,4,14700\n589430,sakura_kyouko,4,14333\n593108,tomoe_mami,4,14245\n1275728,shigure_(kancolle),4,14238\n12306,houraisan_kaguya,4,13824\n1799,koakuma,4,13641\n1350122,kongou_(kancolle),4,13106\n1602203,ganyu_(genshin_impact),4,12981\n12300,mystia_lorelei,4,12948\n12303,inaba_tewi,4,12571\n12313,ibuki_suika,4,12550\n1275720,inazuma_(kancolle),4,12537\n475833,souryuu_asuka_langley,4,12338\n503349,hijiri_byakuren,4,12211\n1630449,akagi_(kancolle),4,12210\n473267,nazrin,4,11834\n393597,kagamine_len,4,11428\n503343,houjuu_nue,4,11360\n1270870,tenryuu_(kancolle),4,11216\n1473623,tamamo_(fate),4,11128\n1631504,yuudachi_(kancolle),4,10901\n12305,yagokoro_eirin,4,10879\n384841,yasaka_kanako,4,10876\n457810,megurine_luka,4,10737\n1275719,ikazuchi_(kancolle),4,10591\n1479739,jeanne_d'arc_alter_(fate),4,10481\n413783,mizuhashi_parsee,4,10406\n1471064,abigail_williams_(fate),4,10091\n1275715,fubuki_(kancolle),4,10071\n456150,akiyama_mio,4,9944\n1275711,akatsuki_(kancolle),4,9910\n1631523,zuikaku_(kancolle),4,9855\n1391754,fujimaru_ritsuka_(female),4,9711\n1391753,fujimaru_ritsuka_(male),4,9444\n638495,toyosatomimi_no_miko,4,9247\n1631219,nagato_(kancolle),4,9212\n1630699,hamakaze_(kancolle),4,9178\n13814,morichika_rinnosuke,4,9131\n1335350,haruna_(kancolle),4,9124\n9466,suzumiya_haruhi,4,8980\n7441,fate_testarossa,4,8966\n6313,link,4,8924\n465977,hoshiguma_yuugi,4,8846\n3861,pikachu,4,8817\n1686237,raiden_shogun,4,8798\n1433170,nero_claudius_(fate),4,8774\n1351818,kashima_(kancolle),4,8665\n472387,nakano_azusa,4,8571\n1593594,gawr_gura,4,8530\n1533309,houshou_marine,4,8514\n384197,kagiyama_hina,4,8503\n465646,tohsaka_rin,4,8476\n599169,shanghai_doll,4,8466\n9715,nagato_yuki,4,8457\n700405,nishizumi_miho,4,8294\n12299,wriggle_nightbug,4,8246\n1333465,rem_(re:zero),4,8222\n7493,daiyousei,4,8196\n638499,mononobe_no_futo,4,8163\n1728232,avatar_(ff14),4,8144\n1631280,ryuujou_(kancolle),4,8080\n455424,hirasawa_yui,4,8070\n465688,shiki_eiki,4,7982\n600200,kyubey,4,7934\n12311,onozuka_komachi,4,7924\n1380245,suzuya_(kancolle),4,7856\n1452630,scathach_(fate),4,7702\n12342,usami_renko,4,7645\n1581467,lumine_(genshin_impact),4,7630\n395918,misaka_mikoto,4,7624\n391888,tifa_lockhart,4,7606\n1277919,inkling,4,7466\n8552,takamachi_nanoha,4,7465\n1401122,yorha_no._2_type_b,4,7437\n503351,toramaru_shou,4,7431\n12108,illyasviel_von_einzbern,4,7414\n1631004,houshou_(kancolle),4,7348\n506041,maribel_hearn,4,7317\n1233711,imaizumi_kagerou,4,7306\n8327,ayanami_rei,4,7267\n1639113,shigure_kai_ni_(kancolle),4,7221\n1408636,jeanne_d'arc_(fate),4,7191\n378876,joseph_joestar,4,7142\n1639135,yuudachi_kai_ni_(kancolle),4,7052\n1231258,matoi_ryuuko,4,7016\n456151,tainaka_ritsu,4,6997\n1635696,hu_tao_(genshin_impact),4,6878\n452135,producer_(idolmaster),4,6859\n1275723,murakumo_(kancolle),4,6824\n1405000,serval_(kemono_friends),4,6806\n592977,nishikino_maki,4,6768\n1765420,jeanne_d'arc_alter_(avenger)_(fate),4,6725\n593046,sonoda_umi,4,6721\n1277794,amatsukaze_(kancolle),4,6636\n544560,himekaidou_hatate,4,6634\n1585358,northern_ocean_princess,4,6621\n1631456,ushio_(kancolle),4,6609\n662185,shibuya_rin,4,6569\n615562,toujou_nozomi,4,6537\n94496,princess_zelda,4,6535\n1600114,zhongli_(genshin_impact),4,6477\n1438816,okita_souji_(fate),4,6473\n1285206,atago_(kancolle),4,6461\n638538,kaku_seiga,4,6444\n1275734,yukikaze_(kancolle),4,6441\n1631360,shoukaku_(kancolle),4,6390\n503350,murasa_minamitsu,4,6356\n1511608,inkling_girl,4,6321\n592974,yazawa_nico,4,6312\n713730,kafuu_chino,4,6283\n593043,ayase_eli,4,6236\n1682530,tamamo_no_mae_(fate/extra),4,6180\n1631488,yamato_(kancolle),4,6121\n466373,dawn_(pokemon),4,6100\n1479836,shirakami_fubuki,4,6085\n413645,nagae_iku,4,6078\n1523054,manjuu_(azur_lane),4,6061\n1531173,usada_pekora,4,6052\n487910,joseph_joestar_(young),4,6030\n1275712,akebono_(kancolle),4,6011\n1233709,hata_no_kokoro,4,5911\n1631124,kitakami_(kancolle),4,5907\n54494,kujo_jotaro,4,5902\n1649146,gilgamesh_(fate),4,5822\n570051,ibaraki_kasen,4,5806\n1287532,megumin,4,5792\n669326,kaito_(vocaloid),4,5790\n1239690,rensouhou-chan,4,5717\n1369674,lillie_(pokemon),4,5687\n7438,matou_sakura,4,5678\n1596257,mona_(genshin_impact),4,5668\n1733099,yae_miko,4,5664\n1435530,shuten_douji_(fate),4,5656\n473328,kumoi_ichirin,4,5646\n413652,kurodani_yamame,4,5643\n619180,kasodani_kyouko,4,5610\n638583,soga_no_tojiko,4,5592\n619181,miyako_yoshika,4,5551\n1427460,prinz_eugen_(kancolle),4,5516\n101752,hoshii_miki,4,5496\n1631257,ooyodo_(kancolle),4,5453\n456152,kotobuki_tsumugi,4,5431\n1600563,keqing_(genshin_impact),4,5414\n1448304,astolfo_(fate),4,5413\n1630648,female_admiral_(kancolle),4,5408\n13108,chun-li,4,5374\n1372463,darjeeling_(girls_und_panzer),4,5362\n382074,hiiragi_kagami,4,5341\n1361469,mutsu_(kancolle),4,5321\n1631410,tatsuta_(kancolle),4,5315\n1478494,cu_chulainn_(fate),4,5269\n1630515,asashio_(kancolle),4,5250\n466360,may_(pokemon),4,5242\n1532746,byleth_(fire_emblem),4,5192\n1243755,kijin_seija,4,5186\n1424365,pyra_(xenoblade),4,5178\n7439,kinomoto_sakura,4,5176\n1593595,mori_calliope,4,5176\n1328621,clownpiece,4,5157\n713720,nishizumi_maho,4,5153\n1593596,ninomae_ina'nis,4,5129\n593045,minami_kotori,4,5119\n717860,rosa_(pokemon),4,5119\n1405300,kaban_(kemono_friends),4,5109\n1648389,yor_briar,4,5092\n10658,emiya_shirou,4,5021\n1495213,minato_aqua,4,5017\n713721,itsumi_erika,4,4979\n466669,c.c.,4,4976\n710903,meiko_(vocaloid),4,4975\n11941,samus_aran,4,4963\n383424,aki_minoriko,4,4963\n615290,hilda_(pokemon),4,4950\n1630531,bismarck_(kancolle),4,4949\n1646740,archer_(fate),4,4941\n1448306,mordred_(fate),4,4940\n1599785,aether_(genshin_impact),4,4932\n1631313,sendai_(kancolle),4,4923\n1533332,marnie_(pokemon),4,4923\n1464562,minamoto_no_raikou_(fate),4,4918\n1631252,ooi_(kancolle),4,4907\n656409,yuzuki_yukari,4,4892\n382075,izumi_konata,4,4858\n452021,hieda_no_akyuu,4,4837\n434767,caesar_anthonio_zeppeli,4,4796\n1350123,akashi_(kancolle),4,4794\n1233706,sekibanki,4,4768\n9071,amami_haruka,4,4760\n727352,anchovy_(girls_und_panzer),4,4757\n1507496,takao_(kancolle),4,4740\n1293664,musashi_(kancolle),4,4740\n699794,nanami_chiaki,4,4735\n1328650,junko_(touhou),4,4731\n510663,yoko_littner,4,4679\n1590956,mythra_(xenoblade),4,4644\n1682533,nero_claudius_(fate/extra),4,4616\n1734279,okita_souji_(koha-ace),4,4610\n1442931,amiya_(arknights),4,4608\n1275730,shiranui_(kancolle),4,4605\n7866,tsukino_usagi,4,4576\n1339317,d.va_(overwatch),4,4559\n593044,kousaka_honoka,4,4558\n638500,futatsuiwa_mamizou,4,4557\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,681\n1227600,mochizuki_anna,4,681\n1275005,ousaka_shizuku,4,681\n1782428,bb_(swimsuit_mooncancer)_(third_ascension)_(fate),4,679\n1693037,le_malin_(listless_lapin)_(azur_lane),4,679\n1768854,mysterious_heroine_x_alter_(first_ascension)_(fate),4,678\n1631348,shinshuu_maru_(kancolle),4,678\n480982,asbel_lhant,4,677\n1316228,nakagawa_natsuki,4,677\n1667164,g41_(girls'_frontline),4,677\n404920,sage_(dq3),4,676\n1331206,kawakami_mai,4,676\n1423942,eris_greyrat,4,676\n1560517,utage_(arknights),4,676\n1405059,sand_cat_(kemono_friends),4,675\n1680295,mika_(blue_archive),4,675\n664407,himekawa_yuki,4,674\n1631118,kishinami_(kancolle),4,674\n1285963,alena_(dq4),4,672\n1645008,smart_falcon_(umamusume),4,672\n497689,cheria_barnes,4,671\n1631097,kasuga_maru_(kancolle),4,671\n1432300,yatadera_narumi,4,669\n1387122,mordred_(swimsuit_rider)_(fate),4,667\n1529378,sakata_kintoki_(fate),4,666\n1408392,golden_snub-nosed_monkey_(kemono_friends),4,666\n1780213,hina_(swimsuit)_(blue_archive),4,666\n1875916,tiki_(adult)_(fire_emblem),4,665\n1386436,amami_rantarou,4,664\n548087,hasegawa_kobato,4,663\n1257848,hoshimiya_kate,4,663\n1335159,sugimoto_saichi,4,663\n596743,fukawa_touko,4,662\n1476820,yozora_mel,4,662\n1789595,saijo_juri,4,661\n1327917,yuel_(granblue_fantasy),4,660\n1460549,nekomiya_hinata,4,660\n688953,igarashi_kyoko,4,658\n1639090,maya_kai_ni_(kancolle),4,658\n1588338,tomoe_gozen_(swimsuit_saber)_(fate),4,658\n1657407,blue_poison_(shoal_beat)_(arknights),4,658\n1856336,hasumi_(gym_uniform)_(blue_archive),4,656\n1598223,kayoko_(blue_archive),4,655\n1434774,uehara_himari,4,654\n1344209,roxy_migurdia,4,653\n1837736,saren_(summer)_(princess_connect!),4,653\n1485459,jean_bart_(azur_lane),4,653\n713347,katagiri_sanae,4,652\n1440285,queen_elizabeth_(azur_lane),4,652\n1667205,9a-91_(girls'_frontline),4,651\n1479271,rengoku_kyoujurou,4,650\n1386290,keebo,4,649\n1599618,whislash_(arknights),4,646\n971934,vampy,4,645\n1639099,naganami_kai_ni_(kancolle),4,644\n1326283,jeanne_d'arc_(granblue_fantasy),4,643\n1644991,matikanefukukitaru_(umamusume),4,643\n1645010,sweep_tosho_(umamusume),4,642\n879608,toyokawa_fuka,4,641\n1387119,kiyohime_(swimsuit_lancer)_(fate),4,641\n1836895,karyl_(summer)_(princess_connect!),4,641\n1596254,razor_(genshin_impact),4,640\n1480768,siro_(dennou_shoujo_youtuber_siro),4,639\n1491126,tsuyuri_kanao,4,637\n1667169,negev_(girls'_frontline),4,636\n1440310,eldridge_(azur_lane),4,636\n1284942,midway_princess,4,635\n1420829,matsubara_kanon,4,633\n1541801,magallan_(arknights),4,633\n1782915,ijichi_nijika,4,632\n1411715,okusawa_misaki,4,628\n1875918,tiki_(young)_(fire_emblem),4,627\n1790615,spicy_nun_(diva),4,625\n1067206,hayasaka_mirei,4,623\n1781831,jeanne_d'arc_(swimsuit_archer)_(second_ascension)_(fate),4,623\n1420746,udagawa_tomoe,4,621\n1445024,shirasagi_chisato,4,621\n1678878,la_pluma_(arknights),4,619\n1265247,sato_shin,4,617\n1491428,takamiya_rion,4,612\n1623487,crypto_(apex_legends),4,612\n1533772,uzuki_sayaka,4,607\n1533771,kumada_masaru,4,600\n"
  },
  {
    "path": "models/configs/anything_v3.yaml",
    "content": "model:\n  base_learning_rate: 1.0e-04\n  target: ldm.models.diffusion.ddpm.LatentDiffusion\n  params:\n    linear_start: 0.00085\n    linear_end: 0.0120\n    num_timesteps_cond: 1\n    log_every_t: 200\n    timesteps: 1000\n    first_stage_key: \"jpg\"\n    cond_stage_key: \"txt\"\n    image_size: 64\n    channels: 4\n    cond_stage_trainable: false   # Note: different from the one we trained before\n    conditioning_key: crossattn\n    monitor: val/loss_simple_ema\n    scale_factor: 0.18215\n    use_ema: False\n\n    scheduler_config: # 10000 warmup steps\n      target: ldm.lr_scheduler.LambdaLinearScheduler\n      params:\n        warm_up_steps: [ 10000 ]\n        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases\n        f_start: [ 1.e-6 ]\n        f_max: [ 1. ]\n        f_min: [ 1. ]\n\n    unet_config:\n      target: ldm.modules.diffusionmodules.openaimodel.UNetModel\n      params:\n        image_size: 32 # unused\n        in_channels: 4\n        out_channels: 4\n        model_channels: 320\n        attention_resolutions: [ 4, 2, 1 ]\n        num_res_blocks: 2\n        channel_mult: [ 1, 2, 4, 4 ]\n        num_heads: 8\n        use_spatial_transformer: True\n        transformer_depth: 1\n        context_dim: 768\n        use_checkpoint: True\n        legacy: False\n\n    first_stage_config:\n      target: ldm.models.autoencoder.AutoencoderKL\n      params:\n        embed_dim: 4\n        monitor: val/rec_loss\n        ddconfig:\n          double_z: true\n          z_channels: 4\n          resolution: 256\n          in_channels: 3\n          out_ch: 3\n          ch: 128\n          ch_mult:\n          - 1\n          - 2\n          - 4\n          - 4\n          num_res_blocks: 2\n          attn_resolutions: []\n          dropout: 0.0\n        lossconfig:\n          target: torch.nn.Identity\n\n    cond_stage_config:\n      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder\n      params:\n        layer: \"hidden\"\n        layer_idx: -2\n"
  },
  {
    "path": "models/configs/v1-inference.yaml",
    "content": "model:\n  base_learning_rate: 1.0e-04\n  target: ldm.models.diffusion.ddpm.LatentDiffusion\n  params:\n    linear_start: 0.00085\n    linear_end: 0.0120\n    num_timesteps_cond: 1\n    log_every_t: 200\n    timesteps: 1000\n    first_stage_key: \"jpg\"\n    cond_stage_key: \"txt\"\n    image_size: 64\n    channels: 4\n    cond_stage_trainable: false   # Note: different from the one we trained before\n    conditioning_key: crossattn\n    monitor: val/loss_simple_ema\n    scale_factor: 0.18215\n    use_ema: False\n\n    scheduler_config: # 10000 warmup steps\n      target: ldm.lr_scheduler.LambdaLinearScheduler\n      params:\n        warm_up_steps: [ 10000 ]\n        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases\n        f_start: [ 1.e-6 ]\n        f_max: [ 1. ]\n        f_min: [ 1. ]\n\n    unet_config:\n      target: ldm.modules.diffusionmodules.openaimodel.UNetModel\n      params:\n        image_size: 32 # unused\n        in_channels: 4\n        out_channels: 4\n        model_channels: 320\n        attention_resolutions: [ 4, 2, 1 ]\n        num_res_blocks: 2\n        channel_mult: [ 1, 2, 4, 4 ]\n        num_heads: 8\n        use_spatial_transformer: True\n        transformer_depth: 1\n        context_dim: 768\n        use_checkpoint: True\n        legacy: False\n\n    first_stage_config:\n      target: ldm.models.autoencoder.AutoencoderKL\n      params:\n        embed_dim: 4\n        monitor: val/rec_loss\n        ddconfig:\n          double_z: true\n          z_channels: 4\n          resolution: 256\n          in_channels: 3\n          out_ch: 3\n          ch: 128\n          ch_mult:\n          - 1\n          - 2\n          - 4\n          - 4\n          num_res_blocks: 2\n          attn_resolutions: []\n          dropout: 0.0\n        lossconfig:\n          target: torch.nn.Identity\n\n    cond_stage_config:\n      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder\n"
  },
  {
    "path": "models/configs/v1-inference_clip_skip_2.yaml",
    "content": "model:\n  base_learning_rate: 1.0e-04\n  target: ldm.models.diffusion.ddpm.LatentDiffusion\n  params:\n    linear_start: 0.00085\n    linear_end: 0.0120\n    num_timesteps_cond: 1\n    log_every_t: 200\n    timesteps: 1000\n    first_stage_key: \"jpg\"\n    cond_stage_key: \"txt\"\n    image_size: 64\n    channels: 4\n    cond_stage_trainable: false   # Note: different from the one we trained before\n    conditioning_key: crossattn\n    monitor: val/loss_simple_ema\n    scale_factor: 0.18215\n    use_ema: False\n\n    scheduler_config: # 10000 warmup steps\n      target: ldm.lr_scheduler.LambdaLinearScheduler\n      params:\n        warm_up_steps: [ 10000 ]\n        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases\n        f_start: [ 1.e-6 ]\n        f_max: [ 1. ]\n        f_min: [ 1. ]\n\n    unet_config:\n      target: ldm.modules.diffusionmodules.openaimodel.UNetModel\n      params:\n        image_size: 32 # unused\n        in_channels: 4\n        out_channels: 4\n        model_channels: 320\n        attention_resolutions: [ 4, 2, 1 ]\n        num_res_blocks: 2\n        channel_mult: [ 1, 2, 4, 4 ]\n        num_heads: 8\n        use_spatial_transformer: True\n        transformer_depth: 1\n        context_dim: 768\n        use_checkpoint: True\n        legacy: False\n\n    first_stage_config:\n      target: ldm.models.autoencoder.AutoencoderKL\n      params:\n        embed_dim: 4\n        monitor: val/rec_loss\n        ddconfig:\n          double_z: true\n          z_channels: 4\n          resolution: 256\n          in_channels: 3\n          out_ch: 3\n          ch: 128\n          ch_mult:\n          - 1\n          - 2\n          - 4\n          - 4\n          num_res_blocks: 2\n          attn_resolutions: []\n          dropout: 0.0\n        lossconfig:\n          target: torch.nn.Identity\n\n    cond_stage_config:\n      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder\n      params:\n        layer: \"hidden\"\n        layer_idx: -2\n"
  },
  {
    "path": "models/configs/v1-inference_clip_skip_2_fp16.yaml",
    "content": "model:\n  base_learning_rate: 1.0e-04\n  target: ldm.models.diffusion.ddpm.LatentDiffusion\n  params:\n    linear_start: 0.00085\n    linear_end: 0.0120\n    num_timesteps_cond: 1\n    log_every_t: 200\n    timesteps: 1000\n    first_stage_key: \"jpg\"\n    cond_stage_key: \"txt\"\n    image_size: 64\n    channels: 4\n    cond_stage_trainable: false   # Note: different from the one we trained before\n    conditioning_key: crossattn\n    monitor: val/loss_simple_ema\n    scale_factor: 0.18215\n    use_ema: False\n\n    scheduler_config: # 10000 warmup steps\n      target: ldm.lr_scheduler.LambdaLinearScheduler\n      params:\n        warm_up_steps: [ 10000 ]\n        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases\n        f_start: [ 1.e-6 ]\n        f_max: [ 1. ]\n        f_min: [ 1. ]\n\n    unet_config:\n      target: ldm.modules.diffusionmodules.openaimodel.UNetModel\n      params:\n        use_fp16: True\n        image_size: 32 # unused\n        in_channels: 4\n        out_channels: 4\n        model_channels: 320\n        attention_resolutions: [ 4, 2, 1 ]\n        num_res_blocks: 2\n        channel_mult: [ 1, 2, 4, 4 ]\n        num_heads: 8\n        use_spatial_transformer: True\n        transformer_depth: 1\n        context_dim: 768\n        use_checkpoint: True\n        legacy: False\n\n    first_stage_config:\n      target: ldm.models.autoencoder.AutoencoderKL\n      params:\n        embed_dim: 4\n        monitor: val/rec_loss\n        ddconfig:\n          double_z: true\n          z_channels: 4\n          resolution: 256\n          in_channels: 3\n          out_ch: 3\n          ch: 128\n          ch_mult:\n          - 1\n          - 2\n          - 4\n          - 4\n          num_res_blocks: 2\n          attn_resolutions: []\n          dropout: 0.0\n        lossconfig:\n          target: torch.nn.Identity\n\n    cond_stage_config:\n      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder\n      params:\n        layer: \"hidden\"\n        layer_idx: -2\n"
  },
  {
    "path": "models/configs/v1-inference_fp16.yaml",
    "content": "model:\n  base_learning_rate: 1.0e-04\n  target: ldm.models.diffusion.ddpm.LatentDiffusion\n  params:\n    linear_start: 0.00085\n    linear_end: 0.0120\n    num_timesteps_cond: 1\n    log_every_t: 200\n    timesteps: 1000\n    first_stage_key: \"jpg\"\n    cond_stage_key: \"txt\"\n    image_size: 64\n    channels: 4\n    cond_stage_trainable: false   # Note: different from the one we trained before\n    conditioning_key: crossattn\n    monitor: val/loss_simple_ema\n    scale_factor: 0.18215\n    use_ema: False\n\n    scheduler_config: # 10000 warmup steps\n      target: ldm.lr_scheduler.LambdaLinearScheduler\n      params:\n        warm_up_steps: [ 10000 ]\n        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases\n        f_start: [ 1.e-6 ]\n        f_max: [ 1. ]\n        f_min: [ 1. ]\n\n    unet_config:\n      target: ldm.modules.diffusionmodules.openaimodel.UNetModel\n      params:\n        use_fp16: True\n        image_size: 32 # unused\n        in_channels: 4\n        out_channels: 4\n        model_channels: 320\n        attention_resolutions: [ 4, 2, 1 ]\n        num_res_blocks: 2\n        channel_mult: [ 1, 2, 4, 4 ]\n        num_heads: 8\n        use_spatial_transformer: True\n        transformer_depth: 1\n        context_dim: 768\n        use_checkpoint: True\n        legacy: False\n\n    first_stage_config:\n      target: ldm.models.autoencoder.AutoencoderKL\n      params:\n        embed_dim: 4\n        monitor: val/rec_loss\n        ddconfig:\n          double_z: true\n          z_channels: 4\n          resolution: 256\n          in_channels: 3\n          out_ch: 3\n          ch: 128\n          ch_mult:\n          - 1\n          - 2\n          - 4\n          - 4\n          num_res_blocks: 2\n          attn_resolutions: []\n          dropout: 0.0\n        lossconfig:\n          target: torch.nn.Identity\n\n    cond_stage_config:\n      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder\n"
  },
  {
    "path": "models/configs/v1-inpainting-inference.yaml",
    "content": "model:\n  base_learning_rate: 7.5e-05\n  target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion\n  params:\n    linear_start: 0.00085\n    linear_end: 0.0120\n    num_timesteps_cond: 1\n    log_every_t: 200\n    timesteps: 1000\n    first_stage_key: \"jpg\"\n    cond_stage_key: \"txt\"\n    image_size: 64\n    channels: 4\n    cond_stage_trainable: false   # Note: different from the one we trained before\n    conditioning_key: hybrid   # important\n    monitor: val/loss_simple_ema\n    scale_factor: 0.18215\n    finetune_keys: null\n\n    scheduler_config: # 10000 warmup steps\n      target: ldm.lr_scheduler.LambdaLinearScheduler\n      params:\n        warm_up_steps: [ 2500 ] # NOTE for resuming. use 10000 if starting from scratch\n        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases\n        f_start: [ 1.e-6 ]\n        f_max: [ 1. ]\n        f_min: [ 1. ]\n\n    unet_config:\n      target: ldm.modules.diffusionmodules.openaimodel.UNetModel\n      params:\n        image_size: 32 # unused\n        in_channels: 9  # 4 data + 4 downscaled image + 1 mask\n        out_channels: 4\n        model_channels: 320\n        attention_resolutions: [ 4, 2, 1 ]\n        num_res_blocks: 2\n        channel_mult: [ 1, 2, 4, 4 ]\n        num_heads: 8\n        use_spatial_transformer: True\n        transformer_depth: 1\n        context_dim: 768\n        use_checkpoint: True\n        legacy: False\n\n    first_stage_config:\n      target: ldm.models.autoencoder.AutoencoderKL\n      params:\n        embed_dim: 4\n        monitor: val/rec_loss\n        ddconfig:\n          double_z: true\n          z_channels: 4\n          resolution: 256\n          in_channels: 3\n          out_ch: 3\n          ch: 128\n          ch_mult:\n          - 1\n          - 2\n          - 4\n          - 4\n          num_res_blocks: 2\n          attn_resolutions: []\n          dropout: 0.0\n        lossconfig:\n          target: torch.nn.Identity\n\n    cond_stage_config:\n      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder\n\n"
  },
  {
    "path": "models/configs/v2-inference-v.yaml",
    "content": "model:\n  base_learning_rate: 1.0e-4\n  target: ldm.models.diffusion.ddpm.LatentDiffusion\n  params:\n    parameterization: \"v\"\n    linear_start: 0.00085\n    linear_end: 0.0120\n    num_timesteps_cond: 1\n    log_every_t: 200\n    timesteps: 1000\n    first_stage_key: \"jpg\"\n    cond_stage_key: \"txt\"\n    image_size: 64\n    channels: 4\n    cond_stage_trainable: false\n    conditioning_key: crossattn\n    monitor: val/loss_simple_ema\n    scale_factor: 0.18215\n    use_ema: False # we set this to false because this is an inference only config\n\n    unet_config:\n      target: ldm.modules.diffusionmodules.openaimodel.UNetModel\n      params:\n        use_checkpoint: True\n        use_fp16: True\n        image_size: 32 # unused\n        in_channels: 4\n        out_channels: 4\n        model_channels: 320\n        attention_resolutions: [ 4, 2, 1 ]\n        num_res_blocks: 2\n        channel_mult: [ 1, 2, 4, 4 ]\n        num_head_channels: 64 # need to fix for flash-attn\n        use_spatial_transformer: True\n        use_linear_in_transformer: True\n        transformer_depth: 1\n        context_dim: 1024\n        legacy: False\n\n    first_stage_config:\n      target: ldm.models.autoencoder.AutoencoderKL\n      params:\n        embed_dim: 4\n        monitor: val/rec_loss\n        ddconfig:\n          #attn_type: \"vanilla-xformers\"\n          double_z: true\n          z_channels: 4\n          resolution: 256\n          in_channels: 3\n          out_ch: 3\n          ch: 128\n          ch_mult:\n          - 1\n          - 2\n          - 4\n          - 4\n          num_res_blocks: 2\n          attn_resolutions: []\n          dropout: 0.0\n        lossconfig:\n          target: torch.nn.Identity\n\n    cond_stage_config:\n      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder\n      params:\n        freeze: True\n        layer: \"penultimate\"\n"
  },
  {
    "path": "models/configs/v2-inference-v_fp32.yaml",
    "content": "model:\n  base_learning_rate: 1.0e-4\n  target: ldm.models.diffusion.ddpm.LatentDiffusion\n  params:\n    parameterization: \"v\"\n    linear_start: 0.00085\n    linear_end: 0.0120\n    num_timesteps_cond: 1\n    log_every_t: 200\n    timesteps: 1000\n    first_stage_key: \"jpg\"\n    cond_stage_key: \"txt\"\n    image_size: 64\n    channels: 4\n    cond_stage_trainable: false\n    conditioning_key: crossattn\n    monitor: val/loss_simple_ema\n    scale_factor: 0.18215\n    use_ema: False # we set this to false because this is an inference only config\n\n    unet_config:\n      target: ldm.modules.diffusionmodules.openaimodel.UNetModel\n      params:\n        use_checkpoint: True\n        use_fp16: False\n        image_size: 32 # unused\n        in_channels: 4\n        out_channels: 4\n        model_channels: 320\n        attention_resolutions: [ 4, 2, 1 ]\n        num_res_blocks: 2\n        channel_mult: [ 1, 2, 4, 4 ]\n        num_head_channels: 64 # need to fix for flash-attn\n        use_spatial_transformer: True\n        use_linear_in_transformer: True\n        transformer_depth: 1\n        context_dim: 1024\n        legacy: False\n\n    first_stage_config:\n      target: ldm.models.autoencoder.AutoencoderKL\n      params:\n        embed_dim: 4\n        monitor: val/rec_loss\n        ddconfig:\n          #attn_type: \"vanilla-xformers\"\n          double_z: true\n          z_channels: 4\n          resolution: 256\n          in_channels: 3\n          out_ch: 3\n          ch: 128\n          ch_mult:\n          - 1\n          - 2\n          - 4\n          - 4\n          num_res_blocks: 2\n          attn_resolutions: []\n          dropout: 0.0\n        lossconfig:\n          target: torch.nn.Identity\n\n    cond_stage_config:\n      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder\n      params:\n        freeze: True\n        layer: \"penultimate\"\n"
  },
  {
    "path": "models/configs/v2-inference.yaml",
    "content": "model:\n  base_learning_rate: 1.0e-4\n  target: ldm.models.diffusion.ddpm.LatentDiffusion\n  params:\n    linear_start: 0.00085\n    linear_end: 0.0120\n    num_timesteps_cond: 1\n    log_every_t: 200\n    timesteps: 1000\n    first_stage_key: \"jpg\"\n    cond_stage_key: \"txt\"\n    image_size: 64\n    channels: 4\n    cond_stage_trainable: false\n    conditioning_key: crossattn\n    monitor: val/loss_simple_ema\n    scale_factor: 0.18215\n    use_ema: False # we set this to false because this is an inference only config\n\n    unet_config:\n      target: ldm.modules.diffusionmodules.openaimodel.UNetModel\n      params:\n        use_checkpoint: True\n        use_fp16: True\n        image_size: 32 # unused\n        in_channels: 4\n        out_channels: 4\n        model_channels: 320\n        attention_resolutions: [ 4, 2, 1 ]\n        num_res_blocks: 2\n        channel_mult: [ 1, 2, 4, 4 ]\n        num_head_channels: 64 # need to fix for flash-attn\n        use_spatial_transformer: True\n        use_linear_in_transformer: True\n        transformer_depth: 1\n        context_dim: 1024\n        legacy: False\n\n    first_stage_config:\n      target: ldm.models.autoencoder.AutoencoderKL\n      params:\n        embed_dim: 4\n        monitor: val/rec_loss\n        ddconfig:\n          #attn_type: \"vanilla-xformers\"\n          double_z: true\n          z_channels: 4\n          resolution: 256\n          in_channels: 3\n          out_ch: 3\n          ch: 128\n          ch_mult:\n          - 1\n          - 2\n          - 4\n          - 4\n          num_res_blocks: 2\n          attn_resolutions: []\n          dropout: 0.0\n        lossconfig:\n          target: torch.nn.Identity\n\n    cond_stage_config:\n      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder\n      params:\n        freeze: True\n        layer: \"penultimate\"\n"
  },
  {
    "path": "models/configs/v2-inference_fp32.yaml",
    "content": "model:\n  base_learning_rate: 1.0e-4\n  target: ldm.models.diffusion.ddpm.LatentDiffusion\n  params:\n    linear_start: 0.00085\n    linear_end: 0.0120\n    num_timesteps_cond: 1\n    log_every_t: 200\n    timesteps: 1000\n    first_stage_key: \"jpg\"\n    cond_stage_key: \"txt\"\n    image_size: 64\n    channels: 4\n    cond_stage_trainable: false\n    conditioning_key: crossattn\n    monitor: val/loss_simple_ema\n    scale_factor: 0.18215\n    use_ema: False # we set this to false because this is an inference only config\n\n    unet_config:\n      target: ldm.modules.diffusionmodules.openaimodel.UNetModel\n      params:\n        use_checkpoint: True\n        use_fp16: False\n        image_size: 32 # unused\n        in_channels: 4\n        out_channels: 4\n        model_channels: 320\n        attention_resolutions: [ 4, 2, 1 ]\n        num_res_blocks: 2\n        channel_mult: [ 1, 2, 4, 4 ]\n        num_head_channels: 64 # need to fix for flash-attn\n        use_spatial_transformer: True\n        use_linear_in_transformer: True\n        transformer_depth: 1\n        context_dim: 1024\n        legacy: False\n\n    first_stage_config:\n      target: ldm.models.autoencoder.AutoencoderKL\n      params:\n        embed_dim: 4\n        monitor: val/rec_loss\n        ddconfig:\n          #attn_type: \"vanilla-xformers\"\n          double_z: true\n          z_channels: 4\n          resolution: 256\n          in_channels: 3\n          out_ch: 3\n          ch: 128\n          ch_mult:\n          - 1\n          - 2\n          - 4\n          - 4\n          num_res_blocks: 2\n          attn_resolutions: []\n          dropout: 0.0\n        lossconfig:\n          target: torch.nn.Identity\n\n    cond_stage_config:\n      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder\n      params:\n        freeze: True\n        layer: \"penultimate\"\n"
  },
  {
    "path": "models/configs/v2-inpainting-inference.yaml",
    "content": "model:\n  base_learning_rate: 5.0e-05\n  target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion\n  params:\n    linear_start: 0.00085\n    linear_end: 0.0120\n    num_timesteps_cond: 1\n    log_every_t: 200\n    timesteps: 1000\n    first_stage_key: \"jpg\"\n    cond_stage_key: \"txt\"\n    image_size: 64\n    channels: 4\n    cond_stage_trainable: false\n    conditioning_key: hybrid\n    scale_factor: 0.18215\n    monitor: val/loss_simple_ema\n    finetune_keys: null\n    use_ema: False\n\n    unet_config:\n      target: ldm.modules.diffusionmodules.openaimodel.UNetModel\n      params:\n        use_checkpoint: True\n        image_size: 32 # unused\n        in_channels: 9\n        out_channels: 4\n        model_channels: 320\n        attention_resolutions: [ 4, 2, 1 ]\n        num_res_blocks: 2\n        channel_mult: [ 1, 2, 4, 4 ]\n        num_head_channels: 64 # need to fix for flash-attn\n        use_spatial_transformer: True\n        use_linear_in_transformer: True\n        transformer_depth: 1\n        context_dim: 1024\n        legacy: False\n\n    first_stage_config:\n      target: ldm.models.autoencoder.AutoencoderKL\n      params:\n        embed_dim: 4\n        monitor: val/rec_loss\n        ddconfig:\n          #attn_type: \"vanilla-xformers\"\n          double_z: true\n          z_channels: 4\n          resolution: 256\n          in_channels: 3\n          out_ch: 3\n          ch: 128\n          ch_mult:\n            - 1\n            - 2\n            - 4\n            - 4\n          num_res_blocks: 2\n          attn_resolutions: [ ]\n          dropout: 0.0\n        lossconfig:\n          target: torch.nn.Identity\n\n    cond_stage_config:\n      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder\n      params:\n        freeze: True\n        layer: \"penultimate\"\n\n\ndata:\n  target: ldm.data.laion.WebDataModuleFromConfig\n  params:\n    tar_base: null  # for concat as in LAION-A\n    p_unsafe_threshold: 0.1\n    filter_word_list: \"data/filters.yaml\"\n    max_pwatermark: 0.45\n    batch_size: 8\n    num_workers: 6\n    multinode: True\n    min_size: 512\n    train:\n      shards:\n        - \"pipe:aws s3 cp s3://stability-aws/laion-a-native/part-0/{00000..18699}.tar -\"\n        - \"pipe:aws s3 cp s3://stability-aws/laion-a-native/part-1/{00000..18699}.tar -\"\n        - \"pipe:aws s3 cp s3://stability-aws/laion-a-native/part-2/{00000..18699}.tar -\"\n        - \"pipe:aws s3 cp s3://stability-aws/laion-a-native/part-3/{00000..18699}.tar -\"\n        - \"pipe:aws s3 cp s3://stability-aws/laion-a-native/part-4/{00000..18699}.tar -\"  #{00000-94333}.tar\"\n      shuffle: 10000\n      image_key: jpg\n      image_transforms:\n      - target: torchvision.transforms.Resize\n        params:\n          size: 512\n          interpolation: 3\n      - target: torchvision.transforms.RandomCrop\n        params:\n          size: 512\n      postprocess:\n        target: ldm.data.laion.AddMask\n        params:\n          mode: \"512train-large\"\n          p_drop: 0.25\n    # NOTE use enough shards to avoid empty validation loops in workers\n    validation:\n      shards:\n        - \"pipe:aws s3 cp s3://deep-floyd-s3/datasets/laion_cleaned-part5/{93001..94333}.tar - \"\n      shuffle: 0\n      image_key: jpg\n      image_transforms:\n      - target: torchvision.transforms.Resize\n        params:\n          size: 512\n          interpolation: 3\n      - target: torchvision.transforms.CenterCrop\n        params:\n          size: 512\n      postprocess:\n        target: ldm.data.laion.AddMask\n        params:\n          mode: \"512train-large\"\n          p_drop: 0.25\n\nlightning:\n  find_unused_parameters: True\n  modelcheckpoint:\n    params:\n      every_n_train_steps: 5000\n\n  callbacks:\n    metrics_over_trainsteps_checkpoint:\n      params:\n        every_n_train_steps: 10000\n\n    image_logger:\n      target: main.ImageLogger\n      params:\n        enable_autocast: False\n        disabled: False\n        batch_frequency: 1000\n        max_images: 4\n        increase_log_steps: False\n        log_first_step: False\n        log_images_kwargs:\n          use_ema_scope: False\n          inpaint: False\n          plot_progressive_rows: False\n          plot_diffusion_rows: False\n          N: 4\n          unconditional_guidance_scale: 5.0\n          unconditional_guidance_label: [\"\"]\n          ddim_steps: 50  # todo check these out for depth2img,\n          ddim_eta: 0.0   # todo check these out for depth2img,\n\n  trainer:\n    benchmark: True\n    val_check_interval: 5000000\n    num_sanity_val_steps: 0\n    accumulate_grad_batches: 1\n"
  },
  {
    "path": "models/controlnet/put_controlnets_and_t2i_here",
    "content": ""
  },
  {
    "path": "models/diffusers/put_diffusers_models_here",
    "content": ""
  },
  {
    "path": "models/embeddings/put_embeddings_or_textual_inversion_concepts_here",
    "content": ""
  },
  {
    "path": "models/gligen/put_gligen_models_here",
    "content": ""
  },
  {
    "path": "models/hypernetworks/put_hypernetworks_here",
    "content": ""
  },
  {
    "path": "models/inpaint/put_inpaint_here",
    "content": ""
  },
  {
    "path": "models/loras/put_loras_here",
    "content": ""
  },
  {
    "path": "models/prompt_expansion/fooocus_expansion/config.json",
    "content": "{\n  \"_name_or_path\": \"gpt2\",\n  \"activation_function\": \"gelu_new\",\n  \"architectures\": [\n    \"GPT2LMHeadModel\"\n  ],\n  \"attn_pdrop\": 0.1,\n  \"bos_token_id\": 50256,\n  \"embd_pdrop\": 0.1,\n  \"eos_token_id\": 50256,\n  \"pad_token_id\": 50256,\n  \"initializer_range\": 0.02,\n  \"layer_norm_epsilon\": 1e-05,\n  \"model_type\": \"gpt2\",\n  \"n_ctx\": 1024,\n  \"n_embd\": 768,\n  \"n_head\": 12,\n  \"n_inner\": null,\n  \"n_layer\": 12,\n  \"n_positions\": 1024,\n  \"reorder_and_upcast_attn\": false,\n  \"resid_pdrop\": 0.1,\n  \"scale_attn_by_inverse_layer_idx\": false,\n  \"scale_attn_weights\": true,\n  \"summary_activation\": null,\n  \"summary_first_dropout\": 0.1,\n  \"summary_proj_to_labels\": true,\n  \"summary_type\": \"cls_index\",\n  \"summary_use_proj\": true,\n  \"task_specific_params\": {\n    \"text-generation\": {\n      \"do_sample\": true,\n      \"max_length\": 50\n    }\n  },\n  \"torch_dtype\": \"float32\",\n  \"transformers_version\": \"4.23.0.dev0\",\n  \"use_cache\": true,\n  \"vocab_size\": 50257\n}\n"
  },
  {
    "path": "models/prompt_expansion/fooocus_expansion/merges.txt",
    "content": "#version: 0.2 - Trained by `huggingface/tokenizers`\nĠ t\nĠ a\nh e\ni n\nr e\no n\nĠt he\ne r\nĠ s\na t\nĠ w\nĠ o\ne n\nĠ c\ni t\ni s\na n\no r\ne s\nĠ b\ne d\nĠ f\nin g\nĠ p\no u\nĠa n\na l\na r\nĠt o\nĠ m\nĠo f\nĠ in\nĠ d\nĠ h\nĠan d\ni c\na s\nl e\nĠt h\ni on\no m\nl l\nen t\nĠ n\nĠ l\ns t\nĠ re\nv e\nĠ e\nr o\nl y\nĠb e\nĠ g\nĠ T\nc t\nĠ S\ni d\no t\nĠ I\nu t\ne t\nĠ A\nĠ is\nĠ on\ni m\na m\no w\na y\na d\ns e\nĠth at\nĠ C\ni g\nĠf or\na c\nĠ y\nv er\nu r\nĠ u\nl d\nĠs t\nĠ M\n' s\nĠ he\nĠ it\nat ion\nit h\ni r\nc e\nĠy ou\ni l\nĠ B\nĠw h\no l\nĠ P\nĠw ith\nĠ 1\nt er\nc h\nĠa s\nĠw e\nĠ (\nn d\ni ll\nĠ D\ni f\nĠ 2\na g\ner s\nk e\nĠ \"\nĠ H\ne m\nĠc on\nĠ W\nĠ R\nhe r\nĠw as\nĠ r\no d\nĠ F\nu l\nat e\nĠa t\nr i\np p\no re\nĠT he\nĠs e\nu s\nĠp ro\nĠh a\nu m\nĠa re\nĠd e\na in\nan d\nĠo r\nig h\nes t\nis t\na b\nr om\nĠ N\nt h\nĠc om\nĠ G\nu n\no p\n0 0\nĠ L\nĠn ot\nes s\nĠe x\nĠ v\nre s\nĠ E\ne w\nit y\nan t\nĠb y\ne l\no s\nor t\no c\nq u\nĠf rom\nĠha ve\nĠs u\ni ve\nou ld\nĠs h\nĠth is\nn t\nr a\np e\nigh t\nar t\nm ent\nĠa l\nu st\nen d\n- -\nal l\nĠ O\nac k\nĠc h\nĠ le\ni es\nre d\nar d\nâ Ģ\nou t\nĠ J\nĠa b\ne ar\ni v\nal ly\nou r\no st\ng h\np t\nĠp l\nas t\nĠc an\na k\nom e\nu d\nT he\nĠh is\nĠd o\nĠg o\nĠh as\ng e\n' t\nĠ U\nr ou\nĠs a\nĠ j\nĠb ut\nĠw or\nĠa ll\ne ct\nĠ k\nam e\nĠw ill\no k\nĠw he\nĠthe y\nid e\n0 1\nf f\nic h\np l\nt her\nĠt r\n. .\nĠin t\ni e\nu re\nag e\nĠn e\ni al\na p\nin e\nic e\nĠm e\nĠo ut\nan s\non e\non g\nion s\nĠwh o\nĠ K\nĠu p\nĠthe ir\nĠa d\nĠ 3\nĠu s\nat ed\nou s\nĠm ore\nu e\no g\nĠS t\nin d\ni ke\nĠs o\nim e\np er\n. \"\nb er\ni z\na ct\nĠon e\nĠsa id\nĠ -\na re\nĠyou r\nc c\nĠT h\nĠc l\ne p\na ke\nab le\ni p\nĠcon t\nĠwh ich\ni a\nĠ im\nĠab out\nĠwe re\nver y\nu b\nĠh ad\nĠ en\nĠcom p\n, \"\nĠI n\nĠu n\nĠa g\ni re\nac e\na u\nar y\nĠw ould\nas s\nr y\nĠ âĢ\nc l\no ok\ne re\ns o\nĠ V\nig n\ni b\nĠof f\nĠt e\nv en\nĠ Y\ni le\no se\nit e\nor m\nĠ2 01\nĠre s\nĠm an\nĠp er\nĠo ther\nor d\nul t\nĠbe en\nĠl ike\nas e\nan ce\nk s\nay s\now n\nen ce\nĠd is\nct ion\nĠan y\nĠa pp\nĠs p\nin t\nres s\nation s\na il\nĠ 4\nic al\nĠthe m\nĠhe r\nou nt\nĠC h\nĠa r\nĠ if\nĠthe re\nĠp e\nĠy ear\na v\nĠm y\nĠs ome\nĠwhe n\nou gh\nac h\nĠth an\nr u\non d\nic k\nĠo ver\nve l\nĠ qu\nĊ Ċ\nĠs c\nre at\nre e\nĠI t\nou nd\np ort\nĠal so\nĠp art\nf ter\nĠk n\nĠbe c\nĠt ime\nen s\nĠ 5\nop le\nĠwh at\nĠn o\nd u\nm er\nan g\nĠn ew\n-- --\nĠg et\nor y\nit ion\ning s\nĠj ust\nĠint o\nĠ 0\nent s\no ve\nt e\nĠpe ople\nĠp re\nĠit s\nĠre c\nĠt w\ni an\nir st\nar k\nor s\nĠwor k\nad e\no b\nĠs he\nĠo ur\nw n\nin k\nl ic\nĠ1 9\nĠH e\nis h\nnd er\nau se\nĠh im\non s\nĠ [\nĠ ro\nf orm\ni ld\nat es\nver s\nĠon ly\no ll\nĠs pe\nc k\ne ll\nam p\nĠa cc\nĠb l\ni ous\nur n\nf t\no od\nĠh ow\nhe d\nĠ '\nĠa fter\na w\nĠat t\no v\nn e\nĠpl ay\ner v\nic t\nĠc ould\nit t\nĠa m\nĠf irst\nĠ 6\nĠa ct\nĠ $\ne c\nh ing\nu al\nu ll\nĠcom m\no y\no ld\nc es\nat er\nĠf e\nĠbe t\nw e\nif f\nĠtw o\noc k\nĠb ack\n) .\nid ent\nĠu nder\nrou gh\nse l\nx t\nĠm ay\nrou nd\nĠp o\np h\nis s\nĠd es\nĠm ost\nĠd id\nĠad d\nj ect\nĠin c\nf ore\nĠp ol\non t\nĠag ain\ncl ud\nter n\nĠkn ow\nĠne ed\nĠcon s\nĠc o\nĠ .\nĠw ant\nĠse e\nĠ 7\nn ing\ni ew\nĠTh is\nc ed\nĠe ven\nĠin d\nt y\nĠW e\nat h\nĠthe se\nĠp r\nĠu se\nĠbec ause\nĠf l\nn g\nĠn ow\nĠâĢ ĵ\nc om\nis e\nĠm ake\nĠthe n\now er\nĠe very\nĠU n\nĠse c\nos s\nu ch\nĠe m\nĠ =\nĠR e\ni ed\nr it\nĠin v\nle ct\nĠsu pp\nat ing\nĠl ook\nm an\npe ct\nĠ 8\nro w\nĠb u\nĠwhe re\nif ic\nĠyear s\ni ly\nĠd iff\nĠsh ould\nĠre m\nT h\nI n\nĠe v\nd ay\n' re\nri b\nĠre l\ns s\nĠde f\nĠr ight\nĠs y\n) ,\nl es\n00 0\nhe n\nĠth rough\nĠT r\n_ _\nĠw ay\nĠd on\nĠ ,\nĠ1 0\nas ed\nĠas s\nub lic\nĠre g\nĠA nd\ni x\nĠ very\nĠin clud\not her\nĠim p\not h\nĠsu b\nĠâĢ Ķ\nĠbe ing\nar g\nĠW h\n= =\nib le\nĠdo es\nan ge\nr am\nĠ 9\ner t\np s\nit ed\nation al\nĠb r\nĠd own\nĠman y\nak ing\nĠc all\nur ing\nit ies\nĠp h\nic s\nal s\nĠde c\nat ive\nen er\nĠbe fore\nil ity\nĠwe ll\nĠm uch\ners on\nĠth ose\nĠsu ch\nĠ ke\nĠ end\nĠB ut\nas on\nt ing\nĠl ong\ne f\nĠth ink\ny s\nĠbe l\nĠs m\nit s\na x\nĠo wn\nĠpro v\nĠs et\nif e\nment s\nb le\nw ard\nĠsh ow\nĠp res\nm s\nom et\nĠo b\nĠs ay\nĠS h\nt s\nf ul\nĠe ff\nĠg u\nĠin st\nu nd\nre n\nc ess\nĠ ent\nĠY ou\nĠgo od\nĠst art\nin ce\nĠm ade\nt t\nst em\nol og\nu p\nĠ |\num p\nĠhe l\nver n\nul ar\nu ally\nĠa c\nĠm on\nĠl ast\nĠ2 00\n1 0\nĠst ud\nu res\nĠA r\nsel f\nar s\nmer ic\nu es\nc y\nĠm in\noll ow\nĠc ol\ni o\nĠm od\nĠc ount\nĠC om\nhe s\nĠf in\na ir\ni er\nâĢ Ķ\nre ad\nan k\nat ch\ne ver\nĠst r\nĠpo int\nor k\nĠN ew\nĠs ur\no ol\nal k\nem ent\nĠus ed\nra ct\nwe en\nĠs ame\nou n\nĠA l\nc i\nĠdiff ere\nĠwh ile\n---- ----\nĠg ame\nce pt\nĠs im\n.. .\nĠin ter\ne k\nĠre port\nĠpro du\nĠst ill\nl ed\na h\nĠhe re\nĠwor ld\nĠth ough\nĠn um\nar ch\nim es\nal e\nĠS e\nĠI f\n/ /\nĠL e\nĠre t\nĠre f\nĠtr ans\nn er\nut ion\nter s\nĠt ake\nĠC l\nĠcon f\nw ay\na ve\nĠgo ing\nĠs l\nu g\nĠA meric\nĠspe c\nĠh and\nĠbet ween\nist s\nĠD e\no ot\nI t\nĠe ar\nĠagain st\nĠh igh\ng an\na z\nat her\nĠex p\nĠo p\nĠin s\nĠg r\nĠhel p\nĠre qu\net s\nin s\nĠP ro\nis m\nĠf ound\nl and\nat a\nus s\nam es\nĠp erson\nĠg reat\np r\nĠs ign\nĠA n\n' ve\nĠs omet\nĠs er\nh ip\nĠr un\nĠ :\nĠt er\nire ct\nĠf ollow\nĠd et\nic es\nĠf ind\n1 2\nĠm em\nĠc r\ne red\ne x\nĠex t\nut h\nen se\nc o\nĠte am\nv ing\nou se\nas h\nat t\nv ed\nĠsy stem\nĠA s\nd er\niv es\nm in\nĠle ad\nĠB l\nc ent\nĠa round\nĠgo vern\nĠc ur\nvel op\nan y\nĠc our\nal th\nag es\niz e\nĠc ar\nod e\nĠl aw\nĠre ad\n' m\nc on\nĠre al\nĠsupp ort\nĠ1 2\n.. ..\nĠre ally\nn ess\nĠf act\nĠd ay\nĠb oth\ny ing\nĠs erv\nĠF or\nĠth ree\nĠw om\nĠm ed\nod y\nĠThe y\n5 0\nĠex per\nt on\nĠe ach\nak es\nĠc he\nĠc re\nin es\nĠre p\n1 9\ng g\nill ion\nĠg rou\nut e\ni k\nW e\ng et\nE R\nĠm et\nĠs ays\no x\nĠd uring\ner n\niz ed\na red\nĠf am\nic ally\nĠha pp\nĠI s\nĠch ar\nm ed\nv ent\nĠg ener\ni ent\np le\ni et\nre nt\n1 1\nv es\npt ion\nĠ2 0\nform ation\nĠc or\nĠoff ic\nie ld\nĠto o\nis ion\nĠin f\nĠ Z\nt he\no ad\nĠp ublic\nĠpro g\nr ic\n* *\nĠw ar\nĠp ower\nv iew\nĠf ew\nĠl oc\nĠdiffere nt\nĠst ate\nĠhe ad\n' ll\nĠp oss\nĠst at\nre t\nant s\nĠv al\nĠis s\nĠc le\ni vers\nan c\nĠex pl\nĠan other\nĠ Q\nĠa v\nth ing\nn ce\nW h\nĠch ild\nĠs ince\ni red\nl ess\nĠl ife\nĠde velop\nitt le\nĠde p\nĠp ass\nã ĥ\nĠt urn\nor n\nTh is\nb ers\nro ss\nĠA d\nĠf r\nĠres p\nĠsec ond\no h\nĠ /\nĠdis c\nĠ &\nĠsomet hing\nĠcomp le\nĠ ed\nĠf il\nĠmon th\na j\nu c\nĠgovern ment\nĠwith out\nĠle g\nĠd ist\nĠp ut\nĠqu est\nan n\nĠpro t\n2 0\nĠne ver\ni ence\nĠle vel\nĠar t\nĠth ings\nĠm ight\nĠeff ect\nĠcont ro\nĠc ent\nĠ1 8\nĠall ow\nĠbel ie\nch ool\not t\nĠinc re\nĠfe el\nĠres ult\nĠl ot\nĠf un\not e\nĠt y\nere st\nĠcont in\nĠus ing\nĠb ig\n2 01\nĠas k\nĠb est\nĠ )\nI N\nĠo pp\n3 0\nĠnum ber\nin ess\nS t\nle ase\nĠc a\nĠm ust\nĠd irect\nĠg l\nĠ <\nĠop en\nĠp ost\nĠcom e\nĠse em\nord ing\nĠwe ek\nate ly\nit al\nĠe l\nri end\nĠf ar\nĠt ra\nin al\nĠp ri\nĠU S\nĠpl ace\nĠfor m\nĠto ld\n\" :\nain s\nat ure\nĠTr ump\nĠst and\nĠ #\nid er\nĠF r\nĠne xt\nĠs oc\nĠp ur\nĠle t\nĠl ittle\nĠh um\nĠ i\nr on\n1 5\nĠ1 5\nĠcomm un\nĠm ark\nĠThe re\nĠw r\nĠTh at\nĠin formation\nw ays\nĠb us\na pp\nĠinv est\nm e\nĠh ard\nain ed\ne ad\nĠim port\nĠapp ro\nĠt est\nĠt ri\nĠre st\nos ed\nĠf ull\nĠc are\nĠS p\nĠc ase\nO N\nĠs k\nĠl ess\nĠ +\nĠpart ic\nĠP l\nab ly\nu ck\nis hed\nch n\nb e\nĠl ist\nat or\nĠto p\nĠad v\nĠB e\nru ct\nĠd em\nr ation\nl ing\ng y\nre en\ng er\nĠh ome\nĠle ft\nĠbet ter\nĠd ata\nĠ1 1\nĠatt ack\nĠpro ble\nl ine\nard s\nĠbe h\nr al\nĠH ow\nĠS he\nar ge\nĠ --\n: //\nĠb ro\nĠP h\nat s\nĠbu ild\nw w\nid ed\na im\nas es\nen cy\nĠm ain\nin ed\nĠinclud ing\nĠ {\nĠg ot\nĠint erest\nĠke ep\nĠ X\nĠe as\nain ing\nĠcl ass\nâĢ ¦\nĠN o\nĠv ar\nĠsm all\namp le\nA T\nĠ ide\nĠS o\nĠre ce\nĠpol it\nĠm ov\nĠpl an\nĠper cent\niv ing\nĠc amp\nĠp ay\n1 4\ns c\nis ed\nĠu nt\none y\npl oy\n== ==\nĠdid n\nĠI nd\nel s\nert ain\nĠp os\n__ __\ni ver\nĠpro cess\nĠprog ram\nif ied\nĠR ep\n1 6\nu ro\nolog y\nat ter\nin a\nĠn ame\nĠA ll\nĠf our\nĠret urn\nv ious\nb s\nĠcall ed\nĠm ove\nĠS c\nir d\nĠgrou p\nĠb re\nĠm en\nĠc ap\nt en\ne e\nĠd ri\nle g\nhe re\nuth or\nĠp at\nĠcur rent\nid es\nĠp op\nt o\nent ion\nĠal ways\nĠm il\nĠwom en\nĠ1 6\nĠo ld\niv en\nra ph\nĠO r\nr or\nent ly\nĠn ear\nĠE x\nre am\ns h\nĠ1 4\nĠf ree\niss ion\nst and\nĠC on\nal ity\nus ed\n1 3\nĠdes ign\nĠch ange\nĠch ang\nĠb o\nĠv is\nem ber\nĠb ook\nread y\nĠk ill\n2 5\npp ed\nĠa way\nĠab le\nĠcount ry\nĠcon st\nar n\nĠor der\nA R\ni or\ni um\nor th\n1 8\nail able\nĠs w\nĠm illion\nĠ1 3\nat ic\nt ed\nĠG o\nĠo per\nen g\nĠth ing\naj or\ncon om\nĠCom m\nĠwh y\nu red\nur al\nĠs chool\nb y\nĠM ar\nĠa ff\nĠd ays\nĠan n\nus h\nan e\nI f\ne g\nĠpro f\nĠhe alth\nou th\nB ut\nion al\n. ,\nĠs ol\nĠal ready\nĠ3 0\nĠchar act\nH e\nĠf riend\nE S\ni ans\nic le\n' d\nĠO n\nĠle ast\nĠp rom\nĠd r\nĠh ist\nit her\nĠ est\ni qu\n1 7\ns on\nĠte ll\nĠt alk\noh n\no int\nle ction\nA N\nĠunt il\nau gh\nĠl ater\nĠ ve\nĠv iew\nend ing\niv ed\nĠwor d\nw are\nĠc ost\nĠen ough\nĠg ive\nĠUn ited\nĠte chn\nare nt\nO R\nĠp ar\nĠD r\nĠ201 6\nr ist\ner ing\nĠ Â\nĠl arge\ns ide\nac y\ncc ess\nĠw in\nĠimport ant\nĠ19 9\nĠdoes n\nĠ1 7\nĠbus iness\nĠcle ar\nĠre se\n\" ,\nur y\nĠe qu\nas ter\nal f\nĠAmeric an\nn ect\nĠex pect\nivers ity\nĠo cc\nĠF l\nĠk ind\nĠme an\nĠp ast\nĠde v\nĠb as\nle t\nra ft\nĠor gan\nĠde l\nĠper form\nĠst ory\nĠse ason\nĠC ol\nĠcl aim\nĠc ame\nĠwith in\nĠl ine\nĠpro ject\nĠA t\nĠcontro l\nend ed\nĠS y\nĠa ir\niz ation\nĠ *\nle y\nĠm oney\nid d\nY ou\nf or\nĠfam ily\nĠm aking\nĠb it\nĠpol ice\nĠhapp en\nĠ vers\non y\nu ff\nĠW hen\nĠs it\nide o\nl f\nis on\nĠsu re\ng in\nĠapp ear\nĠl ight\nĠ es\no f\nĠw ater\nĠt imes\nn ot\nĠg row\nĠcomp any\nĠT e\now s\nĠm ar\nour ce\ni ol\nar m\nb r\nĠex ample\nĠcon c\nĠf ore\nĠT o\np ro\nE N\nri es\nĠ2 5\nĠC an\nne y\nĠact ually\nĠe ver\nur ity\nak en\nap s\nĠt ax\nĠm ajor\nam a\nĠof ten\ner al\nĠhum an\nĠj ob\nis ter\nĠav ailable\noc r\nen n\na id\niv id\nĠrec ord\n? \"\nĠs ing\nĠA m\nid ence\nĠnew s\nst er\nĠe conom\nĠfollow ing\nĠB r\nis ing\nĠh our\nm ost\num ent\nĠse x\nĠdes c\nĠbec ome\nĠE d\nĠto ok\nĠha ving\nĠprodu ct\na ult\nA s\nar ing\nĠme ans\nĠh op\nun e\nĠch o\nĠc ertain\nĠn on\nĠde al\n2 4\nle ment\noc i\nen e\nĠs ide\nĠP r\nĠM ay\nĠre ason\nu ed\nc hed\nul ation\nĠe lect\nĠoffic ial\nĠposs ible\nĠh old\nand s\not s\nĠc ity\nor ies\nĠse ver\nĠchild ren\nĠon ce\nĠact iv\nl er\nĠn ight\nit ions\nĠJ ohn\na pe\npl ay\nĠd one\nĠl im\nĠwork ing\nĠP res\nor ld\ne b\nĠC o\nĠb ody\nail s\nut es\nĠM r\nĠwhe ther\nĠa uthor\nro p\nĠpro per\nĠse en\n) ;\nĠf ac\nĠS u\nĠcon d\nit ing\nĠcour se\nĠ }\n-------- --------\na ign\nĠev ent\nĠen g\nĠp ot\nĠin tern\ni am\nĠsh ort\nem pt\nã Ĥ\nĠG od\nil ar\n8 0\nĠor ig\nI S\nour n\nab ility\nit ive\nĠd am\nĠ1 00\nĠp ress\nĠdo ing\nĠprot ect\nr ing\nĠthough t\nĠquest ion\nre w\nĠW ar\nĠsever al\nĠSt ate\nĠg iven\nĠf und\nĠT w\nĠw ent\nan ces\nw ork\np or\nm y\n4 0\nĠar g\nart ment\nust om\nĠpol ic\nĠme et\nĠc reat\n2 2\nĠSt ates\nĠg ames\nra w\nut ure\nĠunder stand\nur s\nĠO b\nl ish\ns y\nĠm akes\nĠw on\nag on\nĠh tt\nĠl ove\nent ial\nĠcomple te\np ar\nĠI m\nA L\nĠacc ount\n ł\nore d\nver t\nĠ ident\nĠ201 5\nĠother s\nĠM in\ni ber\nver age\nThe re\nition al\nd d\nĠpro b\nĠyou ng\nĠal ong\nĠacc ording\nĠy et\nĠmem bers\nĠWh at\no id\nĠM an\nA nd\nĠam ong\na i\nĠem ploy\nĠR es\nĠ >\nĠinv ol\nĠl ow\na f\nĠC ar\nĠh ig\nĠO ne\nĠS ec\nin ation\nĠlike ly\nĠan t\nag ed\nĠR uss\nĠb en\nĠre le\nF or\nb ack\nĠN ot\nĠpres ident\nb all\nĠacc ess\nivid ual\nĠD em\nĠE uro\n6 0\nĠkn own\nir l\nĠG r\nĠear ly\nu se\niet y\nâĢ ĵ\nĠf ight\nĠs ent\nĠto day\nĠmark et\n\" .\nĠb ased\nĠstr ong\nur ther\nĠde b\nm ber\nĠproble m\nĠde ath\nĠsoc ial\nim ate\nA S\nort un\nĠcamp aign\ner y\nC h\nĠe y\ni ally\nĠm us\nw h\np os\nĠ er\nĠsa f\nĠmonth s\nir on\nĠv iol\nĠf ive\nĠst re\nĠplay ers\nin c\nal d\ny ear\na un\nĠsu ccess\nĠpres ent\nere nce\nĠ201 4\nĠsu gg\nĠpartic ular\nĠtr y\nĠsugg est\nĠCh rist\non es\nĠpri v\n2 3\nĠc rit\nĠl and\nĠloc al\nif y\n2 9\nĠa ut\nE D\nĠG u\nĠm ult\nĠpolit ical\nĠask ed\nĠfor mer\nit ter\nri pt\nĠcl ose\nĠp ract\nĠY ork\nĠget ting\nĠac ross\nĠcom b\nĠbelie ve\nĠ z\nĠto get\nĠtoget her\nĠC ent\nir c\nĠind ividual\nĠM c\n2 7\nis k\nĠE ng\nĠf ace\nĠ2 4\nĠval ue\nĠare a\ne v\nĠw rit\nĠPres ident\nĠv ot\nĠke y\nĠm om\np ut\nĠany thing\nĠexper ience\natt le\nĠm ind\na ff\nom m\nĠf uture\ng ed\nĠc ut\nĠto t\nit ch\nĠv ideo\nĠinvest ig\nĠn et\nĠM y\nr ict\ni en\n. )\nĠimp ro\nth ough\nward s\nĠcon nect\nĠM ed\nsel ves\nens ive\nm b\no ber\nat ors\nA n\nĠ5 0\nĠre du\nres ent\nĠab ove\nĠf re\nĠEuro pe\ns w\nĠam ount\nĠA pp\nĠe ither\nĠmil it\nĠan al\nĠf ail\nĠE n\nal es\nĠspec ial\nĠbl ack\nI T\nc her\nĠlook ing\nĠf ire\ny n\nĠal most\no on\nĠstud y\nĠm iss\nc hes\nro wn\nĠt re\nĠcommun ity\nĠmed ia\nĠf ood\nĠcom es\nĠUn iversity\nĠsing le\nWh at\nu ly\nĠh alf\nag ue\nh od\nĠRep ublic\nĠstart ed\nĠqu ick\not o\nb ook\nĠiss ue\nit or\nĠel se\nĠcons ider\n2 6\nro du\nĠt aken\n2 8\n9 9\nĠW ith\nĠtr ue\nĠw a\nĠtr ad\nĠag o\nĠm ess\nie f\nĠadd ed\no ke\nĠb ad\nĠf av\n3 3\nĠsim ilar\nas k\nĠD on\nĠcharact er\nort s\nĠH ouse\nĠreport ed\nĠty pe\nv al\ni od\nĠHow ever\nĠt arg\nĠent ire\npp ing\nĠhist ory\nĠl ive\nff ic\n.... ....\ned eral\nĠtr ying\nĠdisc uss\nĠH ar\nac es\nl ished\nĠse lf\nos p\nre st\nĠro om\nel t\nĠf all\nol ution\nĠe t\nĠ x\nĠis n\nĠide a\nb o\nĠs ound\nĠD ep\nĠsome one\nci ally\null y\nĠf oc\nĠob ject\nif t\nap er\nĠplay er\nĠr ather\nĠserv ice\nas hing\nĠD o\nĠP art\nru g\nm on\np ly\nĠm or\nĠnot hing\nĠprov ide\nI C\nun g\nĠpart y\nĠex ist\nĠm ag\n7 0\nĠr ul\nĠh ouse\nĠbeh ind\nĠhow ever\nĠW orld\nĠs um\nĠapp lic\nĠ ;\nĠfun ction\ng r\nĠP ol\nĠfr ont\n2 00\nĠser ies\nĠt em\nĠty p\nill s\nĠo pt\nĠpoint s\nĠbel ow\nitt ed\nĠspec ific\nĠ201 7\num b\nĠr a\nĠpre vious\nĠpre t\nre me\nĠc ustom\nĠcour t\nĠM e\nĠre pl\nĠwho le\ng o\nc er\nĠt reat\nĠA ct\nĠprob ably\nĠle arn\nend er\nĠA ss\nĠvers ion\nn ow\nĠche ck\nĠC al\nR E\nmin ist\nO n\nour ces\nĠben ef\nĠd oc\nĠdet er\nĠen c\nĠsu per\nĠadd ress\nĠv ict\nĠ201 3\nĠme as\nt r\nĠf ield\nW hen\nĠsign ific\nu ge\nĠfe at\nĠcomm on\nl oad\nĠbe gin\nĠbr ing\nĠa ction\ner man\nĠdesc rib\nĠind ust\nĠwant ed\nri ed\nm ing\nĠatt empt\n4 5\nf er\nĠd ue\nress ion\n# #\nĠsh all\nĠs ix\no o\nĠst ep\nĠp ub\nĠhim self\nĠ2 3\nĠc op\nĠd est\nĠst op\nA C\nib ility\nĠl ab\nic ult\nĠhour s\nĠcre ate\nĠf urther\nĠAmeric a\nĠC ity\nĠd ou\nhe ad\nS T\nĠN orth\nc ing\nĠn ational\nu le\nĠIn st\nĠt aking\nĠQ u\nir t\nĠre d\nĠrese arch\nv iron\nĠG e\nĠbre ak\nan a\nĠsp ace\nater ial\nĠrec ent\nĠA b\nĠgener al\nĠh it\nĠper iod\nĠevery thing\nive ly\nĠph ys\nĠsay ing\nan ks\nĠc ou\nĠc ult\nac ed\ne al\nu ation\nĠc oun\nl u\nĠinclud e\nĠpos ition\nĠA fter\nĠCan ad\nĠE m\nĠim m\nĠR ed\nĠp ick\nĠcom pl\nĠm atter\nre g\ne xt\nang u\nis c\no le\na ut\nĠcomp et\ne ed\nf ect\nĠ2 1\nĠS en\nĠThe se\nas ing\nĠcan not\nĠin it\nĠrel ations\nac hed\nĠb ar\nĠ4 0\nĠT H\nĠ201 2\nĠv ol\nĠg round\nĠsec urity\nĠup d\nil t\n3 5\nĠconc ern\nĠJ ust\nĠwh ite\nĠseem s\nĠH er\npe cially\ni ents\nĠann oun\nĠf ig\night s\nĠst ri\nl ike\nid s\nĠs us\nĠw atch\nĠ â\nĠw ind\nĠC ont\nĠit self\nĠm ass\nA l\ny le\niqu e\nĠN ational\nĠab s\nĠp ack\nĠout side\nĠan im\nĠp ain\net er\nĠman ag\ndu ct\nog n\nĠ ]\nĠSe pt\nse c\no ff\nĠJ an\nĠf oot\nad es\nĠth ird\nĠm ot\nĠev idence\nint on\nĠth reat\na pt\npl es\nc le\nĠl o\nĠde cl\nĠit em\nmed i\nĠrep resent\nom b\nam er\nĠsignific ant\nog raph\ns u\nĠc al\ni res\n00 00\nI D\nA M\nĠsim ply\nĠlong er\nĠf ile\nO T\nc he\nS o\nate g\nor g\nĠH is\nĠen er\nĠd om\nĠup on\nil i\n\": \"\nĠthem selves\nĠcom ing\nĠqu ite\nĠdiff icult\nĠB ar\nil ities\nre l\nend s\nc ial\n6 4\nĠwom an\nra p\ny r\nĠne cess\nip s\nĠte xt\nĠrequ ire\nĠmilit ary\nĠre view\nĠresp ons\n7 5\nĠsub ject\nĠinst ead\nĠiss ues\nĠg en\n\" ,\"\nĠmin utes\nĠwe ap\nr ay\nam ed\nt ime\nb l\nH ow\nĠc ode\nĠS m\nĠhig her\nĠSt e\nr is\nĠp age\nĠstud ents\nĠIn tern\nĠmet hod\nĠA ug\nĠP er\nĠA g\nĠpolic y\nĠS w\nĠex ec\nĠac cept\num e\nrib ut\nĠword s\nĠfin al\nĠchang es\nĠDem ocr\nĠfriend s\nĠres pect\nĠe p\nĠcomp an\niv il\nĠdam age\n** **\nog le\nviron ment\nĠne g\nent al\nĠa p\nĠtot al\niv al\n! \"\nl im\nĠneed s\nĠag re\nĠdevelop ment\nĠa ge\nip le\n2 1\nĠresult s\nĠA f\nS h\nĠg un\nĠOb ama\nro ll\nĠ @\nĠright s\nĠB rit\nĠrun ning\nĠwas n\nĠp ort\nĠr ate\nĠpret ty\nĠtarg et\nĠsa w\nĠc irc\nĠwor ks\nic ro\nal t\no ver\nww w\nTh at\nl ier\nĠevery one\nud e\nĠp ie\nidd le\nra el\nĠr ad\nĠbl ock\nĠw alk\nT o\nã ģ\nn es\nĠA ust\na ul\nro te\nĠS outh\ness ion\nop h\nĠshow s\nĠs ite\nĠj o\nĠr isk\ncl us\nl t\nĠin j\nid ing\nĠS pe\nĠch all\nir m\nĠ2 2\nitt ing\nst r\nĠh y\nL E\nke y\nĠbe gan\nat ur\nashing ton\nl am\nĠD av\nb it\nĠs ize\nĠP ar\n3 8\nourn al\nf ace\nĠdec ision\nĠl arg\nĠj ud\nre ct\nĠcontin ue\nĠO ct\nove red\nĠI nt\n==== ====\nĠp arent\nĠW ill\nĠeas y\nĠd rug\nang er\nĠs ense\nĠd i\nid ay\nĠener gy\nist ic\nĠass oci\nar ter\nob al\ne ks\nĠE l\nur ch\nĠg irl\no e\nit le\nĠ2 8\nĠC he\nĠrequ est\nĠso on\nĠh ost\nk y\nĠst ates\nom es\nĠm aterial\nle x\nĠmom ent\nĠan sw\non se\nĠes pecially\nĠn orm\nĠserv ices\np ite\nr an\nĠro le\n4 4\n) :\nĠc red\nC l\n____ ____\nĠm at\nĠl og\nĠCl inton\nO U\nĠoff ice\nĠ2 6\nĠch arg\nĠtr ack\nm a\nĠhe art\nĠb all\nĠperson al\nĠbuild ing\nn a\ns et\nb ody\nĠBl ack\nĠincre ase\nitt en\nĠneed ed\n3 6\n3 2\n= \"\nĠl ost\nĠbec ame\nĠgrou ps\nĠM us\nĠw rote\nĠP e\nĠpro p\nj oy\nà ©\nĠWh ite\nĠde ad\n. '\nĠhtt p\nĠwe bs\nO S\nĠins ide\nĠwr ong\nĠstat ement\nĠ ...\ny l\nĠfil m\nĠmus ic\nĠsh are\nific ation\nĠre lease\nĠfor ward\nĠst ay\nĠcomp ut\nit te\ns er\nĠorig inal\nĠc ard\nĠc and\nĠd iv\nat ural\nĠfav or\nO M\nĠc ases\nus es\nĠse ction\nĠle ave\ng ing\nov ed\nĠW ashington\n3 9\nĠG l\nĠrequ ired\nact ion\nap an\no or\nit er\nĠK ing\nĠcount ries\nĠG erman\nll ing\nĠ2 7\n3 4\nĠquest ions\nĠpr im\nĠc ell\nĠsh oot\nĠany one\nĠW est\nĠaff ect\nep end\nĠon line\nĠIs rael\nĠSept ember\nĠab ility\nĠcont ent\nis es\nĠre ve\nĠl aun\nĠind ic\nĠfor ce\nc ast\nĠso ld\nav ing\nf l\nĠso ft\nĠcompan ies\nce ed\nĠart icle\nĠa ud\nĠre v\nĠed uc\nĠplay ing\n0 5\nĠhe ld\nct or\nĠrele ased\nĠf ederal\n3 7\nĠad minist\nĠinter view\nĠinst all\nĠrece ived\nĠs ource\nu k\nP h\nĠser ious\nĠcre ated\nĠc ause\nĠim medi\nĠdef in\nu el\nĠDep artment\nct ions\nĠC our\nĠN ow\nz e\nit es\nit ution\nĠl ate\nĠspe ak\nn ers\nĠleg al\nar i\nĠC or\nĠwe eks\nĠmod el\nĠp red\nĠex act\nB C\nĠB y\nIN G\nos ing\nĠt akes\nĠreg ard\nĠopp ortun\nĠpr ice\nĠ19 8\nĠA pr\nf ully\nĠor d\nĠproble ms\nru ction\nh am\nĠC ount\nle ge\nĠlead ers\nE T\nle v\nĠde ep\nolog ical\nes e\nh aps\nĠS ome\nĠp ers\nĠcont ract\nĠrelations hip\ns p\nou d\nĠb ase\n4 8\nm it\nA d\nanc ial\nĠcons um\nĠpot ential\nĠl angu\nre m\net h\nĠrel ig\nress ed\n6 6\nĠl ink\nĠl ower\nay er\nĠJ une\nĠf em\nun t\ner c\nur d\nĠcont act\nĠ ill\nĠm other\nĠest ab\nh tt\nĠM arch\nĠB ro\nĠCh ina\nĠ2 9\nĠs qu\nĠprov ided\nĠa verage\nas ons\nĠ201 1\nĠex am\nl in\n5 5\nn ed\nĠper fect\nĠt ou\nal se\nu x\nĠbu y\nĠsh ot\nĠcol lect\nĠph ot\nĠplay ed\nĠsur pr\nĠofficial s\nĠsim ple\nav y\nĠindust ry\nĠhand s\ng round\nĠp ull\nĠr ound\nĠus er\nĠr ange\nu ary\nĠpriv ate\nop s\ne es\nĠw ays\nĠM ich\nĠve h\nĠex cept\nĠter ms\nim um\npp er\nI ON\nore s\nĠDr agon\nou l\nĠd en\nĠperform ance\nĠb ill\nc il\n4 7\nĠen vironment\nĠex c\nad d\nĠwor th\nĠp ict\nĠch ance\nĠ201 8\nb or\nĠspe ed\nict ion\nĠal leg\nĠJ apan\nat ory\nre et\nĠm atch\nĠI I\nĠst ru\nord er\nĠst e\nĠl iving\nĠst ruct\nin o\nĠse par\nher n\nĠresp onse\nĠen joy\nĠv ia\nA D\num ents\nace book\nĠmem ber\nib r\niz ing\nĠto ol\nĠM on\nĠWh ile\nh ood\nĠA ng\nĠD ef\nĠoff er\nT r\na ur\nĠturn ed\nĠJ uly\nd own\nan ced\nĠrec ently\nĠE ar\nĠc e\nĠSt ar\nĠC ong\nrough t\nĠbl ood\nĠhop e\nĠcom ment\nain t\nĠar ri\nil es\nĠpartic ip\nough t\nri ption\n0 8\n4 9\nĠg ave\nĠse lect\nĠkill ed\nsy ch\nĠgo es\ni j\nĠc oll\nĠimp act\nat ives\nĠS er\n0 9\nĠAug ust\nĠb oy\nd e\nĠD es\nĠf elt\nU S\nĠexpect ed\nĠim age\nĠM ark\ncc ording\no ice\nE C\nĠM ag\nen ed\nh old\nĠP ost\nĠpre vent\nN o\nĠinvol ved\nĠey es\nĠquick ly\nA t\nun k\nĠbeh av\nĠ ur\nĠl ed\nc ome\ne y\nĠcand id\nĠear lier\nĠfoc us\net y\nP ro\nled ge\nix ed\nill ed\nĠpop ular\nA P\nĠset t\nl ight\nĠvar ious\nin ks\nĠlevel s\nĠro ad\nell ig\nab les\nhe l\nitte e\nĠG ener\ny pe\nĠhe ard\nic les\nĠm is\nĠus ers\nĠS an\nĠimpro ve\nĠf ather\nĠse arch\nThe y\nv il\nĠprof ess\nĠkn ew\nĠl oss\nĠev ents\n6 5\nĠb illion\n0 7\n0 2\nĠNew s\nĠA M\nĠco ver\nw here\nens ion\nĠb ott\nĠare as\nen ces\nop e\nĠTw itter\na el\nĠget s\nĠGo ogle\nĠs n\ni ant\nĠv ote\nĠnear ly\nĠinclud ed\nĠrec ogn\nz z\nm m\nal ed\nĠhappen ed\n0 4\nĠh ot\nĠwho se\nĠc ivil\nĠsu ff\no es\nit iz\nĠSy ri\nĠresp ond\nĠh on\nĠfeat ures\nĠeconom ic\nĠApr il\nr im\nĠtechn ology\nĠo ption\nag ing\nĠpur ch\nR e\nĠl at\nch ie\nis l\nĠrec omm\nu f\nĠtr aining\nĠeffect s\nĠf ast\nĠ201 0\nĠocc ur\nĠwebs ite\nĠem ail\nĠs ens\ne ch\nĠo il\nĠinf lu\nĠcurrent ly\nĠS ch\nĠAd d\nĠgo al\nĠsc ient\nĠcon v\n1 00\nem y\nĠdec ided\nĠtra vel\nĠm ention\nL L\n0 3\nĠe lection\nĠph one\nĠlook s\nĠsit uation\nĠc y\nĠh or\nb ed\nĠCour t\na ily\nav es\nĠqu ality\nĠCom p\nw ise\nĠt able\nĠst aff\nĠW ind\net t\nĠtri ed\nide red\nĠadd ition\nĠb ox\nĠl ack\nar ily\nĠw ide\nĠm id\nĠbo ard\nys is\nĠant i\nh a\nĠd ig\nen ing\nĠd ro\nC on\n6 8\nĠsl ow\nb ased\nse qu\nĠp ath\nE x\nak er\nĠwork ed\nĠp en\nĠeng ine\nĠlook ed\nĠSu per\nĠS erv\nĠvict im\nU n\nĠproper ty\nĠint rodu\nĠexec ut\nĠP M\nL e\nĠcol or\nĠM ore\nĠ6 0\nĠnet work\nĠd ate\nc ul\nid ge\nĠext ra\n3 1\nĠs le\n6 7\nĠw ond\nĠreport s\nj ust\nĠAust ral\nĠcap ital\nĠen s\nĠcomm and\nĠallow ed\nĠpre p\nĠca pt\nh ib\nĠnum bers\nch an\nĠf air\nm p\nom s\nĠre ach\nW ith\nt ain\nĠbro ad\nĠcou ple\nec ause\nly ing\nĠF eb\nĠsc reen\nĠl ives\nĠpri or\nĠCong ress\nA r\nĠappro ach\nĠe mer\nar ies\nĠD is\ns erv\nĠN e\nĠbu ilt\nc ies\nĠre pe\nĠrul es\nfor ce\nĠP al\nĠfin ancial\nĠcons idered\nĠCh ar\nn ces\nĠI S\nĠb rought\nĠb i\ni ers\nĠS im\nO P\nĠproduct s\nĠvis it\nĠdoc ument\nĠcon duct\nĠcomplete ly\nin ing\nĠCal if\nib ly\nĠwr itten\nĠT V\nem ents\nĠd raw\nO ne\nĠpub lished\nĠsec ret\nr ain\nhe t\nĠF acebook\nond ay\nĠU p\nĠsex ual\nĠth ous\nĠP at\nĠ ess\nĠstand ard\nĠar m\ng es\nect ion\nĠf ell\nĠfore ign\nan i\nĠFr iday\nĠreg ular\nin ary\nĠincre ased\nĠus ually\nĠdem on\nĠd ark\nĠadd itional\nro l\nĠO f\nĠprodu ction\n! !\nund red\nĠintern ational\nid ents\nĠF ree\nrou p\nĠr ace\nĠm ach\nĠh uge\nA ll\nle ar\nove mber\nĠto wn\nĠatt ention\nĠO ff\ny ond\nĠThe n\nf ield\nĠter ror\nra z\nĠB o\nĠmeet ing\nĠP ark\nĠar rest\nĠf ear\nĠa w\nĠV al\nor ing\n' ,\nĠext reme\nar r\nĠwork ers\nA fter\nĠ3 1\nn et\nam ent\nĠdirect ly\nĠpop ulation\nub e\nĠOct ober\nĠI N\nĠJan uary\n5 9\nĠDav id\nĠc ross\nce mber\nĠF irst\nĠmess age\nir it\nĠn ation\nĠp oll\nis ions\nĠansw er\nn y\nis ode\nĠcar ry\nĠRuss ia\nĠhe ar\neng th\nro y\nĠn atural\nin ally\nĠdo g\nm itted\nĠtr ade\nĠsub st\nĠmult iple\nĠAf ric\nĠf ans\nĠs ort\nĠgl obal\nic ation\nĠW ed\nar a\nĠa chie\nĠlangu age\nve y\nĠt al\nĠnecess ary\nĠdet ails\nĠs en\nĠS und\nĠRe g\nĠR ec\n0 6\nĠs il\nress ive\nĠmed ical\nun ch\norn ia\nĠu nd\nf ort\noc ks\nĠM onday\nues day\nc raft\n7 7\nur t\nĠ ver\nĠH ill\nĠrece ive\nĠmor ning\nes tern\nĠb ank\nĠs at\nir th\nĠH igh\nĠdev ice\nĠTH E\nĠCent er\nĠsaf e\nĠp le\nĠCanad a\nĠsystem s\nĠass ist\nĠsur v\nĠb attle\nĠS oc\nvert is\nS he\nĠp aper\nĠgrow th\nĠc ast\nS c\nĠpl ans\nll ed\nĠpart s\nĠw all\nĠmove ment\nĠpract ice\nim ately\nĠdis play\nĠsomet imes\nom p\nĠP aul\nĠY es\nk ing\n5 8\no ly\nĠs on\nĠav oid\nok es\nĠJ ew\nĠto wards\nas c\nĠ //\nĠK ore\nĠtalk ing\nĠcor rect\nĠsp ent\nic ks\ni able\ne ared\nĠter m\nĠwant s\nom ing\nĠ ut\nĠdou b\nĠfor ces\nĠp lease\n6 9\nĠN ovember\nat form\nond on\nĠon es\nĠimmedi ately\nĠRuss ian\nĠM et\nĠde g\nĠparent s\nC H\nĠAmeric ans\nal y\nĠM od\nĠsh own\nĠcond itions\nĠst uff\nĠre b\nĠY our\nĠinclud es\nn own\nĠS am\nĠexper ien\nm ission\nĠE ven\naugh t\nĠannoun ced\nĠRepublic an\nĠdeter min\nĠdescrib ed\nĠCount y\n( )\nĠdo or\nĠchang ed\nĠne igh\nĠH ere\nĠcle an\nĠp an\nĠDe cember\nĠEurope an\nir ing\nap ter\nĠcl ub\nĠT uesday\nĠp aid\nĠN et\nĠattack s\nĠcharact ers\nĠal one\nĠdirect or\nd om\nĠ3 5\nĠl oad\nĠr out\nĠCalif ornia\nĠfin ally\nĠr ac\nĠcont r\nĠexact ly\nres h\np ri\nĠIs lam\nĠn ature\nĠcare er\nĠlat est\nĠcon vers\nĠS l\np ose\nci ent\nĠIn c\niv ity\n8 8\nĠA tt\nĠM or\nnes day\nĠwe ight\nk en\nĠnot e\nĠteam s\nĠ \\\nair s\nĠG reen\nĠh undred\non ent\nĠstre ng\nĠcons ist\nic ated\nĠreg ul\nĠl ic\nast ic\nĠt en\nurs day\nellig ence\nous ly\nĠU K\nB I\nĠcost s\nĠind epend\nĠA P\nĠnorm al\nĠh om\nĠob vious\nĠs we\nĠst ar\nĠread y\nac her\nĠimp lement\ng est\nĠs ong\nĠG et\nĠL ab\nĠinterest ing\nus ing\nĠg iving\nĠSund ay\nĠet c\nĠm iddle\nĠrem ember\nr ight\nos ition\nut ions\nĠm ax\n4 6\nĠyour self\nĠdem and\nĠtreat ment\nĠd anger\nĠC ons\nĠgu y\nĠBrit ish\nĠphys ical\nĠrel ated\nĠrem ain\nĠcould n\nĠref er\nĠc itiz\nb ox\nEN T\nbo ard\nĠin n\nI G\ner o\nĠSt reet\nosp ital\nren ch\ncher s\nĠst ra\nO L\nag er\nĠA N\nĠeas ily\nI A\nen ge\nin y\nĠcl os\nock ed\nĠus es\nĠC oun\nI m\nu ild\n? ?\nm ore\nĠan g\nĠwr ite\nol ute\n5 7\nĠlead er\nĠread ing\n< /\nĠaut om\nest s\n4 3\nĠleg isl\nĠG old\nĠdesign ed\nĠS T\nĠLe g\na res\nĠbe aut\nĠT ex\nĠappear s\nĠstru gg\nĠR om\nĠ 00\nĠcho ice\nĠparticular ly\nĠF rom\nop er\nĠL ondon\nann ed\nĠallow s\nob ile\nĠdiffere nce\nâĢ ¢\nĠV iew\nĠWed nesday\nĠal though\nĠrel ative\nĠapplic ation\nate ver\nĠare n\nĠmy self\nĠim ag\nĠdis e\nĠsoc iety\nĠfre qu\nĠEng lish\nĠpo or\nĠD ay\nĠwrit ing\nĠse ven\nĠstart ing\nĠb ud\nĠpr int\nĠTr ans\nuf act\nĠSt ud\nn ew\nĠcr im\nĠg ives\nĠco ol\na e\ni ance\nĠGener al\nĠthink ing\nĠsa ve\nĠlim ited\nĠPart y\nĠmean ing\np en\now ers\nĠJ ack\nE M\nĠn ice\nru pt\nĠg as\nĠe ight\nĠfe et\nĠeff ort\nĠ ign\nic it\nB l\nco in\nĠop in\nĠbr ain\nWh ile\nhe st\nĠTh ursday\nĠwould n\naugh ter\nĠtou ch\nle ments\nĠstud ies\nĠcent er\nc ont\nor ge\nĠcomput er\nĠinvestig ation\nP l\nor ks\nĠ200 8\nĠincre asing\nĠst ore\nĠcom ments\nĠb al\nm en\nĠdo ll\nĠl iber\nĠw ife\nĠlaw s\natur day\nit ness\nĠmod ern\nĠS k\nĠadminist ration\nĠopportun ity\nĠs al\nĠpower ful\nM y\nĠclaim s\nĠEar th\nord s\nĠt itle\nĠes c\nn ame\nN ot\nom en\nĠbe yond\nĠc amer\nĠse ll\nit ute\near ch\nĠapp l\nim ent\n4 2\nĠAr t\nĠun f\nĠviol ence\nur g\nĠE ast\nĠcomp ared\nĠopt ions\nĠthrough out\nĠv s\nig r\n. [\nac hes\n7 8\nĠfil es\nF L\nE L\nar ian\nĠJ ames\nĠA ir\nan ch\nĠdet ail\nĠpie ce\nP S\nĠn amed\nĠeduc ation\nĠdri ve\nĠitem s\nĠstud ent\nic ed\n: :\nic o\nĠth row\nĠsc ene\nĠcomple x\nĠ200 9\nĠpre c\nĠB re\n7 9\nĠcon cept\nĠstat us\nam ing\nĠd ied\nĠknow ledge\nĠbegin ning\nO D\nru ary\nĠcertain ly\nĠgu ys\nĠsl ight\nin n\nound s\nĠf ine\nĠf at\nic ations\nĠper haps\nĠA nt\nĠinc ome\nĠhtt ps\nĠmajor ity\nport s\nst on\nĠgreat er\nĠfe ed\nent ially\nĠsaf ety\nĠun ique\nand om\nĠg one\nĠshow ed\nĠhist or\nĠcoun ter\ni us\nid a\nĠlead ing\ni pe\nĠs end\nĠDon ald\ner ve\nĠdef ense\nines e\nĠy es\nĠF ire\nĠMus lim\nra q\nĠcontin ued\nos h\nĠprov ides\nĠpr ison\nĠP re\nĠhapp y\nĠeconom y\nĠtr ust\nag s\nĠG ame\nĠweap ons\num an\nĠC le\nit ation\nĠanal ysis\nĠT imes\nĠsc ience\n- >\nĠfig ure\nĠdis app\nent y\nĠsoft ware\nĠu lt\nĠoffic ers\nN ew\nI s\nĠrem ains\nĠInd ia\nĠp sych\nri ef\nĠc at\nes c\nĠob serv\nĠst age\nĠD ark\nĠent er\nch ange\nĠpass ed\nĠdes pite\nĠO ut\nĠmov ie\nr s\nĠv oice\nm ine\nĠPl ay\nĠto ward\nĠT er\nĠreg ion\nĠval ues\nor ters\nĠm ount\nĠoffic er\nĠO ther\nb an\nĠh ous\nw ood\nro om\nI V\nĠS un\nse e\nĠO ver\nro g\n9 0\nĠl ay\nĠT ur\na wn\nĠpress ure\nĠS ub\nĠbook s\ned om\nĠS and\nA A\nag o\nĠre asons\nf ord\nĠactiv ity\nU T\nN ow\nĠSen ate\nce ll\nn ight\nĠcall s\nin ter\nĠlet ter\nĠR ob\nĠJ e\nĠcho ose\nĠL aw\nG et\nB e\nĠro b\nĠtyp es\nĠpl atform\nĠqu arter\nR A\nĠT ime\nĠmay be\nĠC r\n9 5\np re\nĠmov ing\nĠl if\nĠgo ld\nĠs om\nĠpat ients\nĠtr uth\nĠK e\nur ance\nant ly\nm ar\nĠchar ge\nĠG reat\nĠce le\n---------------- ----------------\nĠro ck\nro id\nan cy\nĠcred it\na ud\nB y\nĠE very\nĠmov ed\ning er\nrib ution\nĠn ames\nĠstra ight\nĠHe alth\nĠW ell\nĠfe ature\nĠr ule\nĠsc he\nin ated\nĠMich ael\nber g\n4 1\nil ed\nb and\nĠcl ick\nĠAng el\non ents\n Ń\nĠI raq\nĠS aturday\nĠa ware\np art\nĠpat tern\nO W\nĠL et\nĠgr ad\nign ed\nĠassoci ated\nĠst yle\nn o\ni ation\na ith\nil ies\nĠst ories\nur ation\nĠindividual s\nĠâĢ ¦\nm iss\nĠAss oci\nish ing\nab y\nĠsum mer\nĠB en\nĠ3 2\nĠar ch\nut y\nĠTex as\nh ol\nĠfull y\nĠm ill\nĠfollow ed\nĠB ill\nĠInd ian\nĠSec ret\nĠB el\nĠFeb ruary\nĠjob s\nĠseem ed\nĠGo vern\ni pped\nĠreal ity\nĠl ines\nĠp ark\nĠmeas ure\nĠO ur\nI M\nĠbro ther\nĠgrow ing\nĠb an\nĠest im\nĠc ry\nĠS chool\nĠme chan\nĠO F\nĠWind ows\nĠr ates\nĠO h\nĠpos itive\nĠcult ure\nist ics\nic a\nĠh ar\ny a\nite ly\ni pp\nĠm ap\nen cies\nĠWill iam\nI I\nak ers\n5 6\nĠM art\nĠR em\nĠal tern\nit ude\nĠco ach\nrow d\nD on\nĠk ids\nĠj ournal\nĠcor por\nĠf alse\nĠwe b\nĠsle ep\nĠcont ain\nĠst o\nĠb ed\niver se\nĠR ich\nĠCh inese\nĠp un\nĠme ant\nk nown\nĠnot ice\nĠfavor ite\na ven\nĠcond ition\nĠpur pose\n) )\nĠorgan ization\nĠchall eng\nĠman ufact\nĠsus p\nĠA c\nĠcrit ic\nun es\nuc lear\nĠm er\nvent ion\nĠ8 0\nĠm ist\nĠU s\nĠT or\nhtt p\nol f\nĠlarg er\nĠadv ant\nĠrese ar\nĠact ions\nm l\nĠke pt\nĠa im\n, '\nc ol\nĠbenef its\nif ying\nĠact ual\nĠIntern ational\nĠveh icle\nĠch ief\nĠeff orts\nĠLe ague\nĠM ost\nĠwa it\nĠad ult\nĠover all\nĠspe ech\nĠhigh ly\nĠfem ale\nĠer ror\nĠeffect ive\n5 4\nĠenc our\nw ell\nĠfail ed\nĠcons erv\nĠprogram s\nĠt rou\nĠa head\n5 00\nvertis ement\nI P\nĠF ound\np ir\nĠ %\nĠcr ime\nand er\nĠloc ation\nĠI ran\nĠbehav ior\naz ing\nĠr are\nĠem b\nĠca used\nĠsh ip\nĠact ive\nĠcont ribut\nĠg reen\nĠac qu\nĠref lect\nven ue\nĠf irm\nĠb irth\n] .\nĠclear ly\nĠem ot\nĠag ency\nri age\nĠmem ory\n9 8\nS A\nĠSe e\nac ing\nC C\nĠbig gest\nĠr ap\nĠbas ic\nĠb and\ne at\nĠsus pect\nĠM ac\nĠ9 0\nm ark\nist an\nĠsp read\nam s\nk i\nas y\nra v\nĠR ober\nĠdemon str\nr ated\nĠabs olute\nĠpl aces\nĠim pl\nibr ary\nĠc ards\nĠdest roy\nĠv irt\nve re\nĠapp eared\ny an\np oint\nĠbe g\nĠtem per\ns pe\nant ed\near s\nĠD irect\nĠl ength\nĠbl og\nam b\nĠint eg\nĠres ources\nac c\nif ul\nĠsp ot\nĠfor ced\nĠthous ands\nĠMin ister\nĠqu al\nĠF rench\nat ically\nĠgener ally\nĠdr ink\nĠth us\nI L\nod es\nĠappro pri\nĠRe ad\nĠwh om\nĠey e\nĠcol lege\nĠ4 5\nire ction\nĠens ure\nĠapp arent\nid ers\nĠrelig ious\nĠmin or\nol ic\nĠt ro\nĠWh y\nrib ute\nm et\nĠprim ary\nĠdevelop ed\nĠpe ace\nĠsk in\nst e\nav a\nĠbl ue\nĠfam ilies\nĠ ir\nĠapp ly\nĠin form\nĠSm ith\nC T\ni i\nĠlim it\nĠres ist\n........ ........\num n\nĠconf lic\nĠtw e\nud d\nĠT om\nĠl iter\nqu e\nb on\nĠha ir\nĠevent ually\nĠp us\nĠhelp ed\nĠag g\nor ney\nĠApp le\nĠf it\nĠS ur\nĠpre m\nĠs ales\nĠsecond s\nĠstreng th\nĠfeel ing\n¿ ½\nĠt our\nĠknow s\no om\nĠex erc\nĠsom ew\nï ¿½\n> >\nĠsp okes\nĠide as\nĠreg ist\nso ft\nĠD el\nĠP C\nĠpro pos\nĠlaun ch\nĠbott om\nT H\nĠP lease\nv est\nit z\nĠIn ter\nĠsc ript\nĠr at\nar ning\nĠ il\nĠJ er\nĠA re\nĠwh atever\nok en\nci ence\nĠmod e\nĠag ree\nĠs ources\nĠinit ial\nĠrest rict\nĠwond er\nus ion\n## ##\nĠS il\nvil le\nĠb urn\nt w\nas ion\nĠ £\nĠn or\nu ing\nĠre ached\nĠs un\nĠc ateg\nig ration\nĠc ook\nĠprom ot\nĠm ale\nĠcl imate\nĠf ix\nĠalleg ed\nU R\nall ed\nĠim ages\nC ont\not a\nĠschool s\ni os\nĠd rop\nĠst ream\nĠM o\nĠprevious ly\nal ing\nĠp et\nĠdou ble\nĠ( @\nann el\nĠdef ault\nt ies\nĠr ank\nĠD ec\nĠCoun cil\nĠweap on\nĠst ock\nĠanal y\nĠSt r\nĠpict ure\nĠPol ice\nf erence\nĠcent ury\nĠcitiz ens\nĠon to\nĠexp and\nĠhe ro\nĠS ol\nĠw ild\nĠupd ate\nĠcustom ers\nr ont\nd ef\nĠl ik\nĠcrim inal\nĠChrist ian\nS P\n7 6\nĠle aving\nĠother wise\nĠD ist\nĠbas is\n5 2\n5 3\nic ip\nĠB er\nĠrecomm end\nĠfl oor\nĠc rowd\nol es\nĠ7 0\nĠcent ral\nĠE v\nĠd ream\nĠdown load\nĠconf ir\nĠTh om\nĠwind ow\nĠhapp ens\nĠun it\nĠt end\nĠs pl\nĠbec omes\nĠfight ing\nĠpred ict\nĠP ress\nĠP ower\nĠhe avy\nak ed\nĠf an\nor ter\nate gy\nB A\niz es\nĠsp end\nH ere\nĠ200 7\nĠad op\nĠH am\nĠfoot ball\nĠP ort\nod ay\n5 1\namp ions\nĠtrans fer\nh t\nĠ3 8\nter m\nac ity\nĠb ur\n] ,\ntern al\nr ig\nb ut\nĠthere fore\nĠB ecause\nres p\nre y\nĠm ission\nS ome\nĠnot ed\nĠass um\nĠdise ase\nĠed it\nĠprog ress\nr d\nĠB rown\noc al\nĠadd ing\nĠra ised\nĠAn y\nĠt ick\nĠsee ing\nĠPe ople\nĠagre ement\nĠser ver\nĠw at\nĠdeb ate\nĠsupp osed\nil ing\nĠlarg est\nĠsuccess ful\nĠP ri\nĠDemocr atic\nĠj ump\nĠSyri a\nĠown ers\nĠoff ers\nĠshoot ing\nĠeff ic\nse y\nĠha ven\nver se\nte red\nĠL ight\nim al\nĠB ig\nĠdef end\nĠbe at\nĠrecord s\n% )\nĠsc en\nĠemploy ees\nĠdev ices\nhe m\nĠcom mer\nĠM ex\nĠbenef it\nĠPro f\nĠil leg\nĠsur face\nĠAl so\nĠh arm\ning ly\nw ide\nĠA lex\nĠsh ut\nĠC ur\nĠl ose\np m\nĠchall enge\nse mb\nĠst ation\nĠint elligence\nĠacc ur\nĠFl or\nĠrequ ires\nĠM al\nb um\nĠh ospital\nĠsp irit\nĠoff ered\nĠprodu ce\nĠComm un\nĠcreat ing\nĠcr is\ns pect\nĠend ed\nĠd aily\nĠvot ers\nland s\ni as\ni h\non a\nĠsm art\nĠOff ice\nĠL ord\nri al\nĠIntern et\nĠcirc um\nĠextreme ly\n' .\nĠopin ion\nĠM il\nĠg ain\nB S\nĠF in\ny p\nĠuse ful\nĠbud get\nĠcom fort\nis f\nĠback ground\nel ine\nĠep isode\nĠen emy\nĠtri al\nĠestab lish\nd ate\nĠC ap\nĠcontin ues\nĠshow ing\nĠUn ion\nw ith\nĠpost ed\nĠSy stem\nĠe at\nri an\nĠr ise\nĠGerman y\nil s\nĠsign ed\nĠv ill\nĠgr and\nm or\nĠEng land\nĠproject s\num ber\nĠconf erence\nz a\nĠrespons ible\nĠAr ab\nĠlearn ed\nâĢĶ âĢĶ\ni pping\nĠGe orge\nO C\nĠreturn ed\nĠAustral ia\nĠb rief\nQ u\nĠbr and\nill ing\nab led\nĠhig hest\nĠtr ain\nĠComm ission\nwh ile\nĠn om\ncept ion\nĠm ut\nĠBl ue\nĠinc ident\nv ant\n8 6\nĠI D\nĠn uclear\n7 4\nĠL ike\nĠR E\nĠM icro\nl i\nm ail\nĠcharg es\n8 9\nĠad just\nad o\nĠear th\nN A\nĠpr ices\nP A\nĠd raft\nĠrun s\nĠcandid ate\nens es\nĠmanag ement\nĠPh il\nĠM iss\nĠte ach\ng ram\nĠunderstand ing\na it\nic ago\nA dd\nĠE p\nsec ut\nĠsepar ate\nĠinst ance\nĠe th\nĠun less\n**** ****\nĠF ore\nin ate\nĠoper ations\nS p\nĠf aith\ng ar\nĠCh urch\nron ic\nĠconf ig\nos ure\nĠactiv ities\nĠtrad itional\nĠ3 6\nĠd irection\nĠmach ine\nĠsur round\nĠp ush\nun ction\nĠE U\nĠeas ier\nĠarg ument\nG B\nĠm icro\nĠsp ending\niz ations\nĠthe ory\nad ow\nĠcall ing\nĠL ast\nĠd er\nĠinflu ence\nĠcomm it\nĠph oto\nĠun c\nist ry\ng n\nast e\nack s\nĠdis p\nad y\nd o\nĠG ood\nĠ `\nĠw ish\nĠreve aled\nÂł Âł\nl ig\nĠen force\nĠComm ittee\nĠche m\nĠmil es\nĠinterest ed\nĠsol ution\nic y\nin ct\nĠ- >\nĠD et\nĠrem oved\nĠcomp ar\ne ah\nĠpl ant\nĠS ince\nĠachie ve\nĠadvant age\nĠslight ly\nb ing\nĠpl aced\nu nder\n201 5\nĠM ad\nĠt im\nos es\nĠc ru\nĠR ock\nĠmost ly\nĠneg ative\nĠset ting\nĠprodu ced\nĠm ur\nĠconnect ion\nĠM er\nĠdri ver\nĠexecut ive\nĠass ault\nĠb orn\nĠV er\nt ained\nĠstruct ure\nĠredu ce\nĠdec ades\nĠd ed\nu ke\nĠM any\nidd en\nĠle ague\nS e\nĠjo in\nĠdis co\nĠd ie\nc ks\nact ions\nĠass ess\nag n\nĠgo als\nour s\nI R\nĠsen ior\nill er\nm od\nip ment\noc ol\nu y\nĠQ ue\nĠpart ies\nir gin\nĠle arning\nit able\nĠstre et\nĠcamer a\nA pp\nĠsk ills\nb re\nc ious\nĠcele br\nĠFr anc\nĠexist ing\nĠwill ing\nl or\nĠ id\nĠSp ace\nĠcrit ical\nĠL a\nortun ately\nĠser ve\nĠc old\nĠspec ies\nT S\nĠanim als\nĠB ay\nĠold er\nĠU nder\nest ic\nĠT re\nĠte acher\nĠpre fer\nv is\nĠth read\nĠM att\nĠmanag er\nãĥ »\nĠprofess ional\nĠV ol\nĠnot es\nThe se\nul a\nĠf resh\nent ed\nu zz\ned y\nclus ion\nĠR el\nĠdoub t\nE O\nĠopen ed\nĠB it\nAd vertisement\nĠgu ess\nĠU N\nĠse qu\nĠexpl ain\nott en\nĠatt ract\nak s\nĠstr ing\nĠcont ext\noss ible\nĠRepublic ans\nĠsol id\nĠc ities\nĠask ing\nĠr andom\nu ps\nur ies\nar ant\ndd en\ng l\nĠFlor ida\nĠdep end\nĠSc ott\nĠ3 3\nĠi T\nic on\nĠmention ed\nĠ2 000\nĠclaim ed\nĠdefin itely\nul f\nĠc ore\nĠopen ing\nĠCon st\nwh ich\nĠT ra\nA G\n7 2\nĠbelie ved\nad a\nĠ4 8\nĠSec urity\nyr ight\nĠP et\nĠL ou\nĠhold ing\n======== ========\nĠ ice\nĠb row\nĠauthor ities\nh ost\nw ord\nĠsc ore\nĠD iv\nĠcell s\nĠtrans l\nĠneigh bor\nĠrem ove\nu ct\nĠdist rict\nĠA ccording\nĠwor se\nĠconcern s\nĠpresident ial\nĠpolic ies\nĠH all\n7 3\nĠh us\nA Y\nĠ200 6\nĠJ ud\nĠindepend ent\nĠJust ice\nili ar\npr int\nigh ter\nĠprotect ion\nz en\nĠsu dden\nh ouse\nĠJ es\nP R\nĠIn f\nĠb ul\nĠ _\nĠServ ice\nĠP R\nĠstr ategy\nff ect\nĠgirl s\nĠmiss ing\noy al\nĠTe am\nul ated\nĠd at\nĠpolit ics\nab or\nA ccording\nĠspe ll\nĠg raph\nort hern\nT C\nA b\nĠlab or\nis her\nĠk ick\nĠiT unes\nĠstep s\npos es\nĠsmall er\nE n\nber t\nĠro ll\nĠresear chers\nĠcl osed\nĠtrans port\nĠlaw y\n________ ________\nĠCh icago\nĠas pect\nĠn one\nĠmar riage\n9 6\nĠe lements\nĠF re\nĠS al\nĠd ram\nF C\nt op\ne qu\nĠhe aring\nĠsupport ed\nĠtest ing\nco hol\nĠmass ive\nĠst ick\nĠgu ard\nis co\nph one\nF rom\nHow ever\nĠb order\nĠcop y\nograph y\nl ist\n7 1\nĠown er\ncl ass\nru it\nr ate\nĠO nce\nĠdig ital\nĠt ask\nER S\nĠinc red\nt es\n+ +\nĠFr ance\nĠb reat\now l\nĠiss ued\nĠW estern\nĠdet ect\nĠpart ners\nĠsh ared\nĠC all\nĠcan cer\nac he\nrib e\nĠexpl ained\nĠhe at\n{ \"\nĠinvest ment\nĠB ook\nĠw ood\nĠtool s\nĠAl though\nĠbelie f\nĠcris is\nĠg e\nĠM P\nĠoper ation\nty pe\n~ ~\ng a\nĠcont ains\nant a\nĠexp ress\nĠG roup\nĠJ ournal\nk a\nĠam b\nĠUS A\nĠfind ing\nĠfund ing\nh ow\nĠestab lished\nide os\nĠdeg ree\nĠdanger ous\nang ing\nĠfre edom\npp ort\nout hern\nĠch urch\nĠc atch\nĠTw o\nĠpres ence\nĠGu ard\nU p\nĠauthor ity\nĠPro ject\nĠbut ton\nĠcon sequ\nĠval id\nĠwe ak\nĠstart s\nĠref erence\nĠM em\n\" )\nU N\nor age\nĠO pen\nĠcol lection\ny m\ng ency\nĠbeaut iful\nro s\nĠtell s\nĠwa iting\nn el\nĠprov iding\nĠDemocr ats\nĠd aughter\nĠm aster\nĠpur poses\nĠJapan ese\nĠequ al\nĠturn s\nĠdoc uments\nĠwatch ing\nR es\nĠr an\n201 4\nĠre ject\nĠKore a\nĠvictim s\nLe vel\nere nces\nĠw itness\nĠ3 4\nĠre form\ncom ing\nĠocc up\nĠc aught\nĠtra ffic\nad ing\nĠmod els\nar io\nĠserv ed\nĠb atter\nu ate\nĠSecret ary\nĠagre ed\nĠtr uly\nyn am\nĠR et\nĠun its\nĠRes earch\nh and\naz ine\nĠM ike\nĠvar iety\not al\nĠam azing\nĠconfir med\nĠentire ly\nĠpurch ase\nĠe lement\nĠc ash\nĠdeter mine\nD e\nĠc ars\nĠW all\nâ ĸ\nĠview s\nĠdrug s\nĠdep artment\nĠSt ep\nu it\nĠ3 9\nas ure\nĠCl ass\nĠc overed\nĠB ank\nĠme re\nu ana\nĠmult i\nĠm ix\nĠun like\nlev ision\nĠsto pped\nĠs em\nĠG al\nul es\nĠwe l\nĠJohn son\nl a\nĠsk ill\nĠbec oming\nri e\nĠappropri ate\nf e\nell ow\nĠPro t\nul ate\noc ation\nĠweek end\nod ies\nĠsit es\nĠanim al\nĠT im\nĠsc ale\nĠcharg ed\nĠinst ruct\nill a\nĠmethod s\nĠc ert\nĠjud ge\nĠH el\nĠdoll ars\nĠstand ing\nĠS qu\nĠdeb t\nl iam\nĠdri ving\nĠS um\nĠEd ition\nĠal bum\nand on\nI F\nĠU k\n6 3\nad er\nĠcommer cial\nes h\nĠGovern ment\nĠdisc overed\nĠout put\nĠHill ary\nĠCar ol\nĠ200 5\nĠab use\nanc ing\nĠsw itch\nĠann ual\nT w\nĠst ated\nag ement\nin ner\nĠdem ocr\nĠres idents\nĠallow ing\nĠfact ors\nod d\nĠf uck\nem ies\nĠoccur red\not i\nĠn orth\nĠP ublic\nĠinj ury\nĠins urance\nC L\noll y\nã Ģ\nĠrepe ated\nĠar ms\nang ed\nĠconst ruction\nĠf le\nP U\nic ians\nĠfor ms\nĠMc C\nant ic\nĠm ental\np ire\nĠequ ipment\nĠf ant\nĠdiscuss ion\nĠregard ing\nk in\nar p\nĠch air\nog ue\nĠpro ceed\nĠI d\nO ur\nĠmur der\nM an\nĠ4 9\nas p\nĠsupp ly\nĠin put\nĠwe alth\nliam ent\nĠpro ced\nor ial\nĠSt at\nĠN FL\nhen s\nĠInst itute\nĠput ting\nourn ament\net ic\nĠloc ated\nĠk id\ner ia\nr un\nĠpr inc\nĠ !\ngo ing\nĠB et\nĠcl ot\nĠtell ing\nĠprop osed\ni ot\nor ry\nĠfund s\ng ment\nĠL ife\nĠb aby\nĠB ack\nĠsp oke\nIm age\nĠear n\nĠA T\ng u\nĠex change\nĠL in\nov ing\nĠp air\nM ore\naz on\nĠarrest ed\nĠkill ing\nc an\nĠC ard\ny d\nĠident ified\nĠm obile\nĠthan ks\nony m\nĠF orm\nĠhundred s\nĠCh ris\nĠC at\nĠtre nd\nh at\nĠA v\nom an\nĠelect ric\nĠW il\nS E\nO f\nĠrest aur\not ed\nĠtr ig\nĠn ine\nĠb omb\nWh y\n ¯\nĠco verage\nĠapp eal\nĠRober t\nĠS up\nĠfin ished\nĠfl ow\nĠdel iver\nĠcal cul\nĠphot os\nĠph il\nĠpie ces\nĠapp re\nk es\nĠr ough\nD o\nĠpart ner\nĠconcern ed\nĠ3 7\nĠG en\nC ol\nct ors\nĠ= >\nst ate\nĠsuggest ed\nĠFor ce\nC E\nĠher self\nĠPl an\nw orks\no oth\nren cy\nĠcor ner\nĠhus band\nĠintern et\nĠA ut\nem s\nos en\nĠAt l\ng en\nĠbal ance\n6 2\nĠsound s\nte xt\nĠar r\nov es\nĠmill ions\nĠrad io\nĠsat isf\nĠD am\nM r\nG o\nS pe\nĠcomb at\nr ant\nĠG ree\nĠf uel\nĠdist ance\nĠtest s\nĠdec re\nĠE r\nĠman aged\nD S\nĠt it\nĠmeas ures\nĠL iber\nĠatt end\nas hed\nĠJ ose\nĠN ight\nd it\nĠN ov\nĠE nd\nout s\nĠgener ation\nĠadv oc\ny th\nĠconvers ation\nĠS ky\nact ive\nce l\nri er\nĠFr ank\nĠg ender\nĠcon cent\nĠcar ried\nand a\nĠV irgin\nĠarri ved\nic ide\nad ed\nĠfail ure\nĠmin imum\nle ts\nĠwor st\nĠkeep ing\nĠint ended\nĠilleg al\nĠsub sc\nĠdetermin ed\nĠtri p\nY es\nĠra ise\nĠ ~\nĠfeel s\nĠpack age\nĠJ o\nh i\n201 6\nre al\nĠf ra\nĠsy mb\nM e\nuck y\np ret\nĠK h\nĠEd it\nĠWe b\nem ic\nĠCol or\nĠjust ice\nI nt\nĠfar m\nck now\n\" >\nel ess\nĠredu ced\nĠ5 00\nx x\nĠR ad\nĠW ood\nĠcl in\nĠhy p\nil er\nur a\nk ins\n8 5\n6 1\nĠThe ir\nĠM ary\nĠs an\nĠno vel\nĠWh o\nĠcap acity\nĠimp ossible\nĠpl ays\nĠmin ister\nij uana\nic ate\nĠS et\nĠf ram\nĠ ing\nĠcommun ities\nĠF BI\nit a\nĠb on\nĠstr ateg\nĠinterest s\nl ock\ng ers\nm as\nĠAN D\nĠconflic t\nĠrequire ments\nĠs ac\nĠoper ating\nin i\nrel ated\nĠcomm itted\nĠrelative ly\nĠs outh\n¯ ¯\nĠaff ord\nĠident ity\nĠdec isions\nĠacc used\npl ace\nĠvict ory\no ch\ni at\nN ame\nC om\nt ion\ned s\nĠsee k\nĠt ight\nĠIm ages\nĠinit i\nĠhum ans\nĠfam iliar\nĠaud ience\nĠintern al\nvent ure\nĠs ides\nĠT O\nĠd im\nĠcon clud\nĠapp oint\nĠenforce ment\nĠJ im\nĠAssoci ation\nĠcircum st\nĠCanad ian\nĠjo ined\nĠdiffere nces\nĠL os\nĠprot est\nĠtw ice\nw in\nĠgl ass\nars h\nĠAr my\nĠexp ression\nĠdec ide\nĠplan ning\nan ia\nĠhand le\nĠMicro soft\nĠN or\nĠmax imum\nĠRe v\nĠse a\nĠev al\nĠhel ps\nre f\nĠb ound\nĠm outh\nĠstand ards\nĠcl im\nĠC amp\nĠF ox\ncl es\nĠar my\nĠTe chn\nack ing\nx y\nS S\nĠ4 2\nĠbu g\nĠUk rain\nĠM ax\nĠJ ones\nĠSh ow\nl o\nĠplan et\nĠ7 5\nĠwin ning\nĠf aster\nĠspe ct\nĠbro ken\nT R\nĠdef ined\nĠhealth y\nĠcompet ition\nhtt ps\nĠIs land\nĠF e\nĠannoun ce\nĠC up\nĠInst ead\nĠcl ient\nĠposs ibly\nse ction\nock et\nl ook\nĠfin ish\nĠcre w\nĠres erv\nĠed itor\nĠh ate\nĠs ale\nĠcontro vers\nĠp ages\nw ing\nĠnum er\nĠopp osition\nĠ200 4\nĠref uge\nĠfl ight\nĠap art\nĠL at\nA meric\nĠAfric a\nĠapplic ations\nĠPal est\nĠB ur\nĠg ar\nĠSoc ial\nĠup gr\nĠsh ape\nĠspe aking\nans ion\na o\nĠS n\nĠwor ry\nĠBrit ain\nP lease\nrou d\nĠh un\nĠintrodu ced\nĠd iet\nI nd\nĠSec ond\nĠfun ctions\nut s\nĠE ach\nĠJe ff\nĠst ress\nĠaccount s\nĠgu arant\nĠAn n\ned ia\nĠhon est\nĠt ree\nĠAfric an\nĠB ush\n} ,\nĠs ch\nĠOn ly\nĠf if\nig an\nĠexerc ise\nĠEx p\nĠscient ists\nĠlegisl ation\nĠW ork\nĠS pr\nà Ĥ\nĠH uman\nĠ è\nĠsur vey\nĠr ich\nri p\nĠmain tain\nĠfl o\nĠleaders hip\nst ream\nĠIslam ic\nĠ 01\nĠCol lege\nĠmag ic\nĠPr ime\nĠfig ures\n201 7\nind er\nx ual\nĠDe ad\nĠabsolute ly\nĠfour th\nĠpresent ed\nresp ond\nrib le\nĠal cohol\nat o\nĠD E\npor ary\nĠgr ab\nĠvar i\nĠqu ant\nĠPh oto\nĠpl us\nr ick\nar ks\nĠaltern ative\nĠp il\nĠappro x\nth at\nĠobject s\nĠR o\nĠAnd roid\nĠsignificant ly\nĠR oad\nk ay\nR ead\nav or\nĠa cknow\nĠH D\nĠS ing\nO r\nĠM ont\nĠun s\npro f\nĠneg oti\nĠAr ch\nik i\nĠte levision\nĠJew ish\nĠcomm ittee\nĠmot or\nĠappear ance\nĠs itting\nĠstri ke\nĠD own\ncom p\nĠH ist\nĠf old\nac ement\nĠLou is\nĠbel ong\nĠâĢ ¢\nĠm ort\nĠprep ared\nĠ6 4\nĠM aster\nĠind eed\nĠD en\nĠre nt\nT A\nour ney\nar c\nS u\n9 7\nĠadv ice\nĠchang ing\nĠlist ed\nĠlaun ched\nis ation\nĠP eter\nis hes\nĠl ived\nĠM el\nĠSup reme\nĠF ederal\nĠ) ;\nruct ure\nĠset s\nĠphil os\nu ous\nĠ ł\nĠappl ied\nĠN OT\nĠhous ing\nĠM ount\nĠo dd\nĠsu st\nD A\nffic ient\nĠ ?\nol ved\nĠp owers\nĠth r\nĠrem aining\nĠW ater\nL C\nĠca uses\nãģ ®\nĠman ner\nad s\nĠsuggest s\nĠend s\nstand ing\nf ig\nĠD un\nid th\nĠg ay\nĠter min\nĠAngel es\nM S\nĠscient ific\nĠco al\nap ers\nb ar\nĠThom as\nĠsy m\nĠR un\nth is\nP C\nigr ants\nĠmin ute\nĠDist rict\ncell ent\nĠle aves\nĠcomple ted\nam in\nĠfoc used\nĠmon itor\nĠveh icles\nM A\nĠM ass\nĠGr and\nĠaffect ed\nitution al\nĠconst ruct\nĠfollow s\nĠt on\nre ens\nĠh omes\nĠE xt\nĠLe vel\nr ast\nĠI r\nĠel im\nĠlarge ly\nĠJ oe\nĠvot es\nall s\nĠbusiness es\nĠFound ation\nĠCent ral\nĠy ards\nĠmaterial s\nul ner\nĠgu ide\nĠclos er\num s\nĠsp orts\ned er\nJ ust\nĠtax es\n8 4\nĠO ld\nĠdec ade\nol a\nĠv ir\nĠdro pped\nĠdel ay\nit ect\nĠsec ure\nste in\nle vel\nĠtre ated\nĠfil ed\nain e\nĠv an\nĠm ir\nĠcol umn\nict ed\ne per\nĠro t\nĠcons ult\nĠent ry\nĠmar ijuana\nĠD ou\nĠapparent ly\nok ing\nclus ive\nĠincre ases\nan o\nĠspecific ally\nĠte le\nens ions\nĠrelig ion\nab ilities\nĠfr ame\nĠN ote\nĠLe e\nĠhelp ing\nĠed ge\nost on\nĠorgan izations\nà ĥ\nĠB oth\nhip s\nĠbig ger\nĠbo ost\nĠSt and\nĠro w\nul s\nab ase\nĠr id\nL et\nare n\nra ve\nĠst ret\nP D\nĠv ision\nĠwe aring\nĠappre ci\nĠa ward\nĠU se\nĠfact or\nw ar\nul ations\n) (\nĠg od\nĠter rit\nĠpar am\nast s\n8 7\nĠen emies\nĠG ames\nF F\nĠacc ident\nW ell\nĠMart in\nT ER\nĠat h\nĠHe ll\nĠfor g\nĠve ter\nĠMed ic\nf ree\nĠst ars\nĠexp ensive\nĠac ad\nra wn\nĠW he\nĠl ock\nĠform at\nĠsold iers\ns m\nĠag ent\nĠrespons ibility\nor a\nĠS cience\nĠrap id\nĠt ough\nĠJes us\nĠbelie ves\nM L\nĠwe ar\nle te\nÃĥ ÃĤ\nĠD ri\nĠcomm ission\nĠB ob\nO h\nap ed\nĠwar m\nÃĥÃĤ ÃĥÃĤ\nĠ200 3\nort ion\nĠhas n\nust er\nĠun ivers\nĠI ll\nĠk ing\nolog ies\n9 4\nĠT em\nĠM os\nĠpat ient\nĠMex ico\nce an\nĠDe ath\nĠSand ers\ny ou\nĠC ast\nĠComp any\npt y\nĠhappen ing\nF P\nĠB attle\nĠb ought\nA m\nM od\nU s\nut ers\nĠC re\nĠTh ose\nĠ4 4\nis er\nĠs oul\nĠT op\nĠHar ry\nĠA w\nĠse at\nff ee\nĠrev olution\nĠ( \"\nĠD uring\net te\nĠr ing\nĠoff ensive\nĠreturn s\nĠv ideos\nĠdis cl\nĠfam ous\nen ced\nĠS ign\nĠR iver\nĠ3 00\nP M\nĠB us\nĠC H\nĠcandid ates\nard en\nĠpercent age\nĠvis ual\nĠthan k\nĠtrou ble\nner gy\nĠ200 1\nĠpro ve\nash ion\nĠen h\nĠL ong\nU M\nĠconnect ed\nĠposs ibility\nO ver\nĠexper t\nĠl ibrary\nart s\nĠDirect or\nĠfell ow\n9 2\nir ty\nĠd ry\nĠsign s\nĠL ove\nĠqu iet\nf oot\nĠp ure\nĠH un\nĠf illed\nph as\nĠE lect\nend ment\nĠEx pl\nĠun able\nn s\nm o\nĠv ast\nob e\nĠident ify\napp ing\nĠCarol ina\ng ress\nĠpro te\nĠf ish\nĠcircumst ances\nraz y\nĠPh ot\nĠb odies\nĠM ur\nĠdevelop ing\nĠA R\nĠexperien ced\nĠsubst ant\nĠBo ard\nes ome\nĠdom estic\nĠcomb ined\nĠP ut\nĠchem ical\nĠCh ild\nĠpo ol\nĠC y\nĠe gg\nc ons\nst ers\nĠh urt\nĠmark ets\nĠconserv ative\nĠsupp orters\nĠag encies\nid el\nO b\nur b\nĠ4 3\nĠDef ense\ny e\nĠA p\ndu le\nĠtemper ature\nĠconduct ed\nĠCh ief\nĠpull ed\nĠf ol\nL ast\nont o\nos is\nV ER\nD es\nĠP an\nF irst\nĠadv ance\nĠlic ense\nr ors\nĠJ on\nĠimag ine\nĠhe ll\nĠf ixed\nĠinc or\nos ite\nĠL og\nick en\n] :\nĠsurpr ise\nh ab\nĠc raft\nol t\nĠJ ul\nĠd ial\nĠrele vant\nĠent ered\nĠlead s\nĠA D\nĠCle an\nĠpict ures\ness or\nĠal t\nĠpay ing\nP er\nĠMark et\nĠupd ates\nam ily\nĠT ype\nĠH ome\nĠ5 5\nsemb ly\nrom e\n8 3\nĠgreat est\nĠhe ight\nĠhe av\nain ts\nĠlist en\nas er\nĠS H\nĠcap able\nac le\nĠpers pect\nin ating\nĠoff ering\nry pt\nĠDe velop\nab in\nr c\nĠbr ight\nal ty\nar row\nĠsupp l\nind ing\nack ed\ngy pt\nĠAn other\np g\nĠVirgin ia\nĠL u\nĠpl anned\nĠp it\nĠswe et\nT ype\nĠD i\nĠtyp ically\nĠFranc isco\nĠpro spect\nĠD an\nĠte en\nre es\nĠsc hed\nĠh ol\nĠsc r\nĠlot s\nl ife\nĠnews p\nĠfor get\nĠN one\nĠM iddle\nĠR yan\ned d\nĠse vere\nĠsu it\nll er\n9 3\nĠcor respond\nĠexpl os\nu ations\nĠfl ag\ng ame\nr id\nĠpr in\nĠD ata\nĠde ploy\nĠEn ter\nsu it\ngh an\nĠM en\nĠthough ts\nĠmat ters\nĠad apt\nĠA ri\nĠf ill\nĠfor th\nĠs am\nĠ4 1\nĠpay ment\nĠH or\nĠsp ring\ndu c\nĠl osing\nĠbring ing\nF O\nal a\nĠdist ribution\nhe red\nb our\nĠIsrael i\nom a\nĠcomb ination\nĠpl enty\nV E\nC an\nĠH aw\nĠper man\nĠSpe cial\nĠto w\nĠsee king\nĠexam ples\nĠclass es\nc r\nĠbe er\nĠmov es\nĠI P\nĠK n\nĠpan el\nE ven\nĠproper ly\nĠr is\nĠpl ug\nĠestim ated\nE very\nĠdef ensive\nag raph\nĠpre gn\nĠinst it\nĠV ict\nĠvol ume\nĠpos itions\nĠl inks\nĠPro gram\nĠWe ek\nag ues\nĠtrans form\nk er\nĠC EO\nĠc as\nĠopp onent\nĠtwe et\nĠC ode\nĠsh op\nĠf ly\nĠtal ks\nĠb ag\nPh one\nĠa id\nĠpl ants\nĠ6 5\nĠatt orney\nar ters\nqu est\nĠMag ic\nĠbeg ins\nĠmy ster\nĠenvironment al\nĠst orage\nN N\nĠm arg\nĠs ke\nĠmet al\nell y\nĠord ered\nĠrem ained\nĠl oved\nĠprom pt\nĠupd ated\nĠexper ts\nĠwalk ing\nĠan cient\nĠperform ed\nAT E\nĠne ither\ni ency\nĠmanufact ure\nĠP ak\nĠselect ed\nĠm ine\nĠult imately\nĠexpl an\nĠlab el\nĠServ ices\nribut ed\nTr ump\nĠsy n\nĠU lt\nS C\nĠme at\nĠg iant\nĠW ars\nĠO N\nĠad m\nĠinter pret\nĠeven ing\nĠev il\nĠB oston\nĠW ild\nĠ Ã\nĠBit coin\nĠAm azon\nD r\nĠIn formation\nĠobvious ly\nĠadv anced\nPh oto\nol ar\nĠwe ather\nĠsymb 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ples\nĠfact s\n9 1\not es\ner g\nThe n\nĠacc ompl\nN ote\nĠre venue\nĠpass ing\nĠm al\ne en\nĠY et\nĠg ather\nter day\new ork\nĠA uthor\nP e\nĠopt im\nĠr ub\nĠè £ı\nĠun known\nst one\nĠun ion\nol ve\nĠopportun ities\nĠbrow ser\nĠW al\nĠC ost\nĠreport ing\nst s\np et\nĠs and\nĠsudden ly\nĠsurpr ising\nĠV R\nĠsomew hat\nĠB as\nult ure\niz z\nĠC D\nĠchalleng es\nĠsett ings\nĠexperien ces\nĠF ull\nĠcan n\nĠrece iving\nES T\nĠj oint\nĠcult ural\nĠa st\n8 2\nas tern\nce ived\nĠC ru\nĠb ull\np ired\nam m\nĠfac ing\np ower\nĠb oss\nĠH ol\nĠinst r\nĠincreasing ly\nĠsh ift\nĠstre ets\nĠWilliam s\nab b\nĠl ie\nĠl augh\nĠC a\nP L\nĠadult s\nĠcustom er\nĠob tained\nĠsupport ing\nht ml\nf ire\nĠdetail ed\nĠpick ed\nĠR ight\nld er\nE E\nst ood\nĠK im\nĠw ire\nĠs ight\nĠdevelop ers\nĠpers ons\nĠs ad\nĠc up\nĠwar ning\nĠboy s\nl ong\nĠb ird\nf o\nĠw al\nĠobserv ed\nĠz one\niven ess\nĠch annel\nc ript\nĠref used\nĠAg ain\nĠsu c\nĠspokes man\nĠRe f\nr ite\nou ston\nãĥ ³\nĠS her\nĠact s\nĠN ame\nĠstrugg 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es\nĠdri vers\nog en\nĠN Y\nĠnecess arily\nĠregul ations\near ing\ns l\nĠperspect ive\nc are\nic ial\nH is\nĠesc ape\nĠsurpr ised\nĠV an\nur rent\nĠv ac\n8 1\nĠTh us\nĠem phas\nĠCh ampions\nĠI ce\nĠn arr\nĠhead s\nĠca using\nb el\nf ortunately\nĠM a\nĠtarg ets\nci pl\nĠafter noon\nĠadd s\nĠMay be\nĠF our\ness ed\nple te\nĠus ual\nch o\ning u\nĠwith d\nĠE nergy\nĠE conom\nO O\nĠart icles\nĠinj ured\nĠman age\nĠexpl ains\nĠdi agn\nR ec\nat ures\nĠlink ed\nĠdiscuss ed\nĠexpl o\nĠocc asion\nath an\nĠopp osite\nĠfac es\nĠden ied\nĠK night\nĠn ut\nĠapprox imately\nĠdisapp oint\nonym ous\nĠB est\nĠL o\nĠH y\nĠA ff\nĠvot ing\nan while\nĠII I\nĠinstit utions\nag ram\nĠD aily\nĠdr ag\nĠnear by\nĠgu ilty\nĠcon ver\nP re\ns hip\nĠre ward\nĠphilos oph\nĠS S\nu gh\nĠapp s\nf riend\nĠu pper\nĠad vert\nĠs now\nĠfr ust\nĠour selves\nF r\nĠD ie\namp ion\nĠdis miss\nĠc ere\nĠsign al\nf rom\nĠ ).\nĠ5 2\nĠcr imes\nit ors\nest ival\nuse um\nĠcoun cil\nĠS aud\nM ay\nĠG un\nic ian\net her\nĠsu fficient\nĠH 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alk\nĠAnd rew\nĠmen u\nam ental\nĠprotect ed\nv a\nĠadminist r\nor al\nĠre in\nĠS ar\nĠamount s\nĠn ative\nĠM oon\nĠrep resents\nĠab andon\nĠcarry ing\nĠt ank\nm ary\nĠdecl ared\nT ube\nĠh at\nĠpun ish\nel lect\nm es\nĠun iverse\nĠR od\nph y\nĠinf rastructure\nĠ5 1\nĠopp osed\now nt\nc a\nĠM ake\nĠhard ware\nĠco ffee\nR el\nb al\nw orld\nĠS af\nĠSe a\nin als\nĠown ed\nĠh all\ners ion\nĠdescrib e\nĠP ot\nĠport ion\nĠat mosp\nĠgovern ments\nĠdep ending\nĠoff ense\nĠtr ick\naw a\nĠL ine\nĠV is\nĠH ard\nĠOr ig\nĠCl ick\nĠdes k\nĠVal ley\nĠS ov\nĠmov ies\nĠrem ark\nĠm ail\nĠcons cious\nĠrul ing\nĠR ights\nĠmed ic\nhe nt\nĠW omen\n> <\nĠrepl aced\nĠP rem\nĠTh anks\nĠre new\nĠB all\nif orm\nĠsh ots\nC omm\nĠar med\nĠconst ant\nĠt aste\nĠreal ized\nĠbu ff\nĠm o\nĠeffic ient\nM ost\nor ation\nif ies\nĠcommun ication\nĠfl ood\nĠconsequ ences\nĠany way\nig g\nĠG M\nĠTh ank\nĠ iron\nĠev olution\nĠC op\ntw itter\nĠ9 5\nĠrelationship s\nad el\nĠYou ng\nĠpropos al\nay ers\nuild ing\nĠH ot\nOR E\nc 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y\nĠd uty\nĠp itch\nad or\nĠM ach\nb oy\nĠrespond ed\nĠext ended\nher s\nM any\nra id\nif er\nĠIn s\nS er\nĠmed ium\ns he\nĠS ports\nĠmag azine\nut ation\nĠlim its\nĠG all\nĠex ternal\nraz il\nĠyoung er\nt le\nĠrem ind\nĠC ON\nĠimmedi ate\nĠh idden\nĠvol unte\nĠsim pl\nod cast\nĠph ase\nd r\nĠpl ot\nĠexp osure\nR I\nog rap\nv in\nan ish\nĠAc ad\nĠEng ine\nĠexp ansion\nĠP ay\nY our\nĠpus hed\nĠE ll\nĠHe ad\nĠmarket ing\nĠA C\nk et\nĠh its\nĠg ro\nĠA ge\nĠSc ot\n] [\nĠst im\nĠi Phone\nĪ Ĵ\nĠn arrow\nĠGet ty\nĠTur key\nĠperfect ly\nĠen able\nut ch\nĠprec ise\nĠreg ime\nĠsh if\nĠcomp ens\ng un\nd iv\nĠch osen\nĠK en\nAn y\nĠtre es\nĠrecomm ended\nĠR en\nu able\nĠH T\nF ollow\nE G\nĠH and\nĠK enn\nĠarg uments\nĠex ists\nĠb ike\nĠCons erv\nĠbre aking\nĠG ar\nĠc razy\nĠvirt ual\nay lor\nix el\nĠ19 80\nĠper mission\nĠSer ies\nĠconsum er\nĠclose ly\nc alled\nĠ5 4\nĠhop es\nĠar ray\nĠW in\nĠLab our\nĠsp ons\nĠI re\nĠp ow\nĠread ers\nĠemploy ment\nĠcreat ure\nĠresult ing\nĠaccur ate\nĠmom ents\nĠarg ued\nĠp ed\nD uring\nĠ5 3\nĠT al\nĠs ought\nĠsuff ering\nĠ icon\nle e\nĠ( $\nal ian\n °\nĠp ra\nĠbon us\n( \"\nk o\nĠact ing\nD E\nf all\nĠcompar ison\nĠsm ooth\nĠN AS\nu pp\nĠJose ph\nep ing\nĠT ake\nĠM id\nĠs ending\nf ast\nĠF all\nĠdeal ing\nus er\nĠOr gan\nC o\nĠatt ached\nĠse es\n% .\nĠtyp ical\nAR T\nĠfind s\nĠAs ia\num in\nĠC ore\nĠE nt\nin ent\nu ce\nĠBl ood\nĠN ever\nĠem ails\nĠhigh light\nĠconf ront\nat us\nut ed\nĠun us\nĠtop ic\nĠAd am\nĠb le\nat i\nĠunder stood\nS et\nst ruct\nT P\nĠm ob\na a\nĠSt art\npect ed\nse ll\nĠded icated\nĠC A\nu an\nĠsong s\nesc ription\nĠte ch\nĠr ape\nĠas ide\nĠgr ant\nĠ5 6\ns ub\nĠarg ue\nĠcont aining\nĠsche dule\nĠliber al\nĠpublic ly\nĠheav ily\nĠU t\nin er\nĠS ection\nĠC are\nwe et\nl s\nD is\nâĶ Ģ\nĠF ollow\nB ack\nĠI T\nĠb es\nj i\nĠH it\nest ed\nĠevery body\nĠSw ed\nĠfem in\nĠfac ilities\nĠcon ven\nC omp\nĠO S\nc ore\nĠan x\nĠdiv ision\nĠC am\nĠSt an\nm ates\nĠexpl ore\npl om\nĠsh ares\npl oad\nan es\nĠide al\net ers\nĠB ase\nĠpl astic\nĠdist inct\nĠNet work\nĠSe attle\nĠtrad ing\nens us\nint end\nĠex hib\nĠinit ially\nĠF ood\nĠthous and\nĠBus iness\nact er\nĠpar agraph\nĠrough ly\nĠw ww\nĠcreat ive\nĠCon f\nĠconsum ption\nĠfil ms\nag an\nĠob tain\nĠt all\nĠt or\nĠacknow led\nĠg rown\nal o\nK E\nĠ4 00\nend ers\nt aining\nU G\nĠsu icide\nĠwat ched\nĠL ist\nal i\nre hens\nĠsurround ing\nĠp ip\nĠf lying\nĠJ ava\nord an\nĠserv ing\nin ations\np ost\nĠsh o\nA v\nĠj ail\nz y\nĠ199 9\nĠ< /\nĠliter ally\nĠS ir\nĠexp osed\nĠl ies\nst ar\nĠb at\nĠear ned\nĠD ig\nĠspec ified\nĠSe ason\nĠdeg rees\nDon ald\nĠcent re\nĠsh aring\nĠwin ter\nĠC O\nC he\nĠ Î\nM P\nĠun w\nĠfew er\nĠM ir\nĠsomew here\nĠK ey\nĠattack ed\nĠK ir\nĠdom ain\nĠstrong er\nĠ9 9\nĠpen alty\nI d\nSc ript\nĠdecl ined\nĠne ck\nĠfra ud\nĠcur rency\nĠr ising\nR C\nâĢ¦ âĢ¦\nH z\nĠt ab\nĠtal ent\nn am\nĠN BA\nĠvill age\nĠleg s\nĠN ext\nE d\nĠac id\nĠhy d\n8 00\nĠinvol ving\nĠIm age\nĠBe fore\nF l\nĠyes terday\nS ource\nĠterror ist\nĠsu p\nĠsy nt\nĠSaud i\nĠw est\nĠr u\nb urg\nĠvis ible\nĠstru ck\nr ison\nĠaw esome\nĠd rawn\nĠansw ers\nĠG irl\nĠR am\nĠthreat s\nĠdef eat\nos it\nĠv ent\natur ally\nAmeric an\nend a\nĠH oly\nĠr um\n% ,\nc ase\nĠHist ory\nĠYou Tube\nĠsit uations\nĠD NA\nS te\nĠsa ved\nIt em\nĠrec ip\nolog ist\nĠfac ed\nĠel ig\nO nce\nĠL i\nu h\nĠmist ake\nĠDiv ision\nĠB ell\nĠsympt oms\n ®\nĠdom in\nĠfall ing\nĠend ing\nas hes\nĠmat ches\nĠOn line\nĠexplan ation\nD ef\nred it\nĠany more\nĠT otal\nĠF OR\nus hed\nĠlet ters\nĠris ks\nĠO K\nĠreported ly\n: \\\nĠpl ate\nĠsubject s\nĠattempt ed\nif ier\nian a\nĠunlike ly\nĠTh ough\num a\nĠIn vest\nĠPr in\nic an\nĠD ar\nĠColor ado\nau g\nĠve get\na os\nri a\nĠshe l\nĠmark ed\nĠ( )\nĠsp r\np o\nĠL ink\nĠdef e\nĠJ r\nĠthem e\nĠpass ion\nĠP en\nĠinf o\niz er\nĠsh it\nĠC ivil\nap se\nc re\nĠpo ly\nĠcomp onent\nĠChar les\nĠIre land\nĠPro v\nĠdo ctors\nĠgr anted\nĠpain t\nĠhon or\nĠsm oke\nĠpay ments\nĠprim arily\nĠKing dom\nr ich\nate ll\nĠde als\nĠsched uled\nĠfund amental\nĠprote in\nĠnewsp aper\nĠcl ients\nyth on\nĠD ate\nh us\nĠfeed back\nĠstret ch\nĠc ock\nĠhot el\nĠQue en\nĠsu gar\nĠj u\nĠmil k\nĠappro val\nĠL ive\nĠequival ent\nef ully\nĠins ert\nz ona\nĠext ension\nd ri\nJ ohn\nĠacc omp\nS m\nĠF und\nĠconst antly\nĠ` `\nĠgener ated\nĠA ction\nĠP sych\nĠT ri\nĠrecogn ize\nĠv ary\nph a\nĠR a\nd f\net ch\nĠSov iet\nTw o\nĠpattern s\nĠprof ession\nan ing\nT ime\nĠL im\nĠcol ors\nĠA z\nĠT R\nĠinf ect\nĠphen omen\nĠshe ll\nAl so\nĠput s\nĠdel ivery\nĠbro wn\nĠprocess ing\nĠlight s\ness age\nĠBro ok\nĠA ud\nl ation\nĠindust rial\nL ike\nĠB razil\nrou s\nES S\nĠL uc\nĠsome how\nĠ8 5\nĠpro port\nĠpolit icians\nĠindic ate\nĠh ole\nĠtechn iques\nĠcompet itive\nĠph r\nĠv o\nist ent\nĠD ream\nĠcamp us\nĠaspect s\nĠhelp ful\nĠsh ield\nor se\nĠtrig ger\nm al\nĠ5 8\nĠt ort\nĠperson ally\nĠt ag\nĠkeep s\nĠV ideo\nĠben ch\nĠg ap\na ire\nĠe ast\nĠrec overy\nper ial\nĠprof it\nĠM ic\nĠ5 7\nĠcol on\nĠstrong ly\nst yle\nĠalleg ations\nh an\nĠrep orters\nj o\nr ine\narg et\nand al\nĠ0 3\nĠfl ash\ntr ans\nĠstr ict\nĠpark ing\nĠPak istan\nĠl i\nĠwe ird\nĠE ric\nĠreg ions\nĠJ un\nĠint ellect\nĠW H\nod ing\nrib utes\nup id\nĠT it\nĠf inger\nor ia\nĠe lev\nĠF ield\nĠcon clusion\n; ;\nĠfeel ings\nĠext ensive\nĠm ixed\nĠne uro\nv y\nĠhar ass\nĠC irc\nou ch\nĠterrit ory\nĠsuccess fully\nM ar\nĠing red\nĠoverw hel\nĠl ayer\nV iew\nĠall ies\nill ance\nĠTh ree\nĠb unch\nĠnorm ally\nĠnet works\nĠsac r\nĠC IA\nb les\nĠch ose\nĠopp onents\nĠregard less\nĠfr anch\nĠpre f\nĠP o\nĠbr idge\nann a\nĠSil ver\nĠw age\np age\nri or\nĠrad ical\nĠL ittle\nĠman ip\nĠsecret ary\nĠg ang\nD R\nF A\nĠdec ent\nĠSp irit\nĠun cle\nĠDevelop ment\nĠinvest ors\nĠwall s\nĠpub lish\nĠgener ate\niss ions\nc ar\nĠprom ote\nĠcut ting\nĠche st\nĠdrink ing\nĠcollect ed\nĠ7 2\nĠhop ing\nĠem br\ngor ith\nĠwar ned\nĠinstruct ions\nO G\nĠD id\nĠAg ency\nĠg ear\nĠcritic ism\nĠF urther\nĠut il\nann y\nR ed\nĠcoun sel\nĠAs ian\nĠredu ction\np ool\nĠteach ing\nĠdeep ly\ni y\nĠestim ates\nĠcho ices\nĠperman ent\nin em\nke l\nĠf asc\np se\nf ile\nĠL ow\nĠP erson\nĠt ournament\nst al\nĠm el\nU ST\nĠR ay\naz i\nV al\nĠcont ained\nĠH olly\nĠw ake\nĠreve al\nĠprocess es\nĠIS IS\nĠ0 9\nĠbl ind\nĠste el\nĠB ad\nĠcare fully\napp y\nro it\nĠg aming\nĠhous es\nĠC oll\nĠtr uck\ner m\nĠsc ored\nĠocc as\nret urn\nb ound\nv ar\nĠsh arp\nĠaf raid\nĠE X\nam ber\nc ific\nĠsche me\nN C\nĠPol it\nĠdecl ine\nĠ199 8\nĠpus hing\nĠposs ession\nĠpriv ile\nĠteacher s\nĠy ield\nH A\nĠDav is\nit led\n#### ####\nĠr ig\nĠD aniel\nac on\nĠh ide\nut en\nĠcolle agues\nĠprin ciples\nĠl oud\nĠs in\nĠDem on\nĠst one\nĠ0 2\nĠt aught\nĠter rible\nĠst uck\nĠPol icy\nte en\nĠimplement ation\nĠB BC\nĠAP I\nĠwhe el\nall as\nĠch ampions\nol ars\nplay er\nĠrepeated ly\nĠSt ill\nĠlik es\nast y\nes ter\nĠCath olic\nR L\nĠb ath\nĠno ise\nt itle\nĠn orthern\nP art\nĠmag n\nĠf ab\nĠAs h\nĠdis pl\nĠtick et\nĠm urd\nĠalong side\nĠMus ic\nĠr iver\nĠSte el\nĠC L\nĠPl ayer\nĠM ult\now ing\nre p\ns ize\nĠt ur\nĠGeorg ia\nisc al\nra ction\nĠc able\nĠ5 9\nĠw ins\nĠup coming\nĠsurv ive\nĠins pired\nĠEduc ation\nĠstat istics\nĠF oot\niam i\nĠy ellow\nĠP age\n. -\nĠH as\nĠur ban\nĠa x\nes sel\n\\ \"\nĠquarter back\nĠreg ister\nĠLab or\nĠab ilities\nĠF amily\nĠvar iable\nĠPr ice\nĠcont em\nĠth in\nĠE qu\nd ata\nĠg otten\nĠconst it\nĠas ks\nĠt ail\nĠexc iting\nĠE ffect\nĠSp anish\nĠencour age\nins on\nĠA h\nĠcommit ment\nC S\nĠr ally\nĠ: :\nĠsubs id\nĠsp in\nĠcapt ured\n201 8\nĠinn oc\nĠalleged ly\nĠC ome\nĠart ists\nĠN umber\nĠelect ronic\nĠreg ional\nap es\nĠw ra\nĠmy th\npr ise\nĠM iller\nĠC reat\nĠEp isode\nb ell\nĠdirect ed\nĠext ract\nĠs orry\nĠv ice\nag ger\nĠSu pport\nĠ6 6\nĠI ron\nĠwonder ful\nĠg ra\nN et\nion e\nE ng\nĠsh ips\nik es\nĠK evin\nit ar\nĠactiv ists\ntr ue\nĠAri zona\nent h\nĠDes pite\nĠS E\nĠha bit\nern el\nĠin qu\nĠab ortion\nĠv oid\nĠexpl icit\nĠeng aged\nĠang ry\nĠr ating\nĠfr ag\nb ro\nick ing\nd ev\nĠwor ried\nĠob ser\nĠap artment\nĠG T\nĠest ate\nĠConst itution\nem on\nĠS now\nĠcount y\nĠdis ag\nĠStep hen\nĠimm igrants\nw ind\nĠN ations\nĠfol ks\nO ut\nĠg all\nĠtarget ed\nĠst ead\nĠB on\nĠL ib\nĠinform ed\nĠ12 0\nch ain\nidel ines\nor ough\nĠdri ven\nĠregular ly\nĠbas ket\nĠprinc iple\noc ument\nĠst un\nib ilities\nĠRom an\nĠAb out\nĠal ert\nĠdemocr acy\nĠrepresent ed\nH S\nc ers\np arent\nAr t\np ack\nĠdi plom\nre ts\nĠN O\nĠcapt ure\nĠAd v\nĦ ¢\nĠannounce ment\nĠL ear\nĠh ook\nĠpur s\nĠS uch\nĠC amer\nĠrefuge es\nĠV e\nP ol\nĠrecogn ized\nl ib\nĠhad n\nA ss\nĠpil ot\nus hing\nĠreturn ing\nĠtra il\nĠSt one\nĠrout ine\nĠcour ts\nĠdes per\nĠfriend ly\nĠIt aly\nĠpl ed\nĠbreat h\nĠstud io\nN S\nĠimp ressive\nĠAfghan istan\nĠf ing\nĠd ownt\nink ing\nĠR og\ni ary\ncol or\nse x\nar on\nĠf ault\nĠN ick\nD own\nĠR ose\nĠS outhern\nX X\nis odes\nL ist\n6 00\nĠout come\ner r\nĠelse where\nĠret ire\nĠp ounds\nĠGl obal\nPe ople\nĠcommun ications\nĠlo an\nĠrat io\nĠEm pire\nĠg onna\nĠinv ent\nD F\nĠ19 70\nĠComm on\np at\nĠprom ised\nĠd inner\nĠH om\nĠcreat es\nĠoper ate\nver ty\nĠJ ordan\net ime\nĠsust ain\nR eg\nĠincred ible\nim a\nĠwar rant\nĠm m\nA tt\nĠlaw suit\nĠreview s\nit ure\nĠS ource\nl ights\nĠF ord\nĠ6 3\ng roup\nst ore\nĠfeat ured\nĠfore ver\nĠpo verty\nĠP op\nĠC NN\naz z\nab is\nach ing\nĠl aid\nĠSu pp\nĠfil ter\nen a\nĠCommun ity\nĠcreat ures\nu ction\nĠR oyal\nĠassoci ation\nĠCon nect\nĠBr ad\nâĸ Ī\nl ers\nthe re\nĠG i\nĠval uable\nAC K\nĠT aylor\nĠl iquid\nĠAtt orney\nĠCar l\nĠF inal\nag a\nĠWil son\nB ecause\nĠProf essor\nak a\nĠincred ibly\nr ance\n! )\nR ef\ns k\nĠsol utions\nĠatmosp here\nĠbl ame\num es\nĠN ob\nC A\num ps\nr ical\nĠPut in\nĠD est\nor ic\nĠP A\nĠrespect ively\nw an\nĠfif th\nâ Ħ¢\nĠC ry\nĠgovern or\nres ident\nĠpurch ased\nĠh ack\nĠint ense\nob s\nĠorig in\nĠdef ine\nĠcare ful\n** *\nĠshould er\nCl ick\nĠt ied\nĠdest ruction\nou red\nĠno body\nĠh o\nĠEx per\nĠt ip\n\" ;\nĠtechn ique\nĠj ur\nĠP ok\nb ow\nĠleg end\nĠacc ord\nĠbus y\nĠInt el\nĠh ang\nak i\n. ]\nâĢĶâĢĶ âĢĶâĢĶ\nĠsur gery\nĠrep rodu\nĠun iform\nĠscen es\nc ode\nĠ6 2\nl isher\nĠH ave\nph ia\nĠcry pt\nĠrec on\nĠsc ream\nĠadop ted\nĠsc ores\nN e\nĠIt alian\nin cluding\nB O\nĠindic ated\nĠent ertain\nG u\nT ext\ni el\nĠtw enty\nĠeng age\noff s\nĠPac ific\nĠsm ile\nĠperson nel\nĠto ler\nĠdo ors\nĠt one\nĠmach ines\nĠent ering\nten ance\nC O\nĠJer sey\nĠfore st\nĠhor se\nĠcompl aint\nĠSpr ing\ny o\nĠPl us\ned ing\nĠRet urn\nqu arters\nial s\nc ow\nĠacad emic\nĠf ruit\nĠ199 6\nog ether\nĠw ine\nĠpur su\nĠSte ven\nĠlic ens\nWh o\nĠclot hes\nre ction\nĠsqu ad\nĠst able\nĠr aw\nz ens\nSt ar\nut ies\nanc er\nĠke ys\nĠM u\nĠcompl icated\nig er\nĠTe xt\nĠabs or\nĠ6 8\nĠfun ny\nĠrel ief\nĠL ew\nĠC ook\nĠch art\nĠdraw ing\nG E\nĠmod ule\nĠB ull\nI LL\nĠs alt\n0000 0000\nil le\nĠres ource\naw ay\nadel phia\nĠB ru\nĠ6 7\nĠsome body\nĠparticip ate\nĠro se\nwe red\nĠmus cle\nĠcons ent\nĠcontin uing\nĠGuard ian\nĠOr der\nreg on\nĠre ar\nĠprov ision\nĠlik ed\nri ent\nĠb ra\nTr ans\nĠmeet ings\nĠto x\nĠcon vent\nĠaut o\nĠrec ording\nĠSo ft\n00 1\nĠR oll\nĠprogram ming\nĠp ic\nĠprov ed\nĠst ab\nĠA st\nĠca ption\nul ating\nĠAtt ack\nĠnew ly\nĠ199 7\nf r\nĠdis cipl\nĠGree k\nĠed ition\nĠDo es\nĠB ox\nif le\nack et\nĠpass es\nĠgu est\nĠac celer\nit als\nU D\nĠaut hent\nĠR est\nov al\nt a\nu ine\nĠarm or\nĠT own\nĠcomp at\nĠinc hes\nDes pite\nĠass ign\nhe rent\nĠprep are\nĠM eg\noc key\nĠdep ends\nĠtrack s\nw atch\nĠl ists\nĠN orthern\nĠal ter\nre c\nĠE astern\nĠcond em\nĠevery where\n? '\nĠaff ili\nĠf ought\n\": {\"\nĠm ac\nit arian\nĠsc ope\nĠA L\naw s\nar ms\nĠqu e\nĠenjoy ed\nnes ota\nĠagg ressive\nĠSt ory\nĠI V\nĠrec ipe\nĠrare ly\nĠMed ical\nval ue\nang el\nay ing\nomet hing\nĠsub section\nĠs outhern\nĠfrequ ency\nre te\nroll ed\nult s\nĠN ic\nĠbeh alf\nĠsequ ence\nab et\nĠcontrovers ial\nĠcomp rom\nĠwork er\nĠmain ly\nĠal gorith\nĠM ajor\nor ce\ng ender\nĠorgan ized\nĠf ake\nĠconclud ed\nĠE D\nĠEx ec\nr age\nĠch ances\nber ry\nĠTr ad\nĠconfig uration\nĠwithd raw\nĠf ro\nud es\nĠBro ther\nĠB rian\nĠtri es\nĠsam ples\nĠb id\nĠGold en\nĠphot ograph\nif est\nĠD O\nĠPar liament\n******** ********\nR em\nĠcont est\nĠsign ing\np x\nĠZ eal\nâĶĢ âĶĢ\nE ar\nĠex it\nBe fore\nĠCor por\nn ull\nmon th\nĠrac ial\nott ed\nĠV eg\nĠRe uters\nĠsw ord\nps on\nĠRom ney\na ed\nĠt rib\nĠin ner\nĠprot ocol\nĠB i\nĠM iami\never al\np ress\nĠsh ipping\nĠAm endment\nĠHow ard\ncon nect\nĠD isc\nĠJ ac\niam ond\nĠThere fore\ns es\nĠPrin cess\nĠUS B\nĠAn th\nĠsurve illance\nĠap olog\nĠ6 1\now a\nĠf ulf\nj s\nĠl uck\nust ed\nĠ §\nn i\nĠant icip\nem an\nĠwin ner\nĠsil ver\nll a\nic ity\nĠunus ual\nĠcr ack\nĠt ies\ne z\nĠpract ical\nĠprov ince\nĠPl ace\nĠprior ity\nIC E\nĠdescrib es\nĠbr anch\nF orm\nask a\nmiss ions\nb i\nĠp orn\nĠTur k\nĠent hus\nĠf ighters\nĠ0 8\nĠDet roit\nĠfound ation\nav id\nA re\nĠjud gment\ncl ing\nĠsol ve\nĠDes ign\nW here\nhes is\nĠT ro\na fter\nĠne utral\nĠPalestin ian\nĠHolly wood\nĠadv is\nĠN on\ny es\nol is\nĠrep utation\nĠsm ell\nĠb read\nĠB ul\nĠBe ach\nĠclaim ing\nĠgen etic\nĠtechn ologies\nĠupgr ade\nrow s\nĠdevelop er\nĠJ osh\nĠDis ney\nerv ed\nip al\nĠun ex\nĠbare ly\nt hen\nĠP ub\nĠill ness\net ary\nĠB al\nĠp atch\nĠbut t\nĠst upid\nĠD og\nĠD allas\nf ront\nie ce\nĠprot ests\nĠch at\noen ix\nĠw ing\nĠpar liament\nĠ7 7\nose xual\nĠre nder\npt ions\nĠCo ast\nos a\nĠG reg\nh op\nĠMan agement\nĠbit coin\nĠrec over\nĠincor por\nor ne\nĠUs ing\nĠpre ced\nĠthreat ened\nĠspirit ual\nĠE vent\nĠF red\nĠadvert ising\nĠimprove ments\nĠC ustom\nĠer rors\nĠsens itive\nĠN avy\nĠcre am\nL ook\nĠex clusive\nĠcomp rehens\nĠde leg\nĠcon ce\nĠrem em\nĠstruct ures\nĠst ored\nN D\nĠ1 000\nU P\nĠB udd\nA F\nw oman\nĠAcad emy\nð Ł\nse a\nĠtem porary\nAb out\nes ters\nĠtick ets\nĠposs ess\nin ch\no z\nĠl a\nĠcontract s\nĠun p\nĠc ig\nĠK at\nult ural\nas m\nĠmount ain\nĠCapt ain\nSt ep\nm aking\nĠSp ain\nĠequ ally\nĠl ands\nat ers\nĠreject ed\ner a\nim m\nri x\nC D\nĠtrans action\ng ener\nless ly\nĠ| |\nĠc os\nĠHen ry\nĠprov isions\nĠg ained\nĠdirect ory\nĠra ising\nĠS ep\nol en\nond er\nĠcon sole\nin st\nĠb om\nĠunc ertain\n1 50\nock ing\nĠmeas ured\nĠpl ain\nĠse ats\nĠd ict\nS L\naf e\nĠest imate\niz on\nat hered\nĠcontribut ed\nĠep isodes\nomm od\nG r\nAN T\nĠ6 9\nG ener\nĠ2 50\nvious ly\nrog en\nĠterror ism\nĠmove ments\nent le\noun ce\nĠS oul\nĠpre v\nĠT able\nact s\nri ors\nt ab\nĠsuff er\nĠn erv\nĠmain stream\nĠW olf\nĠfranch ise\nb at\nĠdem ands\nĠag enda\nĠdo zen\nĠclin ical\niz ard\nĠO p\nt d\nĠvis ited\nĠPer haps\nĠact or\nĠde lic\nĠcont ribute\nĠin ject\nĠE s\nac co\nĠlist ening\nĠcon gress\nepend ent\nĠprem ium\nĠ7 6\nĠIr ish\nĠass igned\nĠPh ys\nĠworld wide\nĠnarr ative\not ype\nm ont\nb ase\nĠB owl\nĠAdminist ration\nĠrel ation\nĠE V\nC P\nĠco vers\nĠ7 8\nĠcert ific\nĠgr ass\nĠ0 4\npir acy\nir a\nĠengine ering\nĠM ars\nĠun employ\nĠFore ign\nst ract\nĠv en\nĠst eal\nĠrepl ied\nĠult imate\nĠtit les\nd ated\nĠj oy\na us\nĠhy per\nak u\nĠoffic ially\nĠPro duct\nĠdifficult y\nper or\nĠresult ed\nrib ed\nl ink\nwh o\n~~ ~~\nĠSpe ed\nĠV iet\nW ind\nĠBar ack\nĠrestrict ions\nĠSh are\nĠ199 5\nition ally\nĠbeaut y\nop t\nĠm aps\nĠC R\nĠN ation\nĠCru z\nW ill\nĠelectric ity\nĠor g\nĠb urd\nĠviol ation\nĠus age\nĠper mit\nĠCh ron\nĠF ant\nĠn aturally\nĠ0 7\nĠth rown\nĠAw oken\nĠal ien\nĠHer o\nĠK ent\nĠR ick\nri ke\nĠp ace\n}, {\"\nG L\nĠpo ison\nĠT ower\nĠform al\nal ysis\nĠgen uine\nĠk il\na ver\nĠproced ure\nĠPro p\nintend o\nĠM ain\nas ant\nĠtr ained\nG ame\nĠL oad\nĠM A\nĠcru cial\nĠle ts\nĠF R\nĠch ampion\n1 01\nĠCon ference\nĠwrit ers\nĠconnect ions\nĠo kay\nir ms\nĠR and\nĠenc ounter\nĠB uff\nĠachie ved\nĠche cks\nisc ons\nĠassist ant\nĠwhen ever\nĠA ccess\nĠU r\nb in\nĠcl ock\nis p\nop her\nĠb orrow\nĠm ad\nĠperson ality\non ly\nIS T\nab ama\nĠg ains\nĠcommon ly\nĠter r\nĠhyp ot\nĠre ly\nĠt iss\niscons in\nĠrid ic\nf unction\nĠO regon\nĠun com\nr ating\nel and\nĠN C\nĠm oon\nann on\nĠvulner able\nut ive\n³³ ³³\nĠRad io\nĠw estern\nse ct\nĠT ony\nĠocc urs\nĠO s\nĠH on\nà Ń\nĠv essel\nĠScot land\nĠdiscrim ination\nĠsubsequ ent\nst ring\nĠfant asy\nĠSh adow\nĠtest im\nW E\nit i\nr as\nĠbo at\nĠmar ks\nĠord inary\nĠre n\nĠrepresent ative\nĠpet ition\nĠ7 3\nĠad venture\nĠign ore\nĠPhil adelphia\nĠS av\nV P\nĠfact ory\nĠt asks\nĠdep ression\nz ed\n................ ................\nĠSt orm\nĠc ogn\nĠelig ible\nĠredu cing\nv ia\nĠ0 5\nĠstri king\nĠdoll ar\nh o\nO V\nĠinstr ument\nĠphilosoph y\nĠMo ore\nĠA venue\nĠrul ed\nĠFr ont\nIN E\nĠM ah\nĠscen ario\nĠNAS A\nĠen orm\nĠdeb ut\nĠte a\nT oday\nĠabs ence\nS im\nĠh am\nle ep\nĠt ables\nĠHe art\nM I\nK e\nre qu\nV D\nm ap\nĠchair man\nĠp ump\nĠrapid ly\nv i\nĠsubstant ial\nE P\nd es\nch ant\nili pp\nĠS anta\nri ers\nanche ster\nL oad\nĠC ase\nĠsa ving\nĠ7 4\nĠA FP\ner ning\noun ced\nĠMin nesota\nĠW as\nĠrec ru\nĠassess ment\nĠB ron\nU E\nĠdynam ic\nĠf urn\nul ator\nĠprop ag\nh igh\nĠacc ommod\nĠst ack\nĠS us\nw rit\nĠre ven\nĠGod d\nĠZeal and\nab s\nĠbr ut\nĠper pet\nh ot\nĠhard ly\nĠB urn\nãĤ ¹\nĠst y\nĠtrans actions\nĠg ate\nĠsc reens\nĠsub mitted\nĠ1 01\nĠlangu ages\nugh t\nem en\nĠfall s\nĠc oc\nĤ ¬\nĠstri kes\np a\nĠdel iber\nĠI M\nĠrel ax\nann els\nĠSen ator\nĠext rem\nĠ} ,\nĠDe b\nĠbe ll\nĠdis order\nc ut\nĠi OS\nĠl ocked\nĠem issions\nĠshort ly\n\" ]\nĠJud ge\nĠS ometimes\nĠr ival\nĠd ust\nĠreach ing\nF ile\n¯¯ ¯¯\nino is\nĠJ ason\nĠs atell\nare t\nĠst ations\nĠag ric\nĠTechn ology\ncom es\nĠUn fortunately\nĠChild ren\nĠappl ies\nast ed\nĠan ger\nail ability\nĠDam age\nĠcomp are\nĠStand ard\nĠaim ed\nĠB a\nangu age\nĠreg ulation\nĠj ury\nĠair port\nĠse ctions\nĠPr ince\nem ed\nĠmedic ine\nĠh itting\nĠsp ark\nol ves\nĠad s\nSt ate\nĠfood s\nĠrepl acement\nĠch icken\nĠlow est\nĠmind s\nĠinvol ves\nu i\nĠarr ang\nĠproced ures\nĠWh ich\nivers ary\nĠb ills\nĠimprove ment\nĠin ev\nĠexpect ations\nĠintellect ual\nĠsp aces\nĠmechan ism\n2 50\nbre ak\nĠZ e\nĠT enn\nĠB alt\nĠbar rel\nĠstat ic\nman n\nPol ice\nĠt ips\nĠhand ling\nc us\nod ed\nil ton\nir y\nĠjournal ists\nour se\nĠcom ic\nĠnom ine\nIT Y\nĠvers us\nĠlo op\nĠsur f\nĠInd ust\nĠHun ter\nĠbelief s\nis an\nĠset up\nĠbre w\nim age\nĠcomput ers\nf ol\n} ,\"\nĠMed al\nĠtax p\nĠdisplay ed\nĠg rav\nĠf iscal\nM on\nĠMos cow\nĠK ong\nĠCent re\nĠcamer as\nĠMr s\nĠH ay\nĠa ver\nĠK elly\np y\nĠrequire ment\nĠent itled\nomb ie\nĠsh adow\nag ic\nĠA k\nĠel ite\nĠdiv ided\nĠhead ing\nĠcop ies\nĠloss es\nĠv it\nk ed\nĠB ry\nĠan s\nĠSte am\nĠrep orter\nhe im\nĠIt em\nĠsuper ior\nd on\nere nt\nà ¶\nĠtherap y\nĠpe ak\nĠMod el\nĠl ying\nĠg am\nz er\nr itten\nĠrespons es\nĠconsider ation\nĠB ible\nĠl oyal\nĠinst ant\nĠp m\nĠFore st\nà ¼\nĠext end\nĠconv icted\nĠfound er\nĠconv in\nĠO ak\nche ck\nĠsch olars\np ed\nĠover se\nT op\nc ount\nĠAr k\n ·\nĠ0 6\nĠL A\nm d\nĠLat in\nim ental\nĠC PU\nĠsubst ance\nĠminor ity\nĠmanufact uring\nE r\nocol ate\nĠatt ended\nĠMan ager\nr ations\nĠappreci ate\nom y\nGB T\nid ency\nB L\nĠguarant ee\npos ition\nĠo cean\nclud e\nĠhead ed\nĠt ape\nĠlo ose\nĠlog ic\nĠpro ven\nĠsp ir\nĠad mit\nis a\nĠinvestig ate\nĠ199 4\nsy lv\nĠL ost\nc est\nĠ7 1\nĠrequest ed\nĠwind ows\nĠPok é\nĠWith out\nM et\nĠbehavi our\nĠread er\nĠh ung\nĠKe ep\nĠro les\nĠimplement ed\nĠbl ank\nĠserv es\nĠJ ay\nĠc ited\nĠF riend\nprof it\nap on\nĠrep air\nit em\narr ass\nĠcrit ics\nad i\nĠF ather\nĠsh out\nĠf ool\nĠ8 8\nĠprodu cing\nĠl ib\nĠround s\nĠcirc le\nĠpre par\nĠsub mit\nĠn ic\nmor row\nãĥ «\nU nder\nĠv ital\nater n\nĠpass word\nĠpublic ation\nĠprom inent\nĠspeak s\nĠb ars\nĠde eper\nĠM ill\nport ed\nĠw id\nĠbut ter\nĠsm oking\nĠindic ates\nK ey\nrop ri\nĠF ile\nall ing\nast ing\nĠR us\nĠad j\nĠ7 9\nav al\nĠpres um\nbur gh\non ic\nĠf ur\nĠpoll s\nik a\nĠsecond ary\nĠmon ster\nig s\nĠCur rent\nE vent\nĠowners hip\nend ar\nĠarri ve\nĠT ax\nĠn ull\nĠPri v\nĠth ro\nĠk iss\nc at\nĠup set\nang le\nit ches\nect or\nolog ists\nĠGal axy\nĠcor ruption\nĠh int\nent er\nĠH ospital\nĠgreat ly\nĠbeg un\nes y\nĠso il\nĠAnt on\nĠmain tenance\nãĥ ©\nĠdo zens\nĠhuman ity\nĠAl abama\nĠr om\nw orth\nap ing\nsylv ania\nl ah\nĠg athered\nG A\nĠattack ing\nf ound\nĠSqu are\nĠar bit\nict ions\nĠW isconsin\nĠd ance\nĠS aint\narch y\nĠbase ball\nĠcontribut ions\nĠliter ature\nĠex ha\nper ty\nt est\nĠb ab\nĠcontain er\nlet ter\nĠfall en\nĠwebs ites\nĠbott le\nĠS ac\nĠbre ast\nĠP L\nĠveter an\nĠinterview s\nĠA le\nĠb anned\neng ers\nĠRev olution\nin th\nĠconc erning\nIV E\nĠexp enses\nĠMatt hew\nĠColumb ia\nd s\nist ance\nĠent ity\n.. .\"\nĠrel iable\nĠpar alle\nĠChrist ians\nĠopin ions\nĠin du\nl ow\nĠcompet e\nĠth orough\nĠemploy ed\nĠestablish ment\nig en\nĠC ro\nĠlawy ers\nĠSt ation\nT E\nĠL ind\nĠP ur\nit ary\nĠeffic iency\nâĢ IJ\nĠL y\nĠm ask\nĠdis aster\nĠag es\nER E\nes is\nĠH old\nĠcas ual\nb led\nĠen abled\nĠEn vironment\nĠInt elligence\ni per\nĠM ap\nĠB E\nĠemer ged\nis dom\nĠc abin\nĠregist ration\nĠfing ers\nĠro ster\nĠfram ework\nĠDo ctor\net ts\nĠtransport ation\nĠaware ness\nH er\nĠattempt ing\nO ff\nĠSt ore\nÃĥÃĤÃĥÃĤ ÃĥÃĤÃĥÃĤ\nĠK now\nĠdef ence\nĠsc an\nĠT en\nĠCh air\nĠP H\nĠAtl anta\nĠfuck ing\nĠans wered\nb n\nĠK ar\nĠcateg ories\nĠr ational\nĠc ust\nĠrob ot\nĠcorrect ly\nĠg if\nĠgraph ics\nm ic\nĠground s\nĠO pp\ni ate\nĠdist ributed\nĠsan ctions\nĠchalleng ing\nut o\nĠingred ients\nĠinv ited\nĠfound ed\nĠRe qu\nd ed\nĠb owl\nĠbrother s\nĠH a\nI O\nĠw ages\nim ore\noc ial\nĠse ed\native ly\nĠaddress es\nĠI owa\nab eth\nĠatt itude\nis d\nch ild\nĠm ole\nĠdisco very\ny ard\nB r\nĠ8 2\nĠsuppl ies\nell ing\nĠdist ingu\nC R\nĠre cept\nĠ vert\nĠsw im\nb ec\nd oor\nĠY eah\nĠg al\nĠinter act\nĠE SP\nĠC S\namp s\nĠconvin ced\nĠobject ive\nĠdis h\nĠPhot os\nl ad\nĠdownt own\no il\nin ction\nĠto morrow\nĠC OM\nĠsurv ival\nsh ot\nĠsett lement\nC ons\nĠX box\nint erest\nĠS M\narg o\nen ess\nĠeth nic\nb ered\nM in\nĠT ok\nĠinc ent\nĠComm and\nĠmain tained\nĠbreak s\nbr idge\nat ar\nag g\nĠF inally\nun icip\nĠO nt\nle ft\nĠrecogn ition\nĠ* /\nĠP ers\nĠwe lf\nĠaddress ed\nĠK ansas\nĠvir us\nĠwhere as\nĠp apers\nram s\nĠMin istry\nĠple asure\nĠacqu ired\nĠd uration\nj pg\nĠcal m\nĠN HL\nĠburn ing\nĠfold er\nick ed\nĠP y\nĠIll inois\nCl ass\nĠGodd ess\nĠperform ing\nĠwelf are\nj ar\nIn ter\nĠl in\nĠenh ance\nĠnot ion\nf are\nyp es\nĠAre a\nĠcann abis\nĠDie go\nf s\nĠM anchester\ncom m\nin ite\nĠcover ing\nĠS ound\nĠ19 60\nĠ8 4\ne lect\nz ing\nĠcitiz en\nĠph ones\nĠr aid\nĠign ored\nĠOb ject\nĠu pload\nc ard\nĠmod ified\nĠroom s\nia h\nr ange\nhe ast\nach us\nĠsuggest ing\nâĢ ĭ\ngr ade\nE l\nĠclot hing\nĠr h\nĠH an\nun ity\nen cing\nĠAust in\nsec ution\nt ra\nd em\nĠQ ual\nĠhe aven\nĠst ages\nĠw edd\npl us\nific ial\nĠIm m\nĠH o\niet ies\nĠphr ase\nĠbr ill\nact ory\nĠprov iders\nĠsil ence\nĠa er\nĠA I\nĠAd venture\nĠplatform s\nĠdemonstr ated\nĠinter f\ning ton\nĠr aces\nĠgr ade\nult ane\nĠTh rough\nf alse\nĠb ow\nĠA B\nĠfl avor\nĠhistor ic\ng ov\nĠcol our\nĠview ed\nĠEm ail\nel come\nĠinter vention\nĠd iversity\nĠperiod s\nĠre verse\nĠV ery\nĠqu ote\nĠLe ft\nth rough\nĠsc rew\nĠland ing\nĠp ill\nĠw et\nĠprot esters\nĠrepe at\nav ed\ner k\nĠsal ary\nĠPenn sylvania\nSt ill\nĠmay or\nĠkit chen\nĠfeat uring\nĠM useum\nĠT ournament\nĠF al\nĠser vers\nU C\nĠany body\nim g\nĠTr ade\nixt ure\nthe less\nĠfin ance\nĠcl osing\nĠPat ri\ni ac\nab el\nĠ> >\nor ous\nĠf irms\nsc reen\nun a\nĠemb arrass\nul se\nĠlet ting\nĠth rew\nile y\nĠch annels\nl an\nĠVeg as\nĠse ar\nĠfant astic\nar re\nuzz le\nĠD er\nTh ose\nĠsw ing\nĠshe et\nind ex\nco ver\nog an\nĠvari ables\nĠTe ch\nĠsp oken\nac hel\nĠD a\nĠMount ain\nĠload ed\nĠfoot age\nvers ion\nĠun l\nĠPh oenix\nĠthrow ing\nĠf iring\nĠtrack ing\nĠw idth\nĠstrugg ling\nro oms\not ion\nĠmonth ly\nĠSer ver\nĠegg s\nop en\nM C\nĠ199 3\nĠh ired\nĠstay ed\nĠAll en\nĠst ro\nĠ9 8\nst ep\nĠTurk ish\nĠfab ric\nist ing\nĠD om\nĠd ates\nĠpr on\nĠbasket ball\nĠl ucky\nĠArab ia\nĠassum ed\nest y\nĠaff airs\nĠgl ad\nĠInd eed\nĠF A\nĠW ord\nĠjo ining\nif ice\np read\nir ts\nĠSe lect\nĠpop ulations\naw are\nĠn ose\nĠcompl aints\nst art\nĠsc oring\nTh anks\nĠmin ing\nĠvisit ors\nS H\nĠdam aged\nĠcharacter istics\nĠP ent\nD C\nĠ8 3\nĠS ix\nr ates\nĠfl ags\nĠB rew\nd og\nM ark\n// //\nĠexec ution\nĠj oke\nph ones\nĠtestim ony\nĠob st\nQ L\nĠC ut\nĠstud ied\nĠN intendo\nick et\nĠN BC\nĠl ad\nĠB ra\nĠM oh\nĠk ernel\nĠoverwhel ming\nĠag ed\nĠapplic able\nĠC ond\nĠroad s\nĠBl ock\nm ade\nod ge\nĠcomm ands\nĠoff ices\nvel and\nĠt ut\nĠrece iver\nĠF ro\nĠsho pping\nĠi P\nĠSt re\nĠA BC\nĠentertain ment\nĠB ow\nort ed\nM c\nĠread s\ngr ad\nĠCol lect\nĠâ ĪĴ\nĠCap ital\neder ation\nĠemploy er\nĠinvolve ment\nĠanx iety\nal ia\nĠro of\nĠAm ong\nĠDemocr at\nĠstat s\nĠV ill\nĠconst itutional\nĠrefer ring\nitt y\nĠtack le\nout ube\nĠback ed\nĠH ong\nĠBro ad\nĠe le\nĠO tt\nĠ199 2\nh our\nachus etts\nC al\nĠdefe ated\nĠ8 1\nes p\nĠseem ingly\nw as\nĠJ enn\nĠK urd\nĠg ene\nĠdisc ount\nR et\nEC T\n( );\nĠclub s\nĠs id\nĠM arsh\nChe ck\nĠp p\nĠE ag\nides pread\nĠbe ings\nF T\nĠintrodu ction\nĠCh ange\nAR D\nĠ1 10\nad ows\nier ce\nĠme al\na uthor\nĠB ang\nlah oma\nĠr anks\n201 1\n?? ??\nm ax\nĠcoll apse\nĠop ens\nĠe cho\nĠs oph\nĠrac ist\nĠenorm ous\nĠw aves\nĠt ap\nĠcomprehens ive\n. --\nĠR oy\nĠfarm ers\nRel ated\na ired\nron es\nĠC rim\nĠproport ion\nĠdesign s\nĠnegoti ations\nĠvirt ually\nĠBat man\nĠwar n\nĠlegit imate\nm ate\nĠcon vention\n, ,\nnet ic\nĠS D\nĠconsist ently\nĠcompens ation\nĠpunish ment\nĠy e\nĠt ie\nĠB ureau\nir lf\nĠB u\nĠA ren\nĠPh ilipp\nĠkn ife\nĠmem ories\nĠR oss\nĠang le\nĠ8 6\nĠTh under\nĠre nd\nĠT our\nĠcount s\ns ung\nĠIm p\nĠeduc ational\nĠaccess ible\nC OM\nĠd rew\ny er\nG l\nam ine\nOR T\nO B\nI B\nm aster\nĠtri als\nog y\nh ar\nĠTr ust\nĠprefer red\nirlf riend\nĠN ev\nĠb in\nĠc ow\nP age\nĠsign ature\nĠB L\n7 00\nĠret ired\nĠby tes\nĠneigh b\nĠLeg end\nĠdev ast\nĠsuspect ed\nis ons\nĠPoké mon\nsc ale\nĠcap abilities\nĠre vel\nĠche ese\nd y\nigr ant\nĠfail ing\nb its\nĠHer oes\nĠG host\nĠS cient\nĠappoint ed\nur i\nĠinst itution\nĠexpand ed\ng reg\nĠmonitor ing\nĠp odcast\nĠcoal ition\nĠ9 6\nJ o\nĠst olen\nĠS ab\nĠstop s\nĠhol iday\nĠint r\nC ar\nBl ack\nĠL GBT\nĠwar ming\nĠAnd erson\nĠ8 9\nĠprodu cer\nM ed\nĠaccur acy\nĠMar vel\niz abeth\nĠPat rick\nm ony\nĠmin i\nac les\nĠover t\nthe y\nĠmembers hip\nĠV en\nĠex ch\nĠrem oval\nĠD ave\nT Y\nm ad\nĠF ind\nĠad equ\nĠe c\nĠte eth\nĠemot ion\nĠper m\nĠsole ly\nd b\nĠextra ord\nIG HT\nc al\nĠgu idelines\nĠd ying\nĠsusp ended\nĠPrem ier\nĠAnth ony\nel ve\nĠd ad\nĠE th\nĠFoot ball\nĠabandon ed\nĠ< <\nĠm arch\nĠhor ror\nâĢ¦ \"\nĠchild hood\nĠcampaign s\nĠl unch\nĠAl bert\nbl ock\nâĸĪ âĸĪ\nound ing\nĠb one\nor gan\nad ers\nĠFl ash\nĠDri ve\nĠton ight\nĠw ars\nĠF L\nĠform ation\ncon st\nNew s\nĠcom pe\nor ious\nĠSt aff\nĠdiscuss ions\nĠProt ection\nĠJ am\nĠcrit eria\nĠinstall ation\nĠaccompl ish\niz za\nĠpub lisher\nĠresc ue\nĠT ry\nU LL\nĠS om\nĠH op\nore t\nth s\nord on\nĠp ocket\nĠIn v\nDown load\nĠCr ime\nĠb ene\nĠGu ide\nĠAs sembly\nĠparam eters\nI E\nĠAlex ander\nĠconc ert\nĠSc he\nĠsh oes\nĠvis iting\nĠrec all\nĠb ub\nĠr ural\nĠconc rete\nĠR os\nN ext\nR uss\nĠlo ans\nĠSh ield\nĠtre m\nhem at\nk g\nĠHar ris\nis ition\nĠM ove\nĠF C\nĠf ate\nĠCh o\nĠt ired\nĠprinc ipal\nh ist\nien ces\nath y\nĠse vent\nĠm ood\nĠstrateg ic\nĠdise ases\nĠfor um\nĠtem por\nĠhead quarters\nP ar\nig e\nfl ix\nĠgu itar\nĠ9 4\nOn ly\nĠrele ases\nro ph\n================ ================\nĠ6 00\nĠContin ue\nig ate\nĠC rit\nsy stem\nĠdis abled\nĠunex pected\nith ub\nĠuncle ar\nĠE st\nĠcontr ad\nĠstrateg ies\nvent ures\nĠpass age\nAM E\nĠimpro ving\nĠreve als\nĠdecre ase\nov a\nĠann oy\nĠSh ort\nĠL ibrary\nĠcy ber\nn ell\nĠH ur\nĠC B\nĠphot ograp\nU I\nĠs ed\nG e\nĠ8 7\nĠd iverse\nĠencour aged\nĠcons piracy\nĠbird s\nĠoper ator\nĠhand ful\nĠclass ified\n? )\nĠdram atic\nĠinvestig ators\nit o\nĠw idespread\nĠR oom\n-------------------------------- --------------------------------\nĠcollect ive\nĠjournal ist\nSt ring\nĠtemper atures\nil a\nĠgu id\nĠins pect\nĠmiss ile\nĠMay or\nĠman ual\nĠsim ultane\nĠrat ings\nĠsu ck\nĠ9 7\nĠunivers al\nĠph arm\nĠdis rupt\nian o\nA V\nĠf t\nĠstat ist\nold s\nĠWalk er\nph p\nĠunder t\nĠL as\nish op\nnt il\nres hold\nĠWhe ther\nM s\nĠden y\nĠCl oud\nĠprov ider\nĠsurv iv\nĠUp date\nh as\nĠmist akes\nch arge\npl ed\nr ity\nĠn ode\nĠMass achusetts\nool s\nlic ation\nĠf ails\nem ale\nor i\nback s\nĠsh irt\nĠ' '\nĠN AT\nĠwat ers\nels on\nĠe ase\nĠsc ar\nĠcont ents\nm ind\nĠcont ribution\nĠsh r\nĠhand ed\nĠst ability\nĠtra ve\nE m\nĠmir ror\n12 3\nĠwe igh\nĠf iction\nou ver\nist ant\nr ition\nĠF ed\nĠphys ically\nĠst ake\nĠArt icle\nĠAr c\nĠLew is\nĠM ind\nĠdemonstr ate\nĠprof its\nv ision\nom ic\nol id\nĠbatt les\nĠdri ves\nĠeas tern\nĠS ony\n!! !\nar ation\nv ard\nĠG L\nport ation\nĠ9 2\nĠlaw makers\nĠprotect ing\nĠE PA\nĠy eah\nĠsh ame\nol ph\ne ven\nx it\nĠatt ach\nĠrepresent ing\nĠob s\nĠUt ah\niff s\nĠFre edom\nà ³\nA K\nĠinc idents\nit age\nĠview ers\nc d\nĠm ouse\nĠcl ar\nĠaccord ance\nĠb ot\nc or\nĠSum mer\nhe ld\nĠinnoc ent\nĠiniti ative\nol s\n________________ ________________\nĠsp ots\np ace\nĠconvent ional\nĠcorpor ations\nĠblock ed\nH D\nat tered\nĠref ers\nĠbu ck\nĠDig ital\n12 0\nĠtop ics\nT F\nÄ ģ\nbr id\nre ement\nĠunder lying\nĠM ember\nĠinvestig ating\nĠpregn ancy\nĠtouch down\nĠB and\nĠCall er\nĠinst ances\nP P\nw a\nG ood\nĠ199 1\nĠC old\nĠfear s\nĠrem arks\nĨ Ĵ\nat al\nĠm it\nĠexper iments\ni pt\nCol or\nind u\nUp date\nĠ9 3\nA g\nĠ å\nanc ouver\nB oth\nĠjud ges\nOb ject\nĠst ere\numb n\nĠparticip ation\nĠSt ars\nĠJ ere\nĠweek ly\nĠB an\nĠconvers ations\nĠP itt\nu z\nĠIndian a\nĠK ick\nĠinf ection\nĠhero es\nĠsett led\nĠstri p\nĠh al\nĠd ump\nĠS ci\nĠl es\nĠref erences\nĠU RL\nĠBr idge\nĠwant ing\nFor ce\nĠex clus\nMe anwhile\nm n\nĠg entle\nm aker\nsen al\nĠG ro\nou ri\nĠR ain\nĠAll iance\nĠl ift\nel a\nS D\nĠCle veland\nĠrank ed\nĠst adium\nĠdead ly\nä ¸\nĠr iding\nar ia\nĠAr mor\nĠdocument ation\nĠGree ce\nree k\nĠl ens\nĠS a\nĠg ross\nĠE mer\nag ers\nĠD ub\nĠR h\nĠAM D\nĠarri val\nĠdes ert\nĠsupp lement\nĠRes p\nĠkn ee\nĠmarg in\nf ont\nog g\n201 0\nĠP ir\nĠP rom\niv als\nĠint ake\nĠdifferent ly\nug s\nĠb its\nclud ed\nĠsearch ing\nĠD u\num ble\nĠfunction al\nĠBalt imore\nĠC ould\nĠdes ired\nĠcirc uit\nĠL yn\nĠG O\nĠF alse\nre pre\n' :\nalt ies\nĠmin im\nĠdro ve\nĠSh ould\nĠh ip\nĠpro s\nĠut ility\nĠN ature\nĠM ode\nP resident\no pp\nr at\nform ance\nĠconcent ration\nĠf ont\nĠB ud\nĠam id\nĠre vers\nĠM L\nB ar\nĠinter action\nĠjur isd\nĠspell s\nd ep\nf il\nĠcivil ians\nut ter\nĠCo oper\nĠBel ow\nĠent rance\nĠcon vert\nĠcontrovers y\now ered\nĠcontr ary\nĠar c\nĠExec utive\nĠOffic er\nĠpack ages\nĠprog ressive\nw idth\nĠreserv ed\nv ol\nĠSam sung\nĠprint ed\nĠcent ers\nĠintrodu ce\nĠKenn edy\nĠodd s\nĠsure ly\nĠindepend ence\nĠpass engers\nrepre ne\nĠBe h\nĠl oves\nĠESP N\nĠfac ilit\nĠident ical\nĠdo ct\nĠpartners hip\ncon f\nĠH ide\nĠconf used\nĠC ow\nM en\nĠw rest\nĠIraq i\nĠh oles\nĠStud ies\nĠpregn ant\nh ard\nĠsign als\nI X\nĠpull ing\nĠgrad uate\nĠnomine e\nD ate\nĠper mitted\nĠâ Ĥ¬\nĠOk lahoma\nSt art\nĠauthor ized\nĠal arm\nĠC os\nv an\nĠgener ations\nc ular\nĠdr agon\nĠSoft ware\nĠEd ward\nĠcontro ller\nS en\nge red\nĠV ik\nĠappro ached\nTh ank\nĠcan ce\nĠform ula\nĠSm all\nĠweak ness\nĠr amp\nit udes\nj ud\nĠbrill iant\nĠacc us\ns ource\nĠ8 00\nĠE vil\nS w\nĠhom eless\nwe ek\ni ens\nr ics\nĠTh ird\nT O\nĠorgan ic\nĠpresent ation\nag h\nĠDown load\nv ation\nĠas sembly\nor able\nhold ers\nĠBern ie\nĠHel p\nĠt ong\nĠF ight\nĠbe ach\nB ook\nĠL ic\nĠr ush\nĠR ound\nou p\nĠMar x\nĠcalcul ated\nĠDe vil\nĠSar ah\nĠoccasion ally\nĠbul let\nAv ailable\ng ate\nĠ9 1\nĠh osp\nĠprom ises\nĠH IV\nĠSt adium\nĠSt ock\nĠCorpor ation\ng age\nN G\nĠC redit\nĠs ne\nib l\nĠacc um\ns uch\nĠterror ists\nĠconscious ness\nĠZ h\nĠdram a\nool a\npir ation\nĠlab our\nĠN in\nĠut ter\nĠdemocr atic\nĠass ass\nil ation\nĠg est\nĠab road\nĠmet ab\nĠs orts\nĠfl av\nU B\nĠm g\nĠNot hing\nĠO d\nĠmus ical\n200 9\nĠdro ps\noc ated\nater al\n0000 00\nĠg re\nĠequ ality\nĠburd en\nĠv ig\nĠLe ader\n-------- ----\nĠcere mony\nĠf ighter\nĠact ors\nĠ æ\nam an\nF i\nĠal ign\nput er\nĠe lder\nĠN SA\nĠrepresent ation\nĠOnt ario\nIT H\nusal em\nĠharass ment\nitz er\nĠsy mp\nĠbox es\nĠD R\nĠman ifest\nat re\nĠ ^\nĠd ies\nle ton\nĠmiss ions\net he\nĠres olve\nĠfollow ers\nĠas c\nĠk m\nl ord\nam med\nĠsil ent\nĠAssoci ated\nĠtim ing\nĠprison ers\nĠK ings\nĠF ive\nĠtow er\nĠappro aches\nĠprecise ly\nĠb ureau\nĠM other\nĠI ss\nĠkey board\nit ual\nĠfund ed\nĠstay ing\nĠpsych ological\nĠm ile\nĠLe on\nĠBar b\nw ill\nĠw ider\nĠAtl antic\nĠt ill\nĠR ome\nro t\nĠaccomp an\nĠfl our\nac o\nW orld\nĠExp ress\nĠY u\nC or\nĠple ased\npart y\nĠpoint ing\nĠinf lation\nĠro y\nĠ ),\nain er\nĠwedd ing\norm on\nĠrequ iring\nĠqual ified\nĠse gment\nEN D\nĠs izes\ne als\nĠcor rupt\nass ador\nĠcele b\nĠdream s\nĠM ess\nĠcheck ing\nĠV ersion\nĠprep aring\nĠact ively\nĠD iff\nĠl ux\nĠW inter\nact eria\nĠN E\nĠdep uty\nĠtrans gender\nĠsum mary\nĠin her\ner ies\nch ar\nĠY an\nĠkn ock\nĠP ath\nĠl ip\nroll er\nĠimp ression\nĠcelebr ate\nĠsl ide\nĠgu ests\nĠcl ip\nF S\nĠsav ings\nĠcapt ain\nĠleg acy\nĠDen ver\nĠw ounded\ntab oola\nAC T\nĠpurs ue\nĠo xy\nĠ q\nĠsem i\nĠN eed\nĠAff airs\nĠob sc\nĠcheck ed\nĠd ual\nC ode\nĠM D\nle m\nult y\nĠ ©\nĠEl izabeth\nĠcent uries\nard ed\ns rc\nĠev ident\nenn is\nat in\nĠunemploy ment\nĠMar io\nĠint im\nCh rist\nĠbi ological\nĠsold ier\nĠAdd ed\nĠm ath\nĠG il\nĠbi as\nĠd ating\nĠO cean\nĠm ice\nM us\nh ire\nĠT es\nSer ver\nlim ited\nS ize\nĠmet ers\nĠrock et\nes see\nĠcertific ate\nĠIran ian\nAS S\nĠgr id\nD ec\nĠro lling\ncom mun\nĠSwed en\nb ury\nĠtiss ue\nĠrac ism\nĠL ocal\nĠmyster y\nĠexam ine\nĠst em\nĠs its\nĠhop ed\not ing\nĠdial ogue\nĠpers u\nW atch\nl ay\nM AN\nĠch ronic\nĠPort land\nmark et\nĠS EC\nĠparalle l\nĠsc andal\nĠcar ries\nĠphenomen on\nh uman\nack er\nĠO x\nĠretire ment\ntain ment\nov ie\nĠG ear\nĠd uties\nĠdo se\nĠsc roll\nM B\nin f\nĠsa uce\nĠland scape\nred dit\nĠChampions hip\nĠRed dit\nal id\nĠco in\nĠover s\nĠpost ing\nab out\nĠf el\nand y\nĠb old\nĠfocus ing\ne ffect\nG R\nĠde emed\nĠrecommend ations\nĠste pped\nĠvot er\nĠDe ep\nĠInst agram\nĠmoder ate\nĠMary land\nĠrestrict ed\nĠM B\nĠCh all\nĠto b\nĠc ir\nĠO cc\nĠE ver\nĠcoll aps\nIN FO\n= -\nĠP ict\nĠAcc ount\nn c\nĠo ught\nĠex port\nĠdr unk\n( '\nĠw ise\nĠM ort\nne cess\nĠan cest\nĠInc re\nĠfrequ ent\nm ir\nĠinterpret ation\nĠdepend ent\nĠco ins\nĠB ol\nV ideo\nĠJust in\nĠfat al\nĠcook ing\nĠconf usion\nip her\nĠcust ody\nĠMor gan\nom ach\nĠGovern or\nĠrestaur ants\nel ing\nĠacknowled ged\nĠthe r\nĠgen es\nch ing\nHe y\nĠtact ics\nĠMex ican\nĠv end\nĠhe s\nqu er\nĠnot ing\nĠCamer on\nĠtarget ing\nro ck\nĠcred its\nĠemot ions\nĠrepresent atives\nnew s\nĠlegisl ative\nĠrem oving\nĠtweet ed\nĠCar ter\nĠF ixed\nĠfor cing\nĠspeak er\nĠm ales\nĠViet nam\nl ined\nĠconcept s\nĠvo ices\no ir\nĠT rib\nW he\nĠJer usalem\nĠS ant\nĠc ul\nĠl ady\nĠHaw ai\nĠar ts\nĠIn n\nĠMach ine\nĠEm peror\nĠsl ot\ng ly\nĠPro cess\nII I\nĠathlet es\nĠTem ple\nĠRep resent\nĠpres c\nĠt ons\nĠgold en\nĠp unch\nĠG R\niver pool\nĠen act\nĠlob by\nĠm os\nĠpick ing\nĠlif etime\nĠcogn itive\nE ach\nz o\nĠd ub\nĠcons ists\nol n\nĠf estival\nam ous\nĠint ellig\nw ords\nĠSm art\nĠde le\nĠl apt\nĠmag ical\nĠS in\nb us\nur ities\nigh th\nĠRub y\nĠS ure\nol ving\nĠj un\nO ST\nĠimp osed\nĠast ron\nĠcor rel\nĠN S\nĠK it\nĠF uture\nb urn\nĠimm une\noc us\nĠcour ses\nĠSt ring\nĠle an\nĠg host\nĠout comes\nĠexp ense\nĠevery day\nĠaccept able\nA h\nĠequ ipped\nĠor ange\nF R\nĠD utch\nTh ough\nĠR ank\nQ U\nĠRober ts\nwh at\nre nd\nĠdisapp ear\nĠsp awn\nĠL am\no is\nĠdes erve\nĠmin imal\nĠnerv ous\nĠW ould\nĠro ok\nĠV ancouver\nĠres ign\nsh ire\nĠW orks\nĠB uild\nĠafford able\nĠG ary\nĠAren a\nĠh anging\nĠimpl ications\nĠS ong\nĠmain taining\nĠgu ards\nC ON\nĠder ived\nĠexecut ed\nĠthe ories\nĠqu oted\nĠAnd re\nog a\nsel ess\nin fo\nĠBel g\nĠt ears\nĠSur v\nĠbirth day\nig ious\nim mer\nĠspect rum\nĠarchitect ure\nĠrec ruit\narm a\nT able\nĠmon sters\nĠG ov\nĠdest ination\nĠattract ive\nĠf oss\nĠMore over\nĠpres ents\nTH E\nĠrep ly\npt on\nĠc um\nĠdel ight\nĠaffect s\nĠdon ations\nĠT oy\nĠH im\nM ENT\nĠover come\nit ched\nĠFant asy\nĠH at\nĠBe ast\nb ott\nĠinvestig ations\nR un\nĠhun ting\nd i\nf und\nĠs essions\nest yle\nĠport ray\noid s\nY eah\nĠcommun icate\nĠcom edy\nĠY ang\nĠbel t\nĠMar ine\nĠpredict ed\nPl ay\nĠimportant ly\nĠremark able\nĠelim inate\nD avid\nĠb ind\nV ID\nĠadvoc ates\nĠG aza\nim p\nD B\nĠN a\nĠSim ilar\nI ES\nĠchar ity\nv as\nm ath\nĠâ ĸ\nok er\nnd um\nĠcap s\nĠH al\n2 000\ne an\nĠfle et\nĠrec re\nR ight\nĠsleep ing\nij ing\nk ind\nĠdesign ated\nà ¤\nĠanim ation\nke e\nĠInt rodu\nĠ/ >\nĠdelay ed\nĠtrem end\nĠcur ious\nU se\nĠle ct\nd am\nĠinnov ation\nĠPoint s\nĠload ing\nĠdisp ute\nct ic\nird s\nĠB Y\nĠn urs\nĠVal ue\nION S\nĠH um\nĠtem plate\nm ers\nĠappear ances\nĠEnter tainment\nĠtransl ation\nĠsa ke\nĠbene ath\nĠin hib\nĠe uro\nabet es\nĠstud ying\nĠM as\nĠper ceived\nĠexam ined\nĠe ager\nĠco aches\nĠim per\nch i\nĠprodu ces\n\" ).\nĠEvery one\nĠm unicip\nĠg irlfriend\nĠh ire\nĠV ice\nĠsu itable\nop y\nĠin equ\nĠD uke\nf ish\nf irst\nĠO bs\nĠinter ior\nĠBru ce\nĠR y\nĠanal ys\nĠconsider able\nĠfore cast\nĠf ert\nors hip\nĠD rug\nĠA LL\n: \"\nth ur\nĠM ail\nĠball ot\nĠinst antly\nĠCh annel\nĠp icks\nĠ198 9\nĠt ent\nol i\nĠcivil ian\nb ling\nell o\nb u\nĠin ch\nĠlog o\nĠcooper ation\nĠwal ks\nĠinvest ments\nĠimp rison\nĠF estival\nĠK y\nĠleg ally\nĠg ri\nch arg\nS l\nĠthreat ening\ndu ction\nfl ow\nĠdismiss ed\nibr aries\nc ap\ne le\nĠMc G\nĠHar vard\nĠConserv ative\nĠC BS\np ng\nĠro ots\nĠH aving\numb led\nĠF un\n\\ /\nĠS earch\nple x\nĠdiscuss ing\nĠcontin u\nĠT ai\nĠW ik\nF ree\nf it\nĠref use\nĠmanag ing\nĠsy nd\nip edia\nw alk\nĠprofession als\nĠguid ance\nĠunivers ities\nĠas semb\nunt u\nF inally\nAS E\nĠAut o\nĠH ad\nĠann iversary\nL D\nĠD ur\nĠUlt imate\nih ad\npro duct\nĠtrans it\nĠrest ore\nĠexpl aining\nĠass et\nĠtransfer red\nĠbur st\nap olis\nĠMag azine\nĠC ra\nĠB R\ngg ed\nĠH E\nM ich\nb et\nĠL ady\nyl um\nerv es\nĠme ets\nwh ite\nL og\nĠcorrespond ing\nĠins isted\nG G\nĠsurround ed\nĠt ens\nĠl ane\nĠco inc\nh ome\nĠexist ed\nect ed\nĠDou ble\nlam m\nĠske pt\nex p\nĠper ception\nie v\nĠBe ing\no ft\nĠadop t\n. :\n] ;\nWind ows\nĠsatell ite\nAS H\nĠinf ant\nd escription\nĠMe anwhile\nc m\noc a\nĠT reat\nact or\nĠtob acco\nĠN orm\nem ption\nĠfl esh\nĠj e\no op\nĠHe aven\nĠbe ating\nan im\nĠgather ing\nĠcult iv\nG O\nab e\nĠJon athan\nĠSaf ety\nĠbad ly\npro t\nĠcho osing\nĠcontact ed\nĠqu it\nĠdist ur\nĠst ir\nĠto ken\nD et\nĠP a\nĠfunction ality\n00 3\ns ome\nĠlimit ations\nĠmet h\nb uild\ncon fig\nN T\nre ll\nble m\nĠM om\nĠveter ans\nĠH u\nĠtrend s\nare r\nĠG iven\nĠCa ption\nm ay\nAS T\nĠwond ering\nĠCl ark\nn ormal\nĠsepar ated\nĠdes p\nst ic\nb rew\nĠrel ating\nĠN ik\nĠF arm\nĠenthus i\ng ood\nd eb\nĠactiv ist\nĠm art\nĠexplos ion\nĠEconom ic\nL ink\nĠins ight\nĠconven ient\nĠcounter part\nsu pport\nĠV irt\nag en\nĠTenn essee\nĠSim on\nĠA ward\nOC K\nĠF igure\nĠoverse as\nĠpr ide\nĠC as\nn ote\nm g\nC urrent\nĠdispl ays\ncont ent\nĠtravel ing\nĠhosp itals\nĠFin ancial\nĠP ast\nĠdefend ant\nĠstream ing\nm ble\nĠBer lin\nuk i\nĠdist ribut\nĠant ib\nĠch ocolate\nĠCast le\nĠinter rupt\nĠR ow\nĠconvers ion\nĠbug s\nĠR ather\nli est\nL Y\nĠJe an\ncom mon\nak h\nĠ1 30\not ton\nĠDe an\nĠam endment\nĠgame play\nĠWar ren\nod a\nĠhigh lights\nĠir re\nĠNAT O\nĠball s\nĠdemand ing\nU RE\nĠL uke\nF igure\nst op\non ia\nz one\niz ers\nĠW R\nĠaward ed\nĠregul atory\nĠH art\nĠS N\npl ing\nĠs our\nĠP ixel\nus ive\nĠf et\nĠS ent\nĠautom atic\nĠf er\nvern ment\nĠKh an\nT ON\nf ather\nĠextraord inary\nth rop\nĠP ython\nĠG PU\nĠsex ually\nĠdesk top\nit ivity\nĠAnton io\nĠo rient\nĠe ars\nob by\nous es\nvertis ements\nĠmanufacture rs\nic ient\nmin ute\nĠconv iction\nĠg arden\np ublic\nĠsatisf ied\nf old\nO K\nĠin hab\nĠTh ink\nĠprogram me\nĠst omach\nĠcoord in\nĠh oly\nĠth reshold\nĠr het\nĠser ial\nĠemploy ers\nĠEvery thing\nra h\nĠb other\nĠbr ands\nVal ue\nĠT ed\nĠPlan et\nĠp ink\nĠFurther more\ns a\nP E\nre ck\nĠUS D\not te\nĠ& &\nĠland ed\ng ets\nĠprodu cers\nĠhealth care\nĠdomin ant\nĠdest ro\nĠam ended\nch ron\nĠf its\nĠSy d\nĠAuthor ity\nAT CH\nĠfight s\nĠL LC\nĠ-- -\nĠCor p\nĠtox ic\nspe cific\nĠC orn\nĠChe l\nĠtele phone\nĠP ant\nĠmyster ious\naun ch\nod ox\nmed ia\nĠwitness es\nag u\nĠquestion ed\nĠBre xit\nĠRem ember\nene z\nĠend orse\niat ric\nĠId ent\nĠridic ulous\n1 10\nĠpr ayer\nĠscient ist\nĠ19 50\nĠA qu\nĠunder ground\nĠU FC\nm are\nĠL ater\nw ich\nĠsubsc rib\nĠhost s\nĠer r\nĠgr ants\nant om\nĠsum mon\near ly\nĠC lear\nĠPr im\nĠsusp ension\nĠguarant eed\napp er\nĠr ice\nĠSe an\nĠSh in\nĠrefere ndum\nĠfl ed\nr ust\nĠ3 60\nter y\nĠsh ocked\nB R\nĠO il\nĠAll ah\nĠpart ly\nĠign or\nĠtrans mission\nĠhom osexual\nivers al\nĠhop efully\nãĤ ¤\nĠless on\nL eg\nĠ ..\nY et\nt able\napp ropri\nre tt\nĠbo ards\nĠincor rect\nĠb acteria\nar u\nam ac\nĠsn ap\n.' \"\nĠpar ad\nt em\nhe art\nĠav ailability\nĠw isdom\nĠ( +\nĠpri est\nĠÂł ĠÂł\nO pen\nĠsp an\nĠparam eter\nĠconv ince\nĠ( %)\nr ac\nĠf o\nĠsafe ly\nĠconver ted\nĠOlymp ic\nĠres erve\nĠhe aling\nĠM ine\nM ax\nĠin herent\nĠGra ham\nĠinteg rated\nD em\nĠpip eline\nĠapp lying\nĠem bed\nĠCharl ie\nĠc ave\n200 8\nĠcons ensus\nĠre wards\nP al\nĠHT ML\nĠpopular ity\nlook ing\nĠSw ord\nĠAr ts\n' )\nĠelect ron\nclus ions\nĠinteg rity\nĠexclus ively\nĠgr ace\nĠtort ure\nĠburn ed\ntw o\nĠ18 0\nP rodu\nĠent reprene\nraph ics\nĠg ym\nric ane\nĠT am\nĠadministr ative\nĠmanufacture r\nĠ vel\nĠN i\nĠisol ated\nĠMedic ine\nĠback up\nĠpromot ing\nĠcommand er\nĠfle e\nĠRus sell\nĠforg otten\nĠMiss ouri\nĠres idence\nm ons\nĠrese mb\nĠw and\nĠmeaning ful\nP T\nĠb ol\nĠhe lic\nĠwealth y\nĠr ifle\nstr ong\nrow ing\npl an\nas ury\nâĢ¦ .\nĠexpand ing\nĠHam ilton\nĠrece ives\nS I\neat ures\nĠAn im\nRE E\nP ut\nĠbrief ly\nri ve\nĠstim ul\nĠ`` (\nĠ __\nĠch ip\nĠha z\nĠpri ze\nĠTh ings\nAC E\nul in\nd ict\nok u\nĠassoci ate\nock ets\ny outube\nSt ory\nateg ory\nĠm ild\nail ing\nĠY e\nO rig\nĠK a\nor ig\nĠpropag anda\nĠan onymous\nĠstrugg led\nĠout rage\nAT ED\nĠBe ijing\nr ary\nĠle ather\nĠworld s\nĠbroad er\n12 5\nid al\nĠBet ter\nĠt ear\nE xt\nĠpropos als\nĠit er\nĠSqu ad\nĠvol unt\nm i\nD id\nĠP u\np in\nĠspeak ers\nĠb orders\nĠfig ured\n= '\nĠsimultane ously\naed a\nĠcharg ing\nĠur ged\nĠcon j\n25 6\nĠG ordon\nmer ce\nĠdocument ary\nSh are\nit ol\nON E\nĠG arden\nh att\nĠThom pson\nane ous\nap ore\nĠt anks\nĠless ons\ntr ack\nĠout standing\nĠvolunte ers\nĠsp ray\nĠmanag ers\nl arge\nĠcamp s\nĠart ificial\nĠR u\nĠb ags\nth al\nĠcompat ible\nĠBl ade\nĠf ed\nĠarg ues\nF I\nĠunf air\nĠcor n\nĠoff set\nĠdirect ions\nĠdisappoint ed\nĠCon vention\nĠview ing\nM E\noc ity\nĠtown s\nĠlay ers\nĠro lled\nĠjump ed\nĠatt ribute\nĠun necess\ninc oln\nĠsupp ose\nĠNet her\nch a\nĠbur ied\nĠsix th\nB en\nress ing\nOU R\nĠw ound\nĠcy cl\nĠmechan isms\nĠcongress ional\nĠE lement\nĠagre ements\nĠdec or\nĠclos est\nĠM it\nGo ogle\n} }\nĠm ixture\nĠflu id\nS ign\nĠSch olar\nĠp ist\nask et\nab ling\nĠrac ing\nhe ro\nri el\nass y\nĠche aper\nb en\nĠvert ical\namac are\nĠRead ing\ng ments\nĠhelic op\nĠsacr ifice\nay a\np aren\nV A\nĠL es\nĠStud io\nĠviol ations\nĠAn na\nac er\né ¾\nĠR at\nĠBe ck\nĠD ick\nĠA CT\nĠcomp osition\nĠtext ure\nĠO wn\nĠsmart phone\nĠN A\nĠfor b\nim port\nĠdef ending\nil st\nre r\nĠo h\nĠJere my\nĠbank ing\ncept ions\nĠrespect ive\n/ .\nĠdr inks\nĠW i\nĠb ands\nĠL iverpool\nĠg rip\nĠB uy\nĠopen ly\nĠreview ed\nper t\nĠver ify\nĠCo le\nĠW ales\nM O\nĠun pre\nĠshel ter\nĠIm perial\nĠgu i\nĠD ak\nĠsuggest ions\nĠexplicit ly\nĠsl ave\nĠblock chain\nĠcompet ing\nĠprom ising\nS ON\nĠsoc cer\nĠconst itution\n4 29\nĠdist ract\nĠU ser\nes ides\nĠMet hod\nĠTok yo\nĠaccompan ied\nCl ient\ns ur\nal og\nĠident ification\nĠinv asion\nas ma\nĠindust ries\npp ers\nĠsub tle\nĠUn it\nn atural\nĠsurv ived\nĠfl aw\nĺ ħ\nĠH oll\nĠdef icit\nĠtut orial\nĠCh ance\nĠarg uing\nĠcontem porary\nĠinteg ration\nfor ward\nĠt um\nit is\nĠh iding\nĠD omin\nĠT an\nĠB uilding\nĠV in\nĠspokes person\nĠNot es\nĠemer ging\nĠprepar ation\nĠpro st\nĠsuspect s\nĠaut onom\nD escription\nĠdeal t\nĠP ear\nĠstead y\nĠdecre ased\nĠso vere\nĠCl in\nĠgrad ually\nors es\nĠW AR\nS erv\nãĤ ¢\nh r\nĠd irty\nĠB arn\nĠB C\nĠd il\nĠcal endar\nĠcompl iance\nĠch amber\nb b\nĠpass enger\nate ful\nĠT itle\nĠSyd ney\nĠG ot\nĠdark ness\nĠdef ect\nĠpack ed\nass ion\nĠgod s\nĠh arsh\nIC K\nle ans\nĠalgorith m\nĠoxy gen\nĠvis its\nĠbl ade\nĠkil omet\nĠKent ucky\nĠkill er\nP ack\nenn y\nĠdiv ine\nĠnom ination\nbe ing\nĠeng ines\nĠc ats\nĠbuff er\nĠPh ill\nĠtra ff\nAG E\nĠtong ue\nĠrad iation\nere r\nm em\nĠExpl icit\né¾ į\nĠcou ples\nĠphys ics\nĠMc K\nĠpolit ically\naw ks\nĠBl oom\nĠwor ship\ne ger\nut er\nĠF O\nĠmat hemat\nĠsent enced\nĠdis k\nĠM arg\nĠ/ *\nP I\nĠoption al\nĠbab ies\nĠse eds\nĠScott ish\nĠth y\n] ]\nĠHit ler\nP H\nng th\nĠrec overed\ning e\nĠpow der\nĠl ips\nĠdesign er\nĠdis orders\nĠcour age\nĠch aos\n\" },{\"\nĠcar rier\nb ably\nH igh\nĠR T\nes ity\nl en\nĠrout es\nu ating\nF il\nN OT\nw all\ns burgh\nĠeng aging\nĠJava Script\nore r\nli hood\nĠun ions\nĠF ederation\nĠTes la\nĠcomple tion\nĠT a\nĠprivile ge\nĠOr ange\nĠne ur\nparen cy\nĠb ones\nĠtit led\nĠprosecut ors\nĠM E\nĠengine er\nĠUn iverse\nĠH ig\nn ie\no ard\nĠheart s\nĠG re\nuss ion\nĠmin istry\nĠpen et\nĠN ut\nĠO w\nĠX P\nin stein\nĠbul k\nS ystem\nic ism\nĠMarket able\nĠpre val\nĠpost er\nĠatt ending\nur able\nĠlicens ed\nĠG h\net ry\nĠTrad able\nĠbl ast\nà ¤\nĠTit an\nell ed\nd ie\nH ave\nĠFl ame\nĠprof ound\nĠparticip ating\nĠan ime\nĠE ss\nĠspec ify\nĠregard ed\nĠSpe ll\nĠs ons\nown ed\nĠm erc\nĠexper imental\nland o\nh s\nĠDun geon\nin os\nĠcomp ly\nĠSystem s\nar th\nĠse ized\nl ocal\nĠGirl s\nud o\non ed\nĠF le\nĠconstruct ed\nĠhost ed\nĠsc ared\nact ic\nĠIs lands\nĠM ORE\nĠbl ess\nĠblock ing\nĠch ips\nĠev ac\nP s\nĠcorpor ation\nĠo x\nĠlight ing\nĠneighb ors\nĠU b\nar o\nĠbe ef\nĠU ber\nF acebook\nar med\nit ate\nĠR ating\nĠQu ick\nĠoccup ied\nĠaim s\nĠAdd itionally\nĠInt erest\nĠdram atically\nĠhe al\nĠpain ting\nĠengine ers\nM M\nĠM ust\nĠquant ity\nP aul\nĠearn ings\nĠPost s\nst ra\nãĥ¼ ãĥ\nĠst ance\nĠdro pping\nsc ript\nĠd ressed\nM ake\nĠjust ify\nĠL td\nĠprompt ed\nĠscr ut\nĠspeed s\nĠGi ants\nom er\nĠEd itor\nĠdescrib ing\nĠL ie\nment ed\nĠnow here\noc aly\nĠinst ruction\nfort able\nĠent ities\nĠc m\nĠN atural\nĠinqu iry\nĠpress ed\niz ont\nfor ced\nĠra ises\nĠNet flix\nĠS ide\nĠout er\nĠamong st\nim s\nows ki\nĠclim b\nne ver\nĠcomb ine\nd ing\nĠcomp r\nĠsignific ance\nĠremem bered\nĠNev ada\nĠT el\nĠSc ar\nĠWar riors\nĠJ ane\nĠcou p\nb as\nĠtermin al\n, -\nO H\nĠt ension\nĠw ings\nĠMy ster\n�� ��\nĠUn like\nval id\nviron ments\nĠAl i\nĠn aked\nbook s\nĠM un\nĠG ulf\nĠd ensity\nĠdim in\nĠdesper ate\nĠpres idency\nĠ198 6\nh y\nIN D\nĠun lock\nim ens\nĠhand led\nĠE b\nĠdisapp eared\nĠgen re\nĠ198 8\nĠdetermin ation\nSt ream\nik o\nap ters\nĠacknow ledge\nJ an\nĠcapital ism\nP at\nĠ20 20\nĠpain ful\nĠcur ve\nĠbom bs\nst orm\nĠMet al\nen cer\nĠF ig\nĠA aron\nanc hes\nĠins piration\nĠexha ust\nt ains\nash i\nĠdesc ript\nĠr itual\nĠChel sea\nĠpromot ion\nĠH ung\nĠW ard\niv a\nĠE T\nĠto ss\nall ow\nĠFranc is\nD ep\nĠhapp iness\nĠGl ass\nĠbet a\nĠstreng then\nN E\no a\nĠbutt ons\nĠMur ray\nĠkick ed\nQu est\nĠT alk\nĠS everal\nĠZ ero\nĠdr one\nul k\nĠc am\nĠM obile\nĠprevent ing\nĠret ro\nĠA x\nĠcru el\nĠflo at\n. ),\nĠfil ing\nĠGr ant\nĠB or\nĠr ib\nĠchampions hip\nĠM erc\nĠsty les\nĠc ake\nĠbuild s\nĠS elf\nio x\nĠep ic\noy d\nB el\nĠSt ew\n. (\nah u\nĠBe yond\nĠout s\nĠsol o\nĠT ree\nĠpres erve\nĠt ub\nAR E\nro c\nĠIm pro\nĠW right\nĠbu nd\nĠtr aged\nĠoccas ional\nb ian\nSec ond\nr ons\nĠinter actions\nform ed\ns ing\nĠown s\nĠh ockey\nGener al\nĠlog ical\nĠexp end\nĠesc al\nĠGr iff\nĠC rown\nĠRes erve\nĠsto pping\nĠexc use\nsec ond\nĠoper ated\nĠre aches\nĠMal ays\nĠpoll ution\nĠBrook lyn\nĠde lete\nĠhas h\nBl ock\nah a\nâĢ ³\nĠsh orter\np iece\n> </\nĠh orm\nĠW at\nĠBre ak\nĠprohib ited\nĠint ensity\nĠAl an\nĠli ability\n? !\nand ed\nĠneigh bour\nĠCol lection\nĠf ires\nĠrevolution ary\nf ly\nĠOr leans\nWh ite\nĠW rit\nĠD awn\nĠsett le\nĠexec ute\nB M\nĠspokes woman\nĠlif estyle\nĠclick ing\nĠK ill\nĠLiber al\nĠN azi\nĠtra iler\nĠmount ains\nĠdam n\nz es\np es\nĠpress ing\nĠb ail\nĠOrgan ization\nĠp ir\nĠth irty\nĠelect rical\nĠ1 15\nĠP oly\nĠR ap\nĠSt rike\nĠC ann\nĠdemand ed\nĠback ing\ndef ault\nspe ed\nĠLeg isl\nĠmother s\nĠB ody\nĠvar iation\nced ented\np owered\nle ading\nN ever\nĠg rave\nĠAnt i\nA W\nĠinterview ed\nĠG ab\nĠF at\nĠrook ie\nu u\nĠdep os\nix on\nĠam pl\nret ion\nĠHe at\nĠpeace ful\nS M\nie ve\nĠd iver\nĠVict oria\nĠm ic\np df\nĠst ating\nĠl ung\nĠcritic ized\nĠvacc ine\nĠLoad ing\nur se\nT ake\nĠFr an\nĠS old\nĠRob in\nĠdetect ed\nĠSc ript\nĠadjust ed\nĠsen ator\nĠopp osing\nEr ror\nC ount\nĠconflic ts\nĠo w\nĠAr gent\nĠmatch ing\nh h\nĠTre k\nst arter\n\" ),\nĠA F\nod er\nxx xx\nĠAl t\nac re\nĠP ick\nĠSol ar\nĠD al\nO ct\nĠB att\nĠs rc\nĠeng agement\nĠexecut ives\nĠliber ty\nj ava\nĠtal ented\nigen ous\nĠcon secut\n.. ...\nIn fo\nĠhor rible\nĠsurprising ly\nf eed\nic ating\nĠL ED\nĠfem ales\nSt ation\nell er\nĠOak land\nĠmechan ical\ni ology\nĠV ar\nĠrob ust\nett ings\nott a\nĠthe oret\nĠret ain\nk ward\nĠd a\nĠdeploy ed\nd el\nĠAnd y\nĠsubsc ribe\nwe b\nĠn a\nĠMic hel\nĠpart ially\nĠCome y\nĠc rown\nĠM aj\nĠBl u\nr ator\nD ay\nIN T\nĠdocument ed\nĠG DP\ng i\nche ll\nĠbrut al\nĠB ab\nst ration\nĠthe ft\nĠt ube\n@ @\nĠqu ery\nĠL incoln\nĠpublish ing\nĠw ore\nor ical\nĠr ic\nĠnot able\nĠsubsequ ently\nne x\nĠobser ve\nĠB oe\nĠc odes\nm ain\nW H\nĠS L\nĠresident ial\nav an\nĠm as\nare st\nade on\nOU T\nĠsoph istic\nant e\nĠc ens\nĠ **\nĠmort ality\nĠyour s\nĠoccas ions\nĠrec alled\nĠDri ver\nĠv ocal\nĠbath room\nĠsh ops\nĠcollabor ation\nĠOb amacare\nĠC ell\nCh ar\nSu per\nC re\nĠt ends\nĠt orn\nĠeconom ics\na very\nĠR aid\nĠS em\nĠshould ers\nĠexpect ing\nĠexam ination\nen ame\nĠU I\ni ability\nol as\nĠAm b\nĠD ra\nĠmid field\nĠI C\nĠlay out\nĠflo ating\nf i\nit ative\nĠtremend ous\nĠ Ð\nĠab und\nW ork\nĠLight ning\nĠsimilar ly\nĠconserv atives\nĠpr ay\nB E\niz arre\nĠt empt\nĠemphas is\nĠMet ro\nĠf ishing\nĠmar ry\nne g\nĠStud y\nĠrec k\nĠdis pos\non ing\nbs ite\nĠsusp ic\nĠmer ch\nĠG ib\nĠDes cription\nĠD VD\nw he\nĠY emen\nĠen vironments\noot ing\nĠMod ern\ne u\nĠreflect s\nĠh oney\nĠanaly st\nĠg ut\nd ec\nA ction\nĠhousehold s\nĠst er\nĠtem ple\nĠreform s\nĠfavour ite\nĠdead line\nĠL E\nTh ree\nĠWith in\nA ug\nĠnight s\nelt a\nĠinv alid\nĠEx change\nĠDel hi\nw hen\ninc ome\nĠ ðŁ\nĠwire less\nsc ribe\nist a\nĠhost ile\nĠall y\nĠg ig\nĠout lets\nĠD or\nEM ENT\nĠas h\nĠab stract\nOR D\nĠMot or\nĠadv iser\nist le\nĠb ases\nĠcourt esy\nĠcross ing\nĠcle ared\nĠrefuge e\ncos ystem\nĠthrow s\nf un\nbour ne\nd ays\nĠdisag ree\nĠN ative\nĠreflect ed\nĠF ast\nĠY ellow\nĠSing apore\nĠR aven\nĠembr ace\nĠK u\nĠC hen\nĠEar ly\nĠappoint ment\nĠMin i\nit ement\nĠpl acing\nĠb icy\nS R\nĠwh is\nS U\nĠinvestig ated\nĠphotograph s\ng ithub\nĠBe at\nĠR ing\nig hed\ni ar\nĠev olved\neral d\nĠd un\nĠh ub\nI AL\nĠencour aging\nĠPr int\nĠD ays\nĠpro secution\nĠp ants\naz y\nl ive\nĠfoss il\nĠJ u\nĠro cks\nud ge\nĠR ace\nĠg reet\nb ie\nĠf illing\nĠL en\nĠdi abetes\nĠfire arms\num ing\nenez uel\nĠB B\nĠaccept ing\nAT H\nĠres ort\nĠh unt\nri k\nuck er\nam ents\nĠsust ained\nĠcross ed\nĠbreak fast\nĠatt ributes\nlect ed\nat ile\nĠv ibr\nĠK al\nars on\nop les\nĠtou ched\nĠdam ages\nĠimp ressed\nru p\nĠan ch\nĠAd ams\nH el\nĠVict or\nĠmount ed\nĠC C\nĠdelic ious\nsp an\nell a\nĠel abor\nam ples\nĠdef ic\nĠconstit u\nu ates\nĠM ission\nĠT her\nĠMon ster\nb es\nRe uters\nĠInd ones\nh ill\nmun ition\nĠconfirm ation\nĠCons ider\nac ent\nĠj et\nĠEm ploy\nĠGT X\nn an\nĠSp ider\nĠprocess or\nĠpat ri\nĠPent agon\nĠRob inson\nĠreal istic\nà ±\nĠappear ing\nĠp ipe\nom ed\nĠf ru\nĠaw ful\nĠeval uation\nĠintellig ent\nĠC itiz\nĠfund ra\nod ium\nĠtwe ets\nĠwor n\npr ing\nĠkid n\nĠreb els\nĠK am\nĠNether lands\nĠS W\nĠacqu isition\nĠM ale\nãĥ ª\nomb ies\nĠtrad em\nĠStat us\nB re\nĠTH IS\nĠad verse\nĠN EW\ns ign\nĠorgan isation\nen c\nĠHar per\nap or\nĠMem bers\nĠPe ace\nĠAir port\nĠOther s\nĠscr atch\nĠP il\nĠsens or\nĠadop tion\nĠHot el\nĠDr ag\nĠhonest ly\nĠy ard\nĠFor ces\nĠpat ent\nĠb ass\nĠquiet ly\nĠbreat hing\nĠp ose\ni ors\nĠJ ess\nst atic\nIT E\nO ffic\nĠj ew\nw cs\nĠ14 0\nĠpre view\nipp i\nĠunf ortunately\noke mon\nĠh orn\nĠre ass\nĠpe er\nock er\nĠunt o\nĠGr ay\nĠclean ing\nĠattract ed\n200 7\nP oint\nk ill\nĠAg reement\nur ches\nĠhor r\nĠMiss iss\nĠworth y\nĠfl owers\nt own\nd ll\nĠre actions\nĠde ce\nĠindic ating\nM D\nĠpre ference\nĠM VP\ness ional\nĠT arget\ng ence\nĠInd ians\nĠm isc\nĠfree ly\nĠmus cles\nĠline up\nĠimpact s\nous ing\nom i\nac ular\nĠcontro lling\nag ine\nc ery\nhe ll\nĠrank ing\nĠN ich\nĠA ve\n12 8\nĠhigh way\nĠinc ons\nĠb inding\nĠstrugg les\nĠPitt sburgh\nĠgr ay\nr in\nĠcom ics\nĠS port\nĠrel atives\nĠfr ight\nĠpro be\nĠPort ug\nĠv oc\nĠt u\nĠCor ps\nĠposs ibilities\nĠqual ify\nwcs store\nĠl ibraries\nĠm igrants\nĠent ries\nĠconsecut ive\nv als\nĠChair man\nĠh ill\nIM E\nĠG ard\nĠinequ ality\nf ox\nĠS ave\nĠc ort\nclaim ed\nĠtra its\nĠp our\nĠmiss iles\nĠess ence\nĠs ends\nĠall iance\nĠw ishes\nĠChrist opher\nB ig\nN Y\nĠJac ob\ns an\nur red\nĠS O\nll y\nĠadvoc ate\nĠB ond\nĠ\" /\nUs ing\nĠdistrict s\nĠG ate\nĠB ir\nr idge\nĠN az\nĠR s\nbo ards\nĠG a\nĠRe agan\nĠinflu enced\n1 000\nap y\nĠchalleng ed\nĠb arg\nĠfac ulty\nĠF if\nĠacqu ire\nA c\nĠin sect\nĠinstr uments\nĠle af\nth odox\nM essage\nĠt ale\nĠthere by\nĠtra p\nĠstrong est\nĠMil itary\nis ible\nĠ198 4\nethe less\nĠflex ible\nĠkill s\nĠfin ishing\nĠS ize\nĠredu ces\nĠep id\nĠorient ation\nf ull\nĠtr ace\nĠl aser\nĠopp ose\nĠed iting\nĠmoment um\nä º\nsh ow\nV I\nĠL ad\nĠ198 5\nĠmurd ered\n9 00\nut her\nĠprob ability\nĠP oll\nĠrel uct\nĠChe m\nĠMont real\nĠadequ ate\nĠPol and\nĠSher iff\num ph\nĠo k\nĠ 000\nĠ\" [\nĠoper ators\nĠF er\nĠmod es\nĠE ve\nĠdiscipl ine\nN ET\nH and\nĠor al\nĠW E\nem ail\nJ P\nĠPalestin ians\nĠhe nce\nĠL ess\nĠover l\nd ig\nĠintim id\nĠCo al\nĠr anging\nth a\nĠdist ant\nĠf ib\nĠInd ex\nĠW onder\nĠP el\nhatt an\nĠH ug\nà Ĺ\nra it\nĠwra pped\nĠR PG\nĠchemical s\nĠM oney\nĠfro zen\nĠind irect\nĠAgain st\nE nd\nĠuncom fortable\nĠGall ery\nĠPost ed\nØ §\nond uct\nĠconsequ ence\nĠbit ter\nĠ198 7\np op\nĠcount less\nĠAl aska\nff ff\nĠdepart ure\nĠref und\nĠI an\ni ated\nĠsee ks\nĠmechan ics\nĠjurisd iction\nlyn n\nĠal ike\nĠH unt\nath on\nĠres olved\nĠc ache\nĠdist inction\nd irect\nĠenc ount\nou b\nbe at\nĠCount ry\nse arch\nĠcontin uous\nĠmod est\nĠR ail\nth ood\n1 30\nB UG\nĠcrim inals\nĠindic ation\nĠencount ered\nl ast\nĠW y\nĠide ology\nĠP DF\nsec urity\n] )\nĠJim my\nĠE N\nĠh iring\nT em\nĠp ig\naun t\nĠCry stal\nĠpen alties\nĠcap ability\nĠp y\nĠproduct ive\nĠbal anced\nĠGe Force\ncl ick\nolit an\nod s\nĠafter wards\nĠplay offs\nĠG ill\nU ser\nĠback s\np ub\nt ag\nĠabs urd\np iring\nĠc iting\nĠtr illion\nĠoblig ation\nĠmax im\nah oo\nc f\num i\nĠAl pha\nĠN elson\nĠpursu ant\nin itely\nĠf ract\nent ry\nber y\nĠTh or\nAdd ed\nĠD J\nĠG ene\nĠaw kward\nSt ud\nĠwal let\nĠDiv ine\nari os\nĠrele asing\nĠed ited\nĠaccompl ished\nB est\nĠed ges\nĠplan es\nĠfeed ing\n\" },\"\nĠdiscl osure\nĠgr ain\nair y\no ons\nern and\nV R\nĠreason ably\nĠdr um\nĠpart ial\nĠgraph ic\nĠunpre cedented\nĠadv ised\nM icro\nĠAss ad\npoint s\nsc ar\nĠZ one\ntt es\nĠ7 00\nv o\nĠH amp\nĠfix es\nĠca ution\nĠstr ings\nĠpan els\nĠle ak\nĠpr icing\nrow th\nĠEr ror\nĠS aints\nf ix\nĠobserv ations\nĠA bs\nĠsuggest ion\nĠUkrain ian\nĠbar rier\nĠpain ted\nB et\nim ir\nĠS pect\np ot\norne ys\nĠcomp ound\nĠbe ars\nĠR ush\nĠlux ury\nS um\nĠor bit\nĠMar c\nĠex empt\nĠTra il\nĠM O\nĠH ans\nĠWe apon\noc used\numin um\nĠJer ry\nĠb ust\nĠA G\nĠW iki\nĠend less\nĠV lad\nĠB ah\nĠR adeon\nke ys\nĠSur vey\nĠV iol\ndef ine\nle an\nĠcomm od\nĠreven ues\nÅ į\nĠfurn iture\nĠcast ing\nĠdiplom atic\nĠPlay ers\nĠK illed\nĠmod ify\nĠinnov ative\nĠAb u\nn or\nĠbond s\nĠcoach ing\nM er\nĠmod ules\nĠPatri ots\nĠenh anced\nĠproceed ings\nĠteam mates\nĠ12 8\nard o\nĠcomprom ise\nĠM uch\nĠfle w\nĠEd ge\nĠunnecess ary\nĠdoct rine\nre port\nĠOr lando\nĠProf ile\nĠplay off\nfriend ly\nĠcompl ain\nĠM C\nĠO pt\nĠG B\nĠbeat en\nĠg olf\nĠpl acement\nB it\nĠnews letter\nĠ201 9\nvis or\nraw l\nĠiP ad\nĠact ed\nĠju ice\nĠdec ks\nP N\nsu ccess\nĠH alf\nĠdele ted\nĠsec rets\nĠas ylum\nM art\nĠAct iv\nĠGu y\nĠT s\nĠd ys\nĠassum ing\nĠman a\nĠsub ur\nĠ12 5\nM edia\nAR Y\nr ide\nc p\nĠdifficult ies\nĠcollect ing\nĠbank rupt\nn on\nĠcomp osed\nĠvol t\nĠmilit ants\nĠ> >>\nĠM ormon\nt or\nĠpartic les\nĠB art\nry ption\nĠad min\nĠsqu ee\nVID IA\nĠcreat or\niam eter\nic ular\nN BC\nĠgrab bed\nĠn odd\nĠr ated\nĠrot ation\nĠgr asp\nĠexcess ive\nĠE C\nĠWh it\nĠinvent ory\nault s\nĠF B\nĠe cosystem\nĠbill ions\nĠvent ure\nn amed\nĠdef ender\nout e\nInst ead\nir able\nW ar\nĠassum ption\nĠb ite\nĠearth qu\nt ail\nsp ace\nĠgif ts\nboy s\nĠinev itable\nĠstruct ural\nĠbenef icial\nĠcompe lling\nh ole\nerv ation\nĠco at\no j\ninc arn\nĠY ears\nĠdetermin ing\nĠrhet oric\nĠbound aries\nĠwh ites\nA nt\nadd y\n) -\nra ham\neter min\nĠhar vest\nĠCon c\nĠlapt op\nĠM atch\nĠenjoy ing\ncc a\noll ar\nĠtri ps\nĠadd iction\nĠS ak\nĠpow ered\nĠc ous\nĠRuss ians\nie re\nĠret rie\nqu ality\nĠdiff er\nĠking dom\nĠL aur\nĠCap itol\nĠcon clusions\nĠAl tern\nĠN av\nĠtrans parent\nB ER\nG roup\nĠCom plete\nĠinf er\nĠint rig\nĠins ane\nR O\noph ob\nis en\nqu al\nMich ael\nĠm useum\nĠP ope\nĠres et\nr ative\nf ive\nĠagg reg\nitte es\nosit ory\nĠcar b\nĠRec ord\nĠdec ides\nĠF ix\nĠexcept ions\nĠCommission er\nun s\nĠEnvironment al\nĠlegend ary\nist ence\nĠtun nel\nk m\nĠins ult\nĠt roll\nĠsh ake\nĠdet ention\nqu es\nĠCh rome\nĠF iles\nĠsub t\nĠprospect s\nĠpro l\nre nder\npro of\nĠperform ances\nSt r\nĠh ref\nern ame\nĠachieve ment\nĠf ut\nF ull\nĠLe ban\ngo ogle\nãĥ Ī\namp a\nMay be\nĠproject ed\nĠE mb\nĠcol leg\nĠa wards\nĠâ Ķ\nG old\nĠBl ake\nĠR aj\nif ting\nĠp ending\nĠinst inct\nĠdevelop ments\nCon nect\nĠM and\nĠW ITH\nĠPhilipp ines\nprof ile\nĠalt ogether\nĠB und\nĠT D\noo oo\namp ed\nip h\nĠste am\nĠold est\nĠdet ection\nul pt\nĠ ç\nĠWay ne\n200 6\nf a\nĠcir cles\nĠF u\nĠdon ors\nappropri ate\nĠDak ota\nj amin\nĠmotiv ated\nĠpurch ases\nĠLouis iana\nĠS pl\nĠgl obe\nĠ10 5\nz ip\nc all\nĠdepart ments\nĠsustain able\n10 5\nĠO P\nif iers\nĠprevent ed\nĠinc omp\nĠComm ander\nĠdom inated\nĠ »\nĠinvest ed\nĠcomplex ity\nĠin cl\nĠens uring\nĠreal m\nyn c\nĠInd ependent\nr ained\nĠJ en\nĠFl ight\nĠat he\nĠspec ulation\nĠT E\noc ate\nt ic\nĠpl aint\nher ry\nĠto y\nĠ1 11\nĠpl ates\nst atus\nĠIs a\nĠdev oted\nC op\nĠE S\n25 5\nur rency\nM ain\nĠsl aves\nĠpe pper\nĠqu otes\nĠce iling\nĠF ish\nĠtrans formation\nĠfra ction\nĠadvant ages\nĠto ile\nĠstun ning\nĠmo ist\nbre aking\ns i\nĠL ocation\nĠMed ium\nĠtext s\nĠu gly\nĠb io\n. âĢĶ\nĠB ased\nĠtr ains\nĠW ing\nĠAn cient\nĠRec ords\nĠH ope\nSpe cial\nades h\nob i\n[ /\nĠtempor arily\nV er\nh u\nos er\nĠover night\nĠm amm\nĠTre asury\nĠV enezuel\nĠMeg a\nĠt ar\nĠexpect s\nbl ack\nor ph\n\\\\ \\\\\nĠaccept ance\nĠrad ar\ns is\nĠjun ior\nĠfram es\nĠobserv ation\nac ies\nP ower\nĠAdv anced\nM ag\nolog ically\nĠMe chan\nĠsent ences\nĠanaly sts\naugh ters\nforce ment\nĠv ague\nĠcl ause\nĠdirect ors\nĠeval uate\nĠcabin et\nM att\nĠClass ic\nA ng\nĠcl er\nĠB uck\nĠresear cher\nĠ16 0\nĠpoor ly\nĠexperien cing\nĠP ed\nĠMan hattan\nĠfre ed\nĠthem es\nad vant\nĠn in\nĠpra ise\n10 4\nĠLib ya\nb est\nĠtrust ed\nĠce ase\nĠd ign\nD irect\nĠbomb ing\nĠm igration\nĠSci ences\nĠmunicip al\nĠA verage\nĠgl ory\nĠreve aling\nĠare na\nĠuncertain ty\nĠbattle field\nia o\nG od\nĠc inem\nra pe\nel le\nap ons\nĠlist ing\nĠwa ited\nĠsp otted\nke ley\nĠAud io\ne or\nard ing\nidd ing\nig ma\nĠN eg\nĠl one\nĠ ----\nex e\nd eg\nĠtrans f\nĠwas h\nĠsl avery\nĠexpl oring\nĠW W\nats on\nĠen cl\nl ies\nĠC reek\nĠwood en\nMan ager\nĠBr and\num my\nĠAr thur\nĠbureau cr\nĠbl end\nar ians\nF urther\nĠsupposed ly\nĠwind s\nĠ19 79\nĠgrav ity\nĠanalys es\nĠTra vel\nĠV eter\nĠd umb\nĠaltern ate\ng al\nĠconsum ed\nĠeffect iveness\n.' '\nĠpath s\nond a\nL A\nĠStr ong\nĠen ables\nĠesc aped\nĠ\" \"\nĠ1 12\nĠ198 3\nĠsm iled\nĠtend ency\nF ire\nĠp ars\nĠR oc\nĠl ake\nĠf itness\nĠA th\nĠH orn\nĠh ier\nĠimp ose\nm other\nĠp ension\nic ut\nbor ne\nic iary\n. _\nĠS U\nĠpol ar\nis y\neng u\nitial ized\nAT A\nw rite\nĠexerc ises\nĠD iamond\not ypes\nĠharm ful\non z\nĠprint ing\nst ory\nĠexpert ise\nĠG er\nĠtraged y\nĠF ly\nĠd ivid\namp ire\nst ock\nM em\nĠre ign\nĠun ve\nĠam end\nĠProp het\nĠmut ual\nĠF ac\nĠrepl acing\nH ar\nĠCirc uit\nĠthro at\nĠSh ot\nĠbatter ies\nĠto ll\nĠaddress ing\nĠMedic aid\nĠp upp\nĠN ar\nol k\nĠequ ity\nM R\nĠHis pan\nĠL arge\nm id\nD ev\nĠexp ed\nĠdem o\nĠMarsh all\nerg us\nĠf iber\nĠdiv orce\nĠCre ate\nĠsl ower\nĠPark er\nĠStud ent\nĠTr aining\nRet urn\nĠT ru\nĠc ub\nĠRe ached\nĠpan ic\nĠqu arters\nĠre ct\nĠtreat ing\nĠr ats\nĠChristian ity\nol er\nĠsac red\nĠdecl are\nul ative\net ing\nĠdeliver ing\nest one\nĠt el\nĠL arry\nĠmet a\nac cept\nart z\nĠRog er\nhand ed\nĠhead er\nĠtra pped\nĠCent ury\nĠkn ocked\nĠOx ford\nĠsurviv ors\nb ot\nĠdemon stration\nĠd irt\nĠass ists\nOM E\nĠD raft\nortun ate\nfol io\npe red\nust ers\ng t\nĠL ock\nĠjud icial\nver ted\nĠsec ured\nout ing\nĠBook s\nĠhost ing\nĠlif ted\nl ength\nĠj er\nĠwhe els\nĠR ange\numbn ails\nĠdiagn osis\nte ch\nĠStew art\nĠP ract\nĠnation wide\nĠde ar\nĠoblig ations\nĠgrow s\nĠmand atory\nĠsusp icious\n! '\nA pr\nG reat\nĠmort gage\nĠprosecut or\nĠeditor ial\nĠK r\nĠprocess ed\nung le\nĠflex ibility\nEar lier\nĠC art\nĠS ug\nĠfoc uses\nĠstart up\nĠbre ach\nĠT ob\ncy cle\nãĢ Į\nro se\nĠb izarre\nãĢ į\nĠveget ables\n$ $\nĠret reat\nosh i\nĠSh op\nĠG round\nĠSt op\nĠHawai i\nĠA y\nPer haps\nĠBe aut\nuff er\nenn a\nĠproduct ivity\nF ixed\ncont rol\nĠabs ent\nĠCamp aign\nG reen\nĠident ifying\nĠreg ret\nĠpromot ed\nĠSe ven\nĠer u\nne ath\naug hed\nĠP in\nĠL iving\nC ost\nom atic\nme ga\nĠN ig\noc y\nĠin box\nĠem pire\nĠhor izont\nĠbr anches\nĠmet aph\nAct ive\ned i\nĠFil m\nĠS omething\nĠmod s\ninc ial\nĠOrig inal\nG en\nĠspir its\nĠear ning\nH ist\nĠr iders\nĠsacr ific\nM T\nĠV A\nĠS alt\nĠoccup ation\nĠM i\nĠdis g\nlic t\nĠn it\nĠn odes\ne em\nĠP ier\nĠhat red\nps y\nãĥ ī\nĠthe ater\nĠsophistic ated\nĠdef ended\nĠbes ides\nĠthorough ly\nĠMedic are\nĠbl amed\narent ly\nĠcry ing\nF OR\npri v\nĠsing ing\nĠI l\nĠc ute\no ided\nolit ical\nĠNe uro\nå ¤\nĠdon ation\nĠEag les\nĠG ive\nT om\nĠsubstant ially\nĠLic ense\nĠJ a\nĠg rey\nĠAn imal\nĠE R\nĠU nd\nĠke en\nĠconclud e\nĠMississ ippi\nEng ine\nĠStud ios\nP ress\no vers\nll ers\nĠ3 50\nĠR angers\nĠr ou\nert o\nE p\niss a\niv an\nĠse al\nĠReg ist\ndis play\nĠwe aken\nu um\nĠComm ons\nĠS ay\nĠcult ures\nĠl aughed\nĠsl ip\nĠtreat ments\niz able\nm art\nĠR ice\nĠbe ast\nĠob esity\nĠLa ure\nig a\nWh ich\nhold er\nĠelder ly\nĠp ays\nĠcompl ained\nĠc rop\nĠpro c\nĠexplos ive\nĠF an\nĠAr senal\nA uthor\nef ul\nĠme als\nĠ( -\nid ays\nĠimag ination\nĠann ually\nĠm s\nas ures\nH ead\nik h\nm atic\nĠboy friend\nĠCom puter\nĠb ump\nĠsur ge\nĠCra ig\nĠKir k\nD el\nmedi ate\nĠscen arios\nĠM ut\nĠSt ream\nĠcompet itors\nÙ Ħ\nĠStan ford\nĠRes ources\naz ed\nb age\nĠorgan is\nĠRe lease\nĠsepar ately\nĠha bits\nĠmeasure ments\nĠCl ose\nĠaccomp any\nĠg ly\nĠt ang\nĠR ou\nĠplug in\nĠcon vey\nĠChall enge\noot s\nj an\nĠcur s\nĠRel ations\nke eper\nĠapproach ing\np ing\nSpe aking\nĠarrang ement\nĠV I\nare ttes\nĠaffect ing\nĠperm its\nb ecause\nĠu seless\nĠH us\n!! !!\nĠdestro ying\nUn fortunately\nĠfasc inating\nS em\nĠelect oral\nĠtrans parency\nĠCh aos\nĠvolunte er\nĠstatist ical\nĠactiv ated\nro x\nWe b\nH E\nĠHamp shire\nis ive\nM ap\nĠtr ash\nĠLaw rence\nst ick\nC r\nĠr ings\nEX T\nĠoper ational\nop es\nD oes\nĠEv ans\nĠwitness ed\nP ort\nĠlaunch ing\nec onom\nw ear\nĠPart icip\num m\ncul es\nĠR AM\nĠT un\nĠass ured\nĠb inary\nĠbet ray\nĠexpl oration\nĠF el\nĠad mission\nit ated\nS y\nĠav oided\nĠSim ulator\nĠcelebr ated\nĠElect ric\n¥ ŀ\nĠcl uster\nitzer land\nhe alth\nL ine\nĠN ash\nat on\nĠsp are\nĠenter prise\nĠD IS\nclud es\nĠfl ights\nĠreg ards\nĠà Ĺ\nh alf\nĠtr ucks\nĠcontact s\nĠunc ons\nĠCl imate\nĠimm ense\nN EW\noc c\nect ive\nĠemb od\nĠpat rol\nĠbes ide\nĠv iable\nĠcre ep\nĠtrig gered\nver ning\nĠcompar able\nq l\nĠg aining\nass es\nĠ( );\nĠG rey\nĠM LS\ns ized\nĠpros per\n\" ?\nĠpoll ing\nĠsh ar\nĠR C\nĠfire arm\nor ient\nĠf ence\nĠvari ations\ng iving\nĠP i\nosp el\nĠpled ge\nĠc ure\nĠsp y\nĠviol ated\nĠr ushed\nĠstro ke\nĠBl og\nsel s\nĠE c\n,' '\nĠp ale\nĠColl ins\nter ror\nĠCanad ians\nĠt une\nĠlabor atory\nĠn ons\nt arian\nĠdis ability\nĠG am\nĠsing er\nal g\nĠSen ior\nĠtrad ed\nĠWar rior\nĠinf ring\nĠFrank lin\nĠstr ain\nĠSwed ish\nĠsevent h\nĠB enn\nĠT ell\nĠsynd rome\nĠwond ered\nid en\n++ ++\nig o\nĠpur ple\nĠjournal ism\nĠreb el\nĠf u\nbl og\nĠinv ite\nren cies\nĠCont act\nIs rael\nĠCont ent\nĠche er\nĠbed room\nĠEngine ering\nĠQue ens\nĠd well\nĠPlay Station\nĠD im\nĠCol on\nl r\nĠoper ates\nĠmotiv ation\nUS A\nast ered\nC ore\nĠTr uth\nol o\nOS E\nĠMem ory\nĠpred ec\nĠan arch\nĠ19 20\nĠY am\nà ¨\nb id\nĠgr ateful\nĠexc itement\nĠtre asure\nĠlong est\nct ive\nĠdes erves\nĠreserv es\nĠcop s\nĠOtt awa\nĠEgypt ian\nank ed\nĠart if\nĠhypot hesis\n: /\nĠpurch asing\nĠlove ly\nH P\nĠdiv ide\nĠstrict ly\nĠquestion ing\nĠtaxp ayers\nĠJ oy\nĠroll s\nĠHe avy\nĠp orts\nĠmag netic\nĠinf lamm\nĠbr ush\nt ics\nâ ĪĴ\nĠbott les\npp y\nĠp add\nãĤ ¯\nm illion\nĠdevast ating\nĠcomp 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ou\nĠk g\nĠinflu ential\nĠFriend s\nS ur\nain ted\nĠfor ums\nĠst arter\nĠcitizens hip\nĠE lection\non ge\not ation\nos ph\n;; ;;\nut ical\np ur\nere n\nĠaccus ations\nbit ious\nab bit\nĠOr d\nPost ed\nir k\nĠsens itivity\nic he\nĠAm y\nĠF ab\nĠsum mit\nĠped est\nĠrub ber\nĠagric ultural\nĠcan cel\nA E\nĠin aug\nĠcont am\nĠfirm ly\ni w\nst age\nĠK an\nĠt ier\nĠinv ention\nĠtransl ated\nĠR ules\nB ox\nTw itter\nID S\nĠp izza\nĠdeb ug\nĠD rop\nv s\nĠh orses\nb ig\nĠb oring\nĠh ood\nĠMcC ain\nat ched\nĠBro s\nĠsk ip\nĠess ay\nst at\nĠLeg ends\nĠam munition\nau c\nĠshoot er\nĠun h\nĠsuppl ied\nĠgener ic\nĠS K\nib an\nyr ics\nĠ25 5\nĠclim bing\nForm er\nĠfl ip\nĠjump ing\nĠfrust ration\nĠTer ry\nĠneighborhood s\nĠmed ian\nbe an\nĠbr ains\nFollow ing\nĠsh aped\nĠdraw s\nĠal tered\nJ ack\nĠrecip es\nĠsk illed\nwe alth\nach i\ne lection\nĠbehavi ors\nde als\nĠU ntil\nF e\nĠdecl aration\nmar ks\nĠBet ween\ncel ona\nĠres on\nĠbub ble\nAm ong\nĠim perial\nG S\nĠfemin ist\n200 5\nĠK yle\nĠaccount 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ified\nform ing\nĠSh ar\nw iki\nĠE ither\nS ometimes\nĠsen ators\nĠJohn ny\nĠTal iban\nĠG PS\n\":\" /\nãģ® å\nĠanaly zed\nĠRub io\nĠMove ment\nop ard\nii i\nSt and\nf ight\nĠign oring\ni ang\nĠG N\nso ever\nĠST AT\nĠref using\nĠswe at\nĠb ay\nP ORT\nir med\nak y\nĠdis pro\nĠlabel ed\nĠ10 8\nH ello\nĠple asant\nab a\nĠtri umph\nĠab oard\nĠinc om\nĠC row\nle tt\nĠfol k\nĠch ase\n` `\nĠBr us\nĠte ens\nc ue\nĠter rain\nh yd\nil ight\nOR Y\nSu pport\new s\nll i\nrain ts\nĠC and\nĠab used\nach ment\nl arg\nB as\nĠC ancer\nĠ19 78\nĠsupp orter\nac cess\nĠTer min\nĠT ampa\nĠAN Y\nĠnew est\nĠCrim inal\ned u\nĠ19 30\nĠadm its\nĠend e\nĠfail ures\nur ate\nful ness\ncy cl\nĠSub ject\nĠinf inite\nth ree\nW A\np it\nĠInst all\nR ad\nili ation\nG M\nĠcontin ent\nĠaccommod ate\nĠCl ay\nĠp up\nĠF unction\nĠham mer\nĠAlbert a\nĠrev ised\nĠminor ities\nĠmeasure ment\nCon nell\nĠdis able\nĠM ix\nIn cre\nĠfor k\nĠR osen\nĠimpl ies\numb lr\nAN G\nĠprote ins\nĠagg ression\nĠfacilit ate\nS N\nĠilleg ally\nu er\nĠacad em\nĠp uzz\nĠSh ift\np ay\noll o\nĠaud iences\nB uild\nĠno ble\nĠsynt ax\nâ ĺħ\nĠbe am\nĠB ed\nĠA ld\nĠorig ins\nv ideo\nĠ19 77\nĠAss ault\nĠgar age\nTe am\nĠver dict\nĠd war\nĠVirt ual\ne vent\nKe ep\nĠsent iment\nĠwild life\nsh irt\nĠb urg\nĠrecommend ation\nrep resent\nĠgall ery\nown ers\nĠsch olar\nĠconven ience\nĠSw ift\nĠconv inc\nC ap\nĠwar fare\nĠVis ual\nĠconst itute\nĠab ort\nĠWe ather\nĠLook ing\nĠH em\nĠmart ial\nĠinc oming\net ition\nĠtoler ance\nĠCre ated\nĠfl ows\nĠE lder\nĠsoul s\nĠf oul\nĠP ain\nĠC AN\nĠ2 20\nb c\nhe nd\nĠgen ius\nR eal\nĠW r\nomet er\np ad\nĠlim iting\nĠS i\nĠL ore\nĠAd ventures\nĠvar ied\nD isc\nf in\nĠPerson al\nCh ris\nĠinv ented\nĠd ive\nĠR ise\nĠo z\nĠCom ics\nĠexp ose\nĠRe b\nlet ters\ns ite\nim ated\nĠh acking\nĠeduc ated\nĠNob ody\nĠdep ri\nĠincent ive\nãĤ ·\nĠovers ight\nĠtrib es\nĠBelg ium\nĠlicens ing\nour t\nProdu ct\nah l\nĠG em\nĠspecial ist\nĠc ra\nann ers\nĠCor byn\nĠ19 73\nRE AD\nĠsum mar\nĠover look\nĠApp lication\nĠin appropriate\nĠdownload ed\nQ ue\nĠB ears\nĠth umb\nĠChar acter\nĠReincarn ated\nĠS id\nĠdemonstr ates\ns ky\nĠBloom berg\nĠAr ray\nĠRes ults\nĠFour th\nĠED T\nĠO scar\nc end\nĠ10 6\nĠN ULL\nĠH ERE\nm atch\nĠBr un\nĠgluc ose\nie g\neg u\nĠcert ified\nĠrel ie\nĠhuman itarian\nĠpr ayers\nK ing\nĠn an\nh ou\n10 8\nul u\nĠrenew able\nĠdistingu ish\nĠd ense\nĠV ent\nĠPack age\nĠB oss\nĠedit ors\nĠm igr\nT ra\nĠPet ers\nĠAr ctic\n200 4\nĠC ape\nĠloc ally\nĠlast ing\nĠhand y\n. ).\nP an\nĠR ES\nInd ex\nĠt ensions\nĠformer ly\nĠide ological\nĠsens ors\nĠdeal ers\nĠdef ines\nS k\nĠproceed s\nĠpro xy\naz ines\nĠB ash\nĠP ad\nĠC raft\neal ous\nĠshe ets\nomet ry\nJ une\ncl ock\nT T\nĠThe atre\nĠB uzz\nĠch apters\nĠmill enn\nĠd ough\nĠCongress ional\nĠimag ined\nav ior\nĠclin ic\nĠ19 45\nĠhold er\nro ot\noles ter\nĠrest art\nB N\nĠHam as\nĠJ ob\nĠor b\nĠr am\nĠdiscl ose\nĠtransl ate\nĠimm igrant\nĠannoy ing\nĠtreat y\nan ium\nĠTe a\nĠLeg ion\nĠcrowd s\nĠB ec\nĠA er\noh yd\nB ro\nLook ing\nĠl bs\nĠagg ress\nĠse am\nĠinter cept\nĠM I\nmer cial\nact iv\nĠC it\nĠdim ension\nĠconsist ency\nĠr ushing\nĠDou glas\nĠtr im\nInst all\nick er\nĠsh y\n10 6\nĠment ions\npe lled\nĠT ak\nc ost\nĠclass room\nĠfort une\ndri ven\nĠun le\nĠWhe el\nĠinvest or\nĠM asters\nk it\nĠassoci ations\nĠEv olution\nop ing\nus cript\nĠprov incial\nĠWal ter\nav i\nS O\nĠun limited\nEng lish\nĠC ards\nĠEb ola\nne red\nĠreven ge\nĠout right\num per\nĠf itting\nĠSol id\nĠform ally\nĠproblem atic\nĠhaz ard\nĠenc ryption\nĠstraight forward\nĠA K\nĠp se\nĠOr b\nĠCh amber\nĠM ak\nCont ents\nĠloyal ty\nĠl yrics\nĠSy m\nĠwel comed\nĠcook ed\nĠmon op\nĠn urse\nĠmis leading\nĠe ternal\nĠshif ting\nĠ+ =\nV is\nĠinst itutional\nill ary\nĠp ant\nVER T\nĠA CC\nĠEn h\nĠinc on\nĠRE UTERS\nĠdon ated\nâĢ¦âĢ¦ âĢ¦âĢ¦\nIn tern\nĠexhib it\nĠt ire\nĠR ic\nĠCh ampion\nĠMu hammad\nN ING\nĠSoc cer\nĠmob ility\nĠvary ing\nĠM ovie\nĠl ord\no ak\nF ield\nĠve ctor\nus ions\nĠsc rap\nĠen abling\nm ake\nT or\n. *\n| |\nĠWe bsite\nĠN PC\nĠsocial ist\nĠBill y\nĠAdd itional\nĠc argo\nĠfar ms\nĠSo on\nĠPri ze\nĠmid night\nĠ9 00\nse en\nĠSp ot\nĠshe ep\nĠspons ored\nĠH i\nĠJ ump\nĠ19 67\nMicro soft\nĠAg ent\nĠch arts\nd ir\nĠadj acent\nĠtr icks\nĠman ga\nĠex agger\n/ >\nfoot ball\nĠF CC\nG C\nĠT ier\nand ra\nOU ND\n% ),\nĠfru its\nV C\nĠA A\nR ober\nĠmid st\nâ Ĺ\nank a\nĠlegisl ature\nĠNe il\nĠtour ists\n\" \"\nĠWar ning\nĠNever theless\nĠOffic ial\nĠWh atever\nĠm old\nĠdraft ed\nĠsubst ances\nĠbre ed\nĠt ags\nĠT ask\nĠver b\nĠmanufact ured\ncom ments\nĠPol ish\nPro v\nĠdetermin es\nOb ama\nk ers\nĠutter ly\nĠse ct\nsc he\nĠG ates\nĠCh ap\nĠal uminum\nĠz ombie\nĠT ouch\nĠU P\nĠsatisf y\nĠpred omin\nasc ript\nĠelabor ate\nĠ19 68\nĠmeas uring\nĠV ari\nany ahu\nĠs ir\nul ates\nid ges\nick ets\nĠSp encer\nT M\noub ted\nĠpre y\nĠinstall ing\nĠC ab\nre ed\nre ated\nSu pp\nĠwr ist\nĠK erry\n10 7\nĠK le\nĠR achel\nĠc otton\nĠA RE\nĠE le\nCont rol\nĠload s\nĠD od\nan as\nb one\nĠclass ical\nĠReg ional\nĠInt eg\nV M\nĠdes ires\nĠaut ism\nsupport ed\nĠM essage\nĠcomp act\nwrit er\nĠ10 9\nĠHur ricane\nc ision\nĠcy cles\nĠdr ill\nĠcolle ague\nĠm aker\nG erman\nĠmist aken\nS un\nĠG ay\nĠwhat soever\nĠsell s\nĠA irl\nl iv\nĠO ption\nĠsol ved\nĠse ctors\nĠhorizont al\nĠequ ation\nĠSk ill\nĠB io\ng ement\nĠSn ap\nĠLeg al\nĠtradem ark\nĠmake up\nĠassemb led\nĠsa ves\nĠHallow een\nĠVer mont\nĠFR OM\nĠfar ming\nĠP odcast\naccept able\nĠHig her\nĠas leep\null ivan\nĠrefere n\nĠLe v\nĠbul lets\nok o\nH C\nĠst airs\nĠmain tains\nĠL ower\nĠV i\nĠmar ine\nĠac res\nĠcoordin ator\nĠJ oh\nĠcounterpart s\nĠBrother s\nĠind ict\nb ra\nĠch unk\nĠc ents\nH ome\nĠMon th\nĠaccording ly\nif les\nĠGerm ans\nĠSy n\nH ub\nĠey eb\nâĶĢâĶĢ âĶĢâĶĢ\nĠr anges\nĠHoll and\nĠRob ot\nf c\nM ike\nĠpl asma\nĠsw ap\nĠath lete\nĠR ams\n,' \"\nĠinfect ions\nĠcor rid\nĠv ib\nĠpat ches\nĠtradition ally\nĠrevel ation\nĠswe ep\nĠgl ance\nĠin ex\n200 3\nĠR aw\nwork ing\nos ures\nĠD at\nĠLyn ch\nĠle verage\nĠRe id\nĠcorrel ation\nian ces\nav ascript\nĠrep ository\nret ty\nĠ19 72\n24 0\nĠo un\np ol\nĠRe ed\nĠtact ical\nis ite\nApp le\nĠQu inn\nĠrap ed\nill o\nEuro pe\nĠalgorith ms\nĠRod rig\ni u\nĠill um\nĠf ame\nĠintrodu cing\nĠdel ays\nĠRaid ers\nĠwh istle\nĠnovel s\nĠRe ally\nĠder iv\nĠpublic ations\nĠNe ither\nĠCom merce\nĠa ston\nl anguage\nNot es\nĠR oth\nĠF ear\nĠm ate\nĠpar ade\nĠQ B\nĠman eu\nĠC incinnati\nm itting\nĠwa ist\nĠR ew\nĠdisc ont\nÐ °\nĠst aring\nĠal ias\nĠsec urities\nĠtoile t\nĠJ edi\nĠun law\nv ised\n//// ////\n] (\nĠWe iss\nĠpre st\nĠComp an\nĠmem o\nĠGr ace\nJ uly\nĠEl ite\ncent er\nĠSt ay\nĠgal axy\nĠto oth\nĠS ettings\nĠsubject ed\nãĤ ¦\nĠline back\nĠretail ers\nĠW ant\nĠd angers\nA ir\nĠvolunt ary\new ay\nĠinterpret ed\not ine\nà §\nĠp el\nServ ice\nĠEvent ually\nĠcare ers\nĠthreat en\nĠmem or\nĠBrad ley\nanc ies\ns n\nĠUn known\nN ational\nĠsh adows\nail and\nĠD ash\nEvery one\nizz ard\nM arch\n= (\nĠpull s\nĠstr anger\nĠback wards\nĠBern ard\nimens ional\nĠch ron\nĠtheoret ical\nk top\nĠw are\nĠInvest ig\nĠIn iti\nĠOper ations\no ven\noc ide\n* /\nĠfl ames\nĠC ash\nsh it\nĠc ab\nĠAn aly\nĠSe ah\nĠdefin ing\nĠorder ing\nĠimm un\nĠpers istent\nAC H\nRuss ian\nm ans\nĠh ind\nĠphot ography\n ©\nĠh ug\nĠ10 7\nĠH ence\ni ots\nude au\nĠsubsid ies\nĠroutine ly\nĠDev ice\nit ic\nĠdisg ust\nland er\nĠ19 40\nĠassign ment\nĠB esides\nw ick\nĠD ust\nus c\nstruct ed\n11 1\nde velop\nĠf ond\nĠinter section\nĠdign ity\nĠcommission er\nWith out\nre ach\nĠcart oon\nĠsc ales\nãĥ Ń\nF IG\nĠsurve ys\nĠIndones ia\nĠart work\nĠun ch\nĠcy cling\nun ct\nau er\nor ate\nĠOb viously\nĠcharacter ized\nfe ld\nĠaff irm\nĠinn ings\nĠ é\nĠal iens\nĠcl oth\net ooth\nĠC ertain\n §\nĠdig est\nk now\nĠX L\nĠpredict ions\nĠd in\nW AR\nĠafter math\nEx ample\nĠSu ccess\nĠTh r\nIG N\nĠmin er\nB us\nĠcl arity\nheim er\nĠO UT\nĠS end\nĠCirc le\nĠD iet\nĠpron ounced\nĠcreat ors\nĠearthqu ake\natter y\nge ons\nĠo d\nĠlay ing\nor p\nU lt\npro ject\nĠunder min\nĠsequ el\nS am\nĠDark ness\nĠre ception\nb ull\nY S\nĠV ir\nĠsequ ences\nĠCo in\nĠout fit\nĠW ait\n1 19\nĠdel ivers\n.... ..\nĠbl own\nĠE sc\nĠM ath\nper m\nĠU l\nĠgl im\nĠfac ial\nĠgreen house\nĠto kens\n/ -\nĠAnn ual\nĠON E\nĠteen age\nĠPhys ical\nĠL ang\nĠC elt\nĠsu ed\nivid ually\nĠpat ience\nch air\nreg ular\nĠa ug\nin v\nex cept\nĠL il\nĠn est\nf d\ns um\nĠCh ase\nRuss ia\nĠJenn ifer\nĠoff season\nOver all\nF ore\nĠr iot\nA ud\nform er\nĠdefend ers\nĠC T\niot ic\nrib ly\nĠautom ated\nĠpen is\nĠins ist\nĠdi agram\nĠS QL\nĠG arc\nĠw itch\ncl ient\nier ra\nam bers\nĠrec ount\nf ar\nV ery\noster one\nĠappreci ated\nĠPer fect\nS ection\nĠd oses\noca ust\nĠcost ly\nĠg rams\nĠSh i\nĠwrest ling\nĠ19 71\nĠtro phy\nĠn erve\nĠK az\nĠExper ience\nĠpled ged\nĠplay back\nĠcreat ivity\nby e\nĠattack ers\nĠhold ers\nĠCo ach\nĠPh D\nĠtransf ers\nĠcol ored\nĠH indu\nĠd rown\nĠlist ened\nĠW A\nias m\nP O\nĠappeal ing\nĠdiscl osed\nĠCh icken\nag ging\nĠple aded\nĠnav igation\nĠReturn s\nĠ[ [\nR OR\nE A\nĠphotograp her\nĠR ider\nipp ers\nĠsl ice\nĠe rect\nĠhe d\niss ance\nĠVik ings\nur ious\nĠapp et\noubted ly\nCh ild\nĠauthent ic\no os\nĠM aking\nĠannoun cing\nĠb od\nĠmet er\nĠN ine\nĠR ogue\nĠwork force\nĠrenew ed\nĠorganis ations\nac s\nP LE\nSh ort\nĠcomp ounds\nĠVis it\nĠen velop\near th\nĠsupport ive\ngg le\nĠBrus sels\nĠGu ild\nCre ate\nRE L\nĠaver aged\nĠ19 69\nri ages\nĠlength y\nĠforg ot\nO kay\nĠE rd\nĠdeal er\nĠrec ession\nD D\nĠdesper ately\nĠhun ger\nĠst icks\nĠm ph\nĠF aith\nĠintention ally\nĠdem ol\nue ller\nĠS ale\nĠde bris\ns pring\nĠle ap\n>> >>\nĠcontain ers\nse lling\nrane an\natter ing\nĠcomment ed\nĠC M\non ut\nĠwood s\nes pecially\nĠorgan ize\niv ic\nĠWood s\nang a\ns qu\nĠm aj\nam on\nĠax is\nĠ19 74\nĠDen mark\nĠwar rior\nĠP and\nĠout lined\nĠB O\nins ula\nz illa\neb ook\nĠd are\nĠsear ched\nĠnav igate\nS n\nwrit ing\nĠun ited\nJ apan\nĠHe brew\nĠfl ame\nĠrel ies\nĠcatch ing\nĠSh o\nĠimprison ment\nĠp ockets\nĠclos ure\nĠF am\nt im\nade qu\nAct ivity\nĠrecru iting\nĠW ATCH\nĠArgent ina\nd est\nĠapolog ize\nor o\nĠlack s\nĠtun ed\nĠGriff in\nĠinf amous\nĠcelebr ity\nss on\nĠ ----------------------------------------------------------------\nĠIs is\nĠDis play\nĠcred ibility\nĠeconom ies\nĠhead line\nĠCow boys\nĠind ef\nĠl ately\nĠincent ives\nbut ton\nĠM ob\nA ut\nĠres igned\nĠO m\nc amp\nĠprof iles\nĠsche mes\nolph ins\nay ed\nCl inton\nen h\nĠY ahoo\nĠab st\nĠan k\nsu its\nĠw ished\nĠMar co\nudd en\nĠsp here\nĠB ishop\nĠincorpor ated\nĠPl ant\n11 4\nĠh ated\np ic\nĠdon ate\nĠl ined\nĠbe ans\nĠsteal ing\nĠcost ume\nĠsher iff\nĠfor ty\nĠint act\nĠadapt ed\nĠtrave lling\nb art\nĠnice ly\nĠdri ed\nĠsc al\nos ity\nNOT E\nĠB h\nĠBron cos\nĠI gn\nĠint imate\nĠchem istry\nĠopt imal\nD eb\nĠGener ation\nĠ] ,\nich i\nĠW ii\nĠYOU R\nvent ions\nW rite\nĠpop ul\nun ning\nĠW or\nV ol\nĠqu een\nhead s\nK K\nĠanaly ze\nop ic\near chers\nĠd ot\nleg raph\nast ically\nĠupgr ades\nĠca res\nĠext ending\nĠfree ze\nĠin ability\nĠorg ans\nĠpret end\nĠout let\n11 3\nol an\nĠM all\nul ing\nt alk\nĠexpress 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ald\nif ully\nĠcomp iler\nĠD A\nĠdon or\nas i\nĠtrib al\nl ash\nĠCon fig\nĠapplic ants\nĠsal aries\n13 5\nPut in\nĠF ocus\nir s\nĠmisc onduct\nĠH az\nĠeat en\nM obile\nMus lim\nĠMar cus\nv iol\nĠfavor able\nĠst ub\nad in\nĠH ob\nĠfaith ful\nĠelectron ics\nĠvac uum\nw ait\nback ed\neconom ic\nd ist\nĠten ure\nĠsince re\nĠT ogether\nĠW ave\nĠprog ression\nĠden ying\nĠdist ress\nbr aska\nth ird\nĠmix ing\nĠcolon ial\nĠpriv ately\nĠun rest\natern ity\nĠprem ises\nant i\ngreg ation\nĠlic ence\nĠH ind\nĠSam uel\nĠconvinc ing\nĠA ce\nĠR ust\nĠNet anyahu\nĠhand les\nĠP atch\norient ed\nah o\nĠG onz\nĠhack ers\nclaim er\nĠcustom s\nĠGr an\nf ighters\nĠl uc\nĠman uscript\naren thood\nĠdev il\nĠwar riors\nĠoff enders\nWill iam\nĠhol idays\nĠnight mare\nĠle ver\niff erent\nSt at\nĠexhib ition\nput ed\nĠP ure\nĠal pha\nĠenthus iasm\nĠRepresent atives\nE AR\nĠT yp\nĠwhe at\nĠAl f\nĠcor rection\nĠev angel\nAT T\nM iss\nĠs oup\nĠimpl ied\npar am\nĠsex y\nĠL ux\nĠrep ublic\np atch\nab lish\nĠic 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pp\nĠrob bery\nE ffect\nĠobsc ure\nĠProf essional\nĠemb assy\nĠmilit ant\nĠinc arcer\nĠgener ates\nĠlaun ches\nĠadministr ators\nĠsh aft\nĠcirc ular\nĠfresh man\nĠW es\nĠJo el\nĠD rew\nĠDun can\nĠApp arently\ns ight\nĠIntern al\nĠInd ividual\nĠF E\nĠb ore\nĠM t\nĠbroad ly\nĠO ptions\nount ain\nip es\nĠV ideos\n20 4\nĠh ills\nĠsim ulation\nĠdisappoint ment\nit an\nĠLabor atory\nĠup ward\nĠbound ary\nĠdark er\nh art\nĠdomin ance\nC ong\nĠOr acle\nĠL ords\nĠscholars hip\nĠVin cent\ned e\nĠR ah\nĠencour ages\nro v\nĠqu o\nĠprem ise\nĠCris is\nĠHol ocaust\nĠrhyth m\nĠmet ric\ncl ub\nĠtransport ed\nĠn od\nĠP ist\nĠancest ors\nĠFred er\nth umbnails\nĠC E\nON D\nPh il\nven ge\nĠProduct s\ncast le\nĠqual ifying\nĠK aren\nVERTIS EMENT\nĠmight y\nĠexplan ations\nĠfix ing\nD i\nĠdecl aring\nĠanonym ity\nĠju ven\nĠN ord\nĠDo om\nĠAct ually\nO k\nph is\nĠDes ert\nĠ11 6\nI K\nĠF M\nĠinc omes\nV EL\nok ers\nĠpe cul\nĠlight weight\ng ue\nĠacc ent\nĠincre ment\nĠCh an\nĠcompl aining\nĠB aghd\nĠmidfield 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o\nĠprec ip\nĠrecord ings\nĠproceed ed\nĠcol ony\nĠT ang\nab lo\nĠstri pped\nLe ft\nto o\nĠpot atoes\nĠfin est\n% ).\nĠc rap\nĠZ ach\nab ases\nĠG oth\nĠbillion aire\nw olf\nĠsan ction\nS K\nĠlog ged\nP o\ney ed\nun al\nĠcr icket\nĠarm ies\nĠunc overed\nCl oud\nó n\nĠreb ounds\nĠm es\nO per\nP ac\nĠnation ally\nĠinsert ed\np ict\nĠgovern ance\nÐ ¸\nĠprivile ges\nG ET\nĠfavor ites\nim ity\nĠlo ver\nthe m\nem pl\nĠgorge ous\nAn n\nĠsl ipped\nĠve to\nB ob\nĠsl im\nu cc\nĠF ame\nudden ly\nĠden ies\nĠM aur\nĠdist ances\nĠw anna\nt ar\nĠS ER\nĠâ Ī\nĠle mon\nat hetic\nĠlit eral\nĠdistingu ished\nĠansw ering\nG I\nĠrelig ions\nĠPhil os\nĠL ay\nĠcomp os\nire ments\nĠK os\nine z\nroll ing\nĠyoung est\nand ise\nĠB orn\nĠalt ar\nam ina\nĠB oot\nv oc\nĠdig ging\nĠpress ures\nĠl en\n26 4\nĠassass ination\nĠBir mingham\nĠMy th\nĠsovere ign\nĠArt ist\nĠPhot ograph\nĠdep icted\nĠdisp ens\north y\nĠamb ul\nint eg\nĠC ele\nĠTib et\nĠhier archy\nĠc u\nĠpre season\nĠPet erson\nĠcol ours\nĠworry ing\nĠback ers\nĠPal mer\nĠÎ ¼\nĠcontribut or\nĠhear ings\nĠur ine\nĠ Ù\nourge ois\nSim ilar\nĠZ immer\ns omething\nĠUS C\nĠstrength s\nĠF I\nĠlog ging\nAs ked\nĠTh ai\nin qu\nĠW alt\nĠcrew s\nit ism\n3 01\nĠshar ply\num ed\nĠred irect\nr ators\nIn f\nĠWe apons\nĠte asp\n19 99\nL ive\nĠEs pecially\nĠS ter\nĠVeter ans\nĠint ro\nother apy\nĠmal ware\nĠbre eding\nĠmole cular\nĠR oute\nĠCom ment\noc hem\nĠa in\nSe ason\nĠlineback er\nÄ «\nĠEconom ics\nes ar\nĠL ives\nĠEm ma\nĠk in\nĠTer rit\nĠpl anted\not on\nĠBut ter\nĠSp ons\nP ER\nĠdun geon\nĠsymb olic\nĠfil med\nĠdi ets\nĠconclud es\nĠcertain ty\nĠForm at\nĠstr angers\nform at\nĠPh ase\nĠcop ied\nĠmet res\nld a\nĠUs ers\nĠdeliber ate\nĠwas hed\nĠL ance\nim ation\nĠimpro per\nĠGen esis\nick r\nĠK ush\nĠreal ise\nĠembarrass ing\nalk ing\nb ucks\nĠver ified\nĠout line\nyear s\nĠIn come\n20 2\nĠz ombies\nF inal\nĠMill enn\nĠmod ifications\nĠV ision\nĠM oses\nver b\niter ranean\nĠJ et\nĠnav al\nĠA gg\nĠur l\nĠvict ories\nĠnon etheless\nĠinj ust\nĠF act\nç ļ\nĠins ufficient\nre view\nface book\nĠnegoti ating\nĠguarant ees\nim en\nuten berg\nĠg ambling\nĠcon gr\nLoad ing\nĠnever theless\nĠpres idents\nĠIndust rial\nĠ11 8\nĠp oured\nĠT ory\nĠ17 5\nĠ: =\nSc ott\nange red\nT ok\nĠorgan izers\nM at\nĠG rowth\nĠad ul\nĠens ures\nĠ11 7\né¾į å\nĠmass acre\nĠgr ades\nbe fore\nAD VERTISEMENT\nĠSl ow\nĠM MA\nâĢĶ \"\nĠV atican\nQ aeda\nĠo we\n66 66\nĠS orry\nĠGr ass\nĠbackground s\nĠexha usted\nĠcl an\nĠcomprom ised\nĠE lf\nĠIsa ac\nens on\nIn vest\nIF A\nĠinterrupt ed\nãĥī ãĥ©\nĠtw isted\nĠDrag ons\nM ode\nĠK remlin\nĠfert il\nhe res\nph an\nĠN ode\nf ed\nĠOr c\nĠunw illing\nC ent\nĠprior it\nĠgrad uates\nĠsubject ive\nĠiss uing\nĠL t\nĠview er\nĠw oke\nTh us\nbro ok\nĠdep ressed\nĠbr acket\nĠG or\nĠFight ing\nĠstri ker\nRep ort\nĠPortug al\nĠne o\nw ed\n19 9\nĠflee ing\nsh adow\nident ified\nUS E\nSte am\nĠstret ched\nĠrevel ations\nart ed\nĠD w\nĠalign ment\nest on\nĠJ ared\nS ep\nĠblog s\nup date\ng om\nr isk\nĠcl ash\nĠH our\nĠrun time\nĠunw anted\nĠsc am\nĠr ack\nĠen light\non est\nĠF err\nĠconv ictions\nĠp iano\nĠcirc ulation\nĠW elcome\nĠback lash\nĠW ade\nĠrece ivers\not ive\nJ eff\nĠnetwork ing\nĠPre p\nĠExpl orer\nĠlect ure\nĠupload ed\nĠMe at\nB LE\nĠNaz is\nĠSy nd\nst ud\nro ots\nri ans\nĠportray ed\nĠ ??\nĠBudd ha\ns un\nRober t\nĠCom plex\nĠover see\nĠste alth\nT itle\nĠJ obs\nĠK um\nĠappreci ation\nĠM OD\nĠbas ics\nĠcl ips\nĠnurs ing\nĠpropos ition\nĠreal ised\nĠNY C\nĠall ocated\nri um\nar an\nĠPro duction\nĠV ote\nĠsm ugg\nĠhun ter\naz er\nĠCh anges\nĠfl uct\ny on\nAr ray\nĠk its\nW ater\nĠuncom mon\nĠrest ing\nell s\nw ould\nĠpurs ued\nĠassert ion\nomet own\nĠMos ul\nĠPl atform\nio let\nĠshare holders\nĠtra ils\nP ay\nĠEn forcement\nty pes\nĠAn onymous\nĠsatisf ying\nil ogy\nĠ( '\nw ave\nc ity\nSte ve\nĠconfront ation\nĠE ld\nC apt\nah an\nht m\nĠC trl\nON S\n2 30\nif a\nhold ing\nĠdelic ate\nĠj aw\nĠGo ing\nor um\nS al\nĠd ull\nĠB eth\nĠpr isons\nĠe go\nĠEl sa\navor ite\nĠG ang\nĠN uclear\nĠsp ider\nats u\nĠsam pling\nĠabsor bed\nĠPh arm\niet h\nĠbuck et\nĠRec omm\nO F\nĠF actory\nAN CE\nĠb acter\nH as\nĠObs erv\n12 1\nĠprem iere\nDe velop\nĠcur rencies\nC ast\nĠaccompany ing\nĠNash ville\nĠfat ty\nĠBre nd\nĠloc ks\nĠcent ered\nĠU T\naugh s\nor ie\nĠAff ordable\nv ance\nD L\nem et\nĠthr one\nĠBlu etooth\nĠn aming\nif ts\nAD E\nĠcorrect ed\nĠprompt ly\nĠST R\nĠgen ome\nĠcop e\nĠval ley\nĠround ed\nĠK end\nal ion\np ers\nĠtour ism\nĠst ark\nv l\nĠblow ing\nĠSche dule\nst d\nĠunh appy\nĠlit igation\nced es\nĠand roid\nĠinteg ral\nere rs\nud ed\nt ax\nĠre iter\nĠMot ors\noci ated\nĠwond ers\nĠAp ost\nuck ing\nĠRoose velt\nf ram\nĠyield s\nĠconstit utes\naw k\nInt erest\nĠinter im\nĠbreak through\nĠC her\nĠpro sec\nĠD j\nĠM T\nRes p\nĠP T\nĠs perm\ned it\nB T\nLin ux\ncount ry\nle ague\nĠd ick\nĠo ct\nĠinsert ing\nĠsc ra\nĠBrew ing\nĠ19 66\nĠrun ners\nĠpl un\nid y\nĠD ian\nĠdys function\nĠex clusion\nĠdis gr\nĠincorpor ate\nĠrecon c\nĠnom inated\nĠAr cher\nd raw\nachel or\nĠwrit ings\nĠshall ow\nĠh ast\nĠB MW\nĠR S\nĠth igh\nĠ19 63\nĠl amb\nĠfav ored\nag le\nĠcool er\nĠH ours\nĠG U\nĠOrig in\nĠglim pse\n---------------- ----\nL im\nĠche ek\nĠj ealous\n- '\nĠhar ness\nĠPo ison\nĠdis abilities\nne apolis\nĠout look\nĠnot ify\nĠIndian apolis\nĠab rupt\nns ic\nĠenc rypted\nĠfor fe\nreat h\nĠr abb\nĠfound ations\nĠcompl iment\nĠInter view\nĠS we\nĠad olesc\nĠmon itors\nĠSacrament o\nĠtime ly\nĠcontem pl\nĠposition ed\nĠpost ers\nph ies\niov ascular\nv oid\nĠFif th\nĠinvestig ative\nOU N\nĠinteg rate\nĠIN C\nish a\nibl ings\nĠRe quest\nĠRodrig uez\nĠsl ides\nĠD X\nĠfemin ism\nĠdat as\nĠb end\nir us\nĠNig eria\nF ox\nCh ange\nĠair plane\nĠLad en\nĠpublic ity\nixt y\nĠcommit ments\nĠaggreg ate\nĠdisplay ing\nĠAr row\nĠ12 2\nĠrespect s\nand roid\ns ix\nĠSh a\nĠrest oration\n) \\\nW S\noy s\nĠillust rate\nwith out\n12 6\nĠâĶ Ĥ\nĠpick up\nn els\nĠ ....\nf ood\nĠF en\n) ?\nĠphenomen a\nĠcompan ions\nĠW rite\nĠsp ill\nĠbr idges\nĠUp dated\nĠF o\nĠinsect s\nASH INGTON\nĠsc are\nil tr\nĠZh ang\nĠsever ity\nĠind ul\n14 9\nĠCo ffee\nĠnorm s\nĠp ulse\nĠF T\nĠhorr ific\nĠDest roy\nĠJ SON\nĠo live\nĠdiscuss es\nR est\nE lect\nĠW inn\nĠSurv iv\nĠH ait\nS ure\nop ed\nĠro oted\nĠS ke\nĠBron ze\nĠl ol\nDef ault\nĠcommod ity\nred ited\nĠliber tarian\nĠforb idden\nĠgr an\nà ¨\nĠl ag\nen z\ndri ve\nĠmathemat ics\nĠw ires\nĠcrit ically\nĠcarb ohyd\nĠChance llor\nĠEd die\nĠban ning\nĠF ri\nĠcompl ications\net ric\nĠBangl adesh\nĠband width\nSt op\nĠOrig inally\nĠhalf way\nyn asty\nsh ine\nĠt ales\nrit ies\nav ier\nĠspin ning\nĠWH O\nĠneighbour hood\nb ach\nĠcommer ce\nĠS le\nB U\nĠentreprene ur\nĠpecul iar\nĠCom ments\nf re\n3 20\nIC S\nĠimag ery\nĠCan on\nĠElect ronic\nsh ort\n( (\nD ig\nĠcomm em\nu ced\nĠincl ined\nĠSum mon\nĠcl iff\nĠMed iterranean\nĠpo etry\nĠprosper ity\nĠRe ce\nĠp ills\nm ember\nĠfin ale\nun c\nĠG ig\nä ½\nĠl od\nĠback ward\n- +\nĠFor ward\nĠth ri\ns ure\nĠso ap\nĠF X\nR ES\nĠSe xual\noul os\nĠfool ish\nĠright eous\nĠco ff\nterror ism\nust ain\not er\nĠab uses\nne xt\nĠab usive\nĠthere after\nĠprohib ition\nĠS UP\nĠd ip\nĠr ipped\nĠinher ited\nĠb ats\nst ru\nG T\nĠflaw ed\nph abet\nĠf og\ndo ors\nĠim aging\nĠdig its\nĠHung ary\nĠar rog\nĠteach ings\nĠprotocol s\nĠB anks\nà ¸\np ound\nĠC urt\n.\" )\n. /\nĠex emption\nend ix\nĠM ull\nĠimpro ves\nĠG amer\nd imensional\nI con\nĠMarg aret\nSt atus\nd ates\nĠint ends\nĠdep ict\nĠpark ed\nJ oe\nĠMar ines\nchn ology\n! ).\nĠjud ged\nĠwe ights\nR ay\nĠapart ments\nhe ster\nĠrein force\nĠoff ender\nocc up\nĠs ore\ne pt\nĠPH P\nĠB row\nĠauthor ization\nĠR isk\nĠDel aware\nĠQ U\nĠnot ifications\nĠsun light\nĠex clude\nd at\nĠm esh\nĠSud an\nĠbelong ed\nĠsub way\nĠno on\nĠInter ior\nol ics\nĠL akers\nĠc oding\nDis claimer\nCal if\nO ld\nĠdis l\n???? ?\nĠconfir ms\nĠrecruit ment\nĠhom icide\nCons ider\nĠJeff rey\nft y\n} ;\nĠobject ion\ndo ing\nĠLe o\nW ant\nĠgl ow\nĠClar ke\nĠNorm an\nĠver ification\nĠpack et\nĠForm ula\nĠpl ag\nes ville\nĠshout ing\nĠo v\nĠR EC\nĠB ub\nĠn inth\nĠener g\nĠvalid ity\nĠup s\nj ack\nĠneighbor ing\nĠN ec\new orks\nĠH ab\nare z\nĠsp ine\nĠevent ual\nĠLe aders\nĠC arn\nĠprob ation\nĠrom ance\nms g\nĠMechan ical\nER Y\nR ock\nĠpart isan\nN ode\nass ets\nmin ent\nĠforeign ers\nĠtest ify\nĠUs ually\nl ords\nĠG ren\nĠPow ell\nBI L\nĠs r\nĠadd ict\nĠshell s\nĠs igh\nĠY ale\ntern ity\nĠ7 50\nE U\nĠR ifle\nĠpat ron\nem a\nĠB annon\nan ity\nĠtrop ical\nĠV II\nc ross\nEvery thing\nĠIS O\nĠhum ble\nass ing\nĠF IG\nĠupd ating\nys on\nĠcal cium\nĠcompet ent\nĠste ering\nPro t\nĠS Y\nĠFin als\nĠR ug\n15 9\n13 7\nĠG olf\nĠ12 6\nĠaccommod ation\nĠHug hes\nĠaest hetic\nart isan\nĠTw ilight\nĠpr ince\nĠAgric ulture\nĠDis co\nĠpreced ent\nĠtyp ing\nauthor ized\nO ption\nĠA ub\nl ishes\nach t\nm ag\nP eter\nĠU FO\nmont on\nĠL ith\nĠa rom\nĠsec uring\nĠconf ined\npriv ate\nĠsw ords\nĠmark ers\nĠmetab olic\nse lect\nĠCur se\nĠO t\ng ressive\nĠinc umb\nĠS aga\nĠpr iced\nĠclear ance\nCont ent\nĠdr illing\nĠnot ices\nĠb ourgeois\nĠv est\nĠcook ie\nĠGuard ians\nry s\nin yl\nĠ12 4\nĠpl ausible\non gh\nĠOd in\nĠconcept ion\nĠY uk\nĠBaghd ad\nĠFl ag\nAust ral\nĠI BM\nĠintern ationally\nĠWiki Leaks\nI ED\nĠc yn\nĠcho oses\nĠP ill\nĠcomb ining\nĠrad i\nĠMoh ammed\ndef ense\natch ing\nSub ject\nic iency\nFr ame\nĠ{ \"\nĠche ss\nĠtim er\n19 0\nĠt in\nĠord inance\nemet ery\nĠacc using\nĠnotice able\nĠcent res\nĠl id\nĠM ills\nimg ur\nĠz oom\nerg ic\nĠcomp ression\npr im\nf ind\nĠsur g\nĠp and\nĠK ee\nĠCh ad\ncell ence\noy le\nĠsocial ism\nĠT ravis\nĠM Hz\nĠgu ild\nALL Y\nĠSub scribe\nĠRel ated\nĠoccur rence\nitch ing\nĠfict ional\nĠcr ush\nĠE A\nc od\nm ix\nĠTri ple\nĠretrie ve\nĠstimul us\nĠpsych iat\nĠDo or\nĠhomosexual ity\nĠelement ary\nĠcell ular\nid ian\nĠL aun\nĠintrig uing\nĠfo am\nĠB ass\nid i\nits u\nĠass ure\nĠcongr at\nĠbusiness man\nĠBo ost\ncl ose\nĠl ied\nĠsc iences\nĠO mega\nĠG raphics\nĠ< =\nsp oken\nĠconnect ivity\nS aturday\nĠAven gers\nĠto ggle\nĠank le\nĠnational ist\nmod el\nĠP ool\nophob ia\nV ar\nĠM ons\nator 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ieval\nfol k\nĠh acked\nĠd ire\nĠHerm ione\nĠt ended\nce ans\nD aniel\nw ent\nĠlegisl ators\nĠred es\ng ames\nĠg n\nam iliar\nĠ+ +\ngg y\nth reat\nĠmag net\nĠper ceive\nĠz ip\nĠindict ment\nĠcrit ique\ng ard\nĠSaf e\nĠC ream\nĠad vent\nob a\nĠv owed\nous ands\nĠsk i\nĠabort ions\nu art\nĠstun ned\nĠadv ancing\nĠlack ed\nĠ\\ \"\nĠsch izophren\nĠeleg ant\nĠconf erences\nĠcance led\nĠHud son\nĠHop efully\nĠtr ump\nĠfrequ encies\nĠmet eor\nĠJun ior\nĠFle et\nĠMal colm\nĠT ools\nĠ ........\nĠh obby\nĠEurope ans\nĠ15 00\nĠInt o\nĠs way\nĠApp ro\nĠCom pl\nComm unity\nĠt ide\nĠSum mit\nä »\nĠinter vals\nĠE ther\nĠhabit at\nĠSteven s\nlish ing\nĠDom ain\nĠtrig gers\nĠch asing\nĠchar m\nĠFl ower\nit ored\nĠbless ing\nĠtext ures\nF ive\nĠliqu or\nR P\nF IN\nĠ19 62\nC AR\nUn known\nĠres il\nĠL ily\nĠabund ance\nĠpredict able\nr ar\nĠbull shit\nle en\nche t\nM or\nM uch\nä ¹\nĠemphas ized\nĠcr ust\nĠprim itive\nĠenjoy able\nĠPict ures\nĠteam mate\npl er\nĠT ol\nĠK ane\nĠsummon ed\nth y\nram a\nĠH 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--------------------------------\nt ml\n33 3\nĠraid s\nĠsatell ites\nKE Y\nĠlast s\nÑ Ĥ\nIn s\nĠD ame\nĠunp redict\n// /\ngh ai\nĠart illery\nĠcru ise\nĠg el\nĠCabin et\nĠbl ows\nĠE sp\nĠprox imity\not he\nĠSk ills\nĠU pper\nob o\nĠN DP\nĠenjoy s\nĠrepe ating\nĠConst ruction\nĠQuest ions\nH illary\nĠu int\nĠprocess ors\nĠGib son\nĠMult iple\nq a\nĠB om\nĠM iles\nvent ional\nĠhur ts\ns kin\nĠA IDS\nĠadvis ers\nĠR oot\nĠmethod ology\nĠD ale\nĠdet on\nĠKnow ledge\nsequ ently\nĠ12 1\nĠconnect s\nC y\nĠD anger\nĠcontribut ors\nĠB ent\nĠbr ass\nĠGun s\nint o\nĠFort une\nĠbro ker\nbal ance\nĠlength s\nĠv ic\nĠaver aging\nĠappropri ately\nĠCamer a\nĠsand wich\nĠCD C\nĠcoord inate\nĠnav ig\nĠgood ness\nl aim\nĠbra ke\nĠextrem ist\nĠW ake\nĠM end\nĠT iny\nĠC OL\nĠR F\nĠD ual\nĠW ine\nC ase\nĠref ined\nĠl amp\nL ead\nĠb apt\nĠCar b\nĠS add\nĠMin neapolis\nPD F\nEar ly\nĠH idden\nI ts\nĠT IME\nĠp ap\nĠcommission ed\nĠF ew\nĠCol ts\nĠB ren\nĠbot hered\nĠlike wise\nEx per\nĠSch w\nc ry\nn n\nĠM 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ername\nĠImpro ve\nĠbatt ling\nĠM AC\nĠcurric ulum\nĠs oda\nĠC annon\nĠsens ible\nsp ons\nDe cember\nĠw icked\nĠP engu\nĠdict ators\nĠHe arts\nog yn\nĠsimilar ities\nĠSt ats\nĠh ollow\nit ations\n\": [\nĠh over\nĠList en\ns ch\nS und\nĠc ad\nĠPar ks\nĠl ur\nĠhy pe\nĠL em\nN AME\nis ure\nFr iday\nĠshoot s\nĠclos es\nĠd b\nĠR idge\nĠDiff erent\nĠrepl ies\nĠBroad way\nop ers\nĠint oler\nĠZe us\nakes pe\nĠpropri etary\nĠrequest ing\nĠcontro llers\nĠM IN\nim edia\nbe cca\nĠexp ans\nĠoil s\nB ot\nĠCh and\nĠpr inter\nĠto pped\nĠP OL\nĠEar lier\nS ocial\nav in\nĠdecre ases\nĠSe b\nĠspecific ations\nĠBl ast\nĠK urt\nĠfre el\nB rown\nĠdil ig\nro e\nĠPro blem\nĠQu ad\nĠdecent ral\nĠV ector\nan ut\nĠplug ins\nĠGreg ory\nĠfuck ed\nel ines\nĠAmb assador\nt ake\nĠcle ans\nong yang\nAn onymous\nst ro\n\" }\nal ine\nĠO dd\nĠE ug\n2 16\nĠbo il\nĠP owers\nĠnurs es\nOb viously\nĠTechn ical\nĠexceed ed\nOR S\nĠextrem ists\nĠtr aces\nex pl\nĠcom r\nĠS ach\n) /\nĠm asks\nĠsc i\nB on\nĠreg ression\nwe gian\nĠadvis or\nit ures\nĠV o\nex ample\nĠInst ruct\nĠs iege\nĠredu ctions\npt r\nĠstat utory\nĠrem oves\nĠp uck\nred its\nĠbe e\nĠsal ad\nĠpromot ions\nĠJosh ua\nwith standing\nET H\nĠCh a\nim us\nĠexpend iture\naun ting\nĠdelight ed\nĠ15 5\nbe h\nĠcar pet\nĠSp art\nĠj ungle\nl ists\nĠbull ying\nĠNob el\nĠGl en\nĠreferen ced\nĠintrodu ces\nse in\nĠcho pped\ngl ass\nĠW rest\nĠneutral ity\nĠâ Ļ\nĠinvestig ator\nĠshel ves\nĠun constitutional\nĠreprodu ction\nĠmer chant\nm ia\nĠmet rics\nĠexplos ives\nĠSon ia\nĠbod ily\nĠthick ness\nĠpredomin antly\nĠAb ility\nĠmon itored\nIC H\nĠ] .\nĠMart inez\nĠvis ibility\nĠqu eries\nĠgen ocide\nĠWar fare\nQu ery\nĠstud ios\nĠemb ry\nĠcorrid or\nĠclean ed\ncom plete\nĠM H\nĠenroll ment\nING S\nĠimpact ed\nĠdis astrous\nĠY un\nĠCl aire\nĠBas ically\ny t\nuster ity\nĠindirect ly\nw ik\nĠd od\nĠCar r\nĠam p\nĠprohib it\nĠIn itial\nĠR d\nij i\nĠeduc ate\nc orn\ni ott\nĠBeaut y\nĠdetect ive\nĠCon n\ns ince\nĠst agger\nĠob ese\nĠb ree\nolog ic\nis se\nwalk 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rol\nĠsw orn\nc am\nĠshut tle\nĠR alph\nĠh ometown\n- ,\non al\nĠB P\nå ı\nĠpersu ade\nĠAlex and\nĠcomb ines\nĠv ivid\nĠL ag\nĠenc oding\nĠsal vation\nw en\nĠRec overy\ni ya\nUn iversity\nĠB iden\nĠbud gets\nĠTex ans\nf its\nĠhon ored\nĠp ython\nT D\n## #\ncl one\nĠbl ink\nĠL iquid\nĠunemploy ed\nĠcl ashes\nĠCoun sel\nĠdirect ing\nĠpun ct\nĠFal cons\nĠsh ark\nĠDam ascus\nĠje ans\nĠemb ark\nĠse ize\nĠup wards\n2 80\nĠE z\nĠAny thing\nĠex otic\nl ower\nĠCreat or\nĠU m\nĠsubur bs\nber ger\nĠW end\nĠm int\nĠX X\nĠD ro\nĠsuff ers\nĠher b\nt ree\nĠfrag ile\nĠflood ed\nĠAl cohol\nole an\nny der\nĠK O\nF ram\nĠ13 6\nĠow ed\nĠMe lee\nĠH ash\nĠwh isk\nĠsu do\nr r\nQu ick\napp ro\nĠi i\nĠEx amples\nhe e\nĠpromot es\nper ature\nk ar\nĠHon or\nĠs odium\nĠL if\nros so\nintend ent\nĠcorrespond ent\nF ound\nsec ret\nĠident ifies\nag ne\nĠl ou\nĠP P\nĠcoinc idence\nm ove\nĠmilit ia\nĠinf iltr\nĠPrim ary\nĠpitch ing\nĠI b\nĠGO OD\nãĤ ¸\nĠW izards\nir al\nĠVen us\nR R\nĠâĢ ķ\nĠCase y\nĠsad ly\nĠadm ire\nĠembarrass ed\nc b\nM el\nĠtub es\nĠbeaut ifully\nĠQueens land\nBel ow\nre z\nqu et\nple asant\nĠ «\nC amp\nĠdec isive\n19 98\nĠL amb\nut ton\nh n\nĠJ agu\nau nder\nĠC ord\nĠcl erk\nĠca ffe\nĠwip ed\nĠre im\nĠMount ains\nĠimprison ed\nĠdevelop s\nĠP ra\nĠmodel ing\nAny one\nance l\nĠS it\nĠshield s\nĠl awn\nĠcard iovascular\nĠdemonstr ating\nĠpar se\nĠIsrael is\nĠeuro s\n14 3\nĠgl orious\nins ki\nec d\nĠcondition ing\nĠhel pless\nĠmicro sc\nĠHar bor\nĠst akes\nĠ2 60\nĠun equ\nĠFl oyd\nĠd amp\nĠappar atus\nĠLaw s\nĠcoun ters\nĠindu ce\nat able\nĠAh med\nĠsl am\nN ovember\nĠpers ist\nĠim minent\ná n\nĠsh red\nĠph ases\nĠEd monton\nĠArm strong\nĠMe et\nĠK itty\nÑ Ģ\nc irc\nĠAd ult\nĠa rose\nĠX en\nD an\ng ow\nĠsuper f\nĠAd mir\nĠend ure\nĠkey word\nyr us\nĠy arn\nĠpath way\nĠHop kins\nmid t\nĠcens orship\nd ependent\nĠinstruct or\nS ources\nĠto e\nĠball oon\nN ob\nĠsw ear\nĠCast ro\nĠgl oss\nĠK avanaugh\nĠremark ably\nPh otos\nĠN om\nĠS outheast\ny ers\nĠvalid ation\nĠcann on\nĠVict ory\nĠPier re\nĠcaut ious\nAud io\nĠf etch\nĠG ift\nĠH yp\nĠrem edy\nZ E\nĠsc ent\nĠbe ard\nĠR ut\n- \"\nĠpat ents\nH y\nĠun just\nĠpot ato\nĠforth coming\nĠche f\nĠR ift\naff e\nĠR OM\nĠL aunch\nĠp ads\nĠNe o\nĠon set\nĠsquee ze\ns afe\nĠpref ix\nĠT M\nĠN early\nĠClin ical\nĠM ental\not iation\nĠUn ic\nant ry\nĠC ir\nĠep it\nà ¦\nĠextract ed\nverse ly\nri ad\nĠstr ains\nĠto ps\nĠpo em\nĠRand y\nĠMap le\nTH ER\nup iter\nĠSS D\nļ é\nĠun con\nper ing\nĠsle pt\nin ers\nĠunder water\nĠEv idence\ng one\n20 5\nĠhistor ians\nĠsynt hesis\nĠf rog\nb asketball\nĠvibr ant\nĠsub ord\nĠ3 65\nĠD ial\nĠcooper ate\nHA HA\nĠgreet ed\n15 8\nĠj azz\nĠinto x\nĠWalk ing\nĠsuper visor\nĠF usion\nĠMer cedes\ns end\nH am\ns d\nn l\nĠtour s\nĠF IFA\nĠcul p\ng d\n30 4\nĠple as\nĠillust rates\nĠColomb ia\nĠhighlight ing\nĠSum mary\nĠexp osing\nĠD ru\nĠir ony\nr itional\nĠCar roll\nĠEll is\nP ict\nĠR apt\nĠad apter\nĠun m\nĠcor pse\nĠceleb rities\nD en\nat um\nĠAp ocalypse\nĠW ag\nlin ing\nĠhorm ones\nR ub\nĠX i\nĠV aults\n20 8\nalky rie\ninos aur\nĠfeed s\nv ity\nĠdefe ating\nW ait\nĠemphas ize\nĠSteel ers\nyr inth\nle ys\nĠWhe never\nCurrent ly\nĠCl ock\nĠcollect ively\nany on\nĠJ P\nĠment ality\nĠdownload s\nĠsurround ings\nĠBarn es\nĠflags hip\nĠindic ators\nĠgra pp\nJan uary\nĠElement al\nĠAthen a\nib al\nĠs ights\nĠcap ita\nĠTreat y\nĠvo iced\nĠG az\nlet te\nĠy a\nĠexp ired\nLeg end\nH ot\nn ature\nĠunst able\nĠ2 80\nà º\nCom ment\nAL E\nĠquest s\nĠhand ler\nn is\nĠvers atile\nĠconce al\nenge ance\nĠInter active\nĠobs essed\nĠDog s\nĠcr acked\nS ound\ns v\nĠD ylan\nro ads\nf x\nĠCath olics\nĠH ag\nĠsl ammed\nĠgl owing\ns ale\nĠtiss ues\nĠCh i\nne e\nĠc her\ns ic\nur rection\nĠb acon\nul atory\n) .\"\nĠir regular\nFOR M\nass ed\nĠintention al\nĠcompens ate\nĠSpe aking\nĠS ets\n15 3\nĠconvent ions\nb ands\nem ade\nĠe cc\nĠWin ston\nĠAssass in\nĠBelg ian\nĠdepend ence\nĠnic he\nĠb ark\nĠJ azz\nĠdisadvant age\nĠgas oline\nĠ16 5\nçļ Ħ\ness a\nmod ule\nang ular\nO Y\nĠTreat ment\nit as\nol ation\nĠArn old\nĠfe ud\nĠN est\nĠthe atre\new ater\nĠmin ors\nolic y\nĠH aven\ndiv ision\nĠtr unk\nF ar\nĠP ull\nĠcapt uring\nĠ18 00\nĠTe en\nĠex empl\nĠclin ics\nĠB urg\nĠsubst it\nĠpay load\nĠL av\nĠT roy\nĠW itness\nĠfrag ments\nĠpass words\nĠg ospel\nĠG in\nĠten ants\nol ith\nS ix\nPre vious\nĠAg es\nĠDar win\nĠbl at\nĠem pathy\nsm ith\nb ag\nĠE cho\nĠC amb\nĠM add\nĠB oo\nĠred e\nĠBurn ing\nĠsmooth ly\nĠAd rian\nĠV ampire\nĠMon sters\nste am\nSty le\nM a\nre a\nĠD war\naly st\nurs or\nĠelim ination\nĠcrypt o\nch t\nĠE ternal\nâĢ¦ ]\nĠS orce\nI ll\nN ER\nĠu h\nCon clusion\nw age\nĠresp ir\nĠrem inis\nhet ical\nĠg y\nĠutil ized\nic idal\nĠ19 00\nĠhun ters\nĠSw an\nĠRe act\nĠvis itor\nĠThanks giving\n30 8\nPost s\nĠh ips\n19 97\nom ers\nĠkn ocking\nĠVeh icle\nĠt il\nĠ13 8\nĠm i\nĠInvest igation\nĠKen ya\nĠcas ino\nĠmot ives\nĠreg ain\nre x\nĠweek ends\nĠstab bed\nbor o\nĠexplo ited\nĠHA VE\nĠTe levision\nc ock\nĠprepar ations\nĠende av\nĠRem ote\nĠM aker\nĠPro du\nĠEv an\nĠinform ational\nĠLouis ville\n15 4\nĠDream s\nĠpl ots\nĠRun ner\nĠhur ting\nĠacad emy\nĠMont gomery\nn m\nĠL anc\nĠAl z\n2 10\nel ong\nĠretail er\nĠar ising\nĠrebell ion\nĠbl onde\nplay ed\nĠinstrument al\nC ross\nĠret ention\nĠtherape utic\nĠse as\nĠinfant ry\nĠCl int\nĠprompt ing\nĠbit ch\nĠst ems\nĠK ra\nĠthe sis\nĠB og\nru ed\nĠk ings\nĠcl ay\nific ent\nĠY ES\nĠTh ing\nĠCub s\nvey ard\nels h\nin arily\nĠE y\nĠRoll ing\nĠev olving\nInd ia\nĠrecogn izes\nĠgrad uation\nis ers\nĠfert ility\nĠMil an\nComm and\nĠbox ing\nĠ19 43\nĠgl uten\nĠEm ir\nĠid ol\nĠcon ceived\nĠCre ation\nMer it\nudd y\nuss ions\nĠLie utenant\niet al\nĠunch anged\nĠSc ale\nĠCrime a\nball s\nator ial\nĠdepth s\nĠempir ical\nĠtrans m\nĠuns afe\nmiss ible\ncom fort\n15 6\nĠmechan ic\n00 2\nl ins\nĠsm oked\nP os\nĠslow ing\nĠl av\nTex as\nĠche ating\nĠMet ropolitan\neth yl\nĠdiscover ing\nas se\nĠpen cil\nĠPy ongyang\nĠclos et\nĠShe et\nĠEnt ry\nou stic\nĠmy st\ner ate\nari at\nĠminer als\nĠmusic ian\nĠP ul\nĠM az\n24 9\nĠper missions\nĠ iv\nen ary\nick ers\nĠB ing\nhe a\nen able\nĠgri ev\nĠassert ed\nĠColon el\nĠaff idav\nw o\nĠse ated\nĠR ide\nĠpaint ings\nĠP ix\nĠ13 7\nish i\numb ai\ng otten\nĠEar l\nĠin ning\nĠc ensus\nĠtrave lled\nĠCons ult\n18 5\nb ind\nĠsimpl icity\nĠoverlook ed\nĠHelp ful\nĠmon key\nĠoverwhelming ly\nBl ood\nĠFl int\nĠJ ama\nĠPres ent\nĠR age\nĠT A\npt ive\nĠturn out\nw ald\nĠD olphins\nĠV PN\nĠon ion\nĠcraft ing\nm ma\nĠMerc ury\nĠarr ange\nĠalert s\nĠO T\nzb ollah\nĠg ases\nĠRichards on\ns al\nl ar\nĠfro st\nĠlower ing\nĠacc laim\nĠstart ups\nĠG ain\ness ment\nĠguard ian\näº º\nĠP ie\nĠL inks\nĠmer its\nĠaw ake\nĠparent al\nĠexceed s\nĠid le\nĠPil ot\nĠe Bay\nĠAc cept\nipe g\nC am\nĠK ot\nĠtrad ers\nolit ics\nunk er\nĠP ale\nos i\nan mar\nĠ19 47\nĠF ell\nest ial\nit ating\nG F\nĠS r\nif ted\nĠconnect or\nĠB one\nill es\n2 60\nh ma\nĠoverl ap\nĠGit Hub\nĠclean er\nĠBapt ist\nĠW AS\nĠlung s\nÑ ģ\nĠB UT\nĠc ite\nĠpit ched\nreat ment\nĠtro phies\nĠN u\n38 6\nĠPr ide\nĠattend ees\n[ ]\n17 9\nĠspat ial\nĠpri zes\nĠRel igion\nĠshow case\nĠC ategory\nvid ia\nT arget\nPro perty\n? ,\nĠf usion\np ie\nĠU CLA\nĠsound track\nĠprin cess\nĠC aval\nsh ould\nĠlim bs\nBack ground\nĠlone ly\nĠc ores\nĠT ail\nshe et\nĠ13 2\nR a\nãĤ «\nĠB olt\nĠbook ed\nĠadmin ister\nĠequ als\nw y\nĠobserv ing\nĠBar on\nĠAd obe\nĠv irgin\nĠSocial ist\nM ove\ngh azi\nĠLind a\n2 12\nĠbre wing\nĠmerch ants\nbur se\nĠdiv or\nĠmet als\nĠN er\nĠsum s\nĠEn emy\nĠen vision\nĠgrant ing\nĠH oney\nĠSk yrim\nĠsoc io\ngr aded\nĠselect ive\nW ASHINGTON\nĠ19 48\nĠSir ius\nĠG ross\nact ivity\nĠI van\nĠfur ious\nBS D\nĠPre vious\nĠrespons ive\nĠchar itable\nĠle aning\nĠP ew\nĠviol ates\n\\\\\\\\ \\\\\\\\\nĠCom ing\nw ire\nĠpo et\nĠres olutions\ncomm and\nĠPortug uese\nĠnick name\nĠde af\nFeb ruary\nĠrecogn ise\nĠentire ty\nĠseason al\npl aced\nĠTe legraph\nĠmicro phone\nour ing\nĠgr ains\nĠgovern ed\nĠpost p\nĠW aters\nin ement\nĠund ocumented\nĠCom cast\nĠf ox\nĠassault s\nre on\nman y\nĠJen kins\nĠAny way\nĠassess ments\nĠdown s\nĠM ouse\nĠsuper b\nk t\nĠD ow\nĠtax ation\n4 01\nĠsm iles\nĠundert aken\nĠex h\nĠenthusi astic\nĠtw ent\nĠgovernment al\nĠautonom y\nĠTechn ologies\nĠCh ain\nĠpreval ent\nf b\nĠnic otine\nog ram\nj ob\nĠawa iting\nĠMen u\nĠdep uties\nk ov\nish ops\nBut ton\nĠShan ghai\nĠdies el\nĠD uck\nR yan\nĠPC s\nN F\nj ury\nent e\nĠinacc urate\nedd y\nWh atever\nĠshow c\nĠN ad\nod us\net r\nĠplaint iffs\nĠW OR\nĠAss ange\nĠpriv at\nĠpremium s\nĠt am\nUR L\nĠel ites\nĠR anger\notten ham\nĠH off\nĠAt hens\nĠdefin ite\nĠs ighed\nĠeven ly\n2 11\nĠAm ber\nak ia\nĠmail ing\nĠcr ashing\nĠConfeder ate\nru gged\nW al\nĠDep ths\nĠjuven ile\nĠreact or\nIntrodu ction\nĠDel uxe\n19 95\nĠS anchez\nĠM ead\niv able\n: -\nĠPlan ning\nĠT rap\nqu in\nĠProt ect\nve red\nIn formation\nĠkid ney\ninn amon\nl as\nĠpolic ing\nĠtoler ate\nĠQ i\nĠbi ased\nF ort\nĠK i\ns ave\nĠprivile ged\nĠbe asts\nĠGl as\nĠC inem\nĠcome back\nSund ay\nĠext inction\nh ops\nĠtrans mit\nĠdoub les\nĠFl at\n16 7\nĠdis puted\nĠinjust ice\nf oo\nV ict\nrole um\nĠJul ie\nCon text\nĠR arity\niss ue\nComp onent\nĠcounsel ing\nan ne\nd ark\nĠobject ions\nu ilt\nĠg ast\nĠpl ac\nĠun used\nãĥ ĩ\nĠT rial\nĠJ as\nhed ral\nob b\nĠtempor al\nĠPR O\nĠN W\nĠAnn iversary\nL arge\nĠther m\nĠd avid\nĠsystem ic\nĠSh ir\nm ut\nĠNe pt\nadd ress\nĠscan ning\nĠunderstand able\nĠcan vas\nC at\nĠZ oo\nĠang els\nL O\nĠStat ement\nĠS ig\nov able\nĠA way\nsh aring\nocr ats\nst ated\nĠweigh ing\nN or\nw ild\nB ey\nĠaston ishing\nĠReyn olds\nĠop ener\nĠtrain er\nĠsurg ical\np n\nĠadjust ing\nwhe el\nĠf rown\nerv ative\nĠsusp end\nWith in\nte in\nĠobst acle\nĠliber ties\nym es\nĠur anium\nans om\nan ol\nub a\nĠL oss\nĠa rous\nĠHend erson\nW ow\ns pl\nc ur\nĠ Ń\nĠtheir s\nDam age\nĠdownload ing\nĠdisc ern\nĠSt o\nĠFl a\nĠh ath\nĠA j\nĠun pleasant\nEurope an\nexp ensive\nĠscreens hot\nĠU V\nĠall ied\nĠPers ian\nĠmonop oly\nĠat om\nĠReds kins\n\"> <\nĠcan cell\nĠcinem a\n13 1\nf air\nĠAlf red\nĠd uck\narg s\n22 3\nĠIS I\nĠsign aling\nin ar\nĠlaugh s\nĠfor wards\nĠreck less\nĠlisten ers\nat ivity\nĠvast ly\nn ant\nL ess\nĠHun ting\nĠScient ific\nIT ED\nĠkn ight\nĠH TC\nus a\nt mp\nĠr ude\nĠLegend ary\nĠar ises\nB ad\nĠCl aim\npe g\nĠreal ities\nTh ink\nĠ °\nĠro de\nĠstri ve\nĠan ecd\nĠshort s\nĠhypot hes\nĠcoord inated\nĠGand hi\nĠF PS\nR ED\nĠsuscept ible\nĠshr ink\nĠCh art\nHel p\nĠ ion\nde ep\nrib es\nĠK ai\nĠCustom er\nSum mary\nĠc ough\nw ife\nĠl end\nĠposition ing\nĠlot tery\nĠC anyon\nĠf ade\nĠbron ze\nĠKenn y\nĠbo asts\nĠEnh anced\nrec ord\nĠemer gence\nĠa kin\nĠB ert\nit ous\nâĸ ij\nĠst ip\nĠexch anged\nom ore\nals h\nĠreserv oir\nĠstand point\nW M\nĠiniti ate\nĠdec ay\nĠbrew ery\nĠter ribly\nĠmort al\nlev ard\nĠrev is\nN I\nel o\nĠconf ess\nĠMS NBC\nĠsub missions\nCont roller\nĠ20 2\nĠR uth\n} );\nĠAz ure\nĠ .\"\n20 6\nĠMarket ing\nĠl aund\nien cies\nĠrenown ed\nĠT rou\nĠN GO\nble ms\nĠterr ified\nĠwar ns\nĠper t\nĠuns ure\n4 80\nale z\nult z\nĠOut side\nĠst yl\nĠUnder ground\nĠp anc\nĠd ictionary\nĠf oe\nrim inal\nĠNor wegian\nĠj ailed\nĠm aternal\né e\nĠLu cy\nc op\nCh o\nĠuns igned\nĠZe lda\nĠIns ider\nĠContin ued\nĠ13 3\nĠNar uto\nĠMajor ity\n16 9\nĠW o\nãĤ ĵ\nĠpast or\nĠinform al\nÐ ½\nan throp\njo in\nãģ Ĺ\nit ational\nN P\nĠWrit ing\nf n\nĠB ever\n19 5\nĠy elling\nĠdr astically\nĠe ject\nĠne ut\nĠth rive\nĠFre qu\nou x\nĠpossess es\nĠSen ators\nĠD ES\nĠSh akespeare\nĠFran co\nĠL B\nuch i\nĠinc arn\nĠfound ers\nF unction\nĠbright ness\nĠB T\nĠwh ale\nĠThe ater\nm ass\nĠD oll\nS omething\nĠecho ed\nĠHe x\nc rit\naf ia\nĠgodd ess\nĠele ven\nĠPre view\nĠAur ora\nĠ4 01\nuls ive\nĠLog an\nin burgh\nĠCent ers\nĠON LY\nĠA id\nĠparad ox\nĠh urd\nĠL C\nD ue\nc ourt\nĠoff ended\nĠeval uating\nĠMatthew s\nĠto mb\nĠpay roll\nĠextra ction\nĠH ands\nif i\nĠsuper natural\nĠCOM M\n] =\ndog s\nĠ5 12\nĠMe eting\nRich ard\nĠMax imum\nĠide als\nTh ings\nm and\nĠReg ardless\nĠhum ili\nb uffer\nL ittle\nĠD ani\nĠN ak\nĠliber ation\nĠA be\nĠO L\nĠstuff ed\nac a\nind a\nraph ic\nĠmos qu\nĠcampaign ing\nĠoccup y\nS qu\nr ina\nĠW el\nĠV S\nĠphys ic\nĠp uls\nr int\noad ed\nET F\nĠArch ives\nĠven ues\nh ner\nĠTur bo\nĠl ust\nĠappeal ed\nque z\nil ib\nĠTim othy\nĠo mn\nd ro\nĠobs ession\nĠSav age\n19 96\nGl obal\nJ es\n2 14\nĠsl iding\nĠdisapp ro\nĠMag ical\nĠvolunt arily\ng b\nane y\nĠprop het\nĠRe in\nĠJul ia\nĠW orth\naur us\nĠb ounds\nie u\n)) )\nĠcro re\nĠCitiz en\nS ky\nĠcolumn ist\nĠseek ers\nond o\nIS A\nĠL ength\nĠnost alg\nĠnew com\nĠdet rim\nent ric\n3 75\nĠG E\nĠaut op\nĠacadem ics\nApp Data\nĠS hen\nĠid iot\nĠTrans it\nĠteasp oon\nW il\nK O\nĠCom edy\n> ,\nĠpop ulated\nW D\nĠp igs\nĠO culus\nĠsymp athetic\nĠmar athon\n19 8\nĠseiz ure\ns ided\nĠd op\nirt ual\nL and\nĠFl oor\nosa urs\n... ]\nĠl os\nĠsubsid iary\nE Y\nĠPart s\nĠSt ef\nĠJud iciary\nĠ13 4\nĠmir rors\nĠk et\nt imes\nĠneuro log\nĠc av\nĠGu est\nĠtum or\nsc ill\nĠLl oyd\nE st\nĠcle arer\nĠstere otypes\nĠd ur\nnot hing\nRed dit\nĠnegoti ated\n---------------- --------\n23 5\nĠfl own\nĠSe oul\nĠRes ident\nĠS CH\nĠdisappear ance\nĠV ince\ng rown\nĠgrab s\nr il\nĠInf inite\nĠTw enty\nĠpedest rian\nĠjer sey\nĠF ur\nĠInf inity\nĠEll iott\nĠment or\nĠmor ally\nĠob ey\nsec ure\niff e\nĠantib iotics\nang led\nĠFre eman\nĠIntrodu ction\nJ un\nĠm arsh\nic ans\nĠEV ENTS\noch ond\nW all\nicult y\nĠmisdem eanor\nĠl y\nTh omas\nĠRes olution\nĠanim ations\nĠD ry\nĠinter course\nĠNew castle\nĠH og\nĠEqu ipment\n17 7\nĠterrit orial\nĠarch ives\n20 3\nFil ter\nĠMun ich\nĠcommand ed\nĠW and\nĠpit ches\nĠCro at\nĠrat ios\nĠM its\nĠaccum ulated\nĠSpecific ally\nĠgentle man\nacer b\nĠp enn\nĠa ka\nĠF uk\nĠinterven e\nĠRef uge\nĠAlz heimer\nĠsuccess ion\noh an\nd oes\nL ord\nĠsepar at\nĠcorrespond ence\nĠsh iny\nP rior\nĠs ulf\nĠmiser able\nĠded ication\n( ).\nĠspecial ists\nĠdefect s\nĠC ult\nĠX ia\nĠje opard\nĠO re\nAb ility\nĠle ar\nĠamb itions\nĠB MI\nĠArab s\nĠ19 42\nĠpres ervation\nific ate\nĠash amed\nl oss\nĠRest aur\nĠrese mble\nĠen rich\nĠK N\nĠCl an\nfl oat\nĠplay able\nIT T\nĠharm ony\narr ison\nĠWe instein\nw ere\nĠpoison ing\nĠCom put\nĠWord Press\nm ajor\nĠVal ve\nF an\nĠTh row\nĠRom ans\nĠDep ression\nad os\nĠtort ured\nĠbal ancing\nbott om\nĠacqu iring\nĠMon te\nard i\nĠa ura\nĠ# #\nĠStand ing\nĠAtl as\nC F\nĠintr ins\nĠBen ghazi\nĠcamp ing\nĠt apped\nbl ade\nst rous\nĠR abb\nĠW ritten\nt ip\nĠNe igh\nster dam\nĠAll ow\nĠHe aling\nĠR hod\nn um\nĠcaffe ine\nĠPer cent\nĠbo o\nĠapp les\n30 5\nĠwel coming\nĠappl aud\nĠa usterity\n ±\nĠRe ality\nef e\nå ®\nĠsu cks\nĠtab s\nĠPay Pal\nĠback pack\nĠgif ted\nabul ary\nĠSc out\nir teen\nĠch in\nĠo mitted\nĠnegative ly\nĠaccess ing\nĠE arn\nĠambul ance\nĠhead phones\nĠ20 5\nĠRef resh\np resident\nĠKit chen\nĠEnt ered\nĠS nyder\n00 5\nom ical\nĠborrow ed\nĠN em\nĠav iation\nĠst all\nrim ination\nĠuniform s\nit ime\nĠSim mons\nener gy\nab lished\ny y\nqual ified\nĠrall ies\nĠSt uart\nfl ight\nĠgang s\nr ag\nĠv ault\nlu x\nĠCom par\nĠdesign ation\n20 9\nĠJ os\nd ollar\nz ero\nĠwell s\n30 3\nĠconstitu ents\nĠhe ck\nĠc 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all\nĠthought ful\nnames e\nĠprost itution\nbus iness\nĠball istic\nperson al\nc ig\niz ational\nR ound\nĠÂłĠÂł ĠÂłĠÂł\nĠCole man\nĠadm itting\nĠPl ug\nĠbit coins\nĠSu z\nĠfair ness\nĠsupp lier\nĠcatast rophic\nĠHel en\no qu\nM arc\nĠArt icles\ng ie\nĠend angered\nĠdest iny\nĠVol t\nol ia\nax is\nĠche at\nĠun ified\nIC O\nqu ote\n30 2\nĠS ed\nĠsupp ression\nĠanaly zing\nĠsqu at\nĠfig uring\nĠcoordin ates\nĠch unks\nĠ19 46\nĠsub p\nĠw iki\nĠFor bes\nĠJ upiter\nĠE rik\nim er\nĠCom mercial\n\\ )\nĠlegitim acy\nĠd ental\nĠMe an\nĠdefic its\n5 50\nOrig inally\nĠHor ror\nĠcontam ination\nll ah\nĠconf isc\nĠCl are\nT B\nĠF ailed\nan ed\nĠrul er\nĠCont roller\nĠfemin ists\nF ix\ng ay\n20 7\nĠr abbit\nTh ird\nownt own\nĠgl ue\nĠvol atile\nĠsh ining\nĠf oll\nĠimp aired\nĠsup ers\næ Ī\nĠcl utch\nļé ĨĴ\nĠpro let\nĠ( !\nĠy elled\nĠK iev\nĠEr n\nĠSh ock\nK B\nĠsit uated\nqu ery\nĠN as\nĠan nex\nchar acter\nĠHol iday\nĠautom ation\nĠJ ill\nĠRem astered\nĠl inem\nĠwild erness\nĠHor izon\nĠGu inea\nA Z\nĠmain land\nĠsec recy\nLE ASE\nĠp unk\nĠProv ince\n( ),\nSpe ed\nĠhand ing\nĠSeb ast\nS ir\nr ase\nĠj ournals\nĠcon gest\nĠT ut\nir rel\nĠschizophren ia\nĠmis ogyn\nhealth y\nI ron\nĠreact ed\n- $\n25 2\nĠpl ural\nĠpl um\nĠbarg ain\nĠground ed\nf inder\nĠdis se\nĠL az\nO OD\nĠat roc\nF actory\nĠmin ions\nĠo ri\nĠB rave\nĠP RE\nĠMy anmar\nĠH od\nĠexped ition\nĠexpl ode\nĠCo ord\nĠext r\nĠB rief\nĠAD HD\nĠhard core\nfeed ing\nĠd ile\nĠF ruit\nĠvacc ination\nĠM ao\nosp here\nĠcont ests\n- |\nĠf ren\nisp here\nR om\nĠSh arp\nĠTre nd\nĠdis connect\nâĢ¢ âĢ¢\nĠper secution\nEar th\nĠhealth ier\n38 4\nĠc ob\nĠTr inity\nOW S\nAN N\nĠspecial ty\nĠg ru\nĠcooper ative\nwh y\nStart ing\nĠIss ues\nst re\nens or\nĠ18 5\nAd v\n! ?\nĠRe vel\nem ia\nĠH ulk\nĠcelebr ations\nĠS ou\nra ud\nĠKle in\nĠun real\ncon text\nĠpartners hips\nĠadop ting\nt ical\nĠspl ash\nĠHe zbollah\nc ategory\ncycl op\nxt on\nĠD ot\nurd y\nt z\nĠenvelop e\nĠN L\nâ ķ\nĠwhere in\nSpe c\n18 4\nĠte lev\nal iation\nĠmyth s\nå °\nĠrig orous\nĠcommun icating\nĠobser ver\nĠre he\nĠW ash\nĠapolog ized\nĠT in\nĠexpend itures\nwork ers\nd ocument\nĠhes itate\nĠLen in\nĠunpredict able\nĠrenew al\ncl er\nok ia\nĠCON T\nĠpost season\nTok ens\nĠex acerb\nĠbet ting\nĠ14 7\nĠelev ation\nW ood\nĠSol omon\n19 4\n00 4\nout put\nĠredu nd\nĠM umbai\nĠp H\nĠreprodu ce\nĠD uration\nMA X\nĠb og\nC BS\nĠBal ance\nĠS gt\nĠRec ent\nĠc d\nĠpo pped\nĠincomp et\npro p\nay an\ng uy\nPac ific\nĠty r\nĠ{ {\nĠMy stic\nĠD ana\nĠmast urb\nĠge ometry\nà ¢\nĠCor rect\nĠtraject ory\nĠdistract ed\nĠf oo\nĠW elsh\nL uc\nm ith\nĠrug by\nĠrespir atory\nĠtri angle\nĠ2 15\nĠunder graduate\nĠSuper ior\nch anging\n_ -\nĠright ly\nĠrefere e\nĠluc rative\nĠun authorized\nĠresemb les\nĠGN U\nĠDer by\nĠpath ways\nĠL ed\nĠend urance\nĠst int\nĠcollect or\nF ast\nĠd ots\nĠnational s\nĠSec urities\nĠwh ip\nPar am\nĠlearn s\nM agic\nĠdetail ing\nm oon\nĠbroadcast ing\nĠb aked\n26 5\nhol m\nĠS ah\nĠHus sein\nĠCourt esy\n17 4\nĠ14 6\nĠge ographic\npe 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eless\nĠSte alth\nĠ ãģ\ness ors\ntt le\nb org\nĠSaf ari\nf ell\nĠw ary\nd ue\nĠAb ove\nH a\nE LL\nĠnot or\nĠW on\nT oo\nĠoccup ations\nĠposs essions\nĠinv iting\nĠpred ators\nĠacceler ated\nĠ15 7\nuter te\nĠC ube\ne ast\nacc ount\nG ive\nĠtrans plant\nred ients\nid able\nĠscreens hots\nĠG und\nĠF S\nĠtravel ers\nĠsens ory\nĠF iat\nĠRock ets\nİ ĭ\n_ {\nF riend\nĠchar ming\nAL S\nĠenjoy ment\nm ph\nĠ5 000\nĠRE G\nÙ Ĩ\nb ia\nĠcomp ilation\nro st\nĠV P\nĠSch ne\n201 9\nĠcop ying\nM ORE\nĠFl ore\nf alls\n2 15\nt otal\nĠdis ciples\nd ouble\nĠexceed ing\nĠsm ashed\nĠconcept ual\nĠRom ania\nĠB rent\nĠI CE\nĠT ou\nĠg rap\nĠn ails\n18 9\nãĥ ĺ\nĠproc ure\ne ur\nĠconfir ming\nĠC ec\naw i\nĠEd en\nĠn g\nĠengine ered\nat ics\nĠhook ed\nĠdisgust ing\nĠMur der\nãĤ ¿\nL ibrary\nĠ16 8\nAl most\nhem atic\nMen u\nĠNot re\nĠJ ur\nĠkidn apped\nĠhack er\nĠJ ade\nĠcreep y\nĠdraw ings\nĠSpons or\nĠcycl ists\nĠGob lin\nĠoptim ized\nĠst aged\nĠMc D\nbet ween\nA ge\nen o\nS ex\nĠW ide\nn ings\nav is\nĠincap able\nĠK ob\nĠreward ing\nĠL one\noles cent\nĠcontract ed\nĠstick y\nJ ose\nB all\nf est\nĠIn put\nĠRec ently\nĠto mat\nsqu are\nApp lication\nĠnit rogen\nĠdupl icate\nĠRec on\nĠD ear\nL ondon\nĠint ra\nĠd ock\nĠout reach\nĠM illion\nĠmamm als\nam pton\nV AL\nĠsn aps\nĠd os\nĠWh ole\nĠRead y\nT ry\nĠWinn ipeg\near ance\nĠinc urred\nren ched\nĠNS W\nil ot\nrain e\nĠc ube\ng ot\nĠrun way\netermin ed\nĠHaw ks\nĠsurviv or\nĠW ish\nĠD in\nĠDE F\nĠV ault\n18 7\nĠmush rooms\nĠcris p\nbe y\nĠDisco very\nĠdevelopment al\nĠparad igm\nĠcha otic\nĠT su\nĠ3 33\nb ons\nĠbacter ial\nĠcomm its\nĠcos mic\nĠme ga\noc ative\nĠP aint\nophob ic\nĠv ain\nĠcar ved\nĠTh ief\nĠG ul\nows hip\nĠc ites\nĠEd inburgh\nĠdimin ished\nĠacknowled ges\nĠK ills\nĠmic row\nĠHer a\nĠsen iors\nĠwhere by\nH op\nat ron\nĠun available\nĠN ate\nĠ4 80\nĠsl ated\nĠRe becca\nĠB attery\nĠgram mar\nĠhead set\nĠcurs or\nĠex cluding\nany e\naunder ing\neb in\nĠfeas ible\nĠPub lishing\nĠLab s\nĠCl iff\nĠFerr ari\nĠp ac\nvis ible\nmark ed\npe ll\nĠpol ite\nĠstagger ing\nĠGal actic\nĠsuper st\nĠpar an\nĠOffic ers\nãĢ ģ\nĠspecific s\nul us\n23 9\nĠP aste\nAM P\nĠPan ama\nĠDe lete\nangu ard\nrest rial\nĠhero ic\nĠD y\nا ÙĦ\nĠincumb ent\nĠcr unch\nt ro\nĠsc oop\nĠblog ger\nĠsell ers\nure n\nĠmedic ines\nĠC aps\nĠAnim ation\nox y\nĠout ward\nĠinqu iries\n22 9\nĠpsych ologist\nĠS ask\nev il\nĠcontam inated\nãĤ ¨\nhe rence\nĠbrand ed\nĠAbd ul\nz h\nĠparagraph s\nĠmin s\nĠcor related\ner b\nĠimp art\nĠmil estone\nĠSol utions\not le\nĠunder cover\nĠmar ched\nĠCharg ers\nf ax\nĠSec rets\nĠr uth\nwe ather\nĠfemin ine\nĠsh am\nĠprest igious\nigg ins\nĠs ung\nhist ory\nett le\ngg ie\nĠout dated\nol and\nĠper ceptions\nĠS ession\nĠDod gers\nu j\nĠE ND\nD oc\nĠdefic iency\nGr and\nĠJ oker\nĠretro spect\nĠdiagn ostic\nĠharm less\nĠro gue\nĠA val\nE qu\nĠtrans c\nĠRoberts on\nĠDep ending\nĠBurn s\niv o\nĠhost ility\nF eatures\nĵ ĺ\nĠdis comfort\nĠL CD\nspec ified\nĠEx pect\n3 40\nĠimper ative\nĠReg ular\nCh inese\nĠstate wide\nĠsy mm\nĠlo ops\nĠaut umn\nN ick\nĠsh aping\nĠqu ot\nĠc herry\nĠCross ref\nè¦ ļéĨĴ\nStand ard\nhe ed\nĠD ell\nĠViet namese\nĠo st\nĠV alkyrie\nO A\nAss ad\nĠreb ound\nĠTra ffic\npl aces\næ ĺ\nĠB uc\n17 2\nĠshel ters\nĠins isting\nĠCertain ly\nĠKenn eth\nĠT CP\nĠpen al\nĠRe play\nhe ard\nĠdial ect\niz a\nĠF Y\nit cher\nĠD L\nĠspir al\nĠquarterback s\nĠh ull\nĠgo ogle\nĠto dd\nĠSter ling\nĠPl ate\nĠsp ying\nmb ol\nĠReal m\nĠPro ced\nĠCr ash\nĠtermin ate\nĠprotest ing\nC enter\ngu ided\nĠun cover\nĠboy cott\nĠreal izes\ns ound\nĠpret ending\nĠV as\n19 80\nĠfram ed\nĠ13 9\nĠdesc ended\nĠrehab ilitation\nĠborrow ing\nĠB uch\nĠbl ur\nR on\nĠFro zen\nen za\nCh ief\nĠP oor\nĠtransl ates\nM IN\nĠ2 12\nJ ECT\nĠerupt ed\nĠsuccess es\nS EC\nĠpl ague\nĠg ems\nd oms\nĠstret ches\nĠSp y\nĠstory telling\nC redit\nĠP ush\nĠtra ction\nĠin effective\nĠL una\nĠt apes\nĠanaly tics\nerc ise\nĠprogram mes\nĠCar bon\nĠbeh old\nhe avy\nĠConserv ation\nĠF IR\nĠs ack\nter min\nric ks\nĠhous ed\nĠunus ually\nI ce\nĠexecut ing\nĠMor oc\ned ay\nĠed itions\nĠsm arter\nĠB A\nĠout law\nĠvan ished\nib a\nAL SE\nĠSil va\n23 8\nC ould\nĠphilos opher\nĠevac uated\nSec ret\n14 2\nĠvis as\nãĤ ¬\nĠM alt\nĠClear ly\nĠN iger\nĠC airo\nĠF ist\n3 80\nĠX ML\naut o\nit ant\nĠrein forced\nRec ord\nĠSurviv or\nG Hz\nĠscrew s\nparent s\nĠo ceans\nma res\nĠbra kes\nvas ive\nĠhell o\nĠS IM\nrim p\nĠo re\nĠArm our\n24 7\nĠterr ific\nĠt ones\n14 1\nĠMin utes\nEp isode\nĠcur ves\nĠinflamm atory\nĠbat ting\nĠBeaut iful\nL ay\nĠunp op\nv able\nĠr iots\nĠTact ics\nb augh\nĠC ock\nĠorg asm\nĠS as\nĠconstruct or\net z\nG ov\nĠant agon\nĠthe at\nĠde eds\nha o\nc uts\nĠMc Cl\nĠu m\nĠScient ists\nĠgrass roots\nys sey\n\"] =>\nĠsurf aced\nĠsh ades\nĠneighb ours\nĠad vertis\noy a\nĠmer ged\nUp on\nĠg ad\nĠanticip ate\nAny way\nĠsl ogan\nĠdis respect\nI ran\nĠT B\nact ed\nĠsubp oen\nmedi ately\nOO OO\nĠwa iver\nĠvulner abilities\nott esville\nĠHuff ington\nJ osh\nĠD H\nM onday\nĠEll en\nK now\nx on\nit ems\n22 8\nĠf ills\nĠN ike\nĠcum ulative\nand als\nI r\nĠ ì\nĠfr iction\nig ator\nĠsc ans\nĠVi enna\nld om\nĠperform ers\nP rim\nĠb idding\nM ur\nĠlean ed\nĠPri x\nal ks\nĠ[ âĢ¦]\nĠTw itch\nĠDevelop er\nĠG ir\nĠcall back\nAb stract\nĠacc ustomed\nĠfreed oms\nĠP G\nur acy\nĠl ump\nis man\n,, ,,\n19 92\nĠR ED\nĠwor m\nM atch\nĠPl atinum\nI J\nĠOwn er\nTri via\ncom pl\nĠnew born\nĠfant as\nO wn\nĠ19 59\nĠsymp ath\nĠub iqu\nĠoutput s\nĠal lev\nĠpr ag\nK evin\nĠfav ors\nĠbur ial\nĠn urt\nso lete\nc ache\nĠ15 6\nĠunl ocks\nte chn\nM aking\nĠcon quer\nad ic\næ ĸ\nĠel f\nĠelect orate\nĠKurd s\nĠSt ack\nĠSam urai\nĠâ ĺħ\nĠ{ }\nĠS aid\nĠFall out\nĠkind ness\nĠCustom s\nĠBou levard\nĠhelicop ters\not ics\nĠVe get\ncom ment\nĠcritic ised\nĠpol ished\nĠRem ix\nĠC ultural\nĠrec ons\nĠdo i\nat em\nSc reen\nĠbar red\nCom ments\nĠGener ally\nĠsl ap\n7 20\nV ari\np ine\nĠem pt\nĠh ats\nĠPlay ing\nl ab\na verage\nform s\nĠC otton\nĠcan s\nĠD ON\nĠSom alia\nC rypt\nĠIncre ases\nE ver\nmod ern\nĠsur geon\n3 000\nĠrandom ized\n================================ ================================\nB ern\nim pl\nĠC OR\nĠpro claim\nth ouse\nĠto es\nĠam ple\nĠpres erving\nĠdis bel\ngr and\nB esides\nĠsil k\nĠPat tern\nh m\nĠenter prises\nĠaffidav it\nĠAdvis ory\nĠadvert ised\nĠRel igious\nse ctions\npsy ch\nĠField s\naw ays\nĠhasht ag\nĠNight mare\nĠv ampire\nĠfore nsic\nrosso ver\nn ar\nĠn avy\nĠvac ant\nĠD uel\nĠhall way\nĠface book\nident ally\nĠN RA\nĠm att\nĠhur ricane\nĠKir by\nĠP uzzle\nĠsk irt\nou st\ndu llah\nĠanal ogy\nin ion\nĠtomat oes\nĠN V\nĠPe ak\nĠMe yer\nĠappoint ments\nĠm asc\nĠal ley\nre hend\nĠchar ities\nĠund o\nĠdest inations\nĠTest ing\n\"> </\nĠdest ined\nĠimp lements\nĠHar old\nRE CT\nĠoptim ization\nĠkilomet res\nĠc md\nĠimpair ment\nĠun successful\nĠswift ly\nĠGlas gow\nart en\nĠSh ares\nĠAn swer\nĠAl bum\nĠnut ritional\nãĥ ĸ\nĠF ut\nĠbl oc\nĠN FC\nĠwholes ale\nĠC W\nĠneg lected\nĠlaun cher\nĠannounce ments\nOU LD\ncom b\nĠrot ating\nĠrest s\nĠT icket\nched el\nL ou\nĠV ic\nĠ\" '\nĠtem plates\nĠrepl aces\nAr c\n:: ::\nĠGil bert\nĠillness es\nĠsched ules\nĠheter osexual\nL INE\nĠhere in\nĠco erc\nĠdecre asing\nĠde portation\ns udo\nĠInd igenous\nĠweigh s\nAl ong\n' );\nĠBeng als\n70 7\nĠjoint s\nver ts\nĠ14 9\nna ire\nĠsimpl est\nĠl ore\n10 80\nf iction\nĠDat abase\nĠreserv ation\nĠs ou\nĠsan ctuary\naud io\nap le\nĠveget arian\nĠanticip ation\nm icro\nĠend uring\nĠdepart ed\nĠsidew alk\nĠprohib its\nĠF ont\nĠcomp ute\nĠS ect\nĠ15 8\nB attle\nĠbom ber\nĠdist raction\nĠend ured\nĠpractition ers\nĠdistur bed\nĠdr ank\nord ered\nĠsurpr ises\nse at\nSec urity\nĠW isdom\nog o\nĠsub paragraph\nĠPen insula\nĠOrig ins\nire n\nĠP av\nigg le\nĠgrat itude\nĠG ravity\nover ty\nim an\nct r\nĠCa esar\nc ould\ng em\nĠsk ies\nĠch amp\nĠagree ing\nF amily\nD iv\n17 6\nĠmess y\num ption\nF ederal\nern o\nĠCh at\nBey ond\nĠdev ote\nĠW alsh\nĠdump ed\nĠaccum ulation\nst ad\nhib ition\nĠsm okers\nĠinspect or\nF rench\niss an\nĠV ita\nĠresearch ing\nR AM\nĠCelt ics\nĠcl oak\nĠTer ra\nM ary\nso ld\nĠD OM\nmod s\nInt el\nĠmult itude\nĠImpro ved\nĠrel iance\nĠartif act\nĠalarm ing\nP rom\nh on\nT ION\nmed ium\nĠref lex\nĠEx cel\nĠweaken ed\n16 3\n2 24\nĠcost umes\nĠunique ly\nĠs orrow\nĠm ansion\nw p\nĠsal v\nĠGro ve\nbs p\nĠSn iper\nĠSh ipping\nĠP OW\nĠund is\nĠbrand ing\nG irl\nĠAh mad\nĠL akes\nĠCore y\nĠinherit ance\nener y\nĠpack ing\nĠP rest\nD est\nF W\nĠregul ator\nl ocked\nĠcont ested\nĠMel issa\nĠD uc\nĠunpop ular\nĠst acked\nĠ19 17\nĠyear ly\nĠst are\nĠassess ing\nà ¸\nĠbe verages\nĠcompet itions\nĠstreng thening\nal ong\nĠL ud\nĠmel ted\nstan bul\nĠb ounty\nEN C\nĠL ands\nĠdecl ares\nĠcustom ize\nĠcomp osite\nãĥ ¬\nC M\nograph ics\nĠTem p\nĠcont ender\nĠins ign\nĠL AN\nĠdis asters\nins pired\nĠjud gments\nustain able\nurs ion\nĠvar iance\nĠUlt imately\nĠ --------\nu ador\nĠR X\nĠmel ting\nĠExt ended\nĠT we\nM ajor\nĠB il\nĠsy rup\nqu ick\nĠHold er\nĠinnoc ence\nU LE\nĠM ight\n99 99\nĠf al\nĠcontinu ity\nĠ19 53\nĠB S\nst ill\nL at\nĠAb use\nĠun supported\nxxxx xxxx\nĠinst itute\nĠfrag ment\nĠP ep\nW estern\nĠC ause\nĠFr ag\nĠAr s\nà ¥\nast ics\nĠb ishop\nĠcross es\nĠ15 4\nĠUp grade\nĠmit igate\nĠRay mond\nMod s\nĠtom ato\nĠst umbled\nĠdiff ers\nIn itial\nĠR aspberry\nĠign ores\nĠt ant\nà ł\nĠrel ay\nĠb isexual\nĠconf ession\nĠd ement\nin as\nĠHe ather\npl atform\ndri ving\nbour g\nĠM ush\nĠhy ster\nDet ails\nĠdr ift\nĠW ald\nĠLuck ily\nor f\nĠexp ire\nĠP unch\nzy me\ng old\nĠunp aid\nĠT rent\nĠun armed\nĠill icit\nĠT ottenham\nĠsm ash\nIntern ational\nink er\nĠst ing\nĠSadd am\nĠAR T\nĠtruth s\nb irth\nĠso ber\nĠN it\nĠ ib\nĠus able\nĠst acks\nĠSy lv\nĠnort heast\nĠdom ination\nĠM our\nEN SE\nĠMe asure\nĠprogram mer\nĠ< -\n18 2\nĠCond ition\nĠback yard\nir ling\nĠJ eb\nĠCre ed\nĠH ang\nĠCOM P\nF ER\nĠIs h\nĠdetect ives\n------------ ---\nĠMess enger\nĠlo oph\nĠgate way\n15 1\nĠMaterial s\nĠD T\nĠdo omed\nod o\nĠslic es\nĠemail ed\nĠPer l\nĠren ov\nUT H\nody nam\nĠSouth west\nget ic\nĠT PP\nĠoptim ism\nĠT ow\nul ators\nprot ected\ny les\n «\nĠex ile\nen v\nP rop\nĠZimmer man\nÙ İ\nC a\nom aly\nãĥ Ĩ\nĠrail road\nL ee\n23 2\nĠrepl icate\nĠcomfort ably\nact ly\nĠr av\nĠtelesc ope\nĠhonest y\nĠPe pper\nĠBr ing\nĠric hest\nĠout doors\nĠh alls\nĠcont end\nIS E\nĠsub mitting\nĠna ive\nar ations\nĠ14 3\nĠpo ised\nrespons ible\nĠsoc ks\nĠSk ull\nQuest ion\nĠdiscover ies\nJo ined\nĠEn emies\nĠWire less\nĠRe venge\nĠpuzz les\nĠce ased\n29 0\ncript ions\nĠCon sole\nĠbo iling\nĠdisc rep\nĠded uction\nĠar senal\nXX XX\nĠAm sterdam\nrox imately\nĠSh ane\nĠpos ing\nĠACL U\nĠCompan ies\nĠthe ology\nĠU g\nqu arter\nĠH ank\nCo in\nĠL v\nĠalleg ation\nĠAv oid\nĠindef initely\nĠcommod ities\nĠbr ig\nĠMan it\nĠt enth\nmet hod\nĠKn icks\nĠâĢ İ\nĠinv oked\nD ial\nAR A\nĠc aucus\n22 7\nĠJ ab\nĠoun ces\nb ay\nĠbud dy\nf an\n23 4\nĠH il\nad h\nĠT Y\nĠIN D\nĠ19 39\nĠiter ation\nĠGonz alez\nĠV ert\nĠI O\nem b\nre ra\nen ch\nĠRequ irements\nĠW ins\nĠlivest ock\nh ours\n\" âĢ¦\nb ral\nM arg\nĠD one\nĠwas ting\ning ed\ng roups\nĠw ishing\nĠT umblr\nĠt apping\nĠnational ism\nĠB yr\nĠsqu ares\nĠAct ions\nãĥ ¥\nIn side\ndeb ug\nĠapp end\nĠstub born\nĠC ind\nT ell\nĠt earing\nĠRe y\nor c\nĠDay ton\nĠN H\nĠMad ness\nCh arl\nĠMor rison\nfil ter\nĠacc use\nĠ. /\nĠtor rent\nĠdecl ines\ng allery\nM ine\nĠneg otiation\nĠBash ar\nop ia\n19 93\nem ort\nĠNo vel\nĠF ang\ners ive\nĠInst ant\nĠroll er\nA round\nĠElect ions\nG ames\nĠin expensive\nĠwor s\nĠv ul\nĠH ole\nĠunbeliev able\nĠn ause\nĠent r\nbo at\nĠST E\nĠbus h\nĠHass an\nĠw o\nĠpa used\nĠM ig\nl ived\nĠsc out\nĠl ith\nPub lished\ndu ino\nc ool\nĠcirc ulating\nid as\nĠP am\nviol ent\nĠCraw ford\nudd le\nĠLet ters\nGu ard\nmor ph\nĠwand ering\nĠsoph omore\nĠque er\nĠBl ind\nr ue\nĠMar riage\nD om\nĠpadd ing\nĠfold ers\nĠmeaning less\nĠcandid acy\naf ort\nĠwhistle bl\nĠIdent ified\nĠcig ar\nĠh id\nĠDub ai\nĠpost ure\nĠh iking\nĠTermin al\nLegend ary\nĠT P\nĠAT K\nĠStar bucks\nĠR iot\n19 91\nĠBott om\ne ffic\nĠEug ene\nĠWy oming\nĠRock y\nĠsal mon\nĠmet ro\nĠb ilateral\nĠcelebr ates\nL ength\nb illion\nB at\nĠre leg\nĠpse udo\nD T\nĠRh ode\nP arent\nple tion\nĠatt ribut\nĠtun ing\nĠNOT E\nĠRe bel\nic us\nF und\nĠcock tail\nĠ5 01\nĠsp oon\nĠbrut ality\nĠun ite\nĠmicro bi\nĠRe ich\npos itive\nĠam azed\nĠN T\nD esc\nECT ION\nĠfalse ly\nĠHigh lander\nĠC rist\nĠVictor ian\nĠdistribut ions\nthe ir\nĠE instein\nĠp od\nĠepid em\nĠhe ap\nĠR anch\nĠan them\nĠre app\nĠAub urn\nĠconc urrent\nĠThrough out\nĠP OST\nâ ĺ\nĠhom emade\nk ick\nB eg\nĠch assis\nc ounter\nĠmer ger\nĠl aps\n2 17\nun ion\nĠTr igger\nĠdeb ated\nĠsil ently\nĠrest raint\nB al\n0000 000\nĠform idable\nĠFil ip\nĠsacrific es\nF ood\nĠdwar f\nĠSe qu\nin ian\nMore over\nĠtang ible\nops is\nĠMine craft\nĠRegist ration\no an\nĠrepresent ations\nĠth irst\nĠcor p\nire ment\nM ade\nl oe\n> \"\nc ats\n* .\nĠgest ures\ngener al\nLe ague\nĠpack ets\nĠInspect or\nĠBer g\nĠfraud ulent\nĠcritic ize\nF un\nĠbl aming\nnd ra\nĠsl ash\nĠE ston\nĠpropos ing\nĠwh ales\nĠtherap ist\nĠsub set\nĠle isure\nEL D\nĠC VE\nĠAct ivity\nĠcul min\nsh op\nĠD AY\nis cher\nĠAdmir al\nĠAtt acks\nĠ19 58\nĠmem oir\nĠfold ed\nĠsex ist\nĠ15 3\nĠL I\nĠread ings\nĠembarrass ment\nĠEmploy ment\nw art\nch in\nĠcontin uation\nl ia\nRec ently\nĠd uel\nĠevac uation\nĠKash mir\nĠdis position\nĠR ig\nĠbol ts\nĠins urers\n4 67\nM ex\nĠret aliation\nĠmis ery\nĠunre asonable\nr aining\nI mm\nĠP U\nem er\nĠgen ital\nãĤ ³\nĠC andy\nĠon ions\nĠP att\nlin er\nĠconced ed\nĠf a\nĠfor c\nĠH ernandez\nĠGe off\ndeb ian\nĠTe ams\nĠc ries\nĠhome owners\n23 7\nA BC\nĠst itch\nĠstat istic\nĠhead ers\nĠBi ology\nĠmot ors\nĠG EN\nĠL ip\nĠh ates\nĠhe el\nS elf\ni pl\nED IT\nort ing\nĠann ot\nĠSpe ech\nold emort\nĠJ avascript\nĠLe Bron\nĠfoot print\nĠf n\nĠseiz ures\nn as\nh ide\nĠ19 54\nĠBe e\nĠDecl aration\nĠKat ie\nĠreserv ations\nN R\nf emale\nĠsatur ated\nĠb iblical\nĠtroll s\nDev ice\nph otos\nĠdr ums\nãĥīãĥ© ãĤ´ãĥ³\nN ight\nf ighter\nĠH ak\nri ber\nĠc ush\nĠdiscipl inary\nba um\nĠG H\nĠSch midt\nilib rium\nĠs ixty\nĠKush ner\nro ts\nĠp und\nĠR ac\nĠspr ings\nĠcon ve\nBus iness\nF all\nĠqual ifications\nĠvers es\nĠnarc iss\nĠK oh\nĠW ow\nĠCharl ottesville\ned o\nĠinterrog ation\nĠW ool\n36 5\nB rian\nĠâľ ĵ\nĠalleg es\nond s\nid ation\nĠJack ie\ny u\nĠl akes\nĠworth while\nĠcryst als\nĠJud a\nĠcomp rehend\nĠfl ush\nĠabsor ption\nĠO C\nĠfright ened\nĠCh ocolate\nMart in\nĠbu ys\nĠbu cks\nĠapp ell\nĠChampions hips\nĠlist ener\nĠDef ensive\nĠc z\nud s\nĠM ate\nĠre play\nĠdecor ated\nĠs unk\nĠV IP\nĠAn k\nĠ19 5\naa aa\nNob ody\nĠMil k\nĠG ur\nĠM k\nĠS ara\nĠse ating\nĠW id\nTr ack\nĠemploy s\nĠgig antic\nAP P\nãĤ §\nin ventory\nĠtow el\nat che\nl asting\nĠT L\nĠlat ency\nĠkn e\nB er\nme aning\nĠup held\nĠplay ground\nĠm ant\nS ide\nĠstere o\nĠnorth west\nĠexception ally\nĠr ays\nĠrec urring\nD rive\nĠup right\nĠab duct\nĠMar athon\nĠgood bye\nĠal phabet\nh p\nĠcourt room\nring ton\not hing\nT ag\nĠdiplom ats\nĠbar bar\nĠAqu a\n18 3\n33 33\nĠmat urity\nĠinst ability\nĠAp ache\nĠ= ==\nĠfast ing\nĠGr id\nMod Loader\nĠ15 2\nA bs\nĠOper ating\nett i\nĠacqu aint\nDon nell\nĠK em\nĠFor ge\nĠarm ored\nM il\nĠphilos ophers\nin vest\nPl ayers\nâ Ī\nĠmy riad\nĠcomr ades\nR ot\nĠremember ing\nĠcorrespond s\nĠprogram mers\nĠLyn n\nĠo lig\nĠco herent\nyn chron\nĠChem ical\nĠj ugg\np air\npost s\nE ye\nĠIn ner\nĠsem ester\nott est\nĠEmir ates\nric anes\nor ously\nm its\nĠW is\nĠd odge\nl ocation\nĠf aded\nAm azon\nĠPro ceed\nĠIN FO\nj ournal\nĠTru ck\nT en\nĠ2 17\nĠstat utes\nm obile\nĠT ypes\nRec omm\nb uster\npe x\nĠleg ends\nĠhead ache\nf aced\nĠWi Fi\nif ty\nĠH ER\nĠcirc uits\nER ROR\n22 6\nol in\nĠcyl inder\nosp ace\nik ers\nP rem\nQu ant\nĠconflic ting\nĠslight est\nĠfor ged\nion age\nStep hen\nĠK ub\nĠOpp ortun\nĠHe al\nĠbl o\nĠrul ers\nĠh uh\nĠsubmar ine\nf y\nass er\nĠallow ance\nĠKas ich\nĠT as\nĠAustral ians\nForge ModLoader\nĠâĨ ij\nĠMat rix\nam ins\nĠ12 00\nĠAc qu\n23 6\nD ocument\nĠBre aking\n19 3\nĠSub st\nĠRoll er\nĠPro perties\nĠN I\nt ier\nĠcr ushing\nĠadvoc ating\nFurther more\nkeep ers\nĠsex ism\nx d\nĠcall er\nĠS ense\nchie ve\nĠT F\nĠfuel ed\nĠreminis cent\nĠobs ess\nur st\nĠup hold\nĠF ans\nhet ics\nĠâ Ĺ\nĠB ath\nĠbe verage\nĠo scill\n25 4\nĠpol es\nĠgrad ual\nĠex ting\nĠS uff\nĠS uddenly\nĠlik ing\nĠ19 49\nun ciation\nam ination\nĠO mar\nĠL V\nĠCon sequently\nĠsynt hes\nĠG IF\nĠp ains\nĠinteract ing\nu ously\ninc re\nĠrum or\nĠScient ology\n19 7\nĠZ ig\nĠspe lling\nĠA SS\nĠexting u\nms on\nĠg h\nĠremark ed\nĠStrateg ic\nĠM ON\nå ¥\ng ae\nĠWH AT\nE ric\nĠCamp us\nĠmeth ane\nĠimag in\nJ UST\nĠAl m\nX T\ni q\nĠR SS\nĠwrong doing\natt a\nĠbig ot\nĠdemonstr ators\nĠCal vin\nĠV illa\nĠmembr ane\nĠAw esome\nĠbenef ic\n26 8\nĠmagn ificent\nĠL ots\nG reg\nĠBor is\nĠdetain ees\nĠH erman\nĠwhis pered\nĠa we\nProf essor\nfund ing\nĠphys iological\nĠDest ruction\nĠlim b\nĠmanip ulated\nĠbub bles\nĠpse ud\nĠhyd ra\nĠBrist ol\nĠst ellar\nĠExp ansion\nĠK ell\nĠInterest ingly\nĠm ans\nĠdrag ging\nĠec ological\nĠF it\nĠg ent\nĠbenef ited\nĠHait i\nĠpoly g\nãĥ İ\nĠ20 30\nĠpro w\nĠrecon struction\nĠwas t\nĠpsych ic\nĠGree ks\nHand ler\n16 2\nĠP ulse\nĠsol icit\nĠsy s\nĠinflu x\nĠG entle\nper cent\nĠprolifer ation\nĠtax able\nĠdisreg ard\nĠesc aping\nĠg inger\nĠwith stand\nĠdevast ated\nĠD ew\nser ies\nĠinject ed\nela ide\nĠturn over\nhe at\nĻ Ĥ\nH appy\nĠSil ent\nãĤ Ń\niv ism\nĠir rational\nAM A\nĠre ef\nr ub\nĠ16 2\nĠbank ers\nĠEth ics\nv v\nĠcritic isms\nK n\n18 6\nM ovie\nĠT ories\nĠno od\nĠdist ortion\nF alse\nod ore\nĠt asty\nRes earch\nĠU ID\n- )\nĠdivor ced\nĠM U\nĠHay es\nĠIs n\nian i\nĠH Q\nĠ\" #\nign ant\nĠtra umatic\nĠL ing\nH un\nĠsab ot\non line\nr andom\nĠren amed\nra red\nK A\nd ead\né t\nĠAss istance\nĠse af\n++++ ++++\nĠse ldom\nĠWeb b\nĠbo olean\nu let\nĠref rain\nĠDI Y\nru le\nĠshut ting\nĠutil izing\nload ing\nĠPar am\nco al\noot er\nĠattract ing\nĠD ol\nĠher s\nag netic\nĠRe ach\nim o\nĠdisc arded\nĠP ip\n01 5\nü r\nĠm ug\nIm agine\nC OL\nĠcurs ed\nĠSh ows\nĠCurt is\nĠSach s\nspe aking\nĠV ista\nĠFram ework\nong o\nĠsub reddit\nĠcr us\nĠO val\nR ow\ng rowing\nĠinstall ment\nĠgl ac\nĠAdv ance\nEC K\nĠLGBT Q\nLE Y\nĠac et\nĠsuccess ive\nĠNic ole\nĠ19 57\nQu ote\nĠcircumst ance\nack ets\nĠ14 2\nort ium\nĠguess ed\nĠFr ame\nĠperpet rators\nĠAv iation\nĠBen ch\nĠhand c\nA p\nĠ19 56\n25 9\nr and\nNet Message\nd in\nurt les\nh ig\nĠV III\nff iti\nĠSw ords\nb ial\nĠkidn apping\ndev ice\nĠb arn\nĠEl i\nauc as\nS end\nCon structed\nĠ ½\nĠneed les\nĠad vertisements\nĠv ou\nĠexhib ited\nĠFort ress\nAs k\nB erry\nTY PE\nĠcan cers\nump ing\nĠTerrit ory\nĠpr ud\nĠn as\nĠathe ist\nĠbal ances\nãģ Ł\nĠSh awn\n& &\nĠland sc\nĠR GB\nĠpet ty\nĠex cellence\nĠtransl ations\nĠpar cel\nĠChe v\nE ast\nĠOut put\nim i\nĠamb ient\nĠTh reat\nĠvill ains\nĠ5 50\nIC A\nĠtall er\nĠle aking\nc up\nĠpol ish\nĠinfect ious\nĠK C\nĠ@ @\nback ground\nĠbureaucr acy\nĠS ai\nun less\nit ious\nĠSky pe\nAt l\nID ENT\n00 8\nĠhyp ocr\nĠpit chers\nĠguess ing\nĠF INAL\nBet ween\nĠvill agers\nĠ25 2\nf ashion\nĠTun is\nBe h\nĠEx c\nĠM ID\n28 8\nĠHas kell\n19 6\nĠN OR\nĠspec s\nĠinv ari\nĠgl ut\nĠC ars\nĠimp ulse\nĠhon ors\ng el\nĠjurisd ictions\nĠBund le\nul as\nCalif ornia\nĠIncre ase\nĠp ear\nĠsing les\nĠc ues\nĠunder went\nĠW S\nĠexagger ated\nĠdub ious\nĠfl ashing\nL OG\n) ].\nJ ournal\nt g\nV an\nĠI stanbul\nĠIn sp\nĠFrank en\nD raw\nĠsad ness\nĠiron ic\nĠF ry\nx c\nĠ16 4\nis ch\nW ay\nĠProtest ant\nh orn\nĠun aff\nĠV iv\nill as\nĠProduct ions\nĠH ogan\nĠper imeter\nĠS isters\nĠspont aneous\nĠdown side\nĠdescend ants\nĠor n\nw orm\nJapan ese\nĠ19 55\nĠ15 1\nĠDo ing\nels en\numb les\nĠrad ically\nĠDr um\nĠB ach\nĠli abilities\nĠO B\nĠElement ary\nĠmem e\nyn es\nĠfinger print\nĠGr ab\nĠundert ake\nMem bers\nĠRead er\nĠSim s\ng od\nĠhypot hetical\ns cient\nĠA J\nĠchar ism\nĠad missions\nĠMiss ile\ntr ade\nĠexerc ising\nĠBack ground\nW ritten\nĠvoc als\nwhe ther\nĠv i\nĠW inner\nĠl itter\nĠSh ooting\nST EM\nãĤ ¡\nĠA FL\nĠvari ability\nĠe ats\nĠD PS\nb row\nĠeleph ants\nĠstr at\nĠ Å\nĠsett lers\nMatt hew\nĠin advert\nH I\nĠIM F\nĠGo al\nĠnerv es\nJohn son\ney e\nablish ment\nTh ursday\nBIL ITY\nH ad\nam oto\nhet amine\nep s\nĠmit ochond\nĠcomp ressed\nĠTre vor\nĠAnim als\nT ool\nL ock\nĠtwe ak\nĠpin ch\nĠcancell ation\nP ot\nĠfoc al\nĠAst ron\n17 3\nĠA SC\nĠO THER\numn i\nĠdem ise\nd l\nÙ ħ\nSem itism\nĠcr acking\nĠcollabor ative\nĠexpl ores\ns ql\nĠher bs\nĠconfig urations\nm is\nĠRes ult\nace y\nĠSm oke\nĠsan ct\nel ia\nĠdeg ener\nĠdeep est\nĠscream ed\nĠn ap\nSoft ware\nĠST AR\nE F\nĠX in\nspons ored\nmans hip\n23 3\nĠprim aries\nĠfilter ing\nĠas semble\nm il\nĠMy ers\nb ows\nĠpun ched\nM ic\nĠinnov ations\nĠfun c\nand o\nĠfr acking\nĠV ul\nо Ð\nosh op\nĠIm mun\nĠsett ling\nĠadolesc ents\nĠreb uilding\nĠtransform ing\nĠpar ole\nĠhar bor\nĠbook ing\not ional\nonge vity\nĠY o\nb ug\nĠemer ges\nĠMethod s\nĠCh u\nP res\nĠDun geons\nĠtra iling\nĠR um\nĠH ugh\nå¤ ©\nĠE ra\nĠBatt les\nRes ults\nĠTr ading\nĠvers a\nc ss\nax ies\nhe et\nĠgre ed\n19 89\nĠgard ens\nĠconting ent\nP ark\nĠLeaf s\nh ook\nro be\nĠdiplom 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hip\nc ause\nĠFl ood\nãĥ ¯\nĠpur ity\nth ose\nĠem ission\nb olt\nĠch ord\nĠScript ure\nL u\nĠ$ {\ncre ated\nOther s\n25 8\nĠelement al\nĠannoy ed\nĠA E\nd an\nĠS ag\nRes earchers\nĠfair y\nâĢĵ âĢĵ\n======== ====\nSm art\nGG GG\nĠskelet ons\nĠpup ils\nlink ed\nĠur gency\nen abled\nĠF uck\nĠcoun cill\nr ab\nU AL\nT I\nĠlif es\nĠconf essed\nB ug\nĠharm on\nĠCON FIG\nĠNe utral\nD ouble\nĠst aple\nĠSH A\nBrit ish\nĠSN P\nAT OR\noc o\nĠswing ing\nge x\nole on\npl ain\nĠMiss ing\nĠTro phy\nv ari\nran ch\nĠ3 01\n4 40\n00000000 00000000\nĠrest oring\nĠha ul\nuc ing\nner g\nĠfut ures\nĠstrateg ist\nquest ion\nĠlater al\nĠB ard\nĠs or\nĠRhod es\nĠD owntown\n????? -\nĠL it\nĠB ened\nĠco il\nst reet\nĠPort al\nFI LE\nĠG ru\n* ,\n23 1\nne um\nĠsuck ed\nĠr apper\nĠtend encies\nĠLaure n\ncell aneous\n26 7\nĠbrow se\nĠover c\nhead er\no ise\nĠbe et\nĠG le\nSt ay\nĠm um\nĠtyp ed\nĠdiscount s\nT alk\nĠO g\nex isting\nĠS ell\nu ph\nC I\nĠAust rian\nĠW arm\nĠdismiss al\nĠaver ages\nc amera\nĠalleg iance\nL AN\n=\" #\nĠcomment ators\nĠSet ting\nĠMid west\nĠpharm ac\nĠEX P\nĠstain less\nCh icago\nĠt an\n24 4\nĠcountry side\nĠV ac\n29 5\nĠpin ned\nĠcr ises\nĠstandard ized\nT ask\nĠJ ail\nĠD ocker\ncol ored\nf orth\n\" },\nĠpat rons\nĠsp ice\nĠm ourn\nĠM ood\nĠlaund ry\nĠequ ip\nĠM ole\ny ll\nĠTH C\nn ation\nĠSher lock\nĠiss u\nĠK re\nĠAmeric as\nĠA AA\nĠsystem atically\nĠcont ra\nĠS ally\nĠrational e\nĠcar riage\nĠpe aks\nĠcontrad iction\nens ation\nĠFail ure\nĠpro ps\nĠnames pace\nĠc ove\nfield s\nãĤ ĭ\nĠw ool\nĠC atch\nĠpresum ed\nĠD iana\nr agon\nig i\nĠh amm\nĠst unt\nĠG UI\nĠObserv atory\nĠSh ore\nĠsmell s\nann ah\nĠcock pit\nĠD uterte\n8 50\nĠopp ressed\nbre aker\nĠCont ribut\nĠPer u\nĠMons anto\nĠAtt empt\nĠcommand ing\nĠfr idge\nĠR in\nĠChe ss\nual ity\nĠo l\nRepublic an\nĠGl ory\nĠW IN\n.... ...\nag ent\nread ing\nĠin h\nJ ones\nĠcl icks\nal an\nĠ[ ];\nĠMaj esty\nĠC ed\nop us\nate l\nà ª\nAR C\nĠEc uador\nãĥ ł\nĠK uro\nĠritual s\nĠcapt ive\nĠoun ce\nĠdisag reement\nĠsl og\nf uel\nP et\nM ail\nĠexerc ised\nĠsol ic\nĠrain fall\nĠdev otion\nĠAss essment\nĠrob otic\nopt ions\nĠR P\nĠFam ilies\nĠFl ames\nĠassign ments\n00 7\naked own\nĠvoc abulary\nRe illy\nĠc aval\ng ars\nĠsupp ressed\nĠS ET\nĠJohn s\nĠwar p\nbro ken\nĠstat ues\nĠadvoc ated\nĠ2 75\nĠper il\nom orph\nĠF emin\nper fect\nĠh atch\nL ib\n5 12\nĠlif elong\n3 13\nĠche eks\nĠnum bered\nĠM ug\nB ody\nra vel\nWe ight\nĠJ ak\nĠHe ath\nĠkiss ing\nĠJ UST\nĠw aving\nu pload\nĠins ider\nĠPro gressive\nĠFil ter\ntt a\nĠBe am\nĠviol ently\nip ation\nĠskept icism\nĠ19 18\nĠAnn ie\nĠS I\nĠgen etics\nĠon board\nat l\nĠFried man\nĠB ri\ncept ive\nĠpir ate\nĠRep orter\n27 8\nĠmyth ology\nĠe clipse\nĠsk ins\nĠgly ph\ning ham\nF iles\nC our\nw omen\nĠreg imes\nĠphotograp hed\nK at\nĠMA X\nOffic ials\nĠunexpected ly\nĠimpress ions\nF ront\n;;;; ;;;;\nĠsuprem acy\nĠs ang\nĠaggrav ated\nĠabrupt ly\nĠS ector\nĠexc uses\nĠcost ing\nide press\nSt ack\nĠR NA\nob il\nĠghost s\nld on\nat ibility\nTop ics\nĠreim burse\nĠH M\nĠDe 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ias\nĠBrig ade\nĠHann ah\nĠcler gy\nĠs outheast\nĠalcohol ic\nĠpropos es\nliv ion\nĠcalcul ating\nĠstim ulate\nĠspl itting\ne ight\nĠInd y\npl ays\nĠP ik\nĠdom est\nĠforg iveness\nĠR ings\npat ient\nkins on\nM ont\nig ible\n; \"\nĠperiod ically\namm ad\nĠBr itt\np ard\nĠarbit ration\nĠSchne ider\nĠCorpor ate\nĠMay a\nĠsn akes\na um\nĠbl asted\nĠmyster ies\nĠrev ive\noc amp\nĠD odge\nĠOper a\n27 9\nĠor phan\nĠspec ifies\nĠM ets\nD uration\nH en\nĠfire works\nĠprosec ute\nĠTill erson\nd p\nus age\nl iness\nĠDeb ian\nĠ2 24\nris es\nĠIn fect\nat ra\nĠR R\nĠL or\nd iff\nĠCharl eston\nĠac oustic\nĠam use\n3 30\nĠc er\nĠT ac\nĠ[ +\nĠcard iac\nĠRestaur ant\ner gy\nĠf uzz\nĠbit es\nĠhazard ous\nĠbr ighter\nr ans\nĠStephan ie\next ra\nRE T\nĠChrist ine\nĠS ue\nstat ement\nĠbol ster\nĠant it\nRad io\nB IT\nãĤ °\nĠvis ions\nĠCon cept\nĠin line\nĠPhilos ophy\nis ans\nĠIr ving\nà £\nt aking\nĠincons ist\nĠKum ar\nĠl ig\nĠSch umer\nĠReg ulations\nĠH z\nth ro\nĠV oldemort\nĠM ED\nĠFreder ick\nP ad\n22 1\nĠalleg ing\nĠCommun ication\nĠ16 7\nĠforecast s\nĠsp iders\nOr gan\nĠParticip ants\nĠO ps\ndes ign\nCl ose\nĠfact o\nĠbom bers\nres istant\nateg ories\nS chool\nĠhom ework\nĠcor ro\nT uesday\nĠBrend an\nĠM X\nĠT S\nĠSt ri\nĠstake holders\nĠMillenn ium\nĠtransfer ring\nJ ud\nĠt ac\nĠ16 00\nĠSD K\nr b\nĠinterpret ations\nĠS G\nĠup stairs\nĠHar vest\nĠvag ina\nĠing est\nx f\nĠOr ion\nĠJoe y\nĠsand wic\nĠimm ortal\nĠfl ipped\nort ex\nthreat ening\nĠsn iper\nĠconver ts\nĠinstall ations\nĠBul gar\nors che\nm ails\nĠl ure\nĠnarrow ly\nĠgren ade\nĠG ing\nĠunder wear\n------------ --\nĠch ased\nĠV AL\nĠparent ing\nĠH amb\nĠBl az\nĠanarch ist\nĠMed ian\nĠProgram s\nÎ ½\nĠob j\nĠN okia\norm an\nan qu\nat ism\nop a\nĠfulf illing\nĠpupp y\nĠent it\nĠSebast ian\nĠshoot ers\nĠric her\nè ¡\nĠtempt ed\nĠAT T\nĠC V\nĠto re\nRes ource\nĠDevil s\n40 8\nin ational\nĠass urance\nĠDar ren\nĠwh ichever\npos ure\nĠf ury\nSt ock\nĠunivers ally\nresp onse\nĠo ak\nĠwork load\nĠCor ner\nee le\n\" ...\nĠdepri ved\nk owski\nĠcast s\nĠaffili ation\nĠA ch\nĠAs ked\nat he\nĠl act\nĠTh u\nr m\nĠair lines\nĠnot ions\nForm at\nĠF AA\nãĥ Ĭ\ndri ver\nĠtrans cend\nS ettings\nĠPro secut\nĠsp inal\nĠdefault s\nF K\nĠpref ers\nrend ered\nth us\nfil m\nĠt iger\nĠSp icer\nrec ogn\nĠRug by\nNet work\nĠp ity\nĠcomp artment\nc asters\nĠMon roe\nĠ7 20\nĠcorrect ions\nĠdop amine\nĠA Z\nC ut\nĠro omm\nĠspec ulate\nH ash\nĠrestrict ive\n11 11\nred ible\non el\nĠramp ant\nre ported\nĠSu ite\nĠMin imum\nal ys\naz ard\nlo op\nĠl ent\nsh a\nĠv andal\nmen u\nĠBoe hner\nĠnarr atives\nĠauthent icity\n26 9\nan ic\nd uty\n28 5\nĠthank ed\nĠbetray ed\nl ift\nĠsouth west\nĠDex ter\nĠB od\nĠkey words\nA verage\nD IS\nĠethnic ity\n! ),\nĠNational s\ná ¹\nĠT ah\niox id\nĠwid get\nĠpast a\nĠbill ing\nĠtr ilogy\nĠL ines\nĠsn iff\nĠnep hew\nL ate\nĠprinc ip\nĠLo op\nĠMarx ist\nĠdiss olved\nĠcontext s\nĠAm ount\nĠSp ike\nĠtot als\nĠorgan izer\nĠup rising\ns hips\nY Y\nĠNort heast\nm oney\ngrad ation\nĠgoal keeper\nĠH ear\nĠste ak\nĠBuzz Feed\nĠsole mn\nĠSc and\nĠpo pping\nĠad here\nĠAl leg\nby te\nĠW olver\nĠun in\nĠrec ol\nit ud\nĠmim ic\nib us\nĠpredict s\nĠKee per\ni ating\nĠde ception\nĠlear nt\nĠdi ary\nĠcond itional\nĠre lic\nĠinv oke\nien ced\nå Ī\nĠP ont\nĠcell phone\nĠspeed ing\nĠtack ling\nĠn ude\nop ened\nĠMan afort\nĠ19 52\nĠmaj ors\nĠSil ence\nĠlog istics\nĠweight ed\nĠPsych iat\n\": [\"\nĠsick ness\nĠdivid ends\nz on\nRe lease\nĠKe ys\nĠI ch\nĠen z\nĠF ernand\nĠÎ ±\nĠmean ings\nĠp enny\nĠst ern\nĠl ar\nĠPub lished\nĠback drop\nK im\nĠSy nt\nĠdeb uted\nw m\nĠIs le\nĠregul ating\nott i\nĠSch olars\nices ter\nĠChe f\nĠpop s\nĠLaun cher\nĠVar ious\nĠcomment ing\nos lav\nenz ie\nĠrival ry\nâ Ĥ¬\nRe ally\nĠor c\nĠbe an\nĠJud y\nNot ice\nĠB ike\n? ]\nĠrent ed\nst en\nĠfore front\nĠBald win\nĠyield ed\nt ails\nPr ime\nĠS ources\nic ator\nSe an\nĠmarch ing\nOut put\nĠJ ungle\nĠres ide\nzz le\nĠAndrew s\nĠtor que\nBas ic\nAct ually\nst rap\np enter\nĠexam s\nĠY a\nĠ15 9\nĠDec ision\nĠr ansom\nete enth\nens ing\n2 13\nĠsun set\n40 4\nĠRap id\nĠHe in\nĠAb original\nĠorgan ism\nĠS ever\nĠcl a\naj i\nSim ple\nĠFl avor\nĠE val\npr us\nĠch orus\nD AY\nĠden ounced\nĠbi ography\nĠTurn bull\nRec ent\nN ormal\nlect ions\nW ord\nĠf erry\nĠWag ner\nh om\nUn it\nĠsuper market\nĠS ith\nĠnomine es\nĠdictators hip\nidd ler\nĠannoun ces\nĠThe m\nĠNept une\nĠde ity\nĠY i\nĠmon arch\nAR R\nĠinv aded\nĠH ok\nunt ary\nC ertain\neg a\nĠk idding\nĠReg ulation\nĠtr ay\nĠphotograp hers\nĠArc ane\nĠdis charged\nĠevangel ical\nĠinter change\nĠfilm maker\nĠEnd less\nĠ29 0\nĠSalv ador\nAS Y\nĠSign al\nĠwr ath\nâ ľ\nl ot\n' /\nĠproject ile\nĠemploy ing\nĠInter face\n19 1\natell ite\nĠR ath\npack age\nĠindic ations\nJ ason\nĠarg s\nĠG Hz\nĠt ilt\nn ants\nw on\nãĤ µ\nred d\nres cent\nĠCal endar\nĠmod ular\nĠassist ing\nĠred eem\nĠBe an\nĠwor sh\nĠdecentral ized\n) ...\n37 7\nĠarr ays\nĠaccomplish ments\nÎ ¿\nd ot\nĠmut ually\nĠob struct\nĠmis represent\nore st\nion ic\nru ce\n% ;\nĠknow ingly\nport ing\nin ently\nA ri\nĠSch ultz\nD a\nĠC ere\nĠob solete\nħ ĭ\ng ive\nĠb ait\nĠen larg\nNe ill\nĠ19 33\nĠrecons ider\nĠSerge ant\nĠDian e\nĠC ogn\nĠI con\nP osition\nĠf ost\nĠstir ring\nse ven\nĠSpace X\nugg ets\nĠmed d\nG al\nĠS ister\nB oy\nĠtrigger ing\nT aking\nĠscream s\nĠca usal\nĠaw aken\nAr m\n29 7\nĠdisp atched\nĠF ALSE\nĠorgan izational\nĠT ong\nĠdile mma\nd emon\nS pl\nĠhook s\nud ing\nĠvalid ate\nĠpot ion\nĠcl aw\nĠburg l\nĠqu ir\nAC A\nĠBren nan\nĠdur ability\nĠbomb ings\nĠWind ow\nĠculp rit\n3 25\nThere fore\numb ered\nper formance\nw arts\nĠen forcing\nĠBl ow\nĠre print\nif ax\nal pha\nĠsin ister\nĠbur ger\nfight ing\nSc ore\nĠSt ones\ni em\n40 5\nche my\nĠvine gar\nn om\nĠprev ailing\nĠLat est\n ¶\nĠb a\nĠWrit er\nĠ17 7\nĠCon way\nĠcollect s\nĠquant itative\nĠhor rors\nog ens\nĠSl ov\nĠl ays\nh aw\nĠSl ash\nĠnight club\nĠDav ies\nĠbr ide\nĠScar let\ny mm\nĠApplic ations\nvel ength\nĠrev ival\nĠsoft ly\nĠz oo\nita ire\nC ur\nĠelect rom\nĠplant ing\nOT O\nĠE lements\nĠsw allow\npor ter\nĠlapt ops\nĠpe anut\nĠlobby ists\nÎ ²\nPan el\nĠJo an\nim il\nt nc\nĠresist ed\nĠout we\nĠret aining\nat ri\nĠpo orer\nĠSyri ans\nĠHam mond\nĠwe ld\nud er\ntop ic\nĠT T\nric ia\nĠth ieves\nL ic\nĠG ust\nĠW ays\nare th\n24 3\nĠbroad caster\nsh ield\nass ium\nub le\nĠairst rikes\non so\nĠped al\nĠcollect ors\nĠV ander\nĠMes a\nĠdict ator\nĠd ir\nent on\nc art\nsc ore\nad der\nC ry\nĠs sh\ngg er\nĠdrunk en\nĠG S\nĠSe at\nĠcorner back\nĠsk ipped\nĠRes earchers\nĠAud i\nRef erence\nĠhaun ted\nà «\nĠClin ic\nc z\nĠp s\nĠPal adin\nĠRec ipe\nĠst igma\nopp y\nĠmon keys\nĠHaw k\nS ad\n\" />\nĠWorks hop\nĠRet ail\nĠAv atar\n6 25\nN a\nĠV C\nĠSec ure\nM Y\n19 88\noss ip\nĠpro state\nĠund en\nĠg amer\nĠCont ents\nĠWar hammer\nĠSent inel\n3 10\nĠse gregation\nĠF lex\nĠM AY\nĠdr ills\nĠDrug s\nIslam ic\nĠsp ur\nĠca fe\nĠimag inary\nĠgu iding\nĠsw ings\nĠThe me\nob y\nĠn ud\nĠbe gging\nĠstr ongh\nĠreject ing\nĠpedest rians\nĠPro spect\nR are\ns le\nĠconcess ions\nĠConst itutional\nĠbe ams\nĠfib ers\np oon\nĠinstinct s\npro perty\nĠB IG\nSand ers\nim ates\nĠco ating\nĠcorps es\nĠTR UE\ncheck ed\nĠ16 6\nA sh\nĠJ S\nĠF iction\nĠcommun al\nĠener getic\noooo oooo\nĠnow adays\nIL D\nib o\nĠSU V\nR en\nĠdwell ing\nSil ver\nĠt ally\nĠM oving\nĠcow ard\nĠgener als\nĠhorn s\nĠcirc ulated\nĠrob bed\nĠUn limited\nĠharass ed\nĠinhib it\nĠcomp oser\nĠSpot ify\nĠspread s\n3 64\nĠsu icidal\nĠno ises\nĠSt ur\nĠs aga\nĠK ag\nis o\nĠtheoret ically\nM oney\nĠsimilar ity\nĠslic ed\nut ils\ning es\n\" -\nĠan th\nĠimp ed\nMod ule\nThrough out\nĠmen us\ncomm ittee\nand i\nob j\nin av\nf ired\nĠAb dullah\nĠund ead\nĠfont s\nH old\nEN G\nĠsustain ability\nĠfl ick\nĠr azor\nĠF est\nĠChar acters\nĠword ing\nĠpopul ist\nĠcritic izing\nĠm use\nv ine\nĠcard board\nĠkind ly\nĠfr inge\nĠThe ft\nicult ural\nĠgovern ors\nĠ ����\nĠ16 3\nĠtime out\nĠA uth\nChild ren\nA U\nĠred emption\nĠAl ger\nĠ19 14\nĠw aved\nĠastron auts\nog rams\nĠsw amp\nĠFinn ish\nĠcand le\nĠton nes\nut m\nĠr ay\nĠsp un\nĠfear 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ography\n24 2\nĠut most\nĠEx xon\nab outs\nE VA\nĠEn able\nĠBar r\nĠdisag reed\nĠCy prus\nĠdement ia\nĠlab s\nĠubiqu itous\nĠLO VE\nĠconsolid ated\ns r\nĠcream y\nĠTim ber\nReg ardless\nĠCert ificate\nĠ\" ...\nogen ous\nCapt ain\nĠinsult ing\nĠSor os\nĠInst r\nĠBulgar ia\nbet ter\nĠsuck ing\nĠDavid son\nat z\nĠcoll ateral\ng if\nĠplag ued\nĠC ancel\nĠGard ner\nR B\nĠsix teen\nRem ove\nur istic\nc ook\nR od\nĠcompr ising\nf le\n) âĢĶ\nĠVik ing\ng rowth\nagon al\nĠsr f\naf ety\nm ot\nN early\nst own\nĠF actor\nĠautom obile\nĠproced ural\nm ask\namp ires\nĠdisapp ears\nj ab\n3 15\nĠ19 51\nne eded\nĠd aring\nle ader\nĠp odium\nĠun healthy\nĠm und\nĠpy ramid\noc re\nĠkiss ed\nĠdream ed\nĠFant astic\nĠG ly\nå Ĭ\nĠgreat ness\nĠsp ices\nĠmet ropolitan\nĠcomp uls\ni ets\n101 6\nĠSh am\nĠP yr\nfl ies\nĠMid night\nĠswall owed\nĠgen res\nĠL ucky\nĠRew ards\nĠdisp atch\nĠI PA\nĠApp ly\nĠa ven\nal ities\n3 12\nth ings\nĠ( ).\nĠm ates\nĠS z\nĠC OP\nol ate\nO FF\nĠre charge\nc aps\nĠYork er\nic one\nĠgal axies\nile aks\nD ave\nĠP uzz\nĠCelt ic\nĠA FC\n27 6\nĠS ons\nĠaffirm ative\nH or\nĠtutorial s\nĠC ITY\nĠR osa\nĠExt ension\nSer ies\nĠf ats\nĠr ab\nl is\nĠun ic\nĠe ve\nĠSp in\nĠadul thood\nty p\nĠsect arian\nĠcheck out\nĠCy cl\nS ingle\nĠmart yr\nĠch illing\n88 8\nou fl\nĠ] ;\nĠcongest ion\nm k\nĠWhere as\nĠ19 38\nur rencies\ner ion\nĠbo ast\nĠPat ients\nĠch ap\nĠB D\nreal DonaldTrump\nĠexam ines\nh ov\nĠstart ling\nĠBab ylon\nw id\nom ew\nbr ance\nĠOd yssey\nw ig\nĠtor ch\nĠV ox\nĠMo z\nĠT roll\nĠAn s\nSimilar ly\nĠF ul\n00 6\nUn less\nĠAl one\nst ead\nĠPub lisher\nr ights\nt u\nĠDoes n\nĠprofession ally\nĠcl o\nic z\nĠste als\nĠ á\n19 86\nĠst urdy\nĠJoh ann\nĠmed als\nĠfil ings\nĠFr aser\nd one\nĠmult inational\nĠf eder\nĠworth less\nĠp est\nYes terday\nank ind\nĠg ays\nĠb orne\nĠP OS\nPict ure\nĠpercent ages\n25 1\nr ame\nĠpot ions\nAM D\nĠLeban ese\nĠr ang\nĠL SU\nong s\nĠpen insula\nĠCl ause\nAL K\noh a\nĠMac Book\nĠunanim ous\nĠl enders\nĠhang s\nĠfranch ises\nore rs\nĠUp dates\nĠisol ate\nand ro\nS oon\nĠdisrupt ive\nĠSur ve\nĠst itches\nĠSc orp\nĠDomin ion\nĠsupp lying\nAr g\nĠtur ret\nĠL uk\nĠbr ackets\n* )\nĠRevolution ary\nĠHon est\nĠnot icing\nĠSh annon\nĠafford ed\nĠth a\nĠJan et\n! --\nĠNare ndra\nĠPl ot\nH ol\nse ver\ne enth\nĠobst ruction\nĠ10 24\nst aff\nj as\nor get\nsc enes\nl aughs\nĠF argo\ncr ime\nĠorche str\nĠde let\nili ary\nrie ved\nĠmilit ar\nĠGreen e\nâĹ ı\nãģ ¦\nĠGu ards\nĠunle ashed\nĠWe ber\nĠadjust able\nĠcal iber\nĠmotiv ations\nĠà ł\nm Ah\nĠL anka\nhand le\nĠp ent\nĠR av\nĠAng ular\nĠK au\numb ing\nĠphil anthrop\nĠde hyd\nĠtox icity\ne er\nĠY ORK\nw itz\nå ¼\nĠI E\ncommun ity\nĠA H\nĠret ali\nĠmass ively\nĠDani els\nĠD EL\nĠcar cin\nUr l\nĠrout ing\nĠNPC s\nĠR AF\nry ce\nĠwa ived\nĠGu atem\nEvery body\nĠco venant\nĠ17 3\nĠrelax ing\nĠqu art\nal most\nĠguard ed\nĠSold iers\nĠPL AY\nĠout going\nL AND\nĠre write\nĠM OV\nĠIm per\nĠS olution\nĠphenomen al\nĠl ongevity\nĠimp at\nĠN issan\nir ie\nĠod or\nĠZ ar\nok s\nĠmilit ias\nĠSP EC\nĠtoler ated\nars er\nĠBrad ford\n+ ,\nĠsur real\ns f\nCan adian\nĠresemb lance\nĠcarbohyd rate\nVI EW\nĠaccess ory\nme al\nlarg est\nieg el\nSome one\nĠtoug hest\nos o\nĠfun nel\nĠcondemn ation\nlu ent\nĠw ired\nĠSun set\nJes us\nĠP ST\nĠP ages\nĠTy coon\nĠP F\nĠselect ions\nĠ à¤\npart isan\nĠhigh s\nĠR une\nĠcraft s\nle ad\nĠParent s\nĠre claim\nek er\nĠAll ied\nae per\nĠlo oming\nĠbenefic iaries\nĠH ull\nStud ents\nJew ish\nd j\nĠp act\ntem plate\nĠOffic ials\nĠBay lor\nĠhe mp\nĠyouth s\nĠLevel s\nĠX iao\nĠC hes\nĠende avor\nĠRem oved\nĠhipp ocamp\nH ell\nãĤ Ĭ\n80 5\nĠd inosaur\nĠWr ath\nĠIndones ian\nĠcalcul ator\nĠD ictionary\nĠ4 20\nĠM AG\n( _\n! ,\nt arians\nĠrestrict ing\nrac use\nĠweek day\nOU NT\nĠsh rugged\nleg round\nĠb ald\nĠDo ctors\nĠt outed\nĠMax well\nĠ2 14\nĠdiplom at\nĠrep ression\nĠconstitu ency\nv ice\nr anked\nĠNap oleon\ng ang\nĠFore ver\nt un\nĠbul b\nĠPD T\nĠC isco\nV EN\nĠres umed\nSte ven\nĠManit oba\nĠfab ulous\nĠAg ents\n19 84\nĠam using\nĠMyster ies\nĠor thodox\nfl oor\nĠquestion naire\nĠpenet rate\nĠfilm makers\nĠUn c\nĠst amped\nĠth irteen\nĠout field\nĠforward ed\nĠapp ra\nĠa ided\nt ry\nĠunf ocused\nĠL iz\nĠWend y\nĠSc ene\nCh arg\nĠreject s\nĠleft ist\nĠProv idence\nĠBr id\nreg n\nĠprophe cy\nĠL IVE\n4 99\nĠfor ge\nĠF ML\nĠintrins ic\nĠF rog\nĠw ont\nĠH olt\nĠfam ed\nCL US\naeper nick\nĠH ate\nĠC ay\nĠregister ing\nort ality\nrop y\nocaly ptic\na an\nn av\nĠfasc ist\nIF IED\nĠimpl icated\nĠRes ort\nĠChand ler\nĠBr ick\nP in\nys c\nUs age\nĠHel m\nus ra\nâĺħ âĺħ\nĠAb bas\nĠunanim ously\nĠke eper\nĠadd icted\n?? ?\nĠhelm ets\nĠant ioxid\naps ed\n80 8\ngi ene\nĠwa its\nĠmin ion\nra ved\nĠP orsche\nĠdream ing\nĠ17 1\nĠC ain\nĠun for\nass o\nĠConfig uration\nk un\nhard t\nĠn ested\nĠL DS\nL ES\nĠt ying\nen os\nĠc ue\nĠMar qu\nsk irts\nĠclick ed\nĠexp iration\nĠAccording ly\nĠW C\nĠbless ings\nĠaddict ive\nĠN arr\ny x\nĠJagu ars\nĠrent s\nĠS iber\nĠt ipped\nous se\nĠFitz gerald\nĠhier arch\nout ine\nĠwa velength\n> .\nch id\nĠProcess ing\n/ +\nr anking\nE asy\nĠConst ruct\nĠt et\nins ured\nH UD\nĠqu oting\nĠcommun icated\nin x\nĠin mate\nĠerect ed\nĠAbs olutely\nĠSure ly\nĠun im\nĠThr one\nhe id\nĠcl aws\nĠsuper star\nĠL enn\nĠWh is\nU k\nab ol\nĠsk et\nĠN iet\nĠper ks\nĠaff inity\nĠopen ings\nphas is\nĠdiscrim inate\nT ip\nv c\nĠgr inding\nĠJenn y\nĠast hma\nhol es\nĠHom er\nĠreg isters\nĠGl ad\nĠcre ations\nĠlith ium\nĠappl ause\nunt il\nJust ice\nĠTur ks\nĠsc andals\nĠb ake\nt ank\nM ech\nĠMe ans\nĠM aid\nRepublic ans\nis al\nwind ows\nĠSant os\nĠveget ation\n33 8\nt ri\nĠfl ux\nins ert\nĠclar ified\nĠmort g\nĠCh im\nĠT ort\nĠdiscl aim\nmet al\nĠAs ide\nĠindu ction\nĠinf l\nĠathe ists\namp h\nĠe ther\nĠV ital\nĠBu ilt\nM ind\nĠweapon ry\nS ET\nĠ18 6\nad min\ng am\ncont ract\naf a\nĠderiv atives\nĠsn acks\nĠch urn\nE conom\nĠca pped\nĠUnder standing\nĠH ers\nĠI z\nĠd uct\nI ENT\naugh ty\nĠâľ Ķ\nĠN P\nĠsa iling\nIn itialized\nĠt ed\nĠreact ors\nĠL omb\nĠcho ke\nĠW orm\nĠadm iration\nĠsw ung\nens ibly\nĠr ash\nĠGo als\nĠImport ant\nSh ot\nĠR as\nĠtrain ers\nĠB un\nWork ing\nĠhar med\nĠPand ora\nĠL TE\nĠmush room\nĠCH AR\nĠF ee\nĠM oy\nB orn\nol iberal\nĠMart ial\nĠgentle men\nĠling ering\nOffic ial\nĠgra ffiti\nĠN ames\nD er\nĠqu int\nist rate\naze era\nĠNOT ICE\nĠFlore nce\nĠpay able\nĠdep icts\nĠSpe cies\nHe art\nâĶĢâĶĢâĶĢâĶĢ âĶĢâĶĢâĶĢâĶĢ\nĠencl osed\nIncre ases\nD aily\nĠL is\nĠenact ment\nĠB acon\nĠSt eele\ndem and\nĠ18 3\nĠmouth s\nĠstr anded\nĠenhance ment\n01 1\nĠWh ats\nĠhe aled\nen y\nĠR ab\nĠ3 40\nĠLab yrinth\nro ach\nĠY osh\nĠCl ippers\nĠconcert s\nIntern et\n35 5\nĠstick ers\nĠter med\nĠAx e\nĠgrand parents\nFr ance\nĠCl im\nĠU h\nul ic\nĠthr ill\ncent ric\nĠOver view\nĠCond uct\nĠsubstant ive\nĠ18 2\nm ur\nĠstr ay\nĠCo ff\nĠrep etitive\nĠFor gotten\nĠqual ification\new itness\nĠZ imbabwe\nĠsim ulated\nĠJ D\n25 3\nĠW are\nĠun sc\nT imes\nĠsum mons\nĠdis connected\nĠ18 4\nci us\nĠGu jar\nod ka\nĠer ase\nĠTob acco\nelect ed\nĠun cont\nĠShe pard\nĠL amp\nĠalert ed\nĠoper ative\narn a\nu int\nĠneglig ence\nac ements\nĠsup ra\nĠprev ail\nĠSh ark\nĠbel ts\nãģ «\nĠt ighter\nEngine ers\nĠin active\nĠexp onent\nĠWill ie\na ples\nĠhe ir\nĠH its\nian n\nĠS ays\nĠcurrent s\nĠBeng al\nĠar ist\nB uffer\nĠbree ze\nĠWes ley\nCol a\nĠpron oun\nĠde ed\nĠK ling\nĠof t\nĠinf lict\nĠpun ishing\nĠn m\nik u\nOD UCT\n01 4\nĠsubsid y\nĠDE A\nĠHer bert\nĠJ al\nB ank\nĠdef erred\nĠship ment\nB ott\nĠal le\nb earing\nHT ML\nOff line\nĠ2 13\nĠscroll ing\nĠsc anned\nĠLib yan\nĠT OP\nch rom\nd t\ncol umn\nPsy NetMessage\nZ ero\nĠtor so\n0 50\nâķ IJ\nĠimp erson\nĠSchw artz\nud ic\nĠpiss ed\nĠS app\n25 7\nĠIS Ps\nog l\nĠsuper vised\nĠad olescent\nĠatt ained\nĠDel ivery\nĠB unny\nĠ19 37\nĠmini ature\nĠo s\nĠ3 70\n60 8\nĠMour inho\nĠinn ate\nĠtem po\nĠN M\nĠFall en\n00 9\nĠprov ocative\nStream er\nĠBened ict\nĠBol she\nĠt urtle\nĠPC B\nĠEqu al\nDirect or\nĠR end\nĠflu ids\nAuthor ities\nĠcous ins\nrequ ency\nĠNeigh bor\ns ets\nsh ared\nChar les\npass word\nĠg ears\nĠ2 11\nĠHard ware\nri ka\nĠup stream\nH om\nĠdisproportion ately\niv ities\nĠund efined\nĠelect rons\nĠcommem or\nEvent ually\nĠ> <\nĠir responsible\n2 18\nĠRe leased\nĠO VER\nĠI GN\nĠB read\nst ellar\nĠS age\ntt ed\ndam age\ned ition\nĠPre c\nĠl ime\nĠconf inement\nĠcal orie\nwe apon\nĠdiff ering\nĠS ina\nm ys\nam d\nĠintric ate\nk k\nĠP AT\nã o\nst ones\nlin ks\nĠr anch\nSem itic\nĠdifferent iate\nĠS inger\noccup ied\nĠfort ress\nc md\nĠinter ception\nĠAnk ara\nĠre pt\nĠSol itaire\nĠrem ake\np red\nĠd ared\naut ions\nĠB ACK\nRun ning\nĠdebug ging\nĠgraph s\n3 99\nĠNig el\nĠb un\nĠpill ow\nĠprog ressed\nfashion ed\nĠob edience\nER N\nĠrehe ars\nC ell\nt l\nS her\nĠher ald\nĠPay ment\nĠC ory\nĠDe pt\nĠrep ent\nĠWe ak\nuck land\nĠple asing\nĠshort ages\nĠjur ors\nĠK ab\nq qa\nAnt i\nĠw ow\nĠRC MP\nĠt sun\nĠS ic\nĠcomp rises\nĠsp ies\nĠprec inct\nn u\nĠur ges\nĠtim ed\nĠstrip es\nĠB oots\nĠy en\nAdv anced\nĠdisc rete\nĠArch angel\nemploy ment\nD iff\nĠmon uments\nĠ20 9\nwork er\nĠ19 6\nĠI g\nutter stock\nT PS\nJ ac\nĠhomeless ness\nĠcomment ator\nĠrac ially\nf ing\nse ed\nE le\nell ation\nĠeth anol\nĠpar ish\nĠD ong\nĠAw akening\nĠdev iation\nĠB earing\nĠTsu k\nĠrec ess\nĠl ymph\nĠCann abis\nå ľ\nĠNEW S\nĠd ra\nĠStef an\nĠWr ong\nĠS AM\nĠloose ly\nĠinterpre ter\nĠPl ain\nGo vernment\nĠbigot ry\nĠgren ades\nave z\npict ured\nĠmand ated\nĠMon k\nĠPed ro\nĠl ava\n27 4\nĠcyn ical\nĠScroll s\nl ocks\nM p\nĠcon gregation\norn ings\nph il\nĠI bid\nĠf erv\nĠdisapp earing\nĠarrog ant\nsy n\nĠMa ver\nĠSu it\n24 1\nĠab bre\nack ers\nP a\nĠY el\nWhe never\nĠ23 5\nĠV ine\nĠAn at\nĠext inct\nLE T\nĠexecut able\nV ERS\nox ide\nD NA\nĠP rel\nĠresent ment\nĠcompr ise\nĠAv iv\nĠinter ceptions\nĠprol ific\nIN A\nĠEr in\nthough t\n2 19\nĠPsychiat ry\nun ky\nchem ist\nH o\nĠMcC oy\nĠbr icks\nL os\nri ly\nĠUS SR\nĠr ud\nĠl aud\nĠW ise\nĠEmer ald\nĠrev ived\nĠdam ned\nĠRep air\nid em\nct ica\nĠpatri arch\nĠN urs\nme g\nĠcheap est\nre ements\nempt y\nĠCele br\nĠdepri vation\nch anted\nĠTh umbnails\nE nergy\nĠEth an\nĠQ ing\nĠopp oses\nW IND\nv ik\nĠM au\nĠS UB\n66 7\nG RE\nĠVol unte\nnt on\nC ook\nå IJ\nes que\nĠplum met\nĠsu ing\nĠpron ounce\nĠresist ing\nĠF ishing\nĠTri als\nĠy ell\nĠ3 10\nĠin duct\nĠpersonal ized\noft en\nR eb\nEM BER\nĠview point\nĠexist ential\n() )\nrem ove\nMENT S\nl asses\nĠev apor\nĠa isle\nmet a\nĠreflect ive\nĠentit lement\nĠdev ised\nmus ic\nasc ade\nĠwind ing\noff set\nĠaccess ibility\nke red\nBet ter\nĠJohn ston\nth inking\nS now\nĠCroat ia\nĠAt omic\n27 1\n34 8\nĠtext book\nĠSix th\nĠ اÙĦ\nĠsl ider\nĠBur ger\nb ol\nS ync\nĠgrand children\nĠc erv\n+ )\nĠe ternity\nĠtweet ing\nĠspec ulative\nĠpiv otal\nĠW P\nĠT ER\nynam ic\nĠu pl\nĠC ats\nper haps\nĠclass mates\nĠblat ant\n' -\nĠl akh\nant ine\nĠB org\ni om\n/ (\nĠAthlet ic\nĠs ar\nOT A\nĠHoff man\nNever theless\nĠad orable\nĠspawn ed\nAss ociated\nĠDom estic\nĠimpl ant\nĠLux em\nĠK ens\nĠp umps\nĠS AT\nAtt ributes\n50 9\nav our\nĠcentral ized\nĠT N\nĠfresh ly\nĠA chieve\nĠouts iders\nher ty\nĠRe e\nĠT owers\nĠD art\nak able\nĠm p\nĠHeaven ly\nĠr ipe\nĠCarol ine\nry an\nĠclass ics\nĠret iring\nĠ2 28\nĠa h\nĠdeal ings\nĠpunch ing\nĠChap man\nO ptions\nmax well\nvol ume\nĠst al\nĠex ported\nĠQu ite\nĠnumer ical\nB urn\nF act\nĠKey stone\nĠtrend ing\nĠalter ing\nĠAfric ans\n47 8\nĠM N\nĠKn ock\nĠtempt ation\nĠprest ige\nOver view\nĠTrad itional\nĠBah rain\nPriv ate\nĠH OU\nĠbar r\nĠT at\nC ube\nUS D\nĠGrand e\nĠG at\nĠFl o\nĠres ides\nĠind ec\nvol ent\nĠperpet ual\nub es\nĠworld view\nĠQuant um\nĠfil tered\nĠen su\norget own\nERS ON\nĠM ild\n37 9\nOT T\nà ¥\nĠvit amins\nĠrib bon\nĠsincere ly\nĠH in\nĠeight een\nĠcontradict ory\nĠgl aring\nĠexpect ancy\nĠcons pir\nĠmon strous\nĠ3 80\nre ci\nĠhand ic\nĠpump ed\nĠindic ative\nĠr app\nĠav ail\nĠLEG O\nĠMar ijuana\n19 85\nert on\nĠtwent ieth\n################ ################\nĠSw amp\nĠval uation\nĠaffili ates\nadjust ed\nĠFac ility\n26 2\nĠenz ymes\nitud inal\nĠimp rint\nS ite\nĠinstall er\nĠT RA\nm ology\nlin ear\nĠCollect ive\nig ating\nĠT oken\nĠspec ulated\nK N\nĠC ly\nor ity\nĠdef er\nĠinspect ors\nappro ved\nR M\nĠSun s\nĠinform ing\nĠSy racuse\nib li\n7 65\nĠgl ove\nĠauthor ize\nâĢ¦âĢ¦âĢ¦âĢ¦ âĢ¦âĢ¦âĢ¦âĢ¦\nĠCru ise\nĠcontract ing\nshe ll\nIF E\nĠJew el\np ract\nĠPhot oshop\nĠKnow ing\nh arm\nĠattract ions\nad an\net us\n01 8\nw agen\nAl t\nĠmultip ly\nĠequ ilibrium\n: {\nĠF ighters\nĠEd gar\nĠfour teen\nGo vern\nĠmis use\nĠab using\nĠancest ry\nram er\n64 4\nĠwor ms\nĠthick er\nĠComb ine\nĠpeas ants\nĠv ind\nĠcon quest\nĠm ocked\nĠc innamon\nĠC ald\nĠGall up\nĠavoid ance\nĠincarn ation\nĠStr at\nĠt asted\nent a\nĠN eal\np ared\nĠtermin ology\nject ion\nScient ists\nĠIN S\nĠDe e\nĠdirect ories\nR oad\nĠSh ap\nbr ight\nĠDirect ors\nĠCol umn\nĠb ob\nĠprefer ably\nĠgl itch\nf urt\nĠe g\nid is\nC BC\nĠsur rendered\nĠtest ament\n33 6\nug gest\nĠN il\nan other\nĠpat hetic\nĠDon na\nĠ2 18\nĠA very\nĠwhis key\nĠf ixture\nĠCon quest\nĠbet s\nO cc\nĠLe icester\n] .\"\nĠ) );\nĠfl ashes\n45 6\nĠmask ed\nge bra\nĠcomput ed\nche l\naud er\nĠdefe ats\nĠLiber ation\nĠOs ama\nĠV ive\nCh anges\nCh annel\nĠtar iffs\nĠm age\nĠS ax\nĠinadvert ently\nĠC RE\nĠRe aper\nink y\ngr ading\nĠstere otyp\nĠcur l\nĠF ANT\nĠfram eworks\nM om\nĠAn ch\nĠflav our\ncar bon\nĠperm itting\nlet cher\nĠMo zilla\nĠPark ing\nĠCh amp\nSc roll\nĠmurd erer\nĠrest ed\nĠow es\nĠP oss\nAD D\nIF F\nres olution\nĠMin ing\nĠcompar ative\nD im\nĠneighbour ing\nĠA ST\nĠT oxic\nĠbi ases\nĠgun fire\nur ous\nĠMom ent\n19 83\nĠper vasive\ntt p\nĠNorm ally\nr ir\nS arah\nĠAlb any\nĠun sett\nĠS MS\nip ers\nl ayer\nĠWh ites\nup le\nĠtur bo\nĠLe eds\nĠthat s\nĠMin er\nM ER\nĠRe ign\nĠper me\nĠBl itz\nĠ19 34\nĠintimid ating\nt ube\nĠecc entric\nab olic\nbox es\nĠAssoci ates\nv otes\nĠsim ulate\num bo\naster y\nĠship ments\nFF FF\nan th\nĠseason ed\nĠexperiment ation\nâĸ ł\nlaw s\nMe et\nidd les\nant ics\nR ating\nIS IS\nh ift\nĠfront s\nb uf\n01 7\nĠun att\nĠD il\nle ases\nĠGard ens\n77 7\nt ouch\nve ll\n45 8\nĠ= ====\ns aving\nĠer osion\nĠQu in\nĠearn s\nĠaccomplish ment\nĠWe i\nĠ< [\n____ _\nĠir rig\nĠT eddy\nĠconqu ered\nĠArm ored\nĠassert s\nĠmanip ulating\nr é\nĠtranscript s\nG allery\nĠplot ting\nNe il\nĠbetray al\nload er\nĠS ul\nĠdispl acement\nĠroy alty\nĠW I\nhe it\nĠDev ices\nalle l\nĠmunicipal ities\nĠcan al\nSt ars\nĠU AE\nĠ\" âĢ¦\nĠC U\nab ove\nĠreson ance\nĠguiActive Un\nadd ed\nĠBra ves\nĠI bn\nĠhere by\nĠB RE\nĠshare holder\nĠH ir\nĠJ i\nĠstrange ly\nĠadm ired\nĠpl ight\nĠb achelor\nĠP ole\ncipl inary\nT ony\nĠArmen ian\nĠun man\nĠZion ist\nSt age\nisco ver\nĠautom otive\nĠs idelines\nĠsl ick\nĠRena issance\nĠF UN\nIm ages\nĠH aj\nĠp ing\nĠshort cut\nĠBl vd\nĠLook s\nĠbur sts\nĠcl amp\nĠm ish\nĠsort ing\nĠpatri ot\nĠcorrect ness\nĠScand inav\nĠCaval iers\np ython\naz ar\nĠ3 75\nĠJa une\n40 9\nĠdetrim ental\nĠstab bing\nĠpoison ed\nĠf ountain\noc ent\nor st\nĠMar i\nĠr ains\nĠO vers\nĠInst itution\nud get\nAM Y\nt ale\nĠK R\nĠPr ices\nĠhead aches\nĠlands l\nĠA ura\nBon 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alk\nĠbre thren\nĠlog os\nĠgo bl\nf ps\nĠsub division\nĠp awn\nĠsquee zed\nĠmor ale\nĠD W\n' \"\nĠkn ot\nook y\nĠdiv isive\nĠboost ed\nch y\nãĥ IJ\nif act\nĠnewcom ers\nĠWrest ling\nĠsc outs\nw olves\nR at\nĠnin eteenth\nĠOs borne\nSt ats\nĠem powered\nĠpsych opath\nĠO EM\nugg age\nĠP K\nĠMoh ammad\nP ak\nĠanarch ists\nĠExt ract\nest hes\nĠStock holm\nl oo\nĠG raph\nĠdeploy ing\nĠStr anger\nĠM old\nĠstaff er\nĠdiscount ed\nuck le\nple ase\nĠLand ing\nÃŃ a\nĠ19 3\nĠan te\nĠrep etition\nĠ+ /-\nĠpar ody\nĠlive ly\nAA A\nĠHor us\nĠp its\nind ers\nL OC\nĠVen ice\n40 6\nĠDis cover\nâ Ĩ\nellect ual\nĠp ens\nĠey el\nig uous\nIm pl\nĠj oking\nĠinv al\nĠBel fast\nĠcredit ors\nĠSky walker\nov sky\nĠcease fire\nĠse als\nis oft\n) ).\nĠFel ix\nIT S\nĠt resp\nĠBlock chain\new are\nĠSch war\nen ne\nmount ed\nĠBe acon\nles h\nĠimmense ly\nĠche ering\nEm ploy\nsc ene\nish ly\natche wan\nĠNic olas\nĠdr ained\nĠEx it\nĠAz erb\nj un\nĠflo ated\nu ania\nDe ep\nĠsuper v\nĠmyst ical\nĠD ollar\nĠApost le\nĠR 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ob\nĠdemol ition\nãĥ ĭ\nĠen listed\nrat ulations\nĠmis guided\nĠens ured\nĠdiscour aged\nm ort\nĠfl ank\nĠc ess\nĠreact s\nĠS ere\ns ensitive\nĠSer pent\nass ad\nĠ24 7\nĠcalm ly\nb usters\nĠble ed\nĠSt ro\nĠamuse ment\nĠAntar ctica\nĠs cept\nĠG aw\na q\nason ic\nĠsp rawling\nn ative\natur ated\nĠBattle field\nIV ERS\nE B\nĠG ems\nĠNorth western\nĠFil ms\nĠAut omatic\nĠappre hend\nãģ ¨\nĠgui Name\nĠback end\nĠevid enced\nge ant\n01 2\nĠS iege\nĠexternal To\nĠunfocused Range\nĠguiActiveUn focused\nĠgui Icon\nĠexternalTo EVA\nĠexternalToEVA Only\nF ri\nch ard\nen aries\nĠchief s\nĠc f\nĠH UD\nĠcorro bor\nĠd B\nĠT aken\nĠPat ricia\nra il\nĠCh arm\nĠLiber tarian\nrie ve\nPerson al\nĠO UR\nger ies\nĠdump ing\nĠneurolog ical\nit imate\nĠClint ons\nraft ed\nĠM olly\nĠtermin als\nreg ister\nĠfl are\nĠenc oded\nĠautop sy\np el\nm achine\nĠexempt ions\nĠRoy als\nd istance\nĠdraft s\nĠl ame\nĠC unning\nĠsp ouses\nĠMark ets\nĠCar rier\nĠimp lying\nĠY ak\ns id\nĠl oser\nĠvigil ant\nĠimpe achment\nĠaug mented\nĠEmploy ees\nĠunint ended\ntern ally\nĠW att\nĠrecogn izable\ness im\næ Ŀ\nĠco ated\nr ha\nĠlie utenant\nĠLegisl ation\npub lished\n44 4\n01 3\nĠide ally\nĠPass word\nĠsimpl ify\nĠMet a\nĠM RI\nĠple ading\norgan ized\nhand ler\nĠun ravel\ncor rect\nĠ icy\nĠparan oid\nĠpass er\nĠinspect ions\nof er\nĠHealth care\n28 3\nĠBr ut\niol a\nfor ge\nĠMed ieval\nMS N\nie vers\nĠProgram ming\nå ī\nĠ2 23\nm u\nĠC LE\nug a\nĠsho ppers\nĠinform ative\nĠPl ans\nĠsupplement ation\nĠT ests\nty ard\nocy tes\nĠVeg a\nĠGujar at\nerman ent\nEx cept\nĠL OT\nall a\nĠC umm\nĠO sw\nĠven om\nĠDeb t\nĠD OWN\nĠreun ion\nĠm uc\nĠRel ief\nĠge op\nĠðŁ ĺ\nal ogue\nAn th\nech o\nĠcor ros\nĠrepl ication\nĠBl azing\nĠD aughter\nĠinf lic\nĠLind sey\nÙ Ī\n28 4\nEx it\nĠgl oom\nTA IN\nĠundermin ing\nĠadv ising\nh idden\nĠover flow\nĠg or\nurd ue\nĠe choes\nenh agen\nĠimp uls\nd rug\nc ash\nĠas ync\nĠmir ac\nat ts\np unk\nĠpiv ot\nĠLegisl ative\nĠblog gers\nĠCl aw\ns burg\nd yl\nĠRecomm end\nĠver te\nĠprohib iting\nĠPant her\nJon athan\nĠo min\nĠhate ful\n28 1\nĠOr che\nĠMurd och\ndown s\nĠas ymm\nG ER\nAl ways\nĠinform s\nĠW M\nĠP ony\nĠApp endix\nĠAr lington\nJ am\nĠmedic inal\nĠS lam\nIT IES\nĠre aff\nĠR i\nF G\nS pring\nb ool\nĠthigh s\nĠmark ings\nĠRa qqa\nĠL ak\np oll\nts ky\nĠMort y\nĠDef inition\nĠdeb unk\nend ered\nĠLe one\na vers\nĠmortg ages\nApp arently\nN ic\nha us\nĠTh ousands\nau ld\nĠm ash\nsh oot\nĠdi arr\nĠconscious ly\nH ero\ne as\nĠN aturally\nĠDestroy er\nĠdash board\nserv ices\nR og\nĠmillenn ials\nĠinv ade\n- (\nĠcomm issions\nĠA uckland\nĠbroadcast s\nĠfront al\nĠcr ank\nĠHist oric\nĠrum ours\nCT V\nĠster il\nĠboost er\nrock et\nãĤ ¼\nut sche\nĠP I\nĠ2 33\nĠProdu cer\nĠAnaly tics\nĠinval uable\nĠunint ention\nĠC Y\nĠscrut in\nĠg igg\nĠeng ulf\nĠprolet ariat\nĠh acks\nĠH ew\nar ak\nĠSl ime\nield ing\nag her\nĠEll iot\nĠtele com\nĠ2 19\nult an\nĠAr bor\nĠSc outs\nB an\nĠlifes pan\nĠbl asp\n38 8\nĠjud iciary\nĠContin ental\nask ing\nMc C\nL ED\nĠbag gage\nĠSorce rer\nĠrem nants\nĠGriff ith\nets u\nĠSub aru\nĠPerson ality\ndes igned\nush ima\nagn ar\nĠrec oil\nĠpass ions\n\\ \":\nĠte e\nĠabol ition\nĠCreat ing\nj ac\nĠ19 4\n01 9\nĠpill ars\nric hed\n/ \"\nt k\nĠlive lihood\nĠro asted\nah on\nĠH utch\nass ert\nĠdivid end\nĠkn it\nĠd aunting\nĠdisturb ance\nĠsh ale\nĠcultiv ated\nĠrefriger ator\nL B\nĠN ET\nĠcommercial s\nĠthink ers\n45 5\nĠch op\nB road\nĠsuspic ions\nĠtag ged\nl ifting\nĠsty lish\nĠShield s\nShort ly\nĠt ails\nA uth\nST E\nĠG AME\nĠse ism\nĠK is\nolog ne\nĠcow ork\nĠforc ibly\nĠthy roid\nĠP B\nAN E\nmar ried\nh orse\nĠpoly mer\nĠCh al\nod or\nDE BUG\nĠCon text\nĠbl iss\nĠpin point\nĠMat hemat\nleg ram\nĠWeek end\nĠlab elled\nĠb art\nit les\nĠest rogen\nâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶ âĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶ\n\" '\nĠvis ibly\nĠouts ider\naid a\nAre a\nĠdisse min\nĠdish onest\nĠCl osed\nĠBullet in\nĠRam sey\nsw ord\nĠX I\nour ced\nS ame\n34 6\nĠRe pe\nĠK ou\nc ake\nem is\nC ache\nĠMe aning\nĠEn light\nonom y\nĠmanifest ation\nsw orth\nJ ay\nĠch ore\nö r\nD ream\nĠsanction ed\nĠcult urally\nĠA ra\nN av\nĠthe ological\nĠstr ut\nĠV O\nĠHand book\nĠconstruct ing\nĠ ¶\nĠBenef its\nĠPsych ological\ns ac\nå ¸\np olicy\nĠMat ters\nĠReport ed\nĠBy te\nĠvit ro\nĠM aiden\nĠl am\nĠJenn ings\nĠgar ment\nĠRut gers\nĠStaff ord\nĠWell ington\nĠinter mitt\nĠn pm\nĠord eal\nĠplug ged\no oming\nin ished\nfram ework\nĠtim ber\nĠc ass\nĠ8 50\nil ess\nĠRed ux\n7 68\nSt re\nĠsurpass ed\nw hel\nĠparalle ls\nĠve il\nĠG I\nĠR EST\nĠread iness\ns ort\nĠmod ifying\nĠSl ate\nru ff\nĠmar ble\nĠinf rared\nĠaud itor\nĠFANT ASY\nĠP overty\nĠS PD\nĠ\" (\nK y\nRA Y\nĠexecut ions\nĠBever ly\nĠMarx ism\nĠBur st\nĠK ali\nest ones\nClear ly\nE ll\nãģ §\nĠProceed ings\nT oken\nIF IC\nñ a\nCent ral\nĠH aley\nĠD rama\nĠform ations\nOR N\nBook s\nĠdom inating\nĠFly ers\nĠCompan ion\nĠdiscipl ined\nĠYug oslav\nĠSpell s\nĠv engeance\nĠland lords\nL en\nĠO gre\nano ia\nĠpier cing\nĠcon greg\nĠscore r\nob ia\nĠnic kel\nĠLear ns\nĠre jo\nĠmaster piece\nFl ash\nĠinhab ited\nĠOpen GL\nĠD ud\nĠI CO\nĠar ter\nĠpl ur\nĠmaster y\nĠlong standing\nst ed\nĠw ines\nĠtelev ised\nĠSh rine\nĠBay ern\nĠâ ĵĺ\nĠencl osure\nj ohn\nĠprophe ts\nĠRes urrection\nĠOrd ers\nĠun even\nr als\nĠd wind\nĠL ah\nĠSl oven\n37 8\nĠins istence\naff le\nĠCl one\nĠhard ship\nĠCongress man\nĠple ad\nĠreview ers\nĠc ured\nĠ19 35\nas ley\nf ake\nĠTh inking\nyd ia\nP ART\nĠD ota\no it\nĠwh ipped\nĠb ouncing\nĠHispan ics\ncom ings\nĠcann abin\nĠCh ambers\nĠZ ack\nOption al\nĠco ats\nĠprow ess\nĠNort on\nĠplain ly\nĠfre ight\nĠinhib ition\nĠcl am\nĠ30 3\nke f\nale igh\nL uke\nĠpsych o\nator ium\nM ED\nĠtreat ies\nĠind isc\nĠd c\nOP S\nĠresil ient\nĠInter state\nĠsl ack\nĠmund ane\nĠestab lishes\n35 9\nĠstr ained\nĠn ond\nS us\nĠcast e\nar ate\nie ving\nĠunfair ly\nĠpars er\non ial\nurs ive\nV ia\nĠOtt o\nĠAuthor ities\nstro ke\nK R\nĠMer cy\nĠfurn ished\nĠout set\nĠmet ic\n19 82\nolith ic\nĠT ent\nog ical\nĠA ircraft\nĠh ides\nĠBec ame\nĠeduc ators\nre aching\nĠvol atility\nĠtodd ler\nĠNAS CAR\nĠTw elve\nĠHigh lights\nĠgra pe\nĠspl its\nĠpe asant\nĠre neg\nĠMS I\nTem p\nst ars\nĠtre k\nĠHy de\nb inding\nĠreal ism\nĠox ide\nĠH os\nĠmount s\nĠbit ing\nĠcollaps ing\nĠpost al\nĠmuse ums\nĠdet ached\nĠrespect ing\nĠmonop ol\nĠwork flow\nĠC ake\nTem plate\nĠOrgan isation\nĠpers istence\n36 9\nC oming\nB rad\nĠredund ant\nĠG TA\nĠb ending\nĠrev oked\nĠoff ending\nĠfram ing\nĠprint f\nComm un\nmem bers\nOut side\nĠconst rued\nĠc oded\nF ORE\nĠch ast\nCh at\nInd ian\nĠY ard\n? !\"\nĠP orts\nĠX avier\nĠR ET\n' .\"\nĠBo at\niv ated\nich t\numer able\nD s\nĠDun n\nĠcoff in\nĠsecure ly\nĠRapt ors\nĠB es\nInstall ation\nĠin ception\nĠHealth y\nend ants\nĠpsych ologists\nĠShe ikh\nc ultural\nĠBlack Berry\nsh ift\nF red\noc he\nĠc akes\nĠS EO\nĠG ian\nĠAs ians\nog ging\ne lement\nĠpund its\nĠV augh\nĠG avin\nĠh itter\nĠdrown ed\nĠch alk\nĠZ ika\nĠmeas les\n80 2\nâĢ¦ ..\nĠAW S\n] \"\nĠdist ort\nĠM ast\nĠantib odies\nĠM ash\nMem ory\nĠUg anda\nĠPro b\nĠvom 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ed\nĠfet us\nResp onse\nĠAlex is\nth ia\nG uy\nĠrecon struct\nĠextrem es\nĠconclud ing\nĠP eg\nook s\nĠded uctions\nR ose\nĠground breaking\nĠT arg\nãĥ ģ\nĠRe ve\nres ource\nĠmo ons\nĠelectrom agnetic\nĠamid st\nĠVik tor\nN ESS\nB ACK\nĠcomm ute\nĠAna heim\nĠfluct uations\n6 40\nĠnood les\nĠCop enhagen\nĠT ide\nĠGri zz\nĠS EE\nĠpip elines\nĠsc ars\nend o\nag us\nĠE TF\n/ #\nĠBec ome\n44 8\nĠvis c\nĠRecomm ended\nĠj umper\nĠcogn ition\nĠassass in\nĠwitness ing\nĠSet up\nĠl ac\nv im\nIS M\np ages\nSS L\n35 8\nĠad ject\nindust rial\nl ore\ncher y\nĠgl itter\nĠc alf\nFlor ida\nĠspoil ers\nĠsucceed s\nĠch anting\nĠslog ans\nĠTr acy\nVis it\nrol ogy\nĠm ornings\nĠline age\nĠs ip\nĠintense ly\nĠflour ish\nĠSle eping\nĠF em\nor por\nĠK lan\nĠDar th\nh ack\nĠNi elsen\nĠtum ors\nĠprocure ment\nĠY orkshire\nĠra ided\nK Y\nAn na\nĠ// [\nĠDis order\nĠMust ang\nĠW en\nĠTry ing\ns q\nĠdeliver ies\nĠshut ter\nĠcere bral\nĠbip olar\nĠC N\nl ass\nj et\nĠdeb ating\n> :\nĠe agle\ngr ades\nĠD ixon\nUG C\nM AS\nĠDr aco\nĠMach ines\naff er\nĠem an\n ²\npr on\nĠG ym\nĠcompar atively\nĠTrib unal\nPR O\nĠle x\nĠfert ile\nĠdep ressing\nĠsuperf icial\ness ential\nĠHun ters\ng p\nĠprom inence\nL iber\nĠAn cest\note chnology\nĠm ocking\nĠTra ff\nĸ ļ\nMed ium\nI raq\nĠpsychiat rist\nQuant ity\nĠL ect\nĠno isy\n5 20\nG Y\nĠsl apped\nĠM TV\nĠpar a\np ull\nMult iple\nas her\nĠn our\nĠSe g\nSpe ll\nv ous\nord ial\nSen ior\nĠGold berg\nĠPl asma\nne ed\nĠmess enger\nere t\nĠteam ed\nĠliter acy\nĠLe ah\nĠD oyle\nĠem itted\nU X\nĠev ade\nĠm aze\nĠwrong ly\nĠL ars\nĠstere otype\nĠpled ges\nĠarom a\nĠM ET\nĠac re\nĠO D\nĠf f\nĠbrew eries\nĠH ilton\nund le\nĠK ak\nĠThank fully\nĠCan ucks\nin ctions\nĠApp ears\nĠco er\nĠundermin ed\nro vers\nAnd re\nĠbl aze\num ers\nĠfam ine\namp hetamine\nulk an\nAm ount\nĠdesper ation\nwik ipedia\ndevelop ment\nĠCor inth\nuss ia\nJack son\nL I\nN ative\nR s\nOh io\nĠKath leen\nF ortunately\nĠattend ant\nĠPre ferred\nĠDid n\nĠV s\nM is\nĠrespond ent\nĠb oun\nst able\nĠp aved\nĠunex pl\nĠChe ney\nL M\nĠC ull\nbl own\nĠconfront ing\noc ese\nserv ing\nW i\nĠLith uania\nann i\nĠst alk\nh d\nĠv ener\nAP H\nynchron ous\nUR R\num ably\nhist oric\nH alf\nH ay\nĠresil ience\nspe ction\nĠabandon ing\nO bs\nĠDeb bie\nĠgrad ient\nĠPl aint\nĠCan al\nAR CH\nĠexpans ive\nĠfun g\nĠb ounced\nU nd\nĠprec autions\nĠclar ification\nĠd agger\nĠgri ps\nĠ µ\nĠRiver a\nĠUnd ead\nis ites\nĠFIR ST\nñ o\naud i\nĠhost ages\nĠcompl iant\nĠal umni\nSe ven\nĠcyber security\ne ither\nCol lect\nĠinvari ably\nĠS oci\nĠlaw maker\nĠa le\nĠPerson ally\nN azi\nĠcustom ization\nĠPro c\nĠSask atchewan\neat uring\nĠsp ared\nĠdiscontin ued\nĠcomput ational\nĠMotor ola\nĠsuprem acist\ngovernment al\nĠparad ise\nĠDown ing\nĠNik on\nĠcat alyst\nber ra\nTor onto\n8 75\nbet a\nĠMac ron\nĠunreal istic\nve ctor\nĠVeh icles\nit iveness\nĠR V\nĠCol bert\ns in\no ji\nent in\nĠKr ish\nhell o\nff ield\nok y\nĠT ate\nĠmap le\nĠa ids\nchem ical\n33 4\nn uts\nĠWar p\nĠx x\nĠRob b\numer ous\n_- _\nft ime\nĠV W\nĠw inger\nĠD ome\nt ools\nĠP V\nĠGe orgetown\nĠg eared\nĠjihad ists\nĠc p\nĠster oids\nM other\ncler osis\nĠDR M\nnes ia\nĠl inger\nĠimm ersive\nĠC OUN\nĠoutwe igh\nens ual\nB and\nĠtransform s\nmat ched\nps ons\nĠJud icial\nf actor\nĠrefer ral\nĠodd ly\nĠW enger\nB ring\nĠB ows\n60 2\nIC LE\nĠl ions\nĠAcad emic\nĠTh orn\nĠRa ider\nkef eller\nSt orage\nL ower\nĠOr t\nĠEqu ality\nAL T\nĠS OC\nT ypes\nĠl yn\nĠAss et\nco at\nTP P\nC VE\nĠPione er\napp lication\nMod ern\nĠH K\nEn vironment\nAl right\nR ain\nIP P\nĠShi ite\nĠm ound\nĠAb ilities\ncond ition\nSt aff\nĠcompet ence\nĠM oor\nĠDi ablo\nĠwith held\nĠost ensibly\nĠB rom\nĠms g\nĠden omin\nĠRef erences\nĠF P\nĠplun ged\nĠp amph\nm oving\ncent ral\nĠdown right\nĠf ading\nT al\nT yp\nĠTh y\nuk es\nit he\nĠo ve\nĠbatt led\nĠseaf ood\nĠfig ur\nĠR D\nc rop\nĠsqu ads\n{ \\\nà ¹\nĠE h\nĠinterview ing\nĠQ in\nĠas piring\nPL IC\nĠcla uses\nĠG ast\nĠN ir\nĠl uggage\nĠh ose\nĠsystem d\nĠdesc ending\nĠRev ised\nĠR ails\nal ign\n70 9\n33 7\nĠf ug\ncharg ing\nt ags\nĠut er\nk ish\nWAR NING\n49 0\nprof its\nĠvoy age\nĠa ce\nĠV anguard\nĠT anks\nĠM uk\nĠ2 26\nS afe\nAr mor\nĠvolcan ic\nĠwom b\nĠM IL\nĠbegin ner\nĠRec ogn\nĠA AP\nPL AY\n) !\nĠdetect ing\nc n\nĠbre aches\nBas ically\nĠP ag\nĠMunicip al\nĠInd ie\nĠL af\nĠDis able\nĠOl son\nĠrest rained\nĠrul ings\nĠhum ane\nev ents\nĠCinem a\ndisplay Text\nĠH atch\naction Date\nonna issance\nĠassault ing\nĠL ug\nCH AT\nĠvig orous\nĠPer se\nĠintoler ance\nĠSnap chat\nĠSh arks\nĠd ummy\nĠDi agn\nĠGu itar\nim eters\n40 3\nRE G\nA x\nĠsepar ates\nĠMah m\nĠt v\nj ah\nO OL\nC irc\nĠWinds or\nuss ian\nĠintu ition\nĠdis dain\nĠDon ovan\nĠ2 21\nE mb\nĠcondem ning\nĠgener osity\nzz y\nĠpant ies\nĠPre vent\nAction Code\nAN A\n34 2\nexternal ActionCode\nĠspec ifying\nĠcryst all\nJ ere\nĠru pt\nĠApp rentice\nĠprof iling\nÐ º\nSt rike\nĠsid eline\nĠoblig ated\nĠocc ult\nĠbureaucr atic\nant ically\nrupt ed\nneg ative\nĠEthiop ia\nĠC ivic\nĠins iders\nel igible\nĠTV s\nĠB AR\nĠT I\ni ologist\nĠA IR\nĠsubstit uted\nAr ab\nĠS aul\nĠY og\np rem\nĠbuild ers\nĠstation ary\nĠdoubt ful\nĠvig orously\nĠthr illing\nPh ysical\nĠCare y\nĠHyd ra\ngeon ing\nĠS ly\ny ton\nĠborrow ers\nĠPark inson\nĠ ë\nĠJama ica\nĠsat ir\nĠinsurg ents\nĠF irm\nĠis ot\nĠK arn\nour ning\nak ens\ndoc s\nl ittle\nĠMon aco\nCL ASS\nTur key\nL y\nĠCon an\nass ic\nĠstar red\nĠPac ers\net ies\nĠt ipping\nM oon\nĠR w\ns ame\nĠcav ity\nĠgo of\nĠZ o\nSh ock\num mer\nĠemphas izes\nĠreg rett\nĠnovel ty\nĠen vy\nĠPass ive\nr w\n50 5\nĠind ifferent\nĠR ica\nĠHim self\nĠFred die\nĠad ip\nä¸ Ģ\nĠbreak out\nĠhur ried\nĠHu ang\nĠD isk\nĠro aming\n?????- ?????-\nU V\nĠRick y\nĠS igma\nĠmarginal ized\nĠed its\nĠ30 4\nmem ory\nĠspec imen\n29 3\nãģ ¯\nĠvert ically\nĠaud ition\nĠHe ck\nĠc aster\nĠHold ings\nad al\nĠC ron\nĠL iam\nĠdef lect\nP ick\nĠDeb ug\nRE F\nĠvers atility\not hes\nclass ified\nĠMah ar\nĠH ort\nC ounter\nst asy\nnot iced\n33 1\nĠSh im\nf uck\nĠB ie\nĠair ing\nĠPro tein\nĠHold ing\nĠspect ators\nili ated\nĠThat cher\nn osis\nãĥ¼ ãĥ³\nTe le\nB oston\nĠTem pl\nst ay\nĠdecl arations\n47 9\nVol ume\nĠDesign er\nĠOver watch\nid ae\nĠon wards\nĠn ets\nĠMan ila\npart icularly\nĠpolit ic\no other\nĠport raits\nĠpave ment\nc ffff\nĠs aints\nĠbegin ners\nES PN\nĠshort comings\nâķIJ âķIJ\nĠcom et\nĠOrgan ic\nqu el\nĠhospital ized\nBre ak\nĠpe el\ndyl ib\nasp x\nur ances\nĠT IM\nP g\nĠread able\nĠMal ik\nĠm uzzle\nĠbench marks\nd al\nĠV acc\nĠH icks\n60 9\nĠB iblical\nhe ng\nĠover load\nĠCivil ization\nĠimm oral\nĠf ries\nãĤ Ĵ\nĠreprodu ced\nĠform ulation\nj ug\nire z\ng ear\nĠco ached\nMp Server\nĠS J\nĠK w\nIn it\nd eal\nĠO ro\nĠL oki\nĠSong s\nĠ23 2\nĠLou ise\nasion ally\nĠunc ond\nolly wood\nĠprogress ives\nĠEn ough\nĠDo e\nĠwreck age\nĠbr ushed\nĠBase Type\nĠz oning\nish able\nhet ically\nĠC aucus\nĠH ue\nĠk arma\nĠSport ing\nĠtrad er\nĠseem ing\nĠCapt ure\n4 30\nb ish\nĠt unes\nĠindo ors\nĠSp here\nĠD ancing\nTER N\nĠno b\nĠG ST\nm aps\nĠpe ppers\nF it\nĠoverse es\nĠRabb i\nĠR uler\nvert ising\noff ice\nxx x\nĠra ft\nCh anged\nĠtext books\nL inks\nĠO mn\nãĢ ij\nĠinconven ience\nĠDon etsk\n= ~\nĠimplicit ly\nĠboost s\nĠB ones\nĠBo om\nCour tesy\nĠsens ational\nAN Y\nĠgre edy\ned en\nĠinex per\nĠL er\nĠV ale\nĠtight en\nĠE AR\nĠN um\nĠancest or\nS ent\nĠH orde\nurg ical\nall ah\nĠsa p\namb a\nĠSp read\ntw itch\nĠgrand son\nĠfract ure\nĠmoder ator\nĠSe venth\nĠRe verse\nĠestim ation\nCho ose\nĠpar ach\nĠbar ric\nãĢ IJ\nĠcomp ass\nĠall ergic\nâĢ ķ\nOT HER\nerr illa\nĠw agon\nĠz inc\nĠrub bed\nĠFull er\nĠLuxem bourg\nĠHoo ver\nĠli ar\nĠEven ing\nĠCob b\nest eem\nĠselect or\nĠB rawl\nis ance\nĠE k\nĠtro op\nĠg uts\nĠApp eal\nĠTibet an\nĠrout ines\nĠM ent\nĠsummar ized\nsteam apps\nĠtr anqu\nĠ19 29\nor an\nĠAut hent\nĠg maxwell\nĠappre hens\nĠpo ems\nĠsa usage\nĠWeb ster\nur us\nĠthem ed\nĠl ounge\nĠcharg er\nSp oiler\nĠsp illed\nh og\nĠSu nder\nĠA in\nĠAng ry\nĠdis qual\nĠFrequ ency\nĠEther net\nĠhel per\nPer cent\nĠhorr ifying\nĠa il\nĠAll an\nEE E\nĠCross ing\n44 9\nĠh olog\nĠPuzz les\nĠGo es\neren n\n60 4\nãģ ı\nĠRaf ael\nĠatt en\nĠE manuel\nĠup ro\nĠSus p\nP sych\nĠTr ainer\nĠN ES\nĠHun ts\nbec ue\nĠcounsel or\nR ule\nĠtox ins\nĠb anners\nr ifice\nĠgreet ing\nĠfren zy\nĠall ocate\nĠ* )\nex pr\n50 3\nĠCh ick\nĠT orn\nĠconsolid ation\nĠF letcher\nsw itch\nfr ac\ncl ips\nĠMcK in\nĠLun ar\nMon th\nIT CH\nĠscholar ly\nrap ed\n39 8\nĠ19 10\nĠe greg\nĠin secure\nĠvict orious\ncffff cc\nĠsing led\nĠel ves\nĠW ond\nbur st\nĠcam oufl\nĠBL ACK\nĠcondition ed\nç ī\nans wered\nĠcompuls ory\nasc ist\nĠpodcast s\nĠFrank furt\nbn b\nĠne oliberal\nĠKey board\nĠBel le\nw arm\nĠtrust s\nĠins ured\nĠBu cc\nus able\n60 7\nĠPl ains\nĠ18 90\nĠsabot age\nĠlod ged\nf elt\nĠg a\nĠN arc\nĠSal em\nĠsevent y\nĠBl ank\np ocket\nĠwhis per\nĠm ating\nom ics\nĠSal man\nĠK ad\nĠan gered\nĠcoll isions\nĠextraord inarily\nĠcoerc ion\nG host\nb irds\nè Ģ\nk ok\nĠper missible\navor able\nĠpo inters\nĠdiss ip\nac i\nĠtheat rical\nĠCos mic\nĠforget ting\nĠfinal ized\nå¤ §\ny out\nl ibrary\nĠbo oming\nĠBel ieve\nĠTe acher\nĠL iv\nĠGOOD MAN\nĠDomin ican\nOR ED\nĠPart ies\nĠprecip itation\nĠSl ot\nR oy\nĠComb ined\nĠinteg rating\nĠch rome\nĠintest inal\nĠRe bell\nĠmatch ups\nĠblock buster\nĠLore n\nĠLe vy\nĠpre aching\nĠS ending\nĠPur pose\nra x\nf if\nĠauthor itative\nĠP ET\nast ical\nĠdish on\nĠchat ting\nĠ\"$ :/\nConnect ion\nĠrecre ate\nĠdel inqu\nĠbro th\nĠD irty\nĠAd min\nz man\nĠscholars hips\nĠ25 3\ncont act\nals a\n7 67\nc reen\nabb age\nĠ19 15\nĠbl ended\nĠal armed\nL anguage\n35 6\nĠbl ends\nĠCh anged\nW olf\nĠhe pat\nCreat ing\nĠper secut\nĠsweet ness\nart e\nĠforfe iture\nĠRober to\nim pro\nN FL\nĠMag net\nDet ailed\nĠinsign ificant\nĠPOL IT\nĠBB Q\nĠC PS\nĠse aw\namin er\nm L\nend if\nf inals\nĠ26 5\nu ish\nĠ} )\nĠPro blems\nĠem blem\nĠserious ness\nĠpars ing\nĠsubst itution\nĠpress ured\nĠrecy cled\nale b\nRub y\nĠprof iciency\nDri ver\nĠW ester\n: '\nAF TA\nĠm antle\nĠClay ton\nfl ag\nĠpractition er\nc overed\nĠSt ruct\nadd afi\n4 25\nĠTown ship\nĠHyd ro\nLou is\n34 3\nĠcond o\nĠT ao\nĠutil ization\nĠnause a\nĠDem s\nrid ges\np ause\nĠform ulas\nĠchall enger\n37 6\nĠdefect ive\nĠRail way\nĠPub Med\nĠyog urt\nl bs\nĠNor folk\nOP E\nĠMood y\nĠdistribut or\nĠscroll s\nĠextract s\nSt an\nĠv iability\nĠexp oses\nĠstar vation\nĠStep s\nĠD odd\nf ew\nST D\n33 2\nĠclos ures\nĠcomplement ary\nĠS asha\nump y\nĠmon et\nĠartic ulate\nĠDo ct\nk iller\nĠsc rim\nĠ2 64\nĠprost itutes\nĠse vered\nĠattach ments\nĠcool ed\nL ev\nĠF alk\nf ail\nĠpolic eman\nĠD ag\nĠpray ed\nĠK ernel\nĠcl ut\nĠc ath\nĠan omaly\nSt orm\nem aker\nĠBreak fast\nul i\no ire\nJ J\nh z\nOper ation\nĠS ick\n35 4\nĠGuatem ala\nR ate\nĠexp osures\nf aces\nĠArch ae\nra f\nĠM ia\nĠ20 25\nĠop aque\nĠdisgu ised\nĠHead quarters\nS ah\nĠp ots\n9 78\nĠM alf\nĠfrown ed\nĠpoison ous\nĠCon vers\nee ks\nĠcr ab\n.\" \"\nĠtre ason\nĠr anc\nĠescal ating\nĠwar r\nĠmob s\nĠl amps\nĠSun shine\nĠBrun swick\nPh ones\nĠspe lled\nĠSk ip\nĠ20 50\nĠ19 11\nĠPl uto\nĠAm end\nĠme ats\n38 7\nĠst omp\nĠZh ou\nĠLevi athan\nĠHaz ard\nad v\nĠOr well\nĠal oud\nĠb umper\nĠAn arch\nub untu\nĠSer ious\nf itting\nĠOption al\nĠCec il\nRE AM\nĠser otonin\nĠcultiv ate\nag ogue\n} \\\nĠmos ques\nĠSun ny\nĠre active\nrev olution\nĠL up\nĠFed ora\nĠdefense man\nĠV ID\nist ine\nĠdrown ing\nĠBroad casting\nĠthr iller\nĠS cy\nĠacceler ating\nĠdirect s\nod ied\nb ike\nd uration\nĠpain fully\nR edd\nĠproduct ions\nĠg ag\nĠwh ist\nĠs ock\nĠinf initely\nĠConc ern\nĠCit adel\nĠlie u\nĠcand les\nogene ous\narg er\nĠheaven ly\ninflamm atory\nPer formance\nC s\nruct ose\naz aki\nĠp essim\nĠinf erence\nĠpow d\nĠZ oe\nĠpain ts\nĠd azz\npt a\n-------- ---\nĠins pir\nĠExper imental\nĠKn ife\nreg or\nb ors\nĠshow ers\nrom eda\nĠs aint\nĠben ign\nĠJ iang\nĠenvision ed\nĠsh roud\nIF T\nH O\nĠsh uff\nĠI CC\nĠse greg\nĠrevis it\nighth ouse\nL i\nĠsub strate\nĠSe as\nĠRew ard\nĠH ep\nĠBr ass\ns bm\nĠelim inates\nĠst amina\nĠV AT\nĠLo an\nĠconst raint\nĠappropri ated\nĠp es\nĠA LE\nr anging\nĠ40 4\n39 2\nĠintellectual s\nach u\nĠrestruct uring\nĠLe vin\nĠrun es\nĠdelight ful\nĠcarbohyd rates\nĠMod els\nĠExp o\nĠtransport ing\nall oc\nĠring ing\nS amsung\nĠscarce ly\nĠURL s\nĠM AS\nĠprot otypes\nĠnarr ator\nĠCPU s\ncd n\nĠBart on\nĠdecided ly\nĠSh u\nix ir\noc ious\nĠMy st\nN intendo\nĠre use\nĠforg iven\nF ew\nin ical\nn at\nĠseam less\nĠEv a\nĠE VE\nĠJ O\nland ers\nĠso fter\nneg ie\nĠtrans ient\nĠorb ital\nĠfulf il\nĠK om\nHop efully\nĠdynam ically\nĠHun ger\nå Ľ\nĠArmen ia\nel man\nber to\nĠp ige\nĠID s\nlim it\nĠve ins\nĠso aring\np acks\nGold en\nĠCr ab\nist or\nĠR PM\nĠ$ $\ng ression\nĠjihad ist\nĠgam ble\nĠcare g\nĠinf lated\nF ace\nĠFire arms\nĠEm manuel\nâ Ŀ\nĠsh ocks\ngr ab\nĠspl end\nĠHP V\nab ortion\nAb ove\nEnt ity\nplay ers\nĠcomm enced\nul ence\nĠfulfill ment\nĠembod iments\nĠW elfare\nĠha il\nĠ< @\ntt en\nĠcat cher\nĠJ azeera\nĠvolcan o\nĠstabil ize\nĠHand ler\nĠintens ified\nĠAb rams\nĠhum iliation\np aced\n60 5\nĠCent OS\nSpe cific\nĠhe ed\nĠC AM\nĠGal ile\nD ie\nĠabol ished\nĠThom son\nĠTe achers\nĠW ass\nj ong\nĠIS BN\nĠAll ies\nsh ake\nå ·\nv ict\nHow ard\nĠde em\nĠexceed ingly\nĠSmart stocks\nib e\nĠdoor way\nĠcompet ed\nig mat\nĠnational ists\nĠg room\nĠKe en\nĠdispos able\nde cl\nĠT olkien\nĠSche me\nĠb iod\nĠav id\nĠEl on\nag ar\nĠT SA\nR oman\nĠartific ially\nĠadvis ors\nX L\nĠInf erno\n36 6\nĠted ious\nĠPhot ography\nĠCar rie\nĠtro pe\nĠSand ra\nĠdec imal\nQue en\nĠGund am\nĠO M\note ch\nN BA\nĠ19 32\nĠent renched\nĠMar ion\nĠfr aternity\nLab our\nHen ry\nĠlat itude\nE ither\nĠenh ances\nĠPot ential\nĠsh ines\nid ad\nĠbread th\nĠcapac ities\nĠðŁ ĻĤ\nĠBron x\nĠsex es\nĠdifferent iation\nĠheavy weight\nĠT aj\nd ra\nĠmigr ate\nĠexhaust ion\nĠR UN\nels ius\nĠCu omo\nĠgu itars\nĠcl ones\nĠSom ew\nĠP ry\n------------ -\nĠwarr anted\ncy cles\nĠsalv age\nĠdis ks\nR ANT\nĠNGO s\nĠMart ian\n\":[ {\"\nĠadd icts\noj ure\nil let\nĠamazing ly\nart ments\np ixel\nĠGPU s\nLay out\nè £\nĠTam il\nĠBas il\nĠimpart ial\nĠSt ructure\nf ork\nb ryce\nĠr 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ally\nĠmant ra\np aying\nĠiv ory\nĠCour ts\nOU GH\naunt let\nSer ial\nB row\nĠH undreds\n3 23\nĠpe e\nĠlin ux\nĠsub mer\nĠPrinc ipal\n48 5\nĠD SL\nĠCous ins\nĠdoctr ines\nĠAthlet ics\nĠ3 15\nĠK arma\nĠatt ent\nur ger\nĠpresc ribe\nĠenc aps\nĠC ame\nĠsecret ive\nĠCr imes\nd n\nC lean\nĠEgypt ians\nĠCar penter\nĠ ll\nH um\nĠMil o\nĠcapital ists\nĠbrief ed\nT we\nĠBas in\nelve t\nM os\nĠplun ge\nĠKa iser\nĠFu j\nill in\nĠsafegu ards\nĠo ste\nĠOpportun ity\nĠM afia\nĠCall ing\nap a\nur ban\nbr ush\nill ard\nc é\nint elligence\nĠL ob\nĠDru id\nĠsm oother\nĠfoot ing\nĠmotor ists\narc ity\nĠmascul inity\nĠm ism\nĠabdom inal\nĠTa vern\nĠR oh\nĠesc apes\ns igned\nAnth ony\nĠsacrific ing\nĠintim acy\nĠan terior\nĠK od\nĠmot if\nĠg raz\nĠvisual ization\nĠguitar ist\nĠTro tsky\nm agic\nD ar\nĠMor i\nĠw ards\nĠtoile ts\nl est\nĠtele port\nĠSund ays\nĠPl at\nET S\nĠe Sports\nPat rick\nĠK atherine\nen ko\nĠhas sle\nĠM ick\ngg les\nĠh ob\naint ain\nĠair borne\nĠsp ans\nĠch ili\nĠa perture\nĠvolunte ered\nĠInc ident\nĠF res\nĠVeter an\naugh tered\ning o\nĠun insured\nCL OSE\nĠf use\nĠer otic\nĠadvert ise\nra ising\nText ure\nĠatt ends\nĠRE AL\nudd led\nĠsm oot\nĠ30 5\nĠWill is\nĠbl ond\nAn alysis\nĠV T\non ica\nĠstrongh old\nR F\nN M\n. >>\nĠprosper ous\nĠbo asted\n29 2\nĠManufact uring\nPR ESS\ng ren\nĠpharm acy\nĠRoc kefeller\nk ai\nĠth umbs\nĠH ut\nĠmother board\nĠguard ians\nĠAl ter\nll ular\nĠsh ack\nĠwise ly\nĠback bone\nerv a\nĠsu icides\nĠMcG regor\nij ah\nE mer\nĠB rav\nĠdesign ate\nP OST\nprodu ced\nĠcleans ing\nirl wind\nex istent\nĠHum ph\nĠPay ne\nĠv ested\nÅ ¡\nĠstring ent\nion a\nĠuns ub\nĠsum med\nĠHer cules\nsub ject\nĠR agnar\nĠN os\nĠcharacter ization\nĠsav vy\nĠDaw son\nĠCas ino\nĠf ri\nĠBar rier\nĠmis information\nĠins ulation\nĠcorrid ors\nĠair planes\nĠNo ct\nah i\nĠ19 16\nk b\narm ac\nĠsh un\nĠsche ma\nĠhorr ified\nĠ23 9\naund ers\nN B\ni ates\ner ity\nĠSh ard\nĠr arity\nĠgroup ed\nĠGh ana\nagain st\nĠBi ological\nĠA ware\now ell\nÏ Ħ\nĠBe au\nsh aw\nH ack\nĠJul ius\nUS S\nol son\naun a\nc ru\nĠMaur ice\nĠI k\nĠsequ encing\nĠradical s\nĠ( ?,\nv irtual\nĠany ways\nĠreper c\nĠhand lers\nĠhes itant\né ĥ\nĠM F\nple mentation\nass ociated\nĠcampaign ed\nĠY ue\nut ations\nĠY oga\nĠsim mer\nĠro ds\nĠmel ody\nĠconv oy\nv ideos\nĠscreen ed\nN eg\nochem ical\nĠ( ))\nĠultr as\nĠant ip\nĠIsland ers\n70 4\nĠfet ish\nĠridic ulously\nĠK art\nĠmitochond rial\nĠinterf ering\nBuild er\nĠover fl\nĠac ne\nĠM ud\nĠK err\nf lex\nĠPost al\nĠBalt ic\n47 7\nĠPers ons\nour age\nH B\nĠM use\nĠImm ortal\nĠDri ving\nĠpet itions\nĠsubsc ript\nĠs orce\nĠProcess or\nut on\nS ony\nĠph on\nĠr aced\nĠAnth rop\nĠday time\nĠEx ercise\nAdd ing\nĠeng ages\nĠQual comm\nĠmir acles\nĠmem es\nĠDr ink\nĠOri oles\nĠhair s\nĠPol ar\nath om\nĠsl ippery\nĠR emy\nĠcar amel\nĠY EAR\nĠal k\nI gn\na ution\nĠMer lin\nĠC ran\nĠap ologies\nĠ4 10\nĠout ing\nĠMem ories\napp ointed\nĠcount ered\nu ld\npos ing\nĠfire wall\nĠW ast\nĠW et\nwork ed\nse ller\nĠrepe aled\nere o\nass uming\nBL IC\nm ite\nĠCEO s\nĠChap el\nellig ent\n________________ ________\nD og\nĠw art\nĠsubsc riber\ns ports\nĠbe gged\nĠM V\nĠsem if\neth ical\nĠpre ach\nĠrev ital\nĠpun itive\nĠshort cuts\nĠinstit uted\nĠWars aw\nĠabdom en\nĠK ING\nĠsuper intendent\nĠf ry\nĠGe o\nT OR\nĠcontrad ictions\napt ic\nĠlandsc apes\nb ugs\nĠcl ust\nĠvol ley\nc ribed\nĠt andem\nĠrob es\nWH AT\nĠpromot er\nĠel oqu\nreview ed\nĠD K\nĠPl ato\nĠf ps\nT ank\nĠDer rick\nĠpriorit ize\nas per\nĠHond uras\nĠCom pleted\nne c\nĠm og\nn ir\nĠMay o\nDE F\nst all\nin ness\nĠVolks wagen\nĠprec aution\nĠM ell\ni ak\nist ries\nĠ24 8\nĠoverl apping\nSen ate\nĠEnh ance\nres y\nrac ial\nOR TS\nĠM ormons\nStr ong\nĠCo ch\nMex ico\nĠMad uro\nĠj ars\nĠcan e\nW ik\noll a\niff erence\nĠphysic ist\nĠMag gie\nĠ28 5\nĠdep iction\nĠMcL aren\nJ u\nĠsl ows\nĠcommission ers\nĠWill ow\nĠExpl os\nhov ah\nĠtechn ician\nĠhom icides\nĠFl av\nĠTr uman\nĠ100 00\nu ctor\nĠsh ader\nNews letter\n45 7\nĠre ver\nĠhard ened\nĠwhere abouts\nĠrede velop\nĠcar bs\nĠtra vers\nĠsqu irrel\nĠfoll ower\nĠs ings\n50 8\nĠrabb its\nemon ium\nĠdocument ing\nĠmisunder stood\n) '\nR ick\ngg ies\nĠprem ie\nĠsk ating\nĠpass ports\nĠf ists\naged don\nH aw\nAC P\n0 80\nĠThough ts\nĠCarl son\nĠpriest hood\nh ua\nĠdun geons\nĠLo ans\nĠant is\nĠfamiliar ity\nĠS abb\nop al\nĠIn k\nst rike\nĠc ram\nĠlegal ized\nĠcu isine\nĠfib re\nTra vel\nĠMon ument\nOD Y\neth y\nĠinter state\nĠP UR\nem porary\nĠArab ian\ndevelop ed\nĠsadd le\nĠg ithub\nĠOff er\nĠIS P\nro let\nĠSUP ER\nĠDen is\nĠmultipl ier\nĠstir red\nInterest ingly\nĠcustom ary\nĠbill ed\nhe x\nĠmultipl ied\nĠfl ipping\nĠCros by\nĠfundament als\nia e\nĠPlay ed\nĠAt om\nam azon\nĠFl am\nee z\nactiv ated\nĠtables poon\nĠliberal ism\nĠPal in\nĠP atel\nN um\nĠT AM\nĠs urn\nĠRel oaded\nĠco ined\n\" ],\nĠCl ash\nĠAg u\nĠprag matic\nĠActiv ate\nĠ8 02\nĠtrail ers\nĠsil hou\nĠprob es\nĠcirc us\nĠB ain\nĠLind say\nĠAb bey\nDel ivery\nĠconcess ion\nĠgast ro\nĠSpr ite\nÄ Ł\nand el\nĠg imm\nĠaut obi\nĠT urtle\nĠwonder fully\nĠHar am\nĠWorld wide\nĠHand le\nĠtheor ists\nĠsle ek\nĠZh u\nograph ically\nEG A\nĠOwn ers\nath s\nĠAntar ctic\nn atal\n=\" \"\nfl ags\n`` ``\nĠs ul\nK h\nĠpot assium\nĠlinem an\nĠcere al\nĠSe asons\nĠ20 22\nĠmat hematic\nĠastron omers\nprof essional\nĠf ares\ncknow led\nĠch i\nĠyoung sters\nĠmistaken ly\nĠhem isphere\nĠDiv inity\nr one\nĠ\" ,\nr ings\nĠattract s\nv ana\nå ¹\nC AP\nĠplay list\nĠpor ch\nãģ £\nĠincorpor ates\nĠso ak\nĠassert ing\nĠTerror ism\nĠP ablo\nJ a\nces ter\nĠfear ing\nĠPr ayer\nĠescal ated\nG W\nĠro be\nĠBright on\nac ists\nĠSym phony\nĠDwar f\nĠPar ade\nĠLe go\nĠinex pl\nĠl ords\nle af\nRA G\nl iber\nĠcig ars\nĠJe hovah\n60 6\nWIND OWS\nĠLiber ia\neb us\nHe avy\nĠl ubric\nĠR W\nangu ages\nĠnarrow ed\ncom puter\nĠE mber\nĠmurder ing\nĠdown stream\nĠT uls\nĠT ables\nTop ic\nĠAcc uracy\n= /\nl ost\nĠRe i\nĠprogress es\nb ear\nĠestablish ments\nJust in\nĠPe ach\nĠG omez\nå ¿\nĠTri angle\nId ent\nĠH ive\nRes ources\nĠmix es\nĠAss uming\nM u\nĠhyp oc\nĠs ane\nĠW an\nid ious\nSu ccess\nĠ io\nAng el\nĠdanger ously\nĠCreat ure\nW ORK\n: [\nĠKat rina\nList ener\nM iller\nĠId lib\nh ang\nĠcircum vent\nh ref\nĠcel estial\nĠWe eks\nĠP ug\nĠDal ton\nĠsubpoen a\nuk u\nĠpers isted\npe i\nold ing\nĠDoc uments\nĠH ast\nĠC ENT\nĠprim er\nĠsyn onymous\nĠn ib\nom bs\nĠnot ation\nĠD ish\nĠAt mosp\nĠforb id\nĠAN G\npat tern\nl os\nĠproject iles\nb rown\n.\" ,\nĠVen om\nĠfierce ly\nub lished\nĠU ran\nĠNic arag\n4 10\nĠC AL\nOT OS\nĠMir acle\nĠEn chant\nĠguard ing\napp end\nAtt ach\nĠlevel ed\nĠcond oms\nih ilation\n64 9\nĠnight mares\nĠTHE Y\nĠST ART\nĠK inn\nĠroomm ate\nĠhy giene\no pping\nJ ob\nĠl vl\nĠV ER\nĠKe eping\nab etic\nĠformat ting\neral a\nĠrev isions\nĠres urg\nT el\nĠGood man\n35 3\np od\nĠind isp\nĠTrans lation\nĠg own\nĠM und\nĠc is\nĠby stand\ncol lect\nĠPun jab\nact ively\nĠG amb\nte ll\nĠimport ing\ng encies\nĠloc om\nĠBr ill\nH oly\nĠBer ger\nĠshow down\nĠrespond ers\nIL Y\nĠt akedown\nle ted\nĠmat tered\nĠpredict ive\nĠover lay\nG PU\nĠV ick\nĠconvey ed\nT ab\npe er\nSc an\nĠdefensive ly\nv ae\nĠappro ving\nĠt iers\nĠV ia\nquer ade\nĠSaud is\nĠdemol ished\nĠProp he\nĠmon o\nĠhospital ity\nH AM\nĠAri el\nM OD\nĠTor ah\nĠbl ah\nĠBel arus\nerent ial\nĠT uc\nĠbank er\n39 7\nĠmosqu it\nĠScient ist\nĠMus ical\nĠh ust\nSh ift\nĠtor ment\nĠstand off\nE duc\nĠF og\nĠampl ifier\nSh ape\nInst ance\nĠCrit ics\nĠda emon\nH ouston\nĠmatt ress\nĠID F\nĠobsc ene\nĠA mer\nhett i\nĠcomp iling\n35 2\nvere tt\nĠRed uction\nist ration\nĠBl essed\nĠB achelor\n3 16\nĠpr ank\nĠVul can\ndd ing\nĠm ourning\nĠQu int\nĠBl aster\ntest ing\nĠsed iment\n>> >\nĠE ternity\nĠWH ERE\nĠM aze\nĠreact ing\nĠAl v\noms day\nĠC RA\nĠtransl ator\nĠbog us\nat u\nWe bsite\noll s\nĠbapt ism\nĠs ibling\nĠAut umn\nve z\nãģ® é\ngu ards\nGe org\nassad ors\nĠFre ud\nĠcontin ents\nĠReg istry\nBern ie\nĸļ 士\nĠtoler ant\nĠU W\nĠhor ribly\n99 5\nĠMID I\nĠimpat ient\noc ado\ner i\nĠWor st\nĠNor ris\nĠTalk ing\nĠdef ends\nens able\nĠ20 21\nĠanat omy\nL ew\nĠdraw er\nĠCan berra\nĠpatri otic\né¾įå ĸļ士\nĠAv g\nAR M\nĠundis closed\nĠfare well\n45 9\nb able\nĠAll ison\nOL OG\nĠcon co\nt ight\nĠAC PI\nĠM ines\nl ich\nĠâĶ ľ\nrepresent ed\n200 000\nĠenthusi ast\nOT S\nb il\nĠIng redients\nĠinvent or\nĠMy SQL\n³³ Âł\nĠAB OUT\nwith in\nĠm k\nB ul\nĠF ake\nĠdracon ian\nW a\nhel m\nĠTer ran\nerv ille\nĠcommon place\nSI ZE\nĠ\" <\nre place\nograph s\nĠSE LECT\ninc ible\nĠMost ly\nĠShe ffield\nĠID E\nugg le\nĠcit ations\nh urst\nĠUn ix\nĠunle ash\nĠP iper\nĠN ano\nĠsucc umb\nĠreluct ance\nĠ25 00\nĠMer chant\nĠwire t\nĠcomb os\nĠBirth day\nĠchar coal\nĠU PS\nĠFair fax\nĠdrive way\nĠT ek\nĠP itch\nove re\nĠtechn icians\nĠAct ual\nfl ation\nĠF iscal\nĠEm pty\nan amo\nĠmag nesium\nĠsl ut\nĠgrow ers\nInvest igators\n( ):\nĠS atellite\nĠKe ynes\nmiss ive\nl ane\nĠb orough\n3 44\nĠTE AM\nĠBet hesda\nC V\nh ower\nĠR AD\nĠch ant\nĠR iy\nĠcompos itions\nĠmild ly\nĠmedd ling\nĠag ility\nane ers\n5 01\nĠsyn th\nling er\n29 1\nĠex claimed\nPart y\nĠcont amin\nĠMan or\nĠResp ond\nĠpra ising\nĠman ners\nfle et\nSum mer\nĠLy nd\nĠDef initely\ngr im\nĠbow ling\nst ri\nç Ľ\ny nt\nĠmand ates\nD IV\nĠreconc ile\nview s\nĠDam on\nvet te\nF lo\nĠGreat est\nil on\nic ia\nĠportray al\nĠcush ion\n50 4\n19 79\noss al\nApp lic\nsc ription\nĠmit igation\nAT S\np ac\nĠer ased\nĠdefic iencies\nĠHolland e\nĠX u\nĠb red\nĠpregn ancies\nf emin\nĠem ph\nĠpl anners\nĠout per\nutter ing\nĠperpet rator\nĠm otto\nĠEll ison\nĠNE VER\nĠadmitted ly\nAR I\nĠAzerbai jan\nĠmill isec\nĠcombust ion\nĠBott le\nĠL und\nĠP s\nĠD ress\nĠfabric ated\nĠbat tered\nĠs idel\nĠNot ting\nFore ign\nĠJer ome\n0 20\nĠAr bit\nĠkn ots\nĠR IGHT\nM oving\nãģ Ļ\nĠsur geries\nĠcour thouse\nĠm astered\nĠhover ing\nĠBr an\nĠAl ison\nĠsaf est\nm ilitary\nĠbull ied\nĠbar rage\nRead er\nES E\nĠGe ographic\nT ools\n3 14\nĠGe ek\nro th\ngl ers\nĠF IN\nÏ ģ\nĠA ston\nal tern\n48 8\nĠveter in\nG amer\nĠint el\nren ches\nSh ield\nĠam nesty\nĠB har\nĠp iled\nĠhonor able\nĠInst itutes\nĠso aked\nĠcom a\nĠE FF\n34 1\nby tes\nĠG mail\nle in\nĠCanad iens\nm aterial\nI l\nĠinstruct ors\nĠK Y\nĠconce ive\nub b\nĠP ossible\nĠeas ing\nĠChrist ina\nĠcar ic\nĠHD R\nR OM\nĠsho vel\nde lete\nĠp uff\nĠCh anging\nĠseam lessly\nAtt ribute\nĠacqu isitions\nak ery\nĠE F\nĠaut istic\nĠT akes\nĠPow der\nĠSt ir\n5 10\nĠBub ble\nsett ings\nĠF owler\nĠmust ard\nĠmore over\nĠcopyright ed\nĠLED s\n15 00\næ ī\nĠH IS\nen f\nĠcust od\nĠH uck\nG i\nĠim g\nAn swer\nC t\nj ay\nĠInf rastructure\nĠfeder ally\nL oc\nĠmicro bes\nĠover run\ndd s\not ent\nadi ator\n>>>> >>>>\nĠtorn ado\nĠadj ud\nĠintrig ued\nĠs i\nĠRevel ation\npro gress\nĠburgl ary\nĠSai yan\nĠK athy\nĠser pent\nĠAndre as\nĠcomp el\ness ler\nĠPl astic\nĠAd vent\nĠPos itive\nĠQ t\nĠHind us\nreg istered\nular ity\nĠrighteous ness\nĠdemon ic\nu itive\nĠB DS\nĠGre gg\nc ia\nĠCrus ade\nĠSina i\nW ARE\n+ (\nĠme ll\nĠder ail\ny ards\nA st\nĠnotice ably\nĠO ber\nR am\nĠun noticed\nĠse q\nav age\nT s\nĠ6 40\nĠconced e\nĠ] )\nF ill\nĠcapt ivity\nĠImprove ment\nĠCrus ader\nara oh\nM AP\næ Ĺ\nĠstr ide\nal ways\nF ly\nN it\nĠal gae\nĠCook ing\nĠDo ors\nMal ley\nĠpolic emen\nãģ į\nĠastron aut\naccess ible\n49 5\nĠR AW\ncl iffe\nudic rous\nĠdep ended\nal ach\nĠvent ures\nra ke\nĠt its\nĠH ou\nĠcond om\normon al\nĠind ent\nĠupload ing\nFoot note\nImport ant\nĠ27 1\nĠmind ful\nĠcont ends\nC ra\nĠcal ibr\nĠO ECD\nplug in\nF at\nĠIS S\nĠDynam ics\nans en\n68 6\n' ),\nĠsp rite\nĠhand held\nĠH ipp\n=~ =~\nTr ust\nĠsem antics\nĠBund es\nĠRen o\nĠLiter ature\ns ense\nG ary\nĠA eg\nĠTr in\nEE K\nĠcler ic\nĠSS H\nĠch rist\nĠinv ading\nib u\nĠen um\naur a\nĠal lege\nĠInc redible\nB BC\nĠth ru\nĠsa iled\nĠem ulate\nĠin security\nĠc rou\nĠaccommod ations\nĠincompet ent\nĠsl ips\nĠEarth qu\ns ama\nIL LE\nĠi Phones\nas aki\nĠby e\nĠar d\nĠext ras\nĠsl aughtered\nĠcrowd funding\nres so\nĠfil ib\nĠER ROR\nĠT LS\ne gg\nĠIt al\nĠen list\nĠCatal onia\nĠSc ots\nĠser geant\nĠdiss olve\nN H\nĠstand ings\nri que\nI Q\nĠbenef iciary\nĠaqu arium\nYou Tube\nĠPower Shell\nĠbright est\nĠWar rant\nS old\nWrit ing\nĠbegin nings\nĠRes erved\nĠLatin os\nhead ing\nĠ4 40\nĠrooft op\nAT ING\nĠ3 90\nVP N\nG s\nk ernel\nturn ed\nĠprefer able\nĠturn overs\nĠH els\nS a\nĠShin ji\nve h\nĠMOD ULE\nV iol\nĠex iting\nĠj ab\nĠVan illa\nĠac ron\nĠG ap\nber n\nA k\nĠMc Gu\nĠend lessly\nĠFar age\nĠNo el\nV a\nM K\nĠbr ute\nĠK ru\nĠES V\nĠOl ivia\nâĢ ł\nĠK af\nĠtrust ing\nĠh ots\n3 24\nĠmal aria\nĠj son\nĠp ounding\nort ment\nCount ry\nĠpostp oned\nĠunequ iv\n? ),\nĠRo oney\nudd ing\nĠLe ap\nur rence\nsh apeshifter\nĠH AS\nos ate\nĠca vern\nĠconserv atism\nĠB AD\nĠmile age\nĠarrest ing\nV aults\nĠmix er\nDem ocratic\nĠB enson\nĠauth ored\n8 000\nĠpro active\nĠSpirit ual\nt re\nĠincarcer ated\nĠS ort\nĠpe aked\nĠwield ing\nre ciation\n×Ļ ×\nP atch\nĠEm my\nĠex qu\ntt o\nĠRat io\nĠP icks\nĠG ry\nph ant\nĠf ret\nĠeth n\nĠarch ived\n% -\nc ases\nĠBl aze\nĠim b\nc v\ny ss\nim ony\nĠcount down\nĠaw akening\nĠTunis ia\nĠRe fer\nĠM J\nĠun natural\nĠCar negie\niz en\nĠN uggets\nhe ss\nĠev ils\n64 7\nĠintrodu ctory\nl oving\nĠMcM ahon\nĠambig uity\nL abel\nĠAlm ighty\nĠcolor ing\nĠCl aus\nset ting\nN ULL\nĠF avorite\nĠS IG\n> (\nĠSh iva\nĠMay er\nĠstorm ed\nĠCo verage\nwe apons\nigh am\nĠun answered\nĠle ve\nĠc oy\nc as\nb ags\nas ured\nSe attle\nĠSant orum\nser ious\nĠcourage ous\nĠS oup\nĠconfisc ated\nĠ// /\nĠuncon ventional\nĠmom s\nĠRohing ya\nĠOrche stra\nĠPot ion\nĠdisc redit\nĠF IL\nf ixed\nĠDe er\ndo i\nĠDim ension\nĠbureaucr ats\net een\nĠaction Group\noh m\nĠb umps\nĠUt ility\nĠsubmar ines\nren heit\nre search\nĠShap iro\nĠsket ches\nĠde ceptive\nĠV il\nes ame\nĠEss entially\nĠramp age\nisk y\nĠmut tered\nth ritis\nĠ23 6\nf et\nb ars\nĠpup il\nĠTh ou\no S\ns ong\nĠfract ured\nĠre vert\npict ure\nĠcrit erion\nus her\nĠreperc ussions\nĠV intage\nĠSuper intendent\nOffic ers\nĠflag ged\nĠbl ames\nĠin verse\nograp hers\nĠmakes hift\nĠdev oid\nĠfoss ils\nĠArist otle\nĠFund s\nĠde pleted\nĠFl u\nĠY uan\nĠw oes\nĠlip id\nĠsit u\nrequ isites\nĠfurn ish\nĠSam ar\nĠshame ful\nĠadverse ly\nĠad ept\nĠrem orse\nĠmurder ous\nuck les\nĠE SL\nĠ3 14\ns ent\nĠred ef\nĠC ache\nĠP urs\nig ans\nĠ4 60\nĠpres criptions\nĠf res\nF uck\nocr ates\nTw enty\nĠWe ird\nĠT oggle\nĠC alled\nitiz ens\nĠp oultry\nĠharvest ing\nãĤ¦ ãĤ¹\nBott om\nĠcaution ed\nt n\n39 6\nĠNik ki\nĠeval uations\nĠharass ing\nĠbind ings\nĠMon etary\nĠhit ters\nĠadvers ary\nun ts\nĠset back\nĠenc rypt\nĠC ait\nĠl ows\neng es\nĠN orn\nĠbul bs\nĠbott led\nĠVoy ager\n3 17\nĠsp heres\np olitics\nĠsubt ract\nĠsens ations\nĠapp alling\nĠ3 16\nĠenvironment ally\nĠST EM\nĠpub lishes\n5 60\nĠdilig ence\n48 4\nĠadv ises\nĠpet rol\nĠimag ining\nĠpatrol s\nĠInt eger\nĠAs hes\nact us\nĠRad iant\nĠL T\nit ability\nht aking\nSet ting\nĠnu anced\nĠRe ef\nĠDevelop ers\nN i\npie ces\n99 0\nLic ense\nĠlow ers\nĠOtt oman\n3 27\noo o\nĠqu itting\nmark ets\nBeh ind\nĠbas in\nĠdoc s\nan ie\nfl ash\nct l\nĠcivil ized\nĠFuk ushima\n\"] ,\"\nĠK S\nĠHonest ly\nar at\nĠconstruct s\nĠL ans\nĠD ire\nĠLI KE\nĠTrou ble\nĠwith holding\nĠOb livion\nĠsan ity\nany a\nCon st\nĠgro cer\nĠC elsius\nĠrecount ed\nĠW ife\nB order\nate red\nh appy\nĠspo iler\nĠlog ically\nH all\nĠsucceed ing\nĠpoly morph\nĠax es\nĠShot gun\nĠS lim\nĠPrin ciples\nĠL eth\nart a\nĠsc or\nSc reenshot\nĠrelax ation\n#$ #$\nĠdeter rent\nidd y\nĠpower less\nĠles bians\nĠch ords\nĠEd ited\nse lected\nĠseparat ists\n000 2\nĠair space\nĠturn around\nĠc unning\nP ATH\nP oly\nĠbomb ed\nĠt ion\nx s\nĠwith hold\nĠw aged\nĠLiber ties\nFl ag\nĠcomfort ing\n45 4\nĠI ris\nare rs\nĠr ag\nĠrel ocated\nĠGu arant\nĠstrateg ically\nĠgam ma\nuber ty\nĠLock heed\ng res\nĠgr illed\nĠLow e\nst ats\nĠR ocks\nĠsens ing\nĠrent ing\nĠGe ological\nا Ø\not rop\nĠse w\nĠimproper ly\n48 6\nĠâĸ ł\nĠstar ving\nĠB j\nDisc ussion\n3 28\nĠCom bo\nĠFix es\nN AT\nĠstri ving\nth ora\nĠharvest ed\nĠP ing\nĠplay ful\nĠaven ues\nĠoccup ational\nĠw akes\nĠCou rier\nĠdrum mer\nĠBrow ser\nĠH outh\nit u\nĠapp arel\np aste\nĠhun ted\nĠSecond ly\nl ain\nX Y\nĠP IN\nic ons\nĠcock tails\nĠs izable\nĠhurd les\nest inal\nĠRecre ation\nĠe co\n64 8\nĠD ied\nm int\nĠfinger prints\nĠdis pose\nĠBos nia\nts y\n22 00\nĠins pected\nĠF ou\nĠf uss\nĠamb ush\nĠR ak\nĠmanif ested\nPro secut\nĠsuff ice\nren ces\nĠcompens ated\nĠC yrus\nĠgen us\nĠWolver ine\nĠTrend s\nĠh ikes\nĠSe en\nĠen rol\nC old\nĠpol itely\nĠSl av\nĠRu pert\nĠey ewitness\nĠAl to\nĠun comp\nĠposter ior\nM ust\nĠHer z\nĠprogress ively\nĠ23 4\nĠind ifference\nĠCunning ham\nĠacadem ia\nĠse wer\nĠast ounding\nĠA ES\nr ather\nĠeld est\nĠclim bs\nĠAdd s\nĠout cry\nĠcont ag\nĠH ouses\nĠpe pt\nĠMel ania\ninterest ed\nĠU CH\nĠR oots\nĠHub bard\nĠT BD\nĠRoman ian\nfil ename\nSt one\nĠIm pl\nĠchromos ome\nC le\nd x\nĠscram bled\nĠP t\nĠ24 2\nOP LE\nĠtremend ously\nSt reet\nĠcra ving\nĠbund led\nĠR G\np ipe\nĠinj uring\nĠarc ane\nPart icip\nĠHero ic\nst y\nĠto pping\nĠTemp est\nrent ices\nb h\nĠpar anoia\nĠUnic ode\nĠegreg ious\nĠ\\ '\nĠOsw ald\nĠgra vel\nĠSim psons\nĠbl and\nĠGuant anamo\nWrit er\nlin ers\nĠD ice\nJ C\nĠpar ity\nĠs ided\nĠ23 7\nĠPyr rha\nat ters\nd k\nF ine\ncomp an\nĠform ulated\nĠId ol\nil ers\nhem oth\nĠF av\nĠintr usion\nĠcar rots\nĠL ayer\nĠH acker\nĠ ----------------\nĠmoder ation\né ģ\noc oc\nĠcharacter ize\nĠTe resa\nĠsocio economic\nĠper k\nĠParticip ation\ntr aining\nĠPaul o\nph ys\nĠtrust worthy\nĠembod ied\nĠMer ch\nc urrency\nĠPrior ity\nĠte asing\nĠabsor bing\nĠunf inished\nĠCompar ison\nĠdis ple\nwrit ers\nĠprofess ions\nĠPengu in\nĠang rily\nĠL INK\n68 8\nĠCor respond\nĠprev ailed\nĠcart el\nl p\nas ms\nĠRed emption\nĠIslam ists\neffect s\nd ose\nĠL atter\nĠHal ifax\nĠv as\nĠTop ics\nĠN amed\nadvert ising\nzz a\nIC ES\nĠret arded\nach able\nĠPupp et\nĠItem Level\nĠret ract\nĠident ifiable\nA aron\nĠB uster\ns ol\nhel le\nas semb\nH ope\nr anged\nB a\nĠP urch\né Ģ\nĠSir i\nĠarri vals\nĠ19 12\nĠshort ened\nĠ3 12\nĠdiscrep ancy\nĠTem perature\nĠWal ton\nĠkind erg\np olit\nĠrem ix\nĠconnect ors\nãĥĺ ãĥ©\nĠKazakh stan\ndom inated\nĠsu gars\nim ble\nĠPan ic\nĠDem and\nĠCol ony\non en\nĠM ER\n7 75\nur ia\naza ar\nĠDeg ree\nP ri\nĠsun shine\nĠ25 1\nĠpsychedel ic\nĠdigit ally\nĠBra un\nĠsh immer\nĠsh ave\nĠTel esc\nĠAst ral\nĠVenezuel an\nĠO G\nĠc rawling\nInt eg\nĠFe ather\nĠunfold ing\nĠappropri ation\nĠè£ı è\nĠMob ility\nĠN ey\n- .\nb ilt\nL IN\nĠT ube\nĠCon versely\nĠkey boards\nĠC ao\nĠover th\nĠla ure\n>> \\\nĠV iper\nach a\nOff set\nĠR aleigh\nĠJ ae\nJ ordan\nj p\nĠtotal itarian\nConnect or\nĠobserv es\nĠSpart an\nĠIm mediately\nĠSc al\nC ool\nĠt aps\nĠro ar\nP ast\nĠch ars\nĠB ender\nĠShe ldon\nĠpain ter\nĠbe acon\nĠCreat ures\nĠdownt urn\nĠh inder\nĠAnd romeda\nà Ľ\ncc oli\nĠF itness\net rical\nĠutil izes\nĠsen ate\nĠen semble\nĠche ers\nT W\nĠaff luent\nk il\nry lic\nord ering\nCom puter\nĠgru esome\nost ics\nĠUb isoft\nĠKel ley\nĠw rench\nĠbourgeois ie\nIB LE\nĠPrest on\nw orn\nar ist\nreat ing\nĠst ained\nar ine\nĠsl ime\nEN N\nĠche sts\nĠground water\nann ot\nĠTr ay\nĠLoc ke\nĠC TR\nĠd udes\nĠEx ternal\nĠDec oder\nĠpar amed\nĠMed line\n80 9\nĠD inner\nrup al\ng z\nĠG um\nĠDem o\nj ee\nĠd h\nber man\narch s\nĠen qu\nĠEp stein\nĠdevast ation\nĠfriends hips\nĠAr d\nĠ23 1\nĠRub in\nĠDist ance\nĠsp urred\nĠd ossier\nĠover looking\n\\\\\\\\\\\\\\\\ \\\\\\\\\\\\\\\\\nFore st\nĠCom es\n\\ \",\nĠIran ians\nĠf ixtures\nL aughs\nĠcur ry\nĠKing ston\nĠsqu ash\nĠcat alogue\nĠabnormal ities\nĠdigest ive\n.... .....\nĠsubord inate\nog ly\nĠ24 9\nM iddle\nĠmass ac\nĠburg ers\nĠdown stairs\nĠ19 31\n39 4\nĠV G\nĠl asers\nĠS ikh\nĠAlex a\nder ived\nĠcycl ist\nãģ® éŃĶ\nonel iness\n!!!! !!!!\nĠbuff s\nleg ate\nĠrap ing\nĠrecomm ending\nro red\nĠmult icultural\nun ique\nĠbusiness men\nĠune asy\nĠM AP\nĠdisp ersed\ncipl ine\nJ ess\nĠK erala\nå §\nĠabst raction\nSur v\nU h\nĠprin ters\nij a\now der\nĠanalog ous\nĠA SP\naf er\nĠunfold ed\nĠlevel ing\nĠbre ached\nĠH earing\nĠn at\nĠtransl ating\ncrit ical\nĠant agonist\nĠYes terday\nĠfuzz y\nw ash\nm ere\nĠbe wild\nĠM ae\nV irgin\nph rase\nĠsign aled\nĠH IGH\nĠprot 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s\nĠsupplement al\nĠpower house\nĠSt ru\nF ocus\naph ael\nĠCol leg\natt i\nZ A\nĠp erenn\nĠSign ature\nĠRod ney\nĠcub es\nidd led\nĠD ante\nĠIN V\niling ual\nĠC th\nĠso fa\nĠintimid ate\nĠR oe\nĠDi plom\nĠCount ries\nays on\nĠextrad ition\nĠdis abling\nĠCard iff\nĠmemor andum\nĠTr ace\nĠ?? ?\nse ctor\nĠRou hani\nĠY ates\nĠFree ze\nĠbl adder\nM otor\nĠProm ise\nant asy\nĠforesee able\nĠC ologne\ncont ainer\nĠTre es\nĠG ors\nĠSin clair\nĠbar ring\nkey e\nĠsl ashed\nĠStat istical\né ĩ\nĠâĸ º\nAll ows\nĠhum ility\nĠdr illed\nĠF urn\n44 3\nĠse wage\nĠhome page\nĠcour tyard\nĠv ile\nĠsubsid iaries\naj o\ndirect ory\nĠam mon\nV ers\ncharg es\nĠ} }\nĠCh ains\nĠ24 6\nn ob\nĠper cept\nĠg rit\nĠfisher men\nĠIraq is\nĠDIS TR\nĠF ULL\nĠEval uation\ng raph\nat ial\nĠcooper ating\nĠmel an\nĠenlight ened\nĠal i\nt ailed\nĠsal ute\nĠweak est\nĠBull dogs\nU A\nĠAll oy\nĠsem en\noc ene\nĠWilliam son\ns pr\n, âĢĶ\nĠG F\nitt ens\nBe at\nĠJ unk\niph ate\nĠFarm ers\nĠBit coins\nig ers\nd h\nĠL oyal\np 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mercial\nSee ing\nS oul\nĠevac uate\nĠc iv\nĠAs he\nĠdiv ides\nĠD agger\nrehens ive\nĠber ries\nĠD F\nĠs ushi\nĠplur ality\nW I\nĠdisadvant aged\nĠbatt alion\nob iles\n45 1\nĠcl ing\nĠunden iable\nĠL ounge\nĠha unt\np he\nĠquant ify\nĠdiff ered\nĠ[* ]\nĠV iz\nc um\nsl ave\nĠvide og\nĠqu ar\nĠbund les\nĠAl onso\nt ackle\nĠneur onal\nĠlandsl ide\nconf irmed\nĠDep th\nĠrenew ables\nB ear\nĠMaced onia\nĠjer seys\nĠb unk\nĠSp awn\nĠControl s\nĠBuch anan\nĠrobot ics\nĠemphas izing\nĠTut orial\nh yp\nist on\nĠmonument al\næ °\nĠCar ry\nĠt bsp\nen ance\nH ill\nart hed\nĠro tten\nDe an\nĠtw isting\nĠgood will\nĠimm ersion\nL iving\nĠbr ushes\nĠC GI\nĠAt k\ntr aditional\nĠph antom\nĠSt amina\nĠexpans ions\nĠMar in\nĠembark ed\nĠE g\nint estinal\nĠPE OPLE\nĠBo oth\nĠApp alach\nĠreleg ated\nV T\nM IT\nĠmust er\nĠwithdraw ing\nĠmicrosc ope\nĠG athering\nĠC rescent\nĠArgent ine\nĠDec re\nĠDomin ic\nĠbud s\nant age\nĠI on\nĠwid ened\nONS ORED\nĠGl oves\niann opoulos\nraz en\nfe el\nĠrepay ment\nĠhind sight\nĠRE ALLY\nĠPist ol\nĠBra h\nĠwat ts\nĠsurv ives\nĠfl urry\niss y\nAl ert\nĠUrug uay\nPh oenix\nS low\nĠG rave\nĠF ir\nĠmanage able\nĠtar iff\nĠU DP\nĠPist ons\nĠNiger ian\nĠstrike outs\nĠcos metics\nwhel ming\nf ab\nc ape\npro xy\nĠre think\nĠover coming\nsim ple\nĠw oo\nĠdistract ing\nĠSt anton\nĠTuls a\nĠD ock\n65 9\nĠdisc ord\nĠEm acs\nĠV es\nĠR OB\nĠreass uring\nĠcons ortium\nMuslim s\n3 21\nĠprompt s\nse i\nĠH itch\nimp osed\nĠF ool\nĠindisc rim\nwr ong\nbu querque\nD avis\n! ]\nĠtim eless\nĠNE ED\nĠpestic ide\nĠrally ing\nĠCal der\nĠå ¤\nĠx p\nĠUn le\nĠEx port\nlu aj\nB uff\n) </\nB oot\nĠChrys ler\nor ative\nM ess\nĠneglig ible\nert odd\nĠMush room\nĠG ale\ng c\nĠCos by\nĠR ural\nrit ical\nB ell\nĠturb ine\n00 200000\nĠlegit imately\nĠAnim ated\nT ED\nĠThe odore\nc onduct\nĠH ier\nĠcounterfe it\nĠAlger ia\nĠun beat\ncont roller\nĠun res\nĠscram bling\nĠFall on\nT es\nĠam ber\nĠroy alties\nĠShel ter\nĠL ester\nĠclass ify\nRem ote\nĠun heard\nĠcontrovers ies\nĠenrich 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ard\nĠche wing\nu cl\norgan ic\nĠlocal ized\nĠEconom ist\nĠacqu ainted\nDef inition\ns ed\nCrit ics\nĠc c\n45 3\n38 1\nĠfell ows\nĠcheck points\n0 25\nĠre election\nĠmed iated\nĠK DE\nĠhurd le\nĠtext ing\nPer fect\nĠtrust ees\nfect ure\nĠd ich\nmon ary\nĠdist inctions\nĠ14 00\nĠus her\nĠparas ites\nĠSh aring\nĠV im\nĠbar becue\nĠMin isters\nere lla\nĠe b\nĠm c\nĠSome how\nĠIn sect\nch anges\nb road\nĠBy z\nĠgrap es\n66 9\nĠ= ================\nĠass imil\nĠhaun ting\nĠfire power\nĠdef amation\nem phasis\nĠcomp ose\nĠallerg ies\nĠstr ang\nroll ers\nb ang\nĠbrew ers\nron gh\nri ot\np oor\nc old\nS ample\nĠbu oy\n0 40\nĠCourt ney\nĠ26 8\nĠWed ding\n70 2\nĠobsess ive\nĠbra king\nĠL al\nan ical\nå ¦\nat en\nCon struction\nĠclin ically\niers hip\nN ames\nĠDisc uss\nĠRam os\nĠloc ale\nĠAgric ultural\nEn able\nĠhorse power\nent ure\nP ref\nC ourt\nĠstaff ing\nĠfut uristic\ndri vers\nĠMarket place\næĪ ¦\nFriend s\nĠdam ning\nĠCustom ers\nĠwe eds\nĠM ai\nĠag ile\nĠT att\nic ent\nR anked\ncro 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ablishment\num sy\nĠgather ings\nĠN aj\nCharl ie\nĠas cent\nĠProt ector\nĠal gebra\nĠbi os\nfor ums\nEL S\nIntrodu ced\nĠ3 35\nĠastron omy\nCont ribut\nĠPol ic\nPl atform\nĠcontain ment\nw rap\nĠcoron ary\nĠJ elly\nman ager\nĠheart breaking\nc air\nĠChe ro\nc gi\nMed ical\nĠAccount ability\n! !\"\noph ile\nĠpsych otic\nĠRest rict\nĠequ itable\niss ues\nĠ19 05\nĠN ek\nc ised\nĠTr acking\nĠo zone\nĠcook er\nros is\nĠre open\nĠinf inity\nĠPharm aceutical\nens ional\nAtt empt\nĠR ory\nMar co\nĠawa its\nH OW\nt reated\nĠbol st\nĠreve red\nĠp ods\nopp ers\n00 10\nĠampl itude\nric an\nSP ONSORED\nĠtrou sers\nĠhal ves\nĠK aine\nĠCut ler\nĠA UTH\nĠsplend id\nĠprevent ive\nĠDud ley\nif acts\numin ati\nĠY in\nĠad mon\nĠV ag\nĠin verted\nĠhast ily\nĠH ague\nL yn\nĠled ger\nĠastron omical\nget ting\nĠcirc a\nĠC ic\nĠTenn is\nLim ited\nĠd ru\nĠBY U\nĠtrave llers\nĠp ane\nĠInt ro\nĠpatient ly\nĠa iding\nĠlo os\nĠT ough\nĠ29 3\nĠconsum es\nSource File\nĠ\"\" \"\nĠbond ing\nĠtil ted\nĠmenstru al\nĠCel estial\nUL AR\nPlug in\nĠrisk ing\nN az\nĠRiy adh\nĠacc redited\nĠsk irm\né Ľ\nĠexam iner\nĠmess ing\nĠnear ing\nĠC hern\nĠBeck ham\nĠsw apped\nĠgo ose\nK ay\nĠlo fty\nĠWal let\nĠ[ '\nĠap ocalypse\nĠb amboo\nĠSP ACE\nĠEl ena\nĠ30 6\nac ons\nĠtight ened\nĠadolesc ence\nĠrain y\nĠvandal ism\nĠNew town\nĠcon ject\nc akes\nĠche ated\nĠmoder ators\npar ams\nE FF\nĠdece it\nĠST L\nĠTanz ania\nĠR I\nĠ19 23\nĠEx ile\nthe l\nĠthe olog\nĠquir ky\nĠIr vine\nĠneed y\nor is\nU m\nK a\nĠmail box\n3 22\nĠb os\nĠPet ra\nK ING\nĠenlarg ed\nO ften\nĠbad ass\nĠ3 43\nĠPl aces\nĠC AD\nĠpr istine\nĠinterven ing\nd irection\nĠl az\nĠD SM\nĠproject ing\nĠF unk\nag og\npay ment\nn ov\nĠch atter\nAR B\nĠexam inations\nĠHouse hold\nĠG us\nF ord\n4 14\nB oss\nĠmy stic\nĠle aps\nĠB av\nul z\nb udget\nFoot ball\nĠsubsid ized\nĠfirst hand\nĠcoinc ide\noc ular\nCon n\nĠColl abor\nĠfool s\nam ura\nah ar\nr ists\nĠsw ollen\nĠexp ended\nĠP au\ns up\nĠsp ar\nĠkey note\ns uff\nĠunequ al\nĠprogress ing\nstr ings\nĠGamer gate\nDis ney\nĠEle ven\nom nia\nĠscript ed\nĠear ners\nbro ther\nĠEn abled\næ ³\nĠlar vae\nĠL OC\nm ess\nWil son\nĠTem plate\nsuccess fully\nĠparam ount\nĠcamoufl age\nĠbind s\nĠQu iet\nĠSh utterstock\nr ush\nĠmasc ot\nfort une\nĠCol t\nĠBe yon\nhab i\nĠha irc\nĠ26 7\nĠDe us\nĠtw itch\nĠconcent rating\nĠn ipples\nc ible\nĠg ir\nN Z\nM ath\nn ih\nRequ ired\nĠp onder\nĠS AN\nĠwedd ings\nĠl oneliness\nN ES\nĠMah jong\n69 5\nadd le\nĠGar ner\nĠC OUR\nBr idge\nĠsp ree\nĠCald well\nĠbri bery\nĠ���� ����\nplug ins\nĠr acket\nĠchamp agne\nvers ible\nV ote\nĠmod ifiers\nMay or\n6 80\nĠassemb lies\nĠS ultan\nĠN ing\nĠLad ies\nĠsulf ur\nĠor bs\nĠ---- -\n____ ___\nĠJournal ism\nĠes ports\nĠl ush\nĠh ue\nĠspect ral\nH onest\nãĥ ı\nĠbus hes\nĠrein forcement\nĠre opened\nĠWhe els\nĠM org\nrie ving\nĠaux iliary\nĠj Query\nĠB AT\ntes que\nĠver tex\np ure\nf rey\nãĤ º\nd os\nĠty ph\nĠc ull\nĠe q\nĠdec on\nĠtoss ing\nĠdispar ate\nĠBr igham\nprint f\nled ged\nĠsu nd\nĠco zy\nĠhepat itis\nper forming\nĠav 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pm\nĠske wed\nĠBl anc\nĠS akuya\nĠhot ter\nĠ19 24\nop ian\nck o\nĠcr umbling\nĠcapt ains\nĠAppropri ations\nle aders\ndro pping\nan uts\nĠrevers ing\nĠP ose\nĠS ek\nSc ot\nĠIde a\nc ise\nĠSloven ia\nĠ3 17\nDo ctor\nĠcro cod\nald i\nSe a\nĠFar rell\nĠmerc enaries\nĠR NC\nĠGu ess\nĠp acing\nM achine\nStreamer Bot\nĠChar ity\nĠ29 8\nĠcann ons\nĠTob y\nTPP StreamerBot\nĠPass ion\ncf g\nTh om\nĠbad ges\nĠBern stein\n. âĢĵ\nĠP OP\nĠCon j\nĠinitial ization\nĠbiod iversity\nD ub\nĠfeud al\nĠdisclaim er\nĠc row\nĠign ition\nar f\nS HA\nĠk Hz\nh azard\nĠArt ists\noe uv\n67 9\nĠRud y\nN ine\nĠRam adan\nå ½\nitt o\nĠadren aline\nC ert\nĠsmell ed\nĠimp unity\nĠag endas\nĠRe born\nĠCon cent\nĠSe ems\nĠo mega\nĠDust in\nĠback er\nĠSau ce\nĠBoy le\nW IN\nĠsp ins\nĠpa uses\nu pt\nĠshred ded\nĠstra pped\nĠCor ruption\nĠscr atches\nĠn i\nĠatt ire\nĠS AF\nFactory Reloaded\nĠI PS\nĠ( %\nĠsem inar\nf ocus\nc ivil\nĠ18 60\nint osh\nĠcontin ual\nĠabbre vi\nĠS ok\noc obo\nX M\nĠfr antic\nĠunavoid able\nĠar tery\nĠannot ations\nb ath\nCl imate\nĠd ors\nĠSl ide\nco ord\nĠRel oad\nĠL DL\nĠLove craft\nĠunim agin\nĠresemb led\nĠbarr acks\nn p\nĠsurrog ate\nĠcategor ized\nãĤ ©\nĠvacc inated\nĠdrain age\nĠind ist\nĠWhats App\nĠ18 70\noler ance\ninv oke\nam orph\nĠrecon nect\nĠem anc\nĠblind ness\nĠ12 80\nintern et\nc ollar\nĠalt ru\nĠab yss\nĠT RI\n65 7\nĠinf used\nHE AD\nĠforest ry\nĠWood y\nĠC i\nw i\ns am\n78 4\nhol iday\nĠmog ul\nĠF ees\nĠD EN\nIn ternal\nur bed\nf usc\nat om\nĠIll usion\nĠpoll ed\nĠfl ap\nĠco ax\nL GBT\nAn aly\nĠSect ions\nĠCalif orn\nem n\nĠh ither\nĠN IGHT\nĠn ailed\nĠPip eline\n39 1\no of\nĠPr imal\nvere nd\nĠsl ashing\nĠret ri\navi our\nĠdepart ing\ng il\nIS C\nĠmid way\nĠultras ound\nĠbeh aving\nĠT ara\nclass es\nV irtual\nĠColon ial\nĠstri pping\nĠorchestr ated\nĠGra ves\n45 2\nĠIron ically\nĠWrit ers\nĠl ends\nĠMan z\nĠra ven\nĠoxid ative\nĠ26 6\nEL F\nact ually\nasc ar\nD raft\nĠfavour able\nĠhumili ating\nĠf idelity\nĠH of\nĠX uan\n49 6\nĠlay ered\nat is\n79 0\nĠpay check\nit on\nK ar\nĠVM ware\nĠFar mer\nĠserv ic\ngl omer\nĠsl ump\nĠFab ric\nĠD OC\nest ing\nĠreass ure\nĠph yl\nv olt\nit ory\nR ules\nĠoxid ation\nĠpri zed\nĠmist ress\nĠDj ango\nWAR N\nå ij\nĠenc ode\nĠFeed back\nĠstupid ity\nI an\nĠYugoslav ia\n× ¨\nac l\nUT E\n19 77\nĠqual ifies\nĠpuls es\npret ty\nĠfro ze\nĠs s\nIter ator\nĠur gently\nĠm ailed\nĠCh am\nĠsust aining\nĠbas il\nĠpupp ies\nil ant\nĠP LEASE\nl ap\nace ous\nF ear\nĠMaster y\naut omatic\nĠT AG\nĠant im\nag les\n47 3\nfram es\nĠwh ispers\nĠWho ever\nĠbra very\nĠUK IP\nract ions\n\"\" \"\nĠt ame\nĠpart ed\nevery thing\nCON T\nĠind ebted\nĠadd r\nre k\nIR ED\nĠem inent\ncl inton\nĠo usted\nĠreview er\nĠmelt down\nĠre arr\nĠY ao\nthe real\naby te\nĠst umbling\nĠbat ches\nĠ25 9\nĠcontrace ptive\nĠprost itute\nens is\nDe cl\nĠSt rikes\nM ilitary\nĠO ath\nv acc\npp ings\n05 2\nĠpart Name\namp ing\nRep orts\nK I\nCH R\nĠsubt ly\nsw ers\nBl ake\nus ual\nĠcontest ants\nĠcart ridges\nĠGRE AT\nĠbl ush\nĠâĢ º\n47 2\nĠreason 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title\nOver ride\nĠorig inating\nĠC CP\nĠsw ore\nĠSo le\nĠDis orders\n3 29\nĠprocess ion\nĠref urb\nĠimm ersed\nrequ ently\nĠskept ics\nĠcer amic\nm itter\nen stein\nb elt\nĠT IT\nb idden\nĠf ir\nm ist\n> ]\nĠwe ave\nĠParad ox\nĠentr usted\nĠBarcl ays\nĠnovel ist\nog ie\n80 6\nĠnin ety\nĠdisag reements\n@@@@ @@@@\nĠAus chwitz\nc ars\nĠL ET\nt ub\narant ine\nP OS\nĠback story\nĠcheer ful\nĠR ag\nek a\nbi ased\nĠinexper ienced\nak ra\nĠW itt\nt an\nĠrap ist\nĠplate au\nch al\nĠInqu is\nexp ression\nĠc ipher\nĠsh aving\nadd en\nre ly\n( \\\nism a\nĠReg ulatory\nCH AR\nily n\nN VIDIA\nG U\nĠmur m\nla us\nChrist opher\nĠcontract ual\nĠPro xy\nĠJa ime\nĠMethod ist\nĠstew ards\nst a\nper ia\nĠphys iology\nĠbump ed\nĠf ructose\nAustral ian\nĠMet allic\nĠMas querade\nar b\nĠprom ul\nĠdown fall\nĠbut cher\nĠb our\nĠIN FORMATION\nĠB is\npect s\nad ena\nĠcontempl ating\nar oo\ncent ered\nĠPe aks\nUs ed\nĠmod em\nĠg enders\nĠ8 000\n37 1\nĠm aternity\nĠR az\nĠrock ing\nĠhandgun s\nĠD ACA\nAut om\nĠN 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eral\nĠC BO\nĠcollabor ated\nz ig\nLeg al\nĠapart heid\nĠbr id\nĠpres et\nĠD ET\nĠAM A\n× Ķ\narch ing\nauc uses\nbuild er\nĠpo etic\nĠem ulator\nĠMole cular\nĠhon oring\nise um\nĠtract or\nĠCl uster\nĠCal m\nared evil\nĠsidew alks\nĠviol in\nĠgeneral ized\nĠAle c\nĠemb argo\nĠfast ball\nĠHT TPS\nĠL ack\nĠCh ill\nri ver\nC hel\nĠSw arm\nĠLev ine\nro ying\nL aunch\nĠkick er\nĠadd itive\nĠDe als\nW idget\ncont aining\nĠescal ate\nĠOP EN\nĠtwe aked\nĠst ash\nĠsp arks\nĠEs sex\nĠE cc\nĠconv ict\nĠblog ging\nI ER\nĠH L\nĠmurd erers\n75 9\nĠH ib\nĠde pl\nĠJ ord\nS ac\nĠdis sect\nĠHow e\nos her\nĠcustom izable\nĠFran z\nĠat ro\nÄ ĩ\nĠ000 4\nĠout post\nR oss\nĠglyph osate\nĠHast ings\nĠBE FORE\nĠsh ove\no pped\nĠSc ala\nĠam ulet\nan ian\nĠexacerb ated\nĠe ater\n47 1\nUM E\nĠpul p\nizont al\nĠZ am\nĠAT I\nimm une\naby tes\nĠunnecess arily\nĠC AT\nĠAx is\nĠvisual ize\nà ī\nĠRad ical\nf m\nDoc uments\nĠFor rest\nĠcontext ual\nĠSy mbol\nĠtent ative\nĠDO ES\nĠGood s\nĠintermitt ent\n} :\nmedi 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er\nR h\nly cer\nĠreinforce ments\nĠJohann es\nĠGall agher\nĠgym n\nS uddenly\nĠext ortion\nk r\ni ator\nT a\nĠhippocamp us\nN PR\nĠComput ing\nĠsquare ly\nĠmod elling\nĠFor ums\nĠL isp\nĠKrish na\nĠ3 24\nĠr ushes\nĠens ued\nĠcre eping\non te\nn ai\nil ater\nĠHorn ets\nĠob livious\nIN ST\n55 9\nĠjeopard y\nĠdistingu ishing\nj ured\nĠbeg s\nsim ilar\nph ot\n5 30\nĠPark way\nĠs inks\nĠHearth stone\nib ur\nĠBat on\nAv oid\nĠd ancer\nĠmag istrate\nary n\nĠdisturb ances\nĠRom ero\nĠpar aph\nĠmis chief\nâĸ ĵ\nĠSh aria\nĠur inary\nr oute\niv as\nf itted\nĠeject ed\nĠAl buquerque\nĠ4 70\nĠirrit ated\nĠZ ip\nĠB iol\nà į\nĠden ounce\nĠbin aries\nĠVer se\nĠopp os\nĠKend rick\nĠG PL\nĠsp ew\nĠEl ijah\nĠE as\nĠdr ifted\nso far\nĠannoy ance\nĠB ET\n47 4\nĠSt rongh\nit ates\nĠCogn itive\noph one\nĠIdent ification\nocr ine\nconnect ion\nĠbox er\nĠAS D\nĠAre as\nY ang\nt ch\null ah\nĠdece ive\nComb at\nep isode\ncre te\nW itness\nĠcondol ences\nht ar\nĠhe als\nĠbuck ets\nĠLA W\nB lu\nĠsl ab\nĠOR 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u\nĠback log\nĠElect ro\nTr uth\n48 3\nĠcond ensed\nr ules\nĠCons piracy\nĠacron ym\nhand led\nĠMat te\nj ri\nĠImp ossible\nl ude\ncre ation\nĠwar med\nĠSl ave\nĠmis led\nĠfer ment\nĠK ah\nink i\nke leton\ncy l\nĠKar in\nHun ter\nReg ister\nĠSur rey\nĠst ares\nĠW idth\nĠN ay\nĠSk i\nĠblack list\nuck et\nĠexp ulsion\nim et\nĠret weet\nvant age\nFe ature\nĠtro opers\nĠhom ers\n9 69\nĠconting ency\nĠW TC\nĠBrew er\nfore ign\nW are\nS olar\nĠund ue\nRE C\nulner able\npath ic\nĠBo ise\nĠ3 22\nĠarous ed\nĠY ing\nä¸ į\nuel ess\nĠp as\nĠmor p\nĠfl oral\nEx press\nud ging\nk B\nĠGr anted\nØ ¯\nĠMich a\nĠGoth ic\nĠSPEC IAL\nĠRic ardo\nF ran\nĠadminister ing\n6 20\npor a\nĠ ®\nĠcomprom ises\nĠb itten\nAc cept\nTh irty\nÐ ²\nĠmater ially\nĠTer r\nig matic\nch ains\nĠdo ve\nstad t\nMar vel\nFA ULT\nĠwind shield\nĠ3 36\nad ier\nĠsw apping\nĠflaw less\nĠPred ator\nĠMiche le\nĠprop ulsion\nĠPsych ic\nĠassign ing\nĠfabric ation\nĠbar ley\nl ust\nĠtow ering\nĠalter cation\nĠBent ley\nSp here\nĠtun 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uddy\nĠMc Int\nĠDec ay\n5 25\nĠconspic uous\nĠEx posure\nĠresc ind\nĠwear able\nĠ3 28\nour met\nah s\nĠRob ots\nĠe clips\ninst ance\nĠRE PORT\nĠApp l\n0 30\nĠSk ies\n01 00\nĠfall acy\nS ocket\nĠRece iver\nĠsol ves\nĠButter fly\nĠSho pping\nĠFI RE\n65 4\nMed ic\nĠsing ers\nĠNeed less\n'' ''\nisher s\nĠD ive\n58 8\nĠselect ively\nĠcl umsy\n88 9\nĠpurch aser\near ned\nard y\nĠbenef iting\neng lish\nĠyield ing\nĠP our\nĠspin ach\nĠdel ve\nĠC rom\n6 10\nĠexport ing\nĠMA KE\nĠ26 3\nĠg rop\nĠenv oy\nĠInqu iry\nĠLu igi\nd ry\nĠT uring\nThumbnail Image\nĠVar iety\nĠfac et\nĠfl uffy\nĠexcerpt s\nĠsh orth\nĠOl sen\nCL UD\nĠrel iant\nĠUN C\nT our\nĠbat hing\nComp any\nĠglobal ization\nP red\nĠMalf oy\nĠh oc\nj am\ncraft ed\nĠBond s\nĠKiss inger\nEng land\nĠorder ly\ncat entry\nĠ26 1\nĠexch anging\nĠInt ent\nĠAmend ments\nD OM\nĠst out\n³³³³³³³³ ³³³³³³³³\nĠAir bus\nĠ27 8\nhy de\nP oll\nItem ThumbnailImage\nĠlooph oles\nĠPill ar\nĠexpl or\nSt retch\nA part\nĠun married\nLim it\nĠTransform ers\nĠintellect ually\nunct ure\n18 00\nĠd arn\nB razil\nĠleft over\nber us\nf red\nMine craft\n3 26\nĠForm s\nĠproof s\nĠDes igned\nĠindex es\nĠSupp ose\nEM S\nĠL oving\nĠBon nie\nim ating\nOT US\nĠconduct or\nĠbehav ed\nĠF ren\nĠsy nerg\nĠmillenn ium\nĠcater ing\nĠL auder\nW r\nĠY iannopoulos\nĠAT F\nĠensl aved\nĠawaken ed\nD VD\nĠED ITION\nĠConc ert\nĠChall enger\nĠH aku\numer ic\nĠdep recated\nĠSH AR\n4 12\nĠdy stop\nĠtremb ling\nĠdread ed\nĠSp ac\np adding\nRe pl\nĠG arrison\nM ini\nĠun paralleled\nam ar\nURR ENT\nw reck\nc ertain\nt al\nĠC LS\napp ings\nĠsens ed\nĠf encing\nĠPas o\nĠDes k\nĠsc off\nĠcontem plate\nĠL iga\nl iquid\n75 7\nĠapp rentice\nĠUCH IJ\n5 70\nĠTh ousand\nĠIll um\nĠchampion ed\nãĤ Į\nĠelect ors\nĠ3 98\nĠH ancock\nround ed\nĠJ OHN\nĠuns atisf\nĠqual ifier\nĠGad get\nEN E\nĠdead liest\nĠPl ants\nĠ ions\nĠacc ents\nĠtwe aking\nĠsh aved\nF REE\nĠCh aser\nAgain st\n9 60\nĠmeth amphetamine\nĠnormal ized\nĠ$ \\\nĠPre cision\nĠGu am\nĠch oked\nĠX II\nĠCast ing\nTor 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e\nĠFen rir\nĠtherap ists\nore al\n^^ ^^\nĠtotal ed\nĠboo ze\nĠR PC\nProsecut ors\nĠdis eng\nĠSh ared\nĠmotor cycles\nĠinvent ions\nĠlett uce\nĠMer ge\nĠJ C\nĠspiritual ity\nĠWAR NING\nĠunl ucky\nĠT ess\nĠtong ues\nĠD UI\nT umblr\nĠle ans\nĠinv aders\nĠcan opy\nĠHur ricanes\nĠB ret\nĠAP PLIC\nid ine\nick le\nReg arding\nĠve ggies\nĠe jac\nju ven\nF ish\nD EM\nĠD ino\nTh row\nĠCheck ing\nbe ard\n( &\nĠj ails\nĠh r\ntrans fer\niv ating\nĠfle ets\nĠIm ag\nĠMc Donnell\nĠsnipp et\nIs a\nĠCh att\nĠSt ain\nĠSet FontSize\nĠO y\nĠMathemat ics\n49 4\nĠelectro ly\nĠG ott\nĠBr as\nB OOK\nĠF inger\nd ump\nĠmut ants\nĠrent als\nĠinter tw\nĠc reek\nail a\nBro ther\nĠDisc ord\npe e\nraw ler\nĠcar p\nĠ27 9\nãĤ· ãĥ£\nrel ations\nĠcontr asts\nCol umn\nĠrec onnaissance\nĠun know\nĠl ooting\nĠregul ates\nĠopt imum\nĠChero kee\nĠA ry\nLat est\nĠroad side\nĠd anced\nĠUnic orn\nA cknowled\nĠuncont roll\nĠM US\nat io\nch ance\nha ven\nVAL UE\nĠfavour ites\nĠceremon ial\nb inary\npe ed\nwood s\nEM P\nĠv 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s\nĠcommun ion\nĠX Y\nĠph ony\nĠnewcom er\nĠ3 32\n.\" ,\"\nĠredist ribution\nProt ect\nĠSo f\nK al\nĠlip stick\nw orst\nĠtang led\nĠretrospect ive\nint eger\nĠvolunte ering\nĠ19 07\nĠ --------------------\nic hen\nĠunve iling\nĠsen seless\nĠfisher ies\n\\ -\nĠh inges\nĠcalcul us\nMy th\nĠund efeated\nĠoptim izations\nĠdep ress\nĠbill board\nĠY ad\nĠPy ramid\nIs n\nI de\nĠleg ion\nĠK ramer\nent anyl\nĠpenet rating\nĠHaw th\nĠPR ODUCT\nĠGer ard\nĠP act\nĠIn cluding\nĠEl ias\nĠEl aine\nvis ual\nĠhum ming\nĠcond esc\nĠF asc\nä¸ Ĭ\nĠe galitarian\nĠdev s\nĠD ahl\nO ps\nD H\nĠB ounce\nid ated\nald o\nĠrepublic an\nĠh amb\nĠS ett\nograph ies\nCH APTER\nĠtrans sexual\nĠsky rocket\nans wer\nĠmark up\nØ ª\nĠhero ine\nComp are\nĠT av\nBe ast\nĠsuccess ors\nĠna ïve\nĠBuck ley\nst ress\nme at\nĠdownload able\nĠindex ed\nĠsc aff\nĠL ump\nĠHom o\nStud io\nIn sp\nĠr acked\nfar ious\nĠPet ty\nEx ternal\nĠ19 09\nW ars\ncom mit\nput ers\nĠun ob\nĠEr r\nĠE G\nĠAl am\nĠSiber ia\nĠAtmosp heric\nIS TER\nĠSatan ic\ntrans lation\nĠL oud\ntra umatic\nl ique\nĠreson ate\nĠWel ch\nĠspark ing\nĠT OM\nt one\nĠout l\nĠhandc uffed\nĠSer ie\n8 01\nĠland marks\nĠRee ves\nĠsoft ened\nĠdazz ling\nĠW anted\nmonth s\nMag ikarp\nĠunt reated\nĠBed ford\nM i\nĠDynam o\nO re\n79 5\nĠwrong ful\nĠl ured\nĠcort isol\nĠve x\nd rawn\nile t\nDownload ha\nĠF action\nĠlab yrinth\nĠhij acked\nw aters\ner ick\nĠsuper iors\nĠRow ling\nĠGu inness\nĠt d\n99 2\nĠune arthed\nĠcentr if\nĠsham eless\nP od\nĠF ib\nĠ icing\nĠpredict or\nĠ29 2\nfore station\ncon struct\nC and\n@ #\nĠag itated\nĠre pr\nOV A\nĠkn itting\nĠLim a\nĠf odder\n68 4\nĠPerson a\nk l\n7 01\nĠbreak up\ná ¸\nĠapp alled\nĠantidepress ants\nĠSus sex\nHar ris\nĠTher mal\nee ee\nU pload\nĠg ulf\nĠdoor step\nĠSh ank\nL U\nĠM EN\nĠP ond\ns orry\nĠmis fortune\nn ance\nĠb ona\nM ut\nĠde graded\nĠL OG\nĠN ess\nan imal\nĠa version\nund own\nĠsupplement ed\nĠC ups\nĠ50 4\nĠdep rive\nĠSpark le\nÅ Ĥ\nĠMed itation\nauth ors\nĠSab an\nĠN aked\nair d\nĠMand arin\nĠScript ures\nĠPerson nel\nĠMahar ashtra\nĠ19 03\nĠP ai\nĠMir age\nomb at\nAccess ory\nĠfrag mented\nT ogether\nĠbelie vable\nĠGl adiator\nal igned\nĠSl ug\nM AT\nĠconvert ible\nĠBour bon\namer on\nĠRe hab\nnt ax\nĠpowd ered\npill ar\nĠsm oker\nĠMans on\nĠB F\n5 11\nĠGood ell\nĠD AR\nm ud\ng art\nĠob edient\nĠTrans mission\nĠDon ation\n8 80\nĠbother ing\nMaterial s\nãĤ ±\ndest roy\nĠfore going\nĠanarch ism\nĠK ry\nice ps\nĠl ittered\nĠSch iff\nĠanecd otal\nun its\nĠf ian\nĠSt im\nĠS OME\nĠInv aders\nĠbehaviour al\nĠVent ures\nĠsub lime\nĠfru ition\nĠPen alty\nĠcorros ion\n¶ ħ\nĠlik ened\nĠbesie ged\nween ey\nĠCre ep\nĠlinem en\nmult i\nic ably\nud der\nĠvital ity\nĠshort fall\nĠP ants\nap ist\nH idden\nĠDro ps\nmed ical\nĠpron unciation\nĠN RL\nĠinsight ful\nJ V\nĠBe ard\nĠCh ou\nĠchar ms\nĠb ins\nĠamb assadors\nĠS aturdays\nĠinhib itor\nĠFr anch\n6 01\n', '\nĠCon or\nart ney\nĠX peria\ng rave\nbe es\nĠProtest ants\nĠso aking\nĠM andal\nĠph ased\nĠ6 60\nĠsc ams\nĠbuzz ing\nĠItal ians\nĠLoren zo\nĠJ A\nĠhes itated\nĠcl iffs\nĠG OT\ningu ishable\nĠk o\nĠinter ruption\nZ ip\nLear ning\nĠundersc ores\nĠBl ink\nK u\n57 9\nĠAut ob\nI RE\nĠwater ing\nĠpast ry\n8 20\nĠvision ary\nĠTempl ar\nawa ited\nĠpist on\nĠant id\ncurrent ly\nĠp ard\nĠw aging\nĠnob ility\nĠY us\nĠinject ing\nf aith\nĠP ASS\nå º\nĠret ake\nĠPR OC\nĠcat hedral\nb ash\nĠwrest lers\nĠpartner ing\nĠn oses\nĠ3 58\nTrans form\nam en\nĠb outs\nĠId eal\nĠConstant in\nĠse p\nĠMon arch\natt en\nĠPe oples\nmod ified\nĠmor atorium\nĠpen chant\nĠoffensive ly\nĠprox ies\nok ane\nĠTaiwan ese\nĠP oo\nĠH OME\nus ional\nĠver bs\nĠO man\nvis ory\nĠpersu asion\nĠmult it\nĠsc issors\nG ay\now ay\noph ysical\nl us\ngn u\nĠap ocalyptic\nĠabsurd ity\nĠplay book\nĠautobi ography\nI UM\nĠsne aking\nĠSim ulation\npp s\nell ery\nPlan et\nĠright fully\nĠn iece\nĠN EC\nĠIP O\nĠDis closure\nlean or\nous y\nST ER\nĠ28 2\nCru z\nCh all\n64 3\nĠSurv ive\nĠF atal\nĠAm id\nap o\nWe apons\nD EN\n7 70\nĠGreen wald\nĠlin en\nal os\nĠpollut ants\nĠPCI e\nk at\nĠp aw\nĠK raft\nC hem\nĠTermin ator\nĠre incarn\nĠ] [\nĠSe eds\nĠsilhou ette\nĠSt ores\nĠgro oming\nĠD irection\nĠIs abel\nĠBr idges\nðŁ ij\nE ED\nĠM orsi\nĠval ves\nĠRank ed\nĠPh arma\nĠOrgan izations\nĠpenet rated\nĠRod ham\nĠProt oss\nĠove rest\nĠex asper\nĠT J\nĠ 000000\nĠtrick le\nĠbour bon\nWH O\nĠw retched\nĠmicrosc opic\nĠcheck list\nĠad orned\nR oyal\nAd minist\nĠRet irement\nĠHig hest\nWe ather\nile ge\nĠincre ments\nĠC osponsors\nĠmas se\nĠS inn\nr f\nĠh ordes\nas sembly\n75 4\nĠNat asha\nĠTY PE\nĠGEN ERAL\nĠarr anging\nĠ40 7\nl ator\nĠg lean\nĠdisc redited\nĠclin icians\nUN E\nĠachie ves\nĠEm erson\ncom plex\n= [\nĠprincip ally\nĠfra il\np icked\nĠthan king\nĠre cl\nĠL AST\nĠsupp ressing\nil ic\nĠantidepress ant\nĠLis bon\nĠth or\nĠsp a\nĠking doms\nĠPear ce\nem o\nĠpl ung\nĠdiv est\nĠ ********************************\nb is\nosp els\nad r\nSp irit\nhall a\nP ink\nend ez\nĠresurrect ed\nesc ape\nĠRosen stein\nĠge ological\nĠnecess ities\nĠcarn iv\nĠE lys\nĠBar ney\nĠ29 6\ndig y\nST ON\nD OWN\nĠmil estones\nĠk er\nĠdismant ling\nĠre prim\nĠcross ings\n19 45\nĠpatri archy\nĠblasp hemy\nĠ3 59\nmet ry\nĠOb esity\nĠDiff erences\nbl ocking\nãĥķ ãĤ¡\nich ita\nĠSab ha\nph alt\nĠCol o\nual a\neffic ients\nĠMed ina\ncon sole\n55 7\nĠHann ibal\nĠHab it\nĠF ever\nĠthen ce\nĠsyn agogue\nĠessential s\nĠw ink\nĠTr ader\nID A\nĠSp oiler\nĠIceland ic\nĠHay ward\nĠpe ac\nĠmal ice\nĠflash back\nĠth w\nĠlay offs\nL iquid\nĠtro oper\nĠh inge\nĠRead ers\nPh ill\nĠB auer\nCre ated\nĠaud its\nac compan\nĠunsus pecting\nier a\n6666 6666\nĠbro ch\nĠapprehend ed\nĠM alk\ncer ning\nĠCod ex\nO VER\nM arsh\nĠD eng\nĠExp ression\nĠdisrespect ful\nĠasc ending\nt ests\nĠPlaint iff\nster y\nĠAl ibaba\ndin and\nĠDem psey\nApplic ations\nmor al\nĠthrough put\nĠquar rel\nĠm ills\nĠhe mor\nĠC ASE\nterror ist\nst im\nifest yle\nro zen\nCE PT\nAr k\nu ci\nlect ic\nĠirrit ating\nshe ets\nA y\nĠrede emed\nĠhorn y\nĠTe ach\nĠS ear\ndem ocracy\n4 65\nĠRest ore\nĠstand by\nĠP is\niff in\nĠsleep y\nĠextr ater\nĠcompl iments\nFram eworks\nĠinstall s\nĠb anging\nsur face\nfound land\nĠmetaph ysical\nĠ28 3\noul s\ndev ices\nAr gs\nĠSac rifice\nĠMcC orm\nes on\nCons ervative\nĠM ikhail\nsee ing\nis ively\nĠRo oms\nĠGener ic\nĠenthusi astically\nĠgri pped\nĠcomed ic\nĠElectric ity\nĠgu errilla\nĠdec oration\nĠPerspect ive\nĠconsult ations\nĠun amb\nĠplag iar\nĠmagic ian\nĠe rection\nĠTour ism\nor ied\nro xy\n11 00\nT am\nĪ è\nÎ ³\n× ª\nĠPred ators\nNit rome\nĠtelesc opes\nproject s\nĠun protected\nĠst ocked\nĠEnt reprene\nnex pected\nĠwast ewater\nV ill\nĠint imately\nĠi Cloud\nĠConst able\nĠspo of\nĠne farious\nĠfin s\nĠcens or\nĠMod es\nĠEs per\nar bon\nĠinter sections\nĠlaud ed\nĠphys i\nĠgener ously\nĠThe Nitrome\nĠTheNitrome Fan\nĠar isen\nĠÙ Ī\nĠg lands\nĠPav ilion\nĠGu pta\nĠuniform ly\nĠr amps\nri et\nĠWH EN\nĠVan essa\nĠrout ed\nĠlim p\nĠC PI\np ter\nint uitive\nĠv aping\nĠexperiment ed\nĠOlymp us\nĠAm on\nĠsight ing\nĠinfiltr ate\nĠGentle man\nĠsign ings\nĠMe ow\nĠNav igation\nche cks\n4 33\nĠel apsed\nĠBulg arian\nesp ie\nĠS OM\nd uring\nĠsp ills\nanc a\nĠPly mouth\nM AL\nĠdomest ically\nĠWater gate\nĠF AM\nk illed\ned ited\nĠYour self\nĠsynchron ization\nĠPract ices\nST EP\nĠgen omes\nĠQ R\nnot ice\nĠloc ating\nz in\nĠ3 29\nal cohol\nĠk itten\nV o\nĠr inse\nĠgrapp le\nĠSc rew\nĠD ul\nA IR\nĠle asing\nĠCaf é\nĠro ses\nĠRes pect\nĠmis lead\nĠperfect ed\nĠnud ity\nĠnon partisan\nĠCons umption\nReport ing\nĠnu ances\nĠdeduct ible\nĠSh ots\nĠ3 77\nĠæ ľ\nano oga\nBen ef\nĠB am\nĠS amp\nif ix\nĠgal van\nĠMed als\nrad ius\nĠno bles\nĠe aves\nigr ate\nK T\nĠHar bour\nu ers\nĠrisk ed\nre q\nĠneuro t\nget table\nain a\nRom ney\nĠunder pin\nĠlo ft\nĠSub committee\nĠMong ol\nb iz\nĠmanif ests\nass isted\nĠG aga\nĠsy nergy\nĠreligious ly\nĠPre f\nĠG erry\nT AG\nĠCho i\n4 66\nbeh ind\nĠO u\nGold Magikarp\nĠhemor rh\nR iver\nĠtend on\nĠinj ure\nĠF iona\nĠp ag\nĠag itation\n|| ||\nur an\nĠE SA\nĠest eem\nĠdod ging\nĠ4 12\nr ss\nĠce ases\nex cluding\nĠint akes\nĠinsert s\nĠemb old\nĠO ral\nup uncture\n4 11\nĠUn ified\nĠDe le\nĠfurn ace\nĠCoy otes\nĠBr ach\nL abor\nĠhand shake\nĠbru ises\nGr ade\néĹ ĺ\nĠGram my\nile en\nSt ates\nĠScandinav ian\nĠKard ash\n8 66\nĠeffort lessly\nĠDI RECT\nĠTH EN\nĠMe i\nert ation\n19 68\nĠgro in\nw itch\nRequ irements\n98 5\nĠroof s\nĠest ates\nĠH F\nĠha ha\nĠdense ly\nĠO CT\nĠpl astics\nĠincident ally\nĠTr acks\nĠTax es\nĠch anted\nĠforce ful\nĠBie ber\nĠK ahn\nK ent\nĠC ot\nlic ts\nF ed\nĠhide ous\nĠVer d\nĠSynd icate\nĠIl legal\nJ et\nĠD AV\nre asonable\nc rew\nĠfundamental ist\nĠtruth ful\nĠJ ing\nĠl il\nĠdown ed\nĠen chanted\nĠPolic ies\nĠMcM aster\nĠH are\nides how\nĠpar ams\nen cers\ngorith m\nĠallow ances\nĠturb ulent\nĠcomplex ities\nĠK T\nĠ3 37\nĠGen etic\nF UN\nD oug\nt ick\nĠg igs\nument hal\nĠpatriarch al\nĠcal c\n, ...\nĠc out\nĠGu an\nĠpath ological\nĠR ivals\nĠunder rated\nĠflu orescent\nĠJ iu\narna ev\nĠQu an\nĠ4 29\nĠ à¨\nM ario\nCon struct\nĠC itation\nĠR acial\nĠR SA\nĠF idel\nĠ3 95\nPerson ally\nC ause\nà »\nrad ical\nin en\nĠvehement ly\nĠPap a\nĠintern ship\nĠfl akes\nĠRe ck\nLuck ily\nB ra\n20 20\nrav ings\nR N\nW onder\nSer iously\nĠre usable\nĠpoll uted\nĠP eng\nle igh\nind le\nĠcircuit ry\nĠMad onna\nĠB ART\nRes idents\natt ribute\nPhil adelphia\nCl ub\nĠplan ner\nĠfr antically\nĠfaith fully\nĠTerrit ories\nĠL AT\nĠAnders en\nan u\nĠP ARK\nĠS ora\ni age\nĠPlay offs\nĠG CC\n4 27\nĠab norm\nĠL ever\nĠdisob edience\nAs ync\nĠShe a\nV ert\nĠsk irts\nĠSaw yer\nx p\nĠwors ening\nĠsc apego\nĠAng le\noth al\nĠtro ve\nĠSt y\nĠN guyen\nmar ine\nide on\nDep ths\nBl og\nĠIll uminati\nĠtract s\nĠorgan ise\nĠo str\nF s\nĠlever aging\nĠD aredevil\nas ar\nĠl ang\nĠex termin\nurs ions\nĠRom o\nãĤ¤ ãĥĪ\nĠcont ended\nĠencounter ing\nĠTable t\nĠAltern ate\nsk ill\nĠswe ets\nĠco hesive\ncap acity\nĠrep ud\nĠl izard\nro o\nĠpilgr ims\nĠR uff\nĠInstr ument\nĠLog o\nuit ous\nE H\nĠsales man\nĠank les\nL ed\nĠPat ty\nud os\nOwn er\nĠdiscrep ancies\nk j\nM U\nĠuncond itional\nDragon Magazine\ni ard\nO ak\nĠConvers ation\nbe er\nĠOs aka\nD elta\nus ky\nĠsecret ion\nĠpl aza\nĠm ing\nĠde pletion\nĠM ous\nĠI TS\nĠH imal\nĠFle ming\nĠcyt ok\nĠH ick\nĠbat ters\nĠInt ellectual\n6 75\né r\nIS ION\nĠQu entin\nĠCh apters\nih adi\nĠco aster\nWAY S\nĠL izard\nĠY or\nand ering\nS kin\nha ust\nab by\nĠportray ing\nĠwield ed\nd ash\nĠprop onent\nĠr ipple\nĠgrap hene\nĠfly er\nĠrec urrent\nĠdev ils\nĠwater fall\næĺ ¯\ngo o\nText Color\nĠtam pering\nIV ES\nTR UMP\nĠAb el\nĠS AL\nĠHend ricks\nĠLu cius\nb ots\nĠ40 96\nIST ORY\nGu est\nĠN X\nin ant\nBen z\nĠLoad ed\nĠCle ver\nt reatment\nĠta vern\nĠ3 39\nĠT NT\nific antly\nTem perature\nF el\nĠunder world\nĠJud ges\nĠ< +\nĠst ump\nĠoccup ancy\nĠab er\nĠF inder\n) \",\nĠN unes\nres et\nin et\nect omy\nĠwell ness\nĠP eb\nquart ered\nand an\nĠneg atives\nĠTh iel\nĠCl ip\nĠL TD\nĠbl ight\nĠreperto ire\nK yle\nĠqu er\nĠC es\nĠha pl\n98 9\nĠTh ames\nisc opal\nDes k\nivari ate\nĠEx cellence\nfound ation\nĠâ ĩ\nX i\nĠmyster iously\nesty les\nĠper ish\nĠEng els\nĠDE AD\n09 0\n}} }\nĠUn real\nĠrest less\nID ES\north odox\nĠInter mediate\nĠdin ners\nĠTr out\nĠSe ym\nĠHall s\nog ged\nĠtraged ies\nĠdid nt\n67 6\nĠail ments\nĠobserv able\nĠV ide\nad apt\nĠD usk\nĠprofessional ism\nĠPres cott\nĠInd ies\np ox\nĠMe hran\nW ide\nĠend emic\nĠPar an\nB ird\nĠped als\nĠI U\nĠAdam ant\nĠH urt\nĠcorrel ates\nurd en\nĠspons oring\ncl imate\nĠUnivers ities\nĠK not\nenn es\nĠDam ian\nĠAx el\nS port\nĠbar b\nĠS no\nsh own\nste en\nud ence\nĠnon violent\nĠhom ophobia\nĠbiom ass\nĠDet ail\nĠsrf N\nĠT une\naccompan ied\nI ENCE\nAl bert\nĠMong o\nz x\nĠCer berus\nor bit\nc ens\nĠsl ay\nSH ARE\nH Y\nĠb rawl\nĠPro be\nĠnonex istent\nĠClare nce\nĠBlack burn\nĠport als\nĠR ita\nĠRem ain\nĠLe vant\nĠtrick ed\nĠF erry\naver ing\nĠStraw berry\nĠAn swers\nĠhorrend ous\nĠA man\nSupp lement\nĠT oad\nĠpe eled\nĠman oeuv\nĠU zbek\nmond s\nĠH ector\nĠ40 2\npe es\nfix es\nĠd j\nĠres umes\nĠaccount ant\nĠadvers ity\nĠham pered\nĠL arson\nĠd oping\npart s\nH ur\nĠbe arded\nĠy r\nĠPlug in\nå¥ ³\nĠ/ **\nrol ley\nĠwaters hed\nĠSub mission\nif lower\nAS C\nĠcho ir\nĠsculpt ures\nm A\nincre asing\nai i\nĠsne akers\nĠconfront s\nĠEle phant\nĠEl ixir\nĠrec al\nĠT TL\nw idget\nĠW ax\nĠGr ayson\nĠha irst\nĠhumili ated\nĠWAR N\napp iness\nĠT TC\nF uel\nĠpol io\nĠcomplex es\nĠbab e\nĠX IV\nP F\n). [\nP arts\nĠ4 35\nM eg\nĠY ards\nĠAL P\nĠy ells\nĠprin ces\nĠbull ies\nĠCapital ism\nex empt\nFA Q\nĠSp onge\nĠAl a\nĠpleas antly\nĠbu f\nĠden ote\nĠunp ublished\nĠkne eling\nasc a\nĠl apse\nal ien\n99 4\nĠrefere es\nĠLaw yers\nS anta\nĠpuzz ling\nĠProm etheus\nĠPh araoh\nĠDel ay\nĠfacilit ates\nĠC ES\nĠjew els\nĠbook let\nond ing\nĠpolar ization\nĠMor an\nĠSal ad\nĠS OS\nĠAdv ice\nPH OTOS\nIC AN\niat ures\nex press\nĠWonder land\nĠC ODE\nĠCL ASS\n9 75\nĠg rep\nĠD iesel\nĠGl ac\n! ?\"\nĠr m\no ine\ndisc rimination\nĠN urse\nm allow\nĠv ortex\nĠCons ortium\nĠlarge Download\nstra ight\naugh lin\nG rad\nĠpublic ized\nĠW aves\nĠRed d\nĠfest ivities\nĠM ane\nar ov\nĠfleet ing\nĠDr unk\nug en\nC ele\nĠchromos omes\nĠD OT\n-+-+ -+-+\nĠbus iest\nĠBe aver\nSy rian\nĠK yr\nk as\nĠCross Ref\n19 50\n76 01\nĠrepe aling\nĠWin ners\nĠMac ro\nĠD OD\nbl ance\nS ort\n64 1\nĠmet re\nĠD irk\nĠgo ggles\nĠdraw backs\nĠcomplain ant\nĠauthor izing\nĠantit rust\noper ated\nĠm ah\nĠexagger ation\nAm azing\nĠSer aph\nĠha ze\nw ow\nĠextingu ished\nĠcan yon\nĠB osh\nĠv ents\nĠsc rape\nCor rect\n4 26\nĠav g\nDem and\nĠâĪ ¼\nĠmicrobi ota\n\"} ],\"\nĠSt ev\nB io\nĠPlan es\nĠsuggest ive\nĠdec ipher\nĠRefuge e\nĠKe jriwal\nĠGreen peace\nĠdecl ass\nĠSound ers\nĠth o\nĠdec rypt\nĠbr ushing\nĠJane iro\nip op\nS i\n8 77\nĠGeoff rey\nĠc pu\nĠHaz el\nĠview points\nĠcris py\nĠNot ification\nĠsold er\nĠMod est\nĠHem isphere\nĠcass ette\nin cludes\nĠident ifiers\nĠC ALL\nin cent\nT odd\nĠSwe ep\nĠ3 34\nb oss\nĠsm ir\ngin x\nĠtown ship\nĠg rieving\nĠMos que\nNet flix\nAS ED\nĠMillenn ials\noc om\n19 67\nĠbold ly\ns leep\nĠes che\narij uana\nĠsw irl\nĠPen al\nĠneglig ent\nĠStephen son\nK ER\nĠZ oro\nris is\nĠlocal ization\nĠSeym our\nĠAng lic\nred itation\nprot ection\nĠPa ige\nĠo mit\nĠR ousse\nĠT ub\nĠinv itations\nt ty\nĠm oss\nph ysical\nC redits\nĠan archy\nĠchild care\nĠl ull\nĠM ek\nĠL anguages\nlat est\nĠSan ford\nĠus ability\nĠdiff use\nĠD ATA\nĠsp rites\nĠVeget a\nĠProm otion\nãĥ¼ ãĤ¯\nrict ing\nz ee\nTur kish\nĠTD s\npro ven\n57 1\nĠsmug glers\n707 10\nĠreform ed\nĠLo is\nĠun fl\nĠWITH OUT\nĠReturn ing\nann ie\nĠTom as\nFr anc\nĠProf it\nĠSER V\nĠR umble\nik uman\nes an\nĠt esters\nĠgad get\nĠbrace let\nĠF SA\ncomp onent\nĠparamed ics\nĠj an\nĠRem em\nĠSk inner\nĠl ov\nĠQu ake\nrom a\nĠfl ask\nPr inc\nĠover power\nĠlod ging\nĠK KK\nret te\nĠabsor bs\nw rote\nĠ ,\"\nK ings\nĠH ail\nĠFall ing\nxt ap\nĠHel ena\nire ns\nL arry\nĠpamph let\nĠC PR\nG ro\nĠHirosh ima\nĠhol istic\n\". [\nĠdet achment\nĠas pire\nĠcompl icit\nĠGreen wood\nĠresp awn\nĠSt upid\nĠFin ished\nf al\nb ass\nĠab hor\nĠmock ery\nĠFe ast\nVID EO\nĠcon sec\nĠHung ry\nP ull\nĠH ust\nit ance\n? ãĢį\n) --\nĠPar allel\ncon v\n4 69\nha ar\nw ant\nP aper\nm ins\nĠTor o\nĠTR UMP\nĠR ai\nD W\nĠW icked\nĠL ep\nĠfun ky\nĠdetrim ent\nios is\nache v\nĠde grade\nim ilation\nĠret ard\nĠfrag mentation\nĠcow boy\nĠY PG\nĠH AL\nParent s\nĠS ieg\nĠStra uss\nĠRub ber\n× IJ\nFr ag\nĠp t\nĠoption ally\nĠZ IP\nĠTrans cript\nĠD well\n88 2\nM erc\nĠM OT\nãĥ¯ ãĥ³\nĠhun ts\nĠexec utes\nIn cludes\nĠacid ic\nĠRespons ibility\nĠD umb\nwe i\nAnd erson\nĠJas per\night on\nabs olutely\nAd ult\nĠpl under\nMor ning\nĠT ours\nĠD ane\nÎ º\nĠT EST\nĠG ina\nĠcan ine\naw an\nĠsocial ists\nĠS oda\nĠimp etus\nĠSupplement ary\noli ath\nĠKinn ikuman\nmitted ly\nsecond s\nĠorganis ers\nĠdocument aries\nVari able\nGRE EN\nĠres orts\nĠbr agging\nĠ3 68\nArt ist\nw k\nbl ers\nUn common\nĠRet rieved\nĠhect ares\nĠtox in\nr ank\nĠfaith s\nĠG raphic\nĠve c\nĠL IA\nAf rican\nĠard ent\nend iary\nL ake\nĠD OS\ncient ious\nĠOk awaru\nĠAll y\nĠTim eline\nD ash\nĠI c\ncontin ue\nĠt idy\nĠinstinct ively\nĠP ossibly\nĠOut door\nĠWould n\nĠl ich\nĠBr ay\nĠA X\nĠà ī\nĠ+ #\n\\ '\nDirect ory\nab iding\nĠf eral\nic ative\nbut t\nĠper verse\nS alt\nĠwar ped\nĠnin eteen\nĠcabin ets\nĠsrf Attach\nĠSl oan\nĠpower ing\nreg ation\nF light\nse vere\nĠst ren\nĠc og\nap ache\nĠâ Ŀ\nĠcaf eteria\np aces\nĠGrim oire\nuton ium\nĠr aining\nĠcir cling\nĠlineback ers\nc redit\nĠrep atri\nĠCam den\nlic ense\nĠly ric\nĠdescript or\nĠval leys\nĠre q\nĠback stage\nĠPro hibition\nĠK et\nOp ening\nS ym\næĸ ¹\nĠserv ings\nĠoverse en\nĠaster oids\nĠMod s\nĠSpr inger\nĠCont ainer\nè »\nĠM ens\nĠmult im\nĠfire fighter\npe c\nĠchlor ine\nÐ ¼\nend i\nĠsp aring\nĠpolyg amy\nĠR N\nĠP ell\nĠt igers\nĠflash y\nĠMad ame\nS word\nĠpref rontal\nĠpre requisite\nuc a\nĠw ifi\nĠmiscon ception\nĠharsh ly\nĠStream ing\not om\nĠGiul iani\nfoot ed\nĠtub ing\nind ividual\nz ek\nn uclear\nm ol\nĠright ful\n49 3\nĠspecial ization\nĠpassion ately\nĠVel ocity\nĠAv ailability\nT enn\nĠl atch\nĠSome body\nĠhel ium\ncl aw\nĠdi pping\nXX X\nĠinter personal\n7 10\nĠsub ter\nĠbi ologists\nĠLight ing\nĠopt ic\nĠden im\nend on\nĠC orm\nĠ3 41\nĠC oup\nĠfear less\nĠal ot\nĠCliff ord\nĠRun time\nĠProv ision\nup dated\nlene ck\nĠneur on\nĠgrad ing\nĠC t\nsequ ence\nin ia\ncon cept\nĠro aring\nri val\nĠCaucas ian\nĠmon og\nkey es\nĠappell ate\nĠlia ison\nEStream Frame\nĠPl um\n! .\nĠsp herical\nĠper ished\nĠbl ot\nĠben ches\nĠ4 11\nĠpione ered\nĠhur led\nJenn ifer\nĠYose mite\nCh air\nĠreef s\nĠelect or\nĠAnt hem\n65 2\nĠun install\nĠimp ede\nĠbl inking\nĠgot o\nDec re\nA ren\nĠstabil ization\nĠDis abled\nĠYanuk ovych\nĠoutlaw ed\nĠVent ura\nten ess\nĠplant ation\nĠy acht\nĠHu awei\nĠsol vent\nĠgr acious\nĠcur iously\nĠcapac itor\nĠc x\nĠRef lex\nPh ys\nĠC f\npt in\ncons ervative\nĠinv ocation\nc our\nF N\nĠNew ly\nH our\nAs ian\nĠLe ading\nĠAer ospace\nAn ne\nĠpre natal\nĠdeterior ating\nH CR\nĠNorm andy\nol ini\nĠAm bro\n9 10\nĠset backs\nĠT RE\nĠs ig\nĠSc ourge\n59 7\n79 8\nGame play\nĠm sec\nM X\nĠprice y\nĠL LP\naker u\nĠover arching\nĠB ale\nĠworld ly\nCl ark\nĠscen ic\nĠdisl iked\nĠCont rolled\nT ickets\nĠE W\nab ies\nĠPl enty\nNon etheless\nĠart isan\nTrans fer\nĠF amous\nĠinf ield\nble y\nĠunres olved\nĠML A\nãĤ Ĥ\nCor rection\nĠdemocr at\nĠMore no\nro cal\nil ings\nĠsail or\nĠr ife\nh ung\nĠtrop es\nĠsn atched\nĠL IN\nĠB ib\nES A\nĠPre v\nĠCam el\nrun time\nĠob noxious\n4 37\nĠsum mers\nĠunexpl ained\nĠWal ters\ncal iber\nĠg ull\nĠEnd urance\nä½ ľ\nĠ3 47\nIr ish\nĠaer obic\nĠcr amped\nĠHon olulu\nà ©\nus erc\nec ast\nAC Y\nĠQu ery\nãĤ¹ ãĥĪ\nBet a\nĠsuscept ibility\nĠSh iv\nĠLim baugh\nĠà ĸ\nĠN XT\nĠM uss\nĠBrit ons\nES CO\nEG IN\nĠ% %\nĠsec ession\nĠPat ron\nĠLu a\nn aires\nĠJPM organ\nus b\nocy te\nĠcouncill ors\nĠLi ang\nf arm\nĠnerv ously\nĠattract iveness\nĠK ov\nj ump\nPl ot\nĠst ains\nĠStat ue\nĠApost les\nhe ter\nĠSUP PORT\nĠoverwhel m\nY ES\nĠ29 1\nd ensity\nĠtra pping\nM it\nĠf ide\nĠPam ela\natl antic\nDam n\nĠp ts\nOP A\nĠserv icing\nĠoverfl owing\nul o\nĠE rit\nt icket\nlight ing\nĠH mm\nãĥ¼ ãĥ«\nim oto\nĠchuck le\n4 23\nãģ ķ\nsh ape\nĠque ues\nĠanch ors\nãĤ¼ ãĤ¦ãĤ¹\nF er\nĠaw oke\nĠ6 66\nh ands\nĠdiver gence\nĠ50 5\nT ips\nĠdep ot\nĠske w\nĠDel iver\nop ot\nĠdiv ul\nĠE B\nuns igned\nĠUn i\nX box\nĠfor ks\nĠ7 02\nå ¯\nĠpromot ers\nĠV apor\nĠlev ied\nsl ot\nĠpig ment\nĠcyl inders\nC RE\nĠsn atch\nĠperpet ually\nĠl icking\nĠFe et\nĠKra ken\nĠHold en\nĠCLS ID\nm r\nĠproject or\nĠden otes\nĠchap el\nĠTor rent\nb ler\nR oute\nĠDef endant\nĠPublisher s\nĠM ales\nĠInn ov\nĠAg ility\nrit er\nty mology\nst ores\nL ind\nĠf olly\nĠZur ich\nB le\nĠnurt ure\nĠcoast line\nuch in\nD omin\nĠfri vol\nĠCons olid\nres ults\nM J\nĠphyl ogen\nĠha uled\nĠW iley\nĠJess ie\nĠPrep are\nĠE ps\nĠtreasure r\nI AS\nĠcolon ists\nĠin und\nĠWW F\nĠCon verted\n6 000\nout side\nĠApp earance\nĠRel ic\nĠM ister\ns aw\nĠresult ant\nĠadject ive\nĠLaure l\nĠHind i\nb da\nPe ace\nĠreb irth\nĠmembr anes\nĠforward ing\nĠcoll ided\nĠCar olyn\nK ansas\n5 99\nĠSolid GoldMagikarp\nBe ck\nĠstress ing\nĠGo o\nĠCooper ative\nĠf s\nĠAr chie\nL iter\nĠK lopp\nJ erry\nĠfoot wear\nWar ren\nĠsc ree\nh are\nUnder standing\nP ed\nĠanth ology\nĠAnn ounce\nM ega\nĠflu ent\nĠbond age\nĠDisc ount\nil ial\nC art\nĠNight mares\nSh am\nĠB oll\nuss ie\nH ttp\nAtl anta\nĠun recogn\nĠB id\nĠunder grad\nĠforg iving\nĠGl over\nAAAA AAAA\n4 45\nV G\npa io\nkill ers\nĠrespons ibly\nĠmobil ize\nĠeffect ed\nĠL umin\nĠk ale\nĠinfring ing\nann ounced\nĠf itt\nb atch\nĠT ackle\nĠL ime\nĠAP P\nuke mia\nĠrub y\nĠex oner\nĠCas ual\n0 70\nĠpel vic\nĠautom ate\nĠK ear\nĠCoast al\nĠcre ed\nĠbored om\nĠSt un\nri ott\nĤ İ\nĠregener ate\nĠcomed ians\nĠOP ER\nSp ons\nid ium\non is\nL ocated\n05 7\nĠsusp ense\nĠD ating\nC ass\nĠneoc ons\nĠShin zo\nĠaw oken\nch rist\nĠMess ages\natt led\nĠSpr ay\nĠSp ice\nC W\nĠshield ing\nĠG aul\nAm id\nĠparam ilitary\nĠmult if\nĠTan ner\nil k\nĠgodd amn\ng ements\nĠbe friend\nm obi\nĠ3 88\nfold er\nacc a\nĠins in\ng ap\nN ev\nfif th\nĠpsychiat ry\nb anks\nTH IS\nĠhar b\nac qu\nĠfac ade\nĠPower Point\n80 3\nĠbl uff\nSh ares\nĠfavor ing\nEl izabeth\nÃį Ãį\nĠr anger\n77 2\nĠAr che\nh ak\nĠGen etics\nĠF EMA\nĠev olves\nĠest e\nĠP ets\nĠM é\nĠInterest ing\nĠCanter bury\nch apter\nĠStar fleet\nSp anish\nĠdraw back\nĠNor wich\n9 70\nn orth\nag anda\nĠtransform ative\nram ids\nbi ology\nad ay\nĠpropag ation\nĠGam ma\nĠDen ise\nĠCalcul ator\nent imes\nĠB ett\nĠapp endix\nĠHD D\nAK ING\nĠst igmat\nĠhol ster\nĠord inarily\nCh ance\nĠCont rary\nĠad hesive\nĠgather s\n6 12\nre au\nony ms\new ays\nĠindu ces\nĠinterchange able\nse m\nWh it\nĠtr ance\nĠincorpor ation\nĠExt ras\nFin ancial\nĠawkward ly\nĠStur geon\nĠH Y\nNorm ally\nĠEnd ing\nĠAss ist\nenc rypted\nĠsub jug\nĠn os\nĠfan atic\nC ub\nC U\n?\" .\nĠirre versible\nå Ĥ\n03 1\nĠH AR\nsp read\nul ia\n= $\nSc ope\nL ots\nĠlif estyles\nol on\nĠf 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le\nAn cient\nĠappell ant\nen forcement\nace a\nans ki\nĠold s\n88 6\nĠsl urs\nĠem pires\nĠbuck le\nĠalien ation\nĠAber deen\nĠunic orn\nĠoverr iding\nĠL X\npp a\nĠdesp ised\nĠB ugs\nĠB ST\nS outhern\n5 33\nĠhall mark\nĠPost er\nĠstem med\nĠprincip als\nĠT ECH\nĠSand wich\nIt aly\nĠche esy\nĠSet TextColor\nĠProt ective\nĠC ohn\nJ O\napt op\nRe ason\nLead er\nĠUnder stand\nĠFr idays\nĠContin uous\nĠcl ipping\nĠR ye\nĠber th\ntim er\nann is\nre act\nĠbuff alo\nĠPar as\nĠ6 55\nĠpres ided\nĠSun rise\nĠve ts\nĠcl oves\nĠMcC ull\nStre ngth\nG AN\nĠill iter\nĠPric ing\nl é\nĠresist or\nĠbr un\nĠSuff olk\nÑ ĭ\nĠL iver\nRe leased\nĠwhat s\n8 60\nĠMe asures\nĠden ouncing\nĠRy zen\nĠsou ven\nĠcareg ivers\nch ini\nĠScar lett\nĠt rough\nCong ratulations\nĠtax is\nĠTrad ition\nj it\nĠtable top\nĠhither to\nĠdis information\noff ensive\nh ra\nĠDISTR ICT\nĠcompl icate\nchen ko\nĠRecon struction\nĠpalp able\nĠa usp\nĠ4 28\nĠshowc ases\nĠPublic ation\nknow ledge\ninn on\n4 19\nĠretri eval\nand ers\nĠref 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xtap\nĠcho pping\nĠSpl ash\nĠS ustainable\nĠFin ch\nĠ18 61\nict ive\nat meal\nĠG ohan\nĠlights aber\nĠG PA\nug u\nĠRE PL\nvari able\nĠher pes\nĠdesert s\nac iously\nĠsitu ational\nweek ly\nob l\nĠtext ile\nĠCorn wall\nĠcontrace ptives\nĠA ke\n] -\nä¹ ĭ\n: ,\nĠW em\nĠB ihar\nĠ' .\nĠbe re\nĠanal ogue\nĠCook ies\nĠtake off\nWhe el\nĠmaj estic\nĠcomm uting\n0 23\nĠCor pse\nass ment\nmin i\nĠgor illa\nĠAl as\nere e\nĠacquaint ances\nĠAd vantage\nĠspirit ually\nĠey ed\npm wiki\nĠE nder\nĠtrans lucent\nĠnight time\nĠIM AGES\n5 45\nĠK amp\nĠFre ak\nĠ ig\nPort land\n4 32\nĠM ata\nĠmar ines\nĠh ors\nater asu\nĠAtt ribution\nĠ-------- -\nĠk ins\nĠBEL OW\n++ +\nĠre eling\nol ed\nĠcl utter\nĠRel ative\nĠ4 27\nB US\nĠa vert\nĠChe ong\nĠA ble\nĠPry or\nDevelop er\nĠen cyclopedia\nĠUSA F\nĠG arry\nSp ain\nBl ocks\nĠexp osition\nĠGamer Gate\nW OR\nĠstockp ile\nĠclot hed\nĠT one\nĠR ue\nt umblr\nĠtreacher ous\nĠf rying\nÑ Į\nĠS ph\nĠrest raints\nĠemb odies\nĠG es\nS afety\nĠnegoti ators\nmin ing\nĠAppalach ian\nL OS\nĠJenn a\nĠpass ers\nç ĭ\nsn ap\nĠshort en\ncreat or\nĠinn umerable\nuther land\n67 4\nĠW OM\nĠAs cend\nĠArm ory\nĠTrans action\nK ick\nĠsuit case\nday Name\nĠwaste ful\nmar riage\nĠMcC abe\nite ch\nĠO ss\nCl osure\nĠTreasure r\nĠindec ent\nĠD ull\nĠresid ences\n19 59\nĠS ettlement\nHam ilton\nĠself ies\nĠRank ing\nĠBark ley\nĠB ore\nĠW CS\nĠMar itime\nĠH uh\nĠForest ry\nĠcultiv ating\nĠBall ard\nĠg arrison\nĠSD L\n9 30\nĠnas cent\nĠirresist ible\nĠaw fully\n\\/ \\/\nĠequ ate\nĠanthrop ology\nĠSylv ia\nĠintest ine\nĠinnoc uous\ncess ive\nag ra\nĠMet roid\nG rant\n8 55\nģ ĸ\nĠ\" _\nãĥĥ ãĥī\nĠappra isal\nĠFred dy\n04 6\nĠ40 6\nĠ18 30\nĠd ocking\nSt atic\nĠp ont\nĠVolt age\nĠSt ead\nĠMort gage\nĠJon ah\nY L\nCLASS IFIED\nĠas bestos\nnik ov\nĠcoll agen\nĠOrb ital\nP ocket\n7 99\nĠhy brids\ninc hes\nĠinv oice\nund y\nĠinequ alities\nT rend\nw ashed\nB ALL\nĠluc id\nĠComment ary\nĠw itty\nBr andon\nĠbru ising\nĠ6 20\nes cent\nbox ing\nP OL\nĠ3 78\nR ect\nĠlic ences\nĠMcG ee\np ressed\nD anny\nĠj ammed\nord inate\nĠle th\nĠdistingu ishes\nĠYam aha\nIL S\nĠH ume\nĠC ategories\nRober ts\nCh art\nĠbeet le\nĠGra veyard\nĠ($ )\no ÄŁ\nĠtw ilight\nare lla\ná ½\nĠbooth s\nĠH HS\nĠFeld man\nĠexcav ation\nĠphilosoph ies\nat ography\nĠGar age\nte chnology\nĠunfor gettable\nĠver ifying\nĠsubord inates\nE ls\nĠne b\nG aming\nEN A\nĠAchieve ment\nit ters\nĠG abe\nĠd umps\nfor cer\nĠpo ignant\nĠM BA\nĠHe idi\nime i\nĠm ages\nĠliber ate\nĠcircum cised\nĠMer maid\nĠMat th\nt ogether\nĠW ichita\nĠstore front\nĠAd in\nV II\nFour th\nĠexplore rs\nW ER\nNot able\nBro ok\nm ens\nF aith\n-------- -\nĠJ ou\n¬ ¼\nĠpine apple\nĠam alg\nel n\nark able\nĠãĤµ ãĥ¼ãĥĨãĤ£\nĠãĤµãĥ¼ãĥĨãĤ£ ãĥ¯ãĥ³\nĠov arian\nĠE choes\nĠhairc ut\nĠp av\nĠch illed\nanas ia\nĠsty led\nĠd ab\nni per\nĠminister ial\nĠD UP\nT an\nĠsul ph\nĠD eter\nĠBo hem\nod an\nĠeduc ator\nâ ĵĺ\nsp ir\nCh icken\nĠE leanor\nĠqu i\nĠheav iest\nĠgrasp ed\nU RA\nĠcro oked\nJess ica\npro blem\nĠpred etermined\nĠman iac\nĠbreath s\nĠLauder dale\nĠh obbies\ny z\nCr ime\nĠcharism a\nd L\nĠle aping\nĠk ittens\nAng elo\nĠJ ACK\nĠSu zanne\nĠhal ting\nENT ION\nĠswall owing\nĠEarthqu ake\nĠeight eenth\nĠN IC\nĠIN F\nĠCons cious\nĠparticular s\ncirc le\n7 40\nĠbene volent\nĠ7 47\nĠ4 90\nĠr undown\nĠVal erie\nĠB UR\nĠcivil isation\nĠS chn\nW B\not ide\nintern ational\nĠj ohn\nĠ19 02\nĠpe anuts\nĠflav ored\nk us\nĠro ared\nĠcut off\né £\nĠorn ament\nĠarchitect ures\nĠ3 69\nol or\nĠWild e\nĠC RC\nĠAdjust ed\nĠprov oking\nland ish\nĠrational ity\nĠjust ifies\nĠdisp el\nĠa meric\nĠPol es\nØ ©\nĠen vis\nĠD oodle\nä½ ¿\nigs aw\nauld ron\nTechn ical\nT een\nup hem\nĠX iang\nĠdetract ors\nĠZ i\nĠJournal ists\nĠconduc ive\nĠVolunte ers\nĠs d\nKnow ing\nĠtrans missions\nĠPL AN\nĠL IB\nĠall uded\nĠob e\nĠd ope\nĠGold stein\nĠwavelength s\nĠDest ination\nnd a\nug i\nĠattent ive\nĠLe an\nral tar\nĠman g\nmb uds\nak ings\nb ender\nĠacc ol\nĠcraw led\nN OW\nMin nesota\nĠflour ished\nĠZ up\nĠSuper visor\nĠOliv ier\nEx cellent\nĠwid en\nD one\nĠw ig\nĠmiscon ceptions\nCor p\nW an\nĠvener able\nĠNot ably\nĠKling on\nan imate\nBo ost\nĠS AY\nmiss ing\nibli ography\nmel on\nĠpay day\nØ ³\nbo le\nĠve iled\nĠAl phabet\nIt alian\nĠever lasting\nĠR IS\nĠC ree\nrom pt\nĠh ating\nĠgrin ning\nĠge ographically\nOS H\nĠwe eping\nĠÂłĠÂłĠÂłĠÂł ĠÂłĠÂłĠÂłĠÂł\nĠimpe cc\nLet ter\nĠblo ated\nPL A\nĠFe in\nĠper sever\nTh under\nĠa ur\nĠR L\nĠpit falls\nâĸ º\nĠpredomin ant\nĠ5 25\n7 18\nAP E\n7 14\nĠfarm land\nĠQ iao\nĠv iolet\nĠBah amas\nĠinflic ting\nĠE fficiency\nĠhome brew\nĠundert ook\nĠcur ly\nĠHard ing\nman ia\n59 6\nĠtem pered\nĠhar rowing\nĠP ledge\nĠFranken stein\nè ª\nM otion\nĠpredict ably\nĠExpl osion\noc using\ner d\ncol o\nFF ER\nĠback field\nĠV IDE\nue bl\nN arr\nĠArg ument\nĠgen omic\nĠbout ique\nĠbatt ed\nĠB inary\nĠg amb\nĠRh ythm\n67 3\nĠa float\nĠOlymp ia\nY ING\nĠend if\nis in\nĠwin ters\nĠsc attering\nI v\nD istance\nĠtr u\nĠCom fort\nĠne xus\nĠair flow\nĠByz antine\np ayers\ncon i\nĠB etsy\nD eal\nĠN ug\nĠContin ent\nred ibly\nĠoptim izing\nal beit\nĠec static\nĠPro to\nç ·\niv ot\nâĸ Ħ\nem p\nrou nder\nĠcl out\nĠI ST\n66 3\nĠDoll ars\nĠD AC\nĠsubsc ribed\nĠrehears al\nĠam ps\nĠSh ang\nes m\nĠspr inkle\nĠassail ant\nĠO o\nĠCoin base\nT act\nĠret ina\nĠn uns\nR ON\natt o\nĠj ug\nĠSV G\nĠb ikini\nĠFI LE\nĠFound ers\nep ort\nĠK P\nĠrest ores\nĠTh ick\nĠash ore\nĠappro vals\nR ender\nM AG\nG raham\nĠCort ana\nãĥ³ ãĤ¸\nss h\nor ians\nars ity\nĠInsp ired\nu pper\nĠsign alling\nĠreb uke\nĠfl ares\nĠdownt ime\nStud ies\nĠstagn ation\nĠSequ ence\nĠgr unt\nĠass ures\nĠPL A\n59 2\nĠintra ven\nd epend\nSus an\nĠManz iel\nMan ia\nCont ract\nĠsl ams\nĠcult ured\nĠcred itor\nL IST\nĠH UM\nĠChatt anooga\nserv ed\nĠclo aked\nĠF TP\np owder\nĠSt ella\nuct ive\nĠcheap ly\nĠMU CH\nĠGalile o\nĠsu ites\nspe ech\nĠdeliber ations\nĠCh ips\n« ĺ\nBal ance\nĠWyn ne\nĠAk ron\nAss et\nĠhon oured\nĠed ged\nLike wise\nanim ous\nĠW age\nĠEz ek\nad vertisement\nĠRT X\nĠM AD\nĠmigr ating\nĠS QU\nĠ4 75\nEd ited\nĠshorth and\nĠBas ics\nĠcro tch\nĠEV EN\nĠv m\neffic iency\nĠcal ves\nĠF rie\nĠBrill iant\nĠstri kers\nĠrepent ance\nĠarter ies\nr l\nB ed\nh ap\nĠcrypt ography\nĠSab res\nĠ4 14\nvi ks\nih ara\naps es\nT alking\nĠintertw ined\nĠdoc ks\nĠalle le\nĠArt ifact\nĠH IM\nt orn\nç ķ\nĠop acity\nĠE ly\nos uke\nĠn ipple\nĠhand written\nĠV K\nĠChamber lain\nĠLa os\nig raph\ng row\nĠtr illions\nĠdescend ant\nĠSail or\nas uring\nĠce ilings\nĠWare house\nf lying\nĠGl ow\nĠn ont\nĠmiscar riage\nĠrig s\nĠmin istries\nĠelabor ated\nĠdel usional\nĠHum ane\nĠ3 79\nn ets\nĠblack out\nadd ers\nĠn p\nĠT ire\nro sc\nĠsub div\nĠlink age\nĠchron ological\nĠHER O\nĠres ettlement\nĠVin yl\nĠpast oral\nĠMob il\nĠBar bar\nCo oldown\nĠF ritz\nc riminal\nre pe\nĠbell ig\nĠBre ed\nĠ4 18\nĠsem blance\nij k\nĠcur tail\nĠclin ch\ncont ained\nĠProm pt\nast on\nĠw i\nĠpursu its\n5 15\nĠGl oss\nĠfl ips\nĠcoup ons\nĠcl oning\nĠLike ly\nRem oved\nĠQu artz\nr ices\nĠSpe ars\nĠp ious\nĠdep reciation\nĠD are\noun ces\nam az\nO nt\nĠp innacle\nd ocker\n0 26\nĠW yr\nĠPro per\nË Ī\nn il\nBy tes\nĠseek er\nt rial\nĠunf olds\nĠMar se\nĠextravag ant\nĠSurviv ors\nRED ACTED\nĠSpeed way\nĠCra igslist\nsub mit\nĠGener ations\nĠup holding\nĠblood stream\nĠMiss ions\nĠL awn\nĠlim bo\nene i\nH uh\nĠWild cats\npre p\nĠMark us\nĠFor bidden\nrit ic\nIN O\nĠexhib iting\nrequ ent\nch uk\nĠhabit ual\nĠComp atibility\nDr ag\nRIP T\nuj ah\nGR OUND\nĠdelinqu ent\nĠburn er\nĠcontempor aries\nĠgimm ick\nload s\nĠno zzle\np odcast\nĠW ak\nĠStat en\nĠK uh\nãģ ĵ\ninter rupted\nĠinv incible\nĠBurn ett\ncig arette\nĠPeb ble\nĠTem porary\nĠMar ino\n58 2\nĠwast eland\nident ly\nT x\nĠr ite\nĠPan asonic\nĠM iddles\nĠHort on\nae us\nĠc uring\nĠm ats\nĠadj ourn\nĠfears ome\npe z\nbo ats\nĠpro pell\nĠconflic ted\nĠAng er\nĠinsurg ent\nK arl\nĠco ales\nĠsouth western\nĠdis su\nĠO vert\n******** ****\nĠbox ed\nĠBr une\naa a\nĠgard ening\nĠEng el\ntr acks\nĠpur ified\nĠplace holder\nĠL ikes\nĠd an\nG ab\nĠe ct\nĠF aw\nĠEl iot\nĠ' ,\notrop ic\nĠRu in\nhed on\nĠca ul\nĠa ft\nĠCad illac\ngh a\nass ian\nud eb\nĠT ick\nĠadjust s\nAR GET\n5 37\nisc he\nant y\nĠFried rich\nĠBl izz\nĠA OL\nCamp aign\nĠmamm al\nĠVe il\nĠK ev\nĠMaur it\nĠDam ien\nN ation\nE astern\nĠ{ :\nĠ= ================================\nĠstereotyp ical\nĠatt ic\nĠCy borg\nrequ ire\nĠaward ing\nĠPap ua\nbt n\nb ent\nB oo\nĠ( =\nĠX ander\nĠSomers et\nĠcatch y\nĠcert ify\nSTR UCT\nĠit al\nĠt ides\nĠBr ands\nG ray\ncomp etitive\nĠcur ator\nĠD G\nomin ium\nĠGM Os\nci ating\nĠCarm en\now ard\nBalt imore\nĠr gb\nC u\nĠwip es\nspe ll\nIT NESS\nĠsummar izes\nĠRe vis\nĠwhistlebl owers\nĠBre ach\nĠcro chet\nk os\news ki\nĠrep et\nĠcrim son\nĠKar achi\nread able\ndim ension\nĠI gor\nild ed\nĠZ ed\nĠKe ane\nĠCos metic\nDE P\nĠretreat ing\nĠU A\nens ical\nĠd usk\nĠDick ens\nĠaren as\nĠPass age\nlevel s\nĠcur v\nP ope\nĠch ores\nĠEl ise\nĠComp ass\nb ub\nĠmamm alian\nĠSans krit\nĠAN C\nĠCr ack\nQ ual\nL aun\namp unk\nĠlearn ers\nĠglam orous\nĠfur the\nerm ott\nc and\nGener 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ging\nĠbother s\nĠPan zer\nĠCh andra\nfig ured\nĠvol ts\nĠCloud s\nĠfee ble\nĠCur ve\nĠAs us\n78 6\nabs or\nĠV ICE\nĠH ess\nĠmanufact ures\nĠgri zz\nĠPower ful\nac id\nĠsub sections\nĠKrug man\nĠAl ps\nis u\nĠsequ est\nĠUlt ron\nĠT inker\nĠGo ose\nĠmism atch\nAtt orney\nĠmorph ology\nĠSix ers\nut tered\nĠE LECT\ngr an\nRus sell\nĠG SL\nĠfort night\nĠ. )\nĠapost le\npr one\nel ist\nUnt itled\nĠIm plementation\nist ors\nĠtank er\nĠpl ush\nĠattend ants\nĠT ik\nĠGreen wich\nĠY on\nĠSP L\ncell s\nunt led\nS olution\nĠQu é\nĠvac ated\nĠupt ick\nĠMer idian\næ ĥ\nĠDr ill\n9 25\n58 4\nĠrenov ated\nĠKub rick\nzy k\nĠl ousy\npp el\nohyd rate\nĠI zzy\nlesi astical\nCC C\nĠAj ax\nĠad apters\nĠPetra eus\nĠaffirm ation\nĠST OR\nle ms\nad oes\nĠConstantin ople\nĠp onies\nĠl ighthouse\nĠadherent s\nĠBre es\nomorph ic\nFight ing\nĠpl aster\nĠP VC\nĠOb st\nĠdear ly\nĠTo oth\nicks on\nĠsh aming\nP lex\nA gg\nĠâĢ¦ \"\nĠsub reddits\nĠpige on\nĠResident ial\nĠPass ing\nĠl um\nĠP ension\nĠpessim istic\nĠ4 32\nz inski\nc ade\n0 75\nĠapolog ised\niy ah\nPut ting\nĠgloom y\nĠLy me\n=-=-=-=- =-=-=-=-\nĠT ome\nĠPsych iatric\nĠH IT\nc ms\nap olog\nĠbreak er\nĠdeep en\nĠtheor ist\nĠHigh lands\nĠb aker\nĠst aples\nĠinterf ered\nĠAb ortion\njo ined\nch u\nĠform ulate\nĠvacc inations\nĠban ter\nphe us\nĠoutfield er\nĠM eter\nĠ# ####\nĠ18 95\nĠnarrow ing\nĠST ORY\nf p\nĠC ST\nign ore\nĠproclaim ing\nĠR U\nĠB ALL\nyn a\n65 3\nĠpos it\nP RE\n59 4\nĠRegist rar\nĠPil grim\nic io\nĠpre tt\nĠlif eless\nĠ__ _\nNe igh\nĠCh urches\norn o\nĠor cs\nĠkind red\nĠAud it\nĠmillenn ial\nĠPers ia\ng ravity\nĠDis ability\nĠD ARK\nW s\nod on\nĠgrand daughter\nĠBro oke\nĠA DA\nER A\nĠpick ups\nĠWil kinson\nĠSh ards\nĠN K\nĠexp el\nĠKis lyak\nĠj argon\nĠpolar ized\nian e\nPub lisher\nĠreb utt\nĠapprehens ion\nĠK essler\nĠpr ism\nF UL\n19 64\nĠL oll\nä ¿\nle thal\nÅ Ł\nĠg hetto\nĠb oulder\nĠSlow ly\nĠOsc ars\nĠInst ruction\nĠUl tr\nĠM oe\nN ich\nĠP ATH\n( *\nĠRE LEASE\nun ing\nrou se\nen eg\nĠre imb\nĠDet ected\nDo S\nĠster ling\nĠaggreg ation\nĠLone ly\nĠAtt end\nhig her\nĠairst rike\nks on\nSE LECT\nĠdef lation\nĠHer rera\nC ole\nrit ch\nĠadvis able\nF ax\nĠwork around\nĠp id\nmort em\ners en\nĠtyp o\nĠal um\n78 2\nĠJam al\nscript s\nĠcapt ives\nĠPres ence\nĠLie berman\nangel o\nĠalcohol ism\nass i\nĠrec ite\nĠgap ing\nĠbask ets\nĠG ou\nBrow ser\nne au\nĠcorrect ive\nund a\nsc oring\nĠX D\nĠfil ament\nĠdeep ening\nĠStain less\nInt eger\nĠbu ggy\nĠten ancy\nĠMub arak\nĠt uple\nĠD roid\nĠS itting\nĠforfe it\nĠRasm ussen\nixt ies\nes i\nĠKim mel\nĠmetic ulously\nĠap opt\nĠS eller\n08 8\nec ake\nhem atically\nT N\nĠmind less\nĠdig s\nĠAcc ord\nons ense\nem ing\nbr ace\nĠe Book\nĠDist ribut\nĠInvest ments\nw t\n] ),\nbeh avior\n56 3\nĠbl inding\nĠPro testers\ntop ia\nĠreb orn\nĠKel vin\nĠDo ver\nĠD airy\nĠOut s\nĠ[ /\nÏ Ģ\nb p\nĠVan ity\nĠRec ap\nĠHOU SE\nĠF ACE\nĠ4 22\n69 2\nĠAnt ioch\ncook ed\nĠcoll ide\nĠa pr\nĠsle eper\nĠJar vis\nĠalternative ly\nĠLe aves\nĠM aw\nĠantiqu ity\nĠAdin ida\nĠab user\nPoké mon\nĠass orted\nĠRev ision\nĠP iano\nĠG ideon\nO cean\nĠsal on\nĠbust ling\nogn itive\nĠRah man\nĠwa iter\nĠpres ets\nĠO sh\nĠG HC\noper ator\nĠrept iles\nĠ4 13\nĠG arr\nĠCh ak\nĠhas hes\nĠfail ings\nĠfolk lore\nĠab l\nĠC ena\nĠMac Arthur\nĠCOUR T\nĠperipher y\napp ers\nĠreck oned\nĠInf lu\nĠC ET\nĠ3 72\nĠDefin itive\nass ault\n4 21\nĠreservoir s\nĠd ives\nĠCo il\nDA Q\nĠvivid ly\nĠR J\nĠBel lev\nĠec lectic\nĠShow down\nĠK M\nip ed\nreet ings\nĠAs uka\nL iberal\nĠÏ Ħ\nĠbystand ers\nĠGood win\nuk ong\nS it\nĠT rem\nĠcrim inally\nĠCirc us\nch rome\n88 7\nĠnan op\nĠOb i\nĠL OW\no gh\nĠAuth ors\nob yl\nUr ban\nĠt i\nĠWe ir\nt rap\nag y\nĠparent heses\nĠout numbered\nĠcounter productive\nĠTob ias\nub is\nP arser\nST AR\nĠsyn aptic\nĠG ears\nĠh iber\nĠdebunk ed\nĠex alted\naw atts\nH OU\nCh urch\nĠPix ie\nĠU ri\nĠForm ation\nĠPred iction\nC EO\nĠthro tt\nĠBrit ann\nĠMad agascar\në ĭ\nĠbill boards\nĠRPG s\nĠBe es\ncomplete ly\nF IL\nĠdoes nt\nĠGreen berg\nre ys\nĠsl ing\nĠempt ied\nĠPix ar\nĠDh arma\nl uck\ningu ished\nĠend ot\nĠbab ys\n05 9\nche st\nr ats\nĠr idden\nĠbeet les\nĠillum inating\nĠfict itious\nĠProv incial\nĠ7 68\nĠshe pherd\nĠR ender\nĠ18 96\nC rew\nĠmold ed\nĠXia omi\nĠSp iral\nĠdel im\nĠorgan ising\nĠho ops\nĠBe i\nz hen\nĠfuck in\nĠdec ad\nĠun biased\nam my\nsw ing\nĠsmugg led\nĠk ios\nĠP ERSON\nĠInquis itor\nĠsnow y\nĠscrap ing\nĠBurg ess\nP tr\nag ame\nR W\nĠdro id\nĠL ys\nĠCass andra\nJac ob\nĠ35 4\nĠpast ure\nĠfr anc\nĠScot ch\nĠEnd s\nĠI GF\ndef inition\nĠhyster ical\nĠBrown e\n77 1\nĠmobil ization\næ ķ\niqu eness\nTh or\nĠspear headed\nĠembro iled\nĠconject ure\njud icial\nCh oice\nĠpaper back\nP ir\nĠrec overs\nĠSur ge\nĠSh ogun\nĠPed iatrics\nãģ ł\nĠsweep s\nĠLabor atories\nĠP acks\nal us\nadd in\nĠhead lights\ng ra\nEv idence\nCOL OR\nAd min\nĬ ±\nĠconco ct\ns ufficient\nĠun marked\nĠrich ness\nĠdiss ertation\nĠseason ing\nĠg ib\nĠM ages\nun ctions\nĠN id\nche at\nĠTM Z\nc itizens\nĠCatholic ism\nn b\nĠdisemb ark\nĠPROG RAM\na 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ial\nMat rix\nĠelect ing\nĠcomment er\nĠNeb ula\nple ts\nĠNad u\nĠAd ren\nĠen shr\nĠR AND\nfin ancial\nĠCly de\nuther ford\nĠsign age\nĠde line\nĠphosph ate\nrovers ial\nf ascist\nĠV all\nĠBeth lehem\nĠfor s\nĠeng lish\nS olid\nN ature\nĠv a\nĠGu ests\nĠtant al\nĠauto immune\n;;;;;;;; ;;;;\nĠTot ally\nĠO v\nĠdef ences\nĠCoc onut\nĠtranqu il\nĠpl oy\nĠflav ours\nĠFl ask\nãĤ¨ ãĥ«\nĠWest on\nĠVol vo\n8 70\nĠmicro phones\nver bal\nR PG\nĠi ii\n; }\n0 28\nĠhead lined\nĠprim ed\nĠho ard\nĠSh ad\nĠEN TER\nĠtri angular\nĠcap it\nl ik\nĠAn cients\nĠl ash\nĠconv ol\nĠcolon el\nen emy\nG ra\nĠpub s\nut ters\nĠassign s\nĠPen et\nĠMon strous\nĠBow en\nil ver\nH aunted\nĠD ing\nstart ed\npl in\nĠcontamin ants\nĠDO E\nff en\nĠTechn ician\nR y\nĠrob bers\nĠhot line\nĠGuard iola\nĠKau fman\nrow er\nĠDres den\nĠAl pine\nE lf\nĠf mt\nĠS ard\nurs es\ng pu\nUn ix\nĠunequiv ocally\nĠCitizens hip\nqu ad\nm ire\nĠS weeney\nB attery\n6 15\nĠpanc akes\nĠo ats\nM aps\nĠCont rast\nmbuds man\nĠE PS\nĠsub committee\nĠsour cing\nĠs izing\nĠBuff er\nĠMand atory\nĠmoder ates\nĠPattern s\nĠCh ocobo\nĠZ an\nĠSTAT ES\nĠJud ging\nĠIn her\n* :\nĠb il\nĠY en\nĠexh ilar\noll ower\nz ers\nĠsn ug\nmax imum\nĠdesp icable\nĠP ACK\nĠAn nex\nĠsarcast ic\nĠlate x\nĠt amp\nĠS ao\nb ah\nĠRe verend\nĠChin atown\nĠA UT\nd ocumented\nĠGA BA\nĠCan aan\nĠÙ ħ\nĠgovern s\npre v\nE sc\nĠEst imates\nOS P\nĠendeav our\nĠCl osing\nomet ime\nevery one\nĠwor sen\nĠsc anners\nĠdev iations\nĠRobot ics\nĠCom pton\nĠsorce rer\nĠend ogenous\nĠem ulation\nĠPier cing\nĠA ph\nĠS ocket\nĠb ould\nĠO U\nĠBorder lands\nĠ18 63\nG ordon\nĠW TO\nĠrestrict s\nĠmosa ic\nĠmel odies\nç Ħ\nT ar\nĠdis son\nĠProv ides\nĠ ......\nb ek\nF IX\nĠbro om\nans hip\nDo ctors\nĠner ds\nĠReg ions\nna issance\nĠmet e\nĠcre pt\npl ings\nĠgirlfriend s\nkn it\nig ent\now e\nĠus hered\nĠB az\nM obil\n4 34\nĠPres ents\norig in\nĠins omnia\nĠA ux\n4 39\nĠCh ili\nirs ch\nG AME\nĠgest ation\nalg ia\nrom ising\n$ ,\nc row\nĠIn spection\nat omic\nRel ations\nJ OHN\nrom an\nĠClock work\nĠBak r\nm one\nM ET\nĠthirst y\nĠb c\nĠfacult ies\nR um\nĠnu ance\nĠD arius\nple ting\nfter s\netch up\nReg istration\nĠK E\nR ah\nĠpref erential\nĠL ash\nĠH H\nVal id\nĠN AV\nĠstar ve\nĠG ong\nz ynski\nĠAct ress\nĠw ik\nĠun accompanied\nlv l\nBr ide\nAD S\nĠCommand o\nĠVaugh n\nWal let\nĠho pping\nĠV ie\nĠcave ats\nĠal as\nif led\nab use\n66 1\nĠib n\nĠg ul\nĠrob bing\nt il\nIL A\nĠmit igating\nĠapt ly\nĠty rant\nĠmid day\nĠGil more\nĠDe cker\nĠ§ §\npart ial\nEx actly\nĠphen otype\nĠ[+ ]\nĠP lex\nĠI ps\nvers ions\nĠe book\nĠch ic\ng ross\n\":\" \"},{\"\nĠSur prisingly\nM organ\nĠresid ues\nĠConf ederation\nin feld\nĠl yr\nmod erate\nĠperpend icular\nV K\nĠsynchron ized\nĠrefres hed\nĠad ore\nĠTor ment\nol ina\nĠ26 00\nItem Tracker\nĠp ies\nĠF AT\nĠR HP\n0 48\nĠRES P\nĠB J\nall ows\nP and\nĠunw elcome\nĠV oc\nĠBast ard\nĠO W\nĠL AR\nĠHeal er\nEnvironment al\nĠKen yan\nĠTr ance\nĠP ats\nĠali ases\nĠGar field\nĠcampaign er\nĠadvance ments\nĠOkin awa\nĠC oh\nows ky\nĠstar ved\nĠsize able\nĠ: -)\nĠm RNA\nĠsusp ensions\nist ar\nScot land\nPr in\n-------------------------------- ----------------\nĠ50 2\nĠteasp oons\nĠ10 50\nĠcoerc ive\nĠMason ic\nedd ed\nĠPass enger\nĠl att\nĠbr aces\nĠSt eal\nĠNY T\nĠK ats\nĠCel est\nae z\nT u\nĠCoul ter\nðŁ ĺ\nFl ickr\nĠWil mington\nith s\n++ ;\nĠv ending\nĠneg ro\nĠPh i\nĠYellow stone\nCall back\nĠsh ampoo\nĠSh ades\nw at\nĠsuper human\nĠridic uled\nĠhol iest\nom bo\nĠintern s\nĠh one\nĠPar agu\nUR I\nĠd angling\nãĤ »\nso v\nict ional\nav ailability\nĠrev ocation\nĠd ow\nin ic\nĠTHE IR\nĠis o\nĠout ings\nĠLeth al\nĠ) ))\nĠinacc ur\nĠout landish\nĠan us\nlet ico\nid on\nl ol\nĠun regulated\nĠsuccumb ed\nĠc uff\nĠWast eland\nlet al\nĠsub str\nĠcoff ers\nĠautom akers\nov i\nĠX ue\nĠDayton a\nĠjar ring\nĠf umes\nĠdisband ed\nz ik\nitt on\nĠstriking ly\nĠsp ores\nAd apter\n.) :\nĠLynd on\nival ry\nĠor ally\nĠtumult uous\nĠdisple asure\nĠcon es\nor rect\nĠappe ase\nĠder by\nĠTrip oli\nĠAl ess\nĠp oked\nĠGu ilty\nv P\nEn ough\nĠorig inals\n6 99\nĠrabb i\nĠproverb ial\nĠpostp one\nel ope\nĠMist y\nĠstaff ed\nĠUn employment\nredit ary\nĠdilig ent\nre comm\nme asures\nas in\n8 25\nĠpond s\nĠmm ol\nĠS AR\nĠC ARE\nĠ3 71\nĠclen ched\nĠCors air\nĠcaric ature\nz n\natt ach\nĠSch ro\nspe ak\np ainted\nĠS uc\nĠE NT\nĠcell ul\nĠP aid\ndi agn\nWH ERE\nĠtext ed\nB arn\nĠret racted\nĠRe ferred\nS av\nĠup keep\nĠwork places\nĠTok ens\nĠampl ify\ncl inical\nĠmult ic\nmber g\nĠconvol uted\nReg ion\n5 65\nĠTop ic\nĠsn ail\nĠsal ine\nĠins urrection\nĠPet r\nf orts\nB AT\nĠNav ajo\nĠrud imentary\nĠLak sh\nOND ON\nMe asure\nĠtransform er\nĠGodd ard\nĠcoinc ides\nir in\nR ex\nĠB ok\nqu it\nĠshotgun s\nĠprolet arian\nĠsc orp\nĠAd a\n5 14\nĠsl ander\nrecord ed\nĠemb ell\nris ome\nĠapolog izing\nĠMul cair\nĠGib raltar\nCl a\nĠall ot\nĠAtt ention\nĠ4 33\nle ave\nĠwh ine\nĠIss a\nĠFa ust\nĠBar ron\nhen y\nĠvictim ized\nJ ews\nĠnurt uring\nett el\nW inged\nĠSub tle\nĠflavor ful\nĠRep s\neng ed\ncall back\nĠdirection al\nĠcl asp\nĠDirect ions\nplan et\nicult ure\nHel per\nic ion\nac ia\nĠç ¥ŀ\nĠsur ges\nĠcan oe\nĠPrem iership\nbe en\nĠdef ied\nĠTro oper\nĠtrip od\nĠgas p\nĠE uph\nĠAd s\nvern ight\nhigh ly\nR ole\nĠent angled\nĠZe it\n6 18\nĠRust y\nĠhaven s\nĠVaugh an\nHA EL\nĠSER VICE\n/ ,\nĠstr icken\nĠdel usions\nĠb is\nĠH af\nĠgrat ification\nĠent icing\nUN CH\nAd ams\nĠOL ED\nĠBeet le\nĠ18 99\nĠSO FTWARE\nateg or\nV L\nĠTot em\nĠG ators\nAT URES\nĠimped ance\nReg istered\nĠC ary\nĠAer ial\non ne\nen ium\nĠd red\nĠBe g\nĠconcurrent ly\nĠsuper power\nĠX an\nj ew\nimes ter\nĠDick inson\nâĶ ģ\nF la\nĠp ree\nĠRoll ins\n© ¶æ\nĠden omination\nĠL ana\n5 16\nĠinc iting\nsc ribed\nj uries\nĠWond ers\napp roximately\nĠsusp ending\nĠmountain ous\nĠL augh\noid al\nN s\nDet ect\n) =\nĠL uthor\nĠSchwarz enegger\nĠMull er\nĠDev i\nec ycle\nJ ar\n6 13\nĠL ongh\nB ah\nĠSP ORTS\nn w\nĠref inement\nĠwater ways\nĠd iner\nBl ade\n68 3\nF ac\nĠinitial s\nĠro g\nĠparan ormal\nB UT\nĠ[ (\nĠSw anson\nĠM esh\nâĸ ¬\nImpro ve\nĠRad iation\nĠEst her\nĠE sk\nĠA ly\nik y\nĠir rad\nĠBuck ingham\nĠref ill\nĠ. _\nRe pe\nCON CLUS\nĠdifferent iated\nĠchi rop\nĠAt kins\nPat tern\nĠexc ise\nĠcab al\nN SA\nĠST A\nĠS IL\nĠPar aly\nĠr ye\nĠHow ell\nĠCount down\nness es\nalys ed\nĠres ize\nãĤ ½\nĠbudget ary\nĠStr as\nw ang\nĠap iece\nĠprecinct s\nĠpe ach\nĠsky line\nĠ35 3\npop ular\nApp earances\nĠMechan ics\nĠDev Online\nS ullivan\nZ en\nĠp u\nop olis\n5 44\nĠde form\nĠcounter act\nĠL ange\nĠ4 17\nCon sole\n77 4\nĠnodd ing\nĠpopul ism\nĠhe p\nĠcoun selling\ncompl iance\nU FF\nĠunden iably\nĠrail ing\nĠHor owitz\nĠSim one\nĠBung ie\nĠa k\nĠTal ks\nx ff\nfl ake\nCr ash\nĠsweat y\nĠban quet\nĠOFF IC\nĠinvent ive\nĠastron omer\nĠStam ford\nĠSc are\nĠGRE EN\nolic ited\nĠr usher\nĠcent rist\night ing\nĠsub class\nĠdis av\nĠdef und\nĠN anto\noci ate\nm ast\nĠpac if\nĠm end\ne ers\nimm igration\nESS ION\nĠnumber ing\nĠlaugh able\nĠEnd ed\nv iation\nem ark\nP itt\nĠmetic ulous\nĠL F\nĠcongrat ulated\nĠBir ch\nĠsway ed\nĠsemif inals\nĠhum ankind\nm atter\nĠEqu ip\nopa usal\nS aid\nĠLay out\nĠvo icing\nĠth ug\nĠporn ographic\nI PS\nĠmo aning\nĠgriev ance\nĠconf essions\nesc al\nTEXT URE\nAut hent\nos aurus\nP urchase\nĠreleg ation\nal ter\nĠÂł Âł\nĠr iddled\nĠo gre\nĠLow ell\nOcc up\nE at\nĠHy der\nĠAdvis er\nCom merce\nH unt\nĠOr th\nĠComp etitive\nĠCL A\nCD C\nĠsal ads\nF le\nĠindustrial ized\n` ,\nĠO WN\nĠbec k\nĠPart icularly\noub t\nĠm M\nĠHuss ain\nĠChen nai\nĠ9 20\nĠappoint ing\nĠCull en\n,,,, ,,,,\nĠp ores\nver ified\nĠbi ochemical\nem ate\nĠcoward ly\nĠHels inki\nĠEthiop ian\nS OURCE\nER C\nest ro\nĠbi otech\nĠS our\nĠbrew er\nBloom berg\nĠintens ify\nGl ass\nan co\nĠF DR\ngre SQL\nĠF ires\n©¶æ ¥µ\nec o\n100 1\nĠHom eless\nĠinstant aneous\nĠH aste\nig el\nD iamond\nĠp aving\nĠland fill\nĠd ads\nh oun\n: ]\nĠinc endiary\nĠLiving ston\nĠHil bert\nĠChe cks\nst yles\nin ators\nĠCl ive\nph rine\nĠchimpan zees\nĠp all\nĠJ M\nĠAad haar\nð Ŀ\nĠachie vable\ndis abled\nP ET\nOOOO OOOO\nM ot\nĠint angible\nĠbal 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ness\nĠport folios\n05 5\nĠInstruct or\naim an\nĠclaim ants\nM bps\nĠBy e\nre ceived\nT weet\nĠind emn\nri z\nam ara\nN at\nĠeval uates\nĠL ur\nep ad\nFO X\nĠTh ro\nĠrust y\nĠbed rock\nĠOp rah\nJ B\nĠmanip ulative\nĠwill ful\nĠrel apse\nĠext ant\nThe me\nS ensor\nĠSt ability\ngo vern\nĠpo ppy\nĠkn ack\nĠins ulated\nĠT ile\nĠExt rem\nĠunt old\nĠconver ge\nĠref uel\nig roup\nĠdistort ions\nĠrav aged\nĠmechan ically\nĠRe illy\nĠN ose\nĠIncarn ation\nĠBeck y\nabb ling\nĠt aco\nĠr ake\nĠmelanch oly\nĠillust rious\nĠDart mouth\nGu ide\nĠR azer\nĠBen z\nUlt imate\nĠSur prise\nĠpage ant\noff er\nWho ever\nĠw iser\nĠchem ist\nĠHE LL\nĠBul k\nĠpl utonium\nĠCO VER\nÖ ¼\nf ailed\nĠtire lessly\nĠinf ertility\nĠTr ident\nĠShow time\nĠC iv\nV ice\nrequ ires\nitt ance\nĠun controlled\ninterest ing\n56 1\nĠinnov ate\nateg ic\nL ie\nĠS elling\nU l\nĠsav ior\nĠT osh\nĠsw ast\nP ASS\nĠr ink\nĠcard io\nĠI ro\nud i\nĠv antage\nĠv ans\nĠNi ño\n+ =\nĠpropag ate\n< ?\nĠmethod ological\n204 39\nĠtrig 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ar\nAss uming\nĠem itting\nĠML G\nĠbirth place\nĠt aunt\nĠL uffy\nĠAm it\nĠcir cled\nĠN ost\nenn ett\nĠde forestation\nĠHist orically\nĠEvery day\nĠovert ake\n79 2\nĠn un\nĠLuc ia\nĠaccompan ies\nĠSe eking\nĠTr ash\nan ism\nR ogue\nĠnorth western\nĠSupplement al\nĠNY U\nĠF RI\nĠSat isf\nx es\n5 17\nĠreass ured\nĠspor adic\nĠ7 01\nĠmed ial\nĠcannabin oid\nĠbarbar ic\nĠep is\nĠExplos ive\nĠD ough\nĠuns olved\nSupport ed\nĠacknowled gment\nsp awn\nĠkit chens\nĠ- =\ntalk ing\nic ist\nĠPeg asus\nĠPS U\nĠphot on\nĠAuthent ication\nR G\n@# &\n76 2\nĠCl air\nĠdi aper\nĠbr ist\nĠProsecut ors\nĠJ em\n6 28\nĠEvery where\nĠJean ne\nequ ality\nãĥ© ãĥ³\nobject s\nĠPel icans\nĠ39 2\nĠbl u\nb ys\nĠA go\nĠinstruction al\nĠdiscrim inating\nĠTR AN\nĠCorn el\nag os\nĠty re\nĠas piration\nĠBrid gewater\n\": -\n! \".\nĠEn s\nĠCoc o\nP ie\nĠdet ach\nĠC ouch\nĠphys ique\nĠOccup ations\nosc opic\nen ough\nB uzz\nApp earance\nY P\nĠrac er\nĠcompl icity\nr pm\nT oy\nĠinterrupt s\nĠCat alyst\nĠut ilitarian\nimp act\nĠsp aghetti\nĠp orous\nĠeste emed\nĠinc iner\nĠI OC\n7 48\nĠesp resso\nĠSm ile\nabil ia\n6 35\nĠmathematic ian\nĠ4 24\nĠK L\nĠH IP\nĠover heard\nĠT ud\nĠT ec\nĠqu izz\nĠfl attering\nĠcon n\nâĢ İ\nĠatt aches\nĠR OS\nĠAC S\nĠt cp\nĠSh ame\nsk ip\nres pected\nĠTrin idad\ngr ain\nĠfooth old\nĠUnch arted\nĠJul io\nz l\nav ored\nĠAn xiety\ner rors\nĠCent auri\nits ch\nD addy\nĠclutch ing\nĠIm plement\nĠGut ierrez\nĠ7 60\nĠtele portation\nend ra\nĠrevers ible\nst ros\nAd venture\n08 3\nĠliber ating\nĠas phalt\nĠSp end\nAR DS\nim sy\nPR ES\nĠEmer ging\nĠwild fires\nĠtechn ologically\nĠem its\nĠART ICLE\nĠirregular ities\nĠcher ish\nçī Ī\nĠst ink\nĠR ost\nEconom ic\nĠcough ing\nĠMcC ann\npro perties\nilant ro\nĠreneg oti\nTrans lation\nĠin quest\nĠGra pe\noot ers\ngu i\nĠSwords man\nace ae\nh itting\nĠr c\nĠexert ed\nĠS AP\nit ent\nĠperil ous\nĠobsc urity\nĠassass inate\nĠab original\nĠresc uing\nĠSh attered\nlock ing\nall ion\nCh anging\nĠHar rington\nĠB ord\nĠAfgh ans\nJam ie\naret z\nĠAugust us\nĠ38 6\n8 30\nĠj og\nok ingly\nTr igger\nĠH OR\nStat istics\nĠviewers hip\nĠadd itives\nh ur\nĠmaxim izing\nĠR ove\nĠLou ie\nĠBuck et\nĠCHR IST\nou sel\nĠstre aks\nir ted\nĠt ert\nĠcolonial ism\nĠbur ying\ny k\nCond ition\nĠDPR K\nBy Id\n75 1\nâĹ ¼\nĠwor risome\nĠvoc ational\nsl ice\nĠsa ils\nĠCorrection al\n95 4\nĠt ul\nK id\nl uster\nĠfam ilial\nĠSp it\nĠEp iscopal\nSpecific ally\nĠVol cano\nrun s\nq s\nĠve tted\nĠcram med\nt rop\nhere r\nThank fully\nĠper cussion\nĠor anges\nĠround up\nĠ4 99\nx ious\nChar acters\nĠZion ism\nĠR ao\nÃĽ ÃĽ\nW F\nĠunintention al\nONE Y\nGr ab\nCom mercial\nĠglut amate\nĠMcK enna\nru ciating\nning ton\nih u\nCh an\nĠSw ap\nĠleaf lets\nĠfunction ally\ner ous\nF arm\nĠcal oric\nĠLiter ally\ncon cert\nĠshe nan\nĠrep aid\ney es\nĠbas hing\nĠG orge\nĠcollabor ations\nĠun account\nitch ie\nĠteam work\npp elin\nĠpip ing\nĠmin ced\nĠd iam\nri eg\nĠmasc ara\nĠsuck er\nĠMo ons\nApp s\nĠPe ck\nĠper v\nĠFl oat\no ley\nĠN ish\nim ize\nĠarom atic\nu in\nend ish\n! /\nĠB icycle\nĠAS IC\nile ged\nĠQuad ro\nios yn\nĠlock out\nĠW ink\nSP EC\nAttempt s\nĠseed ed\nred o\nias is\nĠsn ag\nãĥķ ãĤ©\nãĤ ¶\nĠground ing\nĠrelie ver\nĠfrivol ous\nĠG ifts\nĠF aces\nEs pecially\nĠmicrobi ome\nim ag\nĠSch l\nĠP les\nĠBle ach\nĠIr win\nĠE aton\nĠDisc iple\nĠmultipl ication\nĠcoer ced\nĠ4 19\nst h\nE vil\nB omb\nĠex orc\nĠstag gered\nL ESS\nĠinert ia\nĠED IT\nĠgo b\nTr aditional\nĠclass y\nLear y\nĠP AGE\nyr s\nĠtrans porter\nĠmat ured\nĠhij ab\nĠbi ome\nWhere as\nĠex termination\nĠT ues\nĠT akeru\nĠAud rey\ner ial\nĠAd en\naff les\nĠnarciss istic\nĠB aird\nUT F\nI re\nĠCon nie\nCh amp\nĠwhis pering\nĠH att\nD K\nĠdis infect\nĠdeduct ed\nĠpart ake\nĠdown grade\nĠEs ports\nĠContin uing\nĠdemocr atically\nicro bial\nitt a\nĠlim estone\nĠexempt ed\nĠFren zy\nH erm\n7 28\nĠfled gling\nMet a\n765 61\n69 3\n% :\nw ake\n5 26\nĠDis cipline\nĠvirgin ity\nĠLeg ions\nĠFrank ie\nint ent\nĠrest rooms\nĠRou ter\nda q\nĠobjection able\nâĨ ij\nw ark\nĠRah ul\ng ain\nactiv ation\nabs olute\nĠAccess ed\nĠ24 00\nogg les\nĠsecond ly\nĠDEF ENSE\nĠpost age\nwra pper\nsh arp\n7 29\nĠcommun icates\nĠadd on\nĠMil itia\nH ong\nĠsl umped\nĠJP EG\nĠI car\nad ish\n68 1\nĠmaj esty\nĠWolf gang\nĠEl astic\nu per\nĠv iz\nĠunconscious ly\nĠST D\nĠS ass\nĠflower ing\nĠHel ic\nĠDra per\nĠAm ateur\nĠman ure\nĠdis ingen\nĠLe i\nbr ing\n9 49\nĠinhib ited\nĠhead quartered\nĠen igmatic\n�� �\nĠred ress\nR H\nĠratt led\nĠd iction\nl io\nĠT BA\nĠSN AP\nC alling\nĠfasc ists\nĠD ove\niew icz\n0 36\nĠco asts\nĠR ect\nĠ) ]\nL ot\n6 29\nĠS EM\nĠPeters en\nĠExpl ain\nĠBo ards\nĠBe zos\nĠJ ournals\nĠ20 24\np arser\nĠmist rust\nĠgr ate\nĠL ocked\nbo a\nS aint\ng aming\nĠvow el\nin ately\nbl ow\nAll ah\nĠun matched\nĠb ordering\nĠExp end\nn r\nOr acle\nrou ch\nĠcont iguous\nac us\nĠdist raught\n58 1\nĠanat omical\nO X\nap ixel\n8 33\nĠPL US\nĠres usc\nĠab iding\n57 3\nĠvac ancies\nEm ily\nĠhyp othal\nĠWer ner\nĠWe e\nĠDJ s\n5 13\nĠwitch craft\nĠac upuncture\nent ary\nbenef it\nProduct s\nĠP SP\nĠMP G\nĠJ inn\nĠJ arrett\nĠ4 45\nĠIm aging\nĠP yth\nFin ish\nĠte x\nĠjuven iles\nĠhero ism\nĠdoubt less\nĠA ki\nĠT end\nĠPatri arch\nĠbit ters\nĠTele communications\nit atively\nag na\nĠr g\nĠS OLD\nĠcomp ulsion\nĠN asa\nĠKath ryn\nĠmillion aires\nĠintrins ically\nĠbolst ered\ntime out\nfl o\nĠtut or\np our\nStat ement\nĠ{ *\nĠRud olph\nĠKimber ly\nrog ens\nadi q\n] +\nĠindign ation\nĠfract uring\nĠRe leases\nĠGr ain\npro tein\nL ago\nĠvac ations\nĠboot ed\nĠTH REE\nĠH G\noresc ence\nĠt f\nĠso ar\niosyn cr\nĠgl ances\nĠSp oon\nĠJ ury\nĠCow boy\nĠcreat ively\nHig her\nĠsolic itor\nĠhaw k\nac io\n89 6\nĠsuperf lu\nĠbombs hell\nct ure\nĠbroker age\nĠraid ing\nĠf rench\nĠang led\nTrans action\nĠGen ocide\nu pe\nĠHait ian\n57 2\n! :\nĠunwitting ly\niter ator\nsc roll\nĠtall ied\nĠbi omedical\nĠC ARD\nĠe uphem\nĠbrain storm\na quin\nK o\nMic helle\nĠR unes\nĠBall istic\nud ers\nĠmod esty\nĠiP ads\nĠEzek iel\nY E\nĠstars hip\nĠpower fully\nĠper l\nĠSh ade\nĠQu art\nĠE EG\nĠfisher man\nOS ED\nĠTyp ical\ndf x\nĠmes hes\nĠet ched\nworth iness\nĠtopp led\nĠ3 96\nor ius\nWe iss\nĠmy sql\nĠVal halla\nÙ Ĵ\nle asing\nĠrec omp\nrap nel\nS el\n04 3\nĠder ailed\nĠGu ides\nIR T\nĠde human\nĠBritt any\n\" ))\nĠex claim\nĠb alk\nĠ8 40\nCLA IM\nint el\nL AB\nĠpe gged\nĠast roph\nsm oking\nĠrig ging\nĠfix ation\nĠcat apult\nins ide\nĠC ascade\nĠBolshe vik\nG aza\nDep th\nĠloud spe\nĠalmond s\nme yer\nl eness\nj en\nf resh\nĠunbeat en\nĠSqu id\nĠPres umably\nTim er\nB W\nĠro sters\nĠell ipt\nĠHar riet\ndat abase\nĠMut ual\nĠComm odore\nuk ed\nkn ife\nĠCOMM UN\nh ya\nĠmel ts\narch ives\nĠrat ification\nĠmultip lying\nĠinter oper\nĠasc ert\nw ings\nver ting\nĠScorp ion\nay e\nĠPorts mouth\nĠM TA\nn it\niaz ep\nĠqu arantine\nĠslides how\nĠcent imeters\nĠsyn opsis\nĠsp ate\nth irst\nĠnom inating\nĠMel vin\nPre view\nĠthro b\nĠgener ational\nĠRad ius\nrest ling\nput able\naw ar\nN ECT\nĠunlaw fully\nĠRevel ations\nWik ipedia\nsur v\nĠeye ing\nij n\nĠF W\nĠbr unt\nĠinter stellar\nĠcl itor\nĠCroat ian\nĠCh ic\nev a\nĠDis app\nĠA kin\niner ies\nd ust\nInterest ed\nĠgen esis\nĠE ucl\nö n\np icking\nĠmut ated\nĠdisappro ve\nĠHD L\nĠ6 25\nÌ ¶\nc ancer\nĠsqu ats\nĠle vers\nDisc uss\n= ]\nD ex\nĠVIDE OS\nA UD\nĠtrans act\nĠKin ect\nĠK uala\nĠC yp\n7 47\nĠsh attering\nĠarsen ic\nĠInt ake\nĠAngel o\nĠQu it\nĠK he\nĠ18 93\nM aker\n0 29\nĠPain ting\nDis able\n9 16\nĠanal ges\nĠtact ile\nĠprop hes\nĠd iced\nĠTravel s\nĠHe ader\nĠClub s\nAss istant\nĠinc rim\nĠd ips\nĠcruc ifix\nĠShan ahan\nĠInter pret\nĠ40 90\nal ogy\nabb a\nĠsimul ac\nhus band\nS IM\nĠrecy cle\nuc er\ned ged\nĠre naissance\nĠBomb ay\nCath olic\nĠL INE\nĠCl othing\nre ports\nĠpl aus\nĠd ag\nĠM ace\nZ I\nĠintr uder\nĠVeter inary\ng ru\nĠsne aky\nĠS ie\nĠC innamon\nP OSE\nĠcou rier\nĠC NS\nĠemanc ipation\ns it\nĠplay through\nĠFac ilities\nv irt\nĠG auntlet\nThom pson\nĠunbeliev ably\nParam eters\nĠst itching\nign e\nĠTH ESE\nPriv acy\nĠshenan igans\nĠvit ri\nĠVal id\n59 1\nŃ ·\nĠProt otype\nink a\nSC P\nĠT id\nè Ī\nold ed\nĠindividual ity\nĠbark ing\nĠm ars\nĠW D\nĠ8 20\nĠt ir\nĠsl apping\nĠdisgr untled\nĠAng ola\nri us\nĠTorn ado\nĠTh urs\nĠcapt cha\nĠang st\nĠP og\nĠAssass ins\nĠAd idas\nĠjoy ful\nĠwh ining\nEmer gency\nĠphosph orus\nĠatt rition\noph on\nĠTimber wolves\nĠJ ah\nĠBr inging\nĠW ad\nĠEn sure\noh l\nĠX ie\nomm el\nc mp\nĠz ipper\nĠrel at\nĠCor ridor\nm ilo\nT ING\nAv g\nĠcro pped\n] }\nĠr aged\nĠLump ur\nĠGuer rero\nour ke\nN ut\nĠoff sets\nog lu\ndr m\nĠmort als\nlat able\nĠdismiss ive\nä¸ ī\nĠthro ats\nĠchips et\nĠSpot light\nCatal og\nart ist\nG b\nĠch illy\nĠst oked\nĠ3 74\nW ard\nL atin\nĠf iasco\nĠble ach\nĠb rav\nEnh anced\nĠin oc\nĠFior ina\n_ >\nĠle ukemia\nĠel uc\nĠannoun cer\nĠLith uan\nĠArm ageddon\nå ĩ\nLen in\nĠR uk\nĠpe pp\nĠRom antic\nĠP IT\nĠInter stellar\nĠAt kinson\nR aid\nJ s\nGo al\nC ourse\nĠvan ishing\nes ley\nĠR ounds\nEls a\n59 3\nĠredund ancy\nĠST AND\nĠprop hetic\nĠhabit able\nry u\nĠfaint ly\nM ODE\nĠfl anked\nIR C\nAw esome\nĠsp urious\nĠZ ah\nĠMS G\nĠsh ading\nĠmotiv ational\nĠSant ana\nĠS PR\nĠexc ruciating\nom ial\nĠM iko\nĠLe opard\nA byss\nĠ[ |\nd irty\nĠbath s\nĠdem oral\nand re\nP B\nĠun ification\nĠsac rament\nĠ[ &\nĠpric eless\nĠgel atin\nĠeman ating\nĠAll aah\n98 6\nĠout burst\nĠer as\nĠX VI\nĠSP I\nO tt\nĠLaz arus\nPL IED\nF lying\nblog s\nW isconsin\nR aven\nĠreb ate\nĠcreep s\nĠSp an\nĠPain ter\nĠKir a\nĠAm os\nĠCor vette\nCons umer\nĠRec over\nck i\nĠpes ky\nĠIn vention\nCompan ies\nĠchalleng ers\nad emic\nĠUkrain ians\nĠNeuro log\nĠFors aken\nĠent rants\nĠemb attled\nĠdef unct\nĠGlac ier\nĠpo isons\nĠH orses\nm akes\nĠD irt\nĠ4 23\nhh h\nĠTrans formation\nQUI RE\n................ ..\nĠtrave ller\nĠSe xy\nĠK ern\nip olar\nĠransom ware\noooooooo oooooooo\nE c\nrub y\nProf essional\nĠOut break\narg ument\nG rey\nĠFif a\nĠCH O\nĠFOR M\nĠAm trak\n- [\nĠcr adle\nĠantioxid ants\nãģ®å ®\n7 36\nĠNAS L\nĠContribut ions\nInd iana\nĠST EP\nC SS\nĠsal ient\nĠall ocations\nyr ights\nĠm ashed\nĠCut ter\nSex ual\nĠp ounded\nĠfan base\nĠc asc\nĠTrans parency\nĠanaly tic\nĠSummon er\n× ŀ\nĠAD C\ndet ail\nĠvan quished\nĠcr abs\nar ie\nDest roy\nĠS ack\nĠtrans istor\nAl abama\nĠK oen\nĠFisher ies\nc one\nĠannex ed\nĠM GM\nes a\nĠf aked\nĠCong ratulations\nĠhind ered\nĠcorrection al\nĠI TV\nlee ve\nĠin appropriately\nlic ks\nĠtresp ass\nĠp aws\nĠnegoti ator\nĠChrist ensen\nlim its\nĠDian ne\nĠeleg ance\nĠContract s\nan ke\nOb j\nĠvigil ance\nĠcast les\nĠN AD\nĠHol o\nĠemph atically\nĠTit us\nĠServ ing\nĠRich ie\nĠP igs\n5 68\nĠanim osity\nĠAtt ributes\nĠU riel\nM Q\nmy ra\nĠApplic ant\nĠpsychiat rists\nĠV ij\nĠAb by\nag ree\nP ush\nĠk Wh\nhib a\nĠinc ite\nĠWe asley\nĠTax i\nminist ic\nhy per\nĠF arn\nĠ6 01\nĠNation wide\nF ake\n95 2\nĠma ize\nĠinteract ed\nĠtransition ed\nĠparas itic\nĠharm onic\nĠdec aying\nĠbas eless\nns ics\nĠtrans pired\nĠabund antly\nĠFore nsic\nĠtread mill\nĠJ av\nab and\nĠssh d\nĠfront man\nĠJak arta\noll er\ndro ps\nĠSERV ICES\nrompt u\noph ical\nh ospital\nbled on\n6 45\nĠmid range\nĠEV ENT\ncul ated\nraw led\nĠper ched\nĠover board\nĠPe el\nĠP wr\nĠCar th\nĠCOM PLE\nco e\nsh all\nĠdeter rence\nM ETHOD\nĠAbs ent\nM EN\nĠs ill\nĠLE VEL\nY ork\nĠsin ners\nĠOP EC\nĠN ur\nĠDesign s\nse lection\nĠunw orthy\nCH A\nĠstreng thens\n88 3\ned ly\nĠslic ing\nĠmal nutrition\nĠfilm making\nĠPol k\nur ated\nĠ4 21\nbre akers\n!' \"\nĠwet lands\nĠDisc rimination\nĠallow able\nĠste ered\nĠSic ily\nS AM\nĠmust ache\nĠm ids\nĠcl ipped\nĠcirc ulate\nĠbr ittle\nĠBuild ings\nra ised\nĠRound up\nĠwealth ier\nĠoverw rite\nĠover powered\nĠGerr ard\ns ites\nPD ATED\nĠacute ly\nĠGam ble\nĠp im\nĠK us\nTyp ically\nDe ploy\nĠMoroc can\np otion\ncom be\nĠvigil ante\nĠ36 3\nSt ew\nĠB agg\nĠres ided\nĠSp o\nĠrem nant\nĠempt iness\nbr ainer\nĠout patient\npri ority\nĠle ptin\nĠPay ton\nĠGle aming\nĠS hed\nĠPol o\nĠMormon ism\nrest ricted\narl ane\nw x\nĠcreat ine\nĠAn on\nĠST UD\nĠJ UL\nĠT ee\n5 28\n08 9\nĠhat ched\nDis patch\nĠCompos ite\nĠ45 1\np uff\nĠX COM\nĠOr n\nĠTH ANK\nEND ED\nĠAshe ville\nĠà ľ\nĠman go\nĠS lightly\nworld ly\nĠW ander\nĠExp and\nĠCh r\nM ist\nĠorthodox y\nĠUN ESCO\nreg ate\nElse where\nk ie\nir led\nĠtopp le\nĠadopt ive\nĠLeg s\nd ress\nĠS agan\nb are\nĠGl ou\nCr unch\nĠhelp ers\nĠchron ically\nĠH uma\n1 0000\nĠaccommod ating\näº Ķ\nĠwrink les\nĠdod ged\nfour th\nĠpre con\nĠcompress or\nĠK are\nĠev ict\nĠWar wick\nim ar\nĠmodern ization\nĠband wagon\nĠref uted\nĠnet ted\nĠNa ples\nĠGen ie\nper ors\nĠfield ed\nĠde re\nĠPar ables\nle es\nĠtr out\nasp ers\nĠn ihil\nĠhapp iest\nĠflo ppy\nĠLo ft\nĠHe ard\nĠun ison\nĠl ug\nĠRed mond\nclass ic\nSupp orters\nSH IP\nG MT\nĠfue lled\nç IJ\nĠd d\nĠEmin em\nĠ18 97\nNY SE\nĠsecret aries\nĠF IA\nĠCanaver al\nF avorite\nĠp omp\nĠdetain ee\ners hip\naim on\ni our\nĠA pex\nĠplant ations\nam ia\nac ion\nR ust\nĠtow ed\nĠTru ly\n5 77\nĠshel tered\nr ider\nW o\nĠl air\nĠInt elligent\nimpro ve\nm atically\nĠet iquette\nad ra\nall o\nĠJun o\nany thing\nĠStru ggle\nĠPred ict\nĠGr imes\nĠAMER ICA\nct x\nĠSit uation\nW OOD\nĠsol uble\nme ier\nĠintoler able\nang ering\nĠun interrupted\nĠtool tip\nĠinterrog ated\nĠgun ned\nĠSne ak\næŃ ¦\nĠt ether\nĠcr umble\nL ens\nĠclust ered\nĠSy l\nĠHas an\nĠdystop ian\nw ana\nĠjoy stick\nĠTh ib\namm u\nTom orrow\n5 46\nĠoverc ame\nĠminim ized\ncept or\nRun ner\nENG TH\nĠBrend a\nĠAchieve ments\nĠtor ches\nĠrapp ort\nĠInvestig ator\nĠHand ling\nrel ation\ng rey\n8 15\nĠk cal\nĠComm ands\nd q\nĠcur ls\nĠbe arer\nĠcyn icism\nit ri\nĠUse ful\nB ee\nD CS\nĠab ras\nP ract\nBIL ITIES\n7 12\nĠdebug ger\nĠdebt or\nĠL ia\nĠK ers\nĠexacerb ate\nĠSt acy\nĠB land\nĠSc enes\nĠbranch ing\nâĸĪâĸĪâĸĪâĸĪ âĸĪâĸĪâĸĪâĸĪ\nape ake\nĠs alsa\nĠmish and\nĠKon ami\nĠN ib\nĠanecd ote\nĠagree able\nÏ ī\nĠNath aniel\nĠHe isman\nĠB eware\nĠ18 86\nspect ive\n69 1\n5 22\nĠinhib its\nĠhas hing\nĠ18 89\nå° Ĩ\nv ich\nP ure\nĠsolid ly\nĠaspir in\nim aru\nĠstreet car\nĠU CS\nĠJ udd\nĠflash backs\np ins\nĠ14 40\nĠUN HCR\nĠSym ptoms\nT IT\n5 38\nF ra\n% );\nĠo oz\nĠcur few\nĠcal med\nĠparticip ates\nTe X\nĠnons ensical\nĠfull back\nĠDe L\nmon key\nh ari\nĠmetabol ites\nĠloot ed\nĠAL WAYS\nĠB CC\nL t\noc het\nB one\nĠveto ed\nĠg cc\nĠCL ICK\nĠ18 88\ns af\nĠstiff ness\nĠlow ly\nĠGe h\nvers on\nors et\nĠun foreseen\nĠan esthesia\nĠOpt ical\nĠrecon structed\nĠT up\nsh ows\nNEW S\nĠNewsp aper\nĠA SA\nter a\nN umbers\nĠinexpl icable\n× ij\nĠhard ness\nunt arily\nĠA cer\ngrad ient\nARD IS\nĠwood land\nĠmetaph ors\nĠWem bley\nĠPa vel\nphil is\nĠre writing\nĠpercept ual\nĠ10 70\nworm s\nĠDown s\nĠunsur prisingly\nĠtag ging\nfl ame\nĠlit res\nĠboun ces\nĠB abe\nsh ut\nĠoverd oses\nĠShe ila\nĠCh au\nĠBl ess\nCapt ure\nĠSign ificant\nĠSc ion\nĠ38 9\nĠMc H\nĠTitan ium\nĠMe al\named a\nag ents\nagg ressive\nB illy\n76 3\nĠS aying\nDER R\nit one\nColl ins\nB ound\nĠbol ted\nĠDM CA\n95 3\nĠun iqueness\nĠep igen\nun ci\nant am\nĠreck oning\nch airs\nOG R\nĠSen egal\nĠ18 62\nre levant\nĠ ¯\nĠpharm acies\nĠG eral\nv ier\nY an\nOR PG\nĠrab id\nb ending\nĠUN ITED\nĠ4 65\nAs sembly\nĠwe ep\nĠbe hest\nĠMother s\nĠJ ace\nh id\nĠwh irlwind\nĠUN IVERS\nĠut opian\nĠkidn ap\nPh ilipp\nK in\n89 3\nĠlivest ream\nĠM ISS\nĠsub versive\nĠTechn iques\nĠJUST ICE\nĠB ASE\nĠ38 7\nĠassail ants\nĠHard core\nĠsprink led\nĠP se\né ļ\nprint ed\nĠH au\nOR GE\nĠT OUR\nĠl aced\nĠit ch\nG iving\nĠport ed\n78 1\n//////////////// ////////////////\nbre eding\nĠlog ger\nĠH OL\ninn ie\nFirst ly\nĠembry onic\nĠdeleg ated\np ai\nO IL\nĠcentr ally\nĠR x\nĠSc outing\nD utch\nĠhe reditary\nĠCru iser\ns at\n5 29\nĠMar riott\nother mal\nĠprohib itions\nE arn\nĠSt ab\nĠColleg es\nĠBel ief\nst retched\nĠL H\nĠEntity Item\nC IA\nĠun rem\nĠlaure ate\nĠdenomin ations\nsum mary\nh ler\nS pect\nĠK laus\nĠBe ans\nĠins ur\nĠPA X\nĠfield er\nĠV et\nĠSp arrow\nz ie\nĠS Q\nĠMond ays\nĠOff line\nĠLer ner\nĠExt ensions\nIre land\nĠpatron age\nĠcontrast ed\nĠMan ia\nh irt\nMos cow\nĠcondem ns\nĠAn ge\nĠcomp osing\nĠPe pe\nĠP addock\nĠheter ogeneity\nĠide ologically\nĠf ishes\nĠcur sing\nĠR utherford\nĠFlo ating\nĠAm elia\nTe a\nSyn opsis\nĠstun ts\nĠbe ad\nĠstock ing\nĠM ILL\nob ook\nmass ive\n\\ <\nĠh ump\nĠPref erences\nEngine Debug\nge ist\nĠNiet o\nome ver\nish y\neval uate\ncol onial\nAltern ative\nĠGo Pro\nĠV ortex\nĠNET WORK\nans ky\nSec ure\nĠTh rust\nSn ake\nĠparcel s\nĠsam urai\nĠactress es\nN ap\nM F\nifer ation\nBe er\n5 23\nĠI ly\noint ment\nP ing\nĠstri ped\nĠMell on\noss ession\nĠneut ron\nend ium\nĠa ph\nĠFlav oring\nĠ38 3\nĠrespons iveness\nĠJ indal\nĠHitch cock\nDen ver\nĠDRAG ON\nsm anship\nĠDu pl\nĠs ly\nĠweb cam\nĠTw ain\nĠDar ling\nili ate\ncons umer\nD IT\nĠnames ake\nĠun orthodox\nĠfun er\nĠPL oS\nĠCONTR OL\nozy g\nogl obin\nF ACE\nER G\nĠD ia\nĠF iesta\nce le\n0 34\nĠencl ave\nâĸ¬ âĸ¬\non ement\nal ist\nM and\nĠhome grown\nĠF ancy\nĠconcept ions\nĠCont ains\nure en\nĠreiter ate\nĠme ager\nĠinstall ments\nSp awn\n6 27\nĠphot oc\nĠCab rera\nĠRos enthal\nĠLans ing\nis ner\nĠinvest s\nĠUFO s\nEX P\nHard ware\nĠtr agically\nĠconced es\nie ft\nch am\nbor gh\nĠSch r\nĠMel anie\nĠH oy\nĠvisit ation\nĠid iosyncr\nĠfract ions\nĠfore skin\nob os\nĠpo aching\nĠVI EW\nĠstimul ates\nĠG ork\ncan on\nM IC\nĠNem esis\nĠInd ra\nĠDM V\nĠ5 29\nĠinspect ing\nĠgrand ma\nĠW hedon\nĠSh ant\nĠP urg\nik an\nĠT eg\nĠCL R\nz ac\nVict oria\nĠVer ify\nion ics\nĠpart ying\nĠM ou\ncol our\nĠtestim onies\nl ations\nĠpress uring\nhi ro\nac ers\nĠf id\nang ler\nĠCS I\nĠhere after\nĠdiss idents\nreport ing\niph any\nche v\nĠsol itude\nĠl obe\nĠind is\nĠcred ential\nre cent\nad ult\nĠNir vana\nĠFranch ise\nL ayer\nH yp\nĠBerks hire\nĠwill s\nt if\nĠtot em\nĠJud ah\nrep air\nInst ant\n5 48\nĠemb assies\nĠbott leneck\nĠb ount\nĠtyp ew\nĠAl vin\nj ing\nim ilar\nR ush\nĠbr im\nĠHEL P\nA im\n] '\nĠpass ively\nĠbound ed\nĠR ated\nĠcriminal ity\nĠbiom ark\nĠdisp atcher\nĠTow ards\nĠ+ ++\nright eous\nf rog\nĠP anc\nC arter\n0 32\næ© Ł\nĠult raviolet\nĠLic ensed\nĠT ata\nĠBl essing\nĠG AM\nĠchem ically\nĠSe af\nĠRE LE\nĠMerc enary\ncapital ist\nĠform ulations\nĠann ihilation\nĠVer b\nĠAr gon\nĠun loaded\nĠmorp hed\nĠconqu ering\nback er\nI ELD\nĠtheft s\nĠfront runner\nĠRoy ale\nĠFund amental\nel ight\nC hip\nnecess ary\nay n\nĠSl ip\nĠ4 48\ncern ed\nP ause\nĠshock ingly\nĠAB V\nĠcomp osure\n7 33\nĠMotors port\nah ime\nMur ray\nM ach\nĠgr ids\nĠdeb ian\nĠfurther more\nĠdexter ity\nĠCollect ions\nos lov\nil age\nb j\nĠMont eneg\nĠstrut Connector\nĠmassac res\nĠbrief s\nfet ched\nuv ian\nol ition\nFail ure\nemon ic\nĠfl ared\nĠclaim ant\nĠc ures\nĠgive aways\nĠSubst ance\nal ions\nĠcr inge\nĠK ul\nĠarist ocracy\nĠUl ster\nol ated\nh ousing\nĠM IS\nĠgl ared\nĠWil helm\nne eds\nlam bda\nbuild ers\nĠV IS\nĠradi ator\nĠGhost busters\nĠ4 36\nact ual\nĠher ds\nç a\nwatch ing\nĠcounter ing\nCh arge\nĠchar red\nĠwar heads\nĠiod ine\nĠM acy\n04 1\nĠdepart ures\nĠS ins\nĠdy ed\nĠConcept s\ng ado\n7 13\nĠquot ations\nĠg ist\nĠChrist y\nĠant igen\nĠHem p\nĠD rawn\nĠB arg\nez vous\nĠp aternity\nĠar du\nĠAnch orage\nĠR ik\nĠover loaded\nĠUs ername\nĠTam my\nĠN au\nĠCell ular\nĠw aning\nĠrod ent\nĠWor cester\nil ts\nĠT ad\nĠdwell ings\nĠbull ish\n4 31\nĠretali ate\nĠmig raine\nĠChev ron\nCH ECK\nĠdon key\nc rim\nSP A\nĠAn alog\nĠmarqu ee\nĠHa as\nB ir\nĠGD DR\nĠDownload s\nĠwill power\nĠFor th\nĠRecord ed\nĠimp ossibility\nĠLog ged\nĠFr anks\nĠR att\nin itions\nĠclean ers\nĠsore ly\nĠflick ering\nĠEx amination\nc atching\nallow een\nMs g\nĠdun no\nF a\nĠdys ph\nc razy\n.' '.\nĠmain line\nĠc s\nĠp tr\nĠW ally\nig un\n95 1\nĠBig foot\nf ights\nĠretrie ving\nJ r\nĠdupl ication\nĠExpl an\nĠrel ational\nĠqu aint\nĠbisc uits\nĠad o\nĠsh udder\nĠantid ote\nblood ed\nks h\nĠsa uces\nĠrein vest\nĠdispens ary\nĠD iver\nĠ9 000\nstud ent\nĠin separ\nesc ap\nĠtodd lers\nĠGP IO\nĠAss ignment\nhead ers\nĠlack luster\nĠab ack\n95 6\nĠtool bar\n7 45\nĠo ust\nĠcontempl ation\nĠPRES IDENT\nĠ4 58\n==== ==\nĠguarantee ing\nĠHe ist\nĠCann es\nĻ ½\nĠcollabor ator\nĠAm p\nĠg ou\nĠSH ALL\nst ories\n78 3\nĠmobil ized\nĠbro od\nĠL U\nĠðŁ ij\nĠref in\nĠAnthrop ology\nv ind\nill i\nĠwarrant ies\nĠB abel\nĠsw ath\nĠc aches\nĠantagon ists\nart ifacts\nĠhot ly\nĠSt arts\nĠG ö\nz ag\n!! !!!\nĠsc ourge\nĠcons piring\nru its\nre verse\nĠShe en\nĠJes uit\nĠGiov anni\nad ies\nĠbutt ocks\near cher\nac an\nĠvolley ball\nĠshroud ed\nĠscore board\nb ats\nĠI PM\nĠass es\nĠde regulation\nĠTe legram\nĠReb oot\nĠ7 000\nĠCan ary\nĠk ernels\nĠFranç ois\nĠD uff\nĠP on\nĠLe ica\nĠGar min\nĠor phans\nĠClaud ia\nĠcal endars\nĠLe ilan\nent o\nR ocket\nĠbr unch\nĠHaw king\nain ers\nĠsens ibilities\nĠk W\nĠK and\nĠre claimed\nĠinteresting ly\n× ©\nrom y\nJ M\nĠEnhance ment\nb ush\nSk ip\nĠrapp ers\nĠg azing\np edia\nath lon\nRev olution\nĠsn ipers\nĠre verted\nĠconglomer ate\nT erry\n79 4\nĠhars her\nĠdes olate\nĠHit man\nComm ission\nĠ( /\nâĢ¦ .\"\nCom par\nĠampl ification\nom inated\nĠreg ress\nĠColl ider\nĠinform ants\nĠg azed\n"
  },
  {
    "path": "models/prompt_expansion/fooocus_expansion/positive.txt",
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  {
    "path": "models/prompt_expansion/fooocus_expansion/special_tokens_map.json",
    "content": "{\n  \"bos_token\": \"<|endoftext|>\",\n  \"eos_token\": \"<|endoftext|>\",\n  \"unk_token\": \"<|endoftext|>\"\n}\n"
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    "path": "models/prompt_expansion/fooocus_expansion/tokenizer.json",
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\"Ġaff\": 1527,\n      \"Ġdays\": 1528,\n      \"Ġann\": 1529,\n      \"ush\": 1530,\n      \"ane\": 1531,\n      \"If\": 1532,\n      \"eg\": 1533,\n      \"Ġprof\": 1534,\n      \"Ġhealth\": 1535,\n      \"outh\": 1536,\n      \"But\": 1537,\n      \"ional\": 1538,\n      \".,\": 1539,\n      \"Ġsol\": 1540,\n      \"Ġalready\": 1541,\n      \"Ġ30\": 1542,\n      \"Ġcharact\": 1543,\n      \"He\": 1544,\n      \"Ġfriend\": 1545,\n      \"ES\": 1546,\n      \"ians\": 1547,\n      \"icle\": 1548,\n      \"'d\": 1549,\n      \"ĠOn\": 1550,\n      \"Ġleast\": 1551,\n      \"Ġprom\": 1552,\n      \"Ġdr\": 1553,\n      \"Ġhist\": 1554,\n      \"ither\": 1555,\n      \"Ġest\": 1556,\n      \"iqu\": 1557,\n      \"17\": 1558,\n      \"son\": 1559,\n      \"Ġtell\": 1560,\n      \"Ġtalk\": 1561,\n      \"ohn\": 1562,\n      \"oint\": 1563,\n      \"lection\": 1564,\n      \"AN\": 1565,\n      \"Ġuntil\": 1566,\n      \"augh\": 1567,\n      \"Ġlater\": 1568,\n      \"Ġve\": 1569,\n      \"Ġview\": 1570,\n      \"ending\": 1571,\n      \"ived\": 1572,\n      \"Ġword\": 1573,\n      \"ware\": 1574,\n      \"Ġcost\": 1575,\n      \"Ġenough\": 1576,\n      \"Ġgive\": 1577,\n      \"ĠUnited\": 1578,\n      \"Ġtechn\": 1579,\n      \"arent\": 1580,\n      \"OR\": 1581,\n      \"Ġpar\": 1582,\n      \"ĠDr\": 1583,\n      \"Ġ2016\": 1584,\n      \"rist\": 1585,\n      \"ering\": 1586,\n      \"ĠÂ\": 1587,\n      \"Ġlarge\": 1588,\n      \"side\": 1589,\n      \"acy\": 1590,\n      \"ccess\": 1591,\n      \"Ġwin\": 1592,\n      \"Ġimportant\": 1593,\n      \"Ġ199\": 1594,\n      \"Ġdoesn\": 1595,\n      \"Ġ17\": 1596,\n      \"Ġbusiness\": 1597,\n      \"Ġclear\": 1598,\n      \"Ġrese\": 1599,\n      \"\\\",\": 1600,\n      \"ury\": 1601,\n      \"Ġequ\": 1602,\n      \"aster\": 1603,\n      \"alf\": 1604,\n      \"ĠAmerican\": 1605,\n      \"nect\": 1606,\n      \"Ġexpect\": 1607,\n      \"iversity\": 1608,\n      \"Ġocc\": 1609,\n      \"ĠFl\": 1610,\n      \"Ġkind\": 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\"Ġreason\": 1738,\n      \"ued\": 1739,\n      \"ched\": 1740,\n      \"ulation\": 1741,\n      \"Ġelect\": 1742,\n      \"Ġofficial\": 1743,\n      \"Ġpossible\": 1744,\n      \"Ġhold\": 1745,\n      \"ands\": 1746,\n      \"ots\": 1747,\n      \"Ġcity\": 1748,\n      \"ories\": 1749,\n      \"Ġsever\": 1750,\n      \"Ġchildren\": 1751,\n      \"Ġonce\": 1752,\n      \"Ġactiv\": 1753,\n      \"ler\": 1754,\n      \"Ġnight\": 1755,\n      \"itions\": 1756,\n      \"ĠJohn\": 1757,\n      \"ape\": 1758,\n      \"play\": 1759,\n      \"Ġdone\": 1760,\n      \"Ġlim\": 1761,\n      \"Ġworking\": 1762,\n      \"ĠPres\": 1763,\n      \"orld\": 1764,\n      \"eb\": 1765,\n      \"ĠCo\": 1766,\n      \"Ġbody\": 1767,\n      \"ails\": 1768,\n      \"utes\": 1769,\n      \"ĠMr\": 1770,\n      \"Ġwhether\": 1771,\n      \"Ġauthor\": 1772,\n      \"rop\": 1773,\n      \"Ġproper\": 1774,\n      \"Ġseen\": 1775,\n      \");\": 1776,\n      \"Ġfac\": 1777,\n      \"ĠSu\": 1778,\n      \"Ġcond\": 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\"Ġalong\": 1863,\n      \"Ġaccording\": 1864,\n      \"Ġyet\": 1865,\n      \"Ġmembers\": 1866,\n      \"ĠWhat\": 1867,\n      \"oid\": 1868,\n      \"ĠMan\": 1869,\n      \"And\": 1870,\n      \"Ġamong\": 1871,\n      \"ai\": 1872,\n      \"Ġemploy\": 1873,\n      \"ĠRes\": 1874,\n      \"Ġ>\": 1875,\n      \"Ġinvol\": 1876,\n      \"Ġlow\": 1877,\n      \"af\": 1878,\n      \"ĠCar\": 1879,\n      \"Ġhig\": 1880,\n      \"ĠOne\": 1881,\n      \"ĠSec\": 1882,\n      \"ination\": 1883,\n      \"Ġlikely\": 1884,\n      \"Ġant\": 1885,\n      \"aged\": 1886,\n      \"ĠRuss\": 1887,\n      \"Ġben\": 1888,\n      \"Ġrele\": 1889,\n      \"For\": 1890,\n      \"back\": 1891,\n      \"ĠNot\": 1892,\n      \"Ġpresident\": 1893,\n      \"ball\": 1894,\n      \"Ġaccess\": 1895,\n      \"ividual\": 1896,\n      \"ĠDem\": 1897,\n      \"ĠEuro\": 1898,\n      \"60\": 1899,\n      \"Ġknown\": 1900,\n      \"irl\": 1901,\n      \"ĠGr\": 1902,\n      \"Ġearly\": 1903,\n      \"use\": 1904,\n      \"iety\": 1905,\n      \"âĢĵ\": 1906,\n      \"Ġfight\": 1907,\n      \"Ġsent\": 1908,\n      \"Ġtoday\": 1909,\n      \"Ġmarket\": 1910,\n      \"\\\".\": 1911,\n      \"Ġbased\": 1912,\n      \"Ġstrong\": 1913,\n      \"urther\": 1914,\n      \"Ġdeb\": 1915,\n      \"mber\": 1916,\n      \"Ġproblem\": 1917,\n      \"Ġdeath\": 1918,\n      \"Ġsocial\": 1919,\n      \"imate\": 1920,\n      \"AS\": 1921,\n      \"ortun\": 1922,\n      \"Ġcampaign\": 1923,\n      \"ery\": 1924,\n      \"Ch\": 1925,\n      \"Ġey\": 1926,\n      \"ially\": 1927,\n      \"Ġmus\": 1928,\n      \"wh\": 1929,\n      \"pos\": 1930,\n      \"Ġer\": 1931,\n      \"Ġsaf\": 1932,\n      \"Ġmonths\": 1933,\n      \"iron\": 1934,\n      \"Ġviol\": 1935,\n      \"Ġfive\": 1936,\n      \"Ġstre\": 1937,\n      \"Ġplayers\": 1938,\n      \"inc\": 1939,\n      \"ald\": 1940,\n      \"year\": 1941,\n      \"aun\": 1942,\n      \"Ġsuccess\": 1943,\n      \"Ġpresent\": 1944,\n      \"erence\": 1945,\n      \"Ġ2014\": 1946,\n 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\"Ġvalue\": 1988,\n      \"Ġarea\": 1989,\n      \"ev\": 1990,\n      \"Ġwrit\": 1991,\n      \"ĠPresident\": 1992,\n      \"Ġvot\": 1993,\n      \"Ġkey\": 1994,\n      \"Ġmom\": 1995,\n      \"put\": 1996,\n      \"Ġanything\": 1997,\n      \"Ġexperience\": 1998,\n      \"attle\": 1999,\n      \"Ġmind\": 2000,\n      \"aff\": 2001,\n      \"omm\": 2002,\n      \"Ġfuture\": 2003,\n      \"ged\": 2004,\n      \"Ġcut\": 2005,\n      \"Ġtot\": 2006,\n      \"itch\": 2007,\n      \"Ġvideo\": 2008,\n      \"Ġinvestig\": 2009,\n      \"Ġnet\": 2010,\n      \"ĠMy\": 2011,\n      \"rict\": 2012,\n      \"ien\": 2013,\n      \".)\": 2014,\n      \"Ġimpro\": 2015,\n      \"though\": 2016,\n      \"wards\": 2017,\n      \"Ġconnect\": 2018,\n      \"ĠMed\": 2019,\n      \"selves\": 2020,\n      \"ensive\": 2021,\n      \"mb\": 2022,\n      \"ober\": 2023,\n      \"ators\": 2024,\n      \"An\": 2025,\n      \"Ġ50\": 2026,\n      \"Ġredu\": 2027,\n      \"resent\": 2028,\n      \"Ġabove\": 2029,\n   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\"Ġissue\": 2071,\n      \"itor\": 2072,\n      \"Ġelse\": 2073,\n      \"Ġconsider\": 2074,\n      \"26\": 2075,\n      \"rodu\": 2076,\n      \"Ġtaken\": 2077,\n      \"28\": 2078,\n      \"99\": 2079,\n      \"ĠWith\": 2080,\n      \"Ġtrue\": 2081,\n      \"Ġwa\": 2082,\n      \"Ġtrad\": 2083,\n      \"Ġago\": 2084,\n      \"Ġmess\": 2085,\n      \"ief\": 2086,\n      \"Ġadded\": 2087,\n      \"oke\": 2088,\n      \"Ġbad\": 2089,\n      \"Ġfav\": 2090,\n      \"33\": 2091,\n      \"Ġsimilar\": 2092,\n      \"ask\": 2093,\n      \"ĠDon\": 2094,\n      \"Ġcharacter\": 2095,\n      \"orts\": 2096,\n      \"ĠHouse\": 2097,\n      \"Ġreported\": 2098,\n      \"Ġtype\": 2099,\n      \"val\": 2100,\n      \"iod\": 2101,\n      \"ĠHowever\": 2102,\n      \"Ġtarg\": 2103,\n      \"Ġentire\": 2104,\n      \"pping\": 2105,\n      \"Ġhistory\": 2106,\n      \"Ġlive\": 2107,\n      \"ffic\": 2108,\n      \"........\": 2109,\n      \"ederal\": 2110,\n      \"Ġtrying\": 2111,\n      \"Ġdiscuss\": 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\"Ġsix\": 2237,\n      \"oo\": 2238,\n      \"Ġstep\": 2239,\n      \"Ġpub\": 2240,\n      \"Ġhimself\": 2241,\n      \"Ġ23\": 2242,\n      \"Ġcop\": 2243,\n      \"Ġdest\": 2244,\n      \"Ġstop\": 2245,\n      \"AC\": 2246,\n      \"ibility\": 2247,\n      \"Ġlab\": 2248,\n      \"icult\": 2249,\n      \"Ġhours\": 2250,\n      \"Ġcreate\": 2251,\n      \"Ġfurther\": 2252,\n      \"ĠAmerica\": 2253,\n      \"ĠCity\": 2254,\n      \"Ġdou\": 2255,\n      \"head\": 2256,\n      \"ST\": 2257,\n      \"ĠNorth\": 2258,\n      \"cing\": 2259,\n      \"Ġnational\": 2260,\n      \"ule\": 2261,\n      \"ĠInst\": 2262,\n      \"Ġtaking\": 2263,\n      \"ĠQu\": 2264,\n      \"irt\": 2265,\n      \"Ġred\": 2266,\n      \"Ġresearch\": 2267,\n      \"viron\": 2268,\n      \"ĠGe\": 2269,\n      \"Ġbreak\": 2270,\n      \"ana\": 2271,\n      \"Ġspace\": 2272,\n      \"aterial\": 2273,\n      \"Ġrecent\": 2274,\n      \"ĠAb\": 2275,\n      \"Ġgeneral\": 2276,\n      \"Ġhit\": 2277,\n      \"Ġperiod\": 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\"ĠFred\": 8559,\n      \"Ġadvertising\": 8560,\n      \"Ġimprovements\": 8561,\n      \"ĠCustom\": 8562,\n      \"Ġerrors\": 8563,\n      \"Ġsensitive\": 8564,\n      \"ĠNavy\": 8565,\n      \"Ġcream\": 8566,\n      \"Look\": 8567,\n      \"Ġexclusive\": 8568,\n      \"Ġcomprehens\": 8569,\n      \"Ġdeleg\": 8570,\n      \"Ġconce\": 8571,\n      \"Ġremem\": 8572,\n      \"Ġstructures\": 8573,\n      \"Ġstored\": 8574,\n      \"ND\": 8575,\n      \"Ġ1000\": 8576,\n      \"UP\": 8577,\n      \"ĠBudd\": 8578,\n      \"AF\": 8579,\n      \"woman\": 8580,\n      \"ĠAcademy\": 8581,\n      \"ðŁ\": 8582,\n      \"sea\": 8583,\n      \"Ġtemporary\": 8584,\n      \"About\": 8585,\n      \"esters\": 8586,\n      \"Ġtickets\": 8587,\n      \"Ġpossess\": 8588,\n      \"inch\": 8589,\n      \"oz\": 8590,\n      \"Ġla\": 8591,\n      \"Ġcontracts\": 8592,\n      \"Ġunp\": 8593,\n      \"Ġcig\": 8594,\n      \"ĠKat\": 8595,\n      \"ultural\": 8596,\n      \"asm\": 8597,\n      \"Ġmountain\": 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\"ĠEs\": 8678,\n      \"acco\": 8679,\n      \"Ġlistening\": 8680,\n      \"Ġcongress\": 8681,\n      \"ependent\": 8682,\n      \"Ġpremium\": 8683,\n      \"Ġ76\": 8684,\n      \"ĠIrish\": 8685,\n      \"Ġassigned\": 8686,\n      \"ĠPhys\": 8687,\n      \"Ġworldwide\": 8688,\n      \"Ġnarrative\": 8689,\n      \"otype\": 8690,\n      \"mont\": 8691,\n      \"base\": 8692,\n      \"ĠBowl\": 8693,\n      \"ĠAdministration\": 8694,\n      \"Ġrelation\": 8695,\n      \"ĠEV\": 8696,\n      \"CP\": 8697,\n      \"Ġcovers\": 8698,\n      \"Ġ78\": 8699,\n      \"Ġcertific\": 8700,\n      \"Ġgrass\": 8701,\n      \"Ġ04\": 8702,\n      \"piracy\": 8703,\n      \"ira\": 8704,\n      \"Ġengineering\": 8705,\n      \"ĠMars\": 8706,\n      \"Ġunemploy\": 8707,\n      \"ĠForeign\": 8708,\n      \"stract\": 8709,\n      \"Ġven\": 8710,\n      \"Ġsteal\": 8711,\n      \"Ġreplied\": 8712,\n      \"Ġultimate\": 8713,\n      \"Ġtitles\": 8714,\n      \"dated\": 8715,\n      \"Ġjoy\": 8716,\n      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\"Ġassistant\": 8796,\n      \"Ġwhenever\": 8797,\n      \"ĠAccess\": 8798,\n      \"ĠUr\": 8799,\n      \"bin\": 8800,\n      \"Ġclock\": 8801,\n      \"isp\": 8802,\n      \"opher\": 8803,\n      \"Ġborrow\": 8804,\n      \"Ġmad\": 8805,\n      \"Ġpersonality\": 8806,\n      \"only\": 8807,\n      \"IST\": 8808,\n      \"abama\": 8809,\n      \"Ġgains\": 8810,\n      \"Ġcommonly\": 8811,\n      \"Ġterr\": 8812,\n      \"Ġhypot\": 8813,\n      \"Ġrely\": 8814,\n      \"Ġtiss\": 8815,\n      \"isconsin\": 8816,\n      \"Ġridic\": 8817,\n      \"function\": 8818,\n      \"ĠOregon\": 8819,\n      \"Ġuncom\": 8820,\n      \"rating\": 8821,\n      \"eland\": 8822,\n      \"ĠNC\": 8823,\n      \"Ġmoon\": 8824,\n      \"annon\": 8825,\n      \"Ġvulnerable\": 8826,\n      \"utive\": 8827,\n      \"³³³³\": 8828,\n      \"ĠRadio\": 8829,\n      \"Ġwestern\": 8830,\n      \"sect\": 8831,\n      \"ĠTony\": 8832,\n      \"Ġoccurs\": 8833,\n      \"ĠOs\": 8834,\n      \"ĠHon\": 8835,\n      \"ÃŃ\": 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\"105\": 13348,\n      \"ĠOP\": 13349,\n      \"ifiers\": 13350,\n      \"Ġprevented\": 13351,\n      \"Ġincomp\": 13352,\n      \"ĠCommander\": 13353,\n      \"Ġdominated\": 13354,\n      \"Ġ»\": 13355,\n      \"Ġinvested\": 13356,\n      \"Ġcomplexity\": 13357,\n      \"Ġincl\": 13358,\n      \"Ġensuring\": 13359,\n      \"Ġrealm\": 13360,\n      \"ync\": 13361,\n      \"ĠIndependent\": 13362,\n      \"rained\": 13363,\n      \"ĠJen\": 13364,\n      \"ĠFlight\": 13365,\n      \"Ġathe\": 13366,\n      \"Ġspeculation\": 13367,\n      \"ĠTE\": 13368,\n      \"ocate\": 13369,\n      \"tic\": 13370,\n      \"Ġplaint\": 13371,\n      \"herry\": 13372,\n      \"Ġtoy\": 13373,\n      \"Ġ111\": 13374,\n      \"Ġplates\": 13375,\n      \"status\": 13376,\n      \"ĠIsa\": 13377,\n      \"Ġdevoted\": 13378,\n      \"Cop\": 13379,\n      \"ĠES\": 13380,\n      \"255\": 13381,\n      \"urrency\": 13382,\n      \"Main\": 13383,\n      \"Ġslaves\": 13384,\n      \"Ġpepper\": 13385,\n      \"Ġquotes\": 13386,\n      \"Ġceiling\": 13387,\n      \"ĠFish\": 13388,\n      \"Ġtransformation\": 13389,\n      \"Ġfraction\": 13390,\n      \"Ġadvantages\": 13391,\n      \"Ġtoile\": 13392,\n      \"Ġstunning\": 13393,\n      \"Ġmoist\": 13394,\n      \"breaking\": 13395,\n      \"si\": 13396,\n      \"ĠLocation\": 13397,\n      \"ĠMedium\": 13398,\n      \"Ġtexts\": 13399,\n      \"Ġugly\": 13400,\n      \"Ġbio\": 13401,\n      \".âĢĶ\": 13402,\n      \"ĠBased\": 13403,\n      \"Ġtrains\": 13404,\n      \"ĠWing\": 13405,\n      \"ĠAncient\": 13406,\n      \"ĠRecords\": 13407,\n      \"ĠHope\": 13408,\n      \"Special\": 13409,\n      \"adesh\": 13410,\n      \"obi\": 13411,\n      \"[/\": 13412,\n      \"Ġtemporarily\": 13413,\n      \"Ver\": 13414,\n      \"hu\": 13415,\n      \"oser\": 13416,\n      \"Ġovernight\": 13417,\n      \"Ġmamm\": 13418,\n      \"ĠTreasury\": 13419,\n      \"ĠVenezuel\": 13420,\n      \"ĠMega\": 13421,\n      \"Ġtar\": 13422,\n      \"Ġexpects\": 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\"Ġthemes\": 13460,\n      \"advant\": 13461,\n      \"Ġnin\": 13462,\n      \"Ġpraise\": 13463,\n      \"104\": 13464,\n      \"ĠLibya\": 13465,\n      \"best\": 13466,\n      \"Ġtrusted\": 13467,\n      \"Ġcease\": 13468,\n      \"Ġdign\": 13469,\n      \"Direct\": 13470,\n      \"Ġbombing\": 13471,\n      \"Ġmigration\": 13472,\n      \"ĠSciences\": 13473,\n      \"Ġmunicipal\": 13474,\n      \"ĠAverage\": 13475,\n      \"Ġglory\": 13476,\n      \"Ġrevealing\": 13477,\n      \"Ġarena\": 13478,\n      \"Ġuncertainty\": 13479,\n      \"Ġbattlefield\": 13480,\n      \"iao\": 13481,\n      \"God\": 13482,\n      \"Ġcinem\": 13483,\n      \"rape\": 13484,\n      \"elle\": 13485,\n      \"apons\": 13486,\n      \"Ġlisting\": 13487,\n      \"Ġwaited\": 13488,\n      \"Ġspotted\": 13489,\n      \"keley\": 13490,\n      \"ĠAudio\": 13491,\n      \"eor\": 13492,\n      \"arding\": 13493,\n      \"idding\": 13494,\n      \"igma\": 13495,\n      \"ĠNeg\": 13496,\n      \"Ġlone\": 13497,\n      \"Ġ----\": 13498,\n      \"exe\": 13499,\n      \"deg\": 13500,\n      \"Ġtransf\": 13501,\n      \"Ġwash\": 13502,\n      \"Ġslavery\": 13503,\n      \"Ġexploring\": 13504,\n      \"ĠWW\": 13505,\n      \"atson\": 13506,\n      \"Ġencl\": 13507,\n      \"lies\": 13508,\n      \"ĠCreek\": 13509,\n      \"Ġwooden\": 13510,\n      \"Manager\": 13511,\n      \"ĠBrand\": 13512,\n      \"ummy\": 13513,\n      \"ĠArthur\": 13514,\n      \"Ġbureaucr\": 13515,\n      \"Ġblend\": 13516,\n      \"arians\": 13517,\n      \"Further\": 13518,\n      \"Ġsupposedly\": 13519,\n      \"Ġwinds\": 13520,\n      \"Ġ1979\": 13521,\n      \"Ġgravity\": 13522,\n      \"Ġanalyses\": 13523,\n      \"ĠTravel\": 13524,\n      \"ĠVeter\": 13525,\n      \"Ġdumb\": 13526,\n      \"Ġalternate\": 13527,\n      \"gal\": 13528,\n      \"Ġconsumed\": 13529,\n      \"Ġeffectiveness\": 13530,\n      \".''\": 13531,\n      \"Ġpaths\": 13532,\n      \"onda\": 13533,\n      \"LA\": 13534,\n      \"ĠStrong\": 13535,\n      \"Ġenables\": 13536,\n      \"Ġescaped\": 13537,\n      \"Ġ\\\"\\\"\": 13538,\n      \"Ġ112\": 13539,\n      \"Ġ1983\": 13540,\n      \"Ġsmiled\": 13541,\n      \"Ġtendency\": 13542,\n      \"Fire\": 13543,\n      \"Ġpars\": 13544,\n      \"ĠRoc\": 13545,\n      \"Ġlake\": 13546,\n      \"Ġfitness\": 13547,\n      \"ĠAth\": 13548,\n      \"ĠHorn\": 13549,\n      \"Ġhier\": 13550,\n      \"Ġimpose\": 13551,\n      \"mother\": 13552,\n      \"Ġpension\": 13553,\n      \"icut\": 13554,\n      \"borne\": 13555,\n      \"iciary\": 13556,\n      \"._\": 13557,\n      \"ĠSU\": 13558,\n      \"Ġpolar\": 13559,\n      \"isy\": 13560,\n      \"engu\": 13561,\n      \"itialized\": 13562,\n      \"ATA\": 13563,\n      \"write\": 13564,\n      \"Ġexercises\": 13565,\n      \"ĠDiamond\": 13566,\n      \"otypes\": 13567,\n      \"Ġharmful\": 13568,\n      \"onz\": 13569,\n      \"Ġprinting\": 13570,\n      \"story\": 13571,\n      \"Ġexpertise\": 13572,\n      \"ĠGer\": 13573,\n      \"Ġtragedy\": 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\"ĠDraft\": 13650,\n      \"ortunate\": 13651,\n      \"folio\": 13652,\n      \"pered\": 13653,\n      \"usters\": 13654,\n      \"gt\": 13655,\n      \"ĠLock\": 13656,\n      \"Ġjudicial\": 13657,\n      \"verted\": 13658,\n      \"Ġsecured\": 13659,\n      \"outing\": 13660,\n      \"ĠBooks\": 13661,\n      \"Ġhosting\": 13662,\n      \"Ġlifted\": 13663,\n      \"length\": 13664,\n      \"Ġjer\": 13665,\n      \"Ġwheels\": 13666,\n      \"ĠRange\": 13667,\n      \"umbnails\": 13668,\n      \"Ġdiagnosis\": 13669,\n      \"tech\": 13670,\n      \"ĠStewart\": 13671,\n      \"ĠPract\": 13672,\n      \"Ġnationwide\": 13673,\n      \"Ġdear\": 13674,\n      \"Ġobligations\": 13675,\n      \"Ġgrows\": 13676,\n      \"Ġmandatory\": 13677,\n      \"Ġsuspicious\": 13678,\n      \"!'\": 13679,\n      \"Apr\": 13680,\n      \"Great\": 13681,\n      \"Ġmortgage\": 13682,\n      \"Ġprosecutor\": 13683,\n      \"Ġeditorial\": 13684,\n      \"ĠKr\": 13685,\n      \"Ġprocessed\": 13686,\n      \"ungle\": 13687,\n      \"Ġflexibility\": 13688,\n      \"Earlier\": 13689,\n      \"ĠCart\": 13690,\n      \"ĠSug\": 13691,\n      \"Ġfocuses\": 13692,\n      \"Ġstartup\": 13693,\n      \"Ġbreach\": 13694,\n      \"ĠTob\": 13695,\n      \"cycle\": 13696,\n      \"ãĢĮ\": 13697,\n      \"rose\": 13698,\n      \"Ġbizarre\": 13699,\n      \"ãĢį\": 13700,\n      \"Ġvegetables\": 13701,\n      \"$$\": 13702,\n      \"Ġretreat\": 13703,\n      \"oshi\": 13704,\n      \"ĠShop\": 13705,\n      \"ĠGround\": 13706,\n      \"ĠStop\": 13707,\n      \"ĠHawaii\": 13708,\n      \"ĠAy\": 13709,\n      \"Perhaps\": 13710,\n      \"ĠBeaut\": 13711,\n      \"uffer\": 13712,\n      \"enna\": 13713,\n      \"Ġproductivity\": 13714,\n      \"Fixed\": 13715,\n      \"control\": 13716,\n      \"Ġabsent\": 13717,\n      \"ĠCampaign\": 13718,\n      \"Green\": 13719,\n      \"Ġidentifying\": 13720,\n      \"Ġregret\": 13721,\n      \"Ġpromoted\": 13722,\n      \"ĠSeven\": 13723,\n      \"Ġeru\": 13724,\n      \"neath\": 13725,\n      \"aughed\": 13726,\n      \"ĠPin\": 13727,\n      \"ĠLiving\": 13728,\n      \"Cost\": 13729,\n      \"omatic\": 13730,\n      \"mega\": 13731,\n      \"ĠNig\": 13732,\n      \"ocy\": 13733,\n      \"Ġinbox\": 13734,\n      \"Ġempire\": 13735,\n      \"Ġhorizont\": 13736,\n      \"Ġbranches\": 13737,\n      \"Ġmetaph\": 13738,\n      \"Active\": 13739,\n      \"edi\": 13740,\n      \"ĠFilm\": 13741,\n      \"ĠSomething\": 13742,\n      \"Ġmods\": 13743,\n      \"incial\": 13744,\n      \"ĠOriginal\": 13745,\n      \"Gen\": 13746,\n      \"Ġspirits\": 13747,\n      \"Ġearning\": 13748,\n      \"Hist\": 13749,\n      \"Ġriders\": 13750,\n      \"Ġsacrific\": 13751,\n      \"MT\": 13752,\n      \"ĠVA\": 13753,\n      \"ĠSalt\": 13754,\n      \"Ġoccupation\": 13755,\n      \"ĠMi\": 13756,\n      \"Ġdisg\": 13757,\n      \"lict\": 13758,\n      \"Ġnit\": 13759,\n      \"Ġnodes\": 13760,\n      \"eem\": 13761,\n      \"ĠPier\": 13762,\n      \"Ġhatred\": 13763,\n     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\"Ġmeals\": 13840,\n      \"Ġ(-\": 13841,\n      \"idays\": 13842,\n      \"Ġimagination\": 13843,\n      \"Ġannually\": 13844,\n      \"Ġms\": 13845,\n      \"asures\": 13846,\n      \"Head\": 13847,\n      \"ikh\": 13848,\n      \"matic\": 13849,\n      \"Ġboyfriend\": 13850,\n      \"ĠComputer\": 13851,\n      \"Ġbump\": 13852,\n      \"Ġsurge\": 13853,\n      \"ĠCraig\": 13854,\n      \"ĠKirk\": 13855,\n      \"Del\": 13856,\n      \"mediate\": 13857,\n      \"Ġscenarios\": 13858,\n      \"ĠMut\": 13859,\n      \"ĠStream\": 13860,\n      \"Ġcompetitors\": 13861,\n      \"ÙĦ\": 13862,\n      \"ĠStanford\": 13863,\n      \"ĠResources\": 13864,\n      \"azed\": 13865,\n      \"bage\": 13866,\n      \"Ġorganis\": 13867,\n      \"ĠRelease\": 13868,\n      \"Ġseparately\": 13869,\n      \"Ġhabits\": 13870,\n      \"Ġmeasurements\": 13871,\n      \"ĠClose\": 13872,\n      \"Ġaccompany\": 13873,\n      \"Ġgly\": 13874,\n      \"Ġtang\": 13875,\n      \"ĠRou\": 13876,\n      \"Ġplugin\": 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\"cerpt\": 17040,\n      \"plates\": 17041,\n      \"pher\": 17042,\n      \"clipse\": 17043,\n      \"Ġtwitter\": 17044,\n      \"Ġacids\": 17045,\n      \"ographical\": 17046,\n      \"hum\": 17047,\n      \"ĠBald\": 17048,\n      \"ifully\": 17049,\n      \"Ġcompiler\": 17050,\n      \"ĠDA\": 17051,\n      \"Ġdonor\": 17052,\n      \"asi\": 17053,\n      \"Ġtribal\": 17054,\n      \"lash\": 17055,\n      \"ĠConfig\": 17056,\n      \"Ġapplicants\": 17057,\n      \"Ġsalaries\": 17058,\n      \"135\": 17059,\n      \"Putin\": 17060,\n      \"ĠFocus\": 17061,\n      \"irs\": 17062,\n      \"Ġmisconduct\": 17063,\n      \"ĠHaz\": 17064,\n      \"Ġeaten\": 17065,\n      \"Mobile\": 17066,\n      \"Muslim\": 17067,\n      \"ĠMarcus\": 17068,\n      \"viol\": 17069,\n      \"Ġfavorable\": 17070,\n      \"Ġstub\": 17071,\n      \"adin\": 17072,\n      \"ĠHob\": 17073,\n      \"Ġfaithful\": 17074,\n      \"Ġelectronics\": 17075,\n      \"Ġvacuum\": 17076,\n      \"wait\": 17077,\n      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\"Ġabsorb\": 17565,\n      \"images\": 17566,\n      \"Ġrefuses\": 17567,\n      \"Ġbonuses\": 17568,\n      \"___\": 17569,\n      \"Ġhilar\": 17570,\n      \"ĠFeatures\": 17571,\n      \"220\": 17572,\n      \"ĠCollector\": 17573,\n      \"Foot\": 17574,\n      \"Ġ1964\": 17575,\n      \"culus\": 17576,\n      \"Ġdawn\": 17577,\n      \"Ġworkout\": 17578,\n      \"ĠLO\": 17579,\n      \"Ġphilosophical\": 17580,\n      \"ĠSandy\": 17581,\n      \"ĠYouth\": 17582,\n      \"Ġliable\": 17583,\n      \"Af\": 17584,\n      \"blue\": 17585,\n      \"Ġoverturn\": 17586,\n      \"lessness\": 17587,\n      \"ĠTribune\": 17588,\n      \"ĠIng\": 17589,\n      \"Ġfactories\": 17590,\n      \"Ġcatches\": 17591,\n      \"Ġprone\": 17592,\n      \"Ġmatrix\": 17593,\n      \"Ġlogin\": 17594,\n      \"Ġinacc\": 17595,\n      \"Ġexert\": 17596,\n      \"sys\": 17597,\n      \"Ġneedle\": 17598,\n      \"ĠQur\": 17599,\n      \"Ġnotified\": 17600,\n      \"oulder\": 17601,\n      \"tx\": 17602,\n      \"Ġreminds\": 17603,\n      \"Ġpublishers\": 17604,\n      \"Ġnort\": 17605,\n      \"Ġgit\": 17606,\n      \"Ġflies\": 17607,\n      \"ĠEmily\": 17608,\n      \"Ġflowing\": 17609,\n      \"ĠAlien\": 17610,\n      \"ĠStrateg\": 17611,\n      \"Ġhardest\": 17612,\n      \"Ġmodification\": 17613,\n      \"API\": 17614,\n      \"ĠMY\": 17615,\n      \"Ġcrashes\": 17616,\n      \"stairs\": 17617,\n      \"number\": 17618,\n      \"Ġurging\": 17619,\n      \"channel\": 17620,\n      \"ĠFalcon\": 17621,\n      \"Ġinhabitants\": 17622,\n      \"Ġterrifying\": 17623,\n      \"Ġutilize\": 17624,\n      \"Ġbanner\": 17625,\n      \"Ġcigarettes\": 17626,\n      \"Ġsenses\": 17627,\n      \"ĠHolmes\": 17628,\n      \"Ġpractition\": 17629,\n      \"ĠPhillips\": 17630,\n      \"otto\": 17631,\n      \"Ġcompile\": 17632,\n      \"Model\": 17633,\n      \"ĠKo\": 17634,\n      \"Ġ[]\": 17635,\n      \"Americans\": 17636,\n      \"ĠTerms\": 17637,\n      \"Ġmedications\": 17638,\n      \"ĠAna\": 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   \"Ġswept\": 17676,\n      \"ĠImpact\": 17677,\n      \"Ġredist\": 17678,\n      \"Ġunint\": 17679,\n      \"graduate\": 17680,\n      \"ãĥķ\": 17681,\n      \"ĠWILL\": 17682,\n      \"ãģ®ç\": 17683,\n      \"ĠCritical\": 17684,\n      \"Ġfisher\": 17685,\n      \"Ġvicious\": 17686,\n      \"Ġreversed\": 17687,\n      \"Year\": 17688,\n      \"ĠSox\": 17689,\n      \"Ġshootings\": 17690,\n      \"Ġfilming\": 17691,\n      \"Ġtouchdowns\": 17692,\n      \"aires\": 17693,\n      \"mel\": 17694,\n      \"Ġgrandfather\": 17695,\n      \"Ġaffection\": 17696,\n      \"ingle\": 17697,\n      \"Ġoverly\": 17698,\n      \"Additional\": 17699,\n      \"Ġsupreme\": 17700,\n      \"ĠGrad\": 17701,\n      \"Ġsporting\": 17702,\n      \"Ġmercy\": 17703,\n      \"ĠBrooks\": 17704,\n      \"ounty\": 17705,\n      \"Ġperforms\": 17706,\n      \"Ġtightly\": 17707,\n      \"Ġdemons\": 17708,\n      \"Ġkillings\": 17709,\n      \"Ġfaction\": 17710,\n      \"ĠNova\": 17711,\n      \"auts\": 17712,\n      \"Ġundoubtedly\": 17713,\n      \"arin\": 17714,\n      \"Ġunderway\": 17715,\n      \"rak\": 17716,\n      \"Ġliv\": 17717,\n      \"ĠRegion\": 17718,\n      \"Ġbriefing\": 17719,\n      \"sers\": 17720,\n      \"cloud\": 17721,\n      \"ĠMik\": 17722,\n      \"usp\": 17723,\n      \"Ġprediction\": 17724,\n      \"azor\": 17725,\n      \"Ġportable\": 17726,\n      \"ĠGand\": 17727,\n      \"Ġpresenting\": 17728,\n      \"Ġ1080\": 17729,\n      \"»\": 17730,\n      \"ushi\": 17731,\n      \"ĠSpark\": 17732,\n      \"thereum\": 17733,\n      \"Ġjustification\": 17734,\n      \"ĠNy\": 17735,\n      \"Ġcontractors\": 17736,\n      \"mingham\": 17737,\n      \"ĠStyle\": 17738,\n      \"åħ\": 17739,\n      \"ĠChronicles\": 17740,\n      \"ĠPicture\": 17741,\n      \"Ġproving\": 17742,\n      \"Ġwives\": 17743,\n      \"sett\": 17744,\n      \"Ġmolecules\": 17745,\n      \"ĠFairy\": 17746,\n      \"Ġconsisting\": 17747,\n      \"Ġpier\": 17748,\n      \"alone\": 17749,\n      \"inition\": 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\"Ġbeats\": 17825,\n      \"ĠJamie\": 17826,\n      \"152\": 17827,\n      \"ĠINT\": 17828,\n      \"Contact\": 17829,\n      \"Ġaccounted\": 17830,\n      \"hash\": 17831,\n      \"ĠPackers\": 17832,\n      \"pires\": 17833,\n      \"Ġlesbian\": 17834,\n      \"Ġamendments\": 17835,\n      \"Ġhopeful\": 17836,\n      \"ĠFinland\": 17837,\n      \"Ġspotlight\": 17838,\n      \"Ġconfigured\": 17839,\n      \"Ġtroubled\": 17840,\n      \"Ġgaze\": 17841,\n      \"ĠCalgary\": 17842,\n      \"Ġreliability\": 17843,\n      \"Ġinsurg\": 17844,\n      \"swer\": 17845,\n      \"buy\": 17846,\n      \"ĠSkin\": 17847,\n      \"Ġpixels\": 17848,\n      \"Ġhandgun\": 17849,\n      \"Ġparas\": 17850,\n      \"Ġcategor\": 17851,\n      \"ĠEL\": 17852,\n      \"ĠRex\": 17853,\n      \"Indeed\": 17854,\n      \"Ġkinda\": 17855,\n      \"Ġconjunction\": 17856,\n      \"ĠBryan\": 17857,\n      \"ĠManufact\": 17858,\n      \"yang\": 17859,\n      \"Plus\": 17860,\n      \"SQL\": 17861,\n      \"ishment\": 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\"role\": 18090,\n      \"Ġairline\": 18091,\n      \"ernandez\": 18092,\n      \"ĠAthlet\": 18093,\n      \"Ġcertification\": 18094,\n      \"uxe\": 18095,\n      \"riger\": 18096,\n      \"Ġempir\": 18097,\n      \"Ġsensation\": 18098,\n      \"Ġdism\": 18099,\n      \"Ġbolt\": 18100,\n      \"Ġevolve\": 18101,\n      \"House\": 18102,\n      \"Ġconsultation\": 18103,\n      \"ĠDuty\": 18104,\n      \"Ġtouches\": 18105,\n      \"ĠNathan\": 18106,\n      \"Ġfaint\": 18107,\n      \"had\": 18108,\n      \"\\\"(\": 18109,\n      \"ĠConsumer\": 18110,\n      \"ĠExtreme\": 18111,\n      \"Ġ127\": 18112,\n      \"ĠHerm\": 18113,\n      \"ĠSacrament\": 18114,\n      \"izoph\": 18115,\n      \"Ġanxious\": 18116,\n      \"ulously\": 18117,\n      \"Ġsocially\": 18118,\n      \"ĠUTC\": 18119,\n      \"Ġsolving\": 18120,\n      \"ĠLetter\": 18121,\n      \"History\": 18122,\n      \"educ\": 18123,\n      \"Price\": 18124,\n      \"));\": 18125,\n      \"Ġreload\": 18126,\n      \"amic\": 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   \"ĠFeng\": 18164,\n      \"America\": 18165,\n      \"prises\": 18166,\n      \"stitial\": 18167,\n      \"ĠWeekly\": 18168,\n      \"ĠGreater\": 18169,\n      \"modules\": 18170,\n      \"ifter\": 18171,\n      \"Graphics\": 18172,\n      \"uler\": 18173,\n      \"Ġwholly\": 18174,\n      \"Ġsuppress\": 18175,\n      \"Ġconcealed\": 18176,\n      \"Ġhappily\": 18177,\n      \"Ġaccepts\": 18178,\n      \"ĠEnjoy\": 18179,\n      \"Ġrivers\": 18180,\n      \"ĠExcept\": 18181,\n      \"225\": 18182,\n      \"ĠNHS\": 18183,\n      \"ĠMcConnell\": 18184,\n      \"Ġpussy\": 18185,\n      \"ferred\": 18186,\n      \"utable\": 18187,\n      \"Ġattain\": 18188,\n      \"Ġ>=\": 18189,\n      \"Ġdeposits\": 18190,\n      \"rophic\": 18191,\n      \"Ġnotorious\": 18192,\n      \"ĠShaw\": 18193,\n      \"ilitation\": 18194,\n      \"Ġepidemic\": 18195,\n      \"allic\": 18196,\n      \"Ġsmallest\": 18197,\n      \"ovich\": 18198,\n      \"Ġaccessories\": 18199,\n      \"perties\": 18200,\n      \"Ġsurplus\": 18201,\n      \"ĠMech\": 18202,\n      \"Ġambig\": 18203,\n      \"ĠImmigration\": 18204,\n      \"Ġchim\": 18205,\n      \"eval\": 18206,\n      \"Ġpracticing\": 18207,\n      \"ĠMystery\": 18208,\n      \"Ġdomains\": 18209,\n      \"ĠSilicon\": 18210,\n      \"apps\": 18211,\n      \"Ġkilometers\": 18212,\n      \"ea\": 18213,\n      \"ĠSmash\": 18214,\n      \"Ġwarranty\": 18215,\n      \"Ġnost\": 18216,\n      \"sil\": 18217,\n      \"rev\": 18218,\n      \"Jon\": 18219,\n      \"ĠDublin\": 18220,\n      \"Ġtastes\": 18221,\n      \"Ġbout\": 18222,\n      \"great\": 18223,\n      \"error\": 18224,\n      \"Ġswitches\": 18225,\n      \"ĠBapt\": 18226,\n      \"DO\": 18227,\n      \"oki\": 18228,\n      \"Ġsourced\": 18229,\n      \"produ\": 18230,\n      \"Ġattachment\": 18231,\n      \"ĠIssue\": 18232,\n      \"ĠQuestion\": 18233,\n      \"Join\": 18234,\n      \"Ġfitted\": 18235,\n      \"Ġunlawful\": 18236,\n      \"^^\": 18237,\n      \"erek\": 18238,\n      \"Ġauthentication\": 18239,\n      \"Ġstole\": 18240,\n      \"Ġaccountability\": 18241,\n      \"label\": 18242,\n      \"Search\": 18243,\n      \"Ġalbeit\": 18244,\n      \"atican\": 18245,\n      \"funded\": 18246,\n      \"ĠAdding\": 18247,\n      \"ĠIQ\": 18248,\n      \"Ġsubmar\": 18249,\n      \"lit\": 18250,\n      \"aque\": 18251,\n      \"ĠLearning\": 18252,\n      \"Ġinteger\": 18253,\n      \"Master\": 18254,\n      \"ĠChrom\": 18255,\n      \"Ġpremier\": 18256,\n      \"Op\": 18257,\n      \"ĠLiu\": 18258,\n      \"Ġblessed\": 18259,\n      \"ĠGlobe\": 18260,\n      \"ĠResponse\": 18261,\n      \"Ġlegitim\": 18262,\n      \"ĠMerkel\": 18263,\n      \"Ġdisposal\": 18264,\n      \"´\": 18265,\n      \"Ġgauge\": 18266,\n      \"peat\": 18267,\n      \"Ġinduced\": 18268,\n      \"Ġquestionable\": 18269,\n      \"arthy\": 18270,\n      \"ĠVit\": 18271,\n      \"ĠFeed\": 18272,\n      \"Until\": 18273,\n      \"Ut\": 18274,\n      \"worthy\": 18275,\n      \"RY\": 18276,\n      \"ĠHerald\": 18277,\n      \"ĠHammer\": 18278,\n      \"Ġmedal\": 18279,\n      \"ĠRivers\": 18280,\n      \"ĠHack\": 18281,\n      \"Ġclarify\": 18282,\n      \"Ġtracked\": 18283,\n      \"Ġautonomous\": 18284,\n      \"Ġtenant\": 18285,\n      \"ĠQatar\": 18286,\n      \"erie\": 18287,\n      \"Ġgrim\": 18288,\n      \"ĠMonitor\": 18289,\n      \"Ġresistant\": 18290,\n      \"ĠSpec\": 18291,\n      \"ĠWells\": 18292,\n      \"NAS\": 18293,\n      \"148\": 18294,\n      \"Ġminers\": 18295,\n      \"iotics\": 18296,\n      \"Ġmisses\": 18297,\n      \"116\": 18298,\n      \"gian\": 18299,\n      \"git\": 18300,\n      \"ĠEyes\": 18301,\n      \"pres\": 18302,\n      \"Ġgraduated\": 18303,\n      \"Ġangel\": 18304,\n      \"Ġsynchron\": 18305,\n      \"Ġefficiently\": 18306,\n      \"Ġtransmitted\": 18307,\n      \"Harry\": 18308,\n      \"Ġglobally\": 18309,\n      \"ENCE\": 18310,\n      \"ĠMontana\": 18311,\n      \"raged\": 18312,\n      \"ĠPrevention\": 18313,\n      \"Ġpiss\": 18314,\n      \"ĠLl\": 18315,\n      \"Ġshelf\": 18316,\n      \"ĠBJP\": 18317,\n      \"ĠTestament\": 18318,\n      \"ĠLate\": 18319,\n      \"iker\": 18320,\n      \"ĠHapp\": 18321,\n      \"ĠJulian\": 18322,\n      \"hall\": 18323,\n      \"Ġspont\": 18324,\n      \"Ġshutdown\": 18325,\n      \"Ġinconsistent\": 18326,\n      \"Ġsubscribers\": 18327,\n      \"Ġskeleton\": 18328,\n      \"ĠNebraska\": 18329,\n      \"Ġinspire\": 18330,\n      \"ĠVoid\": 18331,\n      \"Feed\": 18332,\n      \"Ġangles\": 18333,\n      \"ĠSprings\": 18334,\n      \"Ġbenchmark\": 18335,\n      \"Ġvaccines\": 18336,\n      \"izophren\": 18337,\n      \"sexual\": 18338,\n      \"uffed\": 18339,\n      \"Ġshine\": 18340,\n      \"ĠKath\": 18341,\n      \"Ġgesture\": 18342,\n      \"inea\": 18343,\n      \"Ġrip\": 18344,\n      \"Ġoppression\": 18345,\n      \"Ġconscience\": 18346,\n      \"bt\": 18347,\n      \"ĠLum\": 18348,\n      \"Ġincidence\": 18349,\n      \"ĠFa\": 18350,\n      \"wr\": 18351,\n      \"Ġmineral\": 18352,\n      \"ĠSpurs\": 18353,\n      \"alky\": 18354,\n      \"Ġthunder\": 18355,\n      \"Ġopio\": 18356,\n      \"Being\": 18357,\n      \"ĠPalm\": 18358,\n      \"Ġwasted\": 18359,\n      \"Ġlb\": 18360,\n      \"iaries\": 18361,\n      \"ĠInitiative\": 18362,\n      \"Ġcurric\": 18363,\n      \"Ġmarker\": 18364,\n      \"ĠMcL\": 18365,\n      \"Ġextensions\": 18366,\n      \"ĠPv\": 18367,\n      \"ĠArms\": 18368,\n      \"Ġofferings\": 18369,\n      \"Ġdefenses\": 18370,\n      \"Ġvendor\": 18371,\n      \"Ġcontradict\": 18372,\n      \"ĠColin\": 18373,\n      \"Ġreddit\": 18374,\n      \"Ġperipher\": 18375,\n      \"122\": 18376,\n      \"Ġsins\": 18377,\n      \"Edit\": 18378,\n      \"ICT\": 18379,\n      \"Soft\": 18380,\n      \"ĠShah\": 18381,\n      \"Ġadministrator\": 18382,\n      \"ĠTrip\": 18383,\n      \"Ġpornography\": 18384,\n      \"Ġtuition\": 18385,\n      \"inence\": 18386,\n      \"ĠProgress\": 18387,\n      \"Ġcatalog\": 18388,\n      \"Ġsuite\": 18389,\n      \"Ġhike\": 18390,\n      \"Ġreproductive\": 18391,\n      \"engine\": 18392,\n      \"Ġdrought\": 18393,\n      \"ĠNoah\": 18394,\n      \"Ġ230\": 18395,\n      \"Ġdude\": 18396,\n      \"Ġrelaxed\": 18397,\n      \"Ġpartition\": 18398,\n      \"Ġparticipant\": 18399,\n      \"Ġtelesc\": 18400,\n      \"Ġfeas\": 18401,\n      \"ĠFF\": 18402,\n      \"owner\": 18403,\n      \"Ġsweeping\": 18404,\n      \"Ġlenses\": 18405,\n      \"Ġmatchup\": 18406,\n      \"ĠRepl\": 18407,\n      \"ournals\": 18408,\n      \"Ġcredible\": 18409,\n      \"Ġgrandmother\": 18410,\n      \"Ġthermal\": 18411,\n      \"Ġsubscribing\": 18412,\n      \"Ġidentities\": 18413,\n      \"colm\": 18414,\n      \"UCT\": 18415,\n      \"Ġreluctant\": 18416,\n      \"users\": 18417,\n      \"ĠCort\": 18418,\n      \"Ġassisted\": 18419,\n      \"OSS\": 18420,\n      \"ATIONS\": 18421,\n      \"ISH\": 18422,\n      \"Ġpharmaceutical\": 18423,\n      \"icable\": 18424,\n      \"adian\": 18425,\n      \"ĠSonic\": 18426,\n      \"ĠFury\": 18427,\n      \"ĠMong\": 18428,\n      \"AH\": 18429,\n      \"ĠPsychology\": 18430,\n      \"Ġphosph\": 18431,\n      \"Ġtreats\": 18432,\n      \"ŃĶ\": 18433,\n      \"Ġsteadily\": 18434,\n      \"ĠHello\": 18435,\n      \"Ġrelates\": 18436,\n      \"Ġclue\": 18437,\n      \"Expl\": 18438,\n      \"auth\": 18439,\n      \"Ġrevision\": 18440,\n      \"Ġeld\": 18441,\n      \"osion\": 18442,\n      \"Ġbron\": 18443,\n      \"144\": 18444,\n      \"rikes\": 18445,\n      \"Ġmines\": 18446,\n      \"Ġblanket\": 18447,\n      \"ĠFail\": 18448,\n      \"eled\": 18449,\n      \"ĠImagine\": 18450,\n      \"ĠPlanned\": 18451,\n      \"aic\": 18452,\n      \"Request\": 18453,\n      \"Mad\": 18454,\n      \"ĠHorse\": 18455,\n      \"ĠEagle\": 18456,\n      \"Ġcapac\": 18457,\n      \"157\": 18458,\n      \"Ġling\": 18459,\n      \"ĠNice\": 18460,\n      \"ĠParenthood\": 18461,\n      \"minster\": 18462,\n      \"ogs\": 18463,\n      \"ensitive\": 18464,\n      \"Nothing\": 18465,\n      \"Ġcarn\": 18466,\n      \"Fin\": 18467,\n      \"ĠPE\": 18468,\n      \"Ġrifles\": 18469,\n      \"ĠLP\": 18470,\n      \"Sand\": 18471,\n      \"ĠguiActive\": 18472,\n      \"Ġtourist\": 18473,\n      \"CNN\": 18474,\n      \"Ġunveiled\": 18475,\n      \"Ġpredecessor\": 18476,\n      \"}{\": 18477,\n      \"uber\": 18478,\n      \"Ġoffshore\": 18479,\n      \"Ġoptical\": 18480,\n      \"ĠRot\": 18481,\n      \"ĠPearl\": 18482,\n      \"eton\": 18483,\n      \"Ġstared\": 18484,\n      \"Ġfarther\": 18485,\n      \"atility\": 18486,\n      \"contin\": 18487,\n      \"ĠGy\": 18488,\n      \"ĠFoster\": 18489,\n      \"ĠCoc\": 18490,\n      \"rients\": 18491,\n      \"Ġdesigning\": 18492,\n      \"ĠEconomy\": 18493,\n      \"ONG\": 18494,\n      \"Women\": 18495,\n      \"ĠNancy\": 18496,\n      \"erver\": 18497,\n      \"Ġmascul\": 18498,\n      \"Ġcasualties\": 18499,\n      \"Ġ225\": 18500,\n      \"ĠSullivan\": 18501,\n      \"ĠChoice\": 18502,\n      \"Ġaster\": 18503,\n      \"ws\": 18504,\n      \"Ġhotels\": 18505,\n      \"Ġconsiderations\": 18506,\n      \"Ġcouch\": 18507,\n      \"ĠStrip\": 18508,\n      \"ĠGn\": 18509,\n      \"Ġmanipulate\": 18510,\n      \"lied\": 18511,\n      \"Ġsynthetic\": 18512,\n      \"Ġassaulted\": 18513,\n      \"Ġoffenses\": 18514,\n      \"ĠDrake\": 18515,\n      \"Ġimpe\": 18516,\n      \"October\": 18517,\n      \"ĠHeritage\": 18518,\n      \"hl\": 18519,\n      \"ĠBlair\": 18520,\n      \"Unlike\": 18521,\n      \"Ġgrief\": 18522,\n      \"Ġ450\": 18523,\n      \"Ġopted\": 18524,\n      \"Ġresignation\": 18525,\n      \"ilo\": 18526,\n      \"Ġverse\": 18527,\n      \"ĠTomb\": 18528,\n      \"Ġupt\": 18529,\n      \"Ġaired\": 18530,\n      \"ĠHook\": 18531,\n      \"ĠMLB\": 18532,\n      \"Ġassumes\": 18533,\n      \"outed\": 18534,\n      \"ĠVers\": 18535,\n      \"Ġinferior\": 18536,\n      \"Ġbundle\": 18537,\n      \"ĠDNS\": 18538,\n      \"ographer\": 18539,\n      \"Ġmultip\": 18540,\n      \"ĠSouls\": 18541,\n      \"Ġillustrated\": 18542,\n      \"Ġtactic\": 18543,\n      \"Ġdressing\": 18544,\n      \"Ġduo\": 18545,\n      \"Conf\": 18546,\n      \"Ġrelent\": 18547,\n      \"Ġcant\": 18548,\n      \"Ġscarce\": 18549,\n      \"Ġcandy\": 18550,\n      \"ĠCF\": 18551,\n      \"Ġaffiliated\": 18552,\n      \"Ġsprint\": 18553,\n      \"ylan\": 18554,\n      \"ĠGarcia\": 18555,\n      \"Ġjunk\": 18556,\n      \"Print\": 18557,\n      \"exec\": 18558,\n      \"Crit\": 18559,\n      \"Ġportrait\": 18560,\n      \"iries\": 18561,\n      \"ĠOFF\": 18562,\n      \"Ġdisputes\": 18563,\n      \"WR\": 18564,\n      \"Love\": 18565,\n      \"ãģĦ\": 18566,\n      \"ĠReyn\": 18567,\n      \"Ġhipp\": 18568,\n      \"opath\": 18569,\n      \"Ġfloors\": 18570,\n      \"ĠFeel\": 18571,\n      \"Ġworries\": 18572,\n      \"Ġsettlements\": 18573,\n      \"ĠPos\": 18574,\n      \"Ġmosque\": 18575,\n      \"Ġfinals\": 18576,\n      \"Ġcrushed\": 18577,\n      \"ĠProbably\": 18578,\n      \"ĠBot\": 18579,\n      \"ĠMans\": 18580,\n      \"ĠPeriod\": 18581,\n      \"Ġsovereignty\": 18582,\n      \"Ġseller\": 18583,\n      \"Ġapost\": 18584,\n      \"Ġamateur\": 18585,\n      \"Ġdorm\": 18586,\n      \"Ġconsuming\": 18587,\n      \"Ġarmour\": 18588,\n      \"ĠRoose\": 18589,\n      \"Ġintensive\": 18590,\n      \"Ġeliminating\": 18591,\n      \"ĠSunni\": 18592,\n      \"ĠAleppo\": 18593,\n      \"jin\": 18594,\n      \"Ġadvise\": 18595,\n      \"pal\": 18596,\n      \"ĠHalo\": 18597,\n      \"Ġdescent\": 18598,\n      \"Ġsimpler\": 18599,\n      \"Ġbooth\": 18600,\n      \"STR\": 18601,\n      \"Later\": 18602,\n      \"ĠCave\": 18603,\n      \"===\": 18604,\n      \"Ġmol\": 18605,\n      \"Ġfist\": 18606,\n      \"Ġshotgun\": 18607,\n      \"supp\": 18608,\n      \"Ġrobbery\": 18609,\n      \"Effect\": 18610,\n      \"Ġobscure\": 18611,\n      \"ĠProfessional\": 18612,\n      \"Ġembassy\": 18613,\n      \"Ġmilitant\": 18614,\n      \"Ġincarcer\": 18615,\n      \"Ġgenerates\": 18616,\n      \"Ġlaunches\": 18617,\n      \"Ġadministrators\": 18618,\n      \"Ġshaft\": 18619,\n      \"Ġcircular\": 18620,\n      \"Ġfreshman\": 18621,\n      \"ĠWes\": 18622,\n      \"ĠJoel\": 18623,\n      \"ĠDrew\": 18624,\n      \"ĠDuncan\": 18625,\n      \"ĠApparently\": 18626,\n      \"sight\": 18627,\n      \"ĠInternal\": 18628,\n      \"ĠIndividual\": 18629,\n      \"ĠFE\": 18630,\n      \"Ġbore\": 18631,\n      \"ĠMt\": 18632,\n      \"Ġbroadly\": 18633,\n      \"ĠOptions\": 18634,\n      \"ountain\": 18635,\n      \"ipes\": 18636,\n      \"ĠVideos\": 18637,\n      \"204\": 18638,\n      \"Ġhills\": 18639,\n      \"Ġsimulation\": 18640,\n      \"Ġdisappointment\": 18641,\n      \"itan\": 18642,\n      \"ĠLaboratory\": 18643,\n      \"Ġupward\": 18644,\n      \"Ġboundary\": 18645,\n      \"Ġdarker\": 18646,\n      \"hart\": 18647,\n      \"Ġdominance\": 18648,\n      \"Cong\": 18649,\n      \"ĠOracle\": 18650,\n      \"ĠLords\": 18651,\n      \"Ġscholarship\": 18652,\n      \"ĠVincent\": 18653,\n      \"ede\": 18654,\n      \"ĠRah\": 18655,\n      \"Ġencourages\": 18656,\n      \"rov\": 18657,\n      \"Ġquo\": 18658,\n      \"Ġpremise\": 18659,\n      \"ĠCrisis\": 18660,\n      \"ĠHolocaust\": 18661,\n      \"Ġrhythm\": 18662,\n      \"Ġmetric\": 18663,\n      \"club\": 18664,\n      \"Ġtransported\": 18665,\n      \"Ġnod\": 18666,\n      \"ĠPist\": 18667,\n      \"Ġancestors\": 18668,\n      \"ĠFreder\": 18669,\n      \"thumbnails\": 18670,\n      \"ĠCE\": 18671,\n      \"OND\": 18672,\n      \"Phil\": 18673,\n      \"venge\": 18674,\n      \"ĠProducts\": 18675,\n      \"castle\": 18676,\n      \"Ġqualifying\": 18677,\n      \"ĠKaren\": 18678,\n      \"VERTISEMENT\": 18679,\n      \"Ġmighty\": 18680,\n      \"Ġexplanations\": 18681,\n      \"Ġfixing\": 18682,\n      \"Di\": 18683,\n      \"Ġdeclaring\": 18684,\n      \"Ġanonymity\": 18685,\n      \"Ġjuven\": 18686,\n      \"ĠNord\": 18687,\n      \"ĠDoom\": 18688,\n      \"ĠActually\": 18689,\n      \"Ok\": 18690,\n      \"phis\": 18691,\n      \"ĠDesert\": 18692,\n      \"Ġ116\": 18693,\n      \"IK\": 18694,\n      \"ĠFM\": 18695,\n      \"Ġincomes\": 18696,\n      \"VEL\": 18697,\n      \"okers\": 18698,\n      \"Ġpecul\": 18699,\n      \"Ġlightweight\": 18700,\n      \"gue\": 18701,\n      \"Ġaccent\": 18702,\n      \"Ġincrement\": 18703,\n      \"ĠChan\": 18704,\n      \"Ġcomplaining\": 18705,\n      \"ĠBaghd\": 18706,\n      \"Ġmidfielder\": 18707,\n      \"Ġoverhaul\": 18708,\n      \"Process\": 18709,\n      \"ĠHollow\": 18710,\n      \"ĠTitans\": 18711,\n      \"Small\": 18712,\n      \"manuel\": 18713,\n      \"ĠUnity\": 18714,\n      \"ĠEvents\": 18715,\n      \"Sty\": 18716,\n      \"Ġdisproportion\": 18717,\n      \"nesty\": 18718,\n      \"enes\": 18719,\n      \"ĠCod\": 18720,\n      \"Ġdemonstrations\": 18721,\n      \"ĠCrimson\": 18722,\n      \"ĠOH\": 18723,\n      \"Ġenrolled\": 18724,\n      \"Ġcel\": 18725,\n      \"ĠBrett\": 18726,\n      \"Ġaide\": 18727,\n      \"Ġheels\": 18728,\n      \"Ġbroadband\": 18729,\n      \"Ġmarking\": 18730,\n      \"Ġwizard\": 18731,\n      \"ĠNJ\": 18732,\n      \"ĠChiefs\": 18733,\n      \"Ġingredient\": 18734,\n      \"Ġdug\": 18735,\n      \"ĠShut\": 18736,\n      \"urchase\": 18737,\n      \"endor\": 18738,\n      \"Ġfarmer\": 18739,\n      \"ĠGoldman\": 18740,\n      \"129\": 18741,\n      \"155\": 18742,\n      \"Order\": 18743,\n      \"Ġlion\": 18744,\n      \"iably\": 18745,\n      \"Ġstain\": 18746,\n      \"array\": 18747,\n      \"ilitary\": 18748,\n      \"ĠFAQ\": 18749,\n      \"Ġexploded\": 18750,\n      \"ĠMcCarthy\": 18751,\n      \"ĠTweet\": 18752,\n      \"ĠGreens\": 18753,\n      \"eking\": 18754,\n      \"ln\": 18755,\n      \"ensen\": 18756,\n      \"Ġmotorcycle\": 18757,\n      \"Ġparticle\": 18758,\n      \"Ġcholesterol\": 18759,\n      \"Bron\": 18760,\n      \"Ġstair\": 18761,\n      \"Ġoxid\": 18762,\n      \"Ġdesirable\": 18763,\n      \"ibles\": 18764,\n      \"Ġtheor\": 18765,\n      \"forcing\": 18766,\n      \"Ġpromotional\": 18767,\n      \"ovo\": 18768,\n      \"boot\": 18769,\n      \"ĠBonus\": 18770,\n      \"rawling\": 18771,\n      \"Ġshortage\": 18772,\n      \"ĠPsy\": 18773,\n      \"Ġrecruited\": 18774,\n      \"Ġinfants\": 18775,\n      \"Ġtestosterone\": 18776,\n      \"Ġdeduct\": 18777,\n      \"Ġdistinctive\": 18778,\n      \"Ġfirmware\": 18779,\n      \"built\": 18780,\n      \"145\": 18781,\n      \"Ġexplored\": 18782,\n      \"Ġfactions\": 18783,\n      \"Ġvide\": 18784,\n      \"Ġtattoo\": 18785,\n      \"Ġfinancially\": 18786,\n      \"Ġfatigue\": 18787,\n      \"Ġproceeding\": 18788,\n      \"constitutional\": 18789,\n      \"Ġmiser\": 18790,\n      \"Ġchairs\": 18791,\n      \"gging\": 18792,\n      \"ipple\": 18793,\n      \"Ġdent\": 18794,\n      \"Ġdisreg\": 18795,\n      \"çĶ\": 18796,\n      \"stant\": 18797,\n      \"llo\": 18798,\n      \"bps\": 18799,\n      \"akening\": 18800,\n      \"Ġabnormal\": 18801,\n      \"ĠERA\": 18802,\n      \"士\": 18803,\n      \"ĠHBO\": 18804,\n      \"ĠMAR\": 18805,\n      \"Ġconcess\": 18806,\n      \"Ġservant\": 18807,\n      \"Ġaspir\": 18808,\n      \"lav\": 18809,\n      \"ĠPanel\": 18810,\n      \"amo\": 18811,\n      \"Ġprecip\": 18812,\n      \"Ġrecordings\": 18813,\n      \"Ġproceeded\": 18814,\n      \"Ġcolony\": 18815,\n      \"ĠTang\": 18816,\n      \"ablo\": 18817,\n      \"Ġstripped\": 18818,\n      \"Left\": 18819,\n      \"too\": 18820,\n      \"Ġpotatoes\": 18821,\n      \"Ġfinest\": 18822,\n      \"%).\": 18823,\n      \"Ġcrap\": 18824,\n      \"ĠZach\": 18825,\n      \"abases\": 18826,\n      \"ĠGoth\": 18827,\n      \"Ġbillionaire\": 18828,\n      \"wolf\": 18829,\n      \"Ġsanction\": 18830,\n      \"SK\": 18831,\n      \"Ġlogged\": 18832,\n      \"Po\": 18833,\n      \"eyed\": 18834,\n      \"unal\": 18835,\n      \"Ġcricket\": 18836,\n      \"Ġarmies\": 18837,\n      \"Ġuncovered\": 18838,\n      \"Cloud\": 18839,\n      \"ón\": 18840,\n      \"Ġrebounds\": 18841,\n      \"Ġmes\": 18842,\n      \"Oper\": 18843,\n      \"Pac\": 18844,\n      \"Ġnationally\": 18845,\n      \"Ġinserted\": 18846,\n      \"pict\": 18847,\n      \"Ġgovernance\": 18848,\n      \"и\": 18849,\n      \"Ġprivileges\": 18850,\n      \"GET\": 18851,\n      \"Ġfavorites\": 18852,\n      \"imity\": 18853,\n      \"Ġlover\": 18854,\n      \"them\": 18855,\n      \"empl\": 18856,\n      \"Ġgorgeous\": 18857,\n      \"Ann\": 18858,\n      \"Ġslipped\": 18859,\n      \"Ġveto\": 18860,\n      \"Bob\": 18861,\n      \"Ġslim\": 18862,\n      \"ucc\": 18863,\n      \"ĠFame\": 18864,\n      \"uddenly\": 18865,\n      \"Ġdenies\": 18866,\n      \"ĠMaur\": 18867,\n      \"Ġdistances\": 18868,\n      \"Ġwanna\": 18869,\n      \"tar\": 18870,\n      \"ĠSER\": 18871,\n      \"ĠâĪ\": 18872,\n      \"Ġlemon\": 18873,\n      \"athetic\": 18874,\n      \"Ġliteral\": 18875,\n      \"Ġdistinguished\": 18876,\n      \"Ġanswering\": 18877,\n      \"GI\": 18878,\n      \"Ġreligions\": 18879,\n      \"ĠPhilos\": 18880,\n      \"ĠLay\": 18881,\n      \"Ġcompos\": 18882,\n      \"irements\": 18883,\n      \"ĠKos\": 18884,\n      \"inez\": 18885,\n      \"rolling\": 18886,\n      \"Ġyoungest\": 18887,\n      \"andise\": 18888,\n      \"ĠBorn\": 18889,\n      \"Ġaltar\": 18890,\n      \"amina\": 18891,\n      \"ĠBoot\": 18892,\n      \"voc\": 18893,\n      \"Ġdigging\": 18894,\n      \"Ġpressures\": 18895,\n      \"Ġlen\": 18896,\n      \"264\": 18897,\n      \"Ġassassination\": 18898,\n      \"ĠBirmingham\": 18899,\n      \"ĠMyth\": 18900,\n      \"Ġsovereign\": 18901,\n      \"ĠArtist\": 18902,\n      \"ĠPhotograph\": 18903,\n      \"Ġdepicted\": 18904,\n      \"Ġdispens\": 18905,\n      \"orthy\": 18906,\n      \"Ġambul\": 18907,\n      \"integ\": 18908,\n      \"ĠCele\": 18909,\n      \"ĠTibet\": 18910,\n      \"Ġhierarchy\": 18911,\n      \"Ġcu\": 18912,\n      \"Ġpreseason\": 18913,\n      \"ĠPeterson\": 18914,\n      \"Ġcolours\": 18915,\n      \"Ġworrying\": 18916,\n      \"Ġbackers\": 18917,\n      \"ĠPalmer\": 18918,\n      \"Ġμ\": 18919,\n      \"Ġcontributor\": 18920,\n      \"Ġhearings\": 18921,\n      \"Ġurine\": 18922,\n      \"ĠÙ\": 18923,\n      \"ourgeois\": 18924,\n      \"Similar\": 18925,\n      \"ĠZimmer\": 18926,\n      \"something\": 18927,\n      \"ĠUSC\": 18928,\n      \"Ġstrengths\": 18929,\n      \"ĠFI\": 18930,\n      \"Ġlogging\": 18931,\n      \"Asked\": 18932,\n      \"ĠThai\": 18933,\n      \"inqu\": 18934,\n      \"ĠWalt\": 18935,\n      \"Ġcrews\": 18936,\n      \"itism\": 18937,\n      \"301\": 18938,\n      \"Ġsharply\": 18939,\n      \"umed\": 18940,\n      \"Ġredirect\": 18941,\n      \"rators\": 18942,\n      \"Inf\": 18943,\n      \"ĠWeapons\": 18944,\n      \"Ġteasp\": 18945,\n      \"1999\": 18946,\n      \"Live\": 18947,\n      \"ĠEspecially\": 18948,\n      \"ĠSter\": 18949,\n      \"ĠVeterans\": 18950,\n      \"Ġintro\": 18951,\n      \"otherapy\": 18952,\n      \"Ġmalware\": 18953,\n      \"Ġbreeding\": 18954,\n      \"Ġmolecular\": 18955,\n      \"ĠRoute\": 18956,\n      \"ĠComment\": 18957,\n      \"ochem\": 18958,\n      \"Ġain\": 18959,\n      \"Season\": 18960,\n      \"Ġlinebacker\": 18961,\n      \"Ä«\": 18962,\n      \"ĠEconomics\": 18963,\n      \"esar\": 18964,\n      \"ĠLives\": 18965,\n      \"ĠEmma\": 18966,\n      \"Ġkin\": 18967,\n      \"ĠTerrit\": 18968,\n      \"Ġplanted\": 18969,\n      \"oton\": 18970,\n      \"ĠButter\": 18971,\n      \"ĠSpons\": 18972,\n      \"PER\": 18973,\n      \"Ġdungeon\": 18974,\n      \"Ġsymbolic\": 18975,\n      \"Ġfilmed\": 18976,\n      \"Ġdiets\": 18977,\n      \"Ġconcludes\": 18978,\n      \"Ġcertainty\": 18979,\n      \"ĠFormat\": 18980,\n      \"Ġstrangers\": 18981,\n      \"format\": 18982,\n      \"ĠPhase\": 18983,\n      \"Ġcopied\": 18984,\n      \"Ġmetres\": 18985,\n      \"lda\": 18986,\n      \"ĠUsers\": 18987,\n      \"Ġdeliberate\": 18988,\n      \"Ġwashed\": 18989,\n      \"ĠLance\": 18990,\n      \"imation\": 18991,\n      \"Ġimproper\": 18992,\n      \"ĠGenesis\": 18993,\n      \"ickr\": 18994,\n      \"ĠKush\": 18995,\n      \"Ġrealise\": 18996,\n      \"Ġembarrassing\": 18997,\n      \"alking\": 18998,\n      \"bucks\": 18999,\n      \"Ġverified\": 19000,\n      \"Ġoutline\": 19001,\n      \"years\": 19002,\n      \"ĠIncome\": 19003,\n      \"202\": 19004,\n      \"Ġzombies\": 19005,\n      \"Final\": 19006,\n      \"ĠMillenn\": 19007,\n      \"Ġmodifications\": 19008,\n      \"ĠVision\": 19009,\n      \"ĠMoses\": 19010,\n      \"verb\": 19011,\n      \"iterranean\": 19012,\n      \"ĠJet\": 19013,\n      \"Ġnaval\": 19014,\n      \"ĠAgg\": 19015,\n      \"Ġurl\": 19016,\n      \"Ġvictories\": 19017,\n      \"Ġnonetheless\": 19018,\n      \"Ġinjust\": 19019,\n      \"ĠFact\": 19020,\n      \"çļ\": 19021,\n      \"Ġinsufficient\": 19022,\n      \"review\": 19023,\n      \"facebook\": 19024,\n      \"Ġnegotiating\": 19025,\n      \"Ġguarantees\": 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\"Ġinherited\": 19552,\n      \"Ġbats\": 19553,\n      \"stru\": 19554,\n      \"GT\": 19555,\n      \"Ġflawed\": 19556,\n      \"phabet\": 19557,\n      \"Ġfog\": 19558,\n      \"doors\": 19559,\n      \"Ġimaging\": 19560,\n      \"Ġdigits\": 19561,\n      \"ĠHungary\": 19562,\n      \"Ġarrog\": 19563,\n      \"Ġteachings\": 19564,\n      \"Ġprotocols\": 19565,\n      \"ĠBanks\": 19566,\n      \"à¸\": 19567,\n      \"pound\": 19568,\n      \"ĠCurt\": 19569,\n      \".\\\")\": 19570,\n      \"./\": 19571,\n      \"Ġexemption\": 19572,\n      \"endix\": 19573,\n      \"ĠMull\": 19574,\n      \"Ġimproves\": 19575,\n      \"ĠGamer\": 19576,\n      \"dimensional\": 19577,\n      \"Icon\": 19578,\n      \"ĠMargaret\": 19579,\n      \"Status\": 19580,\n      \"dates\": 19581,\n      \"Ġintends\": 19582,\n      \"Ġdepict\": 19583,\n      \"Ġparked\": 19584,\n      \"Joe\": 19585,\n      \"ĠMarines\": 19586,\n      \"chnology\": 19587,\n      \"!).\": 19588,\n      \"Ġjudged\": 19589,\n      \"Ġweights\": 19590,\n      \"Ray\": 19591,\n      \"Ġapartments\": 19592,\n      \"hester\": 19593,\n      \"Ġreinforce\": 19594,\n      \"Ġoffender\": 19595,\n      \"occup\": 19596,\n      \"Ġsore\": 19597,\n      \"ept\": 19598,\n      \"ĠPHP\": 19599,\n      \"ĠBrow\": 19600,\n      \"Ġauthorization\": 19601,\n      \"ĠRisk\": 19602,\n      \"ĠDelaware\": 19603,\n      \"ĠQU\": 19604,\n      \"Ġnotifications\": 19605,\n      \"Ġsunlight\": 19606,\n      \"Ġexclude\": 19607,\n      \"dat\": 19608,\n      \"Ġmesh\": 19609,\n      \"ĠSudan\": 19610,\n      \"Ġbelonged\": 19611,\n      \"Ġsubway\": 19612,\n      \"Ġnoon\": 19613,\n      \"ĠInterior\": 19614,\n      \"olics\": 19615,\n      \"ĠLakers\": 19616,\n      \"Ġcoding\": 19617,\n      \"Disclaimer\": 19618,\n      \"Calif\": 19619,\n      \"Old\": 19620,\n      \"Ġdisl\": 19621,\n      \"?????\": 19622,\n      \"Ġconfirms\": 19623,\n      \"Ġrecruitment\": 19624,\n      \"Ġhomicide\": 19625,\n      \"Consider\": 19626,\n      \"ĠJeffrey\": 19627,\n      \"fty\": 19628,\n      \"};\": 19629,\n      \"Ġobjection\": 19630,\n      \"doing\": 19631,\n      \"ĠLeo\": 19632,\n      \"Want\": 19633,\n      \"Ġglow\": 19634,\n      \"ĠClarke\": 19635,\n      \"ĠNorman\": 19636,\n      \"Ġverification\": 19637,\n      \"Ġpacket\": 19638,\n      \"ĠFormula\": 19639,\n      \"Ġplag\": 19640,\n      \"esville\": 19641,\n      \"Ġshouting\": 19642,\n      \"Ġov\": 19643,\n      \"ĠREC\": 19644,\n      \"ĠBub\": 19645,\n      \"Ġninth\": 19646,\n      \"Ġenerg\": 19647,\n      \"Ġvalidity\": 19648,\n      \"Ġups\": 19649,\n      \"jack\": 19650,\n      \"Ġneighboring\": 19651,\n      \"ĠNec\": 19652,\n      \"eworks\": 19653,\n      \"ĠHab\": 19654,\n      \"arez\": 19655,\n      \"Ġspine\": 19656,\n      \"Ġeventual\": 19657,\n      \"ĠLeaders\": 19658,\n      \"ĠCarn\": 19659,\n      \"Ġprobation\": 19660,\n      \"Ġromance\": 19661,\n      \"msg\": 19662,\n      \"ĠMechanical\": 19663,\n      \"ERY\": 19664,\n      \"Rock\": 19665,\n      \"Ġpartisan\": 19666,\n      \"Node\": 19667,\n      \"assets\": 19668,\n      \"minent\": 19669,\n      \"Ġforeigners\": 19670,\n      \"Ġtestify\": 19671,\n      \"ĠUsually\": 19672,\n      \"lords\": 19673,\n      \"ĠGren\": 19674,\n      \"ĠPowell\": 19675,\n      \"BIL\": 19676,\n      \"Ġsr\": 19677,\n      \"Ġaddict\": 19678,\n      \"Ġshells\": 19679,\n      \"Ġsigh\": 19680,\n      \"ĠYale\": 19681,\n      \"ternity\": 19682,\n      \"Ġ750\": 19683,\n      \"EU\": 19684,\n      \"ĠRifle\": 19685,\n      \"Ġpatron\": 19686,\n      \"ema\": 19687,\n      \"ĠBannon\": 19688,\n      \"anity\": 19689,\n      \"Ġtropical\": 19690,\n      \"ĠVII\": 19691,\n      \"cross\": 19692,\n      \"Everything\": 19693,\n      \"ĠISO\": 19694,\n      \"Ġhumble\": 19695,\n      \"assing\": 19696,\n      \"ĠFIG\": 19697,\n      \"Ġupdating\": 19698,\n      \"yson\": 19699,\n      \"Ġcalcium\": 19700,\n      \"Ġcompetent\": 19701,\n      \"Ġsteering\": 19702,\n      \"Prot\": 19703,\n      \"ĠSY\": 19704,\n      \"ĠFinals\": 19705,\n      \"ĠRug\": 19706,\n      \"159\": 19707,\n      \"137\": 19708,\n      \"ĠGolf\": 19709,\n      \"Ġ126\": 19710,\n      \"Ġaccommodation\": 19711,\n      \"ĠHughes\": 19712,\n      \"Ġaesthetic\": 19713,\n      \"artisan\": 19714,\n      \"ĠTwilight\": 19715,\n      \"Ġprince\": 19716,\n      \"ĠAgriculture\": 19717,\n      \"ĠDisco\": 19718,\n      \"Ġprecedent\": 19719,\n      \"Ġtyping\": 19720,\n      \"authorized\": 19721,\n      \"Option\": 19722,\n      \"ĠAub\": 19723,\n      \"lishes\": 19724,\n      \"acht\": 19725,\n      \"mag\": 19726,\n      \"Peter\": 19727,\n      \"ĠUFO\": 19728,\n      \"monton\": 19729,\n      \"ĠLith\": 19730,\n      \"Ġarom\": 19731,\n      \"Ġsecuring\": 19732,\n      \"Ġconfined\": 19733,\n      \"private\": 19734,\n      \"Ġswords\": 19735,\n      \"Ġmarkers\": 19736,\n      \"Ġmetabolic\": 19737,\n      \"select\": 19738,\n      \"ĠCurse\": 19739,\n      \"ĠOt\": 19740,\n      \"gressive\": 19741,\n      \"Ġincumb\": 19742,\n      \"ĠSaga\": 19743,\n      \"Ġpriced\": 19744,\n      \"Ġclearance\": 19745,\n      \"Content\": 19746,\n      \"Ġdrilling\": 19747,\n      \"Ġnotices\": 19748,\n      \"Ġbourgeois\": 19749,\n      \"Ġvest\": 19750,\n      \"Ġcookie\": 19751,\n      \"ĠGuardians\": 19752,\n      \"rys\": 19753,\n      \"inyl\": 19754,\n      \"Ġ124\": 19755,\n      \"Ġplausible\": 19756,\n      \"ongh\": 19757,\n      \"ĠOdin\": 19758,\n      \"Ġconception\": 19759,\n      \"ĠYuk\": 19760,\n      \"ĠBaghdad\": 19761,\n      \"ĠFlag\": 19762,\n      \"Austral\": 19763,\n      \"ĠIBM\": 19764,\n      \"Ġinternationally\": 19765,\n      \"ĠWikiLeaks\": 19766,\n      \"IED\": 19767,\n      \"Ġcyn\": 19768,\n      \"Ġchooses\": 19769,\n      \"ĠPill\": 19770,\n      \"Ġcombining\": 19771,\n      \"Ġradi\": 19772,\n      \"ĠMohammed\": 19773,\n      \"defense\": 19774,\n      \"atching\": 19775,\n      \"Subject\": 19776,\n      \"iciency\": 19777,\n      \"Frame\": 19778,\n      \"Ġ{\\\"\": 19779,\n      \"Ġchess\": 19780,\n      \"Ġtimer\": 19781,\n      \"190\": 19782,\n      \"Ġtin\": 19783,\n      \"Ġordinance\": 19784,\n      \"emetery\": 19785,\n      \"Ġaccusing\": 19786,\n      \"Ġnoticeable\": 19787,\n      \"Ġcentres\": 19788,\n      \"Ġlid\": 19789,\n      \"ĠMills\": 19790,\n      \"imgur\": 19791,\n      \"Ġzoom\": 19792,\n      \"ergic\": 19793,\n      \"Ġcompression\": 19794,\n      \"prim\": 19795,\n      \"find\": 19796,\n      \"Ġsurg\": 19797,\n      \"Ġpand\": 19798,\n      \"ĠKee\": 19799,\n      \"ĠChad\": 19800,\n      \"cellence\": 19801,\n      \"oyle\": 19802,\n      \"Ġsocialism\": 19803,\n      \"ĠTravis\": 19804,\n      \"ĠMHz\": 19805,\n      \"Ġguild\": 19806,\n      \"ALLY\": 19807,\n      \"ĠSubscribe\": 19808,\n      \"ĠRelated\": 19809,\n      \"Ġoccurrence\": 19810,\n      \"itching\": 19811,\n      \"Ġfictional\": 19812,\n      \"Ġcrush\": 19813,\n      \"ĠEA\": 19814,\n      \"cod\": 19815,\n      \"mix\": 19816,\n      \"ĠTriple\": 19817,\n      \"Ġretrieve\": 19818,\n      \"Ġstimulus\": 19819,\n      \"Ġpsychiat\": 19820,\n      \"ĠDoor\": 19821,\n      \"Ġhomosexuality\": 19822,\n      \"Ġelementary\": 19823,\n      \"Ġcellular\": 19824,\n      \"idian\": 19825,\n      \"ĠLaun\": 19826,\n      \"Ġintriguing\": 19827,\n      \"Ġfoam\": 19828,\n      \"ĠBass\": 19829,\n      \"idi\": 19830,\n      \"itsu\": 19831,\n      \"Ġassure\": 19832,\n      \"Ġcongrat\": 19833,\n      \"Ġbusinessman\": 19834,\n      \"ĠBoost\": 19835,\n      \"close\": 19836,\n      \"Ġlied\": 19837,\n      \"Ġsciences\": 19838,\n      \"ĠOmega\": 19839,\n      \"ĠGraphics\": 19840,\n      \"Ġ<=\": 19841,\n      \"spoken\": 19842,\n      \"Ġconnectivity\": 19843,\n      \"Saturday\": 19844,\n      \"ĠAvengers\": 19845,\n      \"Ġtoggle\": 19846,\n      \"Ġankle\": 19847,\n      \"Ġnationalist\": 19848,\n      \"model\": 19849,\n      \"ĠPool\": 19850,\n      \"ophobia\": 19851,\n      \"Var\": 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     \"Ġmembr\": 19890,\n      \"1990\": 19891,\n      \"older\": 19892,\n      \"Ġpracticed\": 19893,\n      \"inkle\": 19894,\n      \"Arch\": 19895,\n      \"Ġstarters\": 19896,\n      \"arius\": 19897,\n      \"Ġintermediate\": 19898,\n      \"ĠBenef\": 19899,\n      \"ĠKiller\": 19900,\n      \"Ġinterventions\": 19901,\n      \"ĠKil\": 19902,\n      \"ĠFlying\": 19903,\n      \"Inv\": 19904,\n      \"Ġpremature\": 19905,\n      \"Ġpsychiatric\": 19906,\n      \"Ġindie\": 19907,\n      \"Ġcollar\": 19908,\n      \"ĠRainbow\": 19909,\n      \"afi\": 19910,\n      \"Ġdisruption\": 19911,\n      \"ĠFOX\": 19912,\n      \"casting\": 19913,\n      \"Ġmisdem\": 19914,\n      \"cro\": 19915,\n      \"Ġwipe\": 19916,\n      \"ardon\": 19917,\n      \"Ġbast\": 19918,\n      \"ĠTommy\": 19919,\n      \"ĠRepresentative\": 19920,\n      \"Ġbelly\": 19921,\n      \"ĠPO\": 19922,\n      \"ĠBreitbart\": 19923,\n      \"132\": 19924,\n      \"Ġmessaging\": 19925,\n      \"Should\": 19926,\n      \"References\": 19927,\n      \"ĠGRE\": 19928,\n      \"istical\": 19929,\n      \"LP\": 19930,\n      \"ĠCav\": 19931,\n      \"ĠCrazy\": 19932,\n      \"Ġintuitive\": 19933,\n      \"keeping\": 19934,\n      \"ĠMoss\": 19935,\n      \"Ġdiscontin\": 19936,\n      \"ĠModule\": 19937,\n      \"Ġunrelated\": 19938,\n      \"ĠPractice\": 19939,\n      \"ĠTransport\": 19940,\n      \"Ġstatistically\": 19941,\n      \"orns\": 19942,\n      \"Ġsized\": 19943,\n      \"pu\": 19944,\n      \"Ġcaf\": 19945,\n      \"ĠWorlds\": 19946,\n      \"ĠRodgers\": 19947,\n      \"ĠLun\": 19948,\n      \"ĠComic\": 19949,\n      \"living\": 19950,\n      \"Ġcared\": 19951,\n      \"Ġclimbed\": 19952,\n      \"){\": 19953,\n      \"Ġconsisted\": 19954,\n      \"Ġmedieval\": 19955,\n      \"folk\": 19956,\n      \"Ġhacked\": 19957,\n      \"Ġdire\": 19958,\n      \"ĠHermione\": 19959,\n      \"Ġtended\": 19960,\n      \"ceans\": 19961,\n      \"Daniel\": 19962,\n      \"went\": 19963,\n      \"Ġlegislators\": 19964,\n      \"Ġredes\": 19965,\n      \"games\": 19966,\n      \"Ġgn\": 19967,\n      \"amiliar\": 19968,\n      \"Ġ++\": 19969,\n      \"ggy\": 19970,\n      \"threat\": 19971,\n      \"Ġmagnet\": 19972,\n      \"Ġperceive\": 19973,\n      \"Ġzip\": 19974,\n      \"Ġindictment\": 19975,\n      \"Ġcritique\": 19976,\n      \"gard\": 19977,\n      \"ĠSafe\": 19978,\n      \"ĠCream\": 19979,\n      \"Ġadvent\": 19980,\n      \"oba\": 19981,\n      \"Ġvowed\": 19982,\n      \"ousands\": 19983,\n      \"Ġski\": 19984,\n      \"Ġabortions\": 19985,\n      \"uart\": 19986,\n      \"Ġstunned\": 19987,\n      \"Ġadvancing\": 19988,\n      \"Ġlacked\": 19989,\n      \"Ġ\\\\\\\"\": 19990,\n      \"Ġschizophren\": 19991,\n      \"Ġelegant\": 19992,\n      \"Ġconferences\": 19993,\n      \"Ġcanceled\": 19994,\n      \"ĠHudson\": 19995,\n      \"ĠHopefully\": 19996,\n      \"Ġtrump\": 19997,\n      \"Ġfrequencies\": 19998,\n      \"Ġmeteor\": 19999,\n      \"ĠJunior\": 20000,\n      \"ĠFleet\": 20001,\n      \"ĠMalcolm\": 20002,\n      \"ĠTools\": 20003,\n      \"Ġ........\": 20004,\n      \"Ġhobby\": 20005,\n      \"ĠEuropeans\": 20006,\n      \"Ġ1500\": 20007,\n      \"ĠInto\": 20008,\n      \"Ġsway\": 20009,\n      \"ĠAppro\": 20010,\n      \"ĠCompl\": 20011,\n      \"Community\": 20012,\n      \"Ġtide\": 20013,\n      \"ĠSummit\": 20014,\n      \"ä»\": 20015,\n      \"Ġintervals\": 20016,\n      \"ĠEther\": 20017,\n      \"Ġhabitat\": 20018,\n      \"ĠStevens\": 20019,\n      \"lishing\": 20020,\n      \"ĠDomain\": 20021,\n      \"Ġtriggers\": 20022,\n      \"Ġchasing\": 20023,\n      \"Ġcharm\": 20024,\n      \"ĠFlower\": 20025,\n      \"itored\": 20026,\n      \"Ġblessing\": 20027,\n      \"Ġtextures\": 20028,\n      \"Five\": 20029,\n      \"Ġliquor\": 20030,\n      \"RP\": 20031,\n      \"FIN\": 20032,\n      \"Ġ1962\": 20033,\n      \"CAR\": 20034,\n      \"Unknown\": 20035,\n      \"Ġresil\": 20036,\n      \"ĠLily\": 20037,\n      \"Ġabundance\": 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\"Ġanat\": 20076,\n      \"share\": 20077,\n      \"Ġgrind\": 20078,\n      \"hin\": 20079,\n      \"umen\": 20080,\n      \"Ġutilities\": 20081,\n      \"ĠYankees\": 20082,\n      \"Ġdatabases\": 20083,\n      \"ĠDD\": 20084,\n      \"Ġdisplaced\": 20085,\n      \"Ġdependencies\": 20086,\n      \"Ġstimulation\": 20087,\n      \"hun\": 20088,\n      \"houses\": 20089,\n      \"ĠPretty\": 20090,\n      \"ĠRavens\": 20091,\n      \"ĠTODAY\": 20092,\n      \"Ġassociates\": 20093,\n      \"Ġtherape\": 20094,\n      \"cled\": 20095,\n      \"Ġdeer\": 20096,\n      \"Ġrepairs\": 20097,\n      \"rentice\": 20098,\n      \"Ġreceptors\": 20099,\n      \"Ġremed\": 20100,\n      \"ĠCe\": 20101,\n      \"Ġmarriages\": 20102,\n      \"Ġballots\": 20103,\n      \"ĠSoldier\": 20104,\n      \"Ġhilarious\": 20105,\n      \"opl\": 20106,\n      \"138\": 20107,\n      \"Ġinherently\": 20108,\n      \"Ġignorant\": 20109,\n      \"Ġbounce\": 20110,\n      \"ĠEaster\": 20111,\n      \"RELATED\": 20112,\n      \"ĠCurrency\": 20113,\n      \"EV\": 20114,\n      \"ãĥŀ\": 20115,\n      \"ĠLead\": 20116,\n      \"Ġdeceased\": 20117,\n      \"Brien\": 20118,\n      \"ĠMusk\": 20119,\n      \"JS\": 20120,\n      \"Ġmerge\": 20121,\n      \"hearted\": 20122,\n      \"creat\": 20123,\n      \"mitt\": 20124,\n      \"mund\": 20125,\n      \"ĠâĢĭ\": 20126,\n      \"ĠBag\": 20127,\n      \"Ġprojection\": 20128,\n      \"Ġjava\": 20129,\n      \"ĠStandards\": 20130,\n      \"ĠLeonard\": 20131,\n      \"Ġcoconut\": 20132,\n      \"ĠPopulation\": 20133,\n      \"Ġtraject\": 20134,\n      \"Ġimply\": 20135,\n      \"Ġcuriosity\": 20136,\n      \"ĠDB\": 20137,\n      \"ĠFresh\": 20138,\n      \"ĠPor\": 20139,\n      \"Ġheavier\": 20140,\n      \"neys\": 20141,\n      \"gomery\": 20142,\n      \"Ġdeserved\": 20143,\n      \"Ġphrases\": 20144,\n      \"ĠGC\": 20145,\n      \"Ġyeast\": 20146,\n      \"desc\": 20147,\n      \"Death\": 20148,\n      \"Ġreboot\": 20149,\n      \"Ġmetadata\": 20150,\n      \"ICAL\": 20151,\n      \"Ġrepay\": 20152,\n      \"ĠIndependence\": 20153,\n      \"Ġsuburban\": 20154,\n      \"icals\": 20155,\n      \"Ġatop\": 20156,\n      \"Ġallocation\": 20157,\n      \"generation\": 20158,\n      \"ĠGram\": 20159,\n      \"Ġmoisture\": 20160,\n      \"Ġpine\": 20161,\n      \"ĠLiberals\": 20162,\n      \"Ġaides\": 20163,\n      \"Ġunderest\": 20164,\n      \"ĠBerry\": 20165,\n      \"Ġceremon\": 20166,\n      \"370\": 20167,\n      \"astrous\": 20168,\n      \"ĠPirates\": 20169,\n      \"Ġtense\": 20170,\n      \"ĠIndustries\": 20171,\n      \"ĠAppeals\": 20172,\n      \"ĠNear\": 20173,\n      \"Ġè£ıç\": 20174,\n      \"Ġlovers\": 20175,\n      \"ĠCAP\": 20176,\n      \"ĠCraw\": 20177,\n      \"Ġgiants\": 20178,\n      \"Ġefficacy\": 20179,\n      \"Element\": 20180,\n      \"ĠBehavior\": 20181,\n      \"ĠToyota\": 20182,\n      \"Ġintest\": 20183,\n      \"Priv\": 20184,\n      \"AI\": 20185,\n      \"Ġmaneuver\": 20186,\n      \"Ġperfection\": 20187,\n      \"Ġbang\": 20188,\n      \"paper\": 20189,\n      \"rill\": 20190,\n      \"George\": 20191,\n      \"border\": 20192,\n      \"inters\": 20193,\n      \"ĠSeth\": 20194,\n      \"Ġclues\": 20195,\n      \"ĠLevi\": 20196,\n      \"ĠRevenue\": 20197,\n      \"147\": 20198,\n      \"Ġvapor\": 20199,\n      \"Ġfortunate\": 20200,\n      \"Ġthreatens\": 20201,\n      \"Ġvet\": 20202,\n      \"Ġdependency\": 20203,\n      \"ersed\": 20204,\n      \"article\": 20205,\n      \"ĠBlizzard\": 20206,\n      \"Ġchlor\": 20207,\n      \"Ġminus\": 20208,\n      \"ĠBills\": 20209,\n      \"Ġcryptocurrency\": 20210,\n      \"Ġmetabolism\": 20211,\n      \"tering\": 20212,\n      \"Ġpestic\": 20213,\n      \"steps\": 20214,\n      \"ĠTreasure\": 20215,\n      \"racted\": 20216,\n      \"ĠConstant\": 20217,\n      \"Ġtemp\": 20218,\n      \"139\": 20219,\n      \"ĠDetective\": 20220,\n      \"urally\": 20221,\n      \"Ġrecovering\": 20222,\n      \"Ġcortex\": 20223,\n      \"Ġ144\": 20224,\n      \"closed\": 20225,\n      \"Ġprejudice\": 20226,\n      \"aunted\": 20227,\n      \"Ġstorms\": 20228,\n      \"ĠNOW\": 20229,\n      \"Ġmachinery\": 20230,\n      \"Address\": 20231,\n      \"Ġcompelled\": 20232,\n      \"270\": 20233,\n      \"Ġdespair\": 20234,\n      \"bane\": 20235,\n      \"Ġvegetable\": 20236,\n      \"Ġbeds\": 20237,\n      \"Learn\": 20238,\n      \"Ġcolorful\": 20239,\n      \"Ġspike\": 20240,\n      \"Ġmargins\": 20241,\n      \"Ġsympathy\": 20242,\n      \"Ġworkshop\": 20243,\n      \"ĠCBC\": 20244,\n      \"Sat\": 20245,\n      \"Ġburns\": 20246,\n      \"ĠGender\": 20247,\n      \"Ġ129\": 20248,\n      \"ĠCable\": 20249,\n      \"Ġdebts\": 20250,\n      \"ĠTheresa\": 20251,\n      \"Ġreflecting\": 20252,\n      \"Ġairst\": 20253,\n      \"Ġrim\": 20254,\n      \"ramid\": 20255,\n      \"Ġweaknesses\": 20256,\n      \"Writ\": 20257,\n      \"oggle\": 20258,\n      \"ti\": 20259,\n      \"ĠCharge\": 20260,\n      \"Ġweighed\": 20261,\n      \"Ġ(.\": 20262,\n      \"Ġlaughter\": 20263,\n      \"Ġrouter\": 20264,\n      \"ĠDemocracy\": 20265,\n      \"Dear\": 20266,\n      \"Ġhasht\": 20267,\n      \"Ġdy\": 20268,\n      \"Ġhints\": 20269,\n      \"running\": 20270,\n      \"Ġfinishes\": 20271,\n      \"arus\": 20272,\n      \"Mass\": 20273,\n      \"result\": 20274,\n      \"ascus\": 20275,\n      \"Ġvintage\": 20276,\n      \"Ġconqu\": 20277,\n      \"Ġwildly\": 20278,\n      \"acist\": 20279,\n      \"Ġlingu\": 20280,\n      \"Ġprotagonist\": 20281,\n      \"strom\": 20282,\n      \"teenth\": 20283,\n      \"ĠSolo\": 20284,\n      \"mac\": 20285,\n      \"filled\": 20286,\n      \"Ġrenown\": 20287,\n      \"itives\": 20288,\n      \"Ġmotive\": 20289,\n      \"ĠAntar\": 20290,\n      \"ĠMann\": 20291,\n      \"ĠAdjust\": 20292,\n      \"Ġrockets\": 20293,\n      \"Ġtroubling\": 20294,\n      \"ei\": 20295,\n      \"Ġorganisms\": 20296,\n      \"assis\": 20297,\n      \"Christian\": 20298,\n      \"Ġ145\": 20299,\n      \"ĠHass\": 20300,\n      \"Ġswall\": 20301,\n      \"Ġwax\": 20302,\n      \"ĠSurvival\": 20303,\n      \"VS\": 20304,\n      \"ĠMurd\": 20305,\n      \"vd\": 20306,\n      \"standard\": 20307,\n      \"Ġdragons\": 20308,\n      \"Ġacceleration\": 20309,\n      \"rational\": 20310,\n      \"final\": 20311,\n      \"Ġpaired\": 20312,\n      \"ĠEthereum\": 20313,\n      \"Ġinterfaces\": 20314,\n      \"Ġresent\": 20315,\n      \"Ġartifacts\": 20316,\n      \"Å«\": 20317,\n      \"arel\": 20318,\n      \"Ġcompetitor\": 20319,\n      \"ĠNicholas\": 20320,\n      \"ĠSurface\": 20321,\n      \"cpp\": 20322,\n      \"ĠTot\": 20323,\n      \"Ġeconomically\": 20324,\n      \"Ġorganised\": 20325,\n      \"Ġenforced\": 20326,\n      \"inho\": 20327,\n      \"Ġvarieties\": 20328,\n      \"Ġabdom\": 20329,\n      \"ĠBailey\": 20330,\n      \"idav\": 20331,\n      \"ĠSalv\": 20332,\n      \"paid\": 20333,\n      \"Ġaltitude\": 20334,\n      \"essert\": 20335,\n      \"ĠGutenberg\": 20336,\n      \"area\": 20337,\n      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\"Ġmodeling\": 21128,\n      \"Anyone\": 21129,\n      \"ancel\": 21130,\n      \"ĠSit\": 21131,\n      \"Ġshields\": 21132,\n      \"Ġlawn\": 21133,\n      \"Ġcardiovascular\": 21134,\n      \"Ġdemonstrating\": 21135,\n      \"Ġparse\": 21136,\n      \"ĠIsraelis\": 21137,\n      \"Ġeuros\": 21138,\n      \"143\": 21139,\n      \"Ġglorious\": 21140,\n      \"inski\": 21141,\n      \"ecd\": 21142,\n      \"Ġconditioning\": 21143,\n      \"Ġhelpless\": 21144,\n      \"Ġmicrosc\": 21145,\n      \"ĠHarbor\": 21146,\n      \"Ġstakes\": 21147,\n      \"Ġ260\": 21148,\n      \"Ġunequ\": 21149,\n      \"ĠFloyd\": 21150,\n      \"Ġdamp\": 21151,\n      \"Ġapparatus\": 21152,\n      \"ĠLaws\": 21153,\n      \"Ġcounters\": 21154,\n      \"Ġinduce\": 21155,\n      \"atable\": 21156,\n      \"ĠAhmed\": 21157,\n      \"Ġslam\": 21158,\n      \"November\": 21159,\n      \"Ġpersist\": 21160,\n      \"Ġimminent\": 21161,\n      \"án\": 21162,\n      \"Ġshred\": 21163,\n      \"Ġphases\": 21164,\n      \"ĠEdmonton\": 21165,\n      \"ĠArmstrong\": 21166,\n      \"ĠMeet\": 21167,\n      \"ĠKitty\": 21168,\n      \"ÑĢ\": 21169,\n      \"circ\": 21170,\n      \"ĠAdult\": 21171,\n      \"Ġarose\": 21172,\n      \"ĠXen\": 21173,\n      \"Dan\": 21174,\n      \"gow\": 21175,\n      \"Ġsuperf\": 21176,\n      \"ĠAdmir\": 21177,\n      \"Ġendure\": 21178,\n      \"Ġkeyword\": 21179,\n      \"yrus\": 21180,\n      \"Ġyarn\": 21181,\n      \"Ġpathway\": 21182,\n      \"ĠHopkins\": 21183,\n      \"midt\": 21184,\n      \"Ġcensorship\": 21185,\n      \"dependent\": 21186,\n      \"Ġinstructor\": 21187,\n      \"Sources\": 21188,\n      \"Ġtoe\": 21189,\n      \"Ġballoon\": 21190,\n      \"Nob\": 21191,\n      \"Ġswear\": 21192,\n      \"ĠCastro\": 21193,\n      \"Ġgloss\": 21194,\n      \"ĠKavanaugh\": 21195,\n      \"Ġremarkably\": 21196,\n      \"Photos\": 21197,\n      \"ĠNom\": 21198,\n      \"ĠSoutheast\": 21199,\n      \"yers\": 21200,\n      \"Ġvalidation\": 21201,\n      \"Ġcannon\": 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\"æ\": 21241,\n      \"Ġextracted\": 21242,\n      \"versely\": 21243,\n      \"riad\": 21244,\n      \"Ġstrains\": 21245,\n      \"Ġtops\": 21246,\n      \"Ġpoem\": 21247,\n      \"ĠRandy\": 21248,\n      \"ĠMaple\": 21249,\n      \"THER\": 21250,\n      \"upiter\": 21251,\n      \"ĠSSD\": 21252,\n      \"ļé\": 21253,\n      \"Ġuncon\": 21254,\n      \"pering\": 21255,\n      \"Ġslept\": 21256,\n      \"iners\": 21257,\n      \"Ġunderwater\": 21258,\n      \"ĠEvidence\": 21259,\n      \"gone\": 21260,\n      \"205\": 21261,\n      \"Ġhistorians\": 21262,\n      \"Ġsynthesis\": 21263,\n      \"Ġfrog\": 21264,\n      \"basketball\": 21265,\n      \"Ġvibrant\": 21266,\n      \"Ġsubord\": 21267,\n      \"Ġ365\": 21268,\n      \"ĠDial\": 21269,\n      \"Ġcooperate\": 21270,\n      \"HAHA\": 21271,\n      \"Ġgreeted\": 21272,\n      \"158\": 21273,\n      \"Ġjazz\": 21274,\n      \"Ġintox\": 21275,\n      \"ĠWalking\": 21276,\n      \"Ġsupervisor\": 21277,\n      \"ĠFusion\": 21278,\n      \"ĠMercedes\": 21279,\n      \"send\": 21280,\n      \"Ham\": 21281,\n      \"sd\": 21282,\n      \"nl\": 21283,\n      \"Ġtours\": 21284,\n      \"ĠFIFA\": 21285,\n      \"Ġculp\": 21286,\n      \"gd\": 21287,\n      \"304\": 21288,\n      \"Ġpleas\": 21289,\n      \"Ġillustrates\": 21290,\n      \"ĠColombia\": 21291,\n      \"Ġhighlighting\": 21292,\n      \"ĠSummary\": 21293,\n      \"Ġexposing\": 21294,\n      \"ĠDru\": 21295,\n      \"Ġirony\": 21296,\n      \"ritional\": 21297,\n      \"ĠCarroll\": 21298,\n      \"ĠEllis\": 21299,\n      \"Pict\": 21300,\n      \"ĠRapt\": 21301,\n      \"Ġadapter\": 21302,\n      \"Ġunm\": 21303,\n      \"Ġcorpse\": 21304,\n      \"Ġcelebrities\": 21305,\n      \"Den\": 21306,\n      \"atum\": 21307,\n      \"ĠApocalypse\": 21308,\n      \"ĠWag\": 21309,\n      \"lining\": 21310,\n      \"Ġhormones\": 21311,\n      \"Rub\": 21312,\n      \"ĠXi\": 21313,\n      \"ĠVaults\": 21314,\n      \"208\": 21315,\n      \"alkyrie\": 21316,\n      \"inosaur\": 21317,\n      \"Ġfeeds\": 21318,\n      \"vity\": 21319,\n      \"Ġdefeating\": 21320,\n      \"Wait\": 21321,\n      \"Ġemphasize\": 21322,\n      \"ĠSteelers\": 21323,\n      \"yrinth\": 21324,\n      \"leys\": 21325,\n      \"ĠWhenever\": 21326,\n      \"Currently\": 21327,\n      \"ĠClock\": 21328,\n      \"Ġcollectively\": 21329,\n      \"anyon\": 21330,\n      \"ĠJP\": 21331,\n      \"Ġmentality\": 21332,\n      \"Ġdownloads\": 21333,\n      \"Ġsurroundings\": 21334,\n      \"ĠBarnes\": 21335,\n      \"Ġflagship\": 21336,\n      \"Ġindicators\": 21337,\n      \"Ġgrapp\": 21338,\n      \"January\": 21339,\n      \"ĠElemental\": 21340,\n      \"ĠAthena\": 21341,\n      \"ibal\": 21342,\n      \"Ġsights\": 21343,\n      \"Ġcapita\": 21344,\n      \"ĠTreaty\": 21345,\n      \"Ġvoiced\": 21346,\n      \"ĠGaz\": 21347,\n      \"lette\": 21348,\n      \"Ġya\": 21349,\n      \"Ġexpired\": 21350,\n      \"Legend\": 21351,\n      \"Hot\": 21352,\n      \"nature\": 21353,\n      \"Ġunstable\": 21354,\n      \"Ġ280\": 21355,\n      \"ú\": 21356,\n      \"Comment\": 21357,\n      \"ALE\": 21358,\n      \"Ġquests\": 21359,\n      \"Ġhandler\": 21360,\n      \"nis\": 21361,\n      \"Ġversatile\": 21362,\n      \"Ġconceal\": 21363,\n      \"engeance\": 21364,\n      \"ĠInteractive\": 21365,\n      \"Ġobsessed\": 21366,\n      \"ĠDogs\": 21367,\n      \"Ġcracked\": 21368,\n      \"Sound\": 21369,\n      \"sv\": 21370,\n      \"ĠDylan\": 21371,\n      \"roads\": 21372,\n      \"fx\": 21373,\n      \"ĠCatholics\": 21374,\n      \"ĠHag\": 21375,\n      \"Ġslammed\": 21376,\n      \"Ġglowing\": 21377,\n      \"sale\": 21378,\n      \"Ġtissues\": 21379,\n      \"ĠChi\": 21380,\n      \"nee\": 21381,\n      \"Ġcher\": 21382,\n      \"sic\": 21383,\n      \"urrection\": 21384,\n      \"Ġbacon\": 21385,\n      \"ulatory\": 21386,\n      \").\\\"\": 21387,\n      \"Ġirregular\": 21388,\n      \"FORM\": 21389,\n      \"assed\": 21390,\n      \"Ġintentional\": 21391,\n      \"Ġcompensate\": 21392,\n      \"ĠSpeaking\": 21393,\n      \"ĠSets\": 21394,\n      \"153\": 21395,\n      \"Ġconventions\": 21396,\n      \"bands\": 21397,\n      \"emade\": 21398,\n      \"Ġecc\": 21399,\n      \"ĠWinston\": 21400,\n      \"ĠAssassin\": 21401,\n      \"ĠBelgian\": 21402,\n      \"Ġdependence\": 21403,\n      \"Ġniche\": 21404,\n      \"Ġbark\": 21405,\n      \"ĠJazz\": 21406,\n      \"Ġdisadvantage\": 21407,\n      \"Ġgasoline\": 21408,\n      \"Ġ165\": 21409,\n      \"çļĦ\": 21410,\n      \"essa\": 21411,\n      \"module\": 21412,\n      \"angular\": 21413,\n      \"OY\": 21414,\n      \"ĠTreatment\": 21415,\n      \"itas\": 21416,\n      \"olation\": 21417,\n      \"ĠArnold\": 21418,\n      \"Ġfeud\": 21419,\n      \"ĠNest\": 21420,\n      \"Ġtheatre\": 21421,\n      \"ewater\": 21422,\n      \"Ġminors\": 21423,\n      \"olicy\": 21424,\n      \"ĠHaven\": 21425,\n      \"division\": 21426,\n      \"Ġtrunk\": 21427,\n      \"Far\": 21428,\n      \"ĠPull\": 21429,\n      \"Ġcapturing\": 21430,\n      \"Ġ1800\": 21431,\n      \"ĠTeen\": 21432,\n      \"Ġexempl\": 21433,\n      \"Ġclinics\": 21434,\n      \"ĠBurg\": 21435,\n      \"Ġsubstit\": 21436,\n      \"Ġpayload\": 21437,\n      \"ĠLav\": 21438,\n      \"ĠTroy\": 21439,\n      \"ĠWitness\": 21440,\n      \"Ġfragments\": 21441,\n      \"Ġpasswords\": 21442,\n      \"Ġgospel\": 21443,\n      \"ĠGin\": 21444,\n      \"Ġtenants\": 21445,\n      \"olith\": 21446,\n      \"Six\": 21447,\n      \"Previous\": 21448,\n      \"ĠAges\": 21449,\n      \"ĠDarwin\": 21450,\n      \"Ġblat\": 21451,\n      \"Ġempathy\": 21452,\n      \"smith\": 21453,\n      \"bag\": 21454,\n      \"ĠEcho\": 21455,\n      \"ĠCamb\": 21456,\n      \"ĠMadd\": 21457,\n      \"ĠBoo\": 21458,\n      \"Ġrede\": 21459,\n      \"ĠBurning\": 21460,\n      \"Ġsmoothly\": 21461,\n      \"ĠAdrian\": 21462,\n      \"ĠVampire\": 21463,\n      \"ĠMonsters\": 21464,\n      \"steam\": 21465,\n      \"Style\": 21466,\n      \"Ma\": 21467,\n      \"rea\": 21468,\n      \"ĠDwar\": 21469,\n      \"alyst\": 21470,\n      \"ursor\": 21471,\n      \"Ġelimination\": 21472,\n      \"Ġcrypto\": 21473,\n      \"cht\": 21474,\n      \"ĠEternal\": 21475,\n      \"âĢ¦]\": 21476,\n      \"ĠSorce\": 21477,\n      \"Ill\": 21478,\n      \"NER\": 21479,\n      \"Ġuh\": 21480,\n      \"Conclusion\": 21481,\n      \"wage\": 21482,\n      \"Ġrespir\": 21483,\n      \"Ġreminis\": 21484,\n      \"hetical\": 21485,\n      \"Ġgy\": 21486,\n      \"Ġutilized\": 21487,\n      \"icidal\": 21488,\n      \"Ġ1900\": 21489,\n      \"Ġhunters\": 21490,\n      \"ĠSwan\": 21491,\n      \"ĠReact\": 21492,\n      \"Ġvisitor\": 21493,\n      \"ĠThanksgiving\": 21494,\n      \"308\": 21495,\n      \"Posts\": 21496,\n      \"Ġhips\": 21497,\n      \"1997\": 21498,\n      \"omers\": 21499,\n      \"Ġknocking\": 21500,\n      \"ĠVehicle\": 21501,\n      \"Ġtil\": 21502,\n      \"Ġ138\": 21503,\n      \"Ġmi\": 21504,\n      \"ĠInvestigation\": 21505,\n      \"ĠKenya\": 21506,\n      \"Ġcasino\": 21507,\n      \"Ġmotives\": 21508,\n      \"Ġregain\": 21509,\n      \"rex\": 21510,\n      \"Ġweekends\": 21511,\n      \"Ġstabbed\": 21512,\n      \"boro\": 21513,\n      \"Ġexploited\": 21514,\n      \"ĠHAVE\": 21515,\n      \"ĠTelevision\": 21516,\n      \"cock\": 21517,\n      \"Ġpreparations\": 21518,\n      \"Ġendeav\": 21519,\n      \"ĠRemote\": 21520,\n      \"ĠMaker\": 21521,\n      \"ĠProdu\": 21522,\n      \"ĠEvan\": 21523,\n      \"Ġinformational\": 21524,\n      \"ĠLouisville\": 21525,\n      \"154\": 21526,\n      \"ĠDreams\": 21527,\n      \"Ġplots\": 21528,\n      \"ĠRunner\": 21529,\n      \"Ġhurting\": 21530,\n      \"Ġacademy\": 21531,\n      \"ĠMontgomery\": 21532,\n      \"nm\": 21533,\n      \"ĠLanc\": 21534,\n      \"ĠAlz\": 21535,\n      \"210\": 21536,\n      \"elong\": 21537,\n      \"Ġretailer\": 21538,\n      \"Ġarising\": 21539,\n      \"Ġrebellion\": 21540,\n      \"Ġblonde\": 21541,\n      \"played\": 21542,\n      \"Ġinstrumental\": 21543,\n      \"Cross\": 21544,\n      \"Ġretention\": 21545,\n      \"Ġtherapeutic\": 21546,\n      \"Ġseas\": 21547,\n      \"Ġinfantry\": 21548,\n      \"ĠClint\": 21549,\n      \"Ġprompting\": 21550,\n      \"Ġbitch\": 21551,\n      \"Ġstems\": 21552,\n      \"ĠKra\": 21553,\n      \"Ġthesis\": 21554,\n      \"ĠBog\": 21555,\n      \"rued\": 21556,\n      \"Ġkings\": 21557,\n      \"Ġclay\": 21558,\n      \"ificent\": 21559,\n      \"ĠYES\": 21560,\n      \"ĠThing\": 21561,\n      \"ĠCubs\": 21562,\n      \"veyard\": 21563,\n      \"elsh\": 21564,\n      \"inarily\": 21565,\n      \"ĠEy\": 21566,\n      \"ĠRolling\": 21567,\n      \"Ġevolving\": 21568,\n      \"India\": 21569,\n      \"Ġrecognizes\": 21570,\n      \"Ġgraduation\": 21571,\n      \"isers\": 21572,\n      \"Ġfertility\": 21573,\n      \"ĠMilan\": 21574,\n      \"Command\": 21575,\n      \"Ġboxing\": 21576,\n      \"Ġ1943\": 21577,\n      \"Ġgluten\": 21578,\n      \"ĠEmir\": 21579,\n      \"Ġidol\": 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\"Ġawake\": 21693,\n      \"Ġparental\": 21694,\n      \"Ġexceeds\": 21695,\n      \"Ġidle\": 21696,\n      \"ĠPilot\": 21697,\n      \"ĠeBay\": 21698,\n      \"ĠAccept\": 21699,\n      \"ipeg\": 21700,\n      \"Cam\": 21701,\n      \"ĠKot\": 21702,\n      \"Ġtraders\": 21703,\n      \"olitics\": 21704,\n      \"unker\": 21705,\n      \"ĠPale\": 21706,\n      \"osi\": 21707,\n      \"anmar\": 21708,\n      \"Ġ1947\": 21709,\n      \"ĠFell\": 21710,\n      \"estial\": 21711,\n      \"itating\": 21712,\n      \"GF\": 21713,\n      \"ĠSr\": 21714,\n      \"ifted\": 21715,\n      \"Ġconnector\": 21716,\n      \"ĠBone\": 21717,\n      \"illes\": 21718,\n      \"260\": 21719,\n      \"hma\": 21720,\n      \"Ġoverlap\": 21721,\n      \"ĠGitHub\": 21722,\n      \"Ġcleaner\": 21723,\n      \"ĠBaptist\": 21724,\n      \"ĠWAS\": 21725,\n      \"Ġlungs\": 21726,\n      \"Ñģ\": 21727,\n      \"ĠBUT\": 21728,\n      \"Ġcite\": 21729,\n      \"Ġpitched\": 21730,\n      \"reatment\": 21731,\n      \"Ġtrophies\": 21732,\n      \"ĠNu\": 21733,\n      \"386\": 21734,\n      \"ĠPride\": 21735,\n      \"Ġattendees\": 21736,\n      \"[]\": 21737,\n      \"179\": 21738,\n      \"Ġspatial\": 21739,\n      \"Ġprizes\": 21740,\n      \"ĠReligion\": 21741,\n      \"Ġshowcase\": 21742,\n      \"ĠCategory\": 21743,\n      \"vidia\": 21744,\n      \"Target\": 21745,\n      \"Property\": 21746,\n      \"?,\": 21747,\n      \"Ġfusion\": 21748,\n      \"pie\": 21749,\n      \"ĠUCLA\": 21750,\n      \"Ġsoundtrack\": 21751,\n      \"Ġprincess\": 21752,\n      \"ĠCaval\": 21753,\n      \"should\": 21754,\n      \"Ġlimbs\": 21755,\n      \"Background\": 21756,\n      \"Ġlonely\": 21757,\n      \"Ġcores\": 21758,\n      \"ĠTail\": 21759,\n      \"sheet\": 21760,\n      \"Ġ132\": 21761,\n      \"Ra\": 21762,\n      \"ãĤ«\": 21763,\n      \"ĠBolt\": 21764,\n      \"Ġbooked\": 21765,\n      \"Ġadminister\": 21766,\n      \"Ġequals\": 21767,\n      \"wy\": 21768,\n      \"Ġobserving\": 21769,\n      \"ĠBaron\": 21770,\n      \"ĠAdobe\": 21771,\n      \"Ġvirgin\": 21772,\n      \"ĠSocialist\": 21773,\n      \"Move\": 21774,\n      \"ghazi\": 21775,\n      \"ĠLinda\": 21776,\n      \"212\": 21777,\n      \"Ġbrewing\": 21778,\n      \"Ġmerchants\": 21779,\n      \"burse\": 21780,\n      \"Ġdivor\": 21781,\n      \"Ġmetals\": 21782,\n      \"ĠNer\": 21783,\n      \"Ġsums\": 21784,\n      \"ĠEnemy\": 21785,\n      \"Ġenvision\": 21786,\n      \"Ġgranting\": 21787,\n      \"ĠHoney\": 21788,\n      \"ĠSkyrim\": 21789,\n      \"Ġsocio\": 21790,\n      \"graded\": 21791,\n      \"Ġselective\": 21792,\n      \"WASHINGTON\": 21793,\n      \"Ġ1948\": 21794,\n      \"ĠSirius\": 21795,\n      \"ĠGross\": 21796,\n      \"activity\": 21797,\n      \"ĠIvan\": 21798,\n      \"Ġfurious\": 21799,\n      \"BSD\": 21800,\n      \"ĠPrevious\": 21801,\n      \"Ġresponsive\": 21802,\n      \"Ġcharitable\": 21803,\n      \"Ġleaning\": 21804,\n      \"ĠPew\": 21805,\n      \"Ġviolates\": 21806,\n      \"\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\": 21807,\n      \"ĠComing\": 21808,\n      \"wire\": 21809,\n      \"Ġpoet\": 21810,\n      \"Ġresolutions\": 21811,\n      \"command\": 21812,\n      \"ĠPortuguese\": 21813,\n      \"Ġnickname\": 21814,\n      \"Ġdeaf\": 21815,\n      \"February\": 21816,\n      \"Ġrecognise\": 21817,\n      \"Ġentirety\": 21818,\n      \"Ġseasonal\": 21819,\n      \"placed\": 21820,\n      \"ĠTelegraph\": 21821,\n      \"Ġmicrophone\": 21822,\n      \"ouring\": 21823,\n      \"Ġgrains\": 21824,\n      \"Ġgoverned\": 21825,\n      \"Ġpostp\": 21826,\n      \"ĠWaters\": 21827,\n      \"inement\": 21828,\n      \"Ġundocumented\": 21829,\n      \"ĠComcast\": 21830,\n      \"Ġfox\": 21831,\n      \"Ġassaults\": 21832,\n      \"reon\": 21833,\n      \"many\": 21834,\n      \"ĠJenkins\": 21835,\n      \"ĠAnyway\": 21836,\n      \"Ġassessments\": 21837,\n      \"Ġdowns\": 21838,\n      \"ĠMouse\": 21839,\n      \"Ġsuperb\": 21840,\n      \"kt\": 21841,\n      \"ĠDow\": 21842,\n      \"Ġtaxation\": 21843,\n      \"401\": 21844,\n      \"Ġsmiles\": 21845,\n      \"Ġundertaken\": 21846,\n      \"Ġexh\": 21847,\n      \"Ġenthusiastic\": 21848,\n      \"Ġtwent\": 21849,\n      \"Ġgovernmental\": 21850,\n      \"Ġautonomy\": 21851,\n      \"ĠTechnologies\": 21852,\n      \"ĠChain\": 21853,\n      \"Ġprevalent\": 21854,\n      \"fb\": 21855,\n      \"Ġnicotine\": 21856,\n      \"ogram\": 21857,\n      \"job\": 21858,\n      \"Ġawaiting\": 21859,\n      \"ĠMenu\": 21860,\n      \"Ġdeputies\": 21861,\n      \"kov\": 21862,\n      \"ishops\": 21863,\n      \"Button\": 21864,\n      \"ĠShanghai\": 21865,\n      \"Ġdiesel\": 21866,\n      \"ĠDuck\": 21867,\n      \"Ryan\": 21868,\n      \"ĠPCs\": 21869,\n      \"NF\": 21870,\n      \"jury\": 21871,\n      \"ente\": 21872,\n      \"Ġinaccurate\": 21873,\n      \"eddy\": 21874,\n      \"Whatever\": 21875,\n      \"Ġshowc\": 21876,\n      \"ĠNad\": 21877,\n      \"odus\": 21878,\n      \"etr\": 21879,\n      \"Ġplaintiffs\": 21880,\n      \"ĠWOR\": 21881,\n      \"ĠAssange\": 21882,\n      \"Ġprivat\": 21883,\n      \"Ġpremiums\": 21884,\n      \"Ġtam\": 21885,\n      \"URL\": 21886,\n      \"Ġelites\": 21887,\n      \"ĠRanger\": 21888,\n      \"ottenham\": 21889,\n      \"ĠHoff\": 21890,\n      \"ĠAthens\": 21891,\n      \"Ġdefinite\": 21892,\n      \"Ġsighed\": 21893,\n      \"Ġevenly\": 21894,\n      \"211\": 21895,\n      \"ĠAmber\": 21896,\n      \"akia\": 21897,\n      \"Ġmailing\": 21898,\n      \"Ġcrashing\": 21899,\n      \"ĠConfederate\": 21900,\n      \"rugged\": 21901,\n      \"Wal\": 21902,\n      \"ĠDepths\": 21903,\n      \"Ġjuvenile\": 21904,\n      \"Ġreactor\": 21905,\n      \"Introduction\": 21906,\n      \"ĠDeluxe\": 21907,\n      \"1995\": 21908,\n      \"ĠSanchez\": 21909,\n      \"ĠMead\": 21910,\n      \"ivable\": 21911,\n      \":-\": 21912,\n      \"ĠPlanning\": 21913,\n      \"ĠTrap\": 21914,\n      \"quin\": 21915,\n      \"ĠProtect\": 21916,\n      \"vered\": 21917,\n      \"Information\": 21918,\n      \"Ġkidney\": 21919,\n      \"innamon\": 21920,\n      \"las\": 21921,\n      \"Ġpolicing\": 21922,\n      \"Ġtolerate\": 21923,\n      \"ĠQi\": 21924,\n      \"Ġbiased\": 21925,\n      \"Fort\": 21926,\n      \"ĠKi\": 21927,\n      \"save\": 21928,\n      \"Ġprivileged\": 21929,\n      \"Ġbeasts\": 21930,\n      \"ĠGlas\": 21931,\n      \"ĠCinem\": 21932,\n      \"Ġcomeback\": 21933,\n      \"Sunday\": 21934,\n      \"Ġextinction\": 21935,\n      \"hops\": 21936,\n      \"Ġtransmit\": 21937,\n      \"Ġdoubles\": 21938,\n      \"ĠFlat\": 21939,\n      \"167\": 21940,\n      \"Ġdisputed\": 21941,\n      \"Ġinjustice\": 21942,\n      \"foo\": 21943,\n      \"Vict\": 21944,\n      \"roleum\": 21945,\n      \"ĠJulie\": 21946,\n      \"Context\": 21947,\n      \"ĠRarity\": 21948,\n      \"issue\": 21949,\n      \"Component\": 21950,\n      \"Ġcounseling\": 21951,\n      \"anne\": 21952,\n      \"dark\": 21953,\n      \"Ġobjections\": 21954,\n      \"uilt\": 21955,\n      \"Ġgast\": 21956,\n      \"Ġplac\": 21957,\n      \"Ġunused\": 21958,\n      \"ãĥĩ\": 21959,\n      \"ĠTrial\": 21960,\n      \"ĠJas\": 21961,\n      \"hedral\": 21962,\n      \"obb\": 21963,\n      \"Ġtemporal\": 21964,\n      \"ĠPRO\": 21965,\n      \"ĠNW\": 21966,\n      \"ĠAnniversary\": 21967,\n      \"Large\": 21968,\n      \"Ġtherm\": 21969,\n      \"Ġdavid\": 21970,\n      \"Ġsystemic\": 21971,\n      \"ĠShir\": 21972,\n      \"mut\": 21973,\n      \"ĠNept\": 21974,\n      \"address\": 21975,\n      \"Ġscanning\": 21976,\n      \"Ġunderstandable\": 21977,\n      \"Ġcanvas\": 21978,\n      \"Cat\": 21979,\n      \"ĠZoo\": 21980,\n      \"Ġangels\": 21981,\n      \"LO\": 21982,\n      \"ĠStatement\": 21983,\n      \"ĠSig\": 21984,\n      \"ovable\": 21985,\n      \"ĠAway\": 21986,\n      \"sharing\": 21987,\n      \"ocrats\": 21988,\n      \"stated\": 21989,\n      \"Ġweighing\": 21990,\n      \"Nor\": 21991,\n      \"wild\": 21992,\n      \"Bey\": 21993,\n      \"Ġastonishing\": 21994,\n      \"ĠReynolds\": 21995,\n      \"Ġopener\": 21996,\n      \"Ġtrainer\": 21997,\n      \"Ġsurgical\": 21998,\n      \"pn\": 21999,\n      \"Ġadjusting\": 22000,\n      \"wheel\": 22001,\n      \"Ġfrown\": 22002,\n      \"ervative\": 22003,\n      \"Ġsuspend\": 22004,\n      \"Within\": 22005,\n      \"tein\": 22006,\n      \"Ġobstacle\": 22007,\n      \"Ġliberties\": 22008,\n      \"ymes\": 22009,\n      \"Ġuranium\": 22010,\n      \"ansom\": 22011,\n      \"anol\": 22012,\n      \"uba\": 22013,\n      \"ĠLoss\": 22014,\n      \"Ġarous\": 22015,\n      \"ĠHenderson\": 22016,\n      \"Wow\": 22017,\n      \"spl\": 22018,\n      \"cur\": 22019,\n      \"ĠÂŃ\": 22020,\n      \"Ġtheirs\": 22021,\n      \"Damage\": 22022,\n      \"Ġdownloading\": 22023,\n      \"Ġdiscern\": 22024,\n      \"ĠSto\": 22025,\n      \"ĠFla\": 22026,\n      \"Ġhath\": 22027,\n      \"ĠAj\": 22028,\n      \"Ġunpleasant\": 22029,\n      \"European\": 22030,\n      \"expensive\": 22031,\n      \"Ġscreenshot\": 22032,\n      \"ĠUV\": 22033,\n      \"Ġallied\": 22034,\n      \"ĠPersian\": 22035,\n      \"Ġmonopoly\": 22036,\n      \"Ġatom\": 22037,\n      \"ĠRedskins\": 22038,\n      \"\\\"><\": 22039,\n      \"Ġcancell\": 22040,\n      \"Ġcinema\": 22041,\n      \"131\": 22042,\n      \"fair\": 22043,\n      \"ĠAlfred\": 22044,\n      \"Ġduck\": 22045,\n      \"args\": 22046,\n      \"223\": 22047,\n      \"ĠISI\": 22048,\n      \"Ġsignaling\": 22049,\n      \"inar\": 22050,\n      \"Ġlaughs\": 22051,\n      \"Ġforwards\": 22052,\n      \"Ġreckless\": 22053,\n      \"Ġlisteners\": 22054,\n      \"ativity\": 22055,\n      \"Ġvastly\": 22056,\n      \"nant\": 22057,\n      \"Less\": 22058,\n      \"ĠHunting\": 22059,\n      \"ĠScientific\": 22060,\n      \"ITED\": 22061,\n      \"Ġknight\": 22062,\n      \"ĠHTC\": 22063,\n      \"usa\": 22064,\n      \"tmp\": 22065,\n      \"Ġrude\": 22066,\n      \"ĠLegendary\": 22067,\n      \"Ġarises\": 22068,\n      \"Bad\": 22069,\n      \"ĠClaim\": 22070,\n      \"peg\": 22071,\n      \"Ġrealities\": 22072,\n      \"Think\": 22073,\n      \"Ġ°\": 22074,\n      \"Ġrode\": 22075,\n      \"Ġstrive\": 22076,\n      \"Ġanecd\": 22077,\n      \"Ġshorts\": 22078,\n      \"Ġhypothes\": 22079,\n      \"Ġcoordinated\": 22080,\n      \"ĠGandhi\": 22081,\n      \"ĠFPS\": 22082,\n      \"RED\": 22083,\n      \"Ġsusceptible\": 22084,\n      \"Ġshrink\": 22085,\n      \"ĠChart\": 22086,\n      \"Help\": 22087,\n      \"Ġion\": 22088,\n      \"deep\": 22089,\n      \"ribes\": 22090,\n      \"ĠKai\": 22091,\n      \"ĠCustomer\": 22092,\n      \"Summary\": 22093,\n      \"Ġcough\": 22094,\n      \"wife\": 22095,\n      \"Ġlend\": 22096,\n      \"Ġpositioning\": 22097,\n      \"Ġlottery\": 22098,\n      \"ĠCanyon\": 22099,\n      \"Ġfade\": 22100,\n      \"Ġbronze\": 22101,\n      \"ĠKenny\": 22102,\n      \"Ġboasts\": 22103,\n      \"ĠEnhanced\": 22104,\n      \"record\": 22105,\n      \"Ġemergence\": 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\"ĠWriting\": 22183,\n      \"fn\": 22184,\n      \"ĠBever\": 22185,\n      \"195\": 22186,\n      \"Ġyelling\": 22187,\n      \"Ġdrastically\": 22188,\n      \"Ġeject\": 22189,\n      \"Ġneut\": 22190,\n      \"Ġthrive\": 22191,\n      \"ĠFrequ\": 22192,\n      \"oux\": 22193,\n      \"Ġpossesses\": 22194,\n      \"ĠSenators\": 22195,\n      \"ĠDES\": 22196,\n      \"ĠShakespeare\": 22197,\n      \"ĠFranco\": 22198,\n      \"ĠLB\": 22199,\n      \"uchi\": 22200,\n      \"Ġincarn\": 22201,\n      \"Ġfounders\": 22202,\n      \"Function\": 22203,\n      \"Ġbrightness\": 22204,\n      \"ĠBT\": 22205,\n      \"Ġwhale\": 22206,\n      \"ĠTheater\": 22207,\n      \"mass\": 22208,\n      \"ĠDoll\": 22209,\n      \"Something\": 22210,\n      \"Ġechoed\": 22211,\n      \"ĠHex\": 22212,\n      \"crit\": 22213,\n      \"afia\": 22214,\n      \"Ġgoddess\": 22215,\n      \"Ġeleven\": 22216,\n      \"ĠPreview\": 22217,\n      \"ĠAurora\": 22218,\n      \"Ġ401\": 22219,\n      \"ulsive\": 22220,\n      \"ĠLogan\": 22221,\n      \"inburgh\": 22222,\n      \"ĠCenters\": 22223,\n      \"ĠONLY\": 22224,\n      \"ĠAid\": 22225,\n      \"Ġparadox\": 22226,\n      \"Ġhurd\": 22227,\n      \"ĠLC\": 22228,\n      \"Due\": 22229,\n      \"court\": 22230,\n      \"Ġoffended\": 22231,\n      \"Ġevaluating\": 22232,\n      \"ĠMatthews\": 22233,\n      \"Ġtomb\": 22234,\n      \"Ġpayroll\": 22235,\n      \"Ġextraction\": 22236,\n      \"ĠHands\": 22237,\n      \"ifi\": 22238,\n      \"Ġsupernatural\": 22239,\n      \"ĠCOMM\": 22240,\n      \"]=\": 22241,\n      \"dogs\": 22242,\n      \"Ġ512\": 22243,\n      \"ĠMeeting\": 22244,\n      \"Richard\": 22245,\n      \"ĠMaximum\": 22246,\n      \"Ġideals\": 22247,\n      \"Things\": 22248,\n      \"mand\": 22249,\n      \"ĠRegardless\": 22250,\n      \"Ġhumili\": 22251,\n      \"buffer\": 22252,\n      \"Little\": 22253,\n      \"ĠDani\": 22254,\n      \"ĠNak\": 22255,\n      \"Ġliberation\": 22256,\n      \"ĠAbe\": 22257,\n      \"ĠOL\": 22258,\n      \"Ġstuffed\": 22259,\n      \"aca\": 22260,\n      \"inda\": 22261,\n      \"raphic\": 22262,\n      \"Ġmosqu\": 22263,\n      \"Ġcampaigning\": 22264,\n      \"Ġoccupy\": 22265,\n      \"Squ\": 22266,\n      \"rina\": 22267,\n      \"ĠWel\": 22268,\n      \"ĠVS\": 22269,\n      \"Ġphysic\": 22270,\n      \"Ġpuls\": 22271,\n      \"rint\": 22272,\n      \"oaded\": 22273,\n      \"ETF\": 22274,\n      \"ĠArchives\": 22275,\n      \"Ġvenues\": 22276,\n      \"hner\": 22277,\n      \"ĠTurbo\": 22278,\n      \"Ġlust\": 22279,\n      \"Ġappealed\": 22280,\n      \"quez\": 22281,\n      \"ilib\": 22282,\n      \"ĠTimothy\": 22283,\n      \"Ġomn\": 22284,\n      \"dro\": 22285,\n      \"Ġobsession\": 22286,\n      \"ĠSavage\": 22287,\n      \"1996\": 22288,\n      \"Global\": 22289,\n      \"Jes\": 22290,\n      \"214\": 22291,\n      \"Ġsliding\": 22292,\n      \"Ġdisappro\": 22293,\n      \"ĠMagical\": 22294,\n      \"Ġvoluntarily\": 22295,\n      \"gb\": 22296,\n      \"aney\": 22297,\n      \"Ġprophet\": 22298,\n      \"ĠRein\": 22299,\n      \"ĠJulia\": 22300,\n      \"ĠWorth\": 22301,\n      \"aurus\": 22302,\n      \"Ġbounds\": 22303,\n      \"ieu\": 22304,\n      \")))\": 22305,\n      \"Ġcrore\": 22306,\n      \"ĠCitizen\": 22307,\n      \"Sky\": 22308,\n      \"Ġcolumnist\": 22309,\n      \"Ġseekers\": 22310,\n      \"ondo\": 22311,\n      \"ISA\": 22312,\n      \"ĠLength\": 22313,\n      \"Ġnostalg\": 22314,\n      \"Ġnewcom\": 22315,\n      \"Ġdetrim\": 22316,\n      \"entric\": 22317,\n      \"375\": 22318,\n      \"ĠGE\": 22319,\n      \"Ġautop\": 22320,\n      \"Ġacademics\": 22321,\n      \"AppData\": 22322,\n      \"ĠShen\": 22323,\n      \"Ġidiot\": 22324,\n      \"ĠTransit\": 22325,\n      \"Ġteaspoon\": 22326,\n      \"Wil\": 22327,\n      \"KO\": 22328,\n      \"ĠComedy\": 22329,\n      \">,\": 22330,\n      \"Ġpopulated\": 22331,\n      \"WD\": 22332,\n      \"Ġpigs\": 22333,\n      \"ĠOculus\": 22334,\n      \"Ġsympathetic\": 22335,\n      \"Ġmarathon\": 22336,\n      \"198\": 22337,\n      \"Ġseizure\": 22338,\n      \"sided\": 22339,\n      \"Ġdop\": 22340,\n      \"irtual\": 22341,\n      \"Land\": 22342,\n      \"ĠFloor\": 22343,\n      \"osaurs\": 22344,\n      \"...]\": 22345,\n      \"Ġlos\": 22346,\n      \"Ġsubsidiary\": 22347,\n      \"EY\": 22348,\n      \"ĠParts\": 22349,\n      \"ĠStef\": 22350,\n      \"ĠJudiciary\": 22351,\n      \"Ġ134\": 22352,\n      \"Ġmirrors\": 22353,\n      \"Ġket\": 22354,\n      \"times\": 22355,\n      \"Ġneurolog\": 22356,\n      \"Ġcav\": 22357,\n      \"ĠGuest\": 22358,\n      \"Ġtumor\": 22359,\n      \"scill\": 22360,\n      \"ĠLloyd\": 22361,\n      \"Est\": 22362,\n      \"Ġclearer\": 22363,\n      \"Ġstereotypes\": 22364,\n      \"Ġdur\": 22365,\n      \"nothing\": 22366,\n      \"Reddit\": 22367,\n      \"Ġnegotiated\": 22368,\n      \"------------------------\": 22369,\n      \"235\": 22370,\n      \"Ġflown\": 22371,\n      \"ĠSeoul\": 22372,\n      \"ĠResident\": 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\"ĠNewcastle\": 22410,\n      \"ĠHog\": 22411,\n      \"ĠEquipment\": 22412,\n      \"177\": 22413,\n      \"Ġterritorial\": 22414,\n      \"Ġarchives\": 22415,\n      \"203\": 22416,\n      \"Filter\": 22417,\n      \"ĠMunich\": 22418,\n      \"Ġcommanded\": 22419,\n      \"ĠWand\": 22420,\n      \"Ġpitches\": 22421,\n      \"ĠCroat\": 22422,\n      \"Ġratios\": 22423,\n      \"ĠMits\": 22424,\n      \"Ġaccumulated\": 22425,\n      \"ĠSpecifically\": 22426,\n      \"Ġgentleman\": 22427,\n      \"acerb\": 22428,\n      \"Ġpenn\": 22429,\n      \"Ġaka\": 22430,\n      \"ĠFuk\": 22431,\n      \"Ġintervene\": 22432,\n      \"ĠRefuge\": 22433,\n      \"ĠAlzheimer\": 22434,\n      \"Ġsuccession\": 22435,\n      \"ohan\": 22436,\n      \"does\": 22437,\n      \"Lord\": 22438,\n      \"Ġseparat\": 22439,\n      \"Ġcorrespondence\": 22440,\n      \"Ġshiny\": 22441,\n      \"Prior\": 22442,\n      \"Ġsulf\": 22443,\n      \"Ġmiserable\": 22444,\n      \"Ġdedication\": 22445,\n      \"().\": 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\"ĠApart\": 22596,\n      \"ĠChang\": 22597,\n      \"Ġrelieved\": 22598,\n      \"rophe\": 22599,\n      \"Ġairports\": 22600,\n      \"Ġfreak\": 22601,\n      \"util\": 22602,\n      \"Mill\": 22603,\n      \"ĠChin\": 22604,\n      \"ĠOwen\": 22605,\n      \"male\": 22606,\n      \"ĠBroken\": 22607,\n      \"ĠWinds\": 22608,\n      \"rob\": 22609,\n      \"rising\": 22610,\n      \"Ġfirefighters\": 22611,\n      \"Ġauthoritarian\": 22612,\n      \"Ġ148\": 22613,\n      \"Bitcoin\": 22614,\n      \"external\": 22615,\n      \"Ġbrowsers\": 22616,\n      \"ichever\": 22617,\n      \"orian\": 22618,\n      \"Ġunb\": 22619,\n      \"Ġpoke\": 22620,\n      \"ĠZot\": 22621,\n      \"Mid\": 22622,\n      \"ĠPopular\": 22623,\n      \"Ġcovert\": 22624,\n      \"Ġcontributes\": 22625,\n      \"Ġ650\": 22626,\n      \"Ġcontention\": 22627,\n      \"Gate\": 22628,\n      \"Ġconsoles\": 22629,\n      \"Ġchromos\": 22630,\n      \"ĠIX\": 22631,\n      \"Ġvisually\": 22632,\n      \"ĠEisen\": 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\"Ġpunk\": 22782,\n      \"ĠProvince\": 22783,\n      \"(),\": 22784,\n      \"Speed\": 22785,\n      \"Ġhanding\": 22786,\n      \"ĠSebast\": 22787,\n      \"Sir\": 22788,\n      \"rase\": 22789,\n      \"Ġjournals\": 22790,\n      \"Ġcongest\": 22791,\n      \"ĠTut\": 22792,\n      \"irrel\": 22793,\n      \"Ġschizophrenia\": 22794,\n      \"Ġmisogyn\": 22795,\n      \"healthy\": 22796,\n      \"Iron\": 22797,\n      \"Ġreacted\": 22798,\n      \"-$\": 22799,\n      \"252\": 22800,\n      \"Ġplural\": 22801,\n      \"Ġplum\": 22802,\n      \"Ġbargain\": 22803,\n      \"Ġgrounded\": 22804,\n      \"finder\": 22805,\n      \"Ġdisse\": 22806,\n      \"ĠLaz\": 22807,\n      \"OOD\": 22808,\n      \"Ġatroc\": 22809,\n      \"Factory\": 22810,\n      \"Ġminions\": 22811,\n      \"Ġori\": 22812,\n      \"ĠBrave\": 22813,\n      \"ĠPRE\": 22814,\n      \"ĠMyanmar\": 22815,\n      \"ĠHod\": 22816,\n      \"Ġexpedition\": 22817,\n      \"Ġexplode\": 22818,\n      \"ĠCoord\": 22819,\n      \"Ġextr\": 22820,\n      \"ĠBrief\": 22821,\n      \"ĠADHD\": 22822,\n      \"Ġhardcore\": 22823,\n      \"feeding\": 22824,\n      \"Ġdile\": 22825,\n      \"ĠFruit\": 22826,\n      \"Ġvaccination\": 22827,\n      \"ĠMao\": 22828,\n      \"osphere\": 22829,\n      \"Ġcontests\": 22830,\n      \"-|\": 22831,\n      \"Ġfren\": 22832,\n      \"isphere\": 22833,\n      \"Rom\": 22834,\n      \"ĠSharp\": 22835,\n      \"ĠTrend\": 22836,\n      \"Ġdisconnect\": 22837,\n      \"âĢ¢âĢ¢\": 22838,\n      \"Ġpersecution\": 22839,\n      \"Earth\": 22840,\n      \"Ġhealthier\": 22841,\n      \"384\": 22842,\n      \"Ġcob\": 22843,\n      \"ĠTrinity\": 22844,\n      \"OWS\": 22845,\n      \"ANN\": 22846,\n      \"Ġspecialty\": 22847,\n      \"Ġgru\": 22848,\n      \"Ġcooperative\": 22849,\n      \"why\": 22850,\n      \"Starting\": 22851,\n      \"ĠIssues\": 22852,\n      \"stre\": 22853,\n      \"ensor\": 22854,\n      \"Ġ185\": 22855,\n      \"Adv\": 22856,\n      \"!?\": 22857,\n      \"ĠRevel\": 22858,\n      \"emia\": 22859,\n      \"ĠHulk\": 22860,\n      \"Ġcelebrations\": 22861,\n      \"ĠSou\": 22862,\n      \"raud\": 22863,\n      \"ĠKlein\": 22864,\n      \"Ġunreal\": 22865,\n      \"context\": 22866,\n      \"Ġpartnerships\": 22867,\n      \"Ġadopting\": 22868,\n      \"tical\": 22869,\n      \"Ġsplash\": 22870,\n      \"ĠHezbollah\": 22871,\n      \"category\": 22872,\n      \"cyclop\": 22873,\n      \"xton\": 22874,\n      \"ĠDot\": 22875,\n      \"urdy\": 22876,\n      \"tz\": 22877,\n      \"Ġenvelope\": 22878,\n      \"ĠNL\": 22879,\n      \"âķ\": 22880,\n      \"Ġwherein\": 22881,\n      \"Spec\": 22882,\n      \"184\": 22883,\n      \"Ġtelev\": 22884,\n      \"aliation\": 22885,\n      \"Ġmyths\": 22886,\n      \"å°\": 22887,\n      \"Ġrigorous\": 22888,\n      \"Ġcommunicating\": 22889,\n      \"Ġobserver\": 22890,\n      \"Ġrehe\": 22891,\n      \"ĠWash\": 22892,\n      \"Ġapologized\": 22893,\n      \"ĠTin\": 22894,\n      \"Ġexpenditures\": 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\"Ġnationals\": 22970,\n      \"ĠSecurities\": 22971,\n      \"Ġwhip\": 22972,\n      \"Param\": 22973,\n      \"Ġlearns\": 22974,\n      \"Magic\": 22975,\n      \"Ġdetailing\": 22976,\n      \"moon\": 22977,\n      \"Ġbroadcasting\": 22978,\n      \"Ġbaked\": 22979,\n      \"265\": 22980,\n      \"holm\": 22981,\n      \"ĠSah\": 22982,\n      \"ĠHussein\": 22983,\n      \"ĠCourtesy\": 22984,\n      \"174\": 22985,\n      \"Ġ146\": 22986,\n      \"Ġgeographic\": 22987,\n      \"peace\": 22988,\n      \"Ġjudging\": 22989,\n      \"ĠStern\": 22990,\n      \"Bur\": 22991,\n      \"Ġstoryline\": 22992,\n      \"Gun\": 22993,\n      \"ĠStick\": 22994,\n      \"245\": 22995,\n      \"307\": 22996,\n      \"ãĤ´ãĥ³\": 22997,\n      \"ĠAdministrator\": 22998,\n      \"Ġburnt\": 22999,\n      \"Ġpave\": 23000,\n      \"choes\": 23001,\n      \"Exec\": 23002,\n      \"Ġcampuses\": 23003,\n      \"Result\": 23004,\n      \"Ġmutations\": 23005,\n      \"ĠCharter\": 23006,\n      \"Ġcaptures\": 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\"Ġindo\": 23046,\n      \"tc\": 23047,\n      \"endant\": 23048,\n      \"ĠMoto\": 23049,\n      \"Ġlegalization\": 23050,\n      \"OTE\": 23051,\n      \"Ġtasked\": 23052,\n      \"Ġtsp\": 23053,\n      \"ĠACTIONS\": 23054,\n      \"166\": 23055,\n      \"Ġrefreshing\": 23056,\n      \"ĠNR\": 23057,\n      \"ĠPerez\": 23058,\n      \"Ġinfringement\": 23059,\n      \"SY\": 23060,\n      \"Listen\": 23061,\n      \"inning\": 23062,\n      \"ku\": 23063,\n      \"Ġrotate\": 23064,\n      \"program\": 23065,\n      \"arah\": 23066,\n      \"Design\": 23067,\n      \"Ġ(£\": 23068,\n      \"Ġstoring\": 23069,\n      \"Ġwarrants\": 23070,\n      \"Ġjudgement\": 23071,\n      \"ĠBrist\": 23072,\n      \"usually\": 23073,\n      \"photo\": 23074,\n      \"ĠRan\": 23075,\n      \"ĠPine\": 23076,\n      \"Ġoutrageous\": 23077,\n      \"ĠValentine\": 23078,\n      \"luence\": 23079,\n      \"ĠEverybody\": 23080,\n      \"Altern\": 23081,\n      \"Ġrelevance\": 23082,\n      \"Ġterminated\": 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\"Ġmodifier\": 23157,\n      \"ĠPosition\": 23158,\n      \"Adam\": 23159,\n      \"lb\": 23160,\n      \"ubs\": 23161,\n      \"Ġimperfect\": 23162,\n      \"Ġclusters\": 23163,\n      \"ĠEngineer\": 23164,\n      \"ĠCherry\": 23165,\n      \"Ġinauguration\": 23166,\n      \"ĠSau\": 23167,\n      \"Ġembodiment\": 23168,\n      \"ĠUncle\": 23169,\n      \"Ġoverr\": 23170,\n      \"Ġexplosions\": 23171,\n      \"cule\": 23172,\n      \"ĠPrinceton\": 23173,\n      \"ĠAndrea\": 23174,\n      \"Ġincorrectly\": 23175,\n      \"Ġearnest\": 23176,\n      \"Ġpilgr\": 23177,\n      \"ĠSprint\": 23178,\n      \"Ġsleeve\": 23179,\n      \"Ġhears\": 23180,\n      \"ĠAmazing\": 23181,\n      \"Ġbrowsing\": 23182,\n      \"agin\": 23183,\n      \"Ġhomeland\": 23184,\n      \"Ġhaw\": 23185,\n      \"Ġdiving\": 23186,\n      \"istered\": 23187,\n      \"178\": 23188,\n      \"Ġbargaining\": 23189,\n      \"ĠArcade\": 23190,\n      \"Ġdelegate\": 23191,\n      \"terson\": 23192,\n      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\"Vs\": 23266,\n      \"isi\": 23267,\n      \"Ġvow\": 23268,\n      \"Ġreper\": 23269,\n      \"ĠPartnership\": 23270,\n      \"Ġpenetration\": 23271,\n      \"Ġaxe\": 23272,\n      \"Ġshattered\": 23273,\n      \"ĠZombies\": 23274,\n      \"Ġvinyl\": 23275,\n      \"ĠAlert\": 23276,\n      \"eon\": 23277,\n      \"Ġobliged\": 23278,\n      \"ĠIllust\": 23279,\n      \"ĠPlaza\": 23280,\n      \"ĠFrontier\": 23281,\n      \"Ġdavidjl\": 23282,\n      \"ĠSerial\": 23283,\n      \"ĠHav\": 23284,\n      \"ĠNutrition\": 23285,\n      \"Bi\": 23286,\n      \"ĠâĸĪ\": 23287,\n      \"ĠJays\": 23288,\n      \"linux\": 23289,\n      \"Ġhurry\": 23290,\n      \"Ġvoy\": 23291,\n      \"Ġhopeless\": 23292,\n      \"ĠStealth\": 23293,\n      \"Ġãģ\": 23294,\n      \"essors\": 23295,\n      \"ttle\": 23296,\n      \"borg\": 23297,\n      \"ĠSafari\": 23298,\n      \"fell\": 23299,\n      \"Ġwary\": 23300,\n      \"due\": 23301,\n      \"ĠAbove\": 23302,\n      \"Ha\": 23303,\n      \"ELL\": 23304,\n      \"Ġnotor\": 23305,\n      \"ĠWon\": 23306,\n      \"Too\": 23307,\n      \"Ġoccupations\": 23308,\n      \"Ġpossessions\": 23309,\n      \"Ġinviting\": 23310,\n      \"Ġpredators\": 23311,\n      \"Ġaccelerated\": 23312,\n      \"Ġ157\": 23313,\n      \"uterte\": 23314,\n      \"ĠCube\": 23315,\n      \"east\": 23316,\n      \"account\": 23317,\n      \"Give\": 23318,\n      \"Ġtransplant\": 23319,\n      \"redients\": 23320,\n      \"idable\": 23321,\n      \"Ġscreenshots\": 23322,\n      \"ĠGund\": 23323,\n      \"ĠFS\": 23324,\n      \"Ġtravelers\": 23325,\n      \"Ġsensory\": 23326,\n      \"ĠFiat\": 23327,\n      \"ĠRockets\": 23328,\n      \"İĭ\": 23329,\n      \"_{\": 23330,\n      \"Friend\": 23331,\n      \"Ġcharming\": 23332,\n      \"ALS\": 23333,\n      \"Ġenjoyment\": 23334,\n      \"mph\": 23335,\n      \"Ġ5000\": 23336,\n      \"ĠREG\": 23337,\n      \"ÙĨ\": 23338,\n      \"bia\": 23339,\n      \"Ġcompilation\": 23340,\n      \"rost\": 23341,\n      \"ĠVP\": 23342,\n      \"ĠSchne\": 23343,\n      \"2019\": 23344,\n      \"Ġcopying\": 23345,\n      \"MORE\": 23346,\n      \"ĠFlore\": 23347,\n      \"falls\": 23348,\n      \"215\": 23349,\n      \"total\": 23350,\n      \"Ġdisciples\": 23351,\n      \"double\": 23352,\n      \"Ġexceeding\": 23353,\n      \"Ġsmashed\": 23354,\n      \"Ġconceptual\": 23355,\n      \"ĠRomania\": 23356,\n      \"ĠBrent\": 23357,\n      \"ĠICE\": 23358,\n      \"ĠTou\": 23359,\n      \"Ġgrap\": 23360,\n      \"Ġnails\": 23361,\n      \"189\": 23362,\n      \"ãĥĺ\": 23363,\n      \"Ġprocure\": 23364,\n      \"eur\": 23365,\n      \"Ġconfirming\": 23366,\n      \"ĠCec\": 23367,\n      \"awi\": 23368,\n      \"ĠEden\": 23369,\n      \"Ġng\": 23370,\n      \"Ġengineered\": 23371,\n      \"atics\": 23372,\n      \"Ġhooked\": 23373,\n      \"Ġdisgusting\": 23374,\n      \"ĠMurder\": 23375,\n      \"ãĤ¿\": 23376,\n      \"Library\": 23377,\n      \"Ġ168\": 23378,\n      \"Almost\": 23379,\n      \"hematic\": 23380,\n      \"Menu\": 23381,\n      \"ĠNotre\": 23382,\n      \"ĠJur\": 23383,\n      \"Ġkidnapped\": 23384,\n      \"Ġhacker\": 23385,\n      \"ĠJade\": 23386,\n      \"Ġcreepy\": 23387,\n      \"Ġdrawings\": 23388,\n      \"ĠSponsor\": 23389,\n      \"Ġcyclists\": 23390,\n      \"ĠGoblin\": 23391,\n      \"Ġoptimized\": 23392,\n      \"Ġstaged\": 23393,\n      \"ĠMcD\": 23394,\n      \"between\": 23395,\n      \"Age\": 23396,\n      \"eno\": 23397,\n      \"Sex\": 23398,\n      \"ĠWide\": 23399,\n      \"nings\": 23400,\n      \"avis\": 23401,\n      \"Ġincapable\": 23402,\n      \"ĠKob\": 23403,\n      \"Ġrewarding\": 23404,\n      \"ĠLone\": 23405,\n      \"olescent\": 23406,\n      \"Ġcontracted\": 23407,\n      \"Ġsticky\": 23408,\n      \"Jose\": 23409,\n      \"Ball\": 23410,\n      \"fest\": 23411,\n      \"ĠInput\": 23412,\n      \"ĠRecently\": 23413,\n      \"Ġtomat\": 23414,\n      \"square\": 23415,\n      \"Application\": 23416,\n      \"Ġnitrogen\": 23417,\n      \"Ġduplicate\": 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\"Ġsellers\": 23531,\n      \"uren\": 23532,\n      \"Ġmedicines\": 23533,\n      \"ĠCaps\": 23534,\n      \"ĠAnimation\": 23535,\n      \"oxy\": 23536,\n      \"Ġoutward\": 23537,\n      \"Ġinquiries\": 23538,\n      \"229\": 23539,\n      \"Ġpsychologist\": 23540,\n      \"ĠSask\": 23541,\n      \"evil\": 23542,\n      \"Ġcontaminated\": 23543,\n      \"ãĤ¨\": 23544,\n      \"herence\": 23545,\n      \"Ġbranded\": 23546,\n      \"ĠAbdul\": 23547,\n      \"zh\": 23548,\n      \"Ġparagraphs\": 23549,\n      \"Ġmins\": 23550,\n      \"Ġcorrelated\": 23551,\n      \"erb\": 23552,\n      \"Ġimpart\": 23553,\n      \"Ġmilestone\": 23554,\n      \"ĠSolutions\": 23555,\n      \"otle\": 23556,\n      \"Ġundercover\": 23557,\n      \"Ġmarched\": 23558,\n      \"ĠChargers\": 23559,\n      \"fax\": 23560,\n      \"ĠSecrets\": 23561,\n      \"Ġruth\": 23562,\n      \"weather\": 23563,\n      \"Ġfeminine\": 23564,\n      \"Ġsham\": 23565,\n      \"Ġprestigious\": 23566,\n      \"iggins\": 23567,\n 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\"Ġstatewide\": 23605,\n      \"Ġsymm\": 23606,\n      \"Ġloops\": 23607,\n      \"Ġautumn\": 23608,\n      \"Nick\": 23609,\n      \"Ġshaping\": 23610,\n      \"Ġquot\": 23611,\n      \"Ġcherry\": 23612,\n      \"ĠCrossref\": 23613,\n      \"è¦ļéĨĴ\": 23614,\n      \"Standard\": 23615,\n      \"heed\": 23616,\n      \"ĠDell\": 23617,\n      \"ĠVietnamese\": 23618,\n      \"Ġost\": 23619,\n      \"ĠValkyrie\": 23620,\n      \"OA\": 23621,\n      \"Assad\": 23622,\n      \"Ġrebound\": 23623,\n      \"ĠTraffic\": 23624,\n      \"places\": 23625,\n      \"æĺ\": 23626,\n      \"ĠBuc\": 23627,\n      \"172\": 23628,\n      \"Ġshelters\": 23629,\n      \"Ġinsisting\": 23630,\n      \"ĠCertainly\": 23631,\n      \"ĠKenneth\": 23632,\n      \"ĠTCP\": 23633,\n      \"Ġpenal\": 23634,\n      \"ĠReplay\": 23635,\n      \"heard\": 23636,\n      \"Ġdialect\": 23637,\n      \"iza\": 23638,\n      \"ĠFY\": 23639,\n      \"itcher\": 23640,\n      \"ĠDL\": 23641,\n      \"Ġspiral\": 23642,\n      \"Ġquarterbacks\": 23643,\n      \"Ġhull\": 23644,\n      \"Ġgoogle\": 23645,\n      \"Ġtodd\": 23646,\n      \"ĠSterling\": 23647,\n      \"ĠPlate\": 23648,\n      \"Ġspying\": 23649,\n      \"mbol\": 23650,\n      \"ĠRealm\": 23651,\n      \"ĠProced\": 23652,\n      \"ĠCrash\": 23653,\n      \"Ġterminate\": 23654,\n      \"Ġprotesting\": 23655,\n      \"Center\": 23656,\n      \"guided\": 23657,\n      \"Ġuncover\": 23658,\n      \"Ġboycott\": 23659,\n      \"Ġrealizes\": 23660,\n      \"sound\": 23661,\n      \"Ġpretending\": 23662,\n      \"ĠVas\": 23663,\n      \"1980\": 23664,\n      \"Ġframed\": 23665,\n      \"Ġ139\": 23666,\n      \"Ġdescended\": 23667,\n      \"Ġrehabilitation\": 23668,\n      \"Ġborrowing\": 23669,\n      \"ĠBuch\": 23670,\n      \"Ġblur\": 23671,\n      \"Ron\": 23672,\n      \"ĠFrozen\": 23673,\n      \"enza\": 23674,\n      \"Chief\": 23675,\n      \"ĠPoor\": 23676,\n      \"Ġtranslates\": 23677,\n      \"MIN\": 23678,\n      \"Ġ212\": 23679,\n      \"JECT\": 23680,\n      \"Ġerupted\": 23681,\n      \"Ġsuccesses\": 23682,\n      \"SEC\": 23683,\n      \"Ġplague\": 23684,\n      \"Ġgems\": 23685,\n      \"doms\": 23686,\n      \"Ġstretches\": 23687,\n      \"ĠSpy\": 23688,\n      \"Ġstorytelling\": 23689,\n      \"Credit\": 23690,\n      \"ĠPush\": 23691,\n      \"Ġtraction\": 23692,\n      \"Ġineffective\": 23693,\n      \"ĠLuna\": 23694,\n      \"Ġtapes\": 23695,\n      \"Ġanalytics\": 23696,\n      \"ercise\": 23697,\n      \"Ġprogrammes\": 23698,\n      \"ĠCarbon\": 23699,\n      \"Ġbehold\": 23700,\n      \"heavy\": 23701,\n      \"ĠConservation\": 23702,\n      \"ĠFIR\": 23703,\n      \"Ġsack\": 23704,\n      \"termin\": 23705,\n      \"ricks\": 23706,\n      \"Ġhoused\": 23707,\n      \"Ġunusually\": 23708,\n      \"Ice\": 23709,\n      \"Ġexecuting\": 23710,\n      \"ĠMoroc\": 23711,\n      \"eday\": 23712,\n      \"Ġeditions\": 23713,\n      \"Ġsmarter\": 23714,\n      \"ĠBA\": 23715,\n      \"Ġoutlaw\": 23716,\n      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\"Ġbrighter\": 26083,\n      \"rans\": 26084,\n      \"ĠStephanie\": 26085,\n      \"extra\": 26086,\n      \"RET\": 26087,\n      \"ĠChristine\": 26088,\n      \"ĠSue\": 26089,\n      \"statement\": 26090,\n      \"Ġbolster\": 26091,\n      \"Ġantit\": 26092,\n      \"Radio\": 26093,\n      \"BIT\": 26094,\n      \"ãĤ°\": 26095,\n      \"Ġvisions\": 26096,\n      \"ĠConcept\": 26097,\n      \"Ġinline\": 26098,\n      \"ĠPhilosophy\": 26099,\n      \"isans\": 26100,\n      \"ĠIrving\": 26101,\n      \"ã\": 26102,\n      \"taking\": 26103,\n      \"Ġinconsist\": 26104,\n      \"ĠKumar\": 26105,\n      \"Ġlig\": 26106,\n      \"ĠSchumer\": 26107,\n      \"ĠRegulations\": 26108,\n      \"ĠHz\": 26109,\n      \"thro\": 26110,\n      \"ĠVoldemort\": 26111,\n      \"ĠMED\": 26112,\n      \"ĠFrederick\": 26113,\n      \"Pad\": 26114,\n      \"221\": 26115,\n      \"Ġalleging\": 26116,\n      \"ĠCommunication\": 26117,\n      \"Ġ167\": 26118,\n      \"Ġforecasts\": 26119,\n      \"Ġspiders\": 26120,\n      \"Organ\": 26121,\n      \"ĠParticipants\": 26122,\n      \"ĠOps\": 26123,\n      \"design\": 26124,\n      \"Close\": 26125,\n      \"Ġfacto\": 26126,\n      \"Ġbombers\": 26127,\n      \"resistant\": 26128,\n      \"ategories\": 26129,\n      \"School\": 26130,\n      \"Ġhomework\": 26131,\n      \"Ġcorro\": 26132,\n      \"Tuesday\": 26133,\n      \"ĠBrendan\": 26134,\n      \"ĠMX\": 26135,\n      \"ĠTS\": 26136,\n      \"ĠStri\": 26137,\n      \"Ġstakeholders\": 26138,\n      \"ĠMillennium\": 26139,\n      \"Ġtransferring\": 26140,\n      \"Jud\": 26141,\n      \"Ġtac\": 26142,\n      \"Ġ1600\": 26143,\n      \"ĠSDK\": 26144,\n      \"rb\": 26145,\n      \"Ġinterpretations\": 26146,\n      \"ĠSG\": 26147,\n      \"Ġupstairs\": 26148,\n      \"ĠHarvest\": 26149,\n      \"Ġvagina\": 26150,\n      \"Ġingest\": 26151,\n      \"xf\": 26152,\n      \"ĠOrion\": 26153,\n      \"ĠJoey\": 26154,\n      \"Ġsandwic\": 26155,\n      \"Ġimmortal\": 26156,\n      \"Ġflipped\": 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26194,\n      \"ĠATT\": 26195,\n      \"ĠCV\": 26196,\n      \"Ġtore\": 26197,\n      \"Resource\": 26198,\n      \"ĠDevils\": 26199,\n      \"408\": 26200,\n      \"inational\": 26201,\n      \"Ġassurance\": 26202,\n      \"ĠDarren\": 26203,\n      \"Ġwhichever\": 26204,\n      \"posure\": 26205,\n      \"Ġfury\": 26206,\n      \"Stock\": 26207,\n      \"Ġuniversally\": 26208,\n      \"response\": 26209,\n      \"Ġoak\": 26210,\n      \"Ġworkload\": 26211,\n      \"ĠCorner\": 26212,\n      \"eele\": 26213,\n      \"\\\"...\": 26214,\n      \"Ġdeprived\": 26215,\n      \"kowski\": 26216,\n      \"Ġcasts\": 26217,\n      \"Ġaffiliation\": 26218,\n      \"ĠAch\": 26219,\n      \"ĠAsked\": 26220,\n      \"athe\": 26221,\n      \"Ġlact\": 26222,\n      \"ĠThu\": 26223,\n      \"rm\": 26224,\n      \"Ġairlines\": 26225,\n      \"Ġnotions\": 26226,\n      \"Format\": 26227,\n      \"ĠFAA\": 26228,\n      \"ãĥĬ\": 26229,\n      \"driver\": 26230,\n      \"Ġtranscend\": 26231,\n      \"Settings\": 26232,\n      \"ĠProsecut\": 26233,\n      \"Ġspinal\": 26234,\n      \"Ġdefaults\": 26235,\n      \"FK\": 26236,\n      \"Ġprefers\": 26237,\n      \"rendered\": 26238,\n      \"thus\": 26239,\n      \"film\": 26240,\n      \"Ġtiger\": 26241,\n      \"ĠSpicer\": 26242,\n      \"recogn\": 26243,\n      \"ĠRugby\": 26244,\n      \"Network\": 26245,\n      \"Ġpity\": 26246,\n      \"Ġcompartment\": 26247,\n      \"casters\": 26248,\n      \"ĠMonroe\": 26249,\n      \"Ġ720\": 26250,\n      \"Ġcorrections\": 26251,\n      \"Ġdopamine\": 26252,\n      \"ĠAZ\": 26253,\n      \"Cut\": 26254,\n      \"Ġroomm\": 26255,\n      \"Ġspeculate\": 26256,\n      \"Hash\": 26257,\n      \"Ġrestrictive\": 26258,\n      \"1111\": 26259,\n      \"redible\": 26260,\n      \"onel\": 26261,\n      \"Ġrampant\": 26262,\n      \"reported\": 26263,\n      \"ĠSuite\": 26264,\n      \"ĠMinimum\": 26265,\n      \"alys\": 26266,\n      \"azard\": 26267,\n      \"loop\": 26268,\n      \"Ġlent\": 26269,\n      \"sha\": 26270,\n      \"Ġvandal\": 26271,\n      \"menu\": 26272,\n      \"ĠBoehner\": 26273,\n      \"Ġnarratives\": 26274,\n      \"Ġauthenticity\": 26275,\n      \"269\": 26276,\n      \"anic\": 26277,\n      \"duty\": 26278,\n      \"285\": 26279,\n      \"Ġthanked\": 26280,\n      \"Ġbetrayed\": 26281,\n      \"lift\": 26282,\n      \"Ġsouthwest\": 26283,\n      \"ĠDexter\": 26284,\n      \"ĠBod\": 26285,\n      \"Ġkeywords\": 26286,\n      \"Average\": 26287,\n      \"DIS\": 26288,\n      \"Ġethnicity\": 26289,\n      \"!),\": 26290,\n      \"ĠNationals\": 26291,\n      \"á¹\": 26292,\n      \"ĠTah\": 26293,\n      \"ioxid\": 26294,\n      \"Ġwidget\": 26295,\n      \"Ġpasta\": 26296,\n      \"Ġbilling\": 26297,\n      \"Ġtrilogy\": 26298,\n      \"ĠLines\": 26299,\n      \"Ġsniff\": 26300,\n      \"Ġnephew\": 26301,\n      \"Late\": 26302,\n      \"Ġprincip\": 26303,\n      \"ĠLoop\": 26304,\n      \"ĠMarxist\": 26305,\n      \"Ġdissolved\": 26306,\n      \"Ġcontexts\": 26307,\n      \"ĠAmount\": 26308,\n      \"ĠSpike\": 26309,\n      \"Ġtotals\": 26310,\n      \"Ġorganizer\": 26311,\n      \"Ġuprising\": 26312,\n      \"ships\": 26313,\n      \"YY\": 26314,\n      \"ĠNortheast\": 26315,\n      \"money\": 26316,\n      \"gradation\": 26317,\n      \"Ġgoalkeeper\": 26318,\n      \"ĠHear\": 26319,\n      \"Ġsteak\": 26320,\n      \"ĠBuzzFeed\": 26321,\n      \"Ġsolemn\": 26322,\n      \"ĠScand\": 26323,\n      \"Ġpopping\": 26324,\n      \"Ġadhere\": 26325,\n      \"ĠAlleg\": 26326,\n      \"byte\": 26327,\n      \"ĠWolver\": 26328,\n      \"Ġunin\": 26329,\n      \"Ġrecol\": 26330,\n      \"itud\": 26331,\n      \"Ġmimic\": 26332,\n      \"ibus\": 26333,\n      \"Ġpredicts\": 26334,\n      \"ĠKeeper\": 26335,\n      \"iating\": 26336,\n      \"Ġdeception\": 26337,\n      \"Ġlearnt\": 26338,\n      \"Ġdiary\": 26339,\n      \"Ġconditional\": 26340,\n      \"Ġrelic\": 26341,\n      \"Ġinvoke\": 26342,\n      \"ienced\": 26343,\n      \"åĪ\": 26344,\n      \"ĠPont\": 26345,\n      \"Ġcellphone\": 26346,\n      \"Ġspeeding\": 26347,\n      \"Ġtackling\": 26348,\n      \"Ġnude\": 26349,\n      \"opened\": 26350,\n      \"ĠManafort\": 26351,\n      \"Ġ1952\": 26352,\n      \"Ġmajors\": 26353,\n      \"ĠSilence\": 26354,\n      \"Ġlogistics\": 26355,\n      \"Ġweighted\": 26356,\n      \"ĠPsychiat\": 26357,\n      \"\\\":[\\\"\": 26358,\n      \"Ġsickness\": 26359,\n      \"Ġdividends\": 26360,\n      \"zon\": 26361,\n      \"Release\": 26362,\n      \"ĠKeys\": 26363,\n      \"ĠIch\": 26364,\n      \"Ġenz\": 26365,\n      \"ĠFernand\": 26366,\n      \"Ġα\": 26367,\n      \"Ġmeanings\": 26368,\n      \"Ġpenny\": 26369,\n      \"Ġstern\": 26370,\n      \"Ġlar\": 26371,\n      \"ĠPublished\": 26372,\n      \"Ġbackdrop\": 26373,\n      \"Kim\": 26374,\n      \"ĠSynt\": 26375,\n      \"Ġdebuted\": 26376,\n      \"wm\": 26377,\n      \"ĠIsle\": 26378,\n      \"Ġregulating\": 26379,\n      \"otti\": 26380,\n      \"ĠScholars\": 26381,\n      \"icester\": 26382,\n      \"ĠChef\": 26383,\n      \"Ġpops\": 26384,\n      \"ĠLauncher\": 26385,\n      \"ĠVarious\": 26386,\n      \"Ġcommenting\": 26387,\n      \"oslav\": 26388,\n      \"enzie\": 26389,\n      \"Ġrivalry\": 26390,\n      \"âĤ¬\": 26391,\n      \"Really\": 26392,\n      \"Ġorc\": 26393,\n      \"Ġbean\": 26394,\n      \"ĠJudy\": 26395,\n      \"Notice\": 26396,\n      \"ĠBike\": 26397,\n      \"?]\": 26398,\n      \"Ġrented\": 26399,\n      \"sten\": 26400,\n      \"Ġforefront\": 26401,\n      \"ĠBaldwin\": 26402,\n      \"Ġyielded\": 26403,\n      \"tails\": 26404,\n      \"Prime\": 26405,\n      \"ĠSources\": 26406,\n      \"icator\": 26407,\n      \"Sean\": 26408,\n      \"Ġmarching\": 26409,\n      \"Output\": 26410,\n      \"ĠJungle\": 26411,\n      \"Ġreside\": 26412,\n      \"zzle\": 26413,\n      \"ĠAndrews\": 26414,\n      \"Ġtorque\": 26415,\n      \"Basic\": 26416,\n      \"Actually\": 26417,\n      \"strap\": 26418,\n      \"penter\": 26419,\n      \"Ġexams\": 26420,\n      \"ĠYa\": 26421,\n      \"Ġ159\": 26422,\n      \"ĠDecision\": 26423,\n      \"Ġransom\": 26424,\n      \"eteenth\": 26425,\n      \"ensing\": 26426,\n      \"213\": 26427,\n      \"Ġsunset\": 26428,\n      \"404\": 26429,\n      \"ĠRapid\": 26430,\n      \"ĠHein\": 26431,\n      \"ĠAboriginal\": 26432,\n      \"Ġorganism\": 26433,\n      \"ĠSever\": 26434,\n      \"Ġcla\": 26435,\n      \"aji\": 26436,\n      \"Simple\": 26437,\n      \"ĠFlavor\": 26438,\n      \"ĠEval\": 26439,\n      \"prus\": 26440,\n      \"Ġchorus\": 26441,\n      \"DAY\": 26442,\n      \"Ġdenounced\": 26443,\n      \"Ġbiography\": 26444,\n      \"ĠTurnbull\": 26445,\n      \"Recent\": 26446,\n      \"Normal\": 26447,\n      \"lections\": 26448,\n      \"Word\": 26449,\n      \"Ġferry\": 26450,\n      \"ĠWagner\": 26451,\n      \"hom\": 26452,\n      \"Unit\": 26453,\n      \"Ġsupermarket\": 26454,\n      \"ĠSith\": 26455,\n      \"Ġnominees\": 26456,\n      \"Ġdictatorship\": 26457,\n      \"iddler\": 26458,\n      \"Ġannounces\": 26459,\n      \"ĠThem\": 26460,\n      \"ĠNeptune\": 26461,\n      \"Ġdeity\": 26462,\n      \"ĠYi\": 26463,\n      \"Ġmonarch\": 26464,\n      \"ARR\": 26465,\n      \"Ġinvaded\": 26466,\n      \"ĠHok\": 26467,\n      \"untary\": 26468,\n      \"Certain\": 26469,\n      \"ega\": 26470,\n      \"Ġkidding\": 26471,\n      \"ĠRegulation\": 26472,\n      \"Ġtray\": 26473,\n      \"Ġphotographers\": 26474,\n      \"ĠArcane\": 26475,\n      \"Ġdischarged\": 26476,\n      \"Ġevangelical\": 26477,\n      \"Ġinterchange\": 26478,\n      \"Ġfilmmaker\": 26479,\n      \"ĠEndless\": 26480,\n      \"Ġ290\": 26481,\n      \"ĠSalvador\": 26482,\n      \"ASY\": 26483,\n      \"ĠSignal\": 26484,\n      \"Ġwrath\": 26485,\n      \"âľ\": 26486,\n      \"lot\": 26487,\n      \"'/\": 26488,\n      \"Ġprojectile\": 26489,\n      \"Ġemploying\": 26490,\n      \"ĠInterface\": 26491,\n      \"191\": 26492,\n      \"atellite\": 26493,\n      \"ĠRath\": 26494,\n      \"package\": 26495,\n      \"Ġindications\": 26496,\n      \"Jason\": 26497,\n      \"Ġargs\": 26498,\n      \"ĠGHz\": 26499,\n      \"Ġtilt\": 26500,\n      \"nants\": 26501,\n      \"won\": 26502,\n      \"ãĤµ\": 26503,\n      \"redd\": 26504,\n      \"rescent\": 26505,\n      \"ĠCalendar\": 26506,\n      \"Ġmodular\": 26507,\n      \"Ġassisting\": 26508,\n      \"Ġredeem\": 26509,\n      \"ĠBean\": 26510,\n      \"Ġworsh\": 26511,\n      \"Ġdecentralized\": 26512,\n      \")...\": 26513,\n      \"377\": 26514,\n      \"Ġarrays\": 26515,\n      \"Ġaccomplishments\": 26516,\n      \"ο\": 26517,\n      \"dot\": 26518,\n      \"Ġmutually\": 26519,\n      \"Ġobstruct\": 26520,\n      \"Ġmisrepresent\": 26521,\n      \"orest\": 26522,\n      \"ionic\": 26523,\n      \"ruce\": 26524,\n      \"%;\": 26525,\n      \"Ġknowingly\": 26526,\n      \"porting\": 26527,\n      \"inently\": 26528,\n      \"Ari\": 26529,\n      \"ĠSchultz\": 26530,\n      \"Da\": 26531,\n      \"ĠCere\": 26532,\n      \"Ġobsolete\": 26533,\n      \"ħĭ\": 26534,\n      \"give\": 26535,\n      \"Ġbait\": 26536,\n      \"Ġenlarg\": 26537,\n      \"Neill\": 26538,\n      \"Ġ1933\": 26539,\n      \"Ġreconsider\": 26540,\n      \"ĠSergeant\": 26541,\n      \"ĠDiane\": 26542,\n      \"ĠCogn\": 26543,\n      \"ĠIcon\": 26544,\n      \"Position\": 26545,\n      \"Ġfost\": 26546,\n      \"Ġstirring\": 26547,\n      \"seven\": 26548,\n      \"ĠSpaceX\": 26549,\n      \"uggets\": 26550,\n      \"Ġmedd\": 26551,\n      \"Gal\": 26552,\n      \"ĠSister\": 26553,\n      \"Boy\": 26554,\n      \"Ġtriggering\": 26555,\n      \"Taking\": 26556,\n      \"Ġscreams\": 26557,\n      \"Ġcausal\": 26558,\n      \"Ġawaken\": 26559,\n      \"Arm\": 26560,\n      \"297\": 26561,\n      \"Ġdispatched\": 26562,\n      \"ĠFALSE\": 26563,\n      \"Ġorganizational\": 26564,\n      \"ĠTong\": 26565,\n      \"Ġdilemma\": 26566,\n      \"demon\": 26567,\n      \"Spl\": 26568,\n      \"Ġhooks\": 26569,\n      \"uding\": 26570,\n      \"Ġvalidate\": 26571,\n      \"Ġpotion\": 26572,\n      \"Ġclaw\": 26573,\n      \"Ġburgl\": 26574,\n      \"Ġquir\": 26575,\n      \"ACA\": 26576,\n      \"ĠBrennan\": 26577,\n      \"Ġdurability\": 26578,\n      \"Ġbombings\": 26579,\n      \"ĠWindow\": 26580,\n      \"Ġculprit\": 26581,\n      \"325\": 26582,\n      \"Therefore\": 26583,\n      \"umbered\": 26584,\n      \"performance\": 26585,\n      \"warts\": 26586,\n      \"Ġenforcing\": 26587,\n      \"ĠBlow\": 26588,\n      \"Ġreprint\": 26589,\n      \"ifax\": 26590,\n      \"alpha\": 26591,\n      \"Ġsinister\": 26592,\n      \"Ġburger\": 26593,\n      \"fighting\": 26594,\n      \"Score\": 26595,\n      \"ĠStones\": 26596,\n      \"iem\": 26597,\n      \"405\": 26598,\n      \"chemy\": 26599,\n      \"Ġvinegar\": 26600,\n      \"nom\": 26601,\n      \"Ġprevailing\": 26602,\n      \"ĠLatest\": 26603,\n      \"¶\": 26604,\n      \"Ġba\": 26605,\n      \"ĠWriter\": 26606,\n      \"Ġ177\": 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\"Ġretaining\": 26645,\n      \"atri\": 26646,\n      \"Ġpoorer\": 26647,\n      \"ĠSyrians\": 26648,\n      \"ĠHammond\": 26649,\n      \"Ġweld\": 26650,\n      \"uder\": 26651,\n      \"topic\": 26652,\n      \"ĠTT\": 26653,\n      \"ricia\": 26654,\n      \"Ġthieves\": 26655,\n      \"Lic\": 26656,\n      \"ĠGust\": 26657,\n      \"ĠWays\": 26658,\n      \"areth\": 26659,\n      \"243\": 26660,\n      \"Ġbroadcaster\": 26661,\n      \"shield\": 26662,\n      \"assium\": 26663,\n      \"uble\": 26664,\n      \"Ġairstrikes\": 26665,\n      \"onso\": 26666,\n      \"Ġpedal\": 26667,\n      \"Ġcollectors\": 26668,\n      \"ĠVander\": 26669,\n      \"ĠMesa\": 26670,\n      \"Ġdictator\": 26671,\n      \"Ġdir\": 26672,\n      \"enton\": 26673,\n      \"cart\": 26674,\n      \"score\": 26675,\n      \"adder\": 26676,\n      \"Cry\": 26677,\n      \"Ġssh\": 26678,\n      \"gger\": 26679,\n      \"Ġdrunken\": 26680,\n      \"ĠGS\": 26681,\n      \"ĠSeat\": 26682,\n      \"Ġcornerback\": 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     \"ĠDrugs\": 26722,\n      \"Islamic\": 26723,\n      \"Ġspur\": 26724,\n      \"Ġcafe\": 26725,\n      \"Ġimaginary\": 26726,\n      \"Ġguiding\": 26727,\n      \"Ġswings\": 26728,\n      \"ĠTheme\": 26729,\n      \"oby\": 26730,\n      \"Ġnud\": 26731,\n      \"Ġbegging\": 26732,\n      \"Ġstrongh\": 26733,\n      \"Ġrejecting\": 26734,\n      \"Ġpedestrians\": 26735,\n      \"ĠProspect\": 26736,\n      \"Rare\": 26737,\n      \"sle\": 26738,\n      \"Ġconcessions\": 26739,\n      \"ĠConstitutional\": 26740,\n      \"Ġbeams\": 26741,\n      \"Ġfibers\": 26742,\n      \"poon\": 26743,\n      \"Ġinstincts\": 26744,\n      \"property\": 26745,\n      \"ĠBIG\": 26746,\n      \"Sanders\": 26747,\n      \"imates\": 26748,\n      \"Ġcoating\": 26749,\n      \"Ġcorpses\": 26750,\n      \"ĠTRUE\": 26751,\n      \"checked\": 26752,\n      \"Ġ166\": 26753,\n      \"Ash\": 26754,\n      \"ĠJS\": 26755,\n      \"ĠFiction\": 26756,\n      \"Ġcommunal\": 26757,\n      \"Ġenergetic\": 26758,\n      \"oooooooo\": 26759,\n      \"Ġnowadays\": 26760,\n      \"ILD\": 26761,\n      \"ibo\": 26762,\n      \"ĠSUV\": 26763,\n      \"Ren\": 26764,\n      \"Ġdwelling\": 26765,\n      \"Silver\": 26766,\n      \"Ġtally\": 26767,\n      \"ĠMoving\": 26768,\n      \"Ġcoward\": 26769,\n      \"Ġgenerals\": 26770,\n      \"Ġhorns\": 26771,\n      \"Ġcirculated\": 26772,\n      \"Ġrobbed\": 26773,\n      \"ĠUnlimited\": 26774,\n      \"Ġharassed\": 26775,\n      \"Ġinhibit\": 26776,\n      \"Ġcomposer\": 26777,\n      \"ĠSpotify\": 26778,\n      \"Ġspreads\": 26779,\n      \"364\": 26780,\n      \"Ġsuicidal\": 26781,\n      \"Ġnoises\": 26782,\n      \"ĠStur\": 26783,\n      \"Ġsaga\": 26784,\n      \"ĠKag\": 26785,\n      \"iso\": 26786,\n      \"Ġtheoretically\": 26787,\n      \"Money\": 26788,\n      \"Ġsimilarity\": 26789,\n      \"Ġsliced\": 26790,\n      \"utils\": 26791,\n      \"inges\": 26792,\n      \"\\\"-\": 26793,\n      \"Ġanth\": 26794,\n      \"Ġimped\": 26795,\n      \"Module\": 26796,\n      \"Throughout\": 26797,\n      \"Ġmenus\": 26798,\n      \"committee\": 26799,\n      \"andi\": 26800,\n      \"obj\": 26801,\n      \"inav\": 26802,\n      \"fired\": 26803,\n      \"ĠAbdullah\": 26804,\n      \"Ġundead\": 26805,\n      \"Ġfonts\": 26806,\n      \"Hold\": 26807,\n      \"ENG\": 26808,\n      \"Ġsustainability\": 26809,\n      \"Ġflick\": 26810,\n      \"Ġrazor\": 26811,\n      \"ĠFest\": 26812,\n      \"ĠCharacters\": 26813,\n      \"Ġwording\": 26814,\n      \"Ġpopulist\": 26815,\n      \"Ġcriticizing\": 26816,\n      \"Ġmuse\": 26817,\n      \"vine\": 26818,\n      \"Ġcardboard\": 26819,\n      \"Ġkindly\": 26820,\n      \"Ġfringe\": 26821,\n      \"ĠTheft\": 26822,\n      \"icultural\": 26823,\n      \"Ġgovernors\": 26824,\n      \"Ġ����\": 26825,\n      \"Ġ163\": 26826,\n      \"Ġtimeout\": 26827,\n      \"ĠAuth\": 26828,\n      \"Children\": 26829,\n      \"AU\": 26830,\n      \"Ġredemption\": 26831,\n      \"ĠAlger\": 26832,\n      \"Ġ1914\": 26833,\n      \"Ġwaved\": 26834,\n      \"Ġastronauts\": 26835,\n      \"ograms\": 26836,\n      \"Ġswamp\": 26837,\n      \"ĠFinnish\": 26838,\n      \"Ġcandle\": 26839,\n      \"Ġtonnes\": 26840,\n      \"utm\": 26841,\n      \"Ġray\": 26842,\n      \"Ġspun\": 26843,\n      \"Ġfearful\": 26844,\n      \"articles\": 26845,\n      \"Ġcaus\": 26846,\n      \"orically\": 26847,\n      \"ĠRequires\": 26848,\n      \"ĠGol\": 26849,\n      \"Ġpope\": 26850,\n      \"Ġinaugural\": 26851,\n      \"Ġgle\": 26852,\n      \"ADA\": 26853,\n      \"ĠISIL\": 26854,\n      \"ĠOffensive\": 26855,\n      \"Ġwatchdog\": 26856,\n      \"Ġbalcon\": 26857,\n      \"entity\": 26858,\n      \"ĠHoo\": 26859,\n      \"Ġgallon\": 26860,\n      \"ACC\": 26861,\n      \"Ġdoubling\": 26862,\n      \"Ġimplication\": 26863,\n      \"ĠSight\": 26864,\n      \"Ġdoctr\": 26865,\n      \"-------\": 26866,\n      \"Ġ\\\\\\\\\": 26867,\n      \"Ġmalt\": 26868,\n      \"Roll\": 26869,\n      \"Ġâī¥\": 26870,\n      \"Ġrecap\": 26871,\n      \"adding\": 26872,\n      \"uces\": 26873,\n      \"ĠBend\": 26874,\n      \"figure\": 26875,\n      \"Ġturkey\": 26876,\n      \"Ġsocietal\": 26877,\n      \"ĠTickets\": 26878,\n      \"Ġcommercially\": 26879,\n      \"Ġspicy\": 26880,\n      \"Ġ216\": 26881,\n      \"ĠRamp\": 26882,\n      \"Ġsuperiority\": 26883,\n      \"ï\": 26884,\n      \"ĠTracker\": 26885,\n      \"Carl\": 26886,\n      \"ĠCoy\": 26887,\n      \"ĠPatriot\": 26888,\n      \"Ġconsulted\": 26889,\n      \"Ġlistings\": 26890,\n      \"Ġslew\": 26891,\n      \"reenshot\": 26892,\n      \"ĠGone\": 26893,\n      \"Ġ[...]\": 26894,\n      \"309\": 26895,\n      \"Ġhottest\": 26896,\n      \"ر\": 26897,\n      \"Ġrocky\": 26898,\n      \"ĠDiaz\": 26899,\n      \"Ġmassage\": 26900,\n      \"Ġparaly\": 26901,\n      \"Ġpony\": 26902,\n      \"Az\": 26903,\n      \"Ġcartridge\": 26904,\n      \"ĠNZ\": 26905,\n      \"Ġsnack\": 26906,\n      \"ĠLamar\": 26907,\n      \"plement\": 26908,\n      \"ĠLeslie\": 26909,\n      \"Ġmater\": 26910,\n      \"Ġsnipp\": 26911,\n      \"246\": 26912,\n      \"Ġjointly\": 26913,\n      \"ĠBrisbane\": 26914,\n      \"ĠiPod\": 26915,\n      \"Ġpumping\": 26916,\n      \"Ġgoat\": 26917,\n      \"ĠSharon\": 26918,\n      \"ealing\": 26919,\n      \"Ġcoron\": 26920,\n      \"Ġanomal\": 26921,\n      \"rahim\": 26922,\n      \"ĠConnection\": 26923,\n      \"Ġsculpture\": 26924,\n      \"Ġscheduling\": 26925,\n      \"ĠDaddy\": 26926,\n      \"athing\": 26927,\n      \"Ġeyebrows\": 26928,\n      \"Ġcurved\": 26929,\n      \"Ġsentiments\": 26930,\n      \"Ġdrafting\": 26931,\n      \"Drop\": 26932,\n      \"([\": 26933,\n      \"Ġnominal\": 26934,\n      \"ĠLeadership\": 26935,\n      \"ĠGrow\": 26936,\n      \"Ġ176\": 26937,\n      \"Ġconstructive\": 26938,\n      \"ivation\": 26939,\n      \"Ġcorrupted\": 26940,\n      \"gerald\": 26941,\n      \"ĠCros\": 26942,\n      \"ĠChester\": 26943,\n      \"ĠLap\": 26944,\n      \"ãģª\": 26945,\n      \"OTH\": 26946,\n      \"DATA\": 26947,\n      \"Ġalmond\": 26948,\n      \"probably\": 26949,\n      \"Imp\": 26950,\n      \"Ġfeast\": 26951,\n      \"ĠWarcraft\": 26952,\n      \"Flor\": 26953,\n      \"Ġcheckpoint\": 26954,\n      \"Ġtranscription\": 26955,\n      \"Ġ204\": 26956,\n      \"Ġtweaks\": 26957,\n      \"Ġrelieve\": 26958,\n      \"Science\": 26959,\n      \"Ġperformer\": 26960,\n      \"Zone\": 26961,\n      \"Ġturmoil\": 26962,\n      \"igated\": 26963,\n      \"hibit\": 26964,\n      \"ĠCafe\": 26965,\n      \"themed\": 26966,\n      \"Ġfluor\": 26967,\n      \"bench\": 26968,\n      \"Ġdecom\": 26969,\n      \"ĠUnt\": 26970,\n      \"ĠBarrett\": 26971,\n      \"ĠFacts\": 26972,\n      \"Ġtasting\": 26973,\n      \"ĠPTSD\": 26974,\n      \"ĠSeal\": 26975,\n      \"ĠJudaism\": 26976,\n      \"ĠDynamic\": 26977,\n      \"ĠCors\": 26978,\n      \"Ve\": 26979,\n      \"ĠMing\": 26980,\n      \"ĠTransform\": 26981,\n      \"von\": 26982,\n      \"ĠDefenders\": 26983,\n      \"ĠTactical\": 26984,\n      \"ĠVon\": 26985,\n      \"ĠUnivers\": 26986,\n      \"Ġdistorted\": 26987,\n      \"ĠBreath\": 26988,\n      \"?'\\\"\": 26989,\n      \"Ġagon\": 26990,\n      \"ĠDeadly\": 26991,\n      \"Ġlan\": 26992,\n      \"ĠCycle\": 26993,\n      \"orned\": 26994,\n      \"Ġreliably\": 26995,\n      \"Ġglor\": 26996,\n      \"ĠMonkey\": 26997,\n      \"ãĥ¡\": 26998,\n      \"Ġadren\": 26999,\n      \"Ġmicrowave\": 27000,\n      \"ĠAlban\": 27001,\n      \"ircraft\": 27002,\n      \"digit\": 27003,\n      \"smart\": 27004,\n      \"ĠDread\": 27005,\n      \"¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯\": 27006,\n      \"{{\": 27007,\n      \"ĠRochester\": 27008,\n      \"Ġsimplified\": 27009,\n      \"Ġinflicted\": 27010,\n      \"Ġtakeover\": 27011,\n      \"Ġyourselves\": 27012,\n      \"aditional\": 27013,\n      \"Ġmuscular\": 27014,\n      \"KS\": 27015,\n      \"Ġingen\": 27016,\n      \"Tax\": 27017,\n      \"ĠFeature\": 27018,\n      \"277\": 27019,\n      \"Ġcruc\": 27020,\n      \"Ġcrate\": 27021,\n      \"Ġunidentified\": 27022,\n      \"Ġacclaimed\": 27023,\n      \"ĠManga\": 27024,\n      \"ĠFrances\": 27025,\n      \"ĠNepal\": 27026,\n      \"ĠGerald\": 27027,\n      \"ĠKuwait\": 27028,\n      \"Ġslain\": 27029,\n      \"ĠHeb\": 27030,\n      \"ĠGoku\": 27031,\n      \"ãģ®æ\": 27032,\n      \"286\": 27033,\n      \"Mrs\": 27034,\n      \"ĠCody\": 27035,\n      \"ĠSanctuary\": 27036,\n      \"016\": 27037,\n      \"Ġdismant\": 27038,\n      \"Ġdataset\": 27039,\n      \"ĠHond\": 27040,\n      \"buck\": 27041,\n      \"ĠPatterson\": 27042,\n      \"Ġpalette\": 27043,\n      \"ĠGD\": 27044,\n      \"icol\": 27045,\n      \"ĠLodge\": 27046,\n      \"Ġplanetary\": 27047,\n      \"akin\": 27048,\n      \"ĠRegistered\": 27049,\n      \"abwe\": 27050,\n      \"ĠPetersburg\": 27051,\n      \"Ġhailed\": 27052,\n      \"ĠPiece\": 27053,\n      \"Sche\": 27054,\n      \"ĠDOJ\": 27055,\n      \"Ġenumer\": 27056,\n      \"181\": 27057,\n      \"ĠObserver\": 27058,\n      \"ĠBold\": 27059,\n      \"founded\": 27060,\n      \"commerce\": 27061,\n      \"Ġexploits\": 27062,\n      \"ĠFinding\": 27063,\n      \"URN\": 27064,\n      \"ĠSne\": 27065,\n      \"ĠAcid\": 27066,\n      \"ayette\": 27067,\n      \"ĠValues\": 27068,\n      \"Ġdrastic\": 27069,\n      \"Ġarchitectural\": 27070,\n      \"Ġ\\\".\": 27071,\n      \"×ķ\": 27072,\n      \"umped\": 27073,\n      \"Ġwrapping\": 27074,\n      \"Ġwidow\": 27075,\n      \"ĠSlayer\": 27076,\n      \"lace\": 27077,\n      \"once\": 27078,\n      \"Germany\": 27079,\n      \"avoid\": 27080,\n      \"Ġtemples\": 27081,\n      \"PAR\": 27082,\n      \"ô\": 27083,\n      \"ĠLucifer\": 27084,\n      \"ĠFlickr\": 27085,\n      \"lov\": 27086,\n      \"forces\": 27087,\n      \"Ġscouting\": 27088,\n      \"Ġlouder\": 27089,\n      \"tesy\": 27090,\n      \"Ġbeforehand\": 27091,\n      \"Äĵ\": 27092,\n      \"ĠNeon\": 27093,\n      \"ĠWol\": 27094,\n      \"ĠTypically\": 27095,\n      \"ĠPolitico\": 27096,\n      \"-+-+\": 27097,\n      \"Ġbuilder\": 27098,\n      \"Ġderive\": 27099,\n      \"Kill\": 27100,\n      \"Ġpoker\": 27101,\n      \"Ġambiguous\": 27102,\n      \"Ġlifts\": 27103,\n      \"Ġcyt\": 27104,\n      \"Ġribs\": 27105,\n      \"oodle\": 27106,\n      \"ĠSounds\": 27107,\n      \"hair\": 27108,\n      \"ĠSyndrome\": 27109,\n      \"tf\": 27110,\n      \"Ġproportional\": 27111,\n      \"uid\": 27112,\n      \"Ġpertaining\": 27113,\n      \"ĠKindle\": 27114,\n      \"ĠNegro\": 27115,\n      \"Ġreiterated\": 27116,\n      \"ĠTonight\": 27117,\n      \"oths\": 27118,\n      \"ĠCornell\": 27119,\n      \"Ġowing\": 27120,\n      \"Ġ208\": 27121,\n      \"elfare\": 27122,\n      \"ocating\": 27123,\n      \"ĠBirds\": 27124,\n      \"Subscribe\": 27125,\n      \"Ġessays\": 27126,\n      \"Ġburdens\": 27127,\n      \"Ġillustrations\": 27128,\n      \"arious\": 27129,\n      \"ERAL\": 27130,\n      \"ĠCalcul\": 27131,\n      \"Ġxen\": 27132,\n      \"ĠLinkedIn\": 27133,\n      \"ĠJung\": 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\"Ġflatt\": 27172,\n      \"operative\": 27173,\n      \"Ġcouncils\": 27174,\n      \"imbabwe\": 27175,\n      \"elin\": 27176,\n      \"Ġstressful\": 27177,\n      \"ĠLD\": 27178,\n      \"Ġresh\": 27179,\n      \"lake\": 27180,\n      \"Ġwheelchair\": 27181,\n      \"ĠAlternative\": 27182,\n      \"Ġoptimize\": 27183,\n      \"operation\": 27184,\n      \"Ġpeek\": 27185,\n      \"Ġoneself\": 27186,\n      \"igil\": 27187,\n      \"Ġtransitions\": 27188,\n      \"opathy\": 27189,\n      \"blank\": 27190,\n      \"Ġ169\": 27191,\n      \"171\": 27192,\n      \"________________________________________________________________\": 27193,\n      \"Ġlaundering\": 27194,\n      \"Enc\": 27195,\n      \"ĠDEC\": 27196,\n      \"Ġworkouts\": 27197,\n      \"Ġspikes\": 27198,\n      \"Ġdinosaurs\": 27199,\n      \"Ġdiscriminatory\": 27200,\n      \"Pool\": 27201,\n      \"Rather\": 27202,\n      \"385\": 27203,\n      \"RNA\": 27204,\n      \"testers\": 27205,\n      \"eto\": 27206,\n      \"ĠIdentity\": 27207,\n      \"Ġvein\": 27208,\n      \"ĠBurton\": 27209,\n      \"Ġarcade\": 27210,\n      \"420\": 27211,\n      \"Ultimately\": 27212,\n      \"ĠSadly\": 27213,\n      \"ð\": 27214,\n      \"pill\": 27215,\n      \"Ġcubic\": 27216,\n      \"ĠSpectrum\": 27217,\n      \"these\": 27218,\n      \"states\": 27219,\n      \"Ġunofficial\": 27220,\n      \"hawks\": 27221,\n      \"ĠEVERY\": 27222,\n      \"Ġrainbow\": 27223,\n      \"Ġincarceration\": 27224,\n      \"anding\": 27225,\n      \"Ġsyll\": 27226,\n      \"ĠEverton\": 27227,\n      \"Ġ179\": 27228,\n      \"ĠSerbia\": 27229,\n      \"Ġ189\": 27230,\n      \"meter\": 27231,\n      \"ĠMickey\": 27232,\n      \"Ġantiqu\": 27233,\n      \"Ġfactual\": 27234,\n      \"neck\": 27235,\n      \"ĠNare\": 27236,\n      \"norm\": 27237,\n      \"must\": 27238,\n      \"Ġhighways\": 27239,\n      \"Ġglam\": 27240,\n      \"Ġdividing\": 27241,\n      \"ĠSquadron\": 27242,\n      \"ĠMartha\": 27243,\n      \"Ġbirths\": 27244,\n      \"Cover\": 27245,\n      \"////////////////\": 27246,\n      \"ĠWong\": 27247,\n      \"Phot\": 27248,\n      \"ĠALS\": 27249,\n      \"rio\": 27250,\n      \"ĠNonetheless\": 27251,\n      \"ĠLemon\": 27252,\n      \"Ġ206\": 27253,\n      \"ĠEE\": 27254,\n      \"Ġderivative\": 27255,\n      \"ĠWWII\": 27256,\n      \"vote\": 27257,\n      \"Ġtherein\": 27258,\n      \"Ġseparating\": 27259,\n      \"446\": 27260,\n      \"sync\": 27261,\n      \"ĠStreets\": 27262,\n      \"Ġratt\": 27263,\n      \"Ġmunicipality\": 27264,\n      \"ĠShortly\": 27265,\n      \"Ġmonk\": 27266,\n      \"),\\\"\": 27267,\n      \"Ġscrub\": 27268,\n      \"Ġoperatives\": 27269,\n      \"Neither\": 27270,\n      \"Place\": 27271,\n      \"ĠLimit\": 27272,\n      \"Female\": 27273,\n      \"ĠActor\": 27274,\n      \"Character\": 27275,\n      \"Ġconstituted\": 27276,\n      \"357\": 27277,\n      \"Ġprotested\": 27278,\n      \"ĠStraw\": 27279,\n      \"ĠHeight\": 27280,\n      \"ilda\": 27281,\n      \"ĠTyph\": 27282,\n      \"Ġfloods\": 27283,\n      \"Ġcosmetic\": 27284,\n      \"WAY\": 27285,\n      \"perture\": 27286,\n      \"upon\": 27287,\n      \"tons\": 27288,\n      \"essing\": 27289,\n      \"ĠPocket\": 27290,\n      \"Ġrooft\": 27291,\n      \"ĠCaucas\": 27292,\n      \"Ġantidepress\": 27293,\n      \"Ġincompatible\": 27294,\n      \"ECD\": 27295,\n      \"Ġopera\": 27296,\n      \"ĠContest\": 27297,\n      \"Ġgenerators\": 27298,\n      \"lime\": 27299,\n      \"Defense\": 27300,\n      \"1987\": 27301,\n      \"forum\": 27302,\n      \"Ġsavage\": 27303,\n      \"ĠHungarian\": 27304,\n      \"nz\": 27305,\n      \"Ġmetallic\": 27306,\n      \"Ġexpelled\": 27307,\n      \"Ġresidency\": 27308,\n      \"Ġdresses\": 27309,\n      \"666\": 27310,\n      \"ĠClement\": 27311,\n      \"fires\": 27312,\n      \"Category\": 27313,\n      \"Ġgeek\": 27314,\n      \"alis\": 27315,\n      \"Ġcemetery\": 27316,\n      \"educated\": 27317,\n      \"Ġcrawl\": 27318,\n      \"ĠUnable\": 27319,\n      \"ĠTyson\": 27320,\n      \"akis\": 27321,\n      \"Ġpardon\": 27322,\n      \"ĠWra\": 27323,\n      \"Ġstrengthened\": 27324,\n      \"ĠFors\": 27325,\n      \"335\": 27326,\n      \"ĠHC\": 27327,\n      \"ĠMond\": 27328,\n      \"Ġvisuals\": 27329,\n      \"ĠBeatles\": 27330,\n      \"ettlement\": 27331,\n      \"Ġï\": 27332,\n      \"gro\": 27333,\n      \"Ġbash\": 27334,\n      \"Ġpoorest\": 27335,\n      \"Ġexcel\": 27336,\n      \"Ġaspirations\": 27337,\n      \"ĠMunicip\": 27338,\n      \"ensible\": 27339,\n      \"Ġceremonies\": 27340,\n      \"Ġintimidation\": 27341,\n      \"ĠCONTR\": 27342,\n      \"beck\": 27343,\n      \"ĠKap\": 27344,\n      \"asu\": 27345,\n      \"Ġtrademarks\": 27346,\n      \"ĠSew\": 27347,\n      \"ĠCompetition\": 27348,\n      \"network\": 27349,\n      \"ĠArri\": 27350,\n      \"ĠTet\": 27351,\n      \"Roaming\": 27352,\n      \"WC\": 27353,\n      \"Dat\": 27354,\n      \"Ġsob\": 27355,\n      \"Ġpairing\": 27356,\n      \"Ġoverdose\": 27357,\n      \"SAY\": 27358,\n      \"aber\": 27359,\n      \"Ġrevolt\": 27360,\n      \"ĠFah\": 27361,\n      \"acting\": 27362,\n      \"eq\": 27363,\n      \"estation\": 27364,\n      \"Fight\": 27365,\n      \"ĠMarks\": 27366,\n      \"273\": 27367,\n      \"Ġ178\": 27368,\n      \"Raw\": 27369,\n      \"ãģĭ\": 27370,\n      \"349\": 27371,\n      \"blocks\": 27372,\n      \"Ġverge\": 27373,\n      \"estine\": 27374,\n      \"ĠPodesta\": 27375,\n      \"Ġinvasive\": 27376,\n      \"Ġprofoundly\": 27377,\n      \"ĠAo\": 27378,\n      \"each\": 27379,\n      \"Ġlest\": 27380,\n      \"interpret\": 27381,\n      \"Ġshrinking\": 27382,\n      \"Ġerrone\": 27383,\n      \"Ġchees\": 27384,\n      \"lys\": 27385,\n      \"ĠIvy\": 27386,\n      \"ĠDirectory\": 27387,\n      \"Ġhinted\": 27388,\n      \"VICE\": 27389,\n      \"Ġcontacting\": 27390,\n      \"ĠGent\": 27391,\n      \"hei\": 27392,\n      \"Ġlabeling\": 27393,\n      \"Ġmercury\": 27394,\n      \"ĠLite\": 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\"ĠMakes\": 27433,\n      \"Ġdeported\": 27434,\n      \"Ġcentr\": 27435,\n      \"ĠDug\": 27436,\n      \"ĠBoulder\": 27437,\n      \"ĠBuffy\": 27438,\n      \"Ġinjunction\": 27439,\n      \"ĠHarley\": 27440,\n      \"ĠGroups\": 27441,\n      \"ĠDumbledore\": 27442,\n      \"ĠClara\": 27443,\n      \"Ġ\\\"-\": 27444,\n      \"Ġsacrificed\": 27445,\n      \"eph\": 27446,\n      \"Shadow\": 27447,\n      \"ibling\": 27448,\n      \"Ġfreelance\": 27449,\n      \"Ġevidently\": 27450,\n      \"phal\": 27451,\n      \"Ġretains\": 27452,\n      \"Mir\": 27453,\n      \"Ġfinite\": 27454,\n      \"dar\": 27455,\n      \"ĠCous\": 27456,\n      \"Ġrepaired\": 27457,\n      \"Ġperiodic\": 27458,\n      \"Ġchampionships\": 27459,\n      \"Ġasteroid\": 27460,\n      \"blind\": 27461,\n      \"Ġexpressly\": 27462,\n      \"ĠAstros\": 27463,\n      \"Ġscaled\": 27464,\n      \"Ġgeographical\": 27465,\n      \"ĠRapids\": 27466,\n      \"Enjoy\": 27467,\n      \"Ġelastic\": 27468,\n      \"ĠMohamed\": 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\"Ġexpresses\": 27505,\n      \"Ġaux\": 27506,\n      \"ĠPione\": 27507,\n      \"Ġexhibits\": 27508,\n      \"Debug\": 27509,\n      \"OTAL\": 27510,\n      \"ĠAlready\": 27511,\n      \"ĠWheeler\": 27512,\n      \"Ġexpands\": 27513,\n      \"?:\": 27514,\n      \"Ġreconciliation\": 27515,\n      \"Ġpirates\": 27516,\n      \"Ġpurse\": 27517,\n      \"Ġdiscourage\": 27518,\n      \"Ġspectacle\": 27519,\n      \"Rank\": 27520,\n      \"Ġwraps\": 27521,\n      \"ĠThought\": 27522,\n      \"Ġimpending\": 27523,\n      \"Opp\": 27524,\n      \"ĠAnglo\": 27525,\n      \"ĠEUR\": 27526,\n      \"Ġscrewed\": 27527,\n      \"retched\": 27528,\n      \"Ġencouragement\": 27529,\n      \"models\": 27530,\n      \"Ġconfuse\": 27531,\n      \"mmm\": 27532,\n      \"ĠVitamin\": 27533,\n      \"âĸijâĸij\": 27534,\n      \"Cru\": 27535,\n      \"Ġknights\": 27536,\n      \"Ġdiscard\": 27537,\n      \"Ġbishops\": 27538,\n      \"ĠWear\": 27539,\n      \"ĠGarrett\": 27540,\n      \"kan\": 27541,\n      \"ãĥŁ\": 27542,\n      \"Ġmasculine\": 27543,\n      \"capital\": 27544,\n      \"ĠAus\": 27545,\n      \"Ġfatally\": 27546,\n      \"thanks\": 27547,\n      \"ĠAU\": 27548,\n      \"ĠGut\": 27549,\n      \"1200\": 27550,\n      \"Ġ00000000\": 27551,\n      \"Ġsurrog\": 27552,\n      \"ĠBIOS\": 27553,\n      \"raits\": 27554,\n      \"ĠWatts\": 27555,\n      \"Ġresurrection\": 27556,\n      \"ĠElectoral\": 27557,\n      \"ĠTips\": 27558,\n      \"4000\": 27559,\n      \"Ġnutrient\": 27560,\n      \"Ġdepicting\": 27561,\n      \"Ġsprink\": 27562,\n      \"Ġmuff\": 27563,\n      \"ĠLIM\": 27564,\n      \"ĠSample\": 27565,\n      \"psc\": 27566,\n      \"ibi\": 27567,\n      \"generated\": 27568,\n      \"Ġspecimens\": 27569,\n      \"Ġdissatisf\": 27570,\n      \"Ġtailored\": 27571,\n      \"Ġholdings\": 27572,\n      \"ĠMonthly\": 27573,\n      \"ĠEat\": 27574,\n      \"poons\": 27575,\n      \"Ġnec\": 27576,\n      \"ĠCage\": 27577,\n      \"ĠLotus\": 27578,\n      \"ĠLantern\": 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\"ĠConditions\": 27617,\n      \"Ġpics\": 27618,\n      \"nesium\": 27619,\n      \"ĠRash\": 27620,\n      \"Ġ174\": 27621,\n      \"ĠDucks\": 27622,\n      \"Ġembro\": 27623,\n      \"zu\": 27624,\n      \"onian\": 27625,\n      \"religious\": 27626,\n      \"Ġcraz\": 27627,\n      \"ĠACA\": 27628,\n      \"ĠZucker\": 27629,\n      \"EMA\": 27630,\n      \"ĠPros\": 27631,\n      \"Weapon\": 27632,\n      \"ĠKnox\": 27633,\n      \"ĠArduino\": 27634,\n      \"Ġstove\": 27635,\n      \"Ġheavens\": 27636,\n      \"ĠPurchase\": 27637,\n      \"Ġherd\": 27638,\n      \"Ġfundraiser\": 27639,\n      \"Digital\": 27640,\n      \"5000\": 27641,\n      \"Ġproponents\": 27642,\n      \"/âĢĭ\": 27643,\n      \"Ġjelly\": 27644,\n      \"ĠVisa\": 27645,\n      \"Ġmonks\": 27646,\n      \"Ġadvancement\": 27647,\n      \"ĠWer\": 27648,\n      \"Ġ187\": 27649,\n      \"eus\": 27650,\n      \"ertility\": 27651,\n      \"Ġfetal\": 27652,\n      \"Ġ1936\": 27653,\n      \"Lo\": 27654,\n      \"Ġoutfits\": 27655,\n      \"Ġstaircase\": 27656,\n      \"bomb\": 27657,\n      \"Ġcustomized\": 27658,\n      \"clair\": 27659,\n      \"Tree\": 27660,\n      \"Ġmapped\": 27661,\n      \"ĠConsidering\": 27662,\n      \"ĠTorres\": 27663,\n      \"Ġmethyl\": 27664,\n      \"Ġapproximate\": 27665,\n      \"Ġdoom\": 27666,\n      \"ĠHansen\": 27667,\n      \"Ġcrossover\": 27668,\n      \"Ġstandalone\": 27669,\n      \"ä¼\": 27670,\n      \"Ġinvites\": 27671,\n      \"Ġgraveyard\": 27672,\n      \"Ġhp\": 27673,\n      \"DonaldTrump\": 27674,\n      \"Ġescort\": 27675,\n      \"Gar\": 27676,\n      \"Ġpredecessors\": 27677,\n      \"Ġhay\": 27678,\n      \"Ġenzyme\": 27679,\n      \"ĠStraight\": 27680,\n      \"visors\": 27681,\n      \"Ing\": 27682,\n      \"aneously\": 27683,\n      \"ĠApplied\": 27684,\n      \"Ġfec\": 27685,\n      \"ĠDurant\": 27686,\n      \"Ġoutspoken\": 27687,\n      \"orb\": 27688,\n      \"Ġzeal\": 27689,\n      \"Ġdisgrace\": 27690,\n      \"').\": 27691,\n    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\"Ġcatastrophe\": 27767,\n      \"ppe\": 27768,\n      \"gio\": 27769,\n      \"Ġcommunism\": 27770,\n      \"Ġhalted\": 27771,\n      \"ĠGuant\": 27772,\n      \"clean\": 27773,\n      \"ĠSched\": 27774,\n      \"ĠKanye\": 27775,\n      \"Ġwander\": 27776,\n      \"ĠSeriously\": 27777,\n      \"Ġ188\": 27778,\n      \"ennial\": 27779,\n      \"follow\": 27780,\n      \"productive\": 27781,\n      \"ĠFlow\": 27782,\n      \"ĠSail\": 27783,\n      \"Ġcraw\": 27784,\n      \"Ġsimulations\": 27785,\n      \"oru\": 27786,\n      \"angles\": 27787,\n      \"ĠNolan\": 27788,\n      \"Ġmenstru\": 27789,\n      \"470\": 27790,\n      \"Ġ207\": 27791,\n      \"aja\": 27792,\n      \"Ġcasually\": 27793,\n      \"boarding\": 27794,\n      \"Ġ222\": 27795,\n      \"ovy\": 27796,\n      \"ĠNumbers\": 27797,\n      \"umat\": 27798,\n      \"OE\": 27799,\n      \"287\": 27800,\n      \"ĠClemson\": 27801,\n      \"Ġcerts\": 27802,\n      \"Ġslid\": 27803,\n      \"ĠTribe\": 27804,\n      \"Ġtoast\": 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\"Ġants\": 27842,\n      \"ĠCum\": 27843,\n      \"ipples\": 27844,\n      \"ĠFill\": 27845,\n      \"Ġglanced\": 27846,\n      \"Ġindicted\": 27847,\n      \"ĠEff\": 27848,\n      \"Ġmiscon\": 27849,\n      \"ĠDesktop\": 27850,\n      \"Ġabide\": 27851,\n      \"ãĥĢ\": 27852,\n      \"ĠIo\": 27853,\n      \"ĠCoul\": 27854,\n      \"Ġcapsule\": 27855,\n      \"ĠChrys\": 27856,\n      \"MON\": 27857,\n      \"Ġundes\": 27858,\n      \"ĠIRA\": 27859,\n      \"Ġcitation\": 27860,\n      \"Ġdictate\": 27861,\n      \"ĠNetworks\": 27862,\n      \"ĠConflict\": 27863,\n      \"ĠStuff\": 27864,\n      \"xa\": 27865,\n      \"isec\": 27866,\n      \"ĠChemistry\": 27867,\n      \"Ġquarterly\": 27868,\n      \"Williams\": 27869,\n      \"anan\": 27870,\n      \"Opt\": 27871,\n      \"ĠAlexandria\": 27872,\n      \"outheastern\": 27873,\n      \"ĠSpringfield\": 27874,\n      \"ĠBlacks\": 27875,\n      \"Ġgeography\": 27876,\n      \"242\": 27877,\n      \"Ġutmost\": 27878,\n      \"ĠExxon\": 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\"LAND\": 28182,\n      \"Ġrewrite\": 28183,\n      \"ĠMOV\": 28184,\n      \"ĠImper\": 28185,\n      \"ĠSolution\": 28186,\n      \"Ġphenomenal\": 28187,\n      \"Ġlongevity\": 28188,\n      \"Ġimpat\": 28189,\n      \"ĠNissan\": 28190,\n      \"irie\": 28191,\n      \"Ġodor\": 28192,\n      \"ĠZar\": 28193,\n      \"oks\": 28194,\n      \"Ġmilitias\": 28195,\n      \"ĠSPEC\": 28196,\n      \"Ġtolerated\": 28197,\n      \"arser\": 28198,\n      \"ĠBradford\": 28199,\n      \"+,\": 28200,\n      \"Ġsurreal\": 28201,\n      \"sf\": 28202,\n      \"Canadian\": 28203,\n      \"Ġresemblance\": 28204,\n      \"Ġcarbohydrate\": 28205,\n      \"VIEW\": 28206,\n      \"Ġaccessory\": 28207,\n      \"meal\": 28208,\n      \"largest\": 28209,\n      \"iegel\": 28210,\n      \"Someone\": 28211,\n      \"Ġtoughest\": 28212,\n      \"oso\": 28213,\n      \"Ġfunnel\": 28214,\n      \"Ġcondemnation\": 28215,\n      \"luent\": 28216,\n      \"Ġwired\": 28217,\n      \"ĠSunset\": 28218,\n      \"Jesus\": 28219,\n      \"ĠPST\": 28220,\n      \"ĠPages\": 28221,\n      \"ĠTycoon\": 28222,\n      \"ĠPF\": 28223,\n      \"Ġselections\": 28224,\n      \"Ġà¤\": 28225,\n      \"partisan\": 28226,\n      \"Ġhighs\": 28227,\n      \"ĠRune\": 28228,\n      \"Ġcrafts\": 28229,\n      \"lead\": 28230,\n      \"ĠParents\": 28231,\n      \"Ġreclaim\": 28232,\n      \"eker\": 28233,\n      \"ĠAllied\": 28234,\n      \"aeper\": 28235,\n      \"Ġlooming\": 28236,\n      \"Ġbeneficiaries\": 28237,\n      \"ĠHull\": 28238,\n      \"Students\": 28239,\n      \"Jewish\": 28240,\n      \"dj\": 28241,\n      \"Ġpact\": 28242,\n      \"template\": 28243,\n      \"ĠOfficials\": 28244,\n      \"ĠBaylor\": 28245,\n      \"Ġhemp\": 28246,\n      \"Ġyouths\": 28247,\n      \"ĠLevels\": 28248,\n      \"ĠXiao\": 28249,\n      \"ĠChes\": 28250,\n      \"Ġendeavor\": 28251,\n      \"ĠRemoved\": 28252,\n      \"Ġhippocamp\": 28253,\n      \"Hell\": 28254,\n      \"ãĤĬ\": 28255,\n      \"805\": 28256,\n      \"Ġdinosaur\": 28257,\n      \"ĠWrath\": 28258,\n      \"ĠIndonesian\": 28259,\n      \"Ġcalculator\": 28260,\n      \"ĠDictionary\": 28261,\n      \"Ġ420\": 28262,\n      \"ĠMAG\": 28263,\n      \"(_\": 28264,\n      \"!,\": 28265,\n      \"tarians\": 28266,\n      \"Ġrestricting\": 28267,\n      \"racuse\": 28268,\n      \"Ġweekday\": 28269,\n      \"OUNT\": 28270,\n      \"Ġshrugged\": 28271,\n      \"leground\": 28272,\n      \"Ġbald\": 28273,\n      \"ĠDoctors\": 28274,\n      \"Ġtouted\": 28275,\n      \"ĠMaxwell\": 28276,\n      \"Ġ214\": 28277,\n      \"Ġdiplomat\": 28278,\n      \"Ġrepression\": 28279,\n      \"Ġconstituency\": 28280,\n      \"vice\": 28281,\n      \"ranked\": 28282,\n      \"ĠNapoleon\": 28283,\n      \"gang\": 28284,\n      \"ĠForever\": 28285,\n      \"tun\": 28286,\n      \"Ġbulb\": 28287,\n      \"ĠPDT\": 28288,\n      \"ĠCisco\": 28289,\n      \"VEN\": 28290,\n      \"Ġresumed\": 28291,\n      \"Steven\": 28292,\n      \"ĠManitoba\": 28293,\n      \"Ġfabulous\": 28294,\n      \"ĠAgents\": 28295,\n      \"1984\": 28296,\n      \"Ġamusing\": 28297,\n      \"ĠMysteries\": 28298,\n      \"Ġorthodox\": 28299,\n      \"floor\": 28300,\n      \"Ġquestionnaire\": 28301,\n      \"Ġpenetrate\": 28302,\n      \"Ġfilmmakers\": 28303,\n      \"ĠUnc\": 28304,\n      \"Ġstamped\": 28305,\n      \"Ġthirteen\": 28306,\n      \"Ġoutfield\": 28307,\n      \"Ġforwarded\": 28308,\n      \"Ġappra\": 28309,\n      \"Ġaided\": 28310,\n      \"try\": 28311,\n      \"Ġunfocused\": 28312,\n      \"ĠLiz\": 28313,\n      \"ĠWendy\": 28314,\n      \"ĠScene\": 28315,\n      \"Charg\": 28316,\n      \"Ġrejects\": 28317,\n      \"Ġleftist\": 28318,\n      \"ĠProvidence\": 28319,\n      \"ĠBrid\": 28320,\n      \"regn\": 28321,\n      \"Ġprophecy\": 28322,\n      \"ĠLIVE\": 28323,\n      \"499\": 28324,\n      \"Ġforge\": 28325,\n      \"ĠFML\": 28326,\n      \"Ġintrinsic\": 28327,\n      \"ĠFrog\": 28328,\n      \"Ġwont\": 28329,\n      \"ĠHolt\": 28330,\n      \"Ġfamed\": 28331,\n      \"CLUS\": 28332,\n      \"aepernick\": 28333,\n      \"ĠHate\": 28334,\n      \"ĠCay\": 28335,\n      \"Ġregistering\": 28336,\n      \"ortality\": 28337,\n      \"ropy\": 28338,\n      \"ocalyptic\": 28339,\n      \"aan\": 28340,\n      \"nav\": 28341,\n      \"Ġfascist\": 28342,\n      \"IFIED\": 28343,\n      \"Ġimplicated\": 28344,\n      \"ĠResort\": 28345,\n      \"ĠChandler\": 28346,\n      \"ĠBrick\": 28347,\n      \"Pin\": 28348,\n      \"ysc\": 28349,\n      \"Usage\": 28350,\n      \"ĠHelm\": 28351,\n      \"usra\": 28352,\n      \"âĺħâĺħ\": 28353,\n      \"ĠAbbas\": 28354,\n      \"Ġunanimously\": 28355,\n      \"Ġkeeper\": 28356,\n      \"Ġaddicted\": 28357,\n      \"???\": 28358,\n      \"Ġhelmets\": 28359,\n      \"Ġantioxid\": 28360,\n      \"apsed\": 28361,\n      \"808\": 28362,\n      \"giene\": 28363,\n      \"Ġwaits\": 28364,\n      \"Ġminion\": 28365,\n      \"raved\": 28366,\n      \"ĠPorsche\": 28367,\n      \"Ġdreaming\": 28368,\n      \"Ġ171\": 28369,\n      \"ĠCain\": 28370,\n      \"Ġunfor\": 28371,\n      \"asso\": 28372,\n      \"ĠConfiguration\": 28373,\n      \"kun\": 28374,\n      \"hardt\": 28375,\n      \"Ġnested\": 28376,\n      \"ĠLDS\": 28377,\n      \"LES\": 28378,\n      \"Ġtying\": 28379,\n      \"enos\": 28380,\n      \"Ġcue\": 28381,\n      \"ĠMarqu\": 28382,\n      \"skirts\": 28383,\n      \"Ġclicked\": 28384,\n      \"Ġexpiration\": 28385,\n      \"ĠAccordingly\": 28386,\n      \"ĠWC\": 28387,\n      \"Ġblessings\": 28388,\n      \"Ġaddictive\": 28389,\n      \"ĠNarr\": 28390,\n      \"yx\": 28391,\n      \"ĠJaguars\": 28392,\n      \"Ġrents\": 28393,\n      \"ĠSiber\": 28394,\n      \"Ġtipped\": 28395,\n      \"ousse\": 28396,\n      \"ĠFitzgerald\": 28397,\n      \"Ġhierarch\": 28398,\n      \"outine\": 28399,\n      \"Ġwavelength\": 28400,\n      \">.\": 28401,\n      \"chid\": 28402,\n      \"ĠProcessing\": 28403,\n      \"/+\": 28404,\n      \"ranking\": 28405,\n      \"Easy\": 28406,\n      \"ĠConstruct\": 28407,\n      \"Ġtet\": 28408,\n      \"insured\": 28409,\n      \"HUD\": 28410,\n      \"Ġquoting\": 28411,\n      \"Ġcommunicated\": 28412,\n      \"inx\": 28413,\n      \"Ġinmate\": 28414,\n      \"Ġerected\": 28415,\n      \"ĠAbsolutely\": 28416,\n      \"ĠSurely\": 28417,\n      \"Ġunim\": 28418,\n      \"ĠThrone\": 28419,\n      \"heid\": 28420,\n      \"Ġclaws\": 28421,\n      \"Ġsuperstar\": 28422,\n      \"ĠLenn\": 28423,\n      \"ĠWhis\": 28424,\n      \"Uk\": 28425,\n      \"abol\": 28426,\n      \"Ġsket\": 28427,\n      \"ĠNiet\": 28428,\n      \"Ġperks\": 28429,\n      \"Ġaffinity\": 28430,\n      \"Ġopenings\": 28431,\n      \"phasis\": 28432,\n      \"Ġdiscriminate\": 28433,\n      \"Tip\": 28434,\n      \"vc\": 28435,\n      \"Ġgrinding\": 28436,\n      \"ĠJenny\": 28437,\n      \"Ġasthma\": 28438,\n      \"holes\": 28439,\n      \"ĠHomer\": 28440,\n      \"Ġregisters\": 28441,\n      \"ĠGlad\": 28442,\n      \"Ġcreations\": 28443,\n      \"Ġlithium\": 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\"Ġmushroom\": 28520,\n      \"ĠCHAR\": 28521,\n      \"ĠFee\": 28522,\n      \"ĠMoy\": 28523,\n      \"Born\": 28524,\n      \"oliberal\": 28525,\n      \"ĠMartial\": 28526,\n      \"Ġgentlemen\": 28527,\n      \"Ġlingering\": 28528,\n      \"Official\": 28529,\n      \"Ġgraffiti\": 28530,\n      \"ĠNames\": 28531,\n      \"Der\": 28532,\n      \"Ġquint\": 28533,\n      \"istrate\": 28534,\n      \"azeera\": 28535,\n      \"ĠNOTICE\": 28536,\n      \"ĠFlorence\": 28537,\n      \"Ġpayable\": 28538,\n      \"Ġdepicts\": 28539,\n      \"ĠSpecies\": 28540,\n      \"Heart\": 28541,\n      \"âĶĢâĶĢâĶĢâĶĢâĶĢâĶĢâĶĢâĶĢ\": 28542,\n      \"Ġenclosed\": 28543,\n      \"Increases\": 28544,\n      \"Daily\": 28545,\n      \"ĠLis\": 28546,\n      \"Ġenactment\": 28547,\n      \"ĠBacon\": 28548,\n      \"ĠSteele\": 28549,\n      \"demand\": 28550,\n      \"Ġ183\": 28551,\n      \"Ġmouths\": 28552,\n      \"Ġstranded\": 28553,\n      \"Ġenhancement\": 28554,\n      \"011\": 28555,\n      \"ĠWhats\": 28556,\n      \"Ġhealed\": 28557,\n      \"eny\": 28558,\n      \"ĠRab\": 28559,\n      \"Ġ340\": 28560,\n      \"ĠLabyrinth\": 28561,\n      \"roach\": 28562,\n      \"ĠYosh\": 28563,\n      \"ĠClippers\": 28564,\n      \"Ġconcerts\": 28565,\n      \"Internet\": 28566,\n      \"355\": 28567,\n      \"Ġstickers\": 28568,\n      \"Ġtermed\": 28569,\n      \"ĠAxe\": 28570,\n      \"Ġgrandparents\": 28571,\n      \"France\": 28572,\n      \"ĠClim\": 28573,\n      \"ĠUh\": 28574,\n      \"ulic\": 28575,\n      \"Ġthrill\": 28576,\n      \"centric\": 28577,\n      \"ĠOverview\": 28578,\n      \"ĠConduct\": 28579,\n      \"Ġsubstantive\": 28580,\n      \"Ġ182\": 28581,\n      \"mur\": 28582,\n      \"Ġstray\": 28583,\n      \"ĠCoff\": 28584,\n      \"Ġrepetitive\": 28585,\n      \"ĠForgotten\": 28586,\n      \"Ġqualification\": 28587,\n      \"ewitness\": 28588,\n      \"ĠZimbabwe\": 28589,\n      \"Ġsimulated\": 28590,\n      \"ĠJD\": 28591,\n      \"253\": 28592,\n      \"ĠWare\": 28593,\n      \"Ġunsc\": 28594,\n      \"Times\": 28595,\n      \"Ġsummons\": 28596,\n      \"Ġdisconnected\": 28597,\n      \"Ġ184\": 28598,\n      \"cius\": 28599,\n      \"ĠGujar\": 28600,\n      \"odka\": 28601,\n      \"Ġerase\": 28602,\n      \"ĠTobacco\": 28603,\n      \"elected\": 28604,\n      \"Ġuncont\": 28605,\n      \"ĠShepard\": 28606,\n      \"ĠLamp\": 28607,\n      \"Ġalerted\": 28608,\n      \"Ġoperative\": 28609,\n      \"arna\": 28610,\n      \"uint\": 28611,\n      \"Ġnegligence\": 28612,\n      \"acements\": 28613,\n      \"Ġsupra\": 28614,\n      \"Ġprevail\": 28615,\n      \"ĠShark\": 28616,\n      \"Ġbelts\": 28617,\n      \"ãģ«\": 28618,\n      \"Ġtighter\": 28619,\n      \"Engineers\": 28620,\n      \"Ġinactive\": 28621,\n      \"Ġexponent\": 28622,\n      \"ĠWillie\": 28623,\n      \"aples\": 28624,\n      \"Ġheir\": 28625,\n      \"ĠHits\": 28626,\n      \"iann\": 28627,\n      \"ĠSays\": 28628,\n      \"Ġcurrents\": 28629,\n      \"ĠBengal\": 28630,\n      \"Ġarist\": 28631,\n      \"Buffer\": 28632,\n      \"Ġbreeze\": 28633,\n      \"ĠWesley\": 28634,\n      \"Cola\": 28635,\n      \"Ġpronoun\": 28636,\n      \"Ġdeed\": 28637,\n      \"ĠKling\": 28638,\n      \"Ġoft\": 28639,\n      \"Ġinflict\": 28640,\n      \"Ġpunishing\": 28641,\n      \"Ġnm\": 28642,\n      \"iku\": 28643,\n      \"ODUCT\": 28644,\n      \"014\": 28645,\n      \"Ġsubsidy\": 28646,\n      \"ĠDEA\": 28647,\n      \"ĠHerbert\": 28648,\n      \"ĠJal\": 28649,\n      \"Bank\": 28650,\n      \"Ġdeferred\": 28651,\n      \"Ġshipment\": 28652,\n      \"Bott\": 28653,\n      \"Ġalle\": 28654,\n      \"bearing\": 28655,\n      \"HTML\": 28656,\n      \"Offline\": 28657,\n      \"Ġ213\": 28658,\n      \"Ġscrolling\": 28659,\n      \"Ġscanned\": 28660,\n      \"ĠLibyan\": 28661,\n      \"ĠTOP\": 28662,\n      \"chrom\": 28663,\n      \"dt\": 28664,\n      \"column\": 28665,\n      \"PsyNetMessage\": 28666,\n      \"Zero\": 28667,\n      \"Ġtorso\": 28668,\n      \"050\": 28669,\n      \"âķIJ\": 28670,\n      \"Ġimperson\": 28671,\n      \"ĠSchwartz\": 28672,\n      \"udic\": 28673,\n      \"Ġpissed\": 28674,\n      \"ĠSapp\": 28675,\n      \"257\": 28676,\n      \"ĠISPs\": 28677,\n      \"ogl\": 28678,\n      \"Ġsupervised\": 28679,\n      \"Ġadolescent\": 28680,\n      \"Ġattained\": 28681,\n      \"ĠDelivery\": 28682,\n      \"ĠBunny\": 28683,\n      \"Ġ1937\": 28684,\n      \"Ġminiature\": 28685,\n      \"Ġos\": 28686,\n      \"Ġ370\": 28687,\n      \"608\": 28688,\n      \"ĠMourinho\": 28689,\n      \"Ġinnate\": 28690,\n      \"Ġtempo\": 28691,\n      \"ĠNM\": 28692,\n      \"ĠFallen\": 28693,\n      \"009\": 28694,\n      \"Ġprovocative\": 28695,\n      \"Streamer\": 28696,\n      \"ĠBenedict\": 28697,\n      \"ĠBolshe\": 28698,\n      \"Ġturtle\": 28699,\n      \"ĠPCB\": 28700,\n      \"ĠEqual\": 28701,\n      \"Director\": 28702,\n      \"ĠRend\": 28703,\n      \"Ġfluids\": 28704,\n      \"Authorities\": 28705,\n      \"Ġcousins\": 28706,\n      \"requency\": 28707,\n      \"ĠNeighbor\": 28708,\n      \"sets\": 28709,\n      \"shared\": 28710,\n      \"Charles\": 28711,\n      \"password\": 28712,\n      \"Ġgears\": 28713,\n      \"Ġ211\": 28714,\n      \"ĠHardware\": 28715,\n      \"rika\": 28716,\n      \"Ġupstream\": 28717,\n      \"Hom\": 28718,\n      \"Ġdisproportionately\": 28719,\n      \"ivities\": 28720,\n      \"Ġundefined\": 28721,\n      \"Ġelectrons\": 28722,\n      \"Ġcommemor\": 28723,\n      \"Eventually\": 28724,\n      \"Ġ><\": 28725,\n      \"Ġirresponsible\": 28726,\n      \"218\": 28727,\n      \"ĠReleased\": 28728,\n      \"ĠOVER\": 28729,\n      \"ĠIGN\": 28730,\n      \"ĠBread\": 28731,\n      \"stellar\": 28732,\n      \"ĠSage\": 28733,\n      \"tted\": 28734,\n      \"damage\": 28735,\n      \"edition\": 28736,\n      \"ĠPrec\": 28737,\n      \"Ġlime\": 28738,\n      \"Ġconfinement\": 28739,\n      \"Ġcalorie\": 28740,\n      \"weapon\": 28741,\n      \"Ġdiffering\": 28742,\n      \"ĠSina\": 28743,\n      \"mys\": 28744,\n      \"amd\": 28745,\n      \"Ġintricate\": 28746,\n      \"kk\": 28747,\n      \"ĠPAT\": 28748,\n      \"ão\": 28749,\n      \"stones\": 28750,\n      \"links\": 28751,\n      \"Ġranch\": 28752,\n      \"Semitic\": 28753,\n      \"Ġdifferentiate\": 28754,\n      \"ĠSinger\": 28755,\n      \"occupied\": 28756,\n      \"Ġfortress\": 28757,\n      \"cmd\": 28758,\n      \"Ġinterception\": 28759,\n      \"ĠAnkara\": 28760,\n      \"Ġrept\": 28761,\n      \"ĠSolitaire\": 28762,\n      \"Ġremake\": 28763,\n      \"pred\": 28764,\n      \"Ġdared\": 28765,\n      \"autions\": 28766,\n      \"ĠBACK\": 28767,\n      \"Running\": 28768,\n      \"Ġdebugging\": 28769,\n      \"Ġgraphs\": 28770,\n      \"399\": 28771,\n      \"ĠNigel\": 28772,\n      \"Ġbun\": 28773,\n      \"Ġpillow\": 28774,\n      \"Ġprogressed\": 28775,\n      \"fashioned\": 28776,\n      \"Ġobedience\": 28777,\n      \"ERN\": 28778,\n      \"Ġrehears\": 28779,\n      \"Cell\": 28780,\n      \"tl\": 28781,\n      \"Sher\": 28782,\n      \"Ġherald\": 28783,\n      \"ĠPayment\": 28784,\n      \"ĠCory\": 28785,\n      \"ĠDept\": 28786,\n      \"Ġrepent\": 28787,\n      \"ĠWeak\": 28788,\n      \"uckland\": 28789,\n      \"Ġpleasing\": 28790,\n      \"Ġshortages\": 28791,\n      \"Ġjurors\": 28792,\n      \"ĠKab\": 28793,\n      \"qqa\": 28794,\n      \"Anti\": 28795,\n      \"Ġwow\": 28796,\n      \"ĠRCMP\": 28797,\n      \"Ġtsun\": 28798,\n      \"ĠSic\": 28799,\n      \"Ġcomprises\": 28800,\n      \"Ġspies\": 28801,\n      \"Ġprecinct\": 28802,\n      \"nu\": 28803,\n      \"Ġurges\": 28804,\n      \"Ġtimed\": 28805,\n      \"Ġstripes\": 28806,\n      \"ĠBoots\": 28807,\n      \"Ġyen\": 28808,\n      \"Advanced\": 28809,\n      \"Ġdiscrete\": 28810,\n      \"ĠArchangel\": 28811,\n      \"employment\": 28812,\n      \"Diff\": 28813,\n      \"Ġmonuments\": 28814,\n      \"Ġ209\": 28815,\n      \"worker\": 28816,\n      \"Ġ196\": 28817,\n      \"ĠIg\": 28818,\n      \"utterstock\": 28819,\n      \"TPS\": 28820,\n      \"Jac\": 28821,\n      \"Ġhomelessness\": 28822,\n      \"Ġcommentator\": 28823,\n      \"Ġracially\": 28824,\n      \"fing\": 28825,\n      \"seed\": 28826,\n      \"Ele\": 28827,\n      \"ellation\": 28828,\n      \"Ġethanol\": 28829,\n      \"Ġparish\": 28830,\n      \"ĠDong\": 28831,\n      \"ĠAwakening\": 28832,\n      \"Ġdeviation\": 28833,\n      \"ĠBearing\": 28834,\n      \"ĠTsuk\": 28835,\n      \"Ġrecess\": 28836,\n      \"Ġlymph\": 28837,\n      \"ĠCannabis\": 28838,\n      \"åľ\": 28839,\n      \"ĠNEWS\": 28840,\n      \"Ġdra\": 28841,\n      \"ĠStefan\": 28842,\n      \"ĠWrong\": 28843,\n      \"ĠSAM\": 28844,\n      \"Ġloosely\": 28845,\n      \"Ġinterpreter\": 28846,\n      \"ĠPlain\": 28847,\n      \"Government\": 28848,\n      \"Ġbigotry\": 28849,\n      \"Ġgrenades\": 28850,\n      \"avez\": 28851,\n      \"pictured\": 28852,\n      \"Ġmandated\": 28853,\n      \"ĠMonk\": 28854,\n      \"ĠPedro\": 28855,\n      \"Ġlava\": 28856,\n      \"274\": 28857,\n      \"Ġcynical\": 28858,\n      \"ĠScrolls\": 28859,\n      \"locks\": 28860,\n      \"Mp\": 28861,\n      \"Ġcongregation\": 28862,\n      \"ornings\": 28863,\n      \"phil\": 28864,\n      \"ĠIbid\": 28865,\n      \"Ġferv\": 28866,\n      \"Ġdisappearing\": 28867,\n      \"Ġarrogant\": 28868,\n      \"syn\": 28869,\n      \"ĠMaver\": 28870,\n      \"ĠSuit\": 28871,\n      \"241\": 28872,\n      \"Ġabbre\": 28873,\n      \"ackers\": 28874,\n      \"Pa\": 28875,\n      \"ĠYel\": 28876,\n      \"Whenever\": 28877,\n      \"Ġ235\": 28878,\n      \"ĠVine\": 28879,\n      \"ĠAnat\": 28880,\n      \"Ġextinct\": 28881,\n      \"LET\": 28882,\n      \"Ġexecutable\": 28883,\n      \"VERS\": 28884,\n      \"oxide\": 28885,\n      \"DNA\": 28886,\n      \"ĠPrel\": 28887,\n      \"Ġresentment\": 28888,\n      \"Ġcomprise\": 28889,\n      \"ĠAviv\": 28890,\n      \"Ġinterceptions\": 28891,\n      \"Ġprolific\": 28892,\n      \"INA\": 28893,\n      \"ĠErin\": 28894,\n      \"thought\": 28895,\n      \"219\": 28896,\n      \"ĠPsychiatry\": 28897,\n      \"unky\": 28898,\n      \"chemist\": 28899,\n      \"Ho\": 28900,\n      \"ĠMcCoy\": 28901,\n      \"Ġbricks\": 28902,\n      \"Los\": 28903,\n      \"rily\": 28904,\n      \"ĠUSSR\": 28905,\n      \"Ġrud\": 28906,\n      \"Ġlaud\": 28907,\n      \"ĠWise\": 28908,\n      \"ĠEmerald\": 28909,\n      \"Ġrevived\": 28910,\n      \"Ġdamned\": 28911,\n      \"ĠRepair\": 28912,\n      \"idem\": 28913,\n      \"ctica\": 28914,\n      \"Ġpatriarch\": 28915,\n      \"ĠNurs\": 28916,\n      \"meg\": 28917,\n      \"Ġcheapest\": 28918,\n      \"reements\": 28919,\n      \"empty\": 28920,\n      \"ĠCelebr\": 28921,\n      \"Ġdeprivation\": 28922,\n      \"chanted\": 28923,\n      \"ĠThumbnails\": 28924,\n      \"Energy\": 28925,\n      \"ĠEthan\": 28926,\n      \"ĠQing\": 28927,\n      \"Ġopposes\": 28928,\n      \"WIND\": 28929,\n      \"vik\": 28930,\n      \"ĠMau\": 28931,\n      \"ĠSUB\": 28932,\n      \"667\": 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\"OTA\": 29009,\n      \"ĠHoffman\": 29010,\n      \"Nevertheless\": 29011,\n      \"Ġadorable\": 29012,\n      \"Ġspawned\": 29013,\n      \"Associated\": 29014,\n      \"ĠDomestic\": 29015,\n      \"Ġimplant\": 29016,\n      \"ĠLuxem\": 29017,\n      \"ĠKens\": 29018,\n      \"Ġpumps\": 29019,\n      \"ĠSAT\": 29020,\n      \"Attributes\": 29021,\n      \"509\": 29022,\n      \"avour\": 29023,\n      \"Ġcentralized\": 29024,\n      \"ĠTN\": 29025,\n      \"Ġfreshly\": 29026,\n      \"ĠAchieve\": 29027,\n      \"Ġoutsiders\": 29028,\n      \"herty\": 29029,\n      \"ĠRee\": 29030,\n      \"ĠTowers\": 29031,\n      \"ĠDart\": 29032,\n      \"akable\": 29033,\n      \"Ġmp\": 29034,\n      \"ĠHeavenly\": 29035,\n      \"Ġripe\": 29036,\n      \"ĠCaroline\": 29037,\n      \"ryan\": 29038,\n      \"Ġclassics\": 29039,\n      \"Ġretiring\": 29040,\n      \"Ġ228\": 29041,\n      \"Ġah\": 29042,\n      \"Ġdealings\": 29043,\n      \"Ġpunching\": 29044,\n      \"ĠChapman\": 29045,\n      \"Options\": 29046,\n      \"maxwell\": 29047,\n      \"volume\": 29048,\n      \"Ġstal\": 29049,\n      \"Ġexported\": 29050,\n      \"ĠQuite\": 29051,\n      \"Ġnumerical\": 29052,\n      \"Burn\": 29053,\n      \"Fact\": 29054,\n      \"ĠKeystone\": 29055,\n      \"Ġtrending\": 29056,\n      \"Ġaltering\": 29057,\n      \"ĠAfricans\": 29058,\n      \"478\": 29059,\n      \"ĠMN\": 29060,\n      \"ĠKnock\": 29061,\n      \"Ġtemptation\": 29062,\n      \"Ġprestige\": 29063,\n      \"Overview\": 29064,\n      \"ĠTraditional\": 29065,\n      \"ĠBahrain\": 29066,\n      \"Private\": 29067,\n      \"ĠHOU\": 29068,\n      \"Ġbarr\": 29069,\n      \"ĠTat\": 29070,\n      \"Cube\": 29071,\n      \"USD\": 29072,\n      \"ĠGrande\": 29073,\n      \"ĠGat\": 29074,\n      \"ĠFlo\": 29075,\n      \"Ġresides\": 29076,\n      \"Ġindec\": 29077,\n      \"volent\": 29078,\n      \"Ġperpetual\": 29079,\n      \"ubes\": 29080,\n      \"Ġworldview\": 29081,\n      \"ĠQuantum\": 29082,\n      \"Ġfiltered\": 29083,\n      \"Ġensu\": 29084,\n      \"orgetown\": 29085,\n      \"ERSON\": 29086,\n      \"ĠMild\": 29087,\n      \"379\": 29088,\n      \"OTT\": 29089,\n      \"Ã¥\": 29090,\n      \"Ġvitamins\": 29091,\n      \"Ġribbon\": 29092,\n      \"Ġsincerely\": 29093,\n      \"ĠHin\": 29094,\n      \"Ġeighteen\": 29095,\n      \"Ġcontradictory\": 29096,\n      \"Ġglaring\": 29097,\n      \"Ġexpectancy\": 29098,\n      \"Ġconspir\": 29099,\n      \"Ġmonstrous\": 29100,\n      \"Ġ380\": 29101,\n      \"reci\": 29102,\n      \"Ġhandic\": 29103,\n      \"Ġpumped\": 29104,\n      \"Ġindicative\": 29105,\n      \"Ġrapp\": 29106,\n      \"Ġavail\": 29107,\n      \"ĠLEGO\": 29108,\n      \"ĠMarijuana\": 29109,\n      \"1985\": 29110,\n      \"erton\": 29111,\n      \"Ġtwentieth\": 29112,\n      \"################################\": 29113,\n      \"ĠSwamp\": 29114,\n      \"Ġvaluation\": 29115,\n      \"Ġaffiliates\": 29116,\n      \"adjusted\": 29117,\n      \"ĠFacility\": 29118,\n      \"262\": 29119,\n      \"Ġenzymes\": 29120,\n      \"itudinal\": 29121,\n      \"Ġimprint\": 29122,\n      \"Site\": 29123,\n      \"Ġinstaller\": 29124,\n      \"ĠTRA\": 29125,\n      \"mology\": 29126,\n      \"linear\": 29127,\n      \"ĠCollective\": 29128,\n      \"igating\": 29129,\n      \"ĠToken\": 29130,\n      \"Ġspeculated\": 29131,\n      \"KN\": 29132,\n      \"ĠCly\": 29133,\n      \"ority\": 29134,\n      \"Ġdefer\": 29135,\n      \"Ġinspectors\": 29136,\n      \"approved\": 29137,\n      \"RM\": 29138,\n      \"ĠSuns\": 29139,\n      \"Ġinforming\": 29140,\n      \"ĠSyracuse\": 29141,\n      \"ibli\": 29142,\n      \"765\": 29143,\n      \"Ġglove\": 29144,\n      \"Ġauthorize\": 29145,\n      \"âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦\": 29146,\n      \"ĠCruise\": 29147,\n      \"Ġcontracting\": 29148,\n      \"shell\": 29149,\n      \"IFE\": 29150,\n      \"ĠJewel\": 29151,\n      \"pract\": 29152,\n      \"ĠPhotoshop\": 29153,\n      \"ĠKnowing\": 29154,\n      \"harm\": 29155,\n      \"Ġattractions\": 29156,\n      \"adan\": 29157,\n      \"etus\": 29158,\n      \"018\": 29159,\n      \"wagen\": 29160,\n      \"Alt\": 29161,\n      \"Ġmultiply\": 29162,\n      \"Ġequilibrium\": 29163,\n      \":{\": 29164,\n      \"ĠFighters\": 29165,\n      \"ĠEdgar\": 29166,\n      \"Ġfourteen\": 29167,\n      \"Govern\": 29168,\n      \"Ġmisuse\": 29169,\n      \"Ġabusing\": 29170,\n      \"Ġancestry\": 29171,\n      \"ramer\": 29172,\n      \"644\": 29173,\n      \"Ġworms\": 29174,\n      \"Ġthicker\": 29175,\n      \"ĠCombine\": 29176,\n      \"Ġpeasants\": 29177,\n      \"Ġvind\": 29178,\n      \"Ġconquest\": 29179,\n      \"Ġmocked\": 29180,\n      \"Ġcinnamon\": 29181,\n      \"ĠCald\": 29182,\n      \"ĠGallup\": 29183,\n      \"Ġavoidance\": 29184,\n      \"Ġincarnation\": 29185,\n      \"ĠStrat\": 29186,\n      \"Ġtasted\": 29187,\n      \"enta\": 29188,\n      \"ĠNeal\": 29189,\n      \"pared\": 29190,\n      \"Ġterminology\": 29191,\n      \"jection\": 29192,\n      \"Scientists\": 29193,\n      \"ĠINS\": 29194,\n      \"ĠDee\": 29195,\n      \"Ġdirectories\": 29196,\n      \"Road\": 29197,\n      \"ĠShap\": 29198,\n      \"bright\": 29199,\n      \"ĠDirectors\": 29200,\n      \"ĠColumn\": 29201,\n      \"Ġbob\": 29202,\n      \"Ġpreferably\": 29203,\n      \"Ġglitch\": 29204,\n      \"furt\": 29205,\n      \"Ġeg\": 29206,\n      \"idis\": 29207,\n      \"CBC\": 29208,\n      \"Ġsurrendered\": 29209,\n      \"Ġtestament\": 29210,\n      \"336\": 29211,\n      \"uggest\": 29212,\n      \"ĠNil\": 29213,\n      \"another\": 29214,\n      \"Ġpathetic\": 29215,\n      \"ĠDonna\": 29216,\n      \"Ġ218\": 29217,\n      \"ĠAvery\": 29218,\n      \"Ġwhiskey\": 29219,\n      \"Ġfixture\": 29220,\n      \"ĠConquest\": 29221,\n      \"Ġbets\": 29222,\n      \"Occ\": 29223,\n      \"ĠLeicester\": 29224,\n      \"].\\\"\": 29225,\n      \"Ġ));\": 29226,\n      \"Ġflashes\": 29227,\n      \"456\": 29228,\n      \"Ġmasked\": 29229,\n      \"gebra\": 29230,\n      \"Ġcomputed\": 29231,\n      \"chel\": 29232,\n      \"auder\": 29233,\n      \"Ġdefeats\": 29234,\n      \"ĠLiberation\": 29235,\n      \"ĠOsama\": 29236,\n      \"ĠVive\": 29237,\n      \"Changes\": 29238,\n      \"Channel\": 29239,\n      \"Ġtariffs\": 29240,\n      \"Ġmage\": 29241,\n      \"ĠSax\": 29242,\n      \"Ġinadvertently\": 29243,\n      \"ĠCRE\": 29244,\n      \"ĠReaper\": 29245,\n      \"inky\": 29246,\n      \"grading\": 29247,\n      \"Ġstereotyp\": 29248,\n      \"Ġcurl\": 29249,\n      \"ĠFANT\": 29250,\n      \"Ġframeworks\": 29251,\n      \"Mom\": 29252,\n      \"ĠAnch\": 29253,\n      \"Ġflavour\": 29254,\n      \"carbon\": 29255,\n      \"Ġpermitting\": 29256,\n      \"letcher\": 29257,\n      \"ĠMozilla\": 29258,\n      \"ĠParking\": 29259,\n      \"ĠChamp\": 29260,\n      \"Scroll\": 29261,\n      \"Ġmurderer\": 29262,\n      \"Ġrested\": 29263,\n      \"Ġowes\": 29264,\n      \"ĠPoss\": 29265,\n      \"ADD\": 29266,\n      \"IFF\": 29267,\n      \"resolution\": 29268,\n      \"ĠMining\": 29269,\n      \"Ġcomparative\": 29270,\n      \"Dim\": 29271,\n      \"Ġneighbouring\": 29272,\n      \"ĠAST\": 29273,\n      \"ĠToxic\": 29274,\n      \"Ġbiases\": 29275,\n      \"Ġgunfire\": 29276,\n      \"urous\": 29277,\n      \"ĠMoment\": 29278,\n      \"1983\": 29279,\n      \"Ġpervasive\": 29280,\n      \"ttp\": 29281,\n      \"ĠNormally\": 29282,\n      \"rir\": 29283,\n      \"Sarah\": 29284,\n      \"ĠAlbany\": 29285,\n      \"Ġunsett\": 29286,\n      \"ĠSMS\": 29287,\n      \"ipers\": 29288,\n      \"layer\": 29289,\n      \"ĠWhites\": 29290,\n      \"uple\": 29291,\n      \"Ġturbo\": 29292,\n      \"ĠLeeds\": 29293,\n      \"Ġthats\": 29294,\n      \"ĠMiner\": 29295,\n      \"MER\": 29296,\n      \"ĠReign\": 29297,\n      \"Ġperme\": 29298,\n      \"ĠBlitz\": 29299,\n      \"Ġ1934\": 29300,\n      \"Ġintimidating\": 29301,\n      \"tube\": 29302,\n      \"Ġeccentric\": 29303,\n      \"abolic\": 29304,\n      \"boxes\": 29305,\n      \"ĠAssociates\": 29306,\n      \"votes\": 29307,\n      \"Ġsimulate\": 29308,\n      \"umbo\": 29309,\n      \"astery\": 29310,\n      \"Ġshipments\": 29311,\n      \"FFFF\": 29312,\n      \"anth\": 29313,\n      \"Ġseasoned\": 29314,\n      \"Ġexperimentation\": 29315,\n      \"âĸł\": 29316,\n      \"laws\": 29317,\n      \"Meet\": 29318,\n      \"iddles\": 29319,\n      \"antics\": 29320,\n      \"Rating\": 29321,\n      \"ISIS\": 29322,\n      \"hift\": 29323,\n      \"Ġfronts\": 29324,\n      \"buf\": 29325,\n      \"017\": 29326,\n      \"Ġunatt\": 29327,\n      \"ĠDil\": 29328,\n      \"leases\": 29329,\n      \"ĠGardens\": 29330,\n      \"777\": 29331,\n      \"touch\": 29332,\n      \"vell\": 29333,\n      \"458\": 29334,\n      \"Ġ=====\": 29335,\n      \"saving\": 29336,\n      \"Ġerosion\": 29337,\n      \"ĠQuin\": 29338,\n      \"Ġearns\": 29339,\n      \"Ġaccomplishment\": 29340,\n      \"ĠWei\": 29341,\n      \"Ġ<[\": 29342,\n      \"_____\": 29343,\n      \"Ġirrig\": 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\"Ġstrangely\": 29381,\n      \"Ġadmired\": 29382,\n      \"Ġplight\": 29383,\n      \"Ġbachelor\": 29384,\n      \"ĠPole\": 29385,\n      \"ciplinary\": 29386,\n      \"Tony\": 29387,\n      \"ĠArmenian\": 29388,\n      \"Ġunman\": 29389,\n      \"ĠZionist\": 29390,\n      \"Stage\": 29391,\n      \"iscover\": 29392,\n      \"Ġautomotive\": 29393,\n      \"Ġsidelines\": 29394,\n      \"Ġslick\": 29395,\n      \"ĠRenaissance\": 29396,\n      \"ĠFUN\": 29397,\n      \"Images\": 29398,\n      \"ĠHaj\": 29399,\n      \"Ġping\": 29400,\n      \"Ġshortcut\": 29401,\n      \"ĠBlvd\": 29402,\n      \"ĠLooks\": 29403,\n      \"Ġbursts\": 29404,\n      \"Ġclamp\": 29405,\n      \"Ġmish\": 29406,\n      \"Ġsorting\": 29407,\n      \"Ġpatriot\": 29408,\n      \"Ġcorrectness\": 29409,\n      \"ĠScandinav\": 29410,\n      \"ĠCavaliers\": 29411,\n      \"python\": 29412,\n      \"azar\": 29413,\n      \"Ġ375\": 29414,\n      \"ĠJaune\": 29415,\n      \"409\": 29416,\n      \"Ġdetrimental\": 29417,\n 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\"ĠEffective\": 29455,\n      \"orean\": 29456,\n      \"izons\": 29457,\n      \"Joseph\": 29458,\n      \"Ġescalation\": 29459,\n      \"username\": 29460,\n      \"Ġcurtain\": 29461,\n      \"ATES\": 29462,\n      \"ĠPAR\": 29463,\n      \"ĠMiy\": 29464,\n      \"Ġcounterfe\": 29465,\n      \"lene\": 29466,\n      \"Ġcontenders\": 29467,\n      \"daily\": 29468,\n      \"ĠAsc\": 29469,\n      \"ĠPhillip\": 29470,\n      \"mostly\": 29471,\n      \"Ġfilename\": 29472,\n      \"hene\": 29473,\n      \"Ġresembling\": 29474,\n      \"Ġstaging\": 29475,\n      \"ĠChloe\": 29476,\n      \"Ġwiring\": 29477,\n      \"Hon\": 29478,\n      \"ĠRenew\": 29479,\n      \"ottage\": 29480,\n      \"ĠHybrid\": 29481,\n      \"much\": 29482,\n      \"Ġstrokes\": 29483,\n      \"Ġpolicymakers\": 29484,\n      \"APTER\": 29485,\n      \"ĠArkham\": 29486,\n      \"plot\": 29487,\n      \"Ġassistants\": 29488,\n      \"Ġdeport\": 29489,\n      \"ĠSega\": 29490,\n      \"Ġinfluenza\": 29491,\n      \"ĠCursed\": 29492,\n      \"ĠKobe\": 29493,\n      \"Ġskinny\": 29494,\n      \"Provider\": 29495,\n      \"ĠRip\": 29496,\n      \"Ġincremental\": 29497,\n      \"products\": 29498,\n      \"BF\": 29499,\n      \"Ġdome\": 29500,\n      \"ĠCredits\": 29501,\n      \"Ġlosers\": 29502,\n      \"ints\": 29503,\n      \"ĠBetty\": 29504,\n      \"ĠTalent\": 29505,\n      \"ĠDAM\": 29506,\n      \"Lv\": 29507,\n      \"Ess\": 29508,\n      \"Ġdens\": 29509,\n      \"temp\": 29510,\n      \"Judge\": 29511,\n      \"odic\": 29512,\n      \"Ġ'(\": 29513,\n      \"URES\": 29514,\n      \"etsk\": 29515,\n      \"VO\": 29516,\n      \"Ġretrieved\": 29517,\n      \"Ġarchitects\": 29518,\n      \"Ùĩ\": 29519,\n      \"Ġethic\": 29520,\n      \"ĠSecondary\": 29521,\n      \"stocks\": 29522,\n      \"adia\": 29523,\n      \"Ġ325\": 29524,\n      \"ĠOpinion\": 29525,\n      \"Ġsimultaneous\": 29526,\n      \"Ġdizz\": 29527,\n      \"ulp\": 29528,\n      \"Ġsmuggling\": 29529,\n      \"ippery\": 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29567,\n      \"Ġ$(\": 29568,\n      \"ussed\": 29569,\n      \"Ġpioneer\": 29570,\n      \"Eight\": 29571,\n      \"parse\": 29572,\n      \"bread\": 29573,\n      \"ritz\": 29574,\n      \"ĠMiranda\": 29575,\n      \"ĠKant\": 29576,\n      \"++)\": 29577,\n      \"oren\": 29578,\n      \"Ġprovoked\": 29579,\n      \"Ġbreeds\": 29580,\n      \"ĠIncludes\": 29581,\n      \"ĠPastebin\": 29582,\n      \"ĠFlip\": 29583,\n      \"Java\": 29584,\n      \"Ġbrink\": 29585,\n      \"Ġrumored\": 29586,\n      \"Ġunseen\": 29587,\n      \"Ġgarnered\": 29588,\n      \"ĠDefin\": 29589,\n      \"alted\": 29590,\n      \"Ġtattoos\": 29591,\n      \"Ġhesitation\": 29592,\n      \"isitions\": 29593,\n      \"ĠWeaver\": 29594,\n      \"ĠReporting\": 29595,\n      \"Ġtherapies\": 29596,\n      \"Ġconsultants\": 29597,\n      \"Ġresidual\": 29598,\n      \"ĠMali\": 29599,\n      \"ĠRoma\": 29600,\n      \"iago\": 29601,\n      \"ĠResidents\": 29602,\n      \"ubi\": 29603,\n      \"Ġremedies\": 29604,\n   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\"ĠBalk\": 29643,\n      \"Ġbrethren\": 29644,\n      \"Ġlogos\": 29645,\n      \"Ġgobl\": 29646,\n      \"fps\": 29647,\n      \"Ġsubdivision\": 29648,\n      \"Ġpawn\": 29649,\n      \"Ġsqueezed\": 29650,\n      \"Ġmorale\": 29651,\n      \"ĠDW\": 29652,\n      \"'\\\"\": 29653,\n      \"Ġknot\": 29654,\n      \"ooky\": 29655,\n      \"Ġdivisive\": 29656,\n      \"Ġboosted\": 29657,\n      \"chy\": 29658,\n      \"ãĥIJ\": 29659,\n      \"ifact\": 29660,\n      \"Ġnewcomers\": 29661,\n      \"ĠWrestling\": 29662,\n      \"Ġscouts\": 29663,\n      \"wolves\": 29664,\n      \"Rat\": 29665,\n      \"Ġnineteenth\": 29666,\n      \"ĠOsborne\": 29667,\n      \"Stats\": 29668,\n      \"Ġempowered\": 29669,\n      \"Ġpsychopath\": 29670,\n      \"ĠOEM\": 29671,\n      \"uggage\": 29672,\n      \"ĠPK\": 29673,\n      \"ĠMohammad\": 29674,\n      \"Pak\": 29675,\n      \"Ġanarchists\": 29676,\n      \"ĠExtract\": 29677,\n      \"esthes\": 29678,\n      \"ĠStockholm\": 29679,\n      \"loo\": 29680,\n      \"ĠGraph\": 29681,\n      \"Ġdeploying\": 29682,\n      \"ĠStranger\": 29683,\n      \"ĠMold\": 29684,\n      \"Ġstaffer\": 29685,\n      \"Ġdiscounted\": 29686,\n      \"uckle\": 29687,\n      \"please\": 29688,\n      \"ĠLanding\": 29689,\n      \"ÃŃa\": 29690,\n      \"Ġ193\": 29691,\n      \"Ġante\": 29692,\n      \"Ġrepetition\": 29693,\n      \"Ġ+/-\": 29694,\n      \"Ġparody\": 29695,\n      \"Ġlively\": 29696,\n      \"AAA\": 29697,\n      \"ĠHorus\": 29698,\n      \"Ġpits\": 29699,\n      \"inders\": 29700,\n      \"LOC\": 29701,\n      \"ĠVenice\": 29702,\n      \"406\": 29703,\n      \"ĠDiscover\": 29704,\n      \"âĨ\": 29705,\n      \"ellectual\": 29706,\n      \"Ġpens\": 29707,\n      \"Ġeyel\": 29708,\n      \"iguous\": 29709,\n      \"Impl\": 29710,\n      \"Ġjoking\": 29711,\n      \"Ġinval\": 29712,\n      \"ĠBelfast\": 29713,\n      \"Ġcreditors\": 29714,\n      \"ĠSkywalker\": 29715,\n      \"ovsky\": 29716,\n      \"Ġceasefire\": 29717,\n      \"Ġseals\": 29718,\n      \"isoft\": 29719,\n      \")).\": 29720,\n      \"ĠFelix\": 29721,\n      \"ITS\": 29722,\n      \"Ġtresp\": 29723,\n      \"ĠBlockchain\": 29724,\n      \"eware\": 29725,\n      \"ĠSchwar\": 29726,\n      \"enne\": 29727,\n      \"mounted\": 29728,\n      \"ĠBeacon\": 29729,\n      \"lesh\": 29730,\n      \"Ġimmensely\": 29731,\n      \"Ġcheering\": 29732,\n      \"Employ\": 29733,\n      \"scene\": 29734,\n      \"ishly\": 29735,\n      \"atchewan\": 29736,\n      \"ĠNicolas\": 29737,\n      \"Ġdrained\": 29738,\n      \"ĠExit\": 29739,\n      \"ĠAzerb\": 29740,\n      \"jun\": 29741,\n      \"Ġfloated\": 29742,\n      \"uania\": 29743,\n      \"Deep\": 29744,\n      \"Ġsuperv\": 29745,\n      \"Ġmystical\": 29746,\n      \"ĠDollar\": 29747,\n      \"ĠApostle\": 29748,\n      \"ĠREL\": 29749,\n      \"ĠProvided\": 29750,\n      \"ĠBucks\": 29751,\n      \"ãĥ´\": 29752,\n      \"cutting\": 29753,\n      \"Ġenhancements\": 29754,\n      \"ĠPenguins\": 29755,\n      \"ĠIsaiah\": 29756,\n      \"Ġjerk\": 29757,\n      \"ĠWyn\": 29758,\n      \"Ġstalled\": 29759,\n      \"Ġcryptocurrencies\": 29760,\n      \"ĠRoland\": 29761,\n      \"single\": 29762,\n      \"Ġlumin\": 29763,\n      \"ĠFellow\": 29764,\n      \"ĠCapacity\": 29765,\n      \"ĠKazakh\": 29766,\n      \"WN\": 29767,\n      \"Ġfinanced\": 29768,\n      \"389\": 29769,\n      \"Ġtid\": 29770,\n      \"Ġcollusion\": 29771,\n      \"ĠMyr\": 29772,\n      \"îĢ\": 29773,\n      \"Senator\": 29774,\n      \"Ġpediatric\": 29775,\n      \"Ġneatly\": 29776,\n      \"Ġsandwiches\": 29777,\n      \"ĠArchitecture\": 29778,\n      \"Ġtucked\": 29779,\n      \"Ġbalcony\": 29780,\n      \"Ġearthquakes\": 29781,\n      \"quire\": 29782,\n      \"Future\": 29783,\n      \"Ġhefty\": 29784,\n      \"éĹ\": 29785,\n      \"Ġspecializes\": 29786,\n      \"Ġstresses\": 29787,\n      \"Ġsender\": 29788,\n      \"Ġmisunderstanding\": 29789,\n      \"Ġepile\": 29790,\n      \"Ġprovoke\": 29791,\n      \"ĠColors\": 29792,\n      \"Ġdismay\": 29793,\n      \"uko\": 29794,\n      \"[_\": 29795,\n      \"586\": 29796,\n      \"neutral\": 29797,\n      \"Ġdonating\": 29798,\n      \"ĠRandall\": 29799,\n      \"Multi\": 29800,\n      \"Ġconveniently\": 29801,\n      \"ĠSung\": 29802,\n      \"ĠCoca\": 29803,\n      \"Ġtents\": 29804,\n      \"ĠAcceler\": 29805,\n      \"Ġpartnered\": 29806,\n      \"272\": 29807,\n      \"irming\": 29808,\n      \"ĠBAS\": 29809,\n      \"sometimes\": 29810,\n      \"Ġobjected\": 29811,\n      \"ubric\": 29812,\n      \"posed\": 29813,\n      \"LCS\": 29814,\n      \"grass\": 29815,\n      \"Ġattributable\": 29816,\n      \"VIS\": 29817,\n      \"Israeli\": 29818,\n      \"Ġrepeats\": 29819,\n      \"ĠRM\": 29820,\n      \"vag\": 29821,\n      \"uta\": 29822,\n      \"inous\": 29823,\n      \"Ġinert\": 29824,\n      \"ĠMiguel\": 29825,\n      \"æŃ\": 29826,\n      \"ĠHawaiian\": 29827,\n      \"Board\": 29828,\n      \"Ġartific\": 29829,\n      \"ĠAzerbai\": 29830,\n      \"asio\": 29831,\n      \"ĠRent\": 29832,\n      \"AIN\": 29833,\n      \"Ġappliances\": 29834,\n      \"Ġnationality\": 29835,\n      \"Ġasshole\": 29836,\n      \"ĠNeb\": 29837,\n      \"Ġnotch\": 29838,\n      \"hani\": 29839,\n      \"ĠBride\": 29840,\n      \"Availability\": 29841,\n      \"Ġintercepted\": 29842,\n      \"Ġcontinental\": 29843,\n      \"Ġswelling\": 29844,\n      \"ĠPerspect\": 29845,\n      \"bies\": 29846,\n      \".<\": 29847,\n      \"ithmetic\": 29848,\n      \"ĠLara\": 29849,\n      \"Ġtempting\": 29850,\n      \"addr\": 29851,\n      \"Ġoverseeing\": 29852,\n      \"clad\": 29853,\n      \"ĠDV\": 29854,\n      \"ĠGingrich\": 29855,\n      \"Ġmun\": 29856,\n      \"ĠAppropri\": 29857,\n      \"Ġalterations\": 29858,\n      \"ĠPatreon\": 29859,\n      \"Ġhavoc\": 29860,\n      \"Ġdisciplines\": 29861,\n      \"Ġnotoriously\": 29862,\n      \"akuya\": 29863,\n      \"ieri\": 29864,\n      \"?).\": 29865,\n      \"ĠWent\": 29866,\n      \"Ġsilicon\": 29867,\n      \"Ġtremb\": 29868,\n      \"Container\": 29869,\n      \"Known\": 29870,\n      \"Ġmortar\": 29871,\n      \"este\": 29872,\n      \"icka\": 29873,\n      \"Arthur\": 29874,\n      \"ĠPreviously\": 29875,\n      \"ĠMarty\": 29876,\n      \"Ġsparse\": 29877,\n      \"gins\": 29878,\n      \"Ġinward\": 29879,\n      \"ĠParticipant\": 29880,\n      \"Copy\": 29881,\n      \"ĠMisc\": 29882,\n      \"Ġantibiotic\": 29883,\n      \"ĠRetro\": 29884,\n      \"Ġelusive\": 29885,\n      \"Ġassail\": 29886,\n      \"ĠBattalion\": 29887,\n      \"ĠBought\": 29888,\n      \"Ġdiminish\": 29889,\n      \"ĠEuropa\": 29890,\n      \"session\": 29891,\n      \"ĠDangerous\": 29892,\n      \"iesel\": 29893,\n      \"Ġdisbelief\": 29894,\n      \"Ġblasts\": 29895,\n      \"extreme\": 29896,\n      \"ĠBoyd\": 29897,\n      \"ĠProjects\": 29898,\n      \"ĠGuys\": 29899,\n      \"Ġundergone\": 29900,\n      \"Ġgrill\": 29901,\n      \"ĠDwight\": 29902,\n      \"Ġ197\": 29903,\n      \"USER\": 29904,\n      \"Ġfilesystem\": 29905,\n      \"Ġclocks\": 29906,\n      \"Taylor\": 29907,\n      \"Ġwrapper\": 29908,\n      \"Ġfolding\": 29909,\n      \"ousand\": 29910,\n      \"ĠPhilippine\": 29911,\n      \"ATIONAL\": 29912,\n      \"ĠPerth\": 29913,\n      \"Ġashes\": 29914,\n      \"Ġaccumulate\": 29915,\n      \"ĠGateway\": 29916,\n      \"Shop\": 29917,\n      \"orkshire\": 29918,\n      \"Han\": 29919,\n      \"ĠBarrel\": 29920,\n      \"ĠLeh\": 29921,\n      \"ĠXV\": 29922,\n      \"Ġwhim\": 29923,\n      \"Ġrepo\": 29924,\n      \"ĠCG\": 29925,\n      \"ĠMam\": 29926,\n      \"Ġincorporating\": 29927,\n      \"Ġbailout\": 29928,\n      \"Ġlinguistic\": 29929,\n      \"Ġdisinteg\": 29930,\n      \"CLE\": 29931,\n      \"Ġcinematic\": 29932,\n      \"ĠFiber\": 29933,\n      \"Syn\": 29934,\n      \"ilion\": 29935,\n      \"ĠCompos\": 29936,\n      \"chens\": 29937,\n      \"Ġneoc\": 29938,\n      \"Ġboiled\": 29939,\n      \"FINE\": 29940,\n      \"ono\": 29941,\n      \"uncle\": 29942,\n      \"iken\": 29943,\n      \"ĠBM\": 29944,\n      \"ι\": 29945,\n      \"Ġreceipts\": 29946,\n      \"Ġdisposed\": 29947,\n      \"ĠThirty\": 29948,\n      \"ĠRough\": 29949,\n      \"ĠABS\": 29950,\n      \"Ġnotwithstanding\": 29951,\n      \"ollen\": 29952,\n      \"#$\": 29953,\n      \"Ġunreliable\": 29954,\n      \"Ġbloom\": 29955,\n      \"Ġmediocre\": 29956,\n      \"Ġtram\": 29957,\n      \"ĠTasman\": 29958,\n      \"Ġshakes\": 29959,\n      \"Ġmanifesto\": 29960,\n      \"ĠMW\": 29961,\n      \"Ġsatisfactory\": 29962,\n      \"Ġshores\": 29963,\n      \"Ġcomputation\": 29964,\n      \"Ġassertions\": 29965,\n      \"ormons\": 29966,\n      \"arag\": 29967,\n      \"abit\": 29968,\n      \"Democrats\": 29969,\n      \"ĠLoot\": 29970,\n      \"ĠVolks\": 29971,\n      \"haired\": 29972,\n      \"Ġgravitational\": 29973,\n      \"Sing\": 29974,\n      \"ĠMiz\": 29975,\n      \"Ġthrottle\": 29976,\n      \"Ġtyranny\": 29977,\n      \"ĠViews\": 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30017,\n      \"ĠQuran\": 30018,\n      \"ĠAdapt\": 30019,\n      \"Ġecosystems\": 30020,\n      \"ĠRepeat\": 30021,\n      \"ĠSoy\": 30022,\n      \"ĠIMP\": 30023,\n      \"Ġgraduating\": 30024,\n      \"andem\": 30025,\n      \"Pur\": 30026,\n      \"ĠReset\": 30027,\n      \"ĠTrick\": 30028,\n      \"ĠPhilly\": 30029,\n      \"ĠTue\": 30030,\n      \"ĠMalaysian\": 30031,\n      \"Ġclimax\": 30032,\n      \"Ġbury\": 30033,\n      \"Ġconspic\": 30034,\n      \"ĠSouthampton\": 30035,\n      \"ĠFlowers\": 30036,\n      \"Ġescorted\": 30037,\n      \"ĠEducational\": 30038,\n      \"ĠIRC\": 30039,\n      \"Ġbrutally\": 30040,\n      \"eating\": 30041,\n      \"Ġpillar\": 30042,\n      \"ĠSang\": 30043,\n      \"ĠJude\": 30044,\n      \"arling\": 30045,\n      \"ĠAmnesty\": 30046,\n      \"Ġreminding\": 30047,\n      \"ĠAdministrative\": 30048,\n      \"hesda\": 30049,\n      \"Ġflashed\": 30050,\n      \"ĠPBS\": 30051,\n      \"perate\": 30052,\n      \"feature\": 30053,\n      \"Ġswipe\": 30054,\n      \"Ġgraves\": 30055,\n      \"oultry\": 30056,\n      \"261\": 30057,\n      \"breaks\": 30058,\n      \"ĠGuer\": 30059,\n      \"Ġshrimp\": 30060,\n      \"ĠVoting\": 30061,\n      \"quist\": 30062,\n      \"Ġanalytical\": 30063,\n      \"Ġtablespoons\": 30064,\n      \"ĠSOU\": 30065,\n      \"Ġresearched\": 30066,\n      \"Ġdisrupted\": 30067,\n      \"Ġjour\": 30068,\n      \"Ġreplica\": 30069,\n      \"Ġcartoons\": 30070,\n      \"bians\": 30071,\n      \"})\": 30072,\n      \"copy\": 30073,\n      \"Got\": 30074,\n      \"ouched\": 30075,\n      \"PUT\": 30076,\n      \"Ġswarm\": 30077,\n      \"notations\": 30078,\n      \"said\": 30079,\n      \"Ġrebuilt\": 30080,\n      \"Ġcollaborate\": 30081,\n      \"Ġraging\": 30082,\n      \"Ġnar\": 30083,\n      \"Ġdemographics\": 30084,\n      \"ĠDDR\": 30085,\n      \"Ġdistrust\": 30086,\n      \"ossier\": 30087,\n      \"ĠKro\": 30088,\n      \"Ġpumpkin\": 30089,\n      \"Ġregrets\": 30090,\n      \"Ġfatalities\": 30091,\n      \"ĠLens\": 30092,\n      \"ĠOle\": 30093,\n      \"pd\": 30094,\n      \"Ġpuppet\": 30095,\n      \"ĠOutlook\": 30096,\n      \"ĠStam\": 30097,\n      \"Ol\": 30098,\n      \"Fair\": 30099,\n      \"UU\": 30100,\n      \"Ġrewritten\": 30101,\n      \"ı\": 30102,\n      \"Ġfascinated\": 30103,\n      \"Ġvectors\": 30104,\n      \"Ġtribunal\": 30105,\n      \"uay\": 30106,\n      \"ĠMats\": 30107,\n      \"ĠCoins\": 30108,\n      \"[[\": 30109,\n      \"Ġ181\": 30110,\n      \"Ġrenders\": 30111,\n      \"ĠKaepernick\": 30112,\n      \"Ġespionage\": 30113,\n      \"Ġsumm\": 30114,\n      \"Ġditch\": 30115,\n      \"Account\": 30116,\n      \"Ġspreadsheet\": 30117,\n      \"Ġmutant\": 30118,\n      \"past\": 30119,\n      \"407\": 30120,\n      \"Ġdye\": 30121,\n      \"Ġinitiation\": 30122,\n      \"Ġ4000\": 30123,\n      \"Ġpunishable\": 30124,\n      \"Ġthinner\": 30125,\n      \"ĠKhal\": 30126,\n      \"Ġintermedi\": 30127,\n      \"Dun\": 30128,\n      \"ĠGotham\": 30129,\n      \"Ġeagerly\": 30130,\n      \"Ġvaginal\": 30131,\n      \"powers\": 30132,\n      \"VW\": 30133,\n      \"ĠWATCHED\": 30134,\n      \"Ġpredator\": 30135,\n      \"amsung\": 30136,\n      \"Ġdisparity\": 30137,\n      \"Ġ[*\": 30138,\n      \"Ġamph\": 30139,\n      \"Ġoutskirts\": 30140,\n      \"ĠSpirits\": 30141,\n      \"Ġskeletal\": 30142,\n      \"л\": 30143,\n      \"ĠRear\": 30144,\n      \"Ġissuance\": 30145,\n      \"ĠLogic\": 30146,\n      \"released\": 30147,\n      \"ZZ\": 30148,\n      \"ĠBound\": 30149,\n      \"Entry\": 30150,\n      \"Ġexits\": 30151,\n      \"isol\": 30152,\n      \"ĠFounder\": 30153,\n      \"Ġwre\": 30154,\n      \"ĠGreenland\": 30155,\n      \"ĠMMO\": 30156,\n      \"taker\": 30157,\n      \"INC\": 30158,\n      \"ãģ¾\": 30159,\n      \"Ġhourly\": 30160,\n      \"henko\": 30161,\n      \"Ġfantasies\": 30162,\n      \"Ġdisob\": 30163,\n      \"Ġdemolition\": 30164,\n      \"ãĥĭ\": 30165,\n      \"Ġenlisted\": 30166,\n      \"ratulations\": 30167,\n      \"Ġmisguided\": 30168,\n      \"Ġensured\": 30169,\n      \"Ġdiscouraged\": 30170,\n      \"mort\": 30171,\n      \"Ġflank\": 30172,\n      \"Ġcess\": 30173,\n      \"Ġreacts\": 30174,\n      \"ĠSere\": 30175,\n      \"sensitive\": 30176,\n      \"ĠSerpent\": 30177,\n      \"assad\": 30178,\n      \"Ġ247\": 30179,\n      \"Ġcalmly\": 30180,\n      \"busters\": 30181,\n      \"Ġbleed\": 30182,\n      \"ĠStro\": 30183,\n      \"Ġamusement\": 30184,\n      \"ĠAntarctica\": 30185,\n      \"Ġscept\": 30186,\n      \"ĠGaw\": 30187,\n      \"aq\": 30188,\n      \"asonic\": 30189,\n      \"Ġsprawling\": 30190,\n      \"native\": 30191,\n      \"aturated\": 30192,\n      \"ĠBattlefield\": 30193,\n      \"IVERS\": 30194,\n      \"EB\": 30195,\n      \"ĠGems\": 30196,\n      \"ĠNorthwestern\": 30197,\n      \"ĠFilms\": 30198,\n      \"ĠAutomatic\": 30199,\n      \"Ġapprehend\": 30200,\n      \"ãģ¨\": 30201,\n      \"ĠguiName\": 30202,\n      \"Ġbackend\": 30203,\n      \"Ġevidenced\": 30204,\n      \"geant\": 30205,\n      \"012\": 30206,\n      \"ĠSiege\": 30207,\n      \"ĠexternalTo\": 30208,\n      \"ĠunfocusedRange\": 30209,\n      \"ĠguiActiveUnfocused\": 30210,\n      \"ĠguiIcon\": 30211,\n      \"ĠexternalToEVA\": 30212,\n      \"ĠexternalToEVAOnly\": 30213,\n      \"Fri\": 30214,\n      \"chard\": 30215,\n      \"enaries\": 30216,\n      \"Ġchiefs\": 30217,\n      \"Ġcf\": 30218,\n      \"ĠHUD\": 30219,\n      \"Ġcorrobor\": 30220,\n      \"ĠdB\": 30221,\n      \"ĠTaken\": 30222,\n      \"ĠPatricia\": 30223,\n      \"rail\": 30224,\n      \"ĠCharm\": 30225,\n      \"ĠLibertarian\": 30226,\n      \"rieve\": 30227,\n      \"Personal\": 30228,\n      \"ĠOUR\": 30229,\n      \"geries\": 30230,\n      \"Ġdumping\": 30231,\n      \"Ġneurological\": 30232,\n      \"itimate\": 30233,\n      \"ĠClintons\": 30234,\n      \"rafted\": 30235,\n      \"ĠMolly\": 30236,\n      \"Ġterminals\": 30237,\n      \"register\": 30238,\n      \"Ġflare\": 30239,\n      \"Ġencoded\": 30240,\n      \"Ġautopsy\": 30241,\n      \"pel\": 30242,\n      \"machine\": 30243,\n      \"Ġexemptions\": 30244,\n      \"ĠRoyals\": 30245,\n      \"distance\": 30246,\n      \"Ġdrafts\": 30247,\n      \"Ġlame\": 30248,\n      \"ĠCunning\": 30249,\n      \"Ġspouses\": 30250,\n      \"ĠMarkets\": 30251,\n      \"ĠCarrier\": 30252,\n      \"Ġimplying\": 30253,\n      \"ĠYak\": 30254,\n      \"sid\": 30255,\n      \"Ġloser\": 30256,\n      \"Ġvigilant\": 30257,\n      \"Ġimpeachment\": 30258,\n      \"Ġaugmented\": 30259,\n      \"ĠEmployees\": 30260,\n      \"Ġunintended\": 30261,\n      \"ternally\": 30262,\n      \"ĠWatt\": 30263,\n      \"Ġrecognizable\": 30264,\n      \"essim\": 30265,\n      \"æĿ\": 30266,\n      \"Ġcoated\": 30267,\n      \"rha\": 30268,\n      \"Ġlieutenant\": 30269,\n      \"ĠLegislation\": 30270,\n      \"published\": 30271,\n      \"444\": 30272,\n      \"013\": 30273,\n      \"Ġideally\": 30274,\n      \"ĠPassword\": 30275,\n      \"Ġsimplify\": 30276,\n      \"ĠMeta\": 30277,\n      \"ĠMRI\": 30278,\n      \"Ġpleading\": 30279,\n      \"organized\": 30280,\n      \"handler\": 30281,\n      \"Ġunravel\": 30282,\n      \"correct\": 30283,\n      \"Ġicy\": 30284,\n      \"Ġparanoid\": 30285,\n      \"Ġpasser\": 30286,\n      \"Ġinspections\": 30287,\n      \"ofer\": 30288,\n      \"ĠHealthcare\": 30289,\n      \"283\": 30290,\n      \"ĠBrut\": 30291,\n      \"iola\": 30292,\n      \"forge\": 30293,\n      \"ĠMedieval\": 30294,\n      \"MSN\": 30295,\n      \"ievers\": 30296,\n      \"ĠProgramming\": 30297,\n      \"åī\": 30298,\n      \"Ġ223\": 30299,\n      \"mu\": 30300,\n      \"ĠCLE\": 30301,\n      \"uga\": 30302,\n      \"Ġshoppers\": 30303,\n      \"Ġinformative\": 30304,\n      \"ĠPlans\": 30305,\n      \"Ġsupplementation\": 30306,\n      \"ĠTests\": 30307,\n      \"tyard\": 30308,\n      \"ocytes\": 30309,\n      \"ĠVega\": 30310,\n      \"ĠGujarat\": 30311,\n      \"ermanent\": 30312,\n      \"Except\": 30313,\n      \"ĠLOT\": 30314,\n      \"alla\": 30315,\n      \"ĠCumm\": 30316,\n      \"ĠOsw\": 30317,\n      \"Ġvenom\": 30318,\n      \"ĠDebt\": 30319,\n      \"ĠDOWN\": 30320,\n      \"Ġreunion\": 30321,\n      \"Ġmuc\": 30322,\n      \"ĠRelief\": 30323,\n      \"Ġgeop\": 30324,\n      \"ĠðŁĺ\": 30325,\n      \"alogue\": 30326,\n      \"Anth\": 30327,\n      \"echo\": 30328,\n      \"Ġcorros\": 30329,\n      \"Ġreplication\": 30330,\n      \"ĠBlazing\": 30331,\n      \"ĠDaughter\": 30332,\n      \"Ġinflic\": 30333,\n      \"ĠLindsey\": 30334,\n      \"ÙĪ\": 30335,\n      \"284\": 30336,\n      \"Exit\": 30337,\n      \"Ġgloom\": 30338,\n      \"TAIN\": 30339,\n      \"Ġundermining\": 30340,\n      \"Ġadvising\": 30341,\n      \"hidden\": 30342,\n      \"Ġoverflow\": 30343,\n      \"Ġgor\": 30344,\n      \"urdue\": 30345,\n      \"Ġechoes\": 30346,\n      \"enhagen\": 30347,\n      \"Ġimpuls\": 30348,\n      \"drug\": 30349,\n      \"cash\": 30350,\n      \"Ġasync\": 30351,\n      \"Ġmirac\": 30352,\n      \"atts\": 30353,\n      \"punk\": 30354,\n      \"Ġpivot\": 30355,\n      \"ĠLegislative\": 30356,\n      \"Ġbloggers\": 30357,\n      \"ĠClaw\": 30358,\n      \"sburg\": 30359,\n      \"dyl\": 30360,\n      \"ĠRecommend\": 30361,\n      \"Ġverte\": 30362,\n      \"Ġprohibiting\": 30363,\n      \"ĠPanther\": 30364,\n      \"Jonathan\": 30365,\n      \"Ġomin\": 30366,\n      \"Ġhateful\": 30367,\n      \"281\": 30368,\n      \"ĠOrche\": 30369,\n      \"ĠMurdoch\": 30370,\n      \"downs\": 30371,\n      \"Ġasymm\": 30372,\n      \"GER\": 30373,\n      \"Always\": 30374,\n      \"Ġinforms\": 30375,\n      \"ĠWM\": 30376,\n      \"ĠPony\": 30377,\n      \"ĠAppendix\": 30378,\n      \"ĠArlington\": 30379,\n      \"Jam\": 30380,\n      \"Ġmedicinal\": 30381,\n      \"ĠSlam\": 30382,\n      \"ITIES\": 30383,\n      \"Ġreaff\": 30384,\n      \"ĠRi\": 30385,\n      \"FG\": 30386,\n      \"Spring\": 30387,\n      \"bool\": 30388,\n      \"Ġthighs\": 30389,\n      \"Ġmarkings\": 30390,\n      \"ĠRaqqa\": 30391,\n      \"ĠLak\": 30392,\n      \"poll\": 30393,\n      \"tsky\": 30394,\n      \"ĠMorty\": 30395,\n      \"ĠDefinition\": 30396,\n      \"Ġdebunk\": 30397,\n      \"endered\": 30398,\n      \"ĠLeone\": 30399,\n      \"avers\": 30400,\n      \"Ġmortgages\": 30401,\n      \"Apparently\": 30402,\n      \"Nic\": 30403,\n      \"haus\": 30404,\n      \"ĠThousands\": 30405,\n      \"auld\": 30406,\n      \"Ġmash\": 30407,\n      \"shoot\": 30408,\n      \"Ġdiarr\": 30409,\n      \"Ġconsciously\": 30410,\n      \"Hero\": 30411,\n      \"eas\": 30412,\n      \"ĠNaturally\": 30413,\n      \"ĠDestroyer\": 30414,\n      \"Ġdashboard\": 30415,\n      \"services\": 30416,\n      \"Rog\": 30417,\n      \"Ġmillennials\": 30418,\n      \"Ġinvade\": 30419,\n      \"-(\": 30420,\n      \"Ġcommissions\": 30421,\n      \"ĠAuckland\": 30422,\n      \"Ġbroadcasts\": 30423,\n      \"Ġfrontal\": 30424,\n      \"Ġcrank\": 30425,\n      \"ĠHistoric\": 30426,\n      \"Ġrumours\": 30427,\n      \"CTV\": 30428,\n      \"Ġsteril\": 30429,\n      \"Ġbooster\": 30430,\n      \"rocket\": 30431,\n      \"ãĤ¼\": 30432,\n      \"utsche\": 30433,\n      \"ĠPI\": 30434,\n      \"Ġ233\": 30435,\n      \"ĠProducer\": 30436,\n      \"ĠAnalytics\": 30437,\n      \"Ġinvaluable\": 30438,\n      \"Ġunintention\": 30439,\n      \"ĠCY\": 30440,\n      \"Ġscrutin\": 30441,\n      \"Ġgigg\": 30442,\n      \"Ġengulf\": 30443,\n      \"Ġproletariat\": 30444,\n      \"Ġhacks\": 30445,\n      \"ĠHew\": 30446,\n      \"arak\": 30447,\n      \"ĠSlime\": 30448,\n      \"ielding\": 30449,\n      \"agher\": 30450,\n      \"ĠElliot\": 30451,\n      \"Ġtelecom\": 30452,\n      \"Ġ219\": 30453,\n      \"ultan\": 30454,\n      \"ĠArbor\": 30455,\n      \"ĠScouts\": 30456,\n      \"Ban\": 30457,\n      \"Ġlifespan\": 30458,\n      \"Ġblasp\": 30459,\n      \"388\": 30460,\n      \"Ġjudiciary\": 30461,\n      \"ĠContinental\": 30462,\n      \"asking\": 30463,\n      \"McC\": 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30501,\n      \"ĠNET\": 30502,\n      \"Ġcommercials\": 30503,\n      \"Ġthinkers\": 30504,\n      \"455\": 30505,\n      \"Ġchop\": 30506,\n      \"Broad\": 30507,\n      \"Ġsuspicions\": 30508,\n      \"Ġtagged\": 30509,\n      \"lifting\": 30510,\n      \"Ġstylish\": 30511,\n      \"ĠShields\": 30512,\n      \"Shortly\": 30513,\n      \"Ġtails\": 30514,\n      \"Auth\": 30515,\n      \"STE\": 30516,\n      \"ĠGAME\": 30517,\n      \"Ġseism\": 30518,\n      \"ĠKis\": 30519,\n      \"ologne\": 30520,\n      \"Ġcowork\": 30521,\n      \"Ġforcibly\": 30522,\n      \"Ġthyroid\": 30523,\n      \"ĠPB\": 30524,\n      \"ANE\": 30525,\n      \"married\": 30526,\n      \"horse\": 30527,\n      \"Ġpolymer\": 30528,\n      \"ĠChal\": 30529,\n      \"odor\": 30530,\n      \"DEBUG\": 30531,\n      \"ĠContext\": 30532,\n      \"Ġbliss\": 30533,\n      \"Ġpinpoint\": 30534,\n      \"ĠMathemat\": 30535,\n      \"legram\": 30536,\n      \"ĠWeekend\": 30537,\n      \"Ġlabelled\": 30538,\n      \"Ġbart\": 30539,\n      \"itles\": 30540,\n      \"Ġestrogen\": 30541,\n      \"âĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶ\": 30542,\n      \"\\\"'\": 30543,\n      \"Ġvisibly\": 30544,\n      \"Ġoutsider\": 30545,\n      \"aida\": 30546,\n      \"Area\": 30547,\n      \"Ġdissemin\": 30548,\n      \"Ġdishonest\": 30549,\n      \"ĠClosed\": 30550,\n      \"ĠBulletin\": 30551,\n      \"ĠRamsey\": 30552,\n      \"sword\": 30553,\n      \"ĠXI\": 30554,\n      \"ourced\": 30555,\n      \"Same\": 30556,\n      \"346\": 30557,\n      \"ĠRepe\": 30558,\n      \"ĠKou\": 30559,\n      \"cake\": 30560,\n      \"emis\": 30561,\n      \"Cache\": 30562,\n      \"ĠMeaning\": 30563,\n      \"ĠEnlight\": 30564,\n      \"onomy\": 30565,\n      \"Ġmanifestation\": 30566,\n      \"sworth\": 30567,\n      \"Jay\": 30568,\n      \"Ġchore\": 30569,\n      \"ör\": 30570,\n      \"Dream\": 30571,\n      \"Ġsanctioned\": 30572,\n      \"Ġculturally\": 30573,\n      \"ĠAra\": 30574,\n      \"Nav\": 30575,\n      \"Ġtheological\": 30576,\n      \"Ġstrut\": 30577,\n      \"ĠVO\": 30578,\n      \"ĠHandbook\": 30579,\n      \"Ġconstructing\": 30580,\n      \"Ġ¶\": 30581,\n      \"ĠBenefits\": 30582,\n      \"ĠPsychological\": 30583,\n      \"sac\": 30584,\n      \"å¸\": 30585,\n      \"policy\": 30586,\n      \"ĠMatters\": 30587,\n      \"ĠReported\": 30588,\n      \"ĠByte\": 30589,\n      \"Ġvitro\": 30590,\n      \"ĠMaiden\": 30591,\n      \"Ġlam\": 30592,\n      \"ĠJennings\": 30593,\n      \"Ġgarment\": 30594,\n      \"ĠRutgers\": 30595,\n      \"ĠStafford\": 30596,\n      \"ĠWellington\": 30597,\n      \"Ġintermitt\": 30598,\n      \"Ġnpm\": 30599,\n      \"Ġordeal\": 30600,\n      \"Ġplugged\": 30601,\n      \"ooming\": 30602,\n      \"inished\": 30603,\n      \"framework\": 30604,\n      \"Ġtimber\": 30605,\n      \"Ġcass\": 30606,\n      \"Ġ850\": 30607,\n      \"iless\": 30608,\n      \"ĠRedux\": 30609,\n      \"768\": 30610,\n      \"Stre\": 30611,\n      \"Ġsurpassed\": 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30650,\n      \"Ġdominating\": 30651,\n      \"ĠFlyers\": 30652,\n      \"ĠCompanion\": 30653,\n      \"Ġdisciplined\": 30654,\n      \"ĠYugoslav\": 30655,\n      \"ĠSpells\": 30656,\n      \"Ġvengeance\": 30657,\n      \"Ġlandlords\": 30658,\n      \"Len\": 30659,\n      \"ĠOgre\": 30660,\n      \"anoia\": 30661,\n      \"Ġpiercing\": 30662,\n      \"Ġcongreg\": 30663,\n      \"Ġscorer\": 30664,\n      \"obia\": 30665,\n      \"Ġnickel\": 30666,\n      \"ĠLearns\": 30667,\n      \"Ġrejo\": 30668,\n      \"Ġmasterpiece\": 30669,\n      \"Flash\": 30670,\n      \"Ġinhabited\": 30671,\n      \"ĠOpenGL\": 30672,\n      \"ĠDud\": 30673,\n      \"ĠICO\": 30674,\n      \"Ġarter\": 30675,\n      \"Ġplur\": 30676,\n      \"Ġmastery\": 30677,\n      \"Ġlongstanding\": 30678,\n      \"sted\": 30679,\n      \"Ġwines\": 30680,\n      \"Ġtelevised\": 30681,\n      \"ĠShrine\": 30682,\n      \"ĠBayern\": 30683,\n      \"Ġâĵĺ\": 30684,\n      \"Ġenclosure\": 30685,\n      \"john\": 30686,\n      \"Ġprophets\": 30687,\n      \"ĠResurrection\": 30688,\n      \"ĠOrders\": 30689,\n      \"Ġuneven\": 30690,\n      \"rals\": 30691,\n      \"Ġdwind\": 30692,\n      \"ĠLah\": 30693,\n      \"ĠSloven\": 30694,\n      \"378\": 30695,\n      \"Ġinsistence\": 30696,\n      \"affle\": 30697,\n      \"ĠClone\": 30698,\n      \"Ġhardship\": 30699,\n      \"ĠCongressman\": 30700,\n      \"Ġplead\": 30701,\n      \"Ġreviewers\": 30702,\n      \"Ġcured\": 30703,\n      \"Ġ1935\": 30704,\n      \"asley\": 30705,\n      \"fake\": 30706,\n      \"ĠThinking\": 30707,\n      \"ydia\": 30708,\n      \"PART\": 30709,\n      \"ĠDota\": 30710,\n      \"oit\": 30711,\n      \"Ġwhipped\": 30712,\n      \"Ġbouncing\": 30713,\n      \"ĠHispanics\": 30714,\n      \"comings\": 30715,\n      \"Ġcannabin\": 30716,\n      \"ĠChambers\": 30717,\n      \"ĠZack\": 30718,\n      \"Optional\": 30719,\n      \"Ġcoats\": 30720,\n      \"Ġprowess\": 30721,\n      \"ĠNorton\": 30722,\n      \"Ġplainly\": 30723,\n      \"Ġfreight\": 30724,\n      \"Ġinhibition\": 30725,\n      \"Ġclam\": 30726,\n      \"Ġ303\": 30727,\n      \"kef\": 30728,\n      \"aleigh\": 30729,\n      \"Luke\": 30730,\n      \"Ġpsycho\": 30731,\n      \"atorium\": 30732,\n      \"MED\": 30733,\n      \"Ġtreaties\": 30734,\n      \"Ġindisc\": 30735,\n      \"Ġdc\": 30736,\n      \"OPS\": 30737,\n      \"Ġresilient\": 30738,\n      \"ĠInterstate\": 30739,\n      \"Ġslack\": 30740,\n      \"Ġmundane\": 30741,\n      \"Ġestablishes\": 30742,\n      \"359\": 30743,\n      \"Ġstrained\": 30744,\n      \"Ġnond\": 30745,\n      \"Sus\": 30746,\n      \"Ġcaste\": 30747,\n      \"arate\": 30748,\n      \"ieving\": 30749,\n      \"Ġunfairly\": 30750,\n      \"Ġparser\": 30751,\n      \"onial\": 30752,\n      \"ursive\": 30753,\n      \"Via\": 30754,\n      \"ĠOtto\": 30755,\n      \"ĠAuthorities\": 30756,\n      \"stroke\": 30757,\n      \"KR\": 30758,\n      \"ĠMercy\": 30759,\n      \"Ġfurnished\": 30760,\n      \"Ġoutset\": 30761,\n      \"Ġmetic\": 30762,\n      \"1982\": 30763,\n      \"olithic\": 30764,\n      \"ĠTent\": 30765,\n      \"ogical\": 30766,\n      \"ĠAircraft\": 30767,\n      \"Ġhides\": 30768,\n      \"ĠBecame\": 30769,\n      \"Ġeducators\": 30770,\n      \"reaching\": 30771,\n      \"Ġvolatility\": 30772,\n      \"Ġtoddler\": 30773,\n      \"ĠNASCAR\": 30774,\n      \"ĠTwelve\": 30775,\n      \"ĠHighlights\": 30776,\n      \"Ġgrape\": 30777,\n      \"Ġsplits\": 30778,\n      \"Ġpeasant\": 30779,\n      \"Ġreneg\": 30780,\n      \"ĠMSI\": 30781,\n      \"Temp\": 30782,\n      \"stars\": 30783,\n      \"Ġtrek\": 30784,\n      \"ĠHyde\": 30785,\n      \"binding\": 30786,\n      \"Ġrealism\": 30787,\n      \"Ġoxide\": 30788,\n      \"ĠHos\": 30789,\n      \"Ġmounts\": 30790,\n      \"Ġbiting\": 30791,\n      \"Ġcollapsing\": 30792,\n      \"Ġpostal\": 30793,\n      \"Ġmuseums\": 30794,\n      \"Ġdetached\": 30795,\n      \"Ġrespecting\": 30796,\n      \"Ġmonopol\": 30797,\n      \"Ġworkflow\": 30798,\n      \"ĠCake\": 30799,\n      \"Template\": 30800,\n      \"ĠOrganisation\": 30801,\n      \"Ġpersistence\": 30802,\n      \"369\": 30803,\n      \"Coming\": 30804,\n      \"Brad\": 30805,\n      \"Ġredundant\": 30806,\n      \"ĠGTA\": 30807,\n      \"Ġbending\": 30808,\n      \"Ġrevoked\": 30809,\n      \"Ġoffending\": 30810,\n      \"Ġframing\": 30811,\n      \"Ġprintf\": 30812,\n      \"Commun\": 30813,\n      \"members\": 30814,\n      \"Outside\": 30815,\n      \"Ġconstrued\": 30816,\n      \"Ġcoded\": 30817,\n      \"FORE\": 30818,\n      \"Ġchast\": 30819,\n      \"Chat\": 30820,\n      \"Indian\": 30821,\n      \"ĠYard\": 30822,\n      \"?!\\\"\": 30823,\n      \"ĠPorts\": 30824,\n      \"ĠXavier\": 30825,\n      \"ĠRET\": 30826,\n      \"'.\\\"\": 30827,\n      \"ĠBoat\": 30828,\n      \"ivated\": 30829,\n      \"icht\": 30830,\n      \"umerable\": 30831,\n      \"Ds\": 30832,\n      \"ĠDunn\": 30833,\n      \"Ġcoffin\": 30834,\n      \"Ġsecurely\": 30835,\n      \"ĠRaptors\": 30836,\n      \"ĠBes\": 30837,\n      \"Installation\": 30838,\n      \"Ġinception\": 30839,\n      \"ĠHealthy\": 30840,\n      \"endants\": 30841,\n      \"Ġpsychologists\": 30842,\n      \"ĠSheikh\": 30843,\n      \"cultural\": 30844,\n      \"ĠBlackBerry\": 30845,\n      \"shift\": 30846,\n      \"Fred\": 30847,\n      \"oche\": 30848,\n      \"Ġcakes\": 30849,\n      \"ĠSEO\": 30850,\n      \"ĠGian\": 30851,\n      \"ĠAsians\": 30852,\n      \"ogging\": 30853,\n      \"element\": 30854,\n      \"Ġpundits\": 30855,\n      \"ĠVaugh\": 30856,\n      \"ĠGavin\": 30857,\n      \"Ġhitter\": 30858,\n      \"Ġdrowned\": 30859,\n      \"Ġchalk\": 30860,\n      \"ĠZika\": 30861,\n      \"Ġmeasles\": 30862,\n      \"802\": 30863,\n      \"âĢ¦..\": 30864,\n      \"ĠAWS\": 30865,\n      \"]\\\"\": 30866,\n      \"Ġdistort\": 30867,\n      \"ĠMast\": 30868,\n      \"Ġantibodies\": 30869,\n      \"ĠMash\": 30870,\n      \"Memory\": 30871,\n      \"ĠUganda\": 30872,\n      \"ĠProb\": 30873,\n      \"Ġvomiting\": 30874,\n      \"ĠTurns\": 30875,\n      \"Ġoccupying\": 30876,\n      \"Ġevasion\": 30877,\n      \"ĠTherapy\": 30878,\n      \"Ġpromo\": 30879,\n      \"Ġelectr\": 30880,\n      \"Ġblueprint\": 30881,\n      \"ĠDre\": 30882,\n      \"priced\": 30883,\n      \"ĠDepot\": 30884,\n      \"Ġalleviate\": 30885,\n      \"ĠSomali\": 30886,\n      \"marg\": 30887,\n      \"nine\": 30888,\n      \"Ġnostalgia\": 30889,\n      \"ĠShepherd\": 30890,\n      \"Ġcavalry\": 30891,\n      \"Ġtorped\": 30892,\n      \"ĠBloody\": 30893,\n      \"xb\": 30894,\n      \"Ġsank\": 30895,\n      \"Ġgoalt\": 30896,\n      \"reportprint\": 30897,\n      \"embedreportprint\": 30898,\n      \"cloneembedreportprint\": 30899,\n      \"ĠInitially\": 30900,\n      \"ĠFischer\": 30901,\n      \"Ġnoteworthy\": 30902,\n      \"cern\": 30903,\n      \"Ġinefficient\": 30904,\n      \"rawdownload\": 30905,\n      \"rawdownloadcloneembedreportprint\": 30906,\n      \"cation\": 30907,\n      \"ĠDynasty\": 30908,\n      \"lag\": 30909,\n      \"DES\": 30910,\n      \"Ġdistinctly\": 30911,\n      \"ĠEstonia\": 30912,\n      \"Ġopenness\": 30913,\n      \"Ġgossip\": 30914,\n      \"ruck\": 30915,\n      \"Width\": 30916,\n      \"ĠIbrahim\": 30917,\n      \"Ġpetroleum\": 30918,\n      \"Ġavatar\": 30919,\n      \"ĠHed\": 30920,\n      \"atha\": 30921,\n      \"ĠHogwarts\": 30922,\n      \"Ġcaves\": 30923,\n      \"678\": 30924,\n      \"Ġsafeguard\": 30925,\n      \"ĠMog\": 30926,\n      \"isson\": 30927,\n      \"ĠDurham\": 30928,\n      \"slaught\": 30929,\n      \"ĠGraduate\": 30930,\n      \"Ġsubconscious\": 30931,\n      \"ĠExcellent\": 30932,\n      \"ĠDum\": 30933,\n      \"-----\": 30934,\n      \"Ġpiles\": 30935,\n      \"ĠWORK\": 30936,\n      \"ĠGarn\": 30937,\n      \"ĠFol\": 30938,\n      \"ĠATM\": 30939,\n      \"Ġavoids\": 30940,\n      \"ĠTul\": 30941,\n      \"Ġbleak\": 30942,\n      \"ELY\": 30943,\n      \"ivist\": 30944,\n      \"lightly\": 30945,\n      \"Pers\": 30946,\n      \"ĠDob\": 30947,\n      \"ĠLS\": 30948,\n      \"Ġinsanity\": 30949,\n      \"ε\": 30950,\n      \"atalie\": 30951,\n      \"Enlarge\": 30952,\n      \"Ġtwists\": 30953,\n      \"Ġfaulty\": 30954,\n      \"Ġpiracy\": 30955,\n      \"Ġimpover\": 30956,\n      \"Ġrugged\": 30957,\n      \"ĠFashion\": 30958,\n      \"Ġsands\": 30959,\n      \"'?\": 30960,\n      \"swick\": 30961,\n      \"Ġnatives\": 30962,\n      \"Ġhen\": 30963,\n      \"ĠNoise\": 30964,\n      \"ãĥĹ\": 30965,\n      \"Ġgreens\": 30966,\n      \"Ġfreezer\": 30967,\n      \"Ġdynasty\": 30968,\n      \"ĠFathers\": 30969,\n      \"ĠNewark\": 30970,\n      \"Ġarchaeological\": 30971,\n      \"Ġot\": 30972,\n      \"obar\": 30973,\n      \"Ġblockade\": 30974,\n      \"Ġallerg\": 30975,\n      \"LV\": 30976,\n      \"Ġdebit\": 30977,\n      \"ĠRFC\": 30978,\n      \"ĠMilton\": 30979,\n      \"ĠPressure\": 30980,\n      \"Ġwillingly\": 30981,\n      \"Ġdisproportionate\": 30982,\n      \"Ġoppressive\": 30983,\n      \"Ġdiamonds\": 30984,\n      \"Ġbelongings\": 30985,\n      \"1970\": 30986,\n      \"Ġbells\": 30987,\n      \"Ġimperialism\": 30988,\n      \"Ġ227\": 30989,\n      \"Ġexploding\": 30990,\n      \"ĠEclipse\": 30991,\n      \"Ġ1919\": 30992,\n      \"Ġrant\": 30993,\n      \"Ġnominations\": 30994,\n      \"347\": 30995,\n      \"Ġpeacefully\": 30996,\n      \"rica\": 30997,\n      \"ĠFUCK\": 30998,\n      \"Ġvibration\": 30999,\n      \"malink\": 31000,\n      \"Ġropes\": 31001,\n      \"ĠIvanka\": 31002,\n      \"ĠBrewery\": 31003,\n      \"ĠBooker\": 31004,\n      \"ĠOwens\": 31005,\n      \"goers\": 31006,\n      \"Services\": 31007,\n      \"ĠSnape\": 31008,\n      \"Ġ191\": 31009,\n      \"395\": 31010,\n      \"Ġ299\": 31011,\n      \"justice\": 31012,\n      \"Ġbri\": 31013,\n      \"Ġdiscs\": 31014,\n      \"Ġprominently\": 31015,\n      \"Ġvulgar\": 31016,\n      \"Ġskipping\": 31017,\n      \"lves\": 31018,\n      \"Ġtsunami\": 31019,\n      \"374\": 31020,\n      \"ĠUrug\": 31021,\n      \"ĠEid\": 31022,\n      \"recated\": 31023,\n      \"phen\": 31024,\n      \"Ġfaults\": 31025,\n      \"ĠStarted\": 31026,\n      \"950\": 31027,\n      \"Ġpi\": 31028,\n      \"Ġdetector\": 31029,\n      \"Ġbastard\": 31030,\n      \"Ġvalidated\": 31031,\n      \"SpaceEngineers\": 31032,\n      \"OURCE\": 31033,\n      \"Ġ(~\": 31034,\n      \"Ġunsur\": 31035,\n      \"Ġaffirmed\": 31036,\n      \"Ġfascism\": 31037,\n      \"Ġresolving\": 31038,\n      \"ĠChavez\": 31039,\n      \"ĠCyn\": 31040,\n      \"Ġdetract\": 31041,\n      \"Lost\": 31042,\n      \"Ġrigged\": 31043,\n      \"Ġhomage\": 31044,\n      \"ĠBruno\": 31045,\n      \"555\": 31046,\n      \"eca\": 31047,\n      \"Ġpresses\": 31048,\n      \"Ġhumour\": 31049,\n      \"Ġspacing\": 31050,\n      \"Ġ'/\": 31051,\n      \"olkien\": 31052,\n      \"Coun\": 31053,\n      \"OPER\": 31054,\n      \"Tre\": 31055,\n      \"Son\": 31056,\n      \"ĠCambodia\": 31057,\n      \"ierre\": 31058,\n      \"mong\": 31059,\n      \"ozy\": 31060,\n      \"Ġliquidity\": 31061,\n      \"ĠSoviets\": 31062,\n      \"ĠFernando\": 31063,\n      \"Ġ229\": 31064,\n      \"Ġslug\": 31065,\n      \"ĠCatalan\": 31066,\n      \"electric\": 31067,\n      \"Ġscenery\": 31068,\n      \"ĠHearth\": 31069,\n      \"Ġconstrained\": 31070,\n      \"Ġgoalie\": 31071,\n      \"ĠGuidelines\": 31072,\n      \"ĠAmmo\": 31073,\n      \"ĠPearson\": 31074,\n      \"Ġtaxed\": 31075,\n      \"Ġfetus\": 31076,\n      \"Response\": 31077,\n      \"ĠAlexis\": 31078,\n      \"thia\": 31079,\n      \"Guy\": 31080,\n      \"Ġreconstruct\": 31081,\n      \"Ġextremes\": 31082,\n      \"Ġconcluding\": 31083,\n      \"ĠPeg\": 31084,\n      \"ooks\": 31085,\n      \"Ġdeductions\": 31086,\n      \"Rose\": 31087,\n      \"Ġgroundbreaking\": 31088,\n      \"ĠTarg\": 31089,\n      \"ãĥģ\": 31090,\n      \"ĠReve\": 31091,\n      \"resource\": 31092,\n      \"Ġmoons\": 31093,\n      \"Ġelectromagnetic\": 31094,\n      \"Ġamidst\": 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\"Ġbreweries\": 31247,\n      \"ĠHilton\": 31248,\n      \"undle\": 31249,\n      \"ĠKak\": 31250,\n      \"ĠThankfully\": 31251,\n      \"ĠCanucks\": 31252,\n      \"inctions\": 31253,\n      \"ĠAppears\": 31254,\n      \"Ġcoer\": 31255,\n      \"Ġundermined\": 31256,\n      \"rovers\": 31257,\n      \"Andre\": 31258,\n      \"Ġblaze\": 31259,\n      \"umers\": 31260,\n      \"Ġfamine\": 31261,\n      \"amphetamine\": 31262,\n      \"ulkan\": 31263,\n      \"Amount\": 31264,\n      \"Ġdesperation\": 31265,\n      \"wikipedia\": 31266,\n      \"development\": 31267,\n      \"ĠCorinth\": 31268,\n      \"ussia\": 31269,\n      \"Jackson\": 31270,\n      \"LI\": 31271,\n      \"Native\": 31272,\n      \"Rs\": 31273,\n      \"Ohio\": 31274,\n      \"ĠKathleen\": 31275,\n      \"Fortunately\": 31276,\n      \"Ġattendant\": 31277,\n      \"ĠPreferred\": 31278,\n      \"ĠDidn\": 31279,\n      \"ĠVs\": 31280,\n      \"Mis\": 31281,\n      \"Ġrespondent\": 31282,\n      \"Ġboun\": 31283,\n      \"stable\": 31284,\n      \"Ġpaved\": 31285,\n      \"Ġunexpl\": 31286,\n      \"ĠCheney\": 31287,\n      \"LM\": 31288,\n      \"ĠCull\": 31289,\n      \"blown\": 31290,\n      \"Ġconfronting\": 31291,\n      \"ocese\": 31292,\n      \"serving\": 31293,\n      \"Wi\": 31294,\n      \"ĠLithuania\": 31295,\n      \"anni\": 31296,\n      \"Ġstalk\": 31297,\n      \"hd\": 31298,\n      \"Ġvener\": 31299,\n      \"APH\": 31300,\n      \"ynchronous\": 31301,\n      \"URR\": 31302,\n      \"umably\": 31303,\n      \"historic\": 31304,\n      \"Half\": 31305,\n      \"Hay\": 31306,\n      \"Ġresilience\": 31307,\n      \"spection\": 31308,\n      \"Ġabandoning\": 31309,\n      \"Obs\": 31310,\n      \"ĠDebbie\": 31311,\n      \"Ġgradient\": 31312,\n      \"ĠPlaint\": 31313,\n      \"ĠCanal\": 31314,\n      \"ARCH\": 31315,\n      \"Ġexpansive\": 31316,\n      \"Ġfung\": 31317,\n      \"Ġbounced\": 31318,\n      \"Und\": 31319,\n      \"Ġprecautions\": 31320,\n      \"Ġclarification\": 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\"Ġcatalyst\": 31357,\n      \"berra\": 31358,\n      \"Toronto\": 31359,\n      \"875\": 31360,\n      \"beta\": 31361,\n      \"ĠMacron\": 31362,\n      \"Ġunrealistic\": 31363,\n      \"vector\": 31364,\n      \"ĠVehicles\": 31365,\n      \"itiveness\": 31366,\n      \"ĠRV\": 31367,\n      \"ĠColbert\": 31368,\n      \"sin\": 31369,\n      \"oji\": 31370,\n      \"entin\": 31371,\n      \"ĠKrish\": 31372,\n      \"hello\": 31373,\n      \"ffield\": 31374,\n      \"oky\": 31375,\n      \"ĠTate\": 31376,\n      \"Ġmaple\": 31377,\n      \"Ġaids\": 31378,\n      \"chemical\": 31379,\n      \"334\": 31380,\n      \"nuts\": 31381,\n      \"ĠWarp\": 31382,\n      \"Ġxx\": 31383,\n      \"ĠRobb\": 31384,\n      \"umerous\": 31385,\n      \"_-_\": 31386,\n      \"ftime\": 31387,\n      \"ĠVW\": 31388,\n      \"Ġwinger\": 31389,\n      \"ĠDome\": 31390,\n      \"tools\": 31391,\n      \"ĠPV\": 31392,\n      \"ĠGeorgetown\": 31393,\n      \"Ġgeared\": 31394,\n      \"Ġjihadists\": 31395,\n      \"Ġcp\": 31396,\n      \"Ġsteroids\": 31397,\n      \"Mother\": 31398,\n      \"clerosis\": 31399,\n      \"ĠDRM\": 31400,\n      \"nesia\": 31401,\n      \"Ġlinger\": 31402,\n      \"Ġimmersive\": 31403,\n      \"ĠCOUN\": 31404,\n      \"Ġoutweigh\": 31405,\n      \"ensual\": 31406,\n      \"Band\": 31407,\n      \"Ġtransforms\": 31408,\n      \"matched\": 31409,\n      \"psons\": 31410,\n      \"ĠJudicial\": 31411,\n      \"factor\": 31412,\n      \"Ġreferral\": 31413,\n      \"Ġoddly\": 31414,\n      \"ĠWenger\": 31415,\n      \"Bring\": 31416,\n      \"ĠBows\": 31417,\n      \"602\": 31418,\n      \"ICLE\": 31419,\n      \"Ġlions\": 31420,\n      \"ĠAcademic\": 31421,\n      \"ĠThorn\": 31422,\n      \"ĠRaider\": 31423,\n      \"kefeller\": 31424,\n      \"Storage\": 31425,\n      \"Lower\": 31426,\n      \"ĠOrt\": 31427,\n      \"ĠEquality\": 31428,\n      \"ALT\": 31429,\n      \"ĠSOC\": 31430,\n      \"Types\": 31431,\n      \"Ġlyn\": 31432,\n      \"ĠAsset\": 31433,\n      \"coat\": 31434,\n      \"TPP\": 31435,\n      \"CVE\": 31436,\n      \"ĠPioneer\": 31437,\n      \"application\": 31438,\n      \"Modern\": 31439,\n      \"ĠHK\": 31440,\n      \"Environment\": 31441,\n      \"Alright\": 31442,\n      \"Rain\": 31443,\n      \"IPP\": 31444,\n      \"ĠShiite\": 31445,\n      \"Ġmound\": 31446,\n      \"ĠAbilities\": 31447,\n      \"condition\": 31448,\n      \"Staff\": 31449,\n      \"Ġcompetence\": 31450,\n      \"ĠMoor\": 31451,\n      \"ĠDiablo\": 31452,\n      \"Ġwithheld\": 31453,\n      \"Ġostensibly\": 31454,\n      \"ĠBrom\": 31455,\n      \"Ġmsg\": 31456,\n      \"Ġdenomin\": 31457,\n      \"ĠReferences\": 31458,\n      \"ĠFP\": 31459,\n      \"Ġplunged\": 31460,\n      \"Ġpamph\": 31461,\n      \"moving\": 31462,\n      \"central\": 31463,\n      \"Ġdownright\": 31464,\n      \"Ġfading\": 31465,\n      \"Tal\": 31466,\n      \"Typ\": 31467,\n      \"ĠThy\": 31468,\n      \"ukes\": 31469,\n      \"ithe\": 31470,\n      \"Ġove\": 31471,\n      \"Ġbattled\": 31472,\n      \"Ġseafood\": 31473,\n      \"Ġfigur\": 31474,\n      \"ĠRD\": 31475,\n      \"crop\": 31476,\n      \"Ġsquads\": 31477,\n      \"{\\\\\": 31478,\n      \"à¹\": 31479,\n      \"ĠEh\": 31480,\n      \"Ġinterviewing\": 31481,\n      \"ĠQin\": 31482,\n      \"Ġaspiring\": 31483,\n      \"PLIC\": 31484,\n      \"Ġclauses\": 31485,\n      \"ĠGast\": 31486,\n      \"ĠNir\": 31487,\n      \"Ġluggage\": 31488,\n      \"Ġhose\": 31489,\n      \"Ġsystemd\": 31490,\n      \"Ġdescending\": 31491,\n      \"ĠRevised\": 31492,\n      \"ĠRails\": 31493,\n      \"align\": 31494,\n      \"709\": 31495,\n      \"337\": 31496,\n      \"Ġfug\": 31497,\n      \"charging\": 31498,\n      \"tags\": 31499,\n      \"Ġuter\": 31500,\n      \"kish\": 31501,\n      \"WARNING\": 31502,\n      \"490\": 31503,\n      \"profits\": 31504,\n      \"Ġvoyage\": 31505,\n      \"Ġace\": 31506,\n      \"ĠVanguard\": 31507,\n      \"ĠTanks\": 31508,\n      \"ĠMuk\": 31509,\n      \"Ġ226\": 31510,\n 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    \"Ġdummy\": 31548,\n      \"ĠDiagn\": 31549,\n      \"ĠGuitar\": 31550,\n      \"imeters\": 31551,\n      \"403\": 31552,\n      \"REG\": 31553,\n      \"Ax\": 31554,\n      \"Ġseparates\": 31555,\n      \"ĠMahm\": 31556,\n      \"Ġtv\": 31557,\n      \"jah\": 31558,\n      \"OOL\": 31559,\n      \"Circ\": 31560,\n      \"ĠWindsor\": 31561,\n      \"ussian\": 31562,\n      \"Ġintuition\": 31563,\n      \"Ġdisdain\": 31564,\n      \"ĠDonovan\": 31565,\n      \"Ġ221\": 31566,\n      \"Emb\": 31567,\n      \"Ġcondemning\": 31568,\n      \"Ġgenerosity\": 31569,\n      \"zzy\": 31570,\n      \"Ġpanties\": 31571,\n      \"ĠPrevent\": 31572,\n      \"ActionCode\": 31573,\n      \"ANA\": 31574,\n      \"342\": 31575,\n      \"externalActionCode\": 31576,\n      \"Ġspecifying\": 31577,\n      \"Ġcrystall\": 31578,\n      \"Jere\": 31579,\n      \"Ġrupt\": 31580,\n      \"ĠApprentice\": 31581,\n      \"Ġprofiling\": 31582,\n      \"к\": 31583,\n      \"Strike\": 31584,\n      \"Ġsideline\": 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\"Ġinsurgents\": 31622,\n      \"ĠFirm\": 31623,\n      \"Ġisot\": 31624,\n      \"ĠKarn\": 31625,\n      \"ourning\": 31626,\n      \"akens\": 31627,\n      \"docs\": 31628,\n      \"little\": 31629,\n      \"ĠMonaco\": 31630,\n      \"CLASS\": 31631,\n      \"Turkey\": 31632,\n      \"Ly\": 31633,\n      \"ĠConan\": 31634,\n      \"assic\": 31635,\n      \"Ġstarred\": 31636,\n      \"ĠPacers\": 31637,\n      \"eties\": 31638,\n      \"Ġtipping\": 31639,\n      \"Moon\": 31640,\n      \"ĠRw\": 31641,\n      \"same\": 31642,\n      \"Ġcavity\": 31643,\n      \"Ġgoof\": 31644,\n      \"ĠZo\": 31645,\n      \"Shock\": 31646,\n      \"ummer\": 31647,\n      \"Ġemphasizes\": 31648,\n      \"Ġregrett\": 31649,\n      \"Ġnovelty\": 31650,\n      \"Ġenvy\": 31651,\n      \"ĠPassive\": 31652,\n      \"rw\": 31653,\n      \"505\": 31654,\n      \"Ġindifferent\": 31655,\n      \"ĠRica\": 31656,\n      \"ĠHimself\": 31657,\n      \"ĠFreddie\": 31658,\n      \"Ġadip\": 31659,\n      \"ä¸Ģ\": 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\"ĠShim\": 31698,\n      \"fuck\": 31699,\n      \"ĠBie\": 31700,\n      \"Ġairing\": 31701,\n      \"ĠProtein\": 31702,\n      \"ĠHolding\": 31703,\n      \"Ġspectators\": 31704,\n      \"iliated\": 31705,\n      \"ĠThatcher\": 31706,\n      \"nosis\": 31707,\n      \"ãĥ¼ãĥ³\": 31708,\n      \"Tele\": 31709,\n      \"Boston\": 31710,\n      \"ĠTempl\": 31711,\n      \"stay\": 31712,\n      \"Ġdeclarations\": 31713,\n      \"479\": 31714,\n      \"Volume\": 31715,\n      \"ĠDesigner\": 31716,\n      \"ĠOverwatch\": 31717,\n      \"idae\": 31718,\n      \"Ġonwards\": 31719,\n      \"Ġnets\": 31720,\n      \"ĠManila\": 31721,\n      \"particularly\": 31722,\n      \"Ġpolitic\": 31723,\n      \"oother\": 31724,\n      \"Ġportraits\": 31725,\n      \"Ġpavement\": 31726,\n      \"cffff\": 31727,\n      \"Ġsaints\": 31728,\n      \"Ġbeginners\": 31729,\n      \"ESPN\": 31730,\n      \"Ġshortcomings\": 31731,\n      \"âķIJâķIJ\": 31732,\n      \"Ġcomet\": 31733,\n      \"ĠOrganic\": 31734,\n      \"quel\": 31735,\n      \"Ġhospitalized\": 31736,\n      \"Break\": 31737,\n      \"Ġpeel\": 31738,\n      \"dylib\": 31739,\n      \"aspx\": 31740,\n      \"urances\": 31741,\n      \"ĠTIM\": 31742,\n      \"Pg\": 31743,\n      \"Ġreadable\": 31744,\n      \"ĠMalik\": 31745,\n      \"Ġmuzzle\": 31746,\n      \"Ġbenchmarks\": 31747,\n      \"dal\": 31748,\n      \"ĠVacc\": 31749,\n      \"ĠHicks\": 31750,\n      \"609\": 31751,\n      \"ĠBiblical\": 31752,\n      \"heng\": 31753,\n      \"Ġoverload\": 31754,\n      \"ĠCivilization\": 31755,\n      \"Ġimmoral\": 31756,\n      \"Ġfries\": 31757,\n      \"ãĤĴ\": 31758,\n      \"Ġreproduced\": 31759,\n      \"Ġformulation\": 31760,\n      \"jug\": 31761,\n      \"irez\": 31762,\n      \"gear\": 31763,\n      \"Ġcoached\": 31764,\n      \"MpServer\": 31765,\n      \"ĠSJ\": 31766,\n      \"ĠKw\": 31767,\n      \"Init\": 31768,\n      \"deal\": 31769,\n      \"ĠOro\": 31770,\n      \"ĠLoki\": 31771,\n      \"ĠSongs\": 31772,\n      \"Ġ232\": 31773,\n      \"ĠLouise\": 31774,\n      \"asionally\": 31775,\n      \"Ġuncond\": 31776,\n      \"ollywood\": 31777,\n      \"Ġprogressives\": 31778,\n      \"ĠEnough\": 31779,\n      \"ĠDoe\": 31780,\n      \"Ġwreckage\": 31781,\n      \"Ġbrushed\": 31782,\n      \"ĠBaseType\": 31783,\n      \"Ġzoning\": 31784,\n      \"ishable\": 31785,\n      \"hetically\": 31786,\n      \"ĠCaucus\": 31787,\n      \"ĠHue\": 31788,\n      \"Ġkarma\": 31789,\n      \"ĠSporting\": 31790,\n      \"Ġtrader\": 31791,\n      \"Ġseeming\": 31792,\n      \"ĠCapture\": 31793,\n      \"430\": 31794,\n      \"bish\": 31795,\n      \"Ġtunes\": 31796,\n      \"Ġindoors\": 31797,\n      \"ĠSphere\": 31798,\n      \"ĠDancing\": 31799,\n      \"TERN\": 31800,\n      \"Ġnob\": 31801,\n      \"ĠGST\": 31802,\n      \"maps\": 31803,\n      \"Ġpeppers\": 31804,\n      \"Fit\": 31805,\n      \"Ġoversees\": 31806,\n      \"ĠRabbi\": 31807,\n      \"ĠRuler\": 31808,\n      \"vertising\": 31809,\n      \"office\": 31810,\n      \"xxx\": 31811,\n      \"Ġraft\": 31812,\n      \"Changed\": 31813,\n      \"Ġtextbooks\": 31814,\n      \"Links\": 31815,\n      \"ĠOmn\": 31816,\n      \"ãĢij\": 31817,\n      \"Ġinconvenience\": 31818,\n      \"ĠDonetsk\": 31819,\n      \"=~\": 31820,\n      \"Ġimplicitly\": 31821,\n      \"Ġboosts\": 31822,\n      \"ĠBones\": 31823,\n      \"ĠBoom\": 31824,\n      \"Courtesy\": 31825,\n      \"Ġsensational\": 31826,\n      \"ANY\": 31827,\n      \"Ġgreedy\": 31828,\n      \"eden\": 31829,\n      \"Ġinexper\": 31830,\n      \"ĠLer\": 31831,\n      \"ĠVale\": 31832,\n      \"Ġtighten\": 31833,\n      \"ĠEAR\": 31834,\n      \"ĠNum\": 31835,\n      \"Ġancestor\": 31836,\n      \"Sent\": 31837,\n      \"ĠHorde\": 31838,\n      \"urgical\": 31839,\n      \"allah\": 31840,\n      \"Ġsap\": 31841,\n      \"amba\": 31842,\n      \"ĠSpread\": 31843,\n      \"twitch\": 31844,\n      \"Ġgrandson\": 31845,\n      \"Ġfracture\": 31846,\n      \"Ġmoderator\": 31847,\n      \"ĠSeventh\": 31848,\n      \"ĠReverse\": 31849,\n      \"Ġestimation\": 31850,\n      \"Choose\": 31851,\n      \"Ġparach\": 31852,\n      \"Ġbarric\": 31853,\n      \"ãĢIJ\": 31854,\n      \"Ġcompass\": 31855,\n      \"Ġallergic\": 31856,\n      \"âĢķ\": 31857,\n      \"OTHER\": 31858,\n      \"errilla\": 31859,\n      \"Ġwagon\": 31860,\n      \"Ġzinc\": 31861,\n      \"Ġrubbed\": 31862,\n      \"ĠFuller\": 31863,\n      \"ĠLuxembourg\": 31864,\n      \"ĠHoover\": 31865,\n      \"Ġliar\": 31866,\n      \"ĠEvening\": 31867,\n      \"ĠCobb\": 31868,\n      \"esteem\": 31869,\n      \"Ġselector\": 31870,\n      \"ĠBrawl\": 31871,\n      \"isance\": 31872,\n      \"ĠEk\": 31873,\n      \"Ġtroop\": 31874,\n      \"Ġguts\": 31875,\n      \"ĠAppeal\": 31876,\n      \"ĠTibetan\": 31877,\n      \"Ġroutines\": 31878,\n      \"ĠMent\": 31879,\n      \"Ġsummarized\": 31880,\n      \"steamapps\": 31881,\n      \"Ġtranqu\": 31882,\n      \"Ġ1929\": 31883,\n      \"oran\": 31884,\n      \"ĠAuthent\": 31885,\n      \"Ġgmaxwell\": 31886,\n      \"Ġapprehens\": 31887,\n      \"Ġpoems\": 31888,\n      \"Ġsausage\": 31889,\n      \"ĠWebster\": 31890,\n      \"urus\": 31891,\n      \"Ġthemed\": 31892,\n      \"Ġlounge\": 31893,\n      \"Ġcharger\": 31894,\n      \"Spoiler\": 31895,\n      \"Ġspilled\": 31896,\n      \"hog\": 31897,\n      \"ĠSunder\": 31898,\n      \"ĠAin\": 31899,\n      \"ĠAngry\": 31900,\n      \"Ġdisqual\": 31901,\n      \"ĠFrequency\": 31902,\n      \"ĠEthernet\": 31903,\n      \"Ġhelper\": 31904,\n      \"Percent\": 31905,\n      \"Ġhorrifying\": 31906,\n      \"Ġail\": 31907,\n      \"ĠAllan\": 31908,\n      \"EEE\": 31909,\n      \"ĠCrossing\": 31910,\n      \"449\": 31911,\n      \"Ġholog\": 31912,\n      \"ĠPuzzles\": 31913,\n      \"ĠGoes\": 31914,\n      \"erenn\": 31915,\n      \"604\": 31916,\n      \"ãģı\": 31917,\n      \"ĠRafael\": 31918,\n      \"Ġatten\": 31919,\n      \"ĠEmanuel\": 31920,\n      \"Ġupro\": 31921,\n      \"ĠSusp\": 31922,\n      \"Psych\": 31923,\n      \"ĠTrainer\": 31924,\n      \"ĠNES\": 31925,\n      \"ĠHunts\": 31926,\n      \"becue\": 31927,\n      \"Ġcounselor\": 31928,\n      \"Rule\": 31929,\n      \"Ġtoxins\": 31930,\n      \"Ġbanners\": 31931,\n      \"rifice\": 31932,\n      \"Ġgreeting\": 31933,\n      \"Ġfrenzy\": 31934,\n      \"Ġallocate\": 31935,\n      \"Ġ*)\": 31936,\n      \"expr\": 31937,\n      \"503\": 31938,\n      \"ĠChick\": 31939,\n      \"ĠTorn\": 31940,\n      \"Ġconsolidation\": 31941,\n      \"ĠFletcher\": 31942,\n      \"switch\": 31943,\n      \"frac\": 31944,\n      \"clips\": 31945,\n      \"ĠMcKin\": 31946,\n      \"ĠLunar\": 31947,\n      \"Month\": 31948,\n      \"ITCH\": 31949,\n      \"Ġscholarly\": 31950,\n      \"raped\": 31951,\n      \"398\": 31952,\n      \"Ġ1910\": 31953,\n      \"Ġegreg\": 31954,\n      \"Ġinsecure\": 31955,\n      \"Ġvictorious\": 31956,\n      \"cffffcc\": 31957,\n      \"Ġsingled\": 31958,\n      \"Ġelves\": 31959,\n      \"ĠWond\": 31960,\n      \"burst\": 31961,\n      \"Ġcamoufl\": 31962,\n      \"ĠBLACK\": 31963,\n      \"Ġconditioned\": 31964,\n      \"çī\": 31965,\n      \"answered\": 31966,\n      \"Ġcompulsory\": 31967,\n      \"ascist\": 31968,\n      \"Ġpodcasts\": 31969,\n      \"ĠFrankfurt\": 31970,\n      \"bnb\": 31971,\n      \"Ġneoliberal\": 31972,\n      \"ĠKeyboard\": 31973,\n      \"ĠBelle\": 31974,\n      \"warm\": 31975,\n      \"Ġtrusts\": 31976,\n      \"Ġinsured\": 31977,\n      \"ĠBucc\": 31978,\n      \"usable\": 31979,\n      \"607\": 31980,\n      \"ĠPlains\": 31981,\n      \"Ġ1890\": 31982,\n      \"Ġsabotage\": 31983,\n      \"Ġlodged\": 31984,\n      \"felt\": 31985,\n      \"Ġga\": 31986,\n      \"ĠNarc\": 31987,\n      \"ĠSalem\": 31988,\n      \"Ġseventy\": 31989,\n      \"ĠBlank\": 31990,\n      \"pocket\": 31991,\n      \"Ġwhisper\": 31992,\n      \"Ġmating\": 31993,\n      \"omics\": 31994,\n      \"ĠSalman\": 31995,\n      \"ĠKad\": 31996,\n      \"Ġangered\": 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     \"Creating\": 32071,\n      \"Ġpersecut\": 32072,\n      \"Ġsweetness\": 32073,\n      \"arte\": 32074,\n      \"Ġforfeiture\": 32075,\n      \"ĠRoberto\": 32076,\n      \"impro\": 32077,\n      \"NFL\": 32078,\n      \"ĠMagnet\": 32079,\n      \"Detailed\": 32080,\n      \"Ġinsignificant\": 32081,\n      \"ĠPOLIT\": 32082,\n      \"ĠBBQ\": 32083,\n      \"ĠCPS\": 32084,\n      \"Ġseaw\": 32085,\n      \"aminer\": 32086,\n      \"mL\": 32087,\n      \"endif\": 32088,\n      \"finals\": 32089,\n      \"Ġ265\": 32090,\n      \"uish\": 32091,\n      \"Ġ})\": 32092,\n      \"ĠProblems\": 32093,\n      \"Ġemblem\": 32094,\n      \"Ġseriousness\": 32095,\n      \"Ġparsing\": 32096,\n      \"Ġsubstitution\": 32097,\n      \"Ġpressured\": 32098,\n      \"Ġrecycled\": 32099,\n      \"aleb\": 32100,\n      \"Ruby\": 32101,\n      \"Ġproficiency\": 32102,\n      \"Driver\": 32103,\n      \"ĠWester\": 32104,\n      \":'\": 32105,\n      \"AFTA\": 32106,\n      \"Ġmantle\": 32107,\n      \"ĠClayton\": 32108,\n      \"flag\": 32109,\n      \"Ġpractitioner\": 32110,\n      \"covered\": 32111,\n      \"ĠStruct\": 32112,\n      \"addafi\": 32113,\n      \"425\": 32114,\n      \"ĠTownship\": 32115,\n      \"ĠHydro\": 32116,\n      \"Louis\": 32117,\n      \"343\": 32118,\n      \"Ġcondo\": 32119,\n      \"ĠTao\": 32120,\n      \"Ġutilization\": 32121,\n      \"Ġnausea\": 32122,\n      \"ĠDems\": 32123,\n      \"ridges\": 32124,\n      \"pause\": 32125,\n      \"Ġformulas\": 32126,\n      \"Ġchallenger\": 32127,\n      \"376\": 32128,\n      \"Ġdefective\": 32129,\n      \"ĠRailway\": 32130,\n      \"ĠPubMed\": 32131,\n      \"Ġyogurt\": 32132,\n      \"lbs\": 32133,\n      \"ĠNorfolk\": 32134,\n      \"OPE\": 32135,\n      \"ĠMoody\": 32136,\n      \"Ġdistributor\": 32137,\n      \"Ġscrolls\": 32138,\n      \"Ġextracts\": 32139,\n      \"Stan\": 32140,\n      \"Ġviability\": 32141,\n      \"Ġexposes\": 32142,\n      \"Ġstarvation\": 32143,\n      \"ĠSteps\": 32144,\n      \"ĠDodd\": 32145,\n      \"few\": 32146,\n      \"STD\": 32147,\n      \"332\": 32148,\n      \"Ġclosures\": 32149,\n      \"Ġcomplementary\": 32150,\n      \"ĠSasha\": 32151,\n      \"umpy\": 32152,\n      \"Ġmonet\": 32153,\n      \"Ġarticulate\": 32154,\n      \"ĠDoct\": 32155,\n      \"killer\": 32156,\n      \"Ġscrim\": 32157,\n      \"Ġ264\": 32158,\n      \"Ġprostitutes\": 32159,\n      \"Ġsevered\": 32160,\n      \"Ġattachments\": 32161,\n      \"Ġcooled\": 32162,\n      \"Lev\": 32163,\n      \"ĠFalk\": 32164,\n      \"fail\": 32165,\n      \"Ġpoliceman\": 32166,\n      \"ĠDag\": 32167,\n      \"Ġprayed\": 32168,\n      \"ĠKernel\": 32169,\n      \"Ġclut\": 32170,\n      \"Ġcath\": 32171,\n      \"Ġanomaly\": 32172,\n      \"Storm\": 32173,\n      \"emaker\": 32174,\n      \"ĠBreakfast\": 32175,\n      \"uli\": 32176,\n      \"oire\": 32177,\n      \"JJ\": 32178,\n      \"hz\": 32179,\n      \"Operation\": 32180,\n      \"ĠSick\": 32181,\n      \"354\": 32182,\n      \"ĠGuatemala\": 32183,\n      \"Rate\": 32184,\n      \"Ġexposures\": 32185,\n      \"faces\": 32186,\n      \"ĠArchae\": 32187,\n      \"raf\": 32188,\n      \"ĠMia\": 32189,\n      \"Ġ2025\": 32190,\n      \"Ġopaque\": 32191,\n      \"Ġdisguised\": 32192,\n      \"ĠHeadquarters\": 32193,\n      \"Sah\": 32194,\n      \"Ġpots\": 32195,\n      \"978\": 32196,\n      \"ĠMalf\": 32197,\n      \"Ġfrowned\": 32198,\n      \"Ġpoisonous\": 32199,\n      \"ĠConvers\": 32200,\n      \"eeks\": 32201,\n      \"Ġcrab\": 32202,\n      \".\\\"\\\"\": 32203,\n      \"Ġtreason\": 32204,\n      \"Ġranc\": 32205,\n      \"Ġescalating\": 32206,\n      \"Ġwarr\": 32207,\n      \"Ġmobs\": 32208,\n      \"Ġlamps\": 32209,\n      \"ĠSunshine\": 32210,\n      \"ĠBrunswick\": 32211,\n      \"Phones\": 32212,\n      \"Ġspelled\": 32213,\n      \"ĠSkip\": 32214,\n      \"Ġ2050\": 32215,\n      \"Ġ1911\": 32216,\n      \"ĠPluto\": 32217,\n      \"ĠAmend\": 32218,\n      \"Ġmeats\": 32219,\n      \"387\": 32220,\n      \"Ġstomp\": 32221,\n      \"ĠZhou\": 32222,\n      \"ĠLeviathan\": 32223,\n      \"ĠHazard\": 32224,\n      \"adv\": 32225,\n      \"ĠOrwell\": 32226,\n      \"Ġaloud\": 32227,\n      \"Ġbumper\": 32228,\n      \"ĠAnarch\": 32229,\n      \"ubuntu\": 32230,\n      \"ĠSerious\": 32231,\n      \"fitting\": 32232,\n      \"ĠOptional\": 32233,\n      \"ĠCecil\": 32234,\n      \"REAM\": 32235,\n      \"Ġserotonin\": 32236,\n      \"Ġcultivate\": 32237,\n      \"agogue\": 32238,\n      \"}\\\\\": 32239,\n      \"Ġmosques\": 32240,\n      \"ĠSunny\": 32241,\n      \"Ġreactive\": 32242,\n      \"revolution\": 32243,\n      \"ĠLup\": 32244,\n      \"ĠFedora\": 32245,\n      \"Ġdefenseman\": 32246,\n      \"ĠVID\": 32247,\n      \"istine\": 32248,\n      \"Ġdrowning\": 32249,\n      \"ĠBroadcasting\": 32250,\n      \"Ġthriller\": 32251,\n      \"ĠScy\": 32252,\n      \"Ġaccelerating\": 32253,\n      \"Ġdirects\": 32254,\n      \"odied\": 32255,\n      \"bike\": 32256,\n      \"duration\": 32257,\n      \"Ġpainfully\": 32258,\n      \"Redd\": 32259,\n      \"Ġproductions\": 32260,\n      \"Ġgag\": 32261,\n      \"Ġwhist\": 32262,\n      \"Ġsock\": 32263,\n      \"Ġinfinitely\": 32264,\n      \"ĠConcern\": 32265,\n      \"ĠCitadel\": 32266,\n      \"Ġlieu\": 32267,\n      \"Ġcandles\": 32268,\n      \"ogeneous\": 32269,\n      \"arger\": 32270,\n      \"Ġheavenly\": 32271,\n      \"inflammatory\": 32272,\n      \"Performance\": 32273,\n      \"Cs\": 32274,\n      \"ructose\": 32275,\n      \"azaki\": 32276,\n      \"Ġpessim\": 32277,\n      \"Ġinference\": 32278,\n      \"Ġpowd\": 32279,\n      \"ĠZoe\": 32280,\n      \"Ġpaints\": 32281,\n      \"Ġdazz\": 32282,\n      \"pta\": 32283,\n      \"-----------\": 32284,\n      \"Ġinspir\": 32285,\n      \"ĠExperimental\": 32286,\n      \"ĠKnife\": 32287,\n      \"regor\": 32288,\n      \"bors\": 32289,\n      \"Ġshowers\": 32290,\n      \"romeda\": 32291,\n      \"Ġsaint\": 32292,\n      \"Ġbenign\": 32293,\n      \"ĠJiang\": 32294,\n      \"Ġenvisioned\": 32295,\n      \"Ġshroud\": 32296,\n      \"IFT\": 32297,\n      \"HO\": 32298,\n      \"Ġshuff\": 32299,\n      \"ĠICC\": 32300,\n      \"Ġsegreg\": 32301,\n      \"Ġrevisit\": 32302,\n      \"ighthouse\": 32303,\n      \"Li\": 32304,\n      \"Ġsubstrate\": 32305,\n      \"ĠSeas\": 32306,\n      \"ĠReward\": 32307,\n      \"ĠHep\": 32308,\n      \"ĠBrass\": 32309,\n      \"sbm\": 32310,\n      \"Ġeliminates\": 32311,\n      \"Ġstamina\": 32312,\n      \"ĠVAT\": 32313,\n      \"ĠLoan\": 32314,\n      \"Ġconstraint\": 32315,\n      \"Ġappropriated\": 32316,\n      \"Ġpes\": 32317,\n      \"ĠALE\": 32318,\n      \"ranging\": 32319,\n      \"Ġ404\": 32320,\n      \"392\": 32321,\n      \"Ġintellectuals\": 32322,\n      \"achu\": 32323,\n      \"Ġrestructuring\": 32324,\n      \"ĠLevin\": 32325,\n      \"Ġrunes\": 32326,\n      \"Ġdelightful\": 32327,\n      \"Ġcarbohydrates\": 32328,\n      \"ĠModels\": 32329,\n      \"ĠExpo\": 32330,\n      \"Ġtransporting\": 32331,\n      \"alloc\": 32332,\n      \"Ġringing\": 32333,\n      \"Samsung\": 32334,\n      \"Ġscarcely\": 32335,\n      \"ĠURLs\": 32336,\n      \"ĠMAS\": 32337,\n      \"Ġprototypes\": 32338,\n      \"Ġnarrator\": 32339,\n      \"ĠCPUs\": 32340,\n      \"cdn\": 32341,\n      \"ĠBarton\": 32342,\n      \"Ġdecidedly\": 32343,\n      \"ĠShu\": 32344,\n      \"ixir\": 32345,\n      \"ocious\": 32346,\n      \"ĠMyst\": 32347,\n      \"Nintendo\": 32348,\n      \"Ġreuse\": 32349,\n      \"Ġforgiven\": 32350,\n      \"Few\": 32351,\n      \"inical\": 32352,\n      \"nat\": 32353,\n      \"Ġseamless\": 32354,\n      \"ĠEva\": 32355,\n      \"ĠEVE\": 32356,\n      \"ĠJO\": 32357,\n      \"landers\": 32358,\n      \"Ġsofter\": 32359,\n      \"negie\": 32360,\n      \"Ġtransient\": 32361,\n      \"Ġorbital\": 32362,\n      \"Ġfulfil\": 32363,\n      \"ĠKom\": 32364,\n      \"Hopefully\": 32365,\n      \"Ġdynamically\": 32366,\n      \"ĠHunger\": 32367,\n      \"åĽ\": 32368,\n      \"ĠArmenia\": 32369,\n      \"elman\": 32370,\n      \"berto\": 32371,\n      \"Ġpige\": 32372,\n      \"ĠIDs\": 32373,\n      \"limit\": 32374,\n      \"Ġveins\": 32375,\n      \"Ġsoaring\": 32376,\n      \"packs\": 32377,\n      \"Golden\": 32378,\n      \"ĠCrab\": 32379,\n      \"istor\": 32380,\n      \"ĠRPM\": 32381,\n      \"Ġ$$\": 32382,\n      \"gression\": 32383,\n      \"Ġjihadist\": 32384,\n      \"Ġgamble\": 32385,\n      \"Ġcareg\": 32386,\n      \"Ġinflated\": 32387,\n      \"Face\": 32388,\n      \"ĠFirearms\": 32389,\n      \"ĠEmmanuel\": 32390,\n      \"âĿ\": 32391,\n      \"Ġshocks\": 32392,\n      \"grab\": 32393,\n      \"Ġsplend\": 32394,\n      \"ĠHPV\": 32395,\n      \"abortion\": 32396,\n      \"Above\": 32397,\n      \"Entity\": 32398,\n      \"players\": 32399,\n      \"Ġcommenced\": 32400,\n      \"ulence\": 32401,\n      \"Ġfulfillment\": 32402,\n      \"Ġembodiments\": 32403,\n      \"ĠWelfare\": 32404,\n      \"Ġhail\": 32405,\n      \"Ġ<@\": 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     \"issors\": 32555,\n      \"Ġimbalance\": 32556,\n      \"Ġspanning\": 32557,\n      \"Ġangular\": 32558,\n      \"ĠCul\": 32559,\n      \"communication\": 32560,\n      \"ĠNora\": 32561,\n      \"ĠGenius\": 32562,\n      \"opter\": 32563,\n      \"Ġsacked\": 32564,\n      \"Spot\": 32565,\n      \"Ġfinely\": 32566,\n      \"ĠCHR\": 32567,\n      \"282\": 32568,\n      \"waves\": 32569,\n      \"Palest\": 32570,\n      \"ĠRohing\": 32571,\n      \"NL\": 32572,\n      \"è¿\": 32573,\n      \"Ġshitty\": 32574,\n      \"ĠScalia\": 32575,\n      \"475\": 32576,\n      \"Progress\": 32577,\n      \"Ġreferencing\": 32578,\n      \"Ġclassrooms\": 32579,\n      \"abee\": 32580,\n      \"Ġsod\": 32581,\n      \"hesion\": 32582,\n      \"708\": 32583,\n      \"ĠZuckerberg\": 32584,\n      \"ĠFinish\": 32585,\n      \"ĠScotia\": 32586,\n      \"ĠSavior\": 32587,\n      \"ĠInstallation\": 32588,\n      \"antha\": 32589,\n      \"(-\": 32590,\n      \"Ġ302\": 32591,\n      \"ĠPunk\": 32592,\n  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\"Ġivory\": 32630,\n      \"ĠCourts\": 32631,\n      \"OUGH\": 32632,\n      \"auntlet\": 32633,\n      \"Serial\": 32634,\n      \"Brow\": 32635,\n      \"ĠHundreds\": 32636,\n      \"323\": 32637,\n      \"Ġpee\": 32638,\n      \"Ġlinux\": 32639,\n      \"Ġsubmer\": 32640,\n      \"ĠPrincipal\": 32641,\n      \"485\": 32642,\n      \"ĠDSL\": 32643,\n      \"ĠCousins\": 32644,\n      \"Ġdoctrines\": 32645,\n      \"ĠAthletics\": 32646,\n      \"Ġ315\": 32647,\n      \"ĠKarma\": 32648,\n      \"Ġattent\": 32649,\n      \"urger\": 32650,\n      \"Ġprescribe\": 32651,\n      \"Ġencaps\": 32652,\n      \"ĠCame\": 32653,\n      \"Ġsecretive\": 32654,\n      \"ĠCrimes\": 32655,\n      \"dn\": 32656,\n      \"Clean\": 32657,\n      \"ĠEgyptians\": 32658,\n      \"ĠCarpenter\": 32659,\n      \"Ġll\": 32660,\n      \"Hum\": 32661,\n      \"ĠMilo\": 32662,\n      \"Ġcapitalists\": 32663,\n      \"Ġbriefed\": 32664,\n      \"Twe\": 32665,\n      \"ĠBasin\": 32666,\n      \"elvet\": 32667,\n      \"Mos\": 32668,\n      \"Ġplunge\": 32669,\n      \"ĠKaiser\": 32670,\n      \"ĠFuj\": 32671,\n      \"illin\": 32672,\n      \"Ġsafeguards\": 32673,\n      \"Ġoste\": 32674,\n      \"ĠOpportunity\": 32675,\n      \"ĠMafia\": 32676,\n      \"ĠCalling\": 32677,\n      \"apa\": 32678,\n      \"urban\": 32679,\n      \"brush\": 32680,\n      \"illard\": 32681,\n      \"cé\": 32682,\n      \"intelligence\": 32683,\n      \"ĠLob\": 32684,\n      \"ĠDruid\": 32685,\n      \"Ġsmoother\": 32686,\n      \"Ġfooting\": 32687,\n      \"Ġmotorists\": 32688,\n      \"arcity\": 32689,\n      \"Ġmasculinity\": 32690,\n      \"Ġmism\": 32691,\n      \"Ġabdominal\": 32692,\n      \"ĠTavern\": 32693,\n      \"ĠRoh\": 32694,\n      \"Ġescapes\": 32695,\n      \"signed\": 32696,\n      \"Anthony\": 32697,\n      \"Ġsacrificing\": 32698,\n      \"Ġintimacy\": 32699,\n      \"Ġanterior\": 32700,\n      \"ĠKod\": 32701,\n      \"Ġmotif\": 32702,\n      \"Ġgraz\": 32703,\n      \"Ġvisualization\": 32704,\n     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\"Texture\": 32742,\n      \"Ġattends\": 32743,\n      \"ĠREAL\": 32744,\n      \"uddled\": 32745,\n      \"Ġsmoot\": 32746,\n      \"Ġ305\": 32747,\n      \"ĠWillis\": 32748,\n      \"Ġblond\": 32749,\n      \"Analysis\": 32750,\n      \"ĠVT\": 32751,\n      \"onica\": 32752,\n      \"Ġstronghold\": 32753,\n      \"RF\": 32754,\n      \"NM\": 32755,\n      \".>>\": 32756,\n      \"Ġprosperous\": 32757,\n      \"Ġboasted\": 32758,\n      \"292\": 32759,\n      \"ĠManufacturing\": 32760,\n      \"PRESS\": 32761,\n      \"gren\": 32762,\n      \"Ġpharmacy\": 32763,\n      \"ĠRockefeller\": 32764,\n      \"kai\": 32765,\n      \"Ġthumbs\": 32766,\n      \"ĠHut\": 32767,\n      \"Ġmotherboard\": 32768,\n      \"Ġguardians\": 32769,\n      \"ĠAlter\": 32770,\n      \"llular\": 32771,\n      \"Ġshack\": 32772,\n      \"Ġwisely\": 32773,\n      \"Ġbackbone\": 32774,\n      \"erva\": 32775,\n      \"Ġsuicides\": 32776,\n      \"ĠMcGregor\": 32777,\n      \"ijah\": 32778,\n      \"Emer\": 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\"ĠYue\": 32854,\n      \"utations\": 32855,\n      \"ĠYoga\": 32856,\n      \"Ġsimmer\": 32857,\n      \"Ġrods\": 32858,\n      \"Ġmelody\": 32859,\n      \"Ġconvoy\": 32860,\n      \"videos\": 32861,\n      \"Ġscreened\": 32862,\n      \"Neg\": 32863,\n      \"ochemical\": 32864,\n      \"Ġ())\": 32865,\n      \"Ġultras\": 32866,\n      \"Ġantip\": 32867,\n      \"ĠIslanders\": 32868,\n      \"704\": 32869,\n      \"Ġfetish\": 32870,\n      \"Ġridiculously\": 32871,\n      \"ĠKart\": 32872,\n      \"Ġmitochondrial\": 32873,\n      \"Ġinterfering\": 32874,\n      \"Builder\": 32875,\n      \"Ġoverfl\": 32876,\n      \"Ġacne\": 32877,\n      \"ĠMud\": 32878,\n      \"ĠKerr\": 32879,\n      \"flex\": 32880,\n      \"ĠPostal\": 32881,\n      \"ĠBaltic\": 32882,\n      \"477\": 32883,\n      \"ĠPersons\": 32884,\n      \"ourage\": 32885,\n      \"HB\": 32886,\n      \"ĠMuse\": 32887,\n      \"ĠImmortal\": 32888,\n      \"ĠDriving\": 32889,\n      \"Ġpetitions\": 32890,\n      \"Ġsubscript\": 32891,\n      \"Ġsorce\": 32892,\n      \"ĠProcessor\": 32893,\n      \"uton\": 32894,\n      \"Sony\": 32895,\n      \"Ġphon\": 32896,\n      \"Ġraced\": 32897,\n      \"ĠAnthrop\": 32898,\n      \"Ġdaytime\": 32899,\n      \"ĠExercise\": 32900,\n      \"Adding\": 32901,\n      \"Ġengages\": 32902,\n      \"ĠQualcomm\": 32903,\n      \"Ġmiracles\": 32904,\n      \"Ġmemes\": 32905,\n      \"ĠDrink\": 32906,\n      \"ĠOrioles\": 32907,\n      \"Ġhairs\": 32908,\n      \"ĠPolar\": 32909,\n      \"athom\": 32910,\n      \"Ġslippery\": 32911,\n      \"ĠRemy\": 32912,\n      \"Ġcaramel\": 32913,\n      \"ĠYEAR\": 32914,\n      \"Ġalk\": 32915,\n      \"Ign\": 32916,\n      \"aution\": 32917,\n      \"ĠMerlin\": 32918,\n      \"ĠCran\": 32919,\n      \"Ġapologies\": 32920,\n      \"Ġ410\": 32921,\n      \"Ġouting\": 32922,\n      \"ĠMemories\": 32923,\n      \"appointed\": 32924,\n      \"Ġcountered\": 32925,\n      \"uld\": 32926,\n      \"posing\": 32927,\n      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\"ODY\": 33076,\n      \"ethy\": 33077,\n      \"Ġinterstate\": 33078,\n      \"ĠPUR\": 33079,\n      \"emporary\": 33080,\n      \"ĠArabian\": 33081,\n      \"developed\": 33082,\n      \"Ġsaddle\": 33083,\n      \"Ġgithub\": 33084,\n      \"ĠOffer\": 33085,\n      \"ĠISP\": 33086,\n      \"rolet\": 33087,\n      \"ĠSUPER\": 33088,\n      \"ĠDenis\": 33089,\n      \"Ġmultiplier\": 33090,\n      \"Ġstirred\": 33091,\n      \"Interestingly\": 33092,\n      \"Ġcustomary\": 33093,\n      \"Ġbilled\": 33094,\n      \"hex\": 33095,\n      \"Ġmultiplied\": 33096,\n      \"Ġflipping\": 33097,\n      \"ĠCrosby\": 33098,\n      \"Ġfundamentals\": 33099,\n      \"iae\": 33100,\n      \"ĠPlayed\": 33101,\n      \"ĠAtom\": 33102,\n      \"amazon\": 33103,\n      \"ĠFlam\": 33104,\n      \"eez\": 33105,\n      \"activated\": 33106,\n      \"Ġtablespoon\": 33107,\n      \"Ġliberalism\": 33108,\n      \"ĠPalin\": 33109,\n      \"ĠPatel\": 33110,\n      \"Num\": 33111,\n      \"ĠTAM\": 33112,\n      \"Ġsurn\": 33113,\n      \"ĠReloaded\": 33114,\n      \"Ġcoined\": 33115,\n      \"\\\"],\": 33116,\n      \"ĠClash\": 33117,\n      \"ĠAgu\": 33118,\n      \"Ġpragmatic\": 33119,\n      \"ĠActivate\": 33120,\n      \"Ġ802\": 33121,\n      \"Ġtrailers\": 33122,\n      \"Ġsilhou\": 33123,\n      \"Ġprobes\": 33124,\n      \"Ġcircus\": 33125,\n      \"ĠBain\": 33126,\n      \"ĠLindsay\": 33127,\n      \"ĠAbbey\": 33128,\n      \"Delivery\": 33129,\n      \"Ġconcession\": 33130,\n      \"Ġgastro\": 33131,\n      \"ĠSprite\": 33132,\n      \"ÄŁ\": 33133,\n      \"andel\": 33134,\n      \"Ġgimm\": 33135,\n      \"Ġautobi\": 33136,\n      \"ĠTurtle\": 33137,\n      \"Ġwonderfully\": 33138,\n      \"ĠHaram\": 33139,\n      \"ĠWorldwide\": 33140,\n      \"ĠHandle\": 33141,\n      \"Ġtheorists\": 33142,\n      \"Ġsleek\": 33143,\n      \"ĠZhu\": 33144,\n      \"ographically\": 33145,\n      \"EGA\": 33146,\n      \"ĠOwners\": 33147,\n      \"aths\": 33148,\n      \"ĠAntarctic\": 33149,\n      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\"cester\": 33187,\n      \"Ġfearing\": 33188,\n      \"ĠPrayer\": 33189,\n      \"Ġescalated\": 33190,\n      \"GW\": 33191,\n      \"Ġrobe\": 33192,\n      \"ĠBrighton\": 33193,\n      \"acists\": 33194,\n      \"ĠSymphony\": 33195,\n      \"ĠDwarf\": 33196,\n      \"ĠParade\": 33197,\n      \"ĠLego\": 33198,\n      \"Ġinexpl\": 33199,\n      \"Ġlords\": 33200,\n      \"leaf\": 33201,\n      \"RAG\": 33202,\n      \"liber\": 33203,\n      \"Ġcigars\": 33204,\n      \"ĠJehovah\": 33205,\n      \"606\": 33206,\n      \"WINDOWS\": 33207,\n      \"ĠLiberia\": 33208,\n      \"ebus\": 33209,\n      \"Heavy\": 33210,\n      \"Ġlubric\": 33211,\n      \"ĠRW\": 33212,\n      \"anguages\": 33213,\n      \"Ġnarrowed\": 33214,\n      \"computer\": 33215,\n      \"ĠEmber\": 33216,\n      \"Ġmurdering\": 33217,\n      \"Ġdownstream\": 33218,\n      \"ĠTuls\": 33219,\n      \"ĠTables\": 33220,\n      \"Topic\": 33221,\n      \"ĠAccuracy\": 33222,\n      \"=/\": 33223,\n      \"lost\": 33224,\n      \"ĠRei\": 33225,\n      \"Ġprogresses\": 33226,\n      \"bear\": 33227,\n      \"Ġestablishments\": 33228,\n      \"Justin\": 33229,\n      \"ĠPeach\": 33230,\n      \"ĠGomez\": 33231,\n      \"å¿\": 33232,\n      \"ĠTriangle\": 33233,\n      \"Ident\": 33234,\n      \"ĠHive\": 33235,\n      \"Resources\": 33236,\n      \"Ġmixes\": 33237,\n      \"ĠAssuming\": 33238,\n      \"Mu\": 33239,\n      \"Ġhypoc\": 33240,\n      \"Ġsane\": 33241,\n      \"ĠWan\": 33242,\n      \"idious\": 33243,\n      \"Success\": 33244,\n      \"Ġio\": 33245,\n      \"Angel\": 33246,\n      \"Ġdangerously\": 33247,\n      \"ĠCreature\": 33248,\n      \"WORK\": 33249,\n      \":[\": 33250,\n      \"ĠKatrina\": 33251,\n      \"Listener\": 33252,\n      \"Miller\": 33253,\n      \"ĠIdlib\": 33254,\n      \"hang\": 33255,\n      \"Ġcircumvent\": 33256,\n      \"href\": 33257,\n      \"Ġcelestial\": 33258,\n      \"ĠWeeks\": 33259,\n      \"ĠPug\": 33260,\n      \"ĠDalton\": 33261,\n      \"Ġsubpoena\": 33262,\n      \"uku\": 33263,\n      \"Ġpersisted\": 33264,\n      \"pei\": 33265,\n      \"olding\": 33266,\n      \"ĠDocuments\": 33267,\n      \"ĠHast\": 33268,\n      \"ĠCENT\": 33269,\n      \"Ġprimer\": 33270,\n      \"Ġsynonymous\": 33271,\n      \"Ġnib\": 33272,\n      \"ombs\": 33273,\n      \"Ġnotation\": 33274,\n      \"ĠDish\": 33275,\n      \"ĠAtmosp\": 33276,\n      \"Ġforbid\": 33277,\n      \"ĠANG\": 33278,\n      \"pattern\": 33279,\n      \"los\": 33280,\n      \"Ġprojectiles\": 33281,\n      \"brown\": 33282,\n      \".\\\",\": 33283,\n      \"ĠVenom\": 33284,\n      \"Ġfiercely\": 33285,\n      \"ublished\": 33286,\n      \"ĠUran\": 33287,\n      \"ĠNicarag\": 33288,\n      \"410\": 33289,\n      \"ĠCAL\": 33290,\n      \"OTOS\": 33291,\n      \"ĠMiracle\": 33292,\n      \"ĠEnchant\": 33293,\n      \"Ġguarding\": 33294,\n      \"append\": 33295,\n      \"Attach\": 33296,\n      \"Ġleveled\": 33297,\n      \"Ġcondoms\": 33298,\n      \"ihilation\": 33299,\n      \"649\": 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\"Ġshowdown\": 33338,\n      \"Ġresponders\": 33339,\n      \"ILY\": 33340,\n      \"Ġtakedown\": 33341,\n      \"leted\": 33342,\n      \"Ġmattered\": 33343,\n      \"Ġpredictive\": 33344,\n      \"Ġoverlay\": 33345,\n      \"GPU\": 33346,\n      \"ĠVick\": 33347,\n      \"Ġconveyed\": 33348,\n      \"Tab\": 33349,\n      \"peer\": 33350,\n      \"Scan\": 33351,\n      \"Ġdefensively\": 33352,\n      \"vae\": 33353,\n      \"Ġapproving\": 33354,\n      \"Ġtiers\": 33355,\n      \"ĠVia\": 33356,\n      \"querade\": 33357,\n      \"ĠSaudis\": 33358,\n      \"Ġdemolished\": 33359,\n      \"ĠProphe\": 33360,\n      \"Ġmono\": 33361,\n      \"Ġhospitality\": 33362,\n      \"HAM\": 33363,\n      \"ĠAriel\": 33364,\n      \"MOD\": 33365,\n      \"ĠTorah\": 33366,\n      \"Ġblah\": 33367,\n      \"ĠBelarus\": 33368,\n      \"erential\": 33369,\n      \"ĠTuc\": 33370,\n      \"Ġbanker\": 33371,\n      \"397\": 33372,\n      \"Ġmosquit\": 33373,\n      \"ĠScientist\": 33374,\n      \"ĠMusical\": 33375,\n      \"Ġhust\": 33376,\n      \"Shift\": 33377,\n      \"Ġtorment\": 33378,\n      \"Ġstandoff\": 33379,\n      \"Educ\": 33380,\n      \"ĠFog\": 33381,\n      \"Ġamplifier\": 33382,\n      \"Shape\": 33383,\n      \"Instance\": 33384,\n      \"ĠCritics\": 33385,\n      \"Ġdaemon\": 33386,\n      \"Houston\": 33387,\n      \"Ġmattress\": 33388,\n      \"ĠIDF\": 33389,\n      \"Ġobscene\": 33390,\n      \"ĠAmer\": 33391,\n      \"hetti\": 33392,\n      \"Ġcompiling\": 33393,\n      \"352\": 33394,\n      \"verett\": 33395,\n      \"ĠReduction\": 33396,\n      \"istration\": 33397,\n      \"ĠBlessed\": 33398,\n      \"ĠBachelor\": 33399,\n      \"316\": 33400,\n      \"Ġprank\": 33401,\n      \"ĠVulcan\": 33402,\n      \"dding\": 33403,\n      \"Ġmourning\": 33404,\n      \"ĠQuint\": 33405,\n      \"ĠBlaster\": 33406,\n      \"testing\": 33407,\n      \"Ġsediment\": 33408,\n      \">>>\": 33409,\n      \"ĠEternity\": 33410,\n      \"ĠWHERE\": 33411,\n      \"ĠMaze\": 33412,\n      \"Ġreacting\": 33413,\n      \"ĠAlv\": 33414,\n      \"omsday\": 33415,\n      \"ĠCRA\": 33416,\n      \"Ġtranslator\": 33417,\n      \"Ġbogus\": 33418,\n      \"atu\": 33419,\n      \"Website\": 33420,\n      \"olls\": 33421,\n      \"Ġbaptism\": 33422,\n      \"Ġsibling\": 33423,\n      \"ĠAutumn\": 33424,\n      \"vez\": 33425,\n      \"ãģ®é\": 33426,\n      \"guards\": 33427,\n      \"Georg\": 33428,\n      \"assadors\": 33429,\n      \"ĠFreud\": 33430,\n      \"Ġcontinents\": 33431,\n      \"ĠRegistry\": 33432,\n      \"Bernie\": 33433,\n      \"ĸļ士\": 33434,\n      \"Ġtolerant\": 33435,\n      \"ĠUW\": 33436,\n      \"Ġhorribly\": 33437,\n      \"995\": 33438,\n      \"ĠMIDI\": 33439,\n      \"Ġimpatient\": 33440,\n      \"ocado\": 33441,\n      \"eri\": 33442,\n      \"ĠWorst\": 33443,\n      \"ĠNorris\": 33444,\n      \"ĠTalking\": 33445,\n      \"Ġdefends\": 33446,\n      \"ensable\": 33447,\n      \"Ġ2021\": 33448,\n      \"Ġanatomy\": 33449,\n      \"Lew\": 33450,\n      \"Ġdrawer\": 33451,\n      \"ĠCanberra\": 33452,\n      \"Ġpatriotic\": 33453,\n      \"é¾įåĸļ士\": 33454,\n      \"ĠAvg\": 33455,\n      \"ARM\": 33456,\n      \"Ġundisclosed\": 33457,\n      \"Ġfarewell\": 33458,\n      \"459\": 33459,\n      \"bable\": 33460,\n      \"ĠAllison\": 33461,\n      \"OLOG\": 33462,\n      \"Ġconco\": 33463,\n      \"tight\": 33464,\n      \"ĠACPI\": 33465,\n      \"ĠMines\": 33466,\n      \"lich\": 33467,\n      \"ĠâĶľ\": 33468,\n      \"represented\": 33469,\n      \"200000\": 33470,\n      \"Ġenthusiast\": 33471,\n      \"OTS\": 33472,\n      \"bil\": 33473,\n      \"ĠIngredients\": 33474,\n      \"Ġinventor\": 33475,\n      \"ĠMySQL\": 33476,\n      \"³³³\": 33477,\n      \"ĠABOUT\": 33478,\n      \"within\": 33479,\n      \"Ġmk\": 33480,\n      \"Bul\": 33481,\n      \"ĠFake\": 33482,\n      \"Ġdraconian\": 33483,\n      \"Wa\": 33484,\n      \"helm\": 33485,\n      \"ĠTerran\": 33486,\n      \"erville\": 33487,\n      \"Ġcommonplace\": 33488,\n      \"SIZE\": 33489,\n      \"Ġ\\\"<\": 33490,\n      \"replace\": 33491,\n      \"ographs\": 33492,\n      \"ĠSELECT\": 33493,\n      \"incible\": 33494,\n      \"ĠMostly\": 33495,\n      \"ĠSheffield\": 33496,\n      \"ĠIDE\": 33497,\n      \"uggle\": 33498,\n      \"Ġcitations\": 33499,\n      \"hurst\": 33500,\n      \"ĠUnix\": 33501,\n      \"Ġunleash\": 33502,\n      \"ĠPiper\": 33503,\n      \"ĠNano\": 33504,\n      \"Ġsuccumb\": 33505,\n      \"Ġreluctance\": 33506,\n      \"Ġ2500\": 33507,\n      \"ĠMerchant\": 33508,\n      \"Ġwiret\": 33509,\n      \"Ġcombos\": 33510,\n      \"ĠBirthday\": 33511,\n      \"Ġcharcoal\": 33512,\n      \"ĠUPS\": 33513,\n      \"ĠFairfax\": 33514,\n      \"Ġdriveway\": 33515,\n      \"ĠTek\": 33516,\n      \"ĠPitch\": 33517,\n      \"overe\": 33518,\n      \"Ġtechnicians\": 33519,\n      \"ĠActual\": 33520,\n      \"flation\": 33521,\n      \"ĠFiscal\": 33522,\n      \"ĠEmpty\": 33523,\n      \"anamo\": 33524,\n      \"Ġmagnesium\": 33525,\n      \"Ġslut\": 33526,\n      \"Ġgrowers\": 33527,\n      \"Investigators\": 33528,\n      \"():\": 33529,\n      \"ĠSatellite\": 33530,\n      \"ĠKeynes\": 33531,\n      \"missive\": 33532,\n      \"lane\": 33533,\n      \"Ġborough\": 33534,\n      \"344\": 33535,\n      \"ĠTEAM\": 33536,\n      \"ĠBethesda\": 33537,\n      \"CV\": 33538,\n      \"hower\": 33539,\n      \"ĠRAD\": 33540,\n      \"Ġchant\": 33541,\n      \"ĠRiy\": 33542,\n      \"Ġcompositions\": 33543,\n      \"Ġmildly\": 33544,\n      \"Ġmeddling\": 33545,\n      \"Ġagility\": 33546,\n      \"aneers\": 33547,\n      \"501\": 33548,\n      \"Ġsynth\": 33549,\n      \"linger\": 33550,\n      \"291\": 33551,\n      \"Ġexclaimed\": 33552,\n      \"Party\": 33553,\n      \"Ġcontamin\": 33554,\n      \"ĠManor\": 33555,\n      \"ĠRespond\": 33556,\n      \"Ġpraising\": 33557,\n      \"Ġmanners\": 33558,\n      \"fleet\": 33559,\n      \"Summer\": 33560,\n      \"ĠLynd\": 33561,\n      \"ĠDefinitely\": 33562,\n      \"grim\": 33563,\n      \"Ġbowling\": 33564,\n      \"stri\": 33565,\n      \"çĽ\": 33566,\n      \"ynt\": 33567,\n      \"Ġmandates\": 33568,\n      \"DIV\": 33569,\n      \"Ġreconcile\": 33570,\n      \"views\": 33571,\n      \"ĠDamon\": 33572,\n      \"vette\": 33573,\n      \"Flo\": 33574,\n      \"ĠGreatest\": 33575,\n      \"ilon\": 33576,\n      \"icia\": 33577,\n      \"Ġportrayal\": 33578,\n      \"Ġcushion\": 33579,\n      \"504\": 33580,\n      \"1979\": 33581,\n      \"ossal\": 33582,\n      \"Applic\": 33583,\n      \"scription\": 33584,\n      \"Ġmitigation\": 33585,\n      \"ATS\": 33586,\n      \"pac\": 33587,\n      \"Ġerased\": 33588,\n      \"Ġdeficiencies\": 33589,\n      \"ĠHollande\": 33590,\n      \"ĠXu\": 33591,\n      \"Ġbred\": 33592,\n      \"Ġpregnancies\": 33593,\n      \"femin\": 33594,\n      \"Ġemph\": 33595,\n      \"Ġplanners\": 33596,\n      \"Ġoutper\": 33597,\n      \"uttering\": 33598,\n      \"Ġperpetrator\": 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\"ĠGeographic\": 33636,\n      \"Tools\": 33637,\n      \"314\": 33638,\n      \"ĠGeek\": 33639,\n      \"roth\": 33640,\n      \"glers\": 33641,\n      \"ĠFIN\": 33642,\n      \"Ïģ\": 33643,\n      \"ĠAston\": 33644,\n      \"altern\": 33645,\n      \"488\": 33646,\n      \"Ġveterin\": 33647,\n      \"Gamer\": 33648,\n      \"Ġintel\": 33649,\n      \"renches\": 33650,\n      \"Shield\": 33651,\n      \"Ġamnesty\": 33652,\n      \"ĠBhar\": 33653,\n      \"Ġpiled\": 33654,\n      \"Ġhonorable\": 33655,\n      \"ĠInstitutes\": 33656,\n      \"Ġsoaked\": 33657,\n      \"Ġcoma\": 33658,\n      \"ĠEFF\": 33659,\n      \"341\": 33660,\n      \"bytes\": 33661,\n      \"ĠGmail\": 33662,\n      \"lein\": 33663,\n      \"ĠCanadiens\": 33664,\n      \"material\": 33665,\n      \"Il\": 33666,\n      \"Ġinstructors\": 33667,\n      \"ĠKY\": 33668,\n      \"Ġconceive\": 33669,\n      \"ubb\": 33670,\n      \"ĠPossible\": 33671,\n      \"Ġeasing\": 33672,\n      \"ĠChristina\": 33673,\n      \"Ġcaric\": 33674,\n      \"ĠHDR\": 33675,\n      \"ROM\": 33676,\n      \"Ġshovel\": 33677,\n      \"delete\": 33678,\n      \"Ġpuff\": 33679,\n      \"ĠChanging\": 33680,\n      \"Ġseamlessly\": 33681,\n      \"Attribute\": 33682,\n      \"Ġacquisitions\": 33683,\n      \"akery\": 33684,\n      \"ĠEF\": 33685,\n      \"Ġautistic\": 33686,\n      \"ĠTakes\": 33687,\n      \"ĠPowder\": 33688,\n      \"ĠStir\": 33689,\n      \"510\": 33690,\n      \"ĠBubble\": 33691,\n      \"settings\": 33692,\n      \"ĠFowler\": 33693,\n      \"Ġmustard\": 33694,\n      \"Ġmoreover\": 33695,\n      \"Ġcopyrighted\": 33696,\n      \"ĠLEDs\": 33697,\n      \"1500\": 33698,\n      \"æī\": 33699,\n      \"ĠHIS\": 33700,\n      \"enf\": 33701,\n      \"Ġcustod\": 33702,\n      \"ĠHuck\": 33703,\n      \"Gi\": 33704,\n      \"Ġimg\": 33705,\n      \"Answer\": 33706,\n      \"Ct\": 33707,\n      \"jay\": 33708,\n      \"ĠInfrastructure\": 33709,\n      \"Ġfederally\": 33710,\n      \"Loc\": 33711,\n      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\"Ġderail\": 33749,\n      \"yards\": 33750,\n      \"Ast\": 33751,\n      \"Ġnoticeably\": 33752,\n      \"ĠOber\": 33753,\n      \"Ram\": 33754,\n      \"Ġunnoticed\": 33755,\n      \"Ġseq\": 33756,\n      \"avage\": 33757,\n      \"Ts\": 33758,\n      \"Ġ640\": 33759,\n      \"Ġconcede\": 33760,\n      \"Ġ])\": 33761,\n      \"Fill\": 33762,\n      \"Ġcaptivity\": 33763,\n      \"ĠImprovement\": 33764,\n      \"ĠCrusader\": 33765,\n      \"araoh\": 33766,\n      \"MAP\": 33767,\n      \"æĹ\": 33768,\n      \"Ġstride\": 33769,\n      \"always\": 33770,\n      \"Fly\": 33771,\n      \"Nit\": 33772,\n      \"Ġalgae\": 33773,\n      \"ĠCooking\": 33774,\n      \"ĠDoors\": 33775,\n      \"Malley\": 33776,\n      \"Ġpolicemen\": 33777,\n      \"ãģį\": 33778,\n      \"Ġastronaut\": 33779,\n      \"accessible\": 33780,\n      \"495\": 33781,\n      \"ĠRAW\": 33782,\n      \"cliffe\": 33783,\n      \"udicrous\": 33784,\n      \"Ġdepended\": 33785,\n      \"alach\": 33786,\n      \"Ġventures\": 33787,\n      \"rake\": 33788,\n      \"Ġtits\": 33789,\n      \"ĠHou\": 33790,\n      \"Ġcondom\": 33791,\n      \"ormonal\": 33792,\n      \"Ġindent\": 33793,\n      \"Ġuploading\": 33794,\n      \"Footnote\": 33795,\n      \"Important\": 33796,\n      \"Ġ271\": 33797,\n      \"Ġmindful\": 33798,\n      \"Ġcontends\": 33799,\n      \"Cra\": 33800,\n      \"Ġcalibr\": 33801,\n      \"ĠOECD\": 33802,\n      \"plugin\": 33803,\n      \"Fat\": 33804,\n      \"ĠISS\": 33805,\n      \"ĠDynamics\": 33806,\n      \"ansen\": 33807,\n      \"686\": 33808,\n      \"'),\": 33809,\n      \"Ġsprite\": 33810,\n      \"Ġhandheld\": 33811,\n      \"ĠHipp\": 33812,\n      \"=~=~\": 33813,\n      \"Trust\": 33814,\n      \"Ġsemantics\": 33815,\n      \"ĠBundes\": 33816,\n      \"ĠReno\": 33817,\n      \"ĠLiterature\": 33818,\n      \"sense\": 33819,\n      \"Gary\": 33820,\n      \"ĠAeg\": 33821,\n      \"ĠTrin\": 33822,\n      \"EEK\": 33823,\n      \"Ġcleric\": 33824,\n      \"ĠSSH\": 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\"bern\": 33900,\n      \"Ak\": 33901,\n      \"ĠMcGu\": 33902,\n      \"Ġendlessly\": 33903,\n      \"ĠFarage\": 33904,\n      \"ĠNoel\": 33905,\n      \"Va\": 33906,\n      \"MK\": 33907,\n      \"Ġbrute\": 33908,\n      \"ĠKru\": 33909,\n      \"ĠESV\": 33910,\n      \"ĠOlivia\": 33911,\n      \"âĢł\": 33912,\n      \"ĠKaf\": 33913,\n      \"Ġtrusting\": 33914,\n      \"Ġhots\": 33915,\n      \"324\": 33916,\n      \"Ġmalaria\": 33917,\n      \"Ġjson\": 33918,\n      \"Ġpounding\": 33919,\n      \"ortment\": 33920,\n      \"Country\": 33921,\n      \"Ġpostponed\": 33922,\n      \"Ġunequiv\": 33923,\n      \"?),\": 33924,\n      \"ĠRooney\": 33925,\n      \"udding\": 33926,\n      \"ĠLeap\": 33927,\n      \"urrence\": 33928,\n      \"shapeshifter\": 33929,\n      \"ĠHAS\": 33930,\n      \"osate\": 33931,\n      \"Ġcavern\": 33932,\n      \"Ġconservatism\": 33933,\n      \"ĠBAD\": 33934,\n      \"Ġmileage\": 33935,\n      \"Ġarresting\": 33936,\n      \"Vaults\": 33937,\n      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\"\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\": 34604,\n      \"Forest\": 34605,\n      \"ĠComes\": 34606,\n      \"\\\\\\\",\": 34607,\n      \"ĠIranians\": 34608,\n      \"Ġfixtures\": 34609,\n      \"Laughs\": 34610,\n      \"Ġcurry\": 34611,\n      \"ĠKingston\": 34612,\n      \"Ġsquash\": 34613,\n      \"Ġcatalogue\": 34614,\n      \"Ġabnormalities\": 34615,\n      \"Ġdigestive\": 34616,\n      \".........\": 34617,\n      \"Ġsubordinate\": 34618,\n      \"ogly\": 34619,\n      \"Ġ249\": 34620,\n      \"Middle\": 34621,\n      \"Ġmassac\": 34622,\n      \"Ġburgers\": 34623,\n      \"Ġdownstairs\": 34624,\n      \"Ġ1931\": 34625,\n      \"394\": 34626,\n      \"ĠVG\": 34627,\n      \"Ġlasers\": 34628,\n      \"ĠSikh\": 34629,\n      \"ĠAlexa\": 34630,\n      \"derived\": 34631,\n      \"Ġcyclist\": 34632,\n      \"ãģ®éŃĶ\": 34633,\n      \"oneliness\": 34634,\n      \"!!!!!!!!\": 34635,\n      \"Ġbuffs\": 34636,\n      \"legate\": 34637,\n      \"Ġraping\": 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\"independent\": 34750,\n      \"sweet\": 34751,\n      \"Ġspac\": 34752,\n      \"ĠKabul\": 34753,\n      \"ĠÄ\": 34754,\n      \"eme\": 34755,\n      \"Ġdictated\": 34756,\n      \"Ġshouts\": 34757,\n      \"={\": 34758,\n      \"Ġripping\": 34759,\n      \"ĠShay\": 34760,\n      \"ĠCricket\": 34761,\n      \"directed\": 34762,\n      \"Ġanalysed\": 34763,\n      \"ĠWARRANT\": 34764,\n      \"agons\": 34765,\n      \"ĠBlazers\": 34766,\n      \"Ġcheered\": 34767,\n      \"Ġarithmetic\": 34768,\n      \"ĠTanz\": 34769,\n      \"373\": 34770,\n      \"ĠFlags\": 34771,\n      \"Ġ295\": 34772,\n      \"Ġwitches\": 34773,\n      \"ĠIncluded\": 34774,\n      \"ĠGained\": 34775,\n      \"ĠBlades\": 34776,\n      \"Gam\": 34777,\n      \"ĠSamantha\": 34778,\n      \"ĠAtlantis\": 34779,\n      \"ĠPratt\": 34780,\n      \"Ġspoiled\": 34781,\n      \"ĠIB\": 34782,\n      \"ĠRamirez\": 34783,\n      \"Probably\": 34784,\n      \"rero\": 34785,\n      \"ĠNg\": 34786,\n      \"ĠWarlock\": 34787,\n 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\"447\": 34825,\n      \"Ġprecursor\": 34826,\n      \"Ġmaiden\": 34827,\n      \"ĠPhen\": 34828,\n      \"ĠVegan\": 34829,\n      \"ĠPatent\": 34830,\n      \"Kelly\": 34831,\n      \"Redditor\": 34832,\n      \"Ġnods\": 34833,\n      \"Ġventilation\": 34834,\n      \"ĠSchwarz\": 34835,\n      \"Ġwizards\": 34836,\n      \"Ġominous\": 34837,\n      \"ĠHeads\": 34838,\n      \"ĠBG\": 34839,\n      \"Ġlumber\": 34840,\n      \"ĠSpiel\": 34841,\n      \"ĠisEnabled\": 34842,\n      \"Ġancestral\": 34843,\n      \"ĠShips\": 34844,\n      \"Ġwrestler\": 34845,\n      \"phi\": 34846,\n      \"Ġyuan\": 34847,\n      \"ĠRebellion\": 34848,\n      \"Ġiceberg\": 34849,\n      \"Ġmagically\": 34850,\n      \"Ġdiversion\": 34851,\n      \"arro\": 34852,\n      \"ythm\": 34853,\n      \"ĠRiders\": 34854,\n      \"ĠRobbie\": 34855,\n      \"ĠKara\": 34856,\n      \"ĠMaintenance\": 34857,\n      \"ĠHerb\": 34858,\n      \"Ġharms\": 34859,\n      \"packed\": 34860,\n      \"ĠFeinstein\": 34861,\n      \"Ġmarrying\": 34862,\n      \"Ġblending\": 34863,\n      \"ĠRates\": 34864,\n      \"Ġ1880\": 34865,\n      \"Ġwrink\": 34866,\n      \"ĠUnch\": 34867,\n      \"ĠTorch\": 34868,\n      \"described\": 34869,\n      \"Ġhumanoid\": 34870,\n      \"ilitating\": 34871,\n      \"ĠConv\": 34872,\n      \"ĠFeld\": 34873,\n      \"IGHTS\": 34874,\n      \"Ġwhistleblower\": 34875,\n      \"ortmund\": 34876,\n      \"etsy\": 34877,\n      \"arrett\": 34878,\n      \"ĠMono\": 34879,\n      \"ĠIke\": 34880,\n      \"ĠCNBC\": 34881,\n      \"ĠWAY\": 34882,\n      \"ĠMDMA\": 34883,\n      \"ĠIndividuals\": 34884,\n      \"Ġsupplemental\": 34885,\n      \"Ġpowerhouse\": 34886,\n      \"ĠStru\": 34887,\n      \"Focus\": 34888,\n      \"aphael\": 34889,\n      \"ĠColleg\": 34890,\n      \"atti\": 34891,\n      \"ZA\": 34892,\n      \"Ġperenn\": 34893,\n      \"ĠSignature\": 34894,\n      \"ĠRodney\": 34895,\n      \"Ġcubes\": 34896,\n      \"iddled\": 34897,\n      \"ĠDante\": 34898,\n      \"ĠINV\": 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\"Ġdrilled\": 34936,\n      \"ĠFurn\": 34937,\n      \"443\": 34938,\n      \"Ġsewage\": 34939,\n      \"Ġhomepage\": 34940,\n      \"Ġcourtyard\": 34941,\n      \"Ġvile\": 34942,\n      \"Ġsubsidiaries\": 34943,\n      \"ajo\": 34944,\n      \"directory\": 34945,\n      \"Ġammon\": 34946,\n      \"Vers\": 34947,\n      \"charges\": 34948,\n      \"Ġ}}\": 34949,\n      \"ĠChains\": 34950,\n      \"Ġ246\": 34951,\n      \"nob\": 34952,\n      \"Ġpercept\": 34953,\n      \"Ġgrit\": 34954,\n      \"Ġfishermen\": 34955,\n      \"ĠIraqis\": 34956,\n      \"ĠDISTR\": 34957,\n      \"ĠFULL\": 34958,\n      \"ĠEvaluation\": 34959,\n      \"graph\": 34960,\n      \"atial\": 34961,\n      \"Ġcooperating\": 34962,\n      \"Ġmelan\": 34963,\n      \"Ġenlightened\": 34964,\n      \"Ġali\": 34965,\n      \"tailed\": 34966,\n      \"Ġsalute\": 34967,\n      \"Ġweakest\": 34968,\n      \"ĠBulldogs\": 34969,\n      \"UA\": 34970,\n      \"ĠAlloy\": 34971,\n      \"Ġsemen\": 34972,\n      \"ocene\": 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\"ĠCooperation\": 35047,\n      \"Ther\": 35048,\n      \"Ġregained\": 35049,\n      \"¶æ\": 35050,\n      \"reflect\": 35051,\n      \"Ġthugs\": 35052,\n      \"ĠShelby\": 35053,\n      \"Ġdictates\": 35054,\n      \"ĠWeiner\": 35055,\n      \"ĠHale\": 35056,\n      \"Ġbattleground\": 35057,\n      \"schild\": 35058,\n      \"Ġcondol\": 35059,\n      \"hunt\": 35060,\n      \"ositories\": 35061,\n      \"Ġaccuses\": 35062,\n      \"Filename\": 35063,\n      \"Ġshri\": 35064,\n      \"Ġmotivate\": 35065,\n      \"Ġreflections\": 35066,\n      \"Null\": 35067,\n      \"ĠLobby\": 35068,\n      \"¥µ\": 35069,\n      \"ĠSATA\": 35070,\n      \"ĠBackup\": 35071,\n      \"Ñĥ\": 35072,\n      \"nin\": 35073,\n      \"ĠCorrection\": 35074,\n      \"Ġjuicy\": 35075,\n      \"utra\": 35076,\n      \"ĠPric\": 35077,\n      \"Ġrestraining\": 35078,\n      \"ĠAirbnb\": 35079,\n      \"ĠArrest\": 35080,\n      \"Ġappropriations\": 35081,\n      \"Ġslopes\": 35082,\n      \"Ġmanslaughter\": 35083,\n      \"Ġworkings\": 35084,\n      \"ĠHuss\": 35085,\n      \"ĠFrey\": 35086,\n      \"Leave\": 35087,\n      \"ĠHarmony\": 35088,\n      \"ĠFeder\": 35089,\n      \"Ġ430\": 35090,\n      \"Ġtrench\": 35091,\n      \"Ġgladly\": 35092,\n      \"Ġbullpen\": 35093,\n      \"ĠGau\": 35094,\n      \"bones\": 35095,\n      \"Ġgroove\": 35096,\n      \"Ġpretext\": 35097,\n      \"ãħĭ\": 35098,\n      \"Ġtransmitter\": 35099,\n      \"ĠComponent\": 35100,\n      \"Ġunderage\": 35101,\n      \"ĠEmpires\": 35102,\n      \"Tile\": 35103,\n      \"Ġoy\": 35104,\n      \"ĠMarvin\": 35105,\n      \"ĠCAS\": 35106,\n      \"Ġbloss\": 35107,\n      \"Ġreplicated\": 35108,\n      \"ĠMariners\": 35109,\n      \"Marcus\": 35110,\n      \"ĠBlocks\": 35111,\n      \"Ġliberated\": 35112,\n      \"Ġbutterfly\": 35113,\n      \"Feel\": 35114,\n      \"Ġfermentation\": 35115,\n      \"Ġyoutube\": 35116,\n      \"Ġoffend\": 35117,\n      \"ĠTerm\": 35118,\n      \"resist\": 35119,\n      \"Ġcessation\": 35120,\n      \"Ġinsurgency\": 35121,\n      \"Ġbir\": 35122,\n      \"ĠRaise\": 35123,\n      \"595\": 35124,\n      \"Ġhypotheses\": 35125,\n      \"502\": 35126,\n      \"Ġplaque\": 35127,\n      \"ocrat\": 35128,\n      \"Ġjackets\": 35129,\n      \"ĠHuffPost\": 35130,\n      \"among\": 35131,\n      \"Ġconfer\": 35132,\n      \"487\": 35133,\n      \"ĠLilly\": 35134,\n      \"Ġadapting\": 35135,\n      \"ĠFay\": 35136,\n      \"Ġshoved\": 35137,\n      \"vec\": 35138,\n      \"Ġrefine\": 35139,\n      \"Ġgon\": 35140,\n      \"Ġgunmen\": 35141,\n      \"zai\": 35142,\n      \"ĠShuttle\": 35143,\n      \"ĠIzan\": 35144,\n      \"Ġ1913\": 35145,\n      \"Ġplethora\": 35146,\n      \"··\": 35147,\n      \"Ġ510\": 35148,\n      \"Ġpuberty\": 35149,\n      \"Ġ241\": 35150,\n      \"ĠWealth\": 35151,\n      \"ĠAlma\": 35152,\n      \"ĠMEM\": 35153,\n      \"ĠAdults\": 35154,\n      \"Cas\": 35155,\n      \"prison\": 35156,\n      \"Race\": 35157,\n      \"Ġwaterproof\": 35158,\n      \"Ġathleticism\": 35159,\n      \"Ġcapitalize\": 35160,\n      \"ĠJuice\": 35161,\n      \"Ġilluminated\": 35162,\n      \"ĠPascal\": 35163,\n      \"Ġirritation\": 35164,\n      \"ĠWitnesses\": 35165,\n      \"adle\": 35166,\n      \"ĠAstro\": 35167,\n      \"Ġfax\": 35168,\n      \"ĠElvis\": 35169,\n      \"Primary\": 35170,\n      \"ĠLich\": 35171,\n      \"ĠElves\": 35172,\n      \"Ġresiding\": 35173,\n      \"Ġstumble\": 35174,\n      \"319\": 35175,\n      \"ĠPKK\": 35176,\n      \"Ġadversaries\": 35177,\n      \"DOS\": 35178,\n      \"ĠRitual\": 35179,\n      \"Ġsmear\": 35180,\n      \"Ġarson\": 35181,\n      \"idental\": 35182,\n      \"Ġscant\": 35183,\n      \"Ġmonarchy\": 35184,\n      \"Ġhalftime\": 35185,\n      \"Ġresidue\": 35186,\n      \"Ġindign\": 35187,\n      \"ĠShaun\": 35188,\n      \"ĠElm\": 35189,\n      \"auri\": 35190,\n      \"Aff\": 35191,\n      \"WATCH\": 35192,\n      \"ĠLyon\": 35193,\n      \"helps\": 35194,\n      \"361\": 35195,\n      \"Ġlobbyist\": 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\"wives\": 35234,\n      \"println\": 35235,\n      \"ĠPlague\": 35236,\n      \"inus\": 35237,\n      \"EEP\": 35238,\n      \"Ġcruiser\": 35239,\n      \"ĠParish\": 35240,\n      \"uminium\": 35241,\n      \"Ġoccupants\": 35242,\n      \"ĠJihad\": 35243,\n      \"mop\": 35244,\n      \"Ġpint\": 35245,\n      \"Ġhect\": 35246,\n      \"ĠMecca\": 35247,\n      \"director\": 35248,\n      \"ĠFunding\": 35249,\n      \"ĠMixed\": 35250,\n      \"Ġstag\": 35251,\n      \"Tier\": 35252,\n      \"Ġgust\": 35253,\n      \"Ġbrightly\": 35254,\n      \"orsi\": 35255,\n      \"Ġuphill\": 35256,\n      \"RD\": 35257,\n      \"Ġlesions\": 35258,\n      \"ĠBundy\": 35259,\n      \"livious\": 35260,\n      \"Ġbiologist\": 35261,\n      \"ĠFaculty\": 35262,\n      \"ĠAuthorization\": 35263,\n      \"Ġ244\": 35264,\n      \"Allow\": 35265,\n      \"ï¸\": 35266,\n      \"ĠGiul\": 35267,\n      \"Ġpertinent\": 35268,\n      \"otaur\": 35269,\n      \"esse\": 35270,\n      \"ĠRoof\": 35271,\n      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   \"Ġbaseman\": 35348,\n      \"APD\": 35349,\n      \"storage\": 35350,\n      \"ĠFifty\": 35351,\n      \"editor\": 35352,\n      \"GEN\": 35353,\n      \"UTION\": 35354,\n      \"irting\": 35355,\n      \"Ġsewing\": 35356,\n      \"rift\": 35357,\n      \"Ġagony\": 35358,\n      \"ĠSands\": 35359,\n      \"Ġ254\": 35360,\n      \"Cash\": 35361,\n      \"Ġlodge\": 35362,\n      \"Ġpunt\": 35363,\n      \"Natural\": 35364,\n      \"ĠIdeas\": 35365,\n      \"Ġerroneous\": 35366,\n      \"ĠSensor\": 35367,\n      \"ĠHannity\": 35368,\n      \"Ġ1921\": 35369,\n      \"Ġmould\": 35370,\n      \"ĠGon\": 35371,\n      \"kaya\": 35372,\n      \"Ġanonymously\": 35373,\n      \"ĠKEY\": 35374,\n      \"Ġsimulator\": 35375,\n      \"Winter\": 35376,\n      \"Ġstreamed\": 35377,\n      \"507\": 35378,\n      \"?\\\",\": 35379,\n      \"Ġteased\": 35380,\n      \"Ġcoefficient\": 35381,\n      \"Ġwartime\": 35382,\n      \"ĠTHR\": 35383,\n      \"''.\": 35384,\n      \"ĠBanking\": 35385,\n      \"mpire\": 35386,\n      \"Ġfandom\": 35387,\n      \"Ġlia\": 35388,\n      \"Ga\": 35389,\n      \"Ġdownhill\": 35390,\n      \"Ġinterpreting\": 35391,\n      \"Individual\": 35392,\n      \"Norm\": 35393,\n      \"Ġjealousy\": 35394,\n      \"bitcoin\": 35395,\n      \"Ġpleasures\": 35396,\n      \"ĠToys\": 35397,\n      \"ĠChevrolet\": 35398,\n      \"ĠAdvisor\": 35399,\n      \"IZE\": 35400,\n      \"Ġreceptions\": 35401,\n      \"706\": 35402,\n      \"Cro\": 35403,\n      \"Ġ262\": 35404,\n      \"Ġcitrus\": 35405,\n      \"iru\": 35406,\n      \"Reviewer\": 35407,\n      \"jected\": 35408,\n      \"UES\": 35409,\n      \"anz\": 35410,\n      \"1981\": 35411,\n      \"ĠWorker\": 35412,\n      \"Ġcomplied\": 35413,\n      \"orescent\": 35414,\n      \"continental\": 35415,\n      \"Ton\": 35416,\n      \"ĠPrism\": 35417,\n      \"ĠSheep\": 35418,\n      \"Ġ288\": 35419,\n      \"nox\": 35420,\n      \"ĠVog\": 35421,\n      \"Ord\": 35422,\n      \"Ġrealms\": 35423,\n      \"tek\": 35424,\n      \"Ġirrigation\": 35425,\n      \"Ġbicycles\": 35426,\n      \"Ġelectronically\": 35427,\n      \"poly\": 35428,\n      \"tall\": 35429,\n      \"());\": 35430,\n      \"Ġaesthetics\": 35431,\n      \"ĠIntegrated\": 35432,\n      \"Explore\": 35433,\n      \"Ġdunk\": 35434,\n      \"476\": 35435,\n      \"pain\": 35436,\n      \"ĠJacques\": 35437,\n      \"ĠDmit\": 35438,\n      \"Frames\": 35439,\n      \"Ġreunited\": 35440,\n      \"Ġhumid\": 35441,\n      \"Dro\": 35442,\n      \"Political\": 35443,\n      \"Ġyouthful\": 35444,\n      \"Ġentails\": 35445,\n      \"Ġmosquito\": 35446,\n      \"363\": 35447,\n      \"species\": 35448,\n      \"Ġcoordinating\": 35449,\n      \"ĠMayhem\": 35450,\n      \"ĠMagnus\": 35451,\n      \"Mount\": 35452,\n      \"Improved\": 35453,\n      \"ĠSTATE\": 35454,\n      \"ATTLE\": 35455,\n      \"Ġflowed\": 35456,\n      \"Ġtackled\": 35457,\n      \"Ġfashioned\": 35458,\n      \"Ġreorgan\": 35459,\n      \"ivari\": 35460,\n      \"finger\": 35461,\n      \"Ġreluctantly\": 35462,\n      \"etting\": 35463,\n      \"ĠVand\": 35464,\n      \"young\": 35465,\n      \"ĠGarland\": 35466,\n      \"Ġpresumption\": 35467,\n      \"Ġamenities\": 35468,\n      \"ĠPleasant\": 35469,\n      \"onential\": 35470,\n      \"ĠOxy\": 35471,\n      \"Ġmorals\": 35472,\n      \"ĠYah\": 35473,\n      \"Ready\": 35474,\n      \"Simon\": 35475,\n      \"Enh\": 35476,\n      \"Demon\": 35477,\n      \"Ġclich\": 35478,\n      \"Monitor\": 35479,\n      \"ĠDU\": 35480,\n      \"Ġwelcomes\": 35481,\n      \"Ġstandout\": 35482,\n      \"Ġdreadful\": 35483,\n      \"Ġbananas\": 35484,\n      \"Ġballoons\": 35485,\n      \"hooting\": 35486,\n      \"basic\": 35487,\n      \"Ġsuffix\": 35488,\n      \"Ġduly\": 35489,\n      \"cano\": 35490,\n      \"Chain\": 35491,\n      \"atos\": 35492,\n      \"Ġgeopolitical\": 35493,\n      \"Ġ(&\": 35494,\n      \"ĠGemini\": 35495,\n      \"ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ\": 35496,\n      \"Ġacquitted\": 35497,\n      \"Luck\": 35498,\n      \"protect\": 35499,\n      \"1024\": 35500,\n      \"Ġscarcity\": 35501,\n      \"Ġmindfulness\": 35502,\n      \"ecided\": 35503,\n      \"DN\": 35504,\n      \"prime\": 35505,\n      \"ĠPresidents\": 35506,\n      \"ĠVIDEO\": 35507,\n      \"Ġ(âĪĴ\": 35508,\n      \"addock\": 35509,\n      \"NOR\": 35510,\n      \"ĠPru\": 35511,\n      \"pun\": 35512,\n      \"ĠLOL\": 35513,\n      \"))))\": 35514,\n      \"ĠLiqu\": 35515,\n      \"ĠSAS\": 35516,\n      \"Ġstyling\": 35517,\n      \"Ġpunishments\": 35518,\n      \"Ġnumb\": 35519,\n      \"Ġascertain\": 35520,\n      \"ĠRockies\": 35521,\n      \"flu\": 35522,\n      \"Thumbnail\": 35523,\n      \"Ġperpetrated\": 35524,\n      \"ĠSemi\": 35525,\n      \"Ġdisarm\": 35526,\n      \"ĠOlder\": 35527,\n      \"ĠException\": 35528,\n      \"Ġexponentially\": 35529,\n      \"ĠCommunities\": 35530,\n      \"Ġabolish\": 35531,\n      \"ĠPartner\": 35532,\n      \"ptoms\": 35533,\n      \"Ġ777\": 35534,\n      \"ĠFoley\": 35535,\n      \"ĠCases\": 35536,\n      \"Ġgrease\": 35537,\n      \"ĠRebirth\": 35538,\n      \"Ground\": 35539,\n      \"Ġ;)\": 35540,\n      \"ĠDoctrine\": 35541,\n      \"ikini\": 35542,\n      \"Ye\": 35543,\n      \"ĠBlossom\": 35544,\n      \"Ġpersists\": 35545,\n      \"bill\": 35546,\n      \"Ġinfusion\": 35547,\n      \"Ġbuddies\": 35548,\n      \"911\": 35549,\n      \"ĠPatient\": 35550,\n      \"Ġdemos\": 35551,\n      \"Ġacquaintance\": 35552,\n      \"ĠPaw\": 35553,\n      \"atari\": 35554,\n      \"Ġxml\": 35555,\n      \"Ġfascination\": 35556,\n      \"ĠServe\": 35557,\n      \"ÏĤ\": 35558,\n      \"branded\": 35559,\n      \"Ġaz\": 35560,\n      \"Returns\": 35561,\n      \"Ġovershadow\": 35562,\n      \"Ġroam\": 35563,\n      \"Ġspeedy\": 35564,\n      \"numbered\": 35565,\n      \"helial\": 35566,\n      \"Ġdisciple\": 35567,\n      \"Ġassurances\": 35568,\n      \"given\": 35569,\n      \"pecting\": 35570,\n      \"ĠNatalie\": 35571,\n      \"çĶ°\": 35572,\n      \"Ġmosquitoes\": 35573,\n      \"rotein\": 35574,\n      \"Ġnumeric\": 35575,\n      \"Ġindependents\": 35576,\n      \"Ġtransitional\": 35577,\n      \"Ġreactionary\": 35578,\n      \"ĠMechdragon\": 35579,\n      \"doctor\": 35580,\n      \"Ġshortest\": 35581,\n      \"Ġsequential\": 35582,\n      \"ĠBac\": 35583,\n      \"ĠAccounts\": 35584,\n      \"ãģĮ\": 35585,\n      \"achy\": 35586,\n      \"ractive\": 35587,\n      \"ĠRegiment\": 35588,\n      \"Ġbreathtaking\": 35589,\n      \"fficiency\": 35590,\n      \"ĠBates\": 35591,\n      \"Ġ311\": 35592,\n      \"Ġwardrobe\": 35593,\n      \"fts\": 35594,\n      \"ĠBerk\": 35595,\n      \"Simply\": 35596,\n      \"ĠRiverside\": 35597,\n      \"ivering\": 35598,\n      \"idential\": 35599,\n      \"lucent\": 35600,\n      \"Ġenriched\": 35601,\n      \"ĠConver\": 35602,\n      \"ĠGiving\": 35603,\n      \"ãĥĻ\": 35604,\n      \"Ġlegalize\": 35605,\n      \"ĠFTC\": 35606,\n      \"Ġfreaking\": 35607,\n      \"Mix\": 35608,\n      \"Ġterrestrial\": 35609,\n      \"esian\": 35610,\n      \"cients\": 35611,\n      \"Wing\": 35612,\n      \"LOAD\": 35613,\n      \"Ġledge\": 35614,\n      \"ĠViolent\": 35615,\n      \"ĠMetall\": 35616,\n      \"Ġ308\": 35617,\n      \"Ġsoutheastern\": 35618,\n      \"hetto\": 35619,\n      \"Meat\": 35620,\n      \"Ġslowdown\": 35621,\n      \"Ġretreated\": 35622,\n      \"Jeremy\": 35623,\n      \"endas\": 35624,\n      \"*****\": 35625,\n      \"eric\": 35626,\n      \"Ġreins\": 35627,\n      \"oppable\": 35628,\n      \"ĠHumanity\": 35629,\n      \"earances\": 35630,\n      \"rigan\": 35631,\n      \"Camera\": 35632,\n      \"Ġwaivers\": 35633,\n      \"soc\": 35634,\n      \"Ġalteration\": 35635,\n      \"transform\": 35636,\n      \"ĠCemetery\": 35637,\n      \"506\": 35638,\n      \"Ġindefinite\": 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\"Ġ...\\\"\": 35713,\n      \"Ġpurge\": 35714,\n      \"Ġpicnic\": 35715,\n      \"ĠUmb\": 35716,\n      \"ĠUPDATE\": 35717,\n      \"ĠSapphire\": 35718,\n      \"ĠStall\": 35719,\n      \"learn\": 35720,\n      \"Ġobjectively\": 35721,\n      \"Ġobliter\": 35722,\n      \"Ġloophole\": 35723,\n      \"Ġjourneys\": 35724,\n      \"Ġomission\": 35725,\n      \"Pros\": 35726,\n      \"ĠSidney\": 35727,\n      \"ploma\": 35728,\n      \"Ġsprayed\": 35729,\n      \"Ġguru\": 35730,\n      \"Ġtraitor\": 35731,\n      \"Ġtimet\": 35732,\n      \"Ġsnapping\": 35733,\n      \"ĠSevent\": 35734,\n      \"urnal\": 35735,\n      \"ĠUkip\": 35736,\n      \"Ġbowed\": 35737,\n      \"poral\": 35738,\n      \"liberal\": 35739,\n      \"Ros\": 35740,\n      \"Questions\": 35741,\n      \"iOS\": 35742,\n      \"Ġsummarize\": 35743,\n      \"STAT\": 35744,\n      \"Ġ1850\": 35745,\n      \"apest\": 35746,\n      \"Ġlender\": 35747,\n      \"ĠVariable\": 35748,\n      \"bringing\": 35749,\n      \"ĠLORD\": 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\"Ġphotographic\": 35788,\n      \"Ġcorpus\": 35789,\n      \"reference\": 35790,\n      \"ĠTrog\": 35791,\n      \"Unt\": 35792,\n      \")=(\": 35793,\n      \"ĠLatvia\": 35794,\n      \"Ġactivating\": 35795,\n      \"Ġlicensee\": 35796,\n      \"Ġdisparities\": 35797,\n      \"ĠNewsletter\": 35798,\n      \"ãĥĥãĥĪ\": 35799,\n      \"Ġfreeing\": 35800,\n      \"ĠJeep\": 35801,\n      \"ĠPerception\": 35802,\n      \"insk\": 35803,\n      \"Ġsilicone\": 35804,\n      \"ĠHayden\": 35805,\n      \"Lean\": 35806,\n      \"ĠSuzuki\": 35807,\n      \"ibrarian\": 35808,\n      \"668\": 35809,\n      \"Ġspor\": 35810,\n      \"Ġcorrelations\": 35811,\n      \"aghetti\": 35812,\n      \"Ġtuber\": 35813,\n      \"ĠIPCC\": 35814,\n      \"ilus\": 35815,\n      \"ĠVu\": 35816,\n      \"Ġwealthiest\": 35817,\n      \"ĠCarbuncle\": 35818,\n      \"anza\": 35819,\n      \"Ġfooled\": 35820,\n      \"ĠZur\": 35821,\n      \"Ġdaddy\": 35822,\n      \"rano\": 35823,\n      \"ilian\": 35824,\n      \"Ġknockout\": 35825,\n      \"fman\": 35826,\n      \"required\": 35827,\n      \"ĠWikileaks\": 35828,\n      \"ĠDuffy\": 35829,\n      \"ONT\": 35830,\n      \"Ġinsol\": 35831,\n      \"ĠObjects\": 35832,\n      \"Ġbou\": 35833,\n      \"ĠNordic\": 35834,\n      \"ĠInsert\": 35835,\n      \"scan\": 35836,\n      \"Ġdancers\": 35837,\n      \"Ġidiots\": 35838,\n      \"majority\": 35839,\n      \"ĠNeville\": 35840,\n      \"ĠFreeBSD\": 35841,\n      \"Ġtart\": 35842,\n      \"panic\": 35843,\n      \"690\": 35844,\n      \"Ġcocoa\": 35845,\n      \"Ġsampled\": 35846,\n      \"Ġlookup\": 35847,\n      \"Indust\": 35848,\n      \"Ġinjections\": 35849,\n      \"genre\": 35850,\n      \"Ġau\": 35851,\n      \"Ġroadway\": 35852,\n      \"Ġgenitals\": 35853,\n      \"Kind\": 35854,\n      \"ĠExaminer\": 35855,\n      \"ĠYaz\": 35856,\n      \"Fresh\": 35857,\n      \"Ġparalysis\": 35858,\n      \"ĠAluminum\": 35859,\n      \"Ġreap\": 35860,\n      \"oké\": 35861,\n      \"Ġsloppy\": 35862,\n      \"ĠTunnel\": 35863,\n      \"posium\": 35864,\n      \"nery\": 35865,\n      \"enic\": 35866,\n      \"Ġherbal\": 35867,\n      \"ĠOuter\": 35868,\n      \"ĠBuilder\": 35869,\n      \"Ġincur\": 35870,\n      \"Ġideologies\": 35871,\n      \"Ġbackups\": 35872,\n      \"consuming\": 35873,\n      \"ĠDetect\": 35874,\n      \"deck\": 35875,\n      \"ĠKNOW\": 35876,\n      \"ĠGret\": 35877,\n      \"ĠMIC\": 35878,\n      \"Ġtoughness\": 35879,\n      \"ĠExhibit\": 35880,\n      \"Ġhive\": 35881,\n      \"Les\": 35882,\n      \"ĠSCHOOL\": 35883,\n      \"ĠAtari\": 35884,\n      \"alde\": 35885,\n      \"ĠNull\": 35886,\n      \"andestine\": 35887,\n      \"mouse\": 35888,\n      \"Ġbrigade\": 35889,\n      \"489\": 35890,\n      \"Ġrevol\": 35891,\n      \"ĠLawson\": 35892,\n      \"ĠWah\": 35893,\n      \"opoly\": 35894,\n      \"ebted\": 35895,\n      \"ĠSaunders\": 35896,\n      \"Ġ313\": 35897,\n      \"ĠWinc\": 35898,\n      \"Ġtaboo\": 35899,\n      \"ĠHelmet\": 35900,\n      \"Ġwedge\": 35901,\n      \"chip\": 35902,\n      \"ĠTina\": 35903,\n      \"bg\": 35904,\n      \"Ġinfuri\": 35905,\n      \"rn\": 35906,\n      \"Ġanomalies\": 35907,\n      \"ĠSync\": 35908,\n      \"ĠExam\": 35909,\n      \"ĠCommit\": 35910,\n      \"ĠDiary\": 35911,\n      \"ĠALSO\": 35912,\n      \"ĠDebor\": 35913,\n      \"omedical\": 35914,\n      \"Ġcomprehension\": 35915,\n      \"655\": 35916,\n      \"Ġempowering\": 35917,\n      \"Ġire\": 35918,\n      \"Ġjuices\": 35919,\n      \"ĠETH\": 35920,\n      \"ĠBoxing\": 35921,\n      \"=\\\"/\": 35922,\n      \"Ġfacilitated\": 35923,\n      \"poke\": 35924,\n      \"ĠParsons\": 35925,\n      \"ĠModer\": 35926,\n      \"travel\": 35927,\n      \"Ġcivilizations\": 35928,\n      \"Ġlibertarians\": 35929,\n      \"Ġrune\": 35930,\n      \"ĠClarks\": 35931,\n      \"athed\": 35932,\n      \"Ġcampaigners\": 35933,\n      \"ĠDispatch\": 35934,\n      \"ĠFahrenheit\": 35935,\n      \"ĠCapcom\": 35936,\n      \"----------\": 35937,\n      \"Ġlace\": 35938,\n      \"Ġdraining\": 35939,\n      \"Ġliner\": 35940,\n      \"ĠArtificial\": 35941,\n      \"én\": 35942,\n      \"task\": 35943,\n      \"]).\": 35944,\n      \"ĠGMO\": 35945,\n      \"ĠOperator\": 35946,\n      \"ordinary\": 35947,\n      \"ĠInfluence\": 35948,\n      \"ĠUps\": 35949,\n      \"Ġpotency\": 35950,\n      \"ussen\": 35951,\n      \"ospons\": 35952,\n      \"ĠSwim\": 35953,\n      \"ĠDeadline\": 35954,\n      \"Unity\": 35955,\n      \"Ġculinary\": 35956,\n      \"Ġenlightenment\": 35957,\n      \"Ġwearer\": 35958,\n      \"Ġmined\": 35959,\n      \"Ġply\": 35960,\n      \"Ġincest\": 35961,\n      \"ĠDVDs\": 35962,\n      \"Walk\": 35963,\n      \"BTC\": 35964,\n      \"Trade\": 35965,\n      \"Ġdeval\": 35966,\n      \"iband\": 35967,\n      \"ĠOversight\": 35968,\n      \"Palestinian\": 35969,\n      \"Ġdart\": 35970,\n      \"Ġmul\": 35971,\n      \"LR\": 35972,\n      \"Ġremovable\": 35973,\n      \"ĠRealms\": 35974,\n      \"ìĿ\": 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\"ĠAFTER\": 36050,\n      \"Ġheartbeat\": 36051,\n      \"ĠLabel\": 36052,\n      \"acky\": 36053,\n      \"ĠCertified\": 36054,\n      \"iltration\": 36055,\n      \"Ze\": 36056,\n      \"ĠUtt\": 36057,\n      \"Ġ1300\": 36058,\n      \"Ġpresume\": 36059,\n      \"ĠDisp\": 36060,\n      \"Ġsurged\": 36061,\n      \"Ġdolls\": 36062,\n      \"Columb\": 36063,\n      \"Ġchimpan\": 36064,\n      \"ĠRazor\": 36065,\n      \"Ġticks\": 36066,\n      \"Ġcouncillor\": 36067,\n      \"Ġpilgrimage\": 36068,\n      \"ĠRebels\": 36069,\n      \"ĠQC\": 36070,\n      \"ĠAuction\": 36071,\n      \"xia\": 36072,\n      \"ikk\": 36073,\n      \"bred\": 36074,\n      \"Ġinsertion\": 36075,\n      \"Ġcoarse\": 36076,\n      \"dB\": 36077,\n      \"SEE\": 36078,\n      \"ĠZap\": 36079,\n      \"ĠFoo\": 36080,\n      \"Ġcontempor\": 36081,\n      \"ĠQuarterly\": 36082,\n      \"otions\": 36083,\n      \"ĠAlchemist\": 36084,\n      \"ĠTrey\": 36085,\n      \"ĠDuo\": 36086,\n      \"Sweet\": 36087,\n      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\"ĠWatkins\": 36832,\n      \"Ġexcludes\": 36833,\n      \"Ġsentient\": 36834,\n      \"Ġepoch\": 36835,\n      \"Ġalloc\": 36836,\n      \"Previously\": 36837,\n      \"ĠShy\": 36838,\n      \"ĠSlovakia\": 36839,\n      \"LOCK\": 36840,\n      \"Ġmarkedly\": 36841,\n      \"Ġknob\": 36842,\n      \"Ġadventurers\": 36843,\n      \"ĠBeen\": 36844,\n      \"ĠCosts\": 36845,\n      \"ammers\": 36846,\n      \"Ġonslaught\": 36847,\n      \"ĠSupported\": 36848,\n      \"ĠTau\": 36849,\n      \"ikarp\": 36850,\n      \"ĠSovere\": 36851,\n      \"ĠHampton\": 36852,\n      \"ãĤī\": 36853,\n      \"Prev\": 36854,\n      \"ĠWorse\": 36855,\n      \"Ġcottage\": 36856,\n      \"ĠHades\": 36857,\n      \"lez\": 36858,\n      \"bowl\": 36859,\n      \"Ġfragrance\": 36860,\n      \"ĠLok\": 36861,\n      \"EMOTE\": 36862,\n      \"ĠPetro\": 36863,\n      \"Ġ1925\": 36864,\n      \"ĠPend\": 36865,\n      \"producing\": 36866,\n      \"Ġrelocate\": 36867,\n      \"vati\": 36868,\n      \"pole\": 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\"ĠTracking\": 37169,\n      \"Ġozone\": 37170,\n      \"Ġcooker\": 37171,\n      \"rosis\": 37172,\n      \"Ġreopen\": 37173,\n      \"Ġinfinity\": 37174,\n      \"ĠPharmaceutical\": 37175,\n      \"ensional\": 37176,\n      \"Attempt\": 37177,\n      \"ĠRory\": 37178,\n      \"Marco\": 37179,\n      \"Ġawaits\": 37180,\n      \"HOW\": 37181,\n      \"treated\": 37182,\n      \"Ġbolst\": 37183,\n      \"Ġrevered\": 37184,\n      \"Ġpods\": 37185,\n      \"oppers\": 37186,\n      \"0010\": 37187,\n      \"Ġamplitude\": 37188,\n      \"rican\": 37189,\n      \"SPONSORED\": 37190,\n      \"Ġtrousers\": 37191,\n      \"Ġhalves\": 37192,\n      \"ĠKaine\": 37193,\n      \"ĠCutler\": 37194,\n      \"ĠAUTH\": 37195,\n      \"Ġsplendid\": 37196,\n      \"Ġpreventive\": 37197,\n      \"ĠDudley\": 37198,\n      \"ifacts\": 37199,\n      \"uminati\": 37200,\n      \"ĠYin\": 37201,\n      \"Ġadmon\": 37202,\n      \"ĠVag\": 37203,\n      \"Ġinverted\": 37204,\n      \"Ġhastily\": 37205,\n      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    \"Ka\": 37281,\n      \"Ġmailbox\": 37282,\n      \"322\": 37283,\n      \"Ġbos\": 37284,\n      \"ĠPetra\": 37285,\n      \"KING\": 37286,\n      \"Ġenlarged\": 37287,\n      \"Often\": 37288,\n      \"Ġbadass\": 37289,\n      \"Ġ343\": 37290,\n      \"ĠPlaces\": 37291,\n      \"ĠCAD\": 37292,\n      \"Ġpristine\": 37293,\n      \"Ġintervening\": 37294,\n      \"direction\": 37295,\n      \"Ġlaz\": 37296,\n      \"ĠDSM\": 37297,\n      \"Ġprojecting\": 37298,\n      \"ĠFunk\": 37299,\n      \"agog\": 37300,\n      \"payment\": 37301,\n      \"nov\": 37302,\n      \"Ġchatter\": 37303,\n      \"ARB\": 37304,\n      \"Ġexaminations\": 37305,\n      \"ĠHousehold\": 37306,\n      \"ĠGus\": 37307,\n      \"Ford\": 37308,\n      \"414\": 37309,\n      \"Boss\": 37310,\n      \"Ġmystic\": 37311,\n      \"Ġleaps\": 37312,\n      \"ĠBav\": 37313,\n      \"ulz\": 37314,\n      \"budget\": 37315,\n      \"Football\": 37316,\n      \"Ġsubsidized\": 37317,\n      \"Ġfirsthand\": 37318,\n      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\"Ġscratched\": 37468,\n      \"Ġbows\": 37469,\n      \"ĠLennon\": 37470,\n      \"ĠGaia\": 37471,\n      \"Ġdripping\": 37472,\n      \"facts\": 37473,\n      \"Ale\": 37474,\n      \"Ġfrogs\": 37475,\n      \"ĠBreast\": 37476,\n      \"ogeneity\": 37477,\n      \"ĠProsecutor\": 37478,\n      \"Ġamplified\": 37479,\n      \"ĠHodg\": 37480,\n      \"ĠFn\": 37481,\n      \"Thousands\": 37482,\n      \"ĠNIH\": 37483,\n      \"ĠMonitoring\": 37484,\n      \"FTWARE\": 37485,\n      \"ĠPriebus\": 37486,\n      \"ĠGrowing\": 37487,\n      \"hunter\": 37488,\n      \"Ġdiagnose\": 37489,\n      \"ĠMald\": 37490,\n      \"ĠLR\": 37491,\n      \"Ġcrowned\": 37492,\n      \"Ġbursting\": 37493,\n      \"Ġdissolution\": 37494,\n      \"javascript\": 37495,\n      \"Ġusefulness\": 37496,\n      \"ĠExecution\": 37497,\n      \":(\": 37498,\n      \"ĠIvory\": 37499,\n      \"aah\": 37500,\n      \"Ġpersecuted\": 37501,\n      \"violence\": 37502,\n      \"istas\": 37503,\n      \"ĠCrate\": 37504,\n      \"Ġimpulses\": 37505,\n      \"ĠSpani\": 37506,\n      \"edes\": 37507,\n      \"Handle\": 37508,\n      \"ĠZerg\": 37509,\n      \"thinkable\": 37510,\n      \"Lastly\": 37511,\n      \"Ġspontaneously\": 37512,\n      \"Ġinconvenient\": 37513,\n      \"Ġdismissing\": 37514,\n      \"Ġplotted\": 37515,\n      \"Ġeighty\": 37516,\n      \"Ġ737\": 37517,\n      \"rish\": 37518,\n      \"ĠThornton\": 37519,\n      \"atham\": 37520,\n      \"Ġsitcom\": 37521,\n      \"Ven\": 37522,\n      \"Recipe\": 37523,\n      \"tel\": 37524,\n      \"lund\": 37525,\n      \"Ġclears\": 37526,\n      \"ĠSasuke\": 37527,\n      \"Ġ258\": 37528,\n      \"Ġopting\": 37529,\n      \"Ġenraged\": 37530,\n      \"esthetic\": 37531,\n      \"ĠAe\": 37532,\n      \"uchs\": 37533,\n      \"Prep\": 37534,\n      \"Flow\": 37535,\n      \"Ġrunoff\": 37536,\n      \"ĠEating\": 37537,\n      \"ĠGiles\": 37538,\n      \"ĠActing\": 37539,\n      \"resources\": 37540,\n      \"ibaba\": 37541,\n      \"Ġrpm\": 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\"Ġbacker\": 37617,\n      \"ĠSauce\": 37618,\n      \"ĠBoyle\": 37619,\n      \"WIN\": 37620,\n      \"Ġspins\": 37621,\n      \"Ġpauses\": 37622,\n      \"upt\": 37623,\n      \"Ġshredded\": 37624,\n      \"Ġstrapped\": 37625,\n      \"ĠCorruption\": 37626,\n      \"Ġscratches\": 37627,\n      \"Ġni\": 37628,\n      \"Ġattire\": 37629,\n      \"ĠSAF\": 37630,\n      \"FactoryReloaded\": 37631,\n      \"ĠIPS\": 37632,\n      \"Ġ(%\": 37633,\n      \"Ġseminar\": 37634,\n      \"focus\": 37635,\n      \"civil\": 37636,\n      \"Ġ1860\": 37637,\n      \"intosh\": 37638,\n      \"Ġcontinual\": 37639,\n      \"Ġabbrevi\": 37640,\n      \"ĠSok\": 37641,\n      \"ocobo\": 37642,\n      \"XM\": 37643,\n      \"Ġfrantic\": 37644,\n      \"Ġunavoidable\": 37645,\n      \"Ġartery\": 37646,\n      \"Ġannotations\": 37647,\n      \"bath\": 37648,\n      \"Climate\": 37649,\n      \"Ġdors\": 37650,\n      \"ĠSlide\": 37651,\n      \"coord\": 37652,\n      \"ĠReload\": 37653,\n      \"ĠLDL\": 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\"ĠDEN\": 37692,\n      \"Internal\": 37693,\n      \"urbed\": 37694,\n      \"fusc\": 37695,\n      \"atom\": 37696,\n      \"ĠIllusion\": 37697,\n      \"Ġpolled\": 37698,\n      \"Ġflap\": 37699,\n      \"Ġcoax\": 37700,\n      \"LGBT\": 37701,\n      \"Analy\": 37702,\n      \"ĠSections\": 37703,\n      \"ĠCaliforn\": 37704,\n      \"emn\": 37705,\n      \"Ġhither\": 37706,\n      \"ĠNIGHT\": 37707,\n      \"Ġnailed\": 37708,\n      \"ĠPipeline\": 37709,\n      \"391\": 37710,\n      \"oof\": 37711,\n      \"ĠPrimal\": 37712,\n      \"verend\": 37713,\n      \"Ġslashing\": 37714,\n      \"Ġretri\": 37715,\n      \"aviour\": 37716,\n      \"Ġdeparting\": 37717,\n      \"gil\": 37718,\n      \"ISC\": 37719,\n      \"Ġmidway\": 37720,\n      \"Ġultrasound\": 37721,\n      \"Ġbehaving\": 37722,\n      \"ĠTara\": 37723,\n      \"classes\": 37724,\n      \"Virtual\": 37725,\n      \"ĠColonial\": 37726,\n      \"Ġstripping\": 37727,\n      \"Ġorchestrated\": 37728,\n      \"ĠGraves\": 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\"çĶŁ\": 37955,\n      \"ĠTropical\": 37956,\n      \"ĠBulg\": 37957,\n      \"ĠCheryl\": 37958,\n      \"Ġadmirable\": 37959,\n      \"Ġsubtitle\": 37960,\n      \"Override\": 37961,\n      \"Ġoriginating\": 37962,\n      \"ĠCCP\": 37963,\n      \"Ġswore\": 37964,\n      \"ĠSole\": 37965,\n      \"ĠDisorders\": 37966,\n      \"329\": 37967,\n      \"Ġprocession\": 37968,\n      \"Ġrefurb\": 37969,\n      \"Ġimmersed\": 37970,\n      \"requently\": 37971,\n      \"Ġskeptics\": 37972,\n      \"Ġceramic\": 37973,\n      \"mitter\": 37974,\n      \"enstein\": 37975,\n      \"belt\": 37976,\n      \"ĠTIT\": 37977,\n      \"bidden\": 37978,\n      \"Ġfir\": 37979,\n      \"mist\": 37980,\n      \">]\": 37981,\n      \"Ġweave\": 37982,\n      \"ĠParadox\": 37983,\n      \"Ġentrusted\": 37984,\n      \"ĠBarclays\": 37985,\n      \"Ġnovelist\": 37986,\n      \"ogie\": 37987,\n      \"806\": 37988,\n      \"Ġninety\": 37989,\n      \"Ġdisagreements\": 37990,\n      \"@@@@@@@@\": 37991,\n      \"ĠAuschwitz\": 37992,\n      \"cars\": 37993,\n      \"ĠLET\": 37994,\n      \"tub\": 37995,\n      \"arantine\": 37996,\n      \"POS\": 37997,\n      \"Ġbackstory\": 37998,\n      \"Ġcheerful\": 37999,\n      \"ĠRag\": 38000,\n      \"eka\": 38001,\n      \"biased\": 38002,\n      \"Ġinexperienced\": 38003,\n      \"akra\": 38004,\n      \"ĠWitt\": 38005,\n      \"tan\": 38006,\n      \"Ġrapist\": 38007,\n      \"Ġplateau\": 38008,\n      \"chal\": 38009,\n      \"ĠInquis\": 38010,\n      \"expression\": 38011,\n      \"Ġcipher\": 38012,\n      \"Ġshaving\": 38013,\n      \"adden\": 38014,\n      \"rely\": 38015,\n      \"(\\\\\": 38016,\n      \"isma\": 38017,\n      \"ĠRegulatory\": 38018,\n      \"CHAR\": 38019,\n      \"ilyn\": 38020,\n      \"NVIDIA\": 38021,\n      \"GU\": 38022,\n      \"Ġmurm\": 38023,\n      \"laus\": 38024,\n      \"Christopher\": 38025,\n      \"Ġcontractual\": 38026,\n      \"ĠProxy\": 38027,\n      \"ĠJaime\": 38028,\n      \"ĠMethodist\": 38029,\n      \"Ġstewards\": 38030,\n      \"sta\": 38031,\n      \"peria\": 38032,\n      \"Ġphysiology\": 38033,\n      \"Ġbumped\": 38034,\n      \"Ġfructose\": 38035,\n      \"Australian\": 38036,\n      \"ĠMetallic\": 38037,\n      \"ĠMasquerade\": 38038,\n      \"arb\": 38039,\n      \"Ġpromul\": 38040,\n      \"Ġdownfall\": 38041,\n      \"Ġbutcher\": 38042,\n      \"Ġbour\": 38043,\n      \"ĠINFORMATION\": 38044,\n      \"ĠBis\": 38045,\n      \"pects\": 38046,\n      \"adena\": 38047,\n      \"Ġcontemplating\": 38048,\n      \"aroo\": 38049,\n      \"centered\": 38050,\n      \"ĠPeaks\": 38051,\n      \"Used\": 38052,\n      \"Ġmodem\": 38053,\n      \"Ġgenders\": 38054,\n      \"Ġ8000\": 38055,\n      \"371\": 38056,\n      \"Ġmaternity\": 38057,\n      \"ĠRaz\": 38058,\n      \"Ġrocking\": 38059,\n      \"Ġhandguns\": 38060,\n      \"ĠDACA\": 38061,\n      \"Autom\": 38062,\n      \"ĠNile\": 38063,\n      \"Ġtumult\": 38064,\n      \"ĠBenefit\": 38065,\n      \"ĠApproach\": 38066,\n      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\"onomous\": 38175,\n      \"Union\": 38176,\n      \"Ġprecedence\": 38177,\n      \"ĠScholarship\": 38178,\n      \"ĠUX\": 38179,\n      \"Ġrollout\": 38180,\n      \"Ġboon\": 38181,\n      \"alm\": 38182,\n      \"ĠCanter\": 38183,\n      \"æµ\": 38184,\n      \"Ġrounding\": 38185,\n      \"Ġclad\": 38186,\n      \"Ġvap\": 38187,\n      \"ĠFeatured\": 38188,\n      \"isations\": 38189,\n      \"Ġ540\": 38190,\n      \"police\": 38191,\n      \"Ġunsettling\": 38192,\n      \"Ġdrifting\": 38193,\n      \"ĠLumia\": 38194,\n      \"ĠObamaCare\": 38195,\n      \"ĠFavor\": 38196,\n      \"Hyper\": 38197,\n      \"ĠRothschild\": 38198,\n      \"ĠMiliband\": 38199,\n      \"analy\": 38200,\n      \"ĠJuliet\": 38201,\n      \"Hu\": 38202,\n      \"Ġrecalling\": 38203,\n      \"ahead\": 38204,\n      \"696\": 38205,\n      \"Ġunfavorable\": 38206,\n      \"Ġdances\": 38207,\n      \"Ox\": 38208,\n      \"Ġlegality\": 38209,\n      \"Ġ403\": 38210,\n      \"romancer\": 38211,\n      \"Ġinquire\": 38212,\n      \"ĠMoves\": 38213,\n      \"\\\\\\\">\": 38214,\n      \"ĠVariant\": 38215,\n      \"ĠMessiah\": 38216,\n      \"ĠLCS\": 38217,\n      \"ĠBahá\": 38218,\n      \"756\": 38219,\n      \"Ġeyebrow\": 38220,\n      \"ĠÂ¥\": 38221,\n      \"ĠMcF\": 38222,\n      \"ĠForty\": 38223,\n      \"Mas\": 38224,\n      \"Ġpanicked\": 38225,\n      \"Ġtransformations\": 38226,\n      \"qq\": 38227,\n      \"Ġrevolves\": 38228,\n      \"ringe\": 38229,\n      \"ĠAi\": 38230,\n      \"axe\": 38231,\n      \"Ġonward\": 38232,\n      \"ĠCFR\": 38233,\n      \"ĠBare\": 38234,\n      \"login\": 38235,\n      \"Ġliquids\": 38236,\n      \"Ġdecomp\": 38237,\n      \"secondary\": 38238,\n      \"ilan\": 38239,\n      \"ĠConvert\": 38240,\n      \"amiya\": 38241,\n      \"Ġprosecuting\": 38242,\n      \"Ġâī¡\": 38243,\n      \"ĠYorkers\": 38244,\n      \"ĠByrne\": 38245,\n      \"slow\": 38246,\n      \"awei\": 38247,\n      \"Jean\": 38248,\n      \"Ġ269\": 38249,\n      \"ĠSkydragon\": 38250,\n      \"Ġé\": 38251,\n      \"ĠNicaragua\": 38252,\n      \"ĠHuckabee\": 38253,\n      \"ĠHighly\": 38254,\n      \"Ġamphib\": 38255,\n      \"ĠPastor\": 38256,\n      \"ĠLets\": 38257,\n      \"Ġblurred\": 38258,\n      \"Ġvisceral\": 38259,\n      \"ĠCBO\": 38260,\n      \"Ġcollaborated\": 38261,\n      \"zig\": 38262,\n      \"Legal\": 38263,\n      \"Ġapartheid\": 38264,\n      \"Ġbrid\": 38265,\n      \"Ġpreset\": 38266,\n      \"ĠDET\": 38267,\n      \"ĠAMA\": 38268,\n      \"×Ķ\": 38269,\n      \"arching\": 38270,\n      \"aucuses\": 38271,\n      \"builder\": 38272,\n      \"Ġpoetic\": 38273,\n      \"Ġemulator\": 38274,\n      \"ĠMolecular\": 38275,\n      \"Ġhonoring\": 38276,\n      \"iseum\": 38277,\n      \"Ġtractor\": 38278,\n      \"ĠCluster\": 38279,\n      \"ĠCalm\": 38280,\n      \"aredevil\": 38281,\n      \"Ġsidewalks\": 38282,\n      \"Ġviolin\": 38283,\n      \"Ġgeneralized\": 38284,\n      \"ĠAlec\": 38285,\n      \"Ġembargo\": 38286,\n      \"Ġfastball\": 38287,\n      \"ĠHTTPS\": 38288,\n      \"ĠLack\": 38289,\n      \"ĠChill\": 38290,\n      \"river\": 38291,\n      \"Chel\": 38292,\n      \"ĠSwarm\": 38293,\n      \"ĠLevine\": 38294,\n      \"roying\": 38295,\n      \"Launch\": 38296,\n      \"Ġkicker\": 38297,\n      \"Ġadditive\": 38298,\n      \"ĠDeals\": 38299,\n      \"Widget\": 38300,\n      \"containing\": 38301,\n      \"Ġescalate\": 38302,\n      \"ĠOPEN\": 38303,\n      \"Ġtweaked\": 38304,\n      \"Ġstash\": 38305,\n      \"Ġsparks\": 38306,\n      \"ĠEssex\": 38307,\n      \"ĠEcc\": 38308,\n      \"Ġconvict\": 38309,\n      \"Ġblogging\": 38310,\n      \"IER\": 38311,\n      \"ĠHL\": 38312,\n      \"Ġmurderers\": 38313,\n      \"759\": 38314,\n      \"ĠHib\": 38315,\n      \"Ġdepl\": 38316,\n      \"ĠJord\": 38317,\n      \"Sac\": 38318,\n      \"Ġdissect\": 38319,\n      \"ĠHowe\": 38320,\n      \"osher\": 38321,\n      \"Ġcustomizable\": 38322,\n      \"ĠFranz\": 38323,\n      \"Ġatro\": 38324,\n      \"Äĩ\": 38325,\n      \"Ġ0004\": 38326,\n      \"Ġoutpost\": 38327,\n      \"Ross\": 38328,\n      \"Ġglyphosate\": 38329,\n      \"ĠHastings\": 38330,\n      \"ĠBEFORE\": 38331,\n      \"Ġshove\": 38332,\n      \"opped\": 38333,\n      \"ĠScala\": 38334,\n      \"Ġamulet\": 38335,\n      \"anian\": 38336,\n      \"Ġexacerbated\": 38337,\n      \"Ġeater\": 38338,\n      \"471\": 38339,\n      \"UME\": 38340,\n      \"Ġpulp\": 38341,\n      \"izontal\": 38342,\n      \"ĠZam\": 38343,\n      \"ĠATI\": 38344,\n      \"immune\": 38345,\n      \"abytes\": 38346,\n      \"Ġunnecessarily\": 38347,\n      \"ĠCAT\": 38348,\n      \"ĠAxis\": 38349,\n      \"Ġvisualize\": 38350,\n      \"Ãī\": 38351,\n      \"ĠRadical\": 38352,\n      \"fm\": 38353,\n      \"Documents\": 38354,\n      \"ĠForrest\": 38355,\n      \"Ġcontextual\": 38356,\n      \"ĠSymbol\": 38357,\n      \"Ġtentative\": 38358,\n      \"ĠDOES\": 38359,\n      \"ĠGoods\": 38360,\n      \"Ġintermittent\": 38361,\n      \"}:\": 38362,\n      \"mediated\": 38363,\n      \"Ġridicule\": 38364,\n      \"Ġatheism\": 38365,\n      \"Ġpathogens\": 38366,\n      \"ĠMum\": 38367,\n      \"Ġreintrodu\": 38368,\n      \"Ġ307\": 38369,\n      \"iHUD\": 38370,\n      \"Ġflashlight\": 38371,\n      \"Ġswearing\": 38372,\n      \"Ġpengu\": 38373,\n      \"Bu\": 38374,\n      \"Ġrotated\": 38375,\n      \"ĠCrane\": 38376,\n      \"Ġ());\": 38377,\n      \"Ġfashionable\": 38378,\n      \"Ġendorsing\": 38379,\n      \"463\": 38380,\n      \")[\": 38381,\n      \"Ġingestion\": 38382,\n      \"Ġcooks\": 38383,\n      \"Ġ950\": 38384,\n      \"otomy\": 38385,\n      \"ĠImam\": 38386,\n      \"Ġka\": 38387,\n      \"Ġteaser\": 38388,\n      \"ĠGhosts\": 38389,\n      \"ĠãĤµ\": 38390,\n      \"1969\": 38391,\n      \"Ïĥ\": 38392,\n      \"ubby\": 38393,\n      \"Ġconverter\": 38394,\n      \"zanne\": 38395,\n      \"ende\": 38396,\n      \"ĠPrepar\": 38397,\n      \"ĠNickel\": 38398,\n      \"ĠChimera\": 38399,\n      \"him\": 38400,\n      \"ĠTyrann\": 38401,\n      \"ĠSabbath\": 38402,\n      \"ĠNichols\": 38403,\n      \"Ġrapt\": 38404,\n      \"ihar\": 38405,\n      \"Ġshelling\": 38406,\n      \"Ġilluminate\": 38407,\n      \"Ġdentist\": 38408,\n      \"utor\": 38409,\n      \"ĠIntegration\": 38410,\n      \"Ġwhims\": 38411,\n      \"ĠLiterary\": 38412,\n      \"Beaut\": 38413,\n      \"Ġparchment\": 38414,\n      \"agara\": 38415,\n      \"Brand\": 38416,\n      \"Ġderog\": 38417,\n      \"âĢ¦)\": 38418,\n      \"ĠNorse\": 38419,\n      \"Ġunwitting\": 38420,\n      \"Ġcuc\": 38421,\n      \"Ġborderline\": 38422,\n      \"Ġupsetting\": 38423,\n      \"Ġrecourse\": 38424,\n      \"Ġdraped\": 38425,\n      \"ĠRadar\": 38426,\n      \"Ġcolder\": 38427,\n      \"ĠPepsi\": 38428,\n      \"iminary\": 38429,\n      \"],[\": 38430,\n      \"658\": 38431,\n      \"Vi\": 38432,\n      \"ĠFrem\": 38433,\n      \"ĠPes\": 38434,\n      \"Ġveterinary\": 38435,\n      \"ĠTED\": 38436,\n      \"ĠEpidem\": 38437,\n      \"nova\": 38438,\n      \"kid\": 38439,\n      \"Ġdevout\": 38440,\n      \"oct\": 38441,\n      \"jad\": 38442,\n      \"Moh\": 38443,\n      \"ĠPAY\": 38444,\n      \"Ġgeometric\": 38445,\n      \"Ġ323\": 38446,\n      \"Ġcircumference\": 38447,\n      \"ichick\": 38448,\n      \"1975\": 38449,\n      \"ĠYuri\": 38450,\n      \"ĠShall\": 38451,\n      \"ĠHover\": 38452,\n      \"unin\": 38453,\n      \"Spr\": 38454,\n      \"Ġgraft\": 38455,\n      \"ĠHappiness\": 38456,\n      \"Ġdisadvantages\": 38457,\n      \"attacks\": 38458,\n      \"Ġhubs\": 38459,\n      \"ĠStarCraft\": 38460,\n      \"éĸ\": 38461,\n      \"Ġgalleries\": 38462,\n      \"ĠKorra\": 38463,\n      \"Ġgroceries\": 38464,\n      \"ĠGorsuch\": 38465,\n      \"Ġrapists\": 38466,\n      \"Ġfungi\": 38467,\n      \"ĠTyphoon\": 38468,\n      \"Vector\": 38469,\n      \"ĠEmpress\": 38470,\n      \"battle\": 38471,\n      \"468\": 38472,\n      \"Ġparasite\": 38473,\n      \"ĠBomber\": 38474,\n      \"SG\": 38475,\n      \"exist\": 38476,\n      \"ĠPf\": 38477,\n      \"Ġunse\": 38478,\n      \"Ġsurgeons\": 38479,\n      \"Birth\": 38480,\n      \"ĠUnsure\": 38481,\n      \"ĠPrinted\": 38482,\n      \"ĠBehavioral\": 38483,\n      \"ĠAster\": 38484,\n      \"Pakistan\": 38485,\n      \"Ġunethical\": 38486,\n      \"Ġsv\": 38487,\n      \"ĠIoT\": 38488,\n      \"Ġlayouts\": 38489,\n      \"Pain\": 38490,\n      \"Ġconstants\": 38491,\n      \"ĠLW\": 38492,\n      \"ĠBake\": 38493,\n      \"Ġtowels\": 38494,\n      \"Ġdeterioration\": 38495,\n      \"ĠBolivia\": 38496,\n      \"Ġblinded\": 38497,\n      \"ĠWarden\": 38498,\n      \"ĠMistress\": 38499,\n      \"Ġonstage\": 38500,\n      \"Ġclans\": 38501,\n      \"ĠBEST\": 38502,\n      \"1960\": 38503,\n      \"Ġantique\": 38504,\n      \"Ġrhetorical\": 38505,\n      \"ĠPercy\": 38506,\n      \"ĠRwanda\": 38507,\n      \",.\": 38508,\n      \"Bruce\": 38509,\n      \"Ġtraumat\": 38510,\n      \"ĠParliamentary\": 38511,\n      \"Ġfootnote\": 38512,\n      \"idia\": 38513,\n      \"ĠLearned\": 38514,\n      \"seeking\": 38515,\n      \"genic\": 38516,\n      \"Ġdimensional\": 38517,\n      \"Hide\": 38518,\n      \"èĢħ\": 38519,\n      \"Ġintrigue\": 38520,\n      \"inse\": 38521,\n      \"Ġleases\": 38522,\n      \"Ġapprentices\": 38523,\n      \"washing\": 38524,\n      \"Ġ1926\": 38525,\n      \"VILLE\": 38526,\n      \"Ġswoop\": 38527,\n      \"scl\": 38528,\n      \"Ġbedrooms\": 38529,\n      \"onics\": 38530,\n      \"ĠCrunch\": 38531,\n      \"compatible\": 38532,\n      \"Ġincapac\": 38533,\n      \"ĠYemeni\": 38534,\n      \"ashtra\": 38535,\n      \"zhou\": 38536,\n      \"danger\": 38537,\n      \"Ġmanifestations\": 38538,\n      \"ĠDemons\": 38539,\n      \"AAF\": 38540,\n      \"Secretary\": 38541,\n      \"ACTED\": 38542,\n      \"LOD\": 38543,\n      \"Ġamy\": 38544,\n      \"raper\": 38545,\n      \"ethnic\": 38546,\n      \"417\": 38547,\n      \"Ġpositives\": 38548,\n      \"Ġ273\": 38549,\n      \"ĠRefugees\": 38550,\n      \"Ġusb\": 38551,\n      \"ĠVald\": 38552,\n      \"oddy\": 38553,\n      \"ĠMahmoud\": 38554,\n      \"Asia\": 38555,\n      \"Ġskulls\": 38556,\n      \"ĠExodus\": 38557,\n      \"ĠCompet\": 38558,\n      \"ĠLIC\": 38559,\n      \"ĠMansion\": 38560,\n      \"ĠAme\": 38561,\n      \"Ġconsolidate\": 38562,\n      \"storms\": 38563,\n      \"ontent\": 38564,\n      \"996\": 38565,\n      \"Ġclen\": 38566,\n      \"Ġmummy\": 38567,\n      \"flat\": 38568,\n      \"758\": 38569,\n      \"ĠVOL\": 38570,\n      \"oteric\": 38571,\n      \"nen\": 38572,\n      \"ĠMinute\": 38573,\n      \"Sov\": 38574,\n      \"Ġfiner\": 38575,\n      \"Rh\": 38576,\n      \"lycer\": 38577,\n      \"Ġreinforcements\": 38578,\n      \"ĠJohannes\": 38579,\n      \"ĠGallagher\": 38580,\n      \"Ġgymn\": 38581,\n      \"Suddenly\": 38582,\n      \"Ġextortion\": 38583,\n      \"kr\": 38584,\n      \"iator\": 38585,\n      \"Ta\": 38586,\n      \"Ġhippocampus\": 38587,\n      \"NPR\": 38588,\n      \"ĠComputing\": 38589,\n      \"Ġsquarely\": 38590,\n      \"Ġmodelling\": 38591,\n      \"ĠForums\": 38592,\n      \"ĠLisp\": 38593,\n      \"ĠKrishna\": 38594,\n      \"Ġ324\": 38595,\n      \"Ġrushes\": 38596,\n      \"Ġensued\": 38597,\n      \"Ġcreeping\": 38598,\n      \"onte\": 38599,\n      \"nai\": 38600,\n      \"ilater\": 38601,\n      \"ĠHornets\": 38602,\n      \"Ġoblivious\": 38603,\n      \"INST\": 38604,\n      \"559\": 38605,\n      \"Ġjeopardy\": 38606,\n      \"Ġdistinguishing\": 38607,\n      \"jured\": 38608,\n      \"Ġbegs\": 38609,\n      \"similar\": 38610,\n      \"phot\": 38611,\n      \"530\": 38612,\n      \"ĠParkway\": 38613,\n      \"Ġsinks\": 38614,\n      \"ĠHearthstone\": 38615,\n      \"ibur\": 38616,\n      \"ĠBaton\": 38617,\n      \"Avoid\": 38618,\n      \"Ġdancer\": 38619,\n      \"Ġmagistrate\": 38620,\n      \"aryn\": 38621,\n      \"Ġdisturbances\": 38622,\n      \"ĠRomero\": 38623,\n      \"Ġparaph\": 38624,\n      \"Ġmischief\": 38625,\n      \"âĸĵ\": 38626,\n      \"ĠSharia\": 38627,\n      \"Ġurinary\": 38628,\n      \"route\": 38629,\n      \"ivas\": 38630,\n      \"fitted\": 38631,\n      \"Ġejected\": 38632,\n      \"ĠAlbuquerque\": 38633,\n      \"Ġ470\": 38634,\n      \"Ġirritated\": 38635,\n      \"ĠZip\": 38636,\n      \"ĠBiol\": 38637,\n      \"Ãį\": 38638,\n      \"Ġdenounce\": 38639,\n      \"Ġbinaries\": 38640,\n      \"ĠVerse\": 38641,\n      \"Ġoppos\": 38642,\n      \"ĠKendrick\": 38643,\n      \"ĠGPL\": 38644,\n      \"Ġspew\": 38645,\n      \"ĠElijah\": 38646,\n      \"ĠEas\": 38647,\n      \"Ġdrifted\": 38648,\n      \"sofar\": 38649,\n      \"Ġannoyance\": 38650,\n      \"ĠBET\": 38651,\n      \"474\": 38652,\n      \"ĠStrongh\": 38653,\n      \"itates\": 38654,\n      \"ĠCognitive\": 38655,\n      \"ophone\": 38656,\n      \"ĠIdentification\": 38657,\n      \"ocrine\": 38658,\n      \"connection\": 38659,\n      \"Ġboxer\": 38660,\n      \"ĠASD\": 38661,\n      \"ĠAreas\": 38662,\n      \"Yang\": 38663,\n      \"tch\": 38664,\n      \"ullah\": 38665,\n      \"Ġdeceive\": 38666,\n      \"Combat\": 38667,\n      \"episode\": 38668,\n      \"crete\": 38669,\n      \"Witness\": 38670,\n      \"Ġcondolences\": 38671,\n      \"htar\": 38672,\n      \"Ġheals\": 38673,\n      \"Ġbuckets\": 38674,\n      \"ĠLAW\": 38675,\n      \"Blu\": 38676,\n      \"Ġslab\": 38677,\n      \"ĠORDER\": 38678,\n      \"ocl\": 38679,\n      \"atton\": 38680,\n      \"ĠStevenson\": 38681,\n      \"ĠGinger\": 38682,\n      \"ĠFriendly\": 38683,\n      \"ĠVanderbilt\": 38684,\n      \"spirit\": 38685,\n      \"igl\": 38686,\n      \"ĠRegarding\": 38687,\n      \"ĠPROG\": 38688,\n      \"Ġsealing\": 38689,\n      \"starting\": 38690,\n      \"Ġcardinal\": 38691,\n      \"ĠVec\": 38692,\n      \"ĠBeir\": 38693,\n      \"Ġmilliseconds\": 38694,\n      \"weak\": 38695,\n      \"perse\": 38696,\n      \"Ġsterile\": 38697,\n      \"ĠContemporary\": 38698,\n      \"ĠPhant\": 38699,\n      \"ĠClo\": 38700,\n      \"Ġoutp\": 38701,\n      \"Ġexiled\": 38702,\n      \"Ġ277\": 38703,\n      \"Ġselfie\": 38704,\n      \"Ġmanic\": 38705,\n      \"Ġnano\": 38706,\n      \"terms\": 38707,\n      \"Alexander\": 38708,\n      \"Ġresolves\": 38709,\n      \"Ġmillennia\": 38710,\n      \"Ġexplodes\": 38711,\n      \"Ġconstellation\": 38712,\n      \"Ġadultery\": 38713,\n      \"motion\": 38714,\n      \"DOC\": 38715,\n      \"Ġbroadcasters\": 38716,\n      \"Ġkindergarten\": 38717,\n      \"ĠMayweather\": 38718,\n      \"ĠEco\": 38719,\n      \"icho\": 38720,\n      \"Ġ287\": 38721,\n      \"laun\": 38722,\n      \"Ġmute\": 38723,\n      \"Ġdiscreet\": 38724,\n      \"Ġpreschool\": 38725,\n      \"Ġpreempt\": 38726,\n      \"Delete\": 38727,\n      \"ĠFreed\": 38728,\n      \"Pi\": 38729,\n      \"HK\": 38730,\n      \"Ġblocker\": 38731,\n      \"ĠCumber\": 38732,\n      \"Ġwrought\": 38733,\n      \"dating\": 38734,\n      \"Ġinsurer\": 38735,\n      \"Ġquotas\": 38736,\n      \"Ġpreached\": 38737,\n      \"Ġeviction\": 38738,\n      \"ĠRegina\": 38739,\n      \"ĠPens\": 38740,\n      \"Ġseventeen\": 38741,\n      \"ĠNass\": 38742,\n      \"Dick\": 38743,\n      \"Ġfolds\": 38744,\n      \"Ġdotted\": 38745,\n      \"ĠAad\": 38746,\n      \"Universal\": 38747,\n      \"Ġpizz\": 38748,\n      \"ĠGuru\": 38749,\n      \"Ġsoils\": 38750,\n      \"Ġnovice\": 38751,\n      \"ĠNeander\": 38752,\n      \"Ġstool\": 38753,\n      \"Ġdetonated\": 38754,\n      \"ĠPikachu\": 38755,\n      \"ĠMassive\": 38756,\n      \"IVER\": 38757,\n      \"ĠAbdel\": 38758,\n      \"Ġsubdued\": 38759,\n      \"Ġtallest\": 38760,\n      \"Ġprecarious\": 38761,\n      \"Ġay\": 38762,\n      \"rification\": 38763,\n      \"ĠObj\": 38764,\n      \"cale\": 38765,\n      \"Ġunquestion\": 38766,\n      \"culosis\": 38767,\n      \"adas\": 38768,\n      \"igrated\": 38769,\n      \"Days\": 38770,\n      \"Ġqueens\": 38771,\n      \"ĠGazette\": 38772,\n      \"ĠColour\": 38773,\n      \"ĠBowman\": 38774,\n      \"ĠJJ\": 38775,\n      \"ïve\": 38776,\n      \"Ġdominates\": 38777,\n      \"Student\": 38778,\n      \"Ġmu\": 38779,\n      \"Ġbacklog\": 38780,\n      \"ĠElectro\": 38781,\n      \"Truth\": 38782,\n      \"483\": 38783,\n      \"Ġcondensed\": 38784,\n      \"rules\": 38785,\n      \"ĠConspiracy\": 38786,\n      \"Ġacronym\": 38787,\n      \"handled\": 38788,\n      \"ĠMatte\": 38789,\n      \"jri\": 38790,\n      \"ĠImpossible\": 38791,\n      \"lude\": 38792,\n      \"creation\": 38793,\n      \"Ġwarmed\": 38794,\n      \"ĠSlave\": 38795,\n      \"Ġmisled\": 38796,\n      \"Ġferment\": 38797,\n      \"ĠKah\": 38798,\n      \"inki\": 38799,\n      \"keleton\": 38800,\n      \"cyl\": 38801,\n      \"ĠKarin\": 38802,\n      \"Hunter\": 38803,\n      \"Register\": 38804,\n      \"ĠSurrey\": 38805,\n      \"Ġstares\": 38806,\n      \"ĠWidth\": 38807,\n      \"ĠNay\": 38808,\n      \"ĠSki\": 38809,\n      \"Ġblacklist\": 38810,\n      \"ucket\": 38811,\n      \"Ġexpulsion\": 38812,\n      \"imet\": 38813,\n      \"Ġretweet\": 38814,\n      \"vantage\": 38815,\n      \"Feature\": 38816,\n      \"Ġtroopers\": 38817,\n      \"Ġhomers\": 38818,\n      \"969\": 38819,\n      \"Ġcontingency\": 38820,\n      \"ĠWTC\": 38821,\n      \"ĠBrewer\": 38822,\n      \"foreign\": 38823,\n      \"Ware\": 38824,\n      \"Solar\": 38825,\n      \"Ġundue\": 38826,\n      \"REC\": 38827,\n      \"ulnerable\": 38828,\n      \"pathic\": 38829,\n      \"ĠBoise\": 38830,\n      \"Ġ322\": 38831,\n      \"Ġaroused\": 38832,\n      \"ĠYing\": 38833,\n      \"ä¸į\": 38834,\n      \"ueless\": 38835,\n      \"Ġpas\": 38836,\n      \"Ġmorp\": 38837,\n      \"Ġfloral\": 38838,\n      \"Express\": 38839,\n      \"udging\": 38840,\n      \"kB\": 38841,\n      \"ĠGranted\": 38842,\n      \"د\": 38843,\n      \"ĠMicha\": 38844,\n      \"ĠGothic\": 38845,\n      \"ĠSPECIAL\": 38846,\n      \"ĠRicardo\": 38847,\n      \"Fran\": 38848,\n      \"Ġadministering\": 38849,\n      \"620\": 38850,\n      \"pora\": 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\"ĠMaxim\": 38962,\n      \"Slot\": 38963,\n      \"Ġquiz\": 38964,\n      \"ĠVid\": 38965,\n      \"ifling\": 38966,\n      \"Ġarchaeologists\": 38967,\n      \"Ġabandonment\": 38968,\n      \"dial\": 38969,\n      \"leon\": 38970,\n      \"ĠFas\": 38971,\n      \"Ted\": 38972,\n      \"Ġraspberry\": 38973,\n      \"Ġmaneuvers\": 38974,\n      \"Ġbehaviours\": 38975,\n      \"Ġinsure\": 38976,\n      \"Ġremod\": 38977,\n      \"Switch\": 38978,\n      \"hoe\": 38979,\n      \"Ġspaced\": 38980,\n      \"Ġaffordability\": 38981,\n      \"ĠFern\": 38982,\n      \"notation\": 38983,\n      \"ĠBalanced\": 38984,\n      \"Ġoccupies\": 38985,\n      \"environment\": 38986,\n      \"Ġnecklace\": 38987,\n      \"Ġsedan\": 38988,\n      \"FU\": 38989,\n      \"ĠBravo\": 38990,\n      \"Ġabusers\": 38991,\n      \"ĠAnita\": 38992,\n      \"metadata\": 38993,\n      \"ĠGithub\": 38994,\n      \"aito\": 38995,\n      \"ĠFaster\": 38996,\n      \"ĠWasserman\": 38997,\n      \"ĠFlesh\": 38998,\n     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\"ĠFirstly\": 41039,\n      \"ĠEighth\": 41040,\n      \"ĠHUN\": 41041,\n      \"ĠDesire\": 41042,\n      \"oras\": 41043,\n      \"Girls\": 41044,\n      \"ĠDesmond\": 41045,\n      \"zar\": 41046,\n      \"amines\": 41047,\n      \"OAD\": 41048,\n      \"execute\": 41049,\n      \"Ġboobs\": 41050,\n      \"ĠATL\": 41051,\n      \"_(\": 41052,\n      \"Chelsea\": 41053,\n      \"Ġmasturbation\": 41054,\n      \"ĠCoC\": 41055,\n      \"Ġdestroyer\": 41056,\n      \"ĠChomsky\": 41057,\n      \"Ġscatter\": 41058,\n      \"ĠAssets\": 41059,\n      \"796\": 41060,\n      \"ĠCargo\": 41061,\n      \"Ġreceptive\": 41062,\n      \"ĠScope\": 41063,\n      \"Ġmarketers\": 41064,\n      \"Ġlaunchers\": 41065,\n      \"Ġaxle\": 41066,\n      \"ĠSEA\": 41067,\n      \"seq\": 41068,\n      \"ĠMoff\": 41069,\n      \"finding\": 41070,\n      \"ĠGibbs\": 41071,\n      \"Georgia\": 41072,\n      \"extremely\": 41073,\n      \"NJ\": 41074,\n      \"Ġlaborers\": 41075,\n      \"stals\": 41076,\n      \"Ġmediation\": 41077,\n      \"ĠHedge\": 41078,\n      \"atown\": 41079,\n      \"Ġiod\": 41080,\n      \"despite\": 41081,\n      \"vill\": 41082,\n      \"Jane\": 41083,\n      \"existence\": 41084,\n      \"Ġcoincided\": 41085,\n      \"ĠUtilities\": 41086,\n      \"ĠCheap\": 41087,\n      \"Ġlogistical\": 41088,\n      \"Ġculmination\": 41089,\n      \"ĠNicotine\": 41090,\n      \"pak\": 41091,\n      \"Folder\": 41092,\n      \"Ġrodents\": 41093,\n      \"stuff\": 41094,\n      \"Ġlawfully\": 41095,\n      \"Ġreperto\": 41096,\n      \"ioch\": 41097,\n      \"jj\": 41098,\n      \"Dialogue\": 41099,\n      \"HHHH\": 41100,\n      \"liction\": 41101,\n      \"Looks\": 41102,\n      \"Ġ297\": 41103,\n      \"Ġturrets\": 41104,\n      \"ĠAbandon\": 41105,\n      \"Ġincess\": 41106,\n      \"ĠTrafford\": 41107,\n      \"Ġcurled\": 41108,\n      \"Ġpreferring\": 41109,\n      \"Ġprivatization\": 41110,\n      \"Ġirresist\": 41111,\n      \"ĠPanda\": 41112,\n      \"ĠShake\": 41113,\n  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\"Ġmoistur\": 41189,\n      \"ĠCPC\": 41190,\n      \"Miami\": 41191,\n      \"Ġtentacles\": 41192,\n      \"ĠPolaris\": 41193,\n      \"exc\": 41194,\n      \"exclusive\": 41195,\n      \"ĠPrairie\": 41196,\n      \"Ġcolossal\": 41197,\n      \"ĠBlend\": 41198,\n      \"surprisingly\": 41199,\n      \"ÃŃs\": 41200,\n      \"Ġindoctr\": 41201,\n      \"Ġbasal\": 41202,\n      \"ĠMPEG\": 41203,\n      \"undo\": 41204,\n      \"Split\": 41205,\n      \"Development\": 41206,\n      \"Ġlantern\": 41207,\n      \"1971\": 41208,\n      \"Ġprovocation\": 41209,\n      \"Ġanguish\": 41210,\n      \"ĠBind\": 41211,\n      \"ĠLeia\": 41212,\n      \"ducers\": 41213,\n      \"ippy\": 41214,\n      \"conservancy\": 41215,\n      \"Ġinitialize\": 41216,\n      \"ĠTwice\": 41217,\n      \"ĠSuk\": 41218,\n      \"Ġpredic\": 41219,\n      \"Ġdiploma\": 41220,\n      \"Ġsociop\": 41221,\n      \"Ingredients\": 41222,\n      \"Ġhammered\": 41223,\n      \"ĠIrma\": 41224,\n      \"Qaida\": 41225,\n      \"Ġglimps\": 41226,\n      \"ĠBian\": 41227,\n      \"Ġstacking\": 41228,\n      \"Ġfend\": 41229,\n      \"govtrack\": 41230,\n      \"Ġunn\": 41231,\n      \"democratic\": 41232,\n      \"igree\": 41233,\n      \"Ġ580\": 41234,\n      \"Ġ294\": 41235,\n      \"Ġstrawberry\": 41236,\n      \"IDER\": 41237,\n      \"Ġcherished\": 41238,\n      \"ĠHots\": 41239,\n      \"Ġinferred\": 41240,\n      \"Ġ808\": 41241,\n      \"ĠSocrates\": 41242,\n      \"Oregon\": 41243,\n      \"ĠRoses\": 41244,\n      \"ĠFOIA\": 41245,\n      \"Ġinsensitive\": 41246,\n      \"Ġ408\": 41247,\n      \"Recommend\": 41248,\n      \"ĠShine\": 41249,\n      \"Ġpainstaking\": 41250,\n      \"UGE\": 41251,\n      \"ĠHeller\": 41252,\n      \"ĠEnterprises\": 41253,\n      \"IOR\": 41254,\n      \"adj\": 41255,\n      \"NRS\": 41256,\n      \"LG\": 41257,\n      \"Ġalienated\": 41258,\n      \"Ġacknowledgement\": 41259,\n      \"ĠAUD\": 41260,\n      \"ĠReneg\": 41261,\n      \"Ġvouchers\": 41262,\n      \"Ġ960\": 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\"Ġscreenings\": 41377,\n      \"Ġdebilitating\": 41378,\n      \"ĠGrind\": 41379,\n      \"natureconservancy\": 41380,\n      \"Ġfades\": 41381,\n      \"termination\": 41382,\n      \"assetsadobe\": 41383,\n      \"Factor\": 41384,\n      \"Ġdefinitively\": 41385,\n      \"Poké\": 41386,\n      \"apult\": 41387,\n      \"ĠLafayette\": 41388,\n      \"Corn\": 41389,\n      \"ĠCoral\": 41390,\n      \"Ġstagnant\": 41391,\n      \"Tue\": 41392,\n      \"Ġdissatisfaction\": 41393,\n      \"Gender\": 41394,\n      \"Ġkidneys\": 41395,\n      \"ĠGow\": 41396,\n      \"ĠDefeat\": 41397,\n      \"ĠAshton\": 41398,\n      \"Ġcartels\": 41399,\n      \"Ġforeclosure\": 41400,\n      \"ĠExplore\": 41401,\n      \"strength\": 41402,\n      \"otin\": 41403,\n      \"Ġveterinarian\": 41404,\n      \"Ġfumble\": 41405,\n      \"Ġparap\": 41406,\n      \"ĠStrait\": 41407,\n      \"rils\": 41408,\n      \"Ġprick\": 41409,\n      \"ĠBermuda\": 41410,\n      \"ĠAmmunition\": 41411,\n      \"skinned\": 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\"Ġmarkup\": 41485,\n      \"ت\": 41486,\n      \"Ġheroine\": 41487,\n      \"Compare\": 41488,\n      \"ĠTav\": 41489,\n      \"Beast\": 41490,\n      \"Ġsuccessors\": 41491,\n      \"Ġnaïve\": 41492,\n      \"ĠBuckley\": 41493,\n      \"stress\": 41494,\n      \"meat\": 41495,\n      \"Ġdownloadable\": 41496,\n      \"Ġindexed\": 41497,\n      \"Ġscaff\": 41498,\n      \"ĠLump\": 41499,\n      \"ĠHomo\": 41500,\n      \"Studio\": 41501,\n      \"Insp\": 41502,\n      \"Ġracked\": 41503,\n      \"farious\": 41504,\n      \"ĠPetty\": 41505,\n      \"External\": 41506,\n      \"Ġ1909\": 41507,\n      \"Wars\": 41508,\n      \"commit\": 41509,\n      \"puters\": 41510,\n      \"Ġunob\": 41511,\n      \"ĠErr\": 41512,\n      \"ĠEG\": 41513,\n      \"ĠAlam\": 41514,\n      \"ĠSiberia\": 41515,\n      \"ĠAtmospheric\": 41516,\n      \"ISTER\": 41517,\n      \"ĠSatanic\": 41518,\n      \"translation\": 41519,\n      \"ĠLoud\": 41520,\n      \"traumatic\": 41521,\n      \"lique\": 41522,\n    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\"Ġbehavioural\": 41672,\n      \"ĠVentures\": 41673,\n      \"Ġsublime\": 41674,\n      \"Ġfruition\": 41675,\n      \"ĠPenalty\": 41676,\n      \"Ġcorrosion\": 41677,\n      \"¶ħ\": 41678,\n      \"Ġlikened\": 41679,\n      \"Ġbesieged\": 41680,\n      \"weeney\": 41681,\n      \"ĠCreep\": 41682,\n      \"Ġlinemen\": 41683,\n      \"multi\": 41684,\n      \"icably\": 41685,\n      \"udder\": 41686,\n      \"Ġvitality\": 41687,\n      \"Ġshortfall\": 41688,\n      \"ĠPants\": 41689,\n      \"apist\": 41690,\n      \"Hidden\": 41691,\n      \"ĠDrops\": 41692,\n      \"medical\": 41693,\n      \"Ġpronunciation\": 41694,\n      \"ĠNRL\": 41695,\n      \"Ġinsightful\": 41696,\n      \"JV\": 41697,\n      \"ĠBeard\": 41698,\n      \"ĠChou\": 41699,\n      \"Ġcharms\": 41700,\n      \"Ġbins\": 41701,\n      \"Ġambassadors\": 41702,\n      \"ĠSaturdays\": 41703,\n      \"Ġinhibitor\": 41704,\n      \"ĠFranch\": 41705,\n      \"601\": 41706,\n      \"','\": 41707,\n      \"ĠConor\": 41708,\n      \"artney\": 41709,\n      \"ĠXperia\": 41710,\n      \"grave\": 41711,\n      \"bees\": 41712,\n      \"ĠProtestants\": 41713,\n      \"Ġsoaking\": 41714,\n      \"ĠMandal\": 41715,\n      \"Ġphased\": 41716,\n      \"Ġ660\": 41717,\n      \"Ġscams\": 41718,\n      \"Ġbuzzing\": 41719,\n      \"ĠItalians\": 41720,\n      \"ĠLorenzo\": 41721,\n      \"ĠJA\": 41722,\n      \"Ġhesitated\": 41723,\n      \"Ġcliffs\": 41724,\n      \"ĠGOT\": 41725,\n      \"inguishable\": 41726,\n      \"Ġko\": 41727,\n      \"Ġinterruption\": 41728,\n      \"Zip\": 41729,\n      \"Learning\": 41730,\n      \"Ġunderscores\": 41731,\n      \"ĠBlink\": 41732,\n      \"Ku\": 41733,\n      \"579\": 41734,\n      \"ĠAutob\": 41735,\n      \"IRE\": 41736,\n      \"Ġwatering\": 41737,\n      \"Ġpastry\": 41738,\n      \"820\": 41739,\n      \"Ġvisionary\": 41740,\n      \"ĠTemplar\": 41741,\n      \"awaited\": 41742,\n      \"Ġpiston\": 41743,\n      \"Ġantid\": 41744,\n      \"currently\": 41745,\n      \"Ġpard\": 41746,\n      \"Ġwaging\": 41747,\n      \"Ġnobility\": 41748,\n      \"ĠYus\": 41749,\n      \"Ġinjecting\": 41750,\n      \"faith\": 41751,\n      \"ĠPASS\": 41752,\n      \"åº\": 41753,\n      \"Ġretake\": 41754,\n      \"ĠPROC\": 41755,\n      \"Ġcathedral\": 41756,\n      \"bash\": 41757,\n      \"Ġwrestlers\": 41758,\n      \"Ġpartnering\": 41759,\n      \"Ġnoses\": 41760,\n      \"Ġ358\": 41761,\n      \"Transform\": 41762,\n      \"amen\": 41763,\n      \"Ġbouts\": 41764,\n      \"ĠIdeal\": 41765,\n      \"ĠConstantin\": 41766,\n      \"Ġsep\": 41767,\n      \"ĠMonarch\": 41768,\n      \"atten\": 41769,\n      \"ĠPeoples\": 41770,\n      \"modified\": 41771,\n      \"Ġmoratorium\": 41772,\n      \"Ġpenchant\": 41773,\n      \"Ġoffensively\": 41774,\n      \"Ġproxies\": 41775,\n      \"okane\": 41776,\n      \"ĠTaiwanese\": 41777,\n      \"ĠPoo\": 41778,\n      \"ĠHOME\": 41779,\n      \"usional\": 41780,\n      \"Ġverbs\": 41781,\n      \"ĠOman\": 41782,\n      \"visory\": 41783,\n      \"Ġpersuasion\": 41784,\n      \"Ġmultit\": 41785,\n      \"Ġscissors\": 41786,\n      \"Gay\": 41787,\n      \"oway\": 41788,\n      \"ophysical\": 41789,\n      \"lus\": 41790,\n      \"gnu\": 41791,\n      \"Ġapocalyptic\": 41792,\n      \"Ġabsurdity\": 41793,\n      \"Ġplaybook\": 41794,\n      \"Ġautobiography\": 41795,\n      \"IUM\": 41796,\n      \"Ġsneaking\": 41797,\n      \"ĠSimulation\": 41798,\n      \"pps\": 41799,\n      \"ellery\": 41800,\n      \"Planet\": 41801,\n      \"Ġrightfully\": 41802,\n      \"Ġniece\": 41803,\n      \"ĠNEC\": 41804,\n      \"ĠIPO\": 41805,\n      \"ĠDisclosure\": 41806,\n      \"leanor\": 41807,\n      \"ousy\": 41808,\n      \"STER\": 41809,\n      \"Ġ282\": 41810,\n      \"Cruz\": 41811,\n      \"Chall\": 41812,\n      \"643\": 41813,\n      \"ĠSurvive\": 41814,\n      \"ĠFatal\": 41815,\n      \"ĠAmid\": 41816,\n      \"apo\": 41817,\n      \"Weapons\": 41818,\n      \"DEN\": 41819,\n      \"770\": 41820,\n      \"ĠGreenwald\": 41821,\n      \"Ġlinen\": 41822,\n      \"alos\": 41823,\n      \"Ġpollutants\": 41824,\n      \"ĠPCIe\": 41825,\n      \"kat\": 41826,\n      \"Ġpaw\": 41827,\n      \"ĠKraft\": 41828,\n      \"Chem\": 41829,\n      \"ĠTerminator\": 41830,\n      \"Ġreincarn\": 41831,\n      \"Ġ][\": 41832,\n      \"ĠSeeds\": 41833,\n      \"Ġsilhouette\": 41834,\n      \"ĠStores\": 41835,\n      \"Ġgrooming\": 41836,\n      \"ĠDirection\": 41837,\n      \"ĠIsabel\": 41838,\n      \"ĠBridges\": 41839,\n      \"ðŁij\": 41840,\n      \"EED\": 41841,\n      \"ĠMorsi\": 41842,\n      \"Ġvalves\": 41843,\n      \"ĠRanked\": 41844,\n      \"ĠPharma\": 41845,\n      \"ĠOrganizations\": 41846,\n      \"Ġpenetrated\": 41847,\n      \"ĠRodham\": 41848,\n      \"ĠProtoss\": 41849,\n      \"Ġoverest\": 41850,\n      \"Ġexasper\": 41851,\n      \"ĠTJ\": 41852,\n      \"Ġ000000\": 41853,\n      \"Ġtrickle\": 41854,\n      \"Ġbourbon\": 41855,\n      \"WHO\": 41856,\n      \"Ġwretched\": 41857,\n      \"Ġmicroscopic\": 41858,\n      \"Ġchecklist\": 41859,\n      \"Ġadorned\": 41860,\n      \"Royal\": 41861,\n      \"Administ\": 41862,\n      \"ĠRetirement\": 41863,\n      \"ĠHighest\": 41864,\n      \"Weather\": 41865,\n      \"ilege\": 41866,\n      \"Ġincrements\": 41867,\n      \"ĠCosponsors\": 41868,\n      \"Ġmasse\": 41869,\n      \"ĠSinn\": 41870,\n      \"rf\": 41871,\n      \"Ġhordes\": 41872,\n      \"assembly\": 41873,\n      \"754\": 41874,\n      \"ĠNatasha\": 41875,\n      \"ĠTYPE\": 41876,\n      \"ĠGENERAL\": 41877,\n      \"Ġarranging\": 41878,\n      \"Ġ407\": 41879,\n      \"lator\": 41880,\n      \"Ġglean\": 41881,\n      \"Ġdiscredited\": 41882,\n      \"Ġclinicians\": 41883,\n      \"UNE\": 41884,\n      \"Ġachieves\": 41885,\n      \"ĠEmerson\": 41886,\n      \"complex\": 41887,\n      \"=[\": 41888,\n      \"Ġprincipally\": 41889,\n      \"Ġfrail\": 41890,\n      \"picked\": 41891,\n      \"Ġthanking\": 41892,\n      \"Ġrecl\": 41893,\n      \"ĠLAST\": 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\"ifestyle\": 42004,\n      \"rozen\": 42005,\n      \"CEPT\": 42006,\n      \"Ark\": 42007,\n      \"uci\": 42008,\n      \"lectic\": 42009,\n      \"Ġirritating\": 42010,\n      \"sheets\": 42011,\n      \"Ay\": 42012,\n      \"Ġredeemed\": 42013,\n      \"Ġhorny\": 42014,\n      \"ĠTeach\": 42015,\n      \"ĠSear\": 42016,\n      \"democracy\": 42017,\n      \"465\": 42018,\n      \"ĠRestore\": 42019,\n      \"Ġstandby\": 42020,\n      \"ĠPis\": 42021,\n      \"iffin\": 42022,\n      \"Ġsleepy\": 42023,\n      \"Ġextrater\": 42024,\n      \"Ġcompliments\": 42025,\n      \"Frameworks\": 42026,\n      \"Ġinstalls\": 42027,\n      \"Ġbanging\": 42028,\n      \"surface\": 42029,\n      \"foundland\": 42030,\n      \"Ġmetaphysical\": 42031,\n      \"Ġ283\": 42032,\n      \"ouls\": 42033,\n      \"devices\": 42034,\n      \"Args\": 42035,\n      \"ĠSacrifice\": 42036,\n      \"ĠMcCorm\": 42037,\n      \"eson\": 42038,\n      \"Conservative\": 42039,\n      \"ĠMikhail\": 42040,\n      \"seeing\": 42041,\n      \"isively\": 42042,\n      \"ĠRooms\": 42043,\n      \"ĠGeneric\": 42044,\n      \"Ġenthusiastically\": 42045,\n      \"Ġgripped\": 42046,\n      \"Ġcomedic\": 42047,\n      \"ĠElectricity\": 42048,\n      \"Ġguerrilla\": 42049,\n      \"Ġdecoration\": 42050,\n      \"ĠPerspective\": 42051,\n      \"Ġconsultations\": 42052,\n      \"Ġunamb\": 42053,\n      \"Ġplagiar\": 42054,\n      \"Ġmagician\": 42055,\n      \"Ġerection\": 42056,\n      \"ĠTourism\": 42057,\n      \"oried\": 42058,\n      \"roxy\": 42059,\n      \"1100\": 42060,\n      \"Tam\": 42061,\n      \"Īè\": 42062,\n      \"γ\": 42063,\n      \"ת\": 42064,\n      \"ĠPredators\": 42065,\n      \"Nitrome\": 42066,\n      \"Ġtelescopes\": 42067,\n      \"projects\": 42068,\n      \"Ġunprotected\": 42069,\n      \"Ġstocked\": 42070,\n      \"ĠEntreprene\": 42071,\n      \"nexpected\": 42072,\n      \"Ġwastewater\": 42073,\n      \"Vill\": 42074,\n      \"Ġintimately\": 42075,\n      \"ĠiCloud\": 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\"Ġleasing\": 42150,\n      \"ĠCafé\": 42151,\n      \"Ġroses\": 42152,\n      \"ĠRespect\": 42153,\n      \"Ġmislead\": 42154,\n      \"Ġperfected\": 42155,\n      \"Ġnudity\": 42156,\n      \"Ġnonpartisan\": 42157,\n      \"ĠConsumption\": 42158,\n      \"Reporting\": 42159,\n      \"Ġnuances\": 42160,\n      \"Ġdeductible\": 42161,\n      \"ĠShots\": 42162,\n      \"Ġ377\": 42163,\n      \"Ġæľ\": 42164,\n      \"anooga\": 42165,\n      \"Benef\": 42166,\n      \"ĠBam\": 42167,\n      \"ĠSamp\": 42168,\n      \"ifix\": 42169,\n      \"Ġgalvan\": 42170,\n      \"ĠMedals\": 42171,\n      \"radius\": 42172,\n      \"Ġnobles\": 42173,\n      \"Ġeaves\": 42174,\n      \"igrate\": 42175,\n      \"KT\": 42176,\n      \"ĠHarbour\": 42177,\n      \"uers\": 42178,\n      \"Ġrisked\": 42179,\n      \"req\": 42180,\n      \"Ġneurot\": 42181,\n      \"gettable\": 42182,\n      \"aina\": 42183,\n      \"Romney\": 42184,\n      \"Ġunderpin\": 42185,\n      \"Ġloft\": 42186,\n      \"ĠSubcommittee\": 42187,\n      \"ĠMongol\": 42188,\n      \"biz\": 42189,\n      \"Ġmanifests\": 42190,\n      \"assisted\": 42191,\n      \"ĠGaga\": 42192,\n      \"Ġsynergy\": 42193,\n      \"Ġreligiously\": 42194,\n      \"ĠPref\": 42195,\n      \"ĠGerry\": 42196,\n      \"TAG\": 42197,\n      \"ĠChoi\": 42198,\n      \"466\": 42199,\n      \"behind\": 42200,\n      \"ĠOu\": 42201,\n      \"GoldMagikarp\": 42202,\n      \"Ġhemorrh\": 42203,\n      \"River\": 42204,\n      \"Ġtendon\": 42205,\n      \"Ġinjure\": 42206,\n      \"ĠFiona\": 42207,\n      \"Ġpag\": 42208,\n      \"Ġagitation\": 42209,\n      \"||||\": 42210,\n      \"uran\": 42211,\n      \"ĠESA\": 42212,\n      \"Ġesteem\": 42213,\n      \"Ġdodging\": 42214,\n      \"Ġ412\": 42215,\n      \"rss\": 42216,\n      \"Ġceases\": 42217,\n      \"excluding\": 42218,\n      \"Ġintakes\": 42219,\n      \"Ġinserts\": 42220,\n      \"Ġembold\": 42221,\n      \"ĠOral\": 42222,\n      \"upuncture\": 42223,\n      \"411\": 42224,\n      \"ĠUnified\": 42225,\n      \"ĠDele\": 42226,\n      \"Ġfurnace\": 42227,\n      \"ĠCoyotes\": 42228,\n      \"ĠBrach\": 42229,\n      \"Labor\": 42230,\n      \"Ġhandshake\": 42231,\n      \"Ġbruises\": 42232,\n      \"Grade\": 42233,\n      \"éĹĺ\": 42234,\n      \"ĠGrammy\": 42235,\n      \"ileen\": 42236,\n      \"States\": 42237,\n      \"ĠScandinavian\": 42238,\n      \"ĠKardash\": 42239,\n      \"866\": 42240,\n      \"Ġeffortlessly\": 42241,\n      \"ĠDIRECT\": 42242,\n      \"ĠTHEN\": 42243,\n      \"ĠMei\": 42244,\n      \"ertation\": 42245,\n      \"1968\": 42246,\n      \"Ġgroin\": 42247,\n      \"witch\": 42248,\n      \"Requirements\": 42249,\n      \"985\": 42250,\n      \"Ġroofs\": 42251,\n      \"Ġestates\": 42252,\n      \"ĠHF\": 42253,\n      \"Ġhaha\": 42254,\n      \"Ġdensely\": 42255,\n      \"ĠOCT\": 42256,\n      \"Ġplastics\": 42257,\n      \"Ġincidentally\": 42258,\n      \"ĠTracks\": 42259,\n      \"ĠTaxes\": 42260,\n      \"Ġchanted\": 42261,\n      \"Ġforceful\": 42262,\n      \"ĠBieber\": 42263,\n      \"ĠKahn\": 42264,\n      \"Kent\": 42265,\n      \"ĠCot\": 42266,\n      \"licts\": 42267,\n      \"Fed\": 42268,\n      \"Ġhideous\": 42269,\n      \"ĠVerd\": 42270,\n      \"ĠSyndicate\": 42271,\n      \"ĠIllegal\": 42272,\n      \"Jet\": 42273,\n      \"ĠDAV\": 42274,\n      \"reasonable\": 42275,\n      \"crew\": 42276,\n      \"Ġfundamentalist\": 42277,\n      \"Ġtruthful\": 42278,\n      \"ĠJing\": 42279,\n      \"Ġlil\": 42280,\n      \"Ġdowned\": 42281,\n      \"Ġenchanted\": 42282,\n      \"ĠPolicies\": 42283,\n      \"ĠMcMaster\": 42284,\n      \"ĠHare\": 42285,\n      \"ideshow\": 42286,\n      \"Ġparams\": 42287,\n      \"encers\": 42288,\n      \"gorithm\": 42289,\n      \"Ġallowances\": 42290,\n      \"Ġturbulent\": 42291,\n      \"Ġcomplexities\": 42292,\n      \"ĠKT\": 42293,\n      \"Ġ337\": 42294,\n      \"ĠGenetic\": 42295,\n      \"FUN\": 42296,\n      \"Doug\": 42297,\n      \"tick\": 42298,\n      \"Ġgigs\": 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\"Ġscapego\": 42374,\n      \"ĠAngle\": 42375,\n      \"othal\": 42376,\n      \"Ġtrove\": 42377,\n      \"ĠSty\": 42378,\n      \"ĠNguyen\": 42379,\n      \"marine\": 42380,\n      \"ideon\": 42381,\n      \"Depths\": 42382,\n      \"Blog\": 42383,\n      \"ĠIlluminati\": 42384,\n      \"Ġtracts\": 42385,\n      \"Ġorganise\": 42386,\n      \"Ġostr\": 42387,\n      \"Fs\": 42388,\n      \"Ġleveraging\": 42389,\n      \"ĠDaredevil\": 42390,\n      \"asar\": 42391,\n      \"Ġlang\": 42392,\n      \"Ġextermin\": 42393,\n      \"ursions\": 42394,\n      \"ĠRomo\": 42395,\n      \"ãĤ¤ãĥĪ\": 42396,\n      \"Ġcontended\": 42397,\n      \"Ġencountering\": 42398,\n      \"ĠTablet\": 42399,\n      \"ĠAlternate\": 42400,\n      \"skill\": 42401,\n      \"Ġsweets\": 42402,\n      \"Ġcohesive\": 42403,\n      \"capacity\": 42404,\n      \"Ġrepud\": 42405,\n      \"Ġlizard\": 42406,\n      \"roo\": 42407,\n      \"Ġpilgrims\": 42408,\n      \"ĠRuff\": 42409,\n      \"ĠInstrument\": 42410,\n      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\"treatment\": 42487,\n      \"Ġtavern\": 42488,\n      \"Ġ339\": 42489,\n      \"ĠTNT\": 42490,\n      \"ificantly\": 42491,\n      \"Temperature\": 42492,\n      \"Fel\": 42493,\n      \"Ġunderworld\": 42494,\n      \"ĠJudges\": 42495,\n      \"Ġ<+\": 42496,\n      \"Ġstump\": 42497,\n      \"Ġoccupancy\": 42498,\n      \"Ġaber\": 42499,\n      \"ĠFinder\": 42500,\n      \")\\\",\": 42501,\n      \"ĠNunes\": 42502,\n      \"reset\": 42503,\n      \"inet\": 42504,\n      \"ectomy\": 42505,\n      \"Ġwellness\": 42506,\n      \"ĠPeb\": 42507,\n      \"quartered\": 42508,\n      \"andan\": 42509,\n      \"Ġnegatives\": 42510,\n      \"ĠThiel\": 42511,\n      \"ĠClip\": 42512,\n      \"ĠLTD\": 42513,\n      \"Ġblight\": 42514,\n      \"Ġrepertoire\": 42515,\n      \"Kyle\": 42516,\n      \"Ġquer\": 42517,\n      \"ĠCes\": 42518,\n      \"Ġhapl\": 42519,\n      \"989\": 42520,\n      \"ĠThames\": 42521,\n      \"iscopal\": 42522,\n      \"Desk\": 42523,\n      \"ivariate\": 42524,\n      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\"Ġblinking\": 43196,\n      \"Ġgoto\": 43197,\n      \"Decre\": 43198,\n      \"Aren\": 43199,\n      \"Ġstabilization\": 43200,\n      \"ĠDisabled\": 43201,\n      \"ĠYanukovych\": 43202,\n      \"Ġoutlawed\": 43203,\n      \"ĠVentura\": 43204,\n      \"teness\": 43205,\n      \"Ġplantation\": 43206,\n      \"Ġyacht\": 43207,\n      \"ĠHuawei\": 43208,\n      \"Ġsolvent\": 43209,\n      \"Ġgracious\": 43210,\n      \"Ġcuriously\": 43211,\n      \"Ġcapacitor\": 43212,\n      \"Ġcx\": 43213,\n      \"ĠReflex\": 43214,\n      \"Phys\": 43215,\n      \"ĠCf\": 43216,\n      \"ptin\": 43217,\n      \"conservative\": 43218,\n      \"Ġinvocation\": 43219,\n      \"cour\": 43220,\n      \"FN\": 43221,\n      \"ĠNewly\": 43222,\n      \"Hour\": 43223,\n      \"Asian\": 43224,\n      \"ĠLeading\": 43225,\n      \"ĠAerospace\": 43226,\n      \"Anne\": 43227,\n      \"Ġprenatal\": 43228,\n      \"Ġdeteriorating\": 43229,\n      \"HCR\": 43230,\n      \"ĠNormandy\": 43231,\n      \"olini\": 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\"rocal\": 43270,\n      \"ilings\": 43271,\n      \"Ġsailor\": 43272,\n      \"Ġrife\": 43273,\n      \"hung\": 43274,\n      \"Ġtropes\": 43275,\n      \"Ġsnatched\": 43276,\n      \"ĠLIN\": 43277,\n      \"ĠBib\": 43278,\n      \"ESA\": 43279,\n      \"ĠPrev\": 43280,\n      \"ĠCamel\": 43281,\n      \"runtime\": 43282,\n      \"Ġobnoxious\": 43283,\n      \"437\": 43284,\n      \"Ġsummers\": 43285,\n      \"Ġunexplained\": 43286,\n      \"ĠWalters\": 43287,\n      \"caliber\": 43288,\n      \"Ġgull\": 43289,\n      \"ĠEndurance\": 43290,\n      \"ä½ľ\": 43291,\n      \"Ġ347\": 43292,\n      \"Irish\": 43293,\n      \"Ġaerobic\": 43294,\n      \"Ġcramped\": 43295,\n      \"ĠHonolulu\": 43296,\n      \"à©\": 43297,\n      \"userc\": 43298,\n      \"ecast\": 43299,\n      \"ACY\": 43300,\n      \"ĠQuery\": 43301,\n      \"ãĤ¹ãĥĪ\": 43302,\n      \"Beta\": 43303,\n      \"Ġsusceptibility\": 43304,\n      \"ĠShiv\": 43305,\n      \"ĠLimbaugh\": 43306,\n      \"ĠÃĸ\": 43307,\n      \"ĠNXT\": 43308,\n      \"ĠMuss\": 43309,\n      \"ĠBritons\": 43310,\n      \"ESCO\": 43311,\n      \"EGIN\": 43312,\n      \"Ġ%%\": 43313,\n      \"Ġsecession\": 43314,\n      \"ĠPatron\": 43315,\n      \"ĠLua\": 43316,\n      \"naires\": 43317,\n      \"ĠJPMorgan\": 43318,\n      \"usb\": 43319,\n      \"ocyte\": 43320,\n      \"Ġcouncillors\": 43321,\n      \"ĠLiang\": 43322,\n      \"farm\": 43323,\n      \"Ġnervously\": 43324,\n      \"Ġattractiveness\": 43325,\n      \"ĠKov\": 43326,\n      \"jump\": 43327,\n      \"Plot\": 43328,\n      \"Ġstains\": 43329,\n      \"ĠStatue\": 43330,\n      \"ĠApostles\": 43331,\n      \"heter\": 43332,\n      \"ĠSUPPORT\": 43333,\n      \"Ġoverwhelm\": 43334,\n      \"YES\": 43335,\n      \"Ġ291\": 43336,\n      \"density\": 43337,\n      \"Ġtrapping\": 43338,\n      \"Mit\": 43339,\n      \"Ġfide\": 43340,\n      \"ĠPamela\": 43341,\n      \"atlantic\": 43342,\n      \"Damn\": 43343,\n      \"Ġpts\": 43344,\n      \"OPA\": 43345,\n      \"Ġservicing\": 43346,\n      \"Ġoverflowing\": 43347,\n      \"ulo\": 43348,\n      \"ĠErit\": 43349,\n      \"ticket\": 43350,\n      \"lighting\": 43351,\n      \"ĠHmm\": 43352,\n      \"ãĥ¼ãĥ«\": 43353,\n      \"imoto\": 43354,\n      \"Ġchuckle\": 43355,\n      \"423\": 43356,\n      \"ãģķ\": 43357,\n      \"shape\": 43358,\n      \"Ġqueues\": 43359,\n      \"Ġanchors\": 43360,\n      \"ãĤ¼ãĤ¦ãĤ¹\": 43361,\n      \"Fer\": 43362,\n      \"Ġawoke\": 43363,\n      \"Ġ666\": 43364,\n      \"hands\": 43365,\n      \"Ġdivergence\": 43366,\n      \"Ġ505\": 43367,\n      \"Tips\": 43368,\n      \"Ġdepot\": 43369,\n      \"Ġskew\": 43370,\n      \"ĠDeliver\": 43371,\n      \"opot\": 43372,\n      \"Ġdivul\": 43373,\n      \"ĠEB\": 43374,\n      \"unsigned\": 43375,\n      \"ĠUni\": 43376,\n      \"Xbox\": 43377,\n      \"Ġforks\": 43378,\n      \"Ġ702\": 43379,\n      \"å¯\": 43380,\n      \"Ġpromoters\": 43381,\n      \"ĠVapor\": 43382,\n      \"Ġlevied\": 43383,\n      \"slot\": 43384,\n 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\"Ġphylogen\": 43422,\n      \"Ġhauled\": 43423,\n      \"ĠWiley\": 43424,\n      \"ĠJessie\": 43425,\n      \"ĠPrepare\": 43426,\n      \"ĠEps\": 43427,\n      \"Ġtreasurer\": 43428,\n      \"IAS\": 43429,\n      \"Ġcolonists\": 43430,\n      \"Ġinund\": 43431,\n      \"ĠWWF\": 43432,\n      \"ĠConverted\": 43433,\n      \"6000\": 43434,\n      \"outside\": 43435,\n      \"ĠAppearance\": 43436,\n      \"ĠRelic\": 43437,\n      \"ĠMister\": 43438,\n      \"saw\": 43439,\n      \"Ġresultant\": 43440,\n      \"Ġadjective\": 43441,\n      \"ĠLaurel\": 43442,\n      \"ĠHindi\": 43443,\n      \"bda\": 43444,\n      \"Peace\": 43445,\n      \"Ġrebirth\": 43446,\n      \"Ġmembranes\": 43447,\n      \"Ġforwarding\": 43448,\n      \"Ġcollided\": 43449,\n      \"ĠCarolyn\": 43450,\n      \"Kansas\": 43451,\n      \"599\": 43452,\n      \"ĠSolidGoldMagikarp\": 43453,\n      \"Beck\": 43454,\n      \"Ġstressing\": 43455,\n      \"ĠGoo\": 43456,\n      \"ĠCooperative\": 43457,\n      \"Ġfs\": 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   \"ĠLumin\": 43496,\n      \"Ġkale\": 43497,\n      \"Ġinfringing\": 43498,\n      \"announced\": 43499,\n      \"Ġfitt\": 43500,\n      \"batch\": 43501,\n      \"ĠTackle\": 43502,\n      \"ĠLime\": 43503,\n      \"ĠAPP\": 43504,\n      \"ukemia\": 43505,\n      \"Ġruby\": 43506,\n      \"Ġexoner\": 43507,\n      \"ĠCasual\": 43508,\n      \"070\": 43509,\n      \"Ġpelvic\": 43510,\n      \"Ġautomate\": 43511,\n      \"ĠKear\": 43512,\n      \"ĠCoastal\": 43513,\n      \"Ġcreed\": 43514,\n      \"Ġboredom\": 43515,\n      \"ĠStun\": 43516,\n      \"riott\": 43517,\n      \"Ĥİ\": 43518,\n      \"Ġregenerate\": 43519,\n      \"Ġcomedians\": 43520,\n      \"ĠOPER\": 43521,\n      \"Spons\": 43522,\n      \"idium\": 43523,\n      \"onis\": 43524,\n      \"Located\": 43525,\n      \"057\": 43526,\n      \"Ġsuspense\": 43527,\n      \"ĠDating\": 43528,\n      \"Cass\": 43529,\n      \"Ġneocons\": 43530,\n      \"ĠShinzo\": 43531,\n      \"Ġawoken\": 43532,\n      \"christ\": 43533,\n      \"ĠMessages\": 43534,\n      \"attled\": 43535,\n      \"ĠSpray\": 43536,\n      \"ĠSpice\": 43537,\n      \"CW\": 43538,\n      \"Ġshielding\": 43539,\n      \"ĠGaul\": 43540,\n      \"Amid\": 43541,\n      \"Ġparamilitary\": 43542,\n      \"Ġmultif\": 43543,\n      \"ĠTanner\": 43544,\n      \"ilk\": 43545,\n      \"Ġgoddamn\": 43546,\n      \"gements\": 43547,\n      \"Ġbefriend\": 43548,\n      \"mobi\": 43549,\n      \"Ġ388\": 43550,\n      \"folder\": 43551,\n      \"acca\": 43552,\n      \"Ġinsin\": 43553,\n      \"gap\": 43554,\n      \"Nev\": 43555,\n      \"fifth\": 43556,\n      \"Ġpsychiatry\": 43557,\n      \"banks\": 43558,\n      \"THIS\": 43559,\n      \"Ġharb\": 43560,\n      \"acqu\": 43561,\n      \"Ġfacade\": 43562,\n      \"ĠPowerPoint\": 43563,\n      \"803\": 43564,\n      \"Ġbluff\": 43565,\n      \"Shares\": 43566,\n      \"Ġfavoring\": 43567,\n      \"Elizabeth\": 43568,\n      \"ÃįÃį\": 43569,\n      \"Ġranger\": 43570,\n      \"772\": 43571,\n      \"ĠArche\": 43572,\n      \"hak\": 43573,\n      \"ĠGenetics\": 43574,\n      \"ĠFEMA\": 43575,\n      \"Ġevolves\": 43576,\n      \"Ġeste\": 43577,\n      \"ĠPets\": 43578,\n      \"ĠMé\": 43579,\n      \"ĠInteresting\": 43580,\n      \"ĠCanterbury\": 43581,\n      \"chapter\": 43582,\n      \"ĠStarfleet\": 43583,\n      \"Spanish\": 43584,\n      \"Ġdrawback\": 43585,\n      \"ĠNorwich\": 43586,\n      \"970\": 43587,\n      \"north\": 43588,\n      \"aganda\": 43589,\n      \"Ġtransformative\": 43590,\n      \"ramids\": 43591,\n      \"biology\": 43592,\n      \"aday\": 43593,\n      \"Ġpropagation\": 43594,\n      \"ĠGamma\": 43595,\n      \"ĠDenise\": 43596,\n      \"ĠCalculator\": 43597,\n      \"entimes\": 43598,\n      \"ĠBett\": 43599,\n      \"Ġappendix\": 43600,\n      \"ĠHDD\": 43601,\n      \"AKING\": 43602,\n      \"Ġstigmat\": 43603,\n      \"Ġholster\": 43604,\n      \"Ġordinarily\": 43605,\n      \"Chance\": 43606,\n      \"ĠContrary\": 43607,\n      \"Ġadhesive\": 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\"ĠAdapter\": 43721,\n      \"Ġ331\": 43722,\n      \"ĠPetition\": 43723,\n      \"kies\": 43724,\n      \"Mars\": 43725,\n      \"Ġundercut\": 43726,\n      \"zees\": 43727,\n      \"ĠLyons\": 43728,\n      \"ĠDHCP\": 43729,\n      \"Missing\": 43730,\n      \"Ġretirees\": 43731,\n      \"Ġinsidious\": 43732,\n      \"eli\": 43733,\n      \">)\": 43734,\n      \".ãĢį\": 43735,\n      \"Ġfinalists\": 43736,\n      \"ĠAure\": 43737,\n      \"Ġaccuser\": 43738,\n      \"Ġwastes\": 43739,\n      \"ĠYs\": 43740,\n      \"ĠLori\": 43741,\n      \"Ġconstituencies\": 43742,\n      \"Ġsupper\": 43743,\n      \"Ġmayhem\": 43744,\n      \"orange\": 43745,\n      \"Ġmisplaced\": 43746,\n      \"Ġmanagerial\": 43747,\n      \"Ġexce\": 43748,\n      \"ĠCLI\": 43749,\n      \"Ġprimal\": 43750,\n      \"ĠLent\": 43751,\n      \"Crystal\": 43752,\n      \"hover\": 43753,\n      \"ĠNTS\": 43754,\n      \"endum\": 43755,\n      \"Ġdw\": 43756,\n      \"ĠAlc\": 43757,\n      \"nostic\": 43758,\n      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\"Ġinconsistency\": 43831,\n      \"Db\": 43832,\n      \"FY\": 43833,\n      \"Export\": 43834,\n      \"Ġape\": 43835,\n      \"Ġpearl\": 43836,\n      \"ibel\": 43837,\n      \"ĠPACs\": 43838,\n      \"Ġ{\\\\\": 43839,\n      \"Ġactu\": 43840,\n      \"ĠHSBC\": 43841,\n      \"campus\": 43842,\n      \"Ġpayoff\": 43843,\n      \"Ġdeities\": 43844,\n      \"ĠNato\": 43845,\n      \"ouple\": 43846,\n      \"Ġcensored\": 43847,\n      \"ĠClojure\": 43848,\n      \"Ġconfounding\": 43849,\n      \"eni\": 43850,\n      \"Ġreckon\": 43851,\n      \"ophe\": 43852,\n      \"Ġspotting\": 43853,\n      \"Ġsignifies\": 43854,\n      \"Ġpropel\": 43855,\n      \"Ġfestive\": 43856,\n      \"Suggest\": 43857,\n      \"Ġpledging\": 43858,\n      \"ĠBerman\": 43859,\n      \"Ġrebellious\": 43860,\n      \"Ġovershadowed\": 43861,\n      \"Ġinfiltrated\": 43862,\n      \"jobs\": 43863,\n      \"672\": 43864,\n      \"Ġscalable\": 43865,\n      \"Ġdominion\": 43866,\n      \"ĠNewfoundland\": 43867,\n      \"ĠMeadow\": 43868,\n      \"Ġpartitions\": 43869,\n      \"AMI\": 43870,\n      \"Ġsupplementary\": 43871,\n      \"strument\": 43872,\n      \"Ġhairy\": 43873,\n      \"Ġperpetuate\": 43874,\n      \"Ġnutshell\": 43875,\n      \"ĠPotato\": 43876,\n      \"ĠHobbit\": 43877,\n      \"Ġcurses\": 43878,\n      \"Float\": 43879,\n      \"Ġquieter\": 43880,\n      \"Ġfueling\": 43881,\n      \"Ġcapsules\": 43882,\n      \"ĠLust\": 43883,\n      \"ĠHaunted\": 43884,\n      \"Executive\": 43885,\n      \"Ġchildbirth\": 43886,\n      \"Gre\": 43887,\n      \"Ġradiant\": 43888,\n      \"åİ\": 43889,\n      \"Ġmalls\": 43890,\n      \"Ġinept\": 43891,\n      \"ĠWarranty\": 43892,\n      \"Ġspectator\": 43893,\n      \"Eh\": 43894,\n      \"thens\": 43895,\n      \"Ġculminating\": 43896,\n      \"æ©\": 43897,\n      \"arya\": 43898,\n      \"ãĤ®\": 43899,\n      \"ilitarian\": 43900,\n      \"ĠORIG\": 43901,\n      \"ĠSpending\": 43902,\n      \"ptives\": 43903,\n      \"ĠSiren\": 43904,\n      \"ĠRecording\": 43905,\n      \"ayne\": 43906,\n      \"Ġvim\": 43907,\n      \"Ġsprang\": 43908,\n      \"Tang\": 43909,\n      \"ĠMFT\": 43910,\n      \"morning\": 43911,\n      \"ĠWeed\": 43912,\n      \"mpeg\": 43913,\n      \"cession\": 43914,\n      \"ĠChung\": 43915,\n      \"730\": 43916,\n      \"warning\": 43917,\n      \"562\": 43918,\n      \"handedly\": 43919,\n      \"Poor\": 43920,\n      \"Politics\": 43921,\n      \":#\": 43922,\n      \"Ġpian\": 43923,\n      \"Ġfeces\": 43924,\n      \"ĠDocumentation\": 43925,\n      \"Ġbanished\": 43926,\n      \"Ġ399\": 43927,\n      \"ĠARC\": 43928,\n      \"Ġheinous\": 43929,\n      \"Jake\": 43930,\n      \"ĠAmir\": 43931,\n      \"wayne\": 43932,\n      \"vre\": 43933,\n      \"oshenko\": 43934,\n      \"Ġnotebooks\": 43935,\n      \"Ġfoundational\": 43936,\n      \"Ġmarvelous\": 43937,\n      \"ixtape\": 43938,\n      \"Ġwithdrawals\": 43939,\n      \"Ġhorde\": 43940,\n      \"ĠDhabi\": 43941,\n      \"isable\": 43942,\n      \"ĠKD\": 43943,\n      \"Ġcontagious\": 43944,\n      \"ĠDip\": 43945,\n      \"ĠArrows\": 43946,\n      \"Ġpronouns\": 43947,\n      \"Ġmorphine\": 43948,\n      \"ĠBUS\": 43949,\n      \"682\": 43950,\n      \"Ġkosher\": 43951,\n      \"finished\": 43952,\n      \"ĠInstruments\": 43953,\n      \"Ġfused\": 43954,\n      \"yden\": 43955,\n      \"ĠSalmon\": 43956,\n      \"Fab\": 43957,\n      \"affected\": 43958,\n      \"KEN\": 43959,\n      \"CENT\": 43960,\n      \"Domain\": 43961,\n      \"Ġpokemon\": 43962,\n      \"ĠDrinking\": 43963,\n      \"Growing\": 43964,\n      \"ĠInvestigative\": 43965,\n      \"ĠAether\": 43966,\n      \"emi\": 43967,\n      \"Ġtabloid\": 43968,\n      \"Ġrepro\": 43969,\n      \"ĠNotwithstanding\": 43970,\n      \"ĠBerserker\": 43971,\n      \"Ġdramas\": 43972,\n      \"Ġcliché\": 43973,\n      \"Ġbung\": 43974,\n      \"ĠURI\": 43975,\n      \"ĠDos\": 43976,\n      \"044\": 43977,\n      \"Ġpastors\": 43978,\n      \"Ġls\": 43979,\n      \"Ġacrylic\": 43980,\n      \"aunts\": 43981,\n      \"Edward\": 43982,\n      \"Ġmajorities\": 43983,\n      \"Bang\": 43984,\n      \"Ġfielding\": 43985,\n      \"ĠReplacement\": 43986,\n      \"ĠAlchemy\": 43987,\n      \"ppard\": 43988,\n      \"ĠRomeo\": 43989,\n      \"ĠSanct\": 43990,\n      \"ĠLavrov\": 43991,\n      \"ibble\": 43992,\n      \"Instruct\": 43993,\n      \"Ġimpractical\": 43994,\n      \"ĠPlayboy\": 43995,\n      \"cephal\": 43996,\n      \"Ġswaps\": 43997,\n      \"Ġkan\": 43998,\n      \"ĠTheo\": 43999,\n      \"Ġillustrating\": 44000,\n      \"Ġdismantled\": 44001,\n      \"ĠTransgender\": 44002,\n      \"ĠGuth\": 44003,\n      \"UGH\": 44004,\n      \"Ġtriumphant\": 44005,\n      \"Ġencompass\": 44006,\n      \"Ġbookmark\": 44007,\n      \"uddin\": 44008,\n      \"jer\": 44009,\n      \"Ġpredicate\": 44010,\n      \"ESH\": 44011,\n      \"Ġwhence\": 44012,\n      \"ĠABE\": 44013,\n      \"Ġnonprofits\": 44014,\n      \"Sequ\": 44015,\n      \"Ġdiabetic\": 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\"Ġsincerity\": 44053,\n      \"Ġmarqu\": 44054,\n      \"Ġelicit\": 44055,\n      \"ĠProposition\": 44056,\n      \"ĠDemonic\": 44057,\n      \"Ġtacos\": 44058,\n      \"Greek\": 44059,\n      \"Ġpostwar\": 44060,\n      \"Ġinsofar\": 44061,\n      \"ĠPork\": 44062,\n      \"Ġ352\": 44063,\n      \"doctoral\": 44064,\n      \"walking\": 44065,\n      \"Ġmidterm\": 44066,\n      \"ĠSammy\": 44067,\n      \"sighted\": 44068,\n      \"ĠTRANS\": 44069,\n      \"ici\": 44070,\n      \"ALD\": 44071,\n      \"ĠUSL\": 44072,\n      \"ĠFISA\": 44073,\n      \"ĠAmpl\": 44074,\n      \"ĠAlexandra\": 44075,\n      \"inelli\": 44076,\n      \"Train\": 44077,\n      \"Ġsignify\": 44078,\n      \"ĠVersus\": 44079,\n      \"Ġobfusc\": 44080,\n      \"Ġkh\": 44081,\n      \"Ġaggro\": 44082,\n      \"ĠRenault\": 44083,\n      \"Ġ348\": 44084,\n      \"518\": 44085,\n      \"oxicity\": 44086,\n      \"022\": 44087,\n      \"ĠTwist\": 44088,\n      \"Ġgoofy\": 44089,\n      \"Dynamic\": 44090,\n      \"Ġbriefings\": 44091,\n      \"might\": 44092,\n      \"899\": 44093,\n      \"Ġderogatory\": 44094,\n      \"Tro\": 44095,\n      \"Ġforging\": 44096,\n      \"ĠKoran\": 44097,\n      \"ĠMarried\": 44098,\n      \"ĠBucs\": 44099,\n      \"Ġpalate\": 44100,\n      \"ĠConversion\": 44101,\n      \"mable\": 44102,\n      \"413\": 44103,\n      \"Ġ(_\": 44104,\n      \"Ġsiph\": 44105,\n      \"ĠNEO\": 44106,\n      \"college\": 44107,\n      \"Ġmarginally\": 44108,\n      \"Ġflirt\": 44109,\n      \"ĠTraps\": 44110,\n      \"ĠPace\": 44111,\n      \"é»Ĵ\": 44112,\n      \"Ġgoaltender\": 44113,\n      \"Ġforbids\": 44114,\n      \"Ġclerks\": 44115,\n      \"ĠTant\": 44116,\n      \"ĠRobbins\": 44117,\n      \"ĠPrinting\": 44118,\n      \"Ġpremiered\": 44119,\n      \"Ġmagnification\": 44120,\n      \"ĠTG\": 44121,\n      \"ĠRouse\": 44122,\n      \"ĠMock\": 44123,\n      \"odynamics\": 44124,\n      \"Ġpreclude\": 44125,\n      \"ismo\": 44126,\n      \"ĠPulitzer\": 44127,\n      \"Ġavalanche\": 44128,\n      \"ĠKodi\": 44129,\n      \"ribune\": 44130,\n      \"ĠLena\": 44131,\n      \"Electric\": 44132,\n      \"Ġrefinery\": 44133,\n      \"Ġendowed\": 44134,\n      \"Ġcounselors\": 44135,\n      \"Ġdolphin\": 44136,\n      \"ĠMith\": 44137,\n      \"Ġarmoured\": 44138,\n      \"hibited\": 44139,\n      \"Begin\": 44140,\n      \"ĠPW\": 44141,\n      \"Oil\": 44142,\n      \"ĠVor\": 44143,\n      \"ĠSharif\": 44144,\n      \"ĠFrazier\": 44145,\n      \"estate\": 44146,\n      \"Ġjams\": 44147,\n      \"Proxy\": 44148,\n      \"Ġbandits\": 44149,\n      \"ĠPresbyterian\": 44150,\n      \"ĠPremiere\": 44151,\n      \"tiny\": 44152,\n      \"ĠCruel\": 44153,\n      \"Testing\": 44154,\n      \"Ġhomer\": 44155,\n      \"ĠVERS\": 44156,\n      \"ĠProl\": 44157,\n      \"ĠDeposit\": 44158,\n      \"ĠCoffin\": 44159,\n      \"Ġseminars\": 44160,\n      \"Ġsql\": 44161,\n      \"ĠDefendants\": 44162,\n      \"Alternatively\": 44163,\n      \"ĠRats\": 44164,\n      \"ç«\": 44165,\n      \"ethyst\": 44166,\n      \"'>\": 44167,\n      \"Ġissuer\": 44168,\n      \"589\": 44169,\n      \"Ġchaired\": 44170,\n      \"ĠAccessories\": 44171,\n      \"manent\": 44172,\n      \"Ġmarrow\": 44173,\n      \"ĠPrimordial\": 44174,\n      \"CN\": 44175,\n      \"Ġlimitless\": 44176,\n      \"ĠCarnage\": 44177,\n      \"Ġundrafted\": 44178,\n      \"qv\": 44179,\n      \"INESS\": 44180,\n      \"onew\": 44181,\n      \"Ġcohesion\": 44182,\n      \"987\": 44183,\n      \"Ġnecks\": 44184,\n      \"Ġfootballer\": 44185,\n      \"ĠGER\": 44186,\n      \"Ġdetectable\": 44187,\n      \"ĠSupporting\": 44188,\n      \"ĠCSV\": 44189,\n      \"ocally\": 44190,\n      \"kHz\": 44191,\n      \"Ġunde\": 44192,\n      \"Ġshone\": 44193,\n      \"Ġbudding\": 44194,\n      \"trak\": 44195,\n      \"Standing\": 44196,\n      \"ĠStarcraft\": 44197,\n      \"ĠKemp\": 44198,\n      \"Bench\": 44199,\n      \"Ġthwarted\": 44200,\n      \"ĠGrounds\": 44201,\n      \"athi\": 44202,\n  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\"Song\": 44241,\n      \"Knight\": 44242,\n      \"Ġdiligently\": 44243,\n      \"ĠBroadcast\": 44244,\n      \"Aid\": 44245,\n      \"Ġafar\": 44246,\n      \"ĠHMS\": 44247,\n      \"atonin\": 44248,\n      \"ĠGrateful\": 44249,\n      \"Ġfireplace\": 44250,\n      \"ĠOmni\": 44251,\n      \"euro\": 44252,\n      \"ĠFRE\": 44253,\n      \"ĠShib\": 44254,\n      \"ĠDigest\": 44255,\n      \"toggle\": 44256,\n      \"Ġheadsets\": 44257,\n      \"Ġdiffusion\": 44258,\n      \"ĠSquirrel\": 44259,\n      \"ĠFN\": 44260,\n      \"Ġdarkened\": 44261,\n      \"outher\": 44262,\n      \"Ġsleeps\": 44263,\n      \"ĠXer\": 44264,\n      \"guns\": 44265,\n      \"Ġsetups\": 44266,\n      \"Ġparsed\": 44267,\n      \"Ġmammoth\": 44268,\n      \"ĠCurious\": 44269,\n      \"gob\": 44270,\n      \"ĠFitzpatrick\": 44271,\n      \"ĠEmil\": 44272,\n      \"imov\": 44273,\n      \".............\": 44274,\n      \"ĠBenny\": 44275,\n      \"Secondly\": 44276,\n      \"Ġhearty\": 44277,\n      \"Ġconson\": 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\"Ġglacier\": 44539,\n      \"ĠBite\": 44540,\n      \"Ġ503\": 44541,\n      \"Ġvomit\": 44542,\n      \"Ġfermented\": 44543,\n      \"ĠKhalid\": 44544,\n      \"Ġgraded\": 44545,\n      \"ĠMagicka\": 44546,\n      \"ĠIchigo\": 44547,\n      \"powerful\": 44548,\n      \"icators\": 44549,\n      \"753\": 44550,\n      \"Ġshrew\": 44551,\n      \"Ġ356\": 44552,\n      \"Ġlegalizing\": 44553,\n      \"Ġallotted\": 44554,\n      \"ĠArchdemon\": 44555,\n      \"ithing\": 44556,\n      \"iggurat\": 44557,\n      \"VOL\": 44558,\n      \"Leod\": 44559,\n      \"Ġoily\": 44560,\n      \"Ġinducing\": 44561,\n      \"Ġamygdala\": 44562,\n      \"Ġadmins\": 44563,\n      \"ĠAcquisition\": 44564,\n      \"CAN\": 44565,\n      \"Ġschematic\": 44566,\n      \"Ġmoan\": 44567,\n      \"ĠCameroon\": 44568,\n      \"Ġtink\": 44569,\n      \"Ġmerry\": 44570,\n      \"Ġbutterflies\": 44571,\n      \"ĠGoff\": 44572,\n      \"Ġworkspace\": 44573,\n      \"ĠCorona\": 44574,\n      \"Ġjavascript\": 44575,\n  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\"Ġregretted\": 44652,\n      \"Ġbroccoli\": 44653,\n      \"ĠScores\": 44654,\n      \"eve\": 44655,\n      \"Ġpostings\": 44656,\n      \"Ġaccumulating\": 44657,\n      \"Ġneedless\": 44658,\n      \"elfth\": 44659,\n      \"Ġmayors\": 44660,\n      \"Ġscrib\": 44661,\n      \"Ġanecdotes\": 44662,\n      \"Ġbotched\": 44663,\n      \"ĠRibbon\": 44664,\n      \"ĠConstantine\": 44665,\n      \"iuses\": 44666,\n      \"esses\": 44667,\n      \"Ġdevise\": 44668,\n      \"Compared\": 44669,\n      \"Ġpudding\": 44670,\n      \"Ġgarg\": 44671,\n      \"Ġevoke\": 44672,\n      \"797\": 44673,\n      \"Ġdetox\": 44674,\n      \"909\": 44675,\n      \"ĠPieces\": 44676,\n      \"ĠMcCartney\": 44677,\n      \"Ġmetast\": 44678,\n      \"ĠKrypt\": 44679,\n      \"POR\": 44680,\n      \"Ġtending\": 44681,\n      \"ĠMerchants\": 44682,\n      \"Proof\": 44683,\n      \"ĠVarg\": 44684,\n      \"ĠPortable\": 44685,\n      \"ãĥ¼ãĥĨãĤ£\": 44686,\n      \"Brain\": 44687,\n      \"2500\": 44688,\n      \"Ġfoliage\": 44689,\n      \"ع\": 44690,\n      \"Ġmentors\": 44691,\n      \"ĠAires\": 44692,\n      \"Ġminimalist\": 44693,\n      \"Ġingested\": 44694,\n      \"ĠTrojan\": 44695,\n      \"ĠQian\": 44696,\n      \"involved\": 44697,\n      \"027\": 44698,\n      \"Ġeroded\": 44699,\n      \"RAFT\": 44700,\n      \"Ġblurry\": 44701,\n      \"Mob\": 44702,\n      \"Ġbuffet\": 44703,\n      \"ĠFnatic\": 44704,\n      \"aea\": 44705,\n      \"KNOWN\": 44706,\n      \"ĠInit\": 44707,\n      \"safety\": 44708,\n      \"enum\": 44709,\n      \"ACTION\": 44710,\n      \"ĠCrusher\": 44711,\n      \"ĠDates\": 44712,\n      \"Ġ................\": 44713,\n      \"calling\": 44714,\n      \"akov\": 44715,\n      \"Ġventured\": 44716,\n      \"Ġ555\": 44717,\n      \"auga\": 44718,\n      \"Hart\": 44719,\n      \"ĠAero\": 44720,\n      \"MAC\": 44721,\n      \"Ġthinly\": 44722,\n      \"Ġarra\": 44723,\n      \"STATE\": 44724,\n      \"ilde\": 44725,\n      \"ĠJacqu\": 44726,\n      \"ĠFemales\": 44727,\n      \"Ġtheorem\": 44728,\n      \"Ġ346\": 44729,\n      \"Ġsmartest\": 44730,\n      \"ĠPUBLIC\": 44731,\n      \"ĠKron\": 44732,\n      \"ĠBits\": 44733,\n      \"ĠVessel\": 44734,\n      \"ĠTelephone\": 44735,\n      \"Ġdecap\": 44736,\n      \"Ġadjunct\": 44737,\n      \"ĠSEN\": 44738,\n      \"merga\": 44739,\n      \"Ġredacted\": 44740,\n      \"Ġprehistoric\": 44741,\n      \"Ġexplanatory\": 44742,\n      \"ĠRuns\": 44743,\n      \"ĠUttar\": 44744,\n      \"ĠManny\": 44745,\n      \"ĠAUTHOR\": 44746,\n      \"ĠUnleashed\": 44747,\n      \"ĠBowling\": 44748,\n      \"beans\": 44749,\n      \"793\": 44750,\n      \"Ġuniverses\": 44751,\n      \"Ġsensit\": 44752,\n      \"ĠKung\": 44753,\n      \"repeat\": 44754,\n      \"ctrl\": 44755,\n      \"Ġpaced\": 44756,\n      \"Ġfuller\": 44757,\n      \"Clock\": 44758,\n      \"Ġrecomb\": 44759,\n      \"ĠFaul\": 44760,\n      \"ĠBunker\": 44761,\n      \"Ġpooled\": 44762,\n      \"Ġana\": 44763,\n      \"ĠMouth\": 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\"ĠRaces\": 45134,\n      \"Submit\": 45135,\n      \"Ġpurposefully\": 45136,\n      \"wav\": 45137,\n      \"ĠYok\": 45138,\n      \"Fest\": 45139,\n      \"ĠGerr\": 45140,\n      \"Metro\": 45141,\n      \"Ġitiner\": 45142,\n      \"famous\": 45143,\n      \"Ġ\\\"{\": 45144,\n      \"inline\": 45145,\n      \"washer\": 45146,\n      \"Issue\": 45147,\n      \"ĠCLIENT\": 45148,\n      \"ozo\": 45149,\n      \"Versions\": 45150,\n      \"725\": 45151,\n      \"ĠGlock\": 45152,\n      \"Ġshielded\": 45153,\n      \"ĠPCR\": 45154,\n      \"ENCY\": 45155,\n      \"ĠWeld\": 45156,\n      \"ĠSimpl\": 45157,\n      \"Ġredirected\": 45158,\n      \"ĠKham\": 45159,\n      \"Ġ(>\": 45160,\n      \"Ġlabou\": 45161,\n      \"Ġdiapers\": 45162,\n      \"ssl\": 45163,\n      \"Ġcellar\": 45164,\n      \"organisms\": 45165,\n      \"oresc\": 45166,\n      \"ĠBerks\": 45167,\n      \"didn\": 45168,\n      \"Shipping\": 45169,\n      \"Chest\": 45170,\n      \"Ġundone\": 45171,\n      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\"ĠMobil\": 45951,\n      \"ĠBarbar\": 45952,\n      \"Cooldown\": 45953,\n      \"ĠFritz\": 45954,\n      \"criminal\": 45955,\n      \"repe\": 45956,\n      \"Ġbellig\": 45957,\n      \"ĠBreed\": 45958,\n      \"Ġ418\": 45959,\n      \"Ġsemblance\": 45960,\n      \"ijk\": 45961,\n      \"Ġcurtail\": 45962,\n      \"Ġclinch\": 45963,\n      \"contained\": 45964,\n      \"ĠPrompt\": 45965,\n      \"aston\": 45966,\n      \"Ġwi\": 45967,\n      \"Ġpursuits\": 45968,\n      \"515\": 45969,\n      \"ĠGloss\": 45970,\n      \"Ġflips\": 45971,\n      \"Ġcoupons\": 45972,\n      \"Ġcloning\": 45973,\n      \"ĠLikely\": 45974,\n      \"Removed\": 45975,\n      \"ĠQuartz\": 45976,\n      \"rices\": 45977,\n      \"ĠSpears\": 45978,\n      \"Ġpious\": 45979,\n      \"Ġdepreciation\": 45980,\n      \"ĠDare\": 45981,\n      \"ounces\": 45982,\n      \"amaz\": 45983,\n      \"Ont\": 45984,\n      \"Ġpinnacle\": 45985,\n      \"docker\": 45986,\n      \"026\": 45987,\n      \"ĠWyr\": 45988,\n      \"ĠProper\": 45989,\n      \"ËĪ\": 45990,\n      \"nil\": 45991,\n      \"Bytes\": 45992,\n      \"Ġseeker\": 45993,\n      \"trial\": 45994,\n      \"Ġunfolds\": 45995,\n      \"ĠMarse\": 45996,\n      \"Ġextravagant\": 45997,\n      \"ĠSurvivors\": 45998,\n      \"REDACTED\": 45999,\n      \"ĠSpeedway\": 46000,\n      \"ĠCraigslist\": 46001,\n      \"submit\": 46002,\n      \"ĠGenerations\": 46003,\n      \"Ġupholding\": 46004,\n      \"Ġbloodstream\": 46005,\n      \"ĠMissions\": 46006,\n      \"ĠLawn\": 46007,\n      \"Ġlimbo\": 46008,\n      \"enei\": 46009,\n      \"Huh\": 46010,\n      \"ĠWildcats\": 46011,\n      \"prep\": 46012,\n      \"ĠMarkus\": 46013,\n      \"ĠForbidden\": 46014,\n      \"ritic\": 46015,\n      \"INO\": 46016,\n      \"Ġexhibiting\": 46017,\n      \"requent\": 46018,\n      \"chuk\": 46019,\n      \"Ġhabitual\": 46020,\n      \"ĠCompatibility\": 46021,\n      \"Drag\": 46022,\n      \"RIPT\": 46023,\n      \"ujah\": 46024,\n      \"GROUND\": 46025,\n      \"Ġdelinquent\": 46026,\n      \"Ġburner\": 46027,\n      \"Ġcontemporaries\": 46028,\n      \"Ġgimmick\": 46029,\n      \"loads\": 46030,\n      \"Ġnozzle\": 46031,\n      \"podcast\": 46032,\n      \"ĠWak\": 46033,\n      \"ĠStaten\": 46034,\n      \"ĠKuh\": 46035,\n      \"ãģĵ\": 46036,\n      \"interrupted\": 46037,\n      \"Ġinvincible\": 46038,\n      \"ĠBurnett\": 46039,\n      \"cigarette\": 46040,\n      \"ĠPebble\": 46041,\n      \"ĠTemporary\": 46042,\n      \"ĠMarino\": 46043,\n      \"582\": 46044,\n      \"Ġwasteland\": 46045,\n      \"idently\": 46046,\n      \"Tx\": 46047,\n      \"Ġrite\": 46048,\n      \"ĠPanasonic\": 46049,\n      \"ĠMiddles\": 46050,\n      \"ĠHorton\": 46051,\n      \"aeus\": 46052,\n      \"Ġcuring\": 46053,\n      \"Ġmats\": 46054,\n      \"Ġadjourn\": 46055,\n      \"Ġfearsome\": 46056,\n      \"pez\": 46057,\n      \"boats\": 46058,\n      \"Ġpropell\": 46059,\n      \"Ġconflicted\": 46060,\n      \"ĠAnger\": 46061,\n      \"Ġinsurgent\": 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   \"ĠAOL\": 46101,\n      \"Campaign\": 46102,\n      \"Ġmammal\": 46103,\n      \"ĠVeil\": 46104,\n      \"ĠKev\": 46105,\n      \"ĠMaurit\": 46106,\n      \"ĠDamien\": 46107,\n      \"Nation\": 46108,\n      \"Eastern\": 46109,\n      \"Ġ{:\": 46110,\n      \"Ġ=================================\": 46111,\n      \"Ġstereotypical\": 46112,\n      \"Ġattic\": 46113,\n      \"ĠCyborg\": 46114,\n      \"require\": 46115,\n      \"Ġawarding\": 46116,\n      \"ĠPapua\": 46117,\n      \"btn\": 46118,\n      \"bent\": 46119,\n      \"Boo\": 46120,\n      \"Ġ(=\": 46121,\n      \"ĠXander\": 46122,\n      \"ĠSomerset\": 46123,\n      \"Ġcatchy\": 46124,\n      \"Ġcertify\": 46125,\n      \"STRUCT\": 46126,\n      \"Ġital\": 46127,\n      \"Ġtides\": 46128,\n      \"ĠBrands\": 46129,\n      \"Gray\": 46130,\n      \"competitive\": 46131,\n      \"Ġcurator\": 46132,\n      \"ĠDG\": 46133,\n      \"ominium\": 46134,\n      \"ĠGMOs\": 46135,\n      \"ciating\": 46136,\n      \"ĠCarmen\": 46137,\n      \"oward\": 46138,\n      \"Baltimore\": 46139,\n      \"Ġrgb\": 46140,\n      \"Cu\": 46141,\n      \"Ġwipes\": 46142,\n      \"spell\": 46143,\n      \"ITNESS\": 46144,\n      \"Ġsummarizes\": 46145,\n      \"ĠRevis\": 46146,\n      \"Ġwhistleblowers\": 46147,\n      \"ĠBreach\": 46148,\n      \"Ġcrochet\": 46149,\n      \"kos\": 46150,\n      \"ewski\": 46151,\n      \"Ġrepet\": 46152,\n      \"Ġcrimson\": 46153,\n      \"ĠKarachi\": 46154,\n      \"readable\": 46155,\n      \"dimension\": 46156,\n      \"ĠIgor\": 46157,\n      \"ilded\": 46158,\n      \"ĠZed\": 46159,\n      \"ĠKeane\": 46160,\n      \"ĠCosmetic\": 46161,\n      \"DEP\": 46162,\n      \"Ġretreating\": 46163,\n      \"ĠUA\": 46164,\n      \"ensical\": 46165,\n      \"Ġdusk\": 46166,\n      \"ĠDickens\": 46167,\n      \"Ġarenas\": 46168,\n      \"ĠPassage\": 46169,\n      \"levels\": 46170,\n      \"Ġcurv\": 46171,\n      \"Pope\": 46172,\n      \"Ġchores\": 46173,\n      \"ĠElise\": 46174,\n      \"ĠCompass\": 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    \"Ġnanop\": 46661,\n      \"ĠObi\": 46662,\n      \"ĠLOW\": 46663,\n      \"ogh\": 46664,\n      \"ĠAuthors\": 46665,\n      \"obyl\": 46666,\n      \"Urban\": 46667,\n      \"Ġti\": 46668,\n      \"ĠWeir\": 46669,\n      \"trap\": 46670,\n      \"agy\": 46671,\n      \"Ġparentheses\": 46672,\n      \"Ġoutnumbered\": 46673,\n      \"Ġcounterproductive\": 46674,\n      \"ĠTobias\": 46675,\n      \"ubis\": 46676,\n      \"Parser\": 46677,\n      \"STAR\": 46678,\n      \"Ġsynaptic\": 46679,\n      \"ĠGears\": 46680,\n      \"Ġhiber\": 46681,\n      \"Ġdebunked\": 46682,\n      \"Ġexalted\": 46683,\n      \"awatts\": 46684,\n      \"HOU\": 46685,\n      \"Church\": 46686,\n      \"ĠPixie\": 46687,\n      \"ĠUri\": 46688,\n      \"ĠFormation\": 46689,\n      \"ĠPrediction\": 46690,\n      \"CEO\": 46691,\n      \"Ġthrott\": 46692,\n      \"ĠBritann\": 46693,\n      \"ĠMadagascar\": 46694,\n      \"ëĭ\": 46695,\n      \"Ġbillboards\": 46696,\n      \"ĠRPGs\": 46697,\n      \"ĠBees\": 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    \"ammy\": 46736,\n      \"swing\": 46737,\n      \"Ġsmuggled\": 46738,\n      \"Ġkios\": 46739,\n      \"ĠPERSON\": 46740,\n      \"ĠInquisitor\": 46741,\n      \"Ġsnowy\": 46742,\n      \"Ġscraping\": 46743,\n      \"ĠBurgess\": 46744,\n      \"Ptr\": 46745,\n      \"agame\": 46746,\n      \"RW\": 46747,\n      \"Ġdroid\": 46748,\n      \"ĠLys\": 46749,\n      \"ĠCassandra\": 46750,\n      \"Jacob\": 46751,\n      \"Ġ354\": 46752,\n      \"Ġpasture\": 46753,\n      \"Ġfranc\": 46754,\n      \"ĠScotch\": 46755,\n      \"ĠEnds\": 46756,\n      \"ĠIGF\": 46757,\n      \"definition\": 46758,\n      \"Ġhysterical\": 46759,\n      \"ĠBrowne\": 46760,\n      \"771\": 46761,\n      \"Ġmobilization\": 46762,\n      \"æķ\": 46763,\n      \"iqueness\": 46764,\n      \"Thor\": 46765,\n      \"Ġspearheaded\": 46766,\n      \"Ġembroiled\": 46767,\n      \"Ġconjecture\": 46768,\n      \"judicial\": 46769,\n      \"Choice\": 46770,\n      \"Ġpaperback\": 46771,\n      \"Pir\": 46772,\n      \"Ġrecovers\": 46773,\n      \"ĠSurge\": 46774,\n      \"ĠShogun\": 46775,\n      \"ĠPediatrics\": 46776,\n      \"ãģł\": 46777,\n      \"Ġsweeps\": 46778,\n      \"ĠLaboratories\": 46779,\n      \"ĠPacks\": 46780,\n      \"alus\": 46781,\n      \"addin\": 46782,\n      \"Ġheadlights\": 46783,\n      \"gra\": 46784,\n      \"Evidence\": 46785,\n      \"COLOR\": 46786,\n      \"Admin\": 46787,\n      \"Ĭ±\": 46788,\n      \"Ġconcoct\": 46789,\n      \"sufficient\": 46790,\n      \"Ġunmarked\": 46791,\n      \"Ġrichness\": 46792,\n      \"Ġdissertation\": 46793,\n      \"Ġseasoning\": 46794,\n      \"Ġgib\": 46795,\n      \"ĠMages\": 46796,\n      \"unctions\": 46797,\n      \"ĠNid\": 46798,\n      \"cheat\": 46799,\n      \"ĠTMZ\": 46800,\n      \"citizens\": 46801,\n      \"ĠCatholicism\": 46802,\n      \"nb\": 46803,\n      \"Ġdisembark\": 46804,\n      \"ĠPROGRAM\": 46805,\n      \"aques\": 46806,\n      \"Tyler\": 46807,\n      \"Org\": 46808,\n      \"ĠSlay\": 46809,\n      \"ĠNero\": 46810,\n      \"ĠTownsend\": 46811,\n      \"INTON\": 46812,\n      \"tele\": 46813,\n      \"Ġmesmer\": 46814,\n      \"901\": 46815,\n      \"Ġfireball\": 46816,\n      \"evidence\": 46817,\n      \"affiliated\": 46818,\n      \"ĠFrenchman\": 46819,\n      \"ĠAugusta\": 46820,\n      \"021\": 46821,\n      \"Ġsled\": 46822,\n      \"Ġreused\": 46823,\n      \"ĠImmunity\": 46824,\n      \"Ġwrestle\": 46825,\n      \"assembled\": 46826,\n      \"Maria\": 46827,\n      \"Ġgunshots\": 46828,\n      \"ĠBarbie\": 46829,\n      \"Ġcannabinoids\": 46830,\n      \"ĠToast\": 46831,\n      \"ĠKinder\": 46832,\n      \"IRD\": 46833,\n      \"Ġrejuven\": 46834,\n      \"Ġgore\": 46835,\n      \"Ġrupture\": 46836,\n      \"Ġbreaching\": 46837,\n      \"ĠCartoon\": 46838,\n      \"Ġ455\": 46839,\n      \"ĠPaleo\": 46840,\n      \"614\": 46841,\n      \"Ġspears\": 46842,\n      \"ĠAmes\": 46843,\n      \"abus\": 46844,\n      \"Madison\": 46845,\n      \"GROUP\": 46846,\n      \"Ġaborted\": 46847,\n      \"yah\": 46848,\n      \"Ġfelon\": 46849,\n      \"Ġcausation\": 46850,\n      \"Ġprepaid\": 46851,\n      \"Ġpitted\": 46852,\n      \"oplan\": 46853,\n      \"ĠShelley\": 46854,\n      \"ĠRusso\": 46855,\n      \"ĠPagan\": 46856,\n      \"Ġwillfully\": 46857,\n      \"ĠCanaver\": 46858,\n      \"undrum\": 46859,\n      \"ĠSalary\": 46860,\n      \"ĠArpaio\": 46861,\n      \"reader\": 46862,\n      \"ĠRational\": 46863,\n      \"ĠOverse\": 46864,\n      \"ĠCauses\": 46865,\n      \"Ġ*.\": 46866,\n      \"Ġwob\": 46867,\n      \"Keith\": 46868,\n      \"ĠConsent\": 46869,\n      \"manac\": 46870,\n      \"773\": 46871,\n      \"623\": 46872,\n      \"Ġfateful\": 46873,\n      \"etimes\": 46874,\n      \"Ġspirited\": 46875,\n      \"ĠDys\": 46876,\n      \"Ġhegemony\": 46877,\n      \"Ġboycot\": 46878,\n      \"ĠEnrique\": 46879,\n      \"emouth\": 46880,\n      \"Ġtimelines\": 46881,\n      \"ĠSahara\": 46882,\n      \"ĠRelax\": 46883,\n      \"ĠQuincy\": 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\"ĠSard\": 46997,\n      \"urses\": 46998,\n      \"gpu\": 46999,\n      \"Unix\": 47000,\n      \"Ġunequivocally\": 47001,\n      \"ĠCitizenship\": 47002,\n      \"quad\": 47003,\n      \"mire\": 47004,\n      \"ĠSweeney\": 47005,\n      \"Battery\": 47006,\n      \"615\": 47007,\n      \"Ġpancakes\": 47008,\n      \"Ġoats\": 47009,\n      \"Maps\": 47010,\n      \"ĠContrast\": 47011,\n      \"mbudsman\": 47012,\n      \"ĠEPS\": 47013,\n      \"Ġsubcommittee\": 47014,\n      \"Ġsourcing\": 47015,\n      \"Ġsizing\": 47016,\n      \"ĠBuffer\": 47017,\n      \"ĠMandatory\": 47018,\n      \"Ġmoderates\": 47019,\n      \"ĠPatterns\": 47020,\n      \"ĠChocobo\": 47021,\n      \"ĠZan\": 47022,\n      \"ĠSTATES\": 47023,\n      \"ĠJudging\": 47024,\n      \"ĠInher\": 47025,\n      \"*:\": 47026,\n      \"Ġbil\": 47027,\n      \"ĠYen\": 47028,\n      \"Ġexhilar\": 47029,\n      \"ollower\": 47030,\n      \"zers\": 47031,\n      \"Ġsnug\": 47032,\n      \"maximum\": 47033,\n      \"Ġdespicable\": 47034,\n      \"ĠPACK\": 47035,\n      \"ĠAnnex\": 47036,\n      \"Ġsarcastic\": 47037,\n      \"Ġlatex\": 47038,\n      \"Ġtamp\": 47039,\n      \"ĠSao\": 47040,\n      \"bah\": 47041,\n      \"ĠReverend\": 47042,\n      \"ĠChinatown\": 47043,\n      \"ĠAUT\": 47044,\n      \"documented\": 47045,\n      \"ĠGABA\": 47046,\n      \"ĠCanaan\": 47047,\n      \"ĠÙħ\": 47048,\n      \"Ġgoverns\": 47049,\n      \"prev\": 47050,\n      \"Esc\": 47051,\n      \"ĠEstimates\": 47052,\n      \"OSP\": 47053,\n      \"Ġendeavour\": 47054,\n      \"ĠClosing\": 47055,\n      \"ometime\": 47056,\n      \"everyone\": 47057,\n      \"Ġworsen\": 47058,\n      \"Ġscanners\": 47059,\n      \"Ġdeviations\": 47060,\n      \"ĠRobotics\": 47061,\n      \"ĠCompton\": 47062,\n      \"Ġsorcerer\": 47063,\n      \"Ġendogenous\": 47064,\n      \"Ġemulation\": 47065,\n      \"ĠPiercing\": 47066,\n      \"ĠAph\": 47067,\n      \"ĠSocket\": 47068,\n      \"Ġbould\": 47069,\n      \"ĠOU\": 47070,\n     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\"GAME\": 47109,\n      \"Ġgestation\": 47110,\n      \"algia\": 47111,\n      \"romising\": 47112,\n      \"$,\": 47113,\n      \"crow\": 47114,\n      \"ĠInspection\": 47115,\n      \"atomic\": 47116,\n      \"Relations\": 47117,\n      \"JOHN\": 47118,\n      \"roman\": 47119,\n      \"ĠClockwork\": 47120,\n      \"ĠBakr\": 47121,\n      \"mone\": 47122,\n      \"MET\": 47123,\n      \"Ġthirsty\": 47124,\n      \"Ġbc\": 47125,\n      \"Ġfaculties\": 47126,\n      \"Rum\": 47127,\n      \"Ġnuance\": 47128,\n      \"ĠDarius\": 47129,\n      \"pleting\": 47130,\n      \"fters\": 47131,\n      \"etchup\": 47132,\n      \"Registration\": 47133,\n      \"ĠKE\": 47134,\n      \"Rah\": 47135,\n      \"Ġpreferential\": 47136,\n      \"ĠLash\": 47137,\n      \"ĠHH\": 47138,\n      \"Valid\": 47139,\n      \"ĠNAV\": 47140,\n      \"Ġstarve\": 47141,\n      \"ĠGong\": 47142,\n      \"zynski\": 47143,\n      \"ĠActress\": 47144,\n      \"Ġwik\": 47145,\n      \"Ġunaccompanied\": 47146,\n      \"lvl\": 47147,\n      \"Bride\": 47148,\n      \"ADS\": 47149,\n      \"ĠCommando\": 47150,\n      \"ĠVaughn\": 47151,\n      \"Wallet\": 47152,\n      \"Ġhopping\": 47153,\n      \"ĠVie\": 47154,\n      \"Ġcaveats\": 47155,\n      \"Ġalas\": 47156,\n      \"ifled\": 47157,\n      \"abuse\": 47158,\n      \"661\": 47159,\n      \"Ġibn\": 47160,\n      \"Ġgul\": 47161,\n      \"Ġrobbing\": 47162,\n      \"til\": 47163,\n      \"ILA\": 47164,\n      \"Ġmitigating\": 47165,\n      \"Ġaptly\": 47166,\n      \"Ġtyrant\": 47167,\n      \"Ġmidday\": 47168,\n      \"ĠGilmore\": 47169,\n      \"ĠDecker\": 47170,\n      \"Ġ§§\": 47171,\n      \"partial\": 47172,\n      \"Exactly\": 47173,\n      \"Ġphenotype\": 47174,\n      \"Ġ[+]\": 47175,\n      \"ĠPlex\": 47176,\n      \"ĠIps\": 47177,\n      \"versions\": 47178,\n      \"Ġebook\": 47179,\n      \"Ġchic\": 47180,\n      \"gross\": 47181,\n      \"\\\":\\\"\\\"},{\\\"\": 47182,\n      \"ĠSurprisingly\": 47183,\n      \"Morgan\": 47184,\n     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\"Callback\": 47258,\n      \"Ġshampoo\": 47259,\n      \"ĠShades\": 47260,\n      \"wat\": 47261,\n      \"Ġsuperhuman\": 47262,\n      \"Ġridiculed\": 47263,\n      \"Ġholiest\": 47264,\n      \"ombo\": 47265,\n      \"Ġinterns\": 47266,\n      \"Ġhone\": 47267,\n      \"ĠParagu\": 47268,\n      \"URI\": 47269,\n      \"Ġdangling\": 47270,\n      \"ãĤ»\": 47271,\n      \"sov\": 47272,\n      \"ictional\": 47273,\n      \"availability\": 47274,\n      \"Ġrevocation\": 47275,\n      \"Ġdow\": 47276,\n      \"inic\": 47277,\n      \"ĠTHEIR\": 47278,\n      \"Ġiso\": 47279,\n      \"Ġoutings\": 47280,\n      \"ĠLethal\": 47281,\n      \"Ġ)))\": 47282,\n      \"Ġinaccur\": 47283,\n      \"Ġoutlandish\": 47284,\n      \"Ġanus\": 47285,\n      \"letico\": 47286,\n      \"idon\": 47287,\n      \"lol\": 47288,\n      \"Ġunregulated\": 47289,\n      \"Ġsuccumbed\": 47290,\n      \"Ġcuff\": 47291,\n      \"ĠWasteland\": 47292,\n      \"letal\": 47293,\n      \"Ġsubstr\": 47294,\n      \"Ġcoffers\": 47295,\n      \"Ġautomakers\": 47296,\n      \"ovi\": 47297,\n      \"ĠXue\": 47298,\n      \"ĠDaytona\": 47299,\n      \"Ġjarring\": 47300,\n      \"Ġfumes\": 47301,\n      \"Ġdisbanded\": 47302,\n      \"zik\": 47303,\n      \"itton\": 47304,\n      \"Ġstrikingly\": 47305,\n      \"Ġspores\": 47306,\n      \"Adapter\": 47307,\n      \".):\": 47308,\n      \"ĠLyndon\": 47309,\n      \"ivalry\": 47310,\n      \"Ġorally\": 47311,\n      \"Ġtumultuous\": 47312,\n      \"Ġdispleasure\": 47313,\n      \"Ġcones\": 47314,\n      \"orrect\": 47315,\n      \"Ġappease\": 47316,\n      \"Ġderby\": 47317,\n      \"ĠTripoli\": 47318,\n      \"ĠAless\": 47319,\n      \"Ġpoked\": 47320,\n      \"ĠGuilty\": 47321,\n      \"vP\": 47322,\n      \"Enough\": 47323,\n      \"Ġoriginals\": 47324,\n      \"699\": 47325,\n      \"Ġrabbi\": 47326,\n      \"Ġproverbial\": 47327,\n      \"Ġpostpone\": 47328,\n      \"elope\": 47329,\n      \"ĠMisty\": 47330,\n      \"Ġstaffed\": 47331,\n      \"ĠUnemployment\": 47332,\n      \"reditary\": 47333,\n      \"Ġdiligent\": 47334,\n      \"recomm\": 47335,\n      \"measures\": 47336,\n      \"asin\": 47337,\n      \"825\": 47338,\n      \"Ġponds\": 47339,\n      \"Ġmmol\": 47340,\n      \"ĠSAR\": 47341,\n      \"ĠCARE\": 47342,\n      \"Ġ371\": 47343,\n      \"Ġclenched\": 47344,\n      \"ĠCorsair\": 47345,\n      \"Ġcaricature\": 47346,\n      \"zn\": 47347,\n      \"attach\": 47348,\n      \"ĠSchro\": 47349,\n      \"speak\": 47350,\n      \"painted\": 47351,\n      \"ĠSuc\": 47352,\n      \"ĠENT\": 47353,\n      \"Ġcellul\": 47354,\n      \"ĠPaid\": 47355,\n      \"diagn\": 47356,\n      \"WHERE\": 47357,\n      \"Ġtexted\": 47358,\n      \"Barn\": 47359,\n      \"Ġretracted\": 47360,\n      \"ĠReferred\": 47361,\n      \"Sav\": 47362,\n      \"Ġupkeep\": 47363,\n      \"Ġworkplaces\": 47364,\n      \"ĠTokens\": 47365,\n      \"Ġamplify\": 47366,\n      \"clinical\": 47367,\n      \"Ġmultic\": 47368,\n      \"mberg\": 47369,\n      \"Ġconvoluted\": 47370,\n      \"Region\": 47371,\n      \"565\": 47372,\n      \"ĠTopic\": 47373,\n      \"Ġsnail\": 47374,\n      \"Ġsaline\": 47375,\n      \"Ġinsurrection\": 47376,\n      \"ĠPetr\": 47377,\n      \"forts\": 47378,\n      \"BAT\": 47379,\n      \"ĠNavajo\": 47380,\n      \"Ġrudimentary\": 47381,\n      \"ĠLaksh\": 47382,\n      \"ONDON\": 47383,\n      \"Measure\": 47384,\n      \"Ġtransformer\": 47385,\n      \"ĠGoddard\": 47386,\n      \"Ġcoincides\": 47387,\n      \"irin\": 47388,\n      \"Rex\": 47389,\n      \"ĠBok\": 47390,\n      \"quit\": 47391,\n      \"Ġshotguns\": 47392,\n      \"Ġproletarian\": 47393,\n      \"Ġscorp\": 47394,\n      \"ĠAda\": 47395,\n      \"514\": 47396,\n      \"Ġslander\": 47397,\n      \"recorded\": 47398,\n      \"Ġembell\": 47399,\n      \"risome\": 47400,\n      \"Ġapologizing\": 47401,\n      \"ĠMulcair\": 47402,\n      \"ĠGibraltar\": 47403,\n      \"Cla\": 47404,\n      \"Ġallot\": 47405,\n      \"ĠAttention\": 47406,\n      \"Ġ433\": 47407,\n      \"leave\": 47408,\n      \"Ġwhine\": 47409,\n      \"ĠIssa\": 47410,\n      \"ĠFaust\": 47411,\n      \"ĠBarron\": 47412,\n      \"heny\": 47413,\n      \"Ġvictimized\": 47414,\n      \"Jews\": 47415,\n      \"Ġnurturing\": 47416,\n      \"ettel\": 47417,\n      \"Winged\": 47418,\n      \"ĠSubtle\": 47419,\n      \"Ġflavorful\": 47420,\n      \"ĠReps\": 47421,\n      \"enged\": 47422,\n      \"callback\": 47423,\n      \"Ġdirectional\": 47424,\n      \"Ġclasp\": 47425,\n      \"ĠDirections\": 47426,\n      \"planet\": 47427,\n      \"iculture\": 47428,\n      \"Helper\": 47429,\n      \"icion\": 47430,\n      \"acia\": 47431,\n      \"Ġç¥ŀ\": 47432,\n      \"Ġsurges\": 47433,\n      \"Ġcanoe\": 47434,\n      \"ĠPremiership\": 47435,\n      \"been\": 47436,\n      \"Ġdefied\": 47437,\n      \"ĠTrooper\": 47438,\n      \"Ġtripod\": 47439,\n      \"Ġgasp\": 47440,\n      \"ĠEuph\": 47441,\n      \"ĠAds\": 47442,\n      \"vernight\": 47443,\n      \"highly\": 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\"ĠXan\": 47482,\n      \"jew\": 47483,\n      \"imester\": 47484,\n      \"ĠDickinson\": 47485,\n      \"âĶģ\": 47486,\n      \"Fla\": 47487,\n      \"Ġpree\": 47488,\n      \"ĠRollins\": 47489,\n      \"©¶æ\": 47490,\n      \"Ġdenomination\": 47491,\n      \"ĠLana\": 47492,\n      \"516\": 47493,\n      \"Ġinciting\": 47494,\n      \"scribed\": 47495,\n      \"juries\": 47496,\n      \"ĠWonders\": 47497,\n      \"approximately\": 47498,\n      \"Ġsuspending\": 47499,\n      \"Ġmountainous\": 47500,\n      \"ĠLaugh\": 47501,\n      \"oidal\": 47502,\n      \"Ns\": 47503,\n      \"Detect\": 47504,\n      \")=\": 47505,\n      \"ĠLuthor\": 47506,\n      \"ĠSchwarzenegger\": 47507,\n      \"ĠMuller\": 47508,\n      \"ĠDevi\": 47509,\n      \"ecycle\": 47510,\n      \"Jar\": 47511,\n      \"613\": 47512,\n      \"ĠLongh\": 47513,\n      \"Bah\": 47514,\n      \"ĠSPORTS\": 47515,\n      \"nw\": 47516,\n      \"Ġrefinement\": 47517,\n      \"Ġwaterways\": 47518,\n      \"Ġdiner\": 47519,\n      \"Blade\": 47520,\n      \"683\": 47521,\n      \"Fac\": 47522,\n      \"Ġinitials\": 47523,\n      \"Ġrog\": 47524,\n      \"Ġparanormal\": 47525,\n      \"BUT\": 47526,\n      \"Ġ[(\": 47527,\n      \"ĠSwanson\": 47528,\n      \"ĠMesh\": 47529,\n      \"âĸ¬\": 47530,\n      \"Improve\": 47531,\n      \"ĠRadiation\": 47532,\n      \"ĠEsther\": 47533,\n      \"ĠEsk\": 47534,\n      \"ĠAly\": 47535,\n      \"iky\": 47536,\n      \"Ġirrad\": 47537,\n      \"ĠBuckingham\": 47538,\n      \"Ġrefill\": 47539,\n      \"Ġ._\": 47540,\n      \"Repe\": 47541,\n      \"CONCLUS\": 47542,\n      \"Ġdifferentiated\": 47543,\n      \"Ġchirop\": 47544,\n      \"ĠAtkins\": 47545,\n      \"Pattern\": 47546,\n      \"Ġexcise\": 47547,\n      \"Ġcabal\": 47548,\n      \"NSA\": 47549,\n      \"ĠSTA\": 47550,\n      \"ĠSIL\": 47551,\n      \"ĠParaly\": 47552,\n      \"Ġrye\": 47553,\n      \"ĠHowell\": 47554,\n      \"ĠCountdown\": 47555,\n      \"nesses\": 47556,\n      \"alysed\": 47557,\n      \"Ġresize\": 47558,\n      \"ãĤ½\": 47559,\n      \"Ġbudgetary\": 47560,\n      \"ĠStras\": 47561,\n      \"wang\": 47562,\n      \"Ġapiece\": 47563,\n      \"Ġprecincts\": 47564,\n      \"Ġpeach\": 47565,\n      \"Ġskyline\": 47566,\n      \"Ġ353\": 47567,\n      \"popular\": 47568,\n      \"Appearances\": 47569,\n      \"ĠMechanics\": 47570,\n      \"ĠDevOnline\": 47571,\n      \"Sullivan\": 47572,\n      \"Zen\": 47573,\n      \"Ġpu\": 47574,\n      \"opolis\": 47575,\n      \"544\": 47576,\n      \"Ġdeform\": 47577,\n      \"Ġcounteract\": 47578,\n      \"ĠLange\": 47579,\n      \"Ġ417\": 47580,\n      \"Console\": 47581,\n      \"774\": 47582,\n      \"Ġnodding\": 47583,\n      \"Ġpopulism\": 47584,\n      \"Ġhep\": 47585,\n      \"Ġcounselling\": 47586,\n      \"compliance\": 47587,\n      \"UFF\": 47588,\n      \"Ġundeniably\": 47589,\n      \"Ġrailing\": 47590,\n      \"ĠHorowitz\": 47591,\n      \"ĠSimone\": 47592,\n      \"ĠBungie\": 47593,\n      \"Ġak\": 47594,\n      \"ĠTalks\": 47595,\n      \"xff\": 47596,\n      \"flake\": 47597,\n      \"Crash\": 47598,\n      \"Ġsweaty\": 47599,\n      \"Ġbanquet\": 47600,\n      \"ĠOFFIC\": 47601,\n      \"Ġinventive\": 47602,\n      \"Ġastronomer\": 47603,\n      \"ĠStamford\": 47604,\n      \"ĠScare\": 47605,\n      \"ĠGREEN\": 47606,\n      \"olicited\": 47607,\n      \"Ġrusher\": 47608,\n      \"Ġcentrist\": 47609,\n      \"ighting\": 47610,\n      \"Ġsubclass\": 47611,\n      \"Ġdisav\": 47612,\n      \"Ġdefund\": 47613,\n      \"ĠNanto\": 47614,\n      \"ociate\": 47615,\n      \"mast\": 47616,\n      \"Ġpacif\": 47617,\n      \"Ġmend\": 47618,\n      \"eers\": 47619,\n      \"immigration\": 47620,\n      \"ESSION\": 47621,\n      \"Ġnumbering\": 47622,\n      \"Ġlaughable\": 47623,\n      \"ĠEnded\": 47624,\n      \"viation\": 47625,\n      \"emark\": 47626,\n      \"Pitt\": 47627,\n      \"Ġmeticulous\": 47628,\n      \"ĠLF\": 47629,\n      \"Ġcongratulated\": 47630,\n      \"ĠBirch\": 47631,\n      \"Ġswayed\": 47632,\n      \"Ġsemifinals\": 47633,\n      \"Ġhumankind\": 47634,\n      \"matter\": 47635,\n      \"ĠEquip\": 47636,\n      \"opausal\": 47637,\n      \"Said\": 47638,\n      \"ĠLayout\": 47639,\n      \"Ġvoicing\": 47640,\n      \"Ġthug\": 47641,\n      \"Ġpornographic\": 47642,\n      \"IPS\": 47643,\n      \"Ġmoaning\": 47644,\n      \"Ġgrievance\": 47645,\n      \"Ġconfessions\": 47646,\n      \"escal\": 47647,\n      \"TEXTURE\": 47648,\n      \"Authent\": 47649,\n      \"osaurus\": 47650,\n      \"Purchase\": 47651,\n      \"Ġrelegation\": 47652,\n      \"alter\": 47653,\n      \"Ġ³³\": 47654,\n      \"Ġriddled\": 47655,\n      \"Ġogre\": 47656,\n      \"ĠLowell\": 47657,\n      \"Occup\": 47658,\n      \"Eat\": 47659,\n      \"ĠHyder\": 47660,\n      \"ĠAdviser\": 47661,\n      \"Commerce\": 47662,\n      \"Hunt\": 47663,\n      \"ĠOrth\": 47664,\n      \"ĠCompetitive\": 47665,\n      \"ĠCLA\": 47666,\n      \"CDC\": 47667,\n      \"Ġsalads\": 47668,\n      \"Fle\": 47669,\n      \"Ġindustrialized\": 47670,\n      \"`,\": 47671,\n      \"ĠOWN\": 47672,\n      \"Ġbeck\": 47673,\n      \"ĠParticularly\": 47674,\n      \"oubt\": 47675,\n      \"ĠmM\": 47676,\n      \"ĠHussain\": 47677,\n      \"ĠChennai\": 47678,\n      \"Ġ920\": 47679,\n      \"Ġappointing\": 47680,\n      \"ĠCullen\": 47681,\n      \",,,,,,,,\": 47682,\n      \"Ġpores\": 47683,\n      \"verified\": 47684,\n      \"Ġbiochemical\": 47685,\n      \"emate\": 47686,\n      \"Ġcowardly\": 47687,\n      \"ĠHelsinki\": 47688,\n      \"ĠEthiopian\": 47689,\n      \"SOURCE\": 47690,\n      \"ERC\": 47691,\n      \"estro\": 47692,\n      \"Ġbiotech\": 47693,\n      \"ĠSour\": 47694,\n      \"Ġbrewer\": 47695,\n      \"Bloomberg\": 47696,\n      \"Ġintensify\": 47697,\n      \"Glass\": 47698,\n      \"anco\": 47699,\n      \"ĠFDR\": 47700,\n      \"greSQL\": 47701,\n      \"ĠFires\": 47702,\n      \"©¶æ¥µ\": 47703,\n      \"eco\": 47704,\n      \"1001\": 47705,\n      \"ĠHomeless\": 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\"ĠDaylight\": 47743,\n      \"Ġ361\": 47744,\n      \"meet\": 47745,\n      \"Ġstatically\": 47746,\n      \"Ġonlook\": 47747,\n      \"Ġki\": 47748,\n      \"illegal\": 47749,\n      \"Ġvelvet\": 47750,\n      \"Ġdehydration\": 47751,\n      \"Ġacquies\": 47752,\n      \"ĠRez\": 47753,\n      \"akura\": 47754,\n      \"ĠUpton\": 47755,\n      \"atro\": 47756,\n      \"Ġincomprehensible\": 47757,\n      \"Ġbackdoor\": 47758,\n      \"ĠRhino\": 47759,\n      \"727\": 47760,\n      \"Ġmaths\": 47761,\n      \")+\": 47762,\n      \"Ġheresy\": 47763,\n      \"Ġdf\": 47764,\n      \"ĠRoche\": 47765,\n      \"ĠLydia\": 47766,\n      \"Ġpancreat\": 47767,\n      \"reply\": 47768,\n      \"arrell\": 47769,\n      \"Ġsolicitation\": 47770,\n      \"Ġcircadian\": 47771,\n      \"BIP\": 47772,\n      \"Ġforay\": 47773,\n      \"Ġcryptic\": 47774,\n      \"izu\": 47775,\n      \"imeo\": 47776,\n      \"ĠTomato\": 47777,\n      \"ĠHoms\": 47778,\n      \"examination\": 47779,\n      \"Ġquarry\": 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    \"Ultimate\": 47892,\n      \"ĠSurprise\": 47893,\n      \"Ġpageant\": 47894,\n      \"offer\": 47895,\n      \"Whoever\": 47896,\n      \"Ġwiser\": 47897,\n      \"Ġchemist\": 47898,\n      \"ĠHELL\": 47899,\n      \"ĠBulk\": 47900,\n      \"Ġplutonium\": 47901,\n      \"ĠCOVER\": 47902,\n      \"Ö¼\": 47903,\n      \"failed\": 47904,\n      \"Ġtirelessly\": 47905,\n      \"Ġinfertility\": 47906,\n      \"ĠTrident\": 47907,\n      \"ĠShowtime\": 47908,\n      \"ĠCiv\": 47909,\n      \"Vice\": 47910,\n      \"requires\": 47911,\n      \"ittance\": 47912,\n      \"Ġuncontrolled\": 47913,\n      \"interesting\": 47914,\n      \"561\": 47915,\n      \"Ġinnovate\": 47916,\n      \"ategic\": 47917,\n      \"Lie\": 47918,\n      \"ĠSelling\": 47919,\n      \"Ul\": 47920,\n      \"Ġsavior\": 47921,\n      \"ĠTosh\": 47922,\n      \"Ġswast\": 47923,\n      \"PASS\": 47924,\n      \"Ġrink\": 47925,\n      \"Ġcardio\": 47926,\n      \"ĠIro\": 47927,\n      \"udi\": 47928,\n      \"Ġvantage\": 47929,\n      \"Ġvans\": 47930,\n      \"ĠNiño\": 47931,\n      \"+=\": 47932,\n      \"Ġpropagate\": 47933,\n      \"<?\": 47934,\n      \"Ġmethodological\": 47935,\n      \"20439\": 47936,\n      \"Ġtriglycer\": 47937,\n      \"Ġingrained\": 47938,\n      \"ĠAnnotations\": 47939,\n      \"arranted\": 47940,\n      \"617\": 47941,\n      \"ĠSodium\": 47942,\n      \"ĠAAC\": 47943,\n      \"technical\": 47944,\n      \"multipl\": 47945,\n      \"Ġ373\": 47946,\n      \"åĭ\": 47947,\n      \"Ġdecisively\": 47948,\n      \"Ġboosters\": 47949,\n      \"Ġdesserts\": 47950,\n      \"ĠGrenade\": 47951,\n      \"Ġtestifying\": 47952,\n      \"ĠScully\": 47953,\n      \"IDs\": 47954,\n      \"Ġlockdown\": 47955,\n      \"ĠScher\": 47956,\n      \"ĠRé\": 47957,\n      \"ĠWhitman\": 47958,\n      \"ĠRamsay\": 47959,\n      \"remote\": 47960,\n      \"Ġhikers\": 47961,\n      \"ĠHyundai\": 47962,\n      \"Ġconscientious\": 47963,\n      \"Ġclerics\": 47964,\n      \"ĠSiberian\": 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\"Adapt\": 48003,\n      \"Ġempath\": 48004,\n      \"nv\": 48005,\n      \"ĠPROT\": 48006,\n      \"ĠDonn\": 48007,\n      \"ĠPax\": 48008,\n      \"ĠBayer\": 48009,\n      \"tis\": 48010,\n      \"Square\": 48011,\n      \"Ġfootprints\": 48012,\n      \"particip\": 48013,\n      \"ĠChilean\": 48014,\n      \"Brend\": 48015,\n      \"inducing\": 48016,\n      \"Magn\": 48017,\n      \"Ġclubhouse\": 48018,\n      \"ĠMagnum\": 48019,\n      \"Ġencamp\": 48020,\n      \"ĠEthnic\": 48021,\n      \"ucha\": 48022,\n      \"erey\": 48023,\n      \"Ġwatered\": 48024,\n      \"ĠCalais\": 48025,\n      \"Ġcomplexion\": 48026,\n      \"Ġsects\": 48027,\n      \"Ġrenters\": 48028,\n      \"Ġbras\": 48029,\n      \"oÄŁan\": 48030,\n      \"Timeout\": 48031,\n      \"Management\": 48032,\n      \"Ġinfographic\": 48033,\n      \"Pokemon\": 48034,\n      \"Clar\": 48035,\n      \"Ġlocality\": 48036,\n      \"Ġflora\": 48037,\n      \"asel\": 48038,\n      \"Pont\": 48039,\n      \"Ġpopulate\": 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\"addon\": 48078,\n      \"ĠHAM\": 48079,\n      \"ĠFrieza\": 48080,\n      \"Woman\": 48081,\n      \".$\": 48082,\n      \"Ġcrib\": 48083,\n      \"ĠHerod\": 48084,\n      \"Ġtimers\": 48085,\n      \"ĠSpaces\": 48086,\n      \"ĠMacintosh\": 48087,\n      \"ataka\": 48088,\n      \"Ġglide\": 48089,\n      \"Ġsmelling\": 48090,\n      \"ĠBAL\": 48091,\n      \"Ġunsu\": 48092,\n      \"Ġcondos\": 48093,\n      \"Ġbicycl\": 48094,\n      \"ĠRevival\": 48095,\n      \"553\": 48096,\n      \"Ġjuggling\": 48097,\n      \"Hug\": 48098,\n      \"ĠKardashian\": 48099,\n      \"ĠBalkans\": 48100,\n      \"multiple\": 48101,\n      \"Ġnutritious\": 48102,\n      \"ocry\": 48103,\n      \"1900\": 48104,\n      \"Ġintegrates\": 48105,\n      \"Ġadjoining\": 48106,\n      \"ĠFolder\": 48107,\n      \"rollment\": 48108,\n      \"venient\": 48109,\n      \"Ġuber\": 48110,\n      \"yi\": 48111,\n      \"Ġwhiff\": 48112,\n      \"ĠJuven\": 48113,\n      \"ĠBorough\": 48114,\n      \"nette\": 48115,\n      \"Ġbilingual\": 48116,\n      \"ĠSparks\": 48117,\n      \"phthal\": 48118,\n      \"manufact\": 48119,\n      \"Ġtouting\": 48120,\n      \"ĠPHI\": 48121,\n      \"Keefe\": 48122,\n      \"Reward\": 48123,\n      \"Ġinfall\": 48124,\n      \"ĠTemper\": 48125,\n      \"typically\": 48126,\n      \"ĠNikol\": 48127,\n      \"Ġregulars\": 48128,\n      \"Ġpseudonym\": 48129,\n      \"Ġexhibitions\": 48130,\n      \"Ġblaster\": 48131,\n      \"Ġ409\": 48132,\n      \"warming\": 48133,\n      \"Ġreverber\": 48134,\n      \"Ġreciprocal\": 48135,\n      \"Ġ670\": 48136,\n      \"ipient\": 48137,\n      \"bett\": 48138,\n      \"ĠBegins\": 48139,\n      \"Ġitching\": 48140,\n      \"ĠPhar\": 48141,\n      \"Assuming\": 48142,\n      \"Ġemitting\": 48143,\n      \"ĠMLG\": 48144,\n      \"Ġbirthplace\": 48145,\n      \"Ġtaunt\": 48146,\n      \"ĠLuffy\": 48147,\n      \"ĠAmit\": 48148,\n      \"Ġcircled\": 48149,\n      \"ĠNost\": 48150,\n      \"ennett\": 48151,\n      \"Ġdeforestation\": 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  \"ĠPSU\": 48189,\n      \"Ġphoton\": 48190,\n      \"ĠAuthentication\": 48191,\n      \"RG\": 48192,\n      \"@#&\": 48193,\n      \"762\": 48194,\n      \"ĠClair\": 48195,\n      \"Ġdiaper\": 48196,\n      \"Ġbrist\": 48197,\n      \"ĠProsecutors\": 48198,\n      \"ĠJem\": 48199,\n      \"628\": 48200,\n      \"ĠEverywhere\": 48201,\n      \"ĠJeanne\": 48202,\n      \"equality\": 48203,\n      \"ãĥ©ãĥ³\": 48204,\n      \"objects\": 48205,\n      \"ĠPelicans\": 48206,\n      \"Ġ392\": 48207,\n      \"Ġblu\": 48208,\n      \"bys\": 48209,\n      \"ĠAgo\": 48210,\n      \"Ġinstructional\": 48211,\n      \"Ġdiscriminating\": 48212,\n      \"ĠTRAN\": 48213,\n      \"ĠCornel\": 48214,\n      \"agos\": 48215,\n      \"Ġtyre\": 48216,\n      \"Ġaspiration\": 48217,\n      \"ĠBridgewater\": 48218,\n      \"\\\":-\": 48219,\n      \"!\\\".\": 48220,\n      \"ĠEns\": 48221,\n      \"ĠCoco\": 48222,\n      \"Pie\": 48223,\n      \"Ġdetach\": 48224,\n      \"ĠCouch\": 48225,\n      \"Ġphysique\": 48226,\n      \"ĠOccupations\": 48227,\n      \"oscopic\": 48228,\n      \"enough\": 48229,\n      \"Buzz\": 48230,\n      \"Appearance\": 48231,\n      \"YP\": 48232,\n      \"Ġracer\": 48233,\n      \"Ġcomplicity\": 48234,\n      \"rpm\": 48235,\n      \"Toy\": 48236,\n      \"Ġinterrupts\": 48237,\n      \"ĠCatalyst\": 48238,\n      \"Ġutilitarian\": 48239,\n      \"impact\": 48240,\n      \"Ġspaghetti\": 48241,\n      \"Ġporous\": 48242,\n      \"Ġesteemed\": 48243,\n      \"Ġinciner\": 48244,\n      \"ĠIOC\": 48245,\n      \"748\": 48246,\n      \"Ġespresso\": 48247,\n      \"ĠSmile\": 48248,\n      \"abilia\": 48249,\n      \"635\": 48250,\n      \"Ġmathematician\": 48251,\n      \"Ġ424\": 48252,\n      \"ĠKL\": 48253,\n      \"ĠHIP\": 48254,\n      \"Ġoverheard\": 48255,\n      \"ĠTud\": 48256,\n      \"ĠTec\": 48257,\n      \"Ġquizz\": 48258,\n      \"Ġflattering\": 48259,\n      \"Ġconn\": 48260,\n      \"âĢİ\": 48261,\n      \"Ġattaches\": 48262,\n      \"ĠROS\": 48263,\n      \"ĠACS\": 48264,\n      \"Ġtcp\": 48265,\n      \"ĠShame\": 48266,\n      \"skip\": 48267,\n      \"respected\": 48268,\n      \"ĠTrinidad\": 48269,\n      \"grain\": 48270,\n      \"Ġfoothold\": 48271,\n      \"ĠUncharted\": 48272,\n      \"ĠJulio\": 48273,\n      \"zl\": 48274,\n      \"avored\": 48275,\n      \"ĠAnxiety\": 48276,\n      \"errors\": 48277,\n      \"ĠCentauri\": 48278,\n      \"itsch\": 48279,\n      \"Daddy\": 48280,\n      \"Ġclutching\": 48281,\n      \"ĠImplement\": 48282,\n      \"ĠGutierrez\": 48283,\n      \"Ġ760\": 48284,\n      \"Ġteleportation\": 48285,\n      \"endra\": 48286,\n      \"Ġreversible\": 48287,\n      \"stros\": 48288,\n      \"Adventure\": 48289,\n      \"083\": 48290,\n      \"Ġliberating\": 48291,\n      \"Ġasphalt\": 48292,\n      \"ĠSpend\": 48293,\n      \"ARDS\": 48294,\n      \"imsy\": 48295,\n      \"PRES\": 48296,\n      \"ĠEmerging\": 48297,\n      \"Ġwildfires\": 48298,\n      \"Ġtechnologically\": 48299,\n      \"Ġemits\": 48300,\n      \"ĠARTICLE\": 48301,\n      \"Ġirregularities\": 48302,\n      \"Ġcherish\": 48303,\n      \"çīĪ\": 48304,\n      \"Ġstink\": 48305,\n      \"ĠRost\": 48306,\n      \"Economic\": 48307,\n      \"Ġcoughing\": 48308,\n      \"ĠMcCann\": 48309,\n      \"properties\": 48310,\n      \"ilantro\": 48311,\n      \"Ġrenegoti\": 48312,\n      \"Translation\": 48313,\n      \"Ġinquest\": 48314,\n      \"ĠGrape\": 48315,\n      \"ooters\": 48316,\n      \"gui\": 48317,\n      \"ĠSwordsman\": 48318,\n      \"aceae\": 48319,\n      \"hitting\": 48320,\n      \"Ġrc\": 48321,\n      \"Ġexerted\": 48322,\n      \"ĠSAP\": 48323,\n      \"itent\": 48324,\n      \"Ġperilous\": 48325,\n      \"Ġobscurity\": 48326,\n      \"Ġassassinate\": 48327,\n      \"Ġaboriginal\": 48328,\n      \"Ġrescuing\": 48329,\n      \"ĠShattered\": 48330,\n      \"locking\": 48331,\n      \"allion\": 48332,\n      \"Changing\": 48333,\n      \"ĠHarrington\": 48334,\n      \"ĠBord\": 48335,\n      \"ĠAfghans\": 48336,\n      \"Jamie\": 48337,\n      \"aretz\": 48338,\n      \"ĠAugustus\": 48339,\n      \"Ġ386\": 48340,\n      \"830\": 48341,\n      \"Ġjog\": 48342,\n      \"okingly\": 48343,\n      \"Trigger\": 48344,\n      \"ĠHOR\": 48345,\n      \"Statistics\": 48346,\n      \"Ġviewership\": 48347,\n      \"Ġadditives\": 48348,\n      \"hur\": 48349,\n      \"Ġmaximizing\": 48350,\n      \"ĠRove\": 48351,\n      \"ĠLouie\": 48352,\n      \"ĠBucket\": 48353,\n      \"ĠCHRIST\": 48354,\n      \"ousel\": 48355,\n      \"Ġstreaks\": 48356,\n      \"irted\": 48357,\n      \"Ġtert\": 48358,\n      \"Ġcolonialism\": 48359,\n      \"Ġburying\": 48360,\n      \"yk\": 48361,\n      \"Condition\": 48362,\n      \"ĠDPRK\": 48363,\n      \"ById\": 48364,\n      \"751\": 48365,\n      \"âĹ¼\": 48366,\n      \"Ġworrisome\": 48367,\n      \"Ġvocational\": 48368,\n      \"slice\": 48369,\n      \"Ġsails\": 48370,\n      \"ĠCorrectional\": 48371,\n      \"954\": 48372,\n      \"Ġtul\": 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\"Ġclassy\": 48486,\n      \"Leary\": 48487,\n      \"ĠPAGE\": 48488,\n      \"yrs\": 48489,\n      \"Ġtransporter\": 48490,\n      \"Ġmatured\": 48491,\n      \"Ġhijab\": 48492,\n      \"Ġbiome\": 48493,\n      \"Whereas\": 48494,\n      \"Ġextermination\": 48495,\n      \"ĠTues\": 48496,\n      \"ĠTakeru\": 48497,\n      \"ĠAudrey\": 48498,\n      \"erial\": 48499,\n      \"ĠAden\": 48500,\n      \"affles\": 48501,\n      \"Ġnarcissistic\": 48502,\n      \"ĠBaird\": 48503,\n      \"UTF\": 48504,\n      \"Ire\": 48505,\n      \"ĠConnie\": 48506,\n      \"Champ\": 48507,\n      \"Ġwhispering\": 48508,\n      \"ĠHatt\": 48509,\n      \"DK\": 48510,\n      \"Ġdisinfect\": 48511,\n      \"Ġdeducted\": 48512,\n      \"Ġpartake\": 48513,\n      \"Ġdowngrade\": 48514,\n      \"ĠEsports\": 48515,\n      \"ĠContinuing\": 48516,\n      \"Ġdemocratically\": 48517,\n      \"icrobial\": 48518,\n      \"itta\": 48519,\n      \"Ġlimestone\": 48520,\n      \"Ġexempted\": 48521,\n      \"ĠFrenzy\": 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\"Ġslumped\": 48560,\n      \"ĠJPEG\": 48561,\n      \"ĠIcar\": 48562,\n      \"adish\": 48563,\n      \"681\": 48564,\n      \"Ġmajesty\": 48565,\n      \"ĠWolfgang\": 48566,\n      \"ĠElastic\": 48567,\n      \"uper\": 48568,\n      \"Ġviz\": 48569,\n      \"Ġunconsciously\": 48570,\n      \"ĠSTD\": 48571,\n      \"ĠSass\": 48572,\n      \"Ġflowering\": 48573,\n      \"ĠHelic\": 48574,\n      \"ĠDraper\": 48575,\n      \"ĠAmateur\": 48576,\n      \"Ġmanure\": 48577,\n      \"Ġdisingen\": 48578,\n      \"ĠLei\": 48579,\n      \"bring\": 48580,\n      \"949\": 48581,\n      \"Ġinhibited\": 48582,\n      \"Ġheadquartered\": 48583,\n      \"Ġenigmatic\": 48584,\n      \"���\": 48585,\n      \"Ġredress\": 48586,\n      \"RH\": 48587,\n      \"Ġrattled\": 48588,\n      \"Ġdiction\": 48589,\n      \"lio\": 48590,\n      \"ĠTBA\": 48591,\n      \"ĠSNAP\": 48592,\n      \"Calling\": 48593,\n      \"Ġfascists\": 48594,\n      \"ĠDove\": 48595,\n      \"iewicz\": 48596,\n      \"036\": 48597,\n      \"Ġcoasts\": 48598,\n      \"ĠRect\": 48599,\n      \"Ġ)]\": 48600,\n      \"Lot\": 48601,\n      \"629\": 48602,\n      \"ĠSEM\": 48603,\n      \"ĠPetersen\": 48604,\n      \"ĠExplain\": 48605,\n      \"ĠBoards\": 48606,\n      \"ĠBezos\": 48607,\n      \"ĠJournals\": 48608,\n      \"Ġ2024\": 48609,\n      \"parser\": 48610,\n      \"Ġmistrust\": 48611,\n      \"Ġgrate\": 48612,\n      \"ĠLocked\": 48613,\n      \"boa\": 48614,\n      \"Saint\": 48615,\n      \"gaming\": 48616,\n      \"Ġvowel\": 48617,\n      \"inately\": 48618,\n      \"blow\": 48619,\n      \"Allah\": 48620,\n      \"Ġunmatched\": 48621,\n      \"Ġbordering\": 48622,\n      \"ĠExpend\": 48623,\n      \"nr\": 48624,\n      \"Oracle\": 48625,\n      \"rouch\": 48626,\n      \"Ġcontiguous\": 48627,\n      \"acus\": 48628,\n      \"Ġdistraught\": 48629,\n      \"581\": 48630,\n      \"Ġanatomical\": 48631,\n      \"OX\": 48632,\n      \"apixel\": 48633,\n      \"833\": 48634,\n      \"ĠPLUS\": 48635,\n      \"Ġresusc\": 48636,\n      \"Ġabiding\": 48637,\n      \"573\": 48638,\n      \"Ġvacancies\": 48639,\n      \"Emily\": 48640,\n      \"Ġhypothal\": 48641,\n      \"ĠWerner\": 48642,\n      \"ĠWee\": 48643,\n      \"ĠDJs\": 48644,\n      \"513\": 48645,\n      \"Ġwitchcraft\": 48646,\n      \"Ġacupuncture\": 48647,\n      \"entary\": 48648,\n      \"benefit\": 48649,\n      \"Products\": 48650,\n      \"ĠPSP\": 48651,\n      \"ĠMPG\": 48652,\n      \"ĠJinn\": 48653,\n      \"ĠJarrett\": 48654,\n      \"Ġ445\": 48655,\n      \"ĠImaging\": 48656,\n      \"ĠPyth\": 48657,\n      \"Finish\": 48658,\n      \"Ġtex\": 48659,\n      \"Ġjuveniles\": 48660,\n      \"Ġheroism\": 48661,\n      \"Ġdoubtless\": 48662,\n      \"ĠAki\": 48663,\n      \"ĠTend\": 48664,\n      \"ĠPatriarch\": 48665,\n      \"Ġbitters\": 48666,\n      \"ĠTelecommunications\": 48667,\n      \"itatively\": 48668,\n      \"agna\": 48669,\n      \"Ġrg\": 48670,\n      \"ĠSOLD\": 48671,\n      \"Ġcompulsion\": 48672,\n      \"ĠNasa\": 48673,\n      \"ĠKathryn\": 48674,\n      \"Ġmillionaires\": 48675,\n      \"Ġintrinsically\": 48676,\n      \"Ġbolstered\": 48677,\n      \"timeout\": 48678,\n      \"flo\": 48679,\n      \"Ġtutor\": 48680,\n      \"pour\": 48681,\n      \"Statement\": 48682,\n      \"Ġ{*\": 48683,\n      \"ĠRudolph\": 48684,\n      \"ĠKimberly\": 48685,\n      \"rogens\": 48686,\n      \"adiq\": 48687,\n      \"]+\": 48688,\n      \"Ġindignation\": 48689,\n      \"Ġfracturing\": 48690,\n      \"ĠReleases\": 48691,\n      \"ĠGrain\": 48692,\n      \"protein\": 48693,\n      \"Lago\": 48694,\n      \"Ġvacations\": 48695,\n      \"Ġbooted\": 48696,\n      \"ĠTHREE\": 48697,\n      \"ĠHG\": 48698,\n      \"orescence\": 48699,\n      \"Ġtf\": 48700,\n      \"Ġsoar\": 48701,\n      \"iosyncr\": 48702,\n      \"Ġglances\": 48703,\n      \"ĠSpoon\": 48704,\n      \"ĠJury\": 48705,\n      \"ĠCowboy\": 48706,\n      \"Ġcreatively\": 48707,\n      \"Higher\": 48708,\n      \"Ġsolicitor\": 48709,\n    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\"ĠShade\": 48747,\n      \"ĠQuart\": 48748,\n      \"ĠEEG\": 48749,\n      \"Ġfisherman\": 48750,\n      \"OSED\": 48751,\n      \"ĠTypical\": 48752,\n      \"dfx\": 48753,\n      \"Ġmeshes\": 48754,\n      \"Ġetched\": 48755,\n      \"worthiness\": 48756,\n      \"Ġtoppled\": 48757,\n      \"Ġ396\": 48758,\n      \"orius\": 48759,\n      \"Weiss\": 48760,\n      \"Ġmysql\": 48761,\n      \"ĠValhalla\": 48762,\n      \"ÙĴ\": 48763,\n      \"leasing\": 48764,\n      \"Ġrecomp\": 48765,\n      \"rapnel\": 48766,\n      \"Sel\": 48767,\n      \"043\": 48768,\n      \"Ġderailed\": 48769,\n      \"ĠGuides\": 48770,\n      \"IRT\": 48771,\n      \"Ġdehuman\": 48772,\n      \"ĠBrittany\": 48773,\n      \"\\\"))\": 48774,\n      \"Ġexclaim\": 48775,\n      \"Ġbalk\": 48776,\n      \"Ġ840\": 48777,\n      \"CLAIM\": 48778,\n      \"intel\": 48779,\n      \"LAB\": 48780,\n      \"Ġpegged\": 48781,\n      \"Ġastroph\": 48782,\n      \"smoking\": 48783,\n      \"Ġrigging\": 48784,\n      \"Ġfixation\": 48785,\n      \"Ġcatapult\": 48786,\n      \"inside\": 48787,\n      \"ĠCascade\": 48788,\n      \"ĠBolshevik\": 48789,\n      \"Gaza\": 48790,\n      \"Depth\": 48791,\n      \"Ġloudspe\": 48792,\n      \"Ġalmonds\": 48793,\n      \"meyer\": 48794,\n      \"leness\": 48795,\n      \"jen\": 48796,\n      \"fresh\": 48797,\n      \"Ġunbeaten\": 48798,\n      \"ĠSquid\": 48799,\n      \"ĠPresumably\": 48800,\n      \"Timer\": 48801,\n      \"BW\": 48802,\n      \"Ġrosters\": 48803,\n      \"Ġellipt\": 48804,\n      \"ĠHarriet\": 48805,\n      \"database\": 48806,\n      \"ĠMutual\": 48807,\n      \"ĠCommodore\": 48808,\n      \"uked\": 48809,\n      \"knife\": 48810,\n      \"ĠCOMMUN\": 48811,\n      \"hya\": 48812,\n      \"Ġmelts\": 48813,\n      \"archives\": 48814,\n      \"Ġratification\": 48815,\n      \"Ġmultiplying\": 48816,\n      \"Ġinteroper\": 48817,\n      \"Ġascert\": 48818,\n      \"wings\": 48819,\n      \"verting\": 48820,\n      \"ĠScorpion\": 48821,\n      \"aye\": 48822,\n      \"ĠPortsmouth\": 48823,\n      \"ĠMTA\": 48824,\n      \"nit\": 48825,\n      \"iazep\": 48826,\n      \"Ġquarantine\": 48827,\n      \"Ġslideshow\": 48828,\n      \"Ġcentimeters\": 48829,\n      \"Ġsynopsis\": 48830,\n      \"Ġspate\": 48831,\n      \"thirst\": 48832,\n      \"Ġnominating\": 48833,\n      \"ĠMelvin\": 48834,\n      \"Preview\": 48835,\n      \"Ġthrob\": 48836,\n      \"Ġgenerational\": 48837,\n      \"ĠRadius\": 48838,\n      \"restling\": 48839,\n      \"putable\": 48840,\n      \"awar\": 48841,\n      \"NECT\": 48842,\n      \"Ġunlawfully\": 48843,\n      \"ĠRevelations\": 48844,\n      \"Wikipedia\": 48845,\n      \"surv\": 48846,\n      \"Ġeyeing\": 48847,\n      \"ijn\": 48848,\n      \"ĠFW\": 48849,\n      \"Ġbrunt\": 48850,\n      \"Ġinterstellar\": 48851,\n      \"Ġclitor\": 48852,\n      \"ĠCroatian\": 48853,\n      \"ĠChic\": 48854,\n      \"eva\": 48855,\n      \"ĠDisapp\": 48856,\n      \"ĠAkin\": 48857,\n      \"ineries\": 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\"Ġprophes\": 48897,\n      \"Ġdiced\": 48898,\n      \"ĠTravels\": 48899,\n      \"ĠHeader\": 48900,\n      \"ĠClubs\": 48901,\n      \"Assistant\": 48902,\n      \"Ġincrim\": 48903,\n      \"Ġdips\": 48904,\n      \"Ġcrucifix\": 48905,\n      \"ĠShanahan\": 48906,\n      \"ĠInterpret\": 48907,\n      \"Ġ4090\": 48908,\n      \"alogy\": 48909,\n      \"abba\": 48910,\n      \"Ġsimulac\": 48911,\n      \"husband\": 48912,\n      \"SIM\": 48913,\n      \"Ġrecycle\": 48914,\n      \"ucer\": 48915,\n      \"edged\": 48916,\n      \"Ġrenaissance\": 48917,\n      \"ĠBombay\": 48918,\n      \"Catholic\": 48919,\n      \"ĠLINE\": 48920,\n      \"ĠClothing\": 48921,\n      \"reports\": 48922,\n      \"Ġplaus\": 48923,\n      \"Ġdag\": 48924,\n      \"ĠMace\": 48925,\n      \"ZI\": 48926,\n      \"Ġintruder\": 48927,\n      \"ĠVeterinary\": 48928,\n      \"gru\": 48929,\n      \"Ġsneaky\": 48930,\n      \"ĠSie\": 48931,\n      \"ĠCinnamon\": 48932,\n      \"POSE\": 48933,\n      \"Ġcourier\": 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\"Ġvanishing\": 49047,\n      \"esley\": 49048,\n      \"ĠRounds\": 49049,\n      \"Elsa\": 49050,\n      \"593\": 49051,\n      \"Ġredundancy\": 49052,\n      \"ĠSTAND\": 49053,\n      \"Ġprophetic\": 49054,\n      \"Ġhabitable\": 49055,\n      \"ryu\": 49056,\n      \"Ġfaintly\": 49057,\n      \"MODE\": 49058,\n      \"Ġflanked\": 49059,\n      \"IRC\": 49060,\n      \"Awesome\": 49061,\n      \"Ġspurious\": 49062,\n      \"ĠZah\": 49063,\n      \"ĠMSG\": 49064,\n      \"Ġshading\": 49065,\n      \"Ġmotivational\": 49066,\n      \"ĠSantana\": 49067,\n      \"ĠSPR\": 49068,\n      \"Ġexcruciating\": 49069,\n      \"omial\": 49070,\n      \"ĠMiko\": 49071,\n      \"ĠLeopard\": 49072,\n      \"Abyss\": 49073,\n      \"Ġ[|\": 49074,\n      \"dirty\": 49075,\n      \"Ġbaths\": 49076,\n      \"Ġdemoral\": 49077,\n      \"andre\": 49078,\n      \"PB\": 49079,\n      \"Ġunification\": 49080,\n      \"Ġsacrament\": 49081,\n      \"Ġ[&\": 49082,\n      \"Ġpriceless\": 49083,\n      \"Ġgelatin\": 49084,\n      \"Ġemanating\": 49085,\n      \"ĠAllaah\": 49086,\n      \"986\": 49087,\n      \"Ġoutburst\": 49088,\n      \"Ġeras\": 49089,\n      \"ĠXVI\": 49090,\n      \"ĠSPI\": 49091,\n      \"Ott\": 49092,\n      \"ĠLazarus\": 49093,\n      \"PLIED\": 49094,\n      \"Flying\": 49095,\n      \"blogs\": 49096,\n      \"Wisconsin\": 49097,\n      \"Raven\": 49098,\n      \"Ġrebate\": 49099,\n      \"Ġcreeps\": 49100,\n      \"ĠSpan\": 49101,\n      \"ĠPainter\": 49102,\n      \"ĠKira\": 49103,\n      \"ĠAmos\": 49104,\n      \"ĠCorvette\": 49105,\n      \"Consumer\": 49106,\n      \"ĠRecover\": 49107,\n      \"cki\": 49108,\n      \"Ġpesky\": 49109,\n      \"ĠInvention\": 49110,\n      \"Companies\": 49111,\n      \"Ġchallengers\": 49112,\n      \"ademic\": 49113,\n      \"ĠUkrainians\": 49114,\n      \"ĠNeurolog\": 49115,\n      \"ĠForsaken\": 49116,\n      \"Ġentrants\": 49117,\n      \"Ġembattled\": 49118,\n      \"Ġdefunct\": 49119,\n      \"ĠGlacier\": 49120,\n    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\"Ġnegotiator\": 49194,\n      \"ĠChristensen\": 49195,\n      \"limits\": 49196,\n      \"ĠDianne\": 49197,\n      \"Ġelegance\": 49198,\n      \"ĠContracts\": 49199,\n      \"anke\": 49200,\n      \"Obj\": 49201,\n      \"Ġvigilance\": 49202,\n      \"Ġcastles\": 49203,\n      \"ĠNAD\": 49204,\n      \"ĠHolo\": 49205,\n      \"Ġemphatically\": 49206,\n      \"ĠTitus\": 49207,\n      \"ĠServing\": 49208,\n      \"ĠRichie\": 49209,\n      \"ĠPigs\": 49210,\n      \"568\": 49211,\n      \"Ġanimosity\": 49212,\n      \"ĠAttributes\": 49213,\n      \"ĠUriel\": 49214,\n      \"MQ\": 49215,\n      \"myra\": 49216,\n      \"ĠApplicant\": 49217,\n      \"Ġpsychiatrists\": 49218,\n      \"ĠVij\": 49219,\n      \"ĠAbby\": 49220,\n      \"agree\": 49221,\n      \"Push\": 49222,\n      \"ĠkWh\": 49223,\n      \"hiba\": 49224,\n      \"Ġincite\": 49225,\n      \"ĠWeasley\": 49226,\n      \"ĠTaxi\": 49227,\n      \"ministic\": 49228,\n      \"hyper\": 49229,\n      \"ĠFarn\": 49230,\n      \"Ġ601\": 49231,\n      \"ĠNationwide\": 49232,\n      \"Fake\": 49233,\n      \"952\": 49234,\n      \"Ġmaize\": 49235,\n      \"Ġinteracted\": 49236,\n      \"Ġtransitioned\": 49237,\n      \"Ġparasitic\": 49238,\n      \"Ġharmonic\": 49239,\n      \"Ġdecaying\": 49240,\n      \"Ġbaseless\": 49241,\n      \"nsics\": 49242,\n      \"Ġtranspired\": 49243,\n      \"Ġabundantly\": 49244,\n      \"ĠForensic\": 49245,\n      \"Ġtreadmill\": 49246,\n      \"ĠJav\": 49247,\n      \"aband\": 49248,\n      \"Ġsshd\": 49249,\n      \"Ġfrontman\": 49250,\n      \"ĠJakarta\": 49251,\n      \"oller\": 49252,\n      \"drops\": 49253,\n      \"ĠSERVICES\": 49254,\n      \"romptu\": 49255,\n      \"ophical\": 49256,\n      \"hospital\": 49257,\n      \"bledon\": 49258,\n      \"645\": 49259,\n      \"Ġmidrange\": 49260,\n      \"ĠEVENT\": 49261,\n      \"culated\": 49262,\n      \"rawled\": 49263,\n      \"Ġperched\": 49264,\n      \"Ġoverboard\": 49265,\n      \"ĠPeel\": 49266,\n      \"ĠPwr\": 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\"ĠPolo\": 49341,\n      \"ĠMormonism\": 49342,\n      \"restricted\": 49343,\n      \"arlane\": 49344,\n      \"wx\": 49345,\n      \"Ġcreatine\": 49346,\n      \"ĠAnon\": 49347,\n      \"ĠSTUD\": 49348,\n      \"ĠJUL\": 49349,\n      \"ĠTee\": 49350,\n      \"528\": 49351,\n      \"089\": 49352,\n      \"Ġhatched\": 49353,\n      \"Dispatch\": 49354,\n      \"ĠComposite\": 49355,\n      \"Ġ451\": 49356,\n      \"puff\": 49357,\n      \"ĠXCOM\": 49358,\n      \"ĠOrn\": 49359,\n      \"ĠTHANK\": 49360,\n      \"ENDED\": 49361,\n      \"ĠAsheville\": 49362,\n      \"ĠÃľ\": 49363,\n      \"Ġmango\": 49364,\n      \"ĠSlightly\": 49365,\n      \"worldly\": 49366,\n      \"ĠWander\": 49367,\n      \"ĠExpand\": 49368,\n      \"ĠChr\": 49369,\n      \"Mist\": 49370,\n      \"Ġorthodoxy\": 49371,\n      \"ĠUNESCO\": 49372,\n      \"regate\": 49373,\n      \"Elsewhere\": 49374,\n      \"kie\": 49375,\n      \"irled\": 49376,\n      \"Ġtopple\": 49377,\n      \"Ġadoptive\": 49378,\n      \"ĠLegs\": 49379,\n      \"dress\": 49380,\n      \"ĠSagan\": 49381,\n      \"bare\": 49382,\n      \"ĠGlou\": 49383,\n      \"Crunch\": 49384,\n      \"Ġhelpers\": 49385,\n      \"Ġchronically\": 49386,\n      \"ĠHuma\": 49387,\n      \"10000\": 49388,\n      \"Ġaccommodating\": 49389,\n      \"äºĶ\": 49390,\n      \"Ġwrinkles\": 49391,\n      \"Ġdodged\": 49392,\n      \"fourth\": 49393,\n      \"Ġprecon\": 49394,\n      \"Ġcompressor\": 49395,\n      \"ĠKare\": 49396,\n      \"Ġevict\": 49397,\n      \"ĠWarwick\": 49398,\n      \"imar\": 49399,\n      \"Ġmodernization\": 49400,\n      \"Ġbandwagon\": 49401,\n      \"Ġrefuted\": 49402,\n      \"Ġnetted\": 49403,\n      \"ĠNaples\": 49404,\n      \"ĠGenie\": 49405,\n      \"perors\": 49406,\n      \"Ġfielded\": 49407,\n      \"Ġdere\": 49408,\n      \"ĠParables\": 49409,\n      \"lees\": 49410,\n      \"Ġtrout\": 49411,\n      \"aspers\": 49412,\n      \"Ġnihil\": 49413,\n      \"Ġhappiest\": 49414,\n      \"Ġfloppy\": 49415,\n      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  },
  {
    "path": "models/prompt_expansion/fooocus_expansion/tokenizer_config.json",
    "content": "{\n  \"add_prefix_space\": false,\n  \"bos_token\": \"<|endoftext|>\",\n  \"eos_token\": \"<|endoftext|>\",\n  \"model_max_length\": 1024,\n  \"name_or_path\": \"gpt2\",\n  \"special_tokens_map_file\": null,\n  \"tokenizer_class\": \"GPT2Tokenizer\",\n  \"unk_token\": \"<|endoftext|>\"\n}\n"
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0094,\"ĠForth\":50095,\"ĠRecorded\":50096,\"Ġimpossibility\":50097,\"ĠLogged\":50098,\"ĠFranks\":50099,\"ĠRatt\":50100,\"initions\":50101,\"Ġcleaners\":50102,\"Ġsorely\":50103,\"Ġflickering\":50104,\"ĠExamination\":50105,\"catching\":50106,\"alloween\":50107,\"Msg\":50108,\"Ġdunno\":50109,\"Fa\":50110,\"Ġdysph\":50111,\"crazy\":50112,\".''.\":50113,\"Ġmainline\":50114,\"Ġcs\":50115,\"Ġptr\":50116,\"ĠWally\":50117,\"igun\":50118,\"951\":50119,\"ĠBigfoot\":50120,\"fights\":50121,\"Ġretrieving\":50122,\"Jr\":50123,\"Ġduplication\":50124,\"ĠExplan\":50125,\"Ġrelational\":50126,\"Ġquaint\":50127,\"Ġbiscuits\":50128,\"Ġado\":50129,\"Ġshudder\":50130,\"Ġantidote\":50131,\"blooded\":50132,\"ksh\":50133,\"Ġsauces\":50134,\"Ġreinvest\":50135,\"Ġdispensary\":50136,\"ĠDiver\":50137,\"Ġ9000\":50138,\"student\":50139,\"Ġinsepar\":50140,\"escap\":50141,\"Ġtoddlers\":50142,\"ĠGPIO\":50143,\"ĠAssignment\":50144,\"headers\":50145,\"Ġlackluster\":50146,\"Ġaback\":50147,\"956\":50148,\"Ġtoolbar\":50149,\"745\":50150,\"Ġoust\":50151,\"Ġcontemplation\":50152,\"ĠPRESIDENT\":50153,\"Ġ458\":50154,\"======\":50155,\"Ġguaranteeing\":50156,\"ĠHeist\":50157,\"ĠCannes\":50158,\"Ļ½\":50159,\"Ġcollaborator\":50160,\"ĠAmp\":50161,\"Ġgou\":50162,\"ĠSHALL\":50163,\"stories\":50164,\"783\":50165,\"Ġmobilized\":50166,\"Ġbrood\":50167,\"ĠLU\":50168,\"ĠðŁij\":50169,\"Ġrefin\":50170,\"ĠAnthropology\":50171,\"vind\":50172,\"illi\":50173,\"Ġwarranties\":50174,\"ĠBabel\":50175,\"Ġswath\":50176,\"Ġcaches\":50177,\"Ġantagonists\":50178,\"artifacts\":50179,\"Ġhotly\":50180,\"ĠStarts\":50181,\"ĠGö\":50182,\"zag\":50183,\"!!!!!\":50184,\"Ġscourge\":50185,\"Ġconspiring\":50186,\"ruits\":50187,\"reverse\":50188,\"ĠSheen\":50189,\"ĠJesuit\":50190,\"ĠGiovanni\":50191,\"adies\":50192,\"Ġbuttocks\":50193,\"earcher\":50194,\"acan\":50195,\"Ġvolleyball\":50196,\"Ġshrouded\":50197,\"Ġscoreboard\":50198,\"bats\":50199,\"ĠIPM\":50200,\"Ġasses\":50201,\"Ġderegulation\":50202,\"ĠTelegram\":50203,\"ĠReboot\":50204,\"Ġ7000\":50205,\"ĠCanary\":50206,\"Ġkernels\":50207,\"ĠFrançois\":50208,\"ĠDuff\":50209,\"ĠPon\":50210,\"ĠLeica\":50211,\"ĠGarmin\":50212,\"Ġorphans\":50213,\"ĠClaudia\":50214,\"Ġcalendars\":50215,\"ĠLeilan\":50216,\"ento\":50217,\"Rocket\":50218,\"Ġbrunch\":50219,\"ĠHawking\":50220,\"ainers\":50221,\"Ġsensibilities\":50222,\"ĠkW\":50223,\"ĠKand\":50224,\"Ġreclaimed\":50225,\"Ġinterestingly\":50226,\"ש\":50227,\"romy\":50228,\"JM\":50229,\"ĠEnhancement\":50230,\"bush\":50231,\"Skip\":50232,\"Ġrappers\":50233,\"Ġgazing\":50234,\"pedia\":50235,\"athlon\":50236,\"Revolution\":50237,\"Ġsnipers\":50238,\"Ġreverted\":50239,\"Ġconglomerate\":50240,\"Terry\":50241,\"794\":50242,\"Ġharsher\":50243,\"Ġdesolate\":50244,\"ĠHitman\":50245,\"Commission\":50246,\"Ġ(/\":50247,\"âĢ¦.\\\"\":50248,\"Compar\":50249,\"Ġamplification\":50250,\"ominated\":50251,\"Ġregress\":50252,\"ĠCollider\":50253,\"Ġinformants\":50254,\"Ġgazed\":50255,\"<|endoftext|>\":50256}"
  },
  {
    "path": "models/prompt_expansion/put_prompt_expansion_here",
    "content": ""
  },
  {
    "path": "models/safety_checker/put_safety_checker_models_here",
    "content": ""
  },
  {
    "path": "models/style_models/put_t2i_style_model_here",
    "content": ""
  },
  {
    "path": "models/unet/put_unet_files_here",
    "content": ""
  },
  {
    "path": "models/upscale_models/put_esrgan_and_other_upscale_models_here",
    "content": ""
  },
  {
    "path": "models/vae/put_vae_here",
    "content": ""
  },
  {
    "path": "models/vae_approx/put_taesd_encoder_pth_and_taesd_decoder_pth_here",
    "content": ""
  },
  {
    "path": "modules/__init__.py",
    "content": ""
  },
  {
    "path": "modules/anisotropic.py",
    "content": "import torch\n\n\nTensor = torch.Tensor\nDevice = torch.DeviceObjType\nDtype = torch.Type\npad = torch.nn.functional.pad\n\n\ndef _compute_zero_padding(kernel_size: tuple[int, int] | int) -> tuple[int, int]:\n    ky, kx = _unpack_2d_ks(kernel_size)\n    return (ky - 1) // 2, (kx - 1) // 2\n\n\ndef _unpack_2d_ks(kernel_size: tuple[int, int] | int) -> tuple[int, int]:\n    if isinstance(kernel_size, int):\n        ky = kx = kernel_size\n    else:\n        assert len(kernel_size) == 2, '2D Kernel size should have a length of 2.'\n        ky, kx = kernel_size\n\n    ky = int(ky)\n    kx = int(kx)\n    return ky, kx\n\n\ndef gaussian(\n    window_size: int, sigma: Tensor | float, *, device: Device | None = None, dtype: Dtype | None = None\n) -> Tensor:\n\n    batch_size = sigma.shape[0]\n\n    x = (torch.arange(window_size, device=sigma.device, dtype=sigma.dtype) - window_size // 2).expand(batch_size, -1)\n\n    if window_size % 2 == 0:\n        x = x + 0.5\n\n    gauss = torch.exp(-x.pow(2.0) / (2 * sigma.pow(2.0)))\n\n    return gauss / gauss.sum(-1, keepdim=True)\n\n\ndef get_gaussian_kernel1d(\n    kernel_size: int,\n    sigma: float | Tensor,\n    force_even: bool = False,\n    *,\n    device: Device | None = None,\n    dtype: Dtype | None = None,\n) -> Tensor:\n\n    return gaussian(kernel_size, sigma, device=device, dtype=dtype)\n\n\ndef get_gaussian_kernel2d(\n    kernel_size: tuple[int, int] | int,\n    sigma: tuple[float, float] | Tensor,\n    force_even: bool = False,\n    *,\n    device: Device | None = None,\n    dtype: Dtype | None = None,\n) -> Tensor:\n\n    sigma = torch.Tensor([[sigma, sigma]]).to(device=device, dtype=dtype)\n\n    ksize_y, ksize_x = _unpack_2d_ks(kernel_size)\n    sigma_y, sigma_x = sigma[:, 0, None], sigma[:, 1, None]\n\n    kernel_y = get_gaussian_kernel1d(ksize_y, sigma_y, force_even, device=device, dtype=dtype)[..., None]\n    kernel_x = get_gaussian_kernel1d(ksize_x, sigma_x, force_even, device=device, dtype=dtype)[..., None]\n\n    return kernel_y * kernel_x.view(-1, 1, ksize_x)\n\n\ndef _bilateral_blur(\n    input: Tensor,\n    guidance: Tensor | None,\n    kernel_size: tuple[int, int] | int,\n    sigma_color: float | Tensor,\n    sigma_space: tuple[float, float] | Tensor,\n    border_type: str = 'reflect',\n    color_distance_type: str = 'l1',\n) -> Tensor:\n\n    if isinstance(sigma_color, Tensor):\n        sigma_color = sigma_color.to(device=input.device, dtype=input.dtype).view(-1, 1, 1, 1, 1)\n\n    ky, kx = _unpack_2d_ks(kernel_size)\n    pad_y, pad_x = _compute_zero_padding(kernel_size)\n\n    padded_input = pad(input, (pad_x, pad_x, pad_y, pad_y), mode=border_type)\n    unfolded_input = padded_input.unfold(2, ky, 1).unfold(3, kx, 1).flatten(-2)  # (B, C, H, W, Ky x Kx)\n\n    if guidance is None:\n        guidance = input\n        unfolded_guidance = unfolded_input\n    else:\n        padded_guidance = pad(guidance, (pad_x, pad_x, pad_y, pad_y), mode=border_type)\n        unfolded_guidance = padded_guidance.unfold(2, ky, 1).unfold(3, kx, 1).flatten(-2)  # (B, C, H, W, Ky x Kx)\n\n    diff = unfolded_guidance - guidance.unsqueeze(-1)\n    if color_distance_type == \"l1\":\n        color_distance_sq = diff.abs().sum(1, keepdim=True).square()\n    elif color_distance_type == \"l2\":\n        color_distance_sq = diff.square().sum(1, keepdim=True)\n    else:\n        raise ValueError(\"color_distance_type only acceps l1 or l2\")\n    color_kernel = (-0.5 / sigma_color**2 * color_distance_sq).exp()  # (B, 1, H, W, Ky x Kx)\n\n    space_kernel = get_gaussian_kernel2d(kernel_size, sigma_space, device=input.device, dtype=input.dtype)\n    space_kernel = space_kernel.view(-1, 1, 1, 1, kx * ky)\n\n    kernel = space_kernel * color_kernel\n    out = (unfolded_input * kernel).sum(-1) / kernel.sum(-1)\n    return out\n\n\ndef bilateral_blur(\n    input: Tensor,\n    kernel_size: tuple[int, int] | int = (13, 13),\n    sigma_color: float | Tensor = 3.0,\n    sigma_space: tuple[float, float] | Tensor = 3.0,\n    border_type: str = 'reflect',\n    color_distance_type: str = 'l1',\n) -> Tensor:\n    return _bilateral_blur(input, None, kernel_size, sigma_color, sigma_space, border_type, color_distance_type)\n\n\ndef adaptive_anisotropic_filter(x, g=None):\n    if g is None:\n        g = x\n    s, m = torch.std_mean(g, dim=(1, 2, 3), keepdim=True)\n    s = s + 1e-5\n    guidance = (g - m) / s\n    y = _bilateral_blur(x, guidance,\n                        kernel_size=(13, 13),\n                        sigma_color=3.0,\n                        sigma_space=3.0,\n                        border_type='reflect',\n                        color_distance_type='l1')\n    return y\n\n\ndef joint_bilateral_blur(\n    input: Tensor,\n    guidance: Tensor,\n    kernel_size: tuple[int, int] | int,\n    sigma_color: float | Tensor,\n    sigma_space: tuple[float, float] | Tensor,\n    border_type: str = 'reflect',\n    color_distance_type: str = 'l1',\n) -> Tensor:\n    return _bilateral_blur(input, guidance, kernel_size, sigma_color, sigma_space, border_type, color_distance_type)\n\n\nclass _BilateralBlur(torch.nn.Module):\n    def __init__(\n        self,\n        kernel_size: tuple[int, int] | int,\n        sigma_color: float | Tensor,\n        sigma_space: tuple[float, float] | Tensor,\n        border_type: str = 'reflect',\n        color_distance_type: str = \"l1\",\n    ) -> None:\n        super().__init__()\n        self.kernel_size = kernel_size\n        self.sigma_color = sigma_color\n        self.sigma_space = sigma_space\n        self.border_type = border_type\n        self.color_distance_type = color_distance_type\n\n    def __repr__(self) -> str:\n        return (\n            f\"{self.__class__.__name__}\"\n            f\"(kernel_size={self.kernel_size}, \"\n            f\"sigma_color={self.sigma_color}, \"\n            f\"sigma_space={self.sigma_space}, \"\n            f\"border_type={self.border_type}, \"\n            f\"color_distance_type={self.color_distance_type})\"\n        )\n\n\nclass BilateralBlur(_BilateralBlur):\n    def forward(self, input: Tensor) -> Tensor:\n        return bilateral_blur(\n            input, self.kernel_size, self.sigma_color, self.sigma_space, self.border_type, self.color_distance_type\n        )\n\n\nclass JointBilateralBlur(_BilateralBlur):\n    def forward(self, input: Tensor, guidance: Tensor) -> Tensor:\n        return joint_bilateral_blur(\n            input,\n            guidance,\n            self.kernel_size,\n            self.sigma_color,\n            self.sigma_space,\n            self.border_type,\n            self.color_distance_type,\n        )\n"
  },
  {
    "path": "modules/async_worker.py",
    "content": "import threading\n\nfrom extras.inpaint_mask import generate_mask_from_image, SAMOptions\nfrom modules.patch import PatchSettings, patch_settings, patch_all\nimport modules.config\n\npatch_all()\n\n\nclass AsyncTask:\n    def __init__(self, args):\n        from modules.flags import Performance, MetadataScheme, ip_list, disabled\n        from modules.util import get_enabled_loras\n        from modules.config import default_max_lora_number\n        import args_manager\n\n        self.args = args.copy()\n        self.yields = []\n        self.results = []\n        self.last_stop = False\n        self.processing = False\n\n        self.performance_loras = []\n\n        if len(args) == 0:\n            return\n\n        args.reverse()\n        self.generate_image_grid = args.pop()\n        self.prompt = args.pop()\n        self.negative_prompt = args.pop()\n        self.style_selections = args.pop()\n\n        self.performance_selection = Performance(args.pop())\n        self.steps = self.performance_selection.steps()\n        self.original_steps = self.steps\n\n        self.aspect_ratios_selection = args.pop()\n        self.image_number = args.pop()\n        self.output_format = args.pop()\n        self.seed = int(args.pop())\n        self.read_wildcards_in_order = args.pop()\n        self.sharpness = args.pop()\n        self.cfg_scale = args.pop()\n        self.base_model_name = args.pop()\n        self.refiner_model_name = args.pop()\n        self.refiner_switch = args.pop()\n        self.loras = get_enabled_loras([(bool(args.pop()), str(args.pop()), float(args.pop())) for _ in\n                                        range(default_max_lora_number)])\n        self.input_image_checkbox = args.pop()\n        self.current_tab = args.pop()\n        self.uov_method = args.pop()\n        self.uov_input_image = args.pop()\n        self.outpaint_selections = args.pop()\n        self.inpaint_input_image = args.pop()\n        self.inpaint_additional_prompt = args.pop()\n        self.inpaint_mask_image_upload = args.pop()\n\n        self.disable_preview = args.pop()\n        self.disable_intermediate_results = args.pop()\n        self.disable_seed_increment = args.pop()\n        self.black_out_nsfw = args.pop()\n        self.adm_scaler_positive = args.pop()\n        self.adm_scaler_negative = args.pop()\n        self.adm_scaler_end = args.pop()\n        self.adaptive_cfg = args.pop()\n        self.clip_skip = args.pop()\n        self.sampler_name = args.pop()\n        self.scheduler_name = args.pop()\n        self.vae_name = args.pop()\n        self.overwrite_step = args.pop()\n        self.overwrite_switch = args.pop()\n        self.overwrite_width = args.pop()\n        self.overwrite_height = args.pop()\n        self.overwrite_vary_strength = args.pop()\n        self.overwrite_upscale_strength = args.pop()\n        self.mixing_image_prompt_and_vary_upscale = args.pop()\n        self.mixing_image_prompt_and_inpaint = args.pop()\n        self.debugging_cn_preprocessor = args.pop()\n        self.skipping_cn_preprocessor = args.pop()\n        self.canny_low_threshold = args.pop()\n        self.canny_high_threshold = args.pop()\n        self.refiner_swap_method = args.pop()\n        self.controlnet_softness = args.pop()\n        self.freeu_enabled = args.pop()\n        self.freeu_b1 = args.pop()\n        self.freeu_b2 = args.pop()\n        self.freeu_s1 = args.pop()\n        self.freeu_s2 = args.pop()\n        self.debugging_inpaint_preprocessor = args.pop()\n        self.inpaint_disable_initial_latent = args.pop()\n        self.inpaint_engine = args.pop()\n        self.inpaint_strength = args.pop()\n        self.inpaint_respective_field = args.pop()\n        self.inpaint_advanced_masking_checkbox = args.pop()\n        self.invert_mask_checkbox = args.pop()\n        self.inpaint_erode_or_dilate = args.pop()\n        self.save_final_enhanced_image_only = args.pop() if not args_manager.args.disable_image_log else False\n        self.save_metadata_to_images = args.pop() if not args_manager.args.disable_metadata else False\n        self.metadata_scheme = MetadataScheme(\n            args.pop()) if not args_manager.args.disable_metadata else MetadataScheme.FOOOCUS\n\n        self.cn_tasks = {x: [] for x in ip_list}\n        for _ in range(modules.config.default_controlnet_image_count):\n            cn_img = args.pop()\n            cn_stop = args.pop()\n            cn_weight = args.pop()\n            cn_type = args.pop()\n            if cn_img is not None:\n                self.cn_tasks[cn_type].append([cn_img, cn_stop, cn_weight])\n\n        self.debugging_dino = args.pop()\n        self.dino_erode_or_dilate = args.pop()\n        self.debugging_enhance_masks_checkbox = args.pop()\n\n        self.enhance_input_image = args.pop()\n        self.enhance_checkbox = args.pop()\n        self.enhance_uov_method = args.pop()\n        self.enhance_uov_processing_order = args.pop()\n        self.enhance_uov_prompt_type = args.pop()\n        self.enhance_ctrls = []\n        for _ in range(modules.config.default_enhance_tabs):\n            enhance_enabled = args.pop()\n            enhance_mask_dino_prompt_text = args.pop()\n            enhance_prompt = args.pop()\n            enhance_negative_prompt = args.pop()\n            enhance_mask_model = args.pop()\n            enhance_mask_cloth_category = args.pop()\n            enhance_mask_sam_model = args.pop()\n            enhance_mask_text_threshold = args.pop()\n            enhance_mask_box_threshold = args.pop()\n            enhance_mask_sam_max_detections = args.pop()\n            enhance_inpaint_disable_initial_latent = args.pop()\n            enhance_inpaint_engine = args.pop()\n            enhance_inpaint_strength = args.pop()\n            enhance_inpaint_respective_field = args.pop()\n            enhance_inpaint_erode_or_dilate = args.pop()\n            enhance_mask_invert = args.pop()\n            if enhance_enabled:\n                self.enhance_ctrls.append([\n                    enhance_mask_dino_prompt_text,\n                    enhance_prompt,\n                    enhance_negative_prompt,\n                    enhance_mask_model,\n                    enhance_mask_cloth_category,\n                    enhance_mask_sam_model,\n                    enhance_mask_text_threshold,\n                    enhance_mask_box_threshold,\n                    enhance_mask_sam_max_detections,\n                    enhance_inpaint_disable_initial_latent,\n                    enhance_inpaint_engine,\n                    enhance_inpaint_strength,\n                    enhance_inpaint_respective_field,\n                    enhance_inpaint_erode_or_dilate,\n                    enhance_mask_invert\n                ])\n        self.should_enhance = self.enhance_checkbox and (self.enhance_uov_method != disabled.casefold() or len(self.enhance_ctrls) > 0)\n        self.images_to_enhance_count = 0\n        self.enhance_stats = {}\n\nasync_tasks = []\n\n\nclass EarlyReturnException(BaseException):\n    pass\n\n\ndef worker():\n    global async_tasks\n\n    import os\n    import traceback\n    import math\n    import numpy as np\n    import torch\n    import time\n    import shared\n    import random\n    import copy\n    import cv2\n    import modules.default_pipeline as pipeline\n    import modules.core as core\n    import modules.flags as flags\n    import modules.patch\n    import ldm_patched.modules.model_management\n    import extras.preprocessors as preprocessors\n    import modules.inpaint_worker as inpaint_worker\n    import modules.constants as constants\n    import extras.ip_adapter as ip_adapter\n    import extras.face_crop\n    import fooocus_version\n\n    from extras.censor import default_censor\n    from modules.sdxl_styles import apply_style, get_random_style, fooocus_expansion, apply_arrays, random_style_name\n    from modules.private_logger import log\n    from extras.expansion import safe_str\n    from modules.util import (remove_empty_str, HWC3, resize_image, get_image_shape_ceil, set_image_shape_ceil,\n                              get_shape_ceil, resample_image, erode_or_dilate, parse_lora_references_from_prompt,\n                              apply_wildcards)\n    from modules.upscaler import perform_upscale\n    from modules.flags import Performance\n    from modules.meta_parser import get_metadata_parser\n\n    pid = os.getpid()\n    print(f'Started worker with PID {pid}')\n\n    try:\n        async_gradio_app = shared.gradio_root\n        flag = f'''App started successful. Use the app with {str(async_gradio_app.local_url)} or {str(async_gradio_app.server_name)}:{str(async_gradio_app.server_port)}'''\n        if async_gradio_app.share:\n            flag += f''' or {async_gradio_app.share_url}'''\n        print(flag)\n    except Exception as e:\n        print(e)\n\n    def progressbar(async_task, number, text):\n        print(f'[Fooocus] {text}')\n        async_task.yields.append(['preview', (number, text, None)])\n\n    def yield_result(async_task, imgs, progressbar_index, black_out_nsfw, censor=True, do_not_show_finished_images=False):\n        if not isinstance(imgs, list):\n            imgs = [imgs]\n\n        if censor and (modules.config.default_black_out_nsfw or black_out_nsfw):\n            progressbar(async_task, progressbar_index, 'Checking for NSFW content ...')\n            imgs = default_censor(imgs)\n\n        async_task.results = async_task.results + imgs\n\n        if do_not_show_finished_images:\n            return\n\n        async_task.yields.append(['results', async_task.results])\n        return\n\n    def build_image_wall(async_task):\n        results = []\n\n        if len(async_task.results) < 2:\n            return\n\n        for img in async_task.results:\n            if isinstance(img, str) and os.path.exists(img):\n                img = cv2.imread(img)\n                img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n            if not isinstance(img, np.ndarray):\n                return\n            if img.ndim != 3:\n                return\n            results.append(img)\n\n        H, W, C = results[0].shape\n\n        for img in results:\n            Hn, Wn, Cn = img.shape\n            if H != Hn:\n                return\n            if W != Wn:\n                return\n            if C != Cn:\n                return\n\n        cols = float(len(results)) ** 0.5\n        cols = int(math.ceil(cols))\n        rows = float(len(results)) / float(cols)\n        rows = int(math.ceil(rows))\n\n        wall = np.zeros(shape=(H * rows, W * cols, C), dtype=np.uint8)\n\n        for y in range(rows):\n            for x in range(cols):\n                if y * cols + x < len(results):\n                    img = results[y * cols + x]\n                    wall[y * H:y * H + H, x * W:x * W + W, :] = img\n\n        # must use deep copy otherwise gradio is super laggy. Do not use list.append() .\n        async_task.results = async_task.results + [wall]\n        return\n\n    def process_task(all_steps, async_task, callback, controlnet_canny_path, controlnet_cpds_path, current_task_id,\n                     denoising_strength, final_scheduler_name, goals, initial_latent, steps, switch, positive_cond,\n                     negative_cond, task, loras, tiled, use_expansion, width, height, base_progress, preparation_steps,\n                     total_count, show_intermediate_results, persist_image=True):\n        if async_task.last_stop is not False:\n            ldm_patched.modules.model_management.interrupt_current_processing()\n        if 'cn' in goals:\n            for cn_flag, cn_path in [\n                (flags.cn_canny, controlnet_canny_path),\n                (flags.cn_cpds, controlnet_cpds_path)\n            ]:\n                for cn_img, cn_stop, cn_weight in async_task.cn_tasks[cn_flag]:\n                    positive_cond, negative_cond = core.apply_controlnet(\n                        positive_cond, negative_cond,\n                        pipeline.loaded_ControlNets[cn_path], cn_img, cn_weight, 0, cn_stop)\n        imgs = pipeline.process_diffusion(\n            positive_cond=positive_cond,\n            negative_cond=negative_cond,\n            steps=steps,\n            switch=switch,\n            width=width,\n            height=height,\n            image_seed=task['task_seed'],\n            callback=callback,\n            sampler_name=async_task.sampler_name,\n            scheduler_name=final_scheduler_name,\n            latent=initial_latent,\n            denoise=denoising_strength,\n            tiled=tiled,\n            cfg_scale=async_task.cfg_scale,\n            refiner_swap_method=async_task.refiner_swap_method,\n            disable_preview=async_task.disable_preview\n        )\n        del positive_cond, negative_cond  # Save memory\n        if inpaint_worker.current_task is not None:\n            imgs = [inpaint_worker.current_task.post_process(x) for x in imgs]\n        current_progress = int(base_progress + (100 - preparation_steps) / float(all_steps) * steps)\n        if modules.config.default_black_out_nsfw or async_task.black_out_nsfw:\n            progressbar(async_task, current_progress, 'Checking for NSFW content ...')\n            imgs = default_censor(imgs)\n        progressbar(async_task, current_progress, f'Saving image {current_task_id + 1}/{total_count} to system ...')\n        img_paths = save_and_log(async_task, height, imgs, task, use_expansion, width, loras, persist_image)\n        yield_result(async_task, img_paths, current_progress, async_task.black_out_nsfw, False,\n                     do_not_show_finished_images=not show_intermediate_results or async_task.disable_intermediate_results)\n\n        return imgs, img_paths, current_progress\n\n    def apply_patch_settings(async_task):\n        patch_settings[pid] = PatchSettings(\n            async_task.sharpness,\n            async_task.adm_scaler_end,\n            async_task.adm_scaler_positive,\n            async_task.adm_scaler_negative,\n            async_task.controlnet_softness,\n            async_task.adaptive_cfg\n        )\n\n    def save_and_log(async_task, height, imgs, task, use_expansion, width, loras, persist_image=True) -> list:\n        img_paths = []\n        for x in imgs:\n            d = [('Prompt', 'prompt', task['log_positive_prompt']),\n                 ('Negative Prompt', 'negative_prompt', task['log_negative_prompt']),\n                 ('Fooocus V2 Expansion', 'prompt_expansion', task['expansion']),\n                 ('Styles', 'styles',\n                  str(task['styles'] if not use_expansion else [fooocus_expansion] + task['styles'])),\n                 ('Performance', 'performance', async_task.performance_selection.value),\n                 ('Steps', 'steps', async_task.steps),\n                 ('Resolution', 'resolution', str((width, height))),\n                 ('Guidance Scale', 'guidance_scale', async_task.cfg_scale),\n                 ('Sharpness', 'sharpness', async_task.sharpness),\n                 ('ADM Guidance', 'adm_guidance', str((\n                     modules.patch.patch_settings[pid].positive_adm_scale,\n                     modules.patch.patch_settings[pid].negative_adm_scale,\n                     modules.patch.patch_settings[pid].adm_scaler_end))),\n                 ('Base Model', 'base_model', async_task.base_model_name),\n                 ('Refiner Model', 'refiner_model', async_task.refiner_model_name),\n                 ('Refiner Switch', 'refiner_switch', async_task.refiner_switch)]\n\n            if async_task.refiner_model_name != 'None':\n                if async_task.overwrite_switch > 0:\n                    d.append(('Overwrite Switch', 'overwrite_switch', async_task.overwrite_switch))\n                if async_task.refiner_swap_method != flags.refiner_swap_method:\n                    d.append(('Refiner Swap Method', 'refiner_swap_method', async_task.refiner_swap_method))\n            if modules.patch.patch_settings[pid].adaptive_cfg != modules.config.default_cfg_tsnr:\n                d.append(\n                    ('CFG Mimicking from TSNR', 'adaptive_cfg', modules.patch.patch_settings[pid].adaptive_cfg))\n\n            if async_task.clip_skip > 1:\n                d.append(('CLIP Skip', 'clip_skip', async_task.clip_skip))\n            d.append(('Sampler', 'sampler', async_task.sampler_name))\n            d.append(('Scheduler', 'scheduler', async_task.scheduler_name))\n            d.append(('VAE', 'vae', async_task.vae_name))\n            d.append(('Seed', 'seed', str(task['task_seed'])))\n\n            if async_task.freeu_enabled:\n                d.append(('FreeU', 'freeu',\n                          str((async_task.freeu_b1, async_task.freeu_b2, async_task.freeu_s1, async_task.freeu_s2))))\n\n            for li, (n, w) in enumerate(loras):\n                if n != 'None':\n                    d.append((f'LoRA {li + 1}', f'lora_combined_{li + 1}', f'{n} : {w}'))\n\n            metadata_parser = None\n            if async_task.save_metadata_to_images:\n                metadata_parser = modules.meta_parser.get_metadata_parser(async_task.metadata_scheme)\n                metadata_parser.set_data(task['log_positive_prompt'], task['positive'],\n                                         task['log_negative_prompt'], task['negative'],\n                                         async_task.steps, async_task.base_model_name, async_task.refiner_model_name,\n                                         loras, async_task.vae_name)\n            d.append(('Metadata Scheme', 'metadata_scheme',\n                      async_task.metadata_scheme.value if async_task.save_metadata_to_images else async_task.save_metadata_to_images))\n            d.append(('Version', 'version', 'Fooocus v' + fooocus_version.version))\n            img_paths.append(log(x, d, metadata_parser, async_task.output_format, task, persist_image))\n\n        return img_paths\n\n    def apply_control_nets(async_task, height, ip_adapter_face_path, ip_adapter_path, width, current_progress):\n        for task in async_task.cn_tasks[flags.cn_canny]:\n            cn_img, cn_stop, cn_weight = task\n            cn_img = resize_image(HWC3(cn_img), width=width, height=height)\n\n            if not async_task.skipping_cn_preprocessor:\n                cn_img = preprocessors.canny_pyramid(cn_img, async_task.canny_low_threshold,\n                                                     async_task.canny_high_threshold)\n\n            cn_img = HWC3(cn_img)\n            task[0] = core.numpy_to_pytorch(cn_img)\n            if async_task.debugging_cn_preprocessor:\n                yield_result(async_task, cn_img, current_progress, async_task.black_out_nsfw, do_not_show_finished_images=True)\n        for task in async_task.cn_tasks[flags.cn_cpds]:\n            cn_img, cn_stop, cn_weight = task\n            cn_img = resize_image(HWC3(cn_img), width=width, height=height)\n\n            if not async_task.skipping_cn_preprocessor:\n                cn_img = preprocessors.cpds(cn_img)\n\n            cn_img = HWC3(cn_img)\n            task[0] = core.numpy_to_pytorch(cn_img)\n            if async_task.debugging_cn_preprocessor:\n                yield_result(async_task, cn_img, current_progress, async_task.black_out_nsfw, do_not_show_finished_images=True)\n        for task in async_task.cn_tasks[flags.cn_ip]:\n            cn_img, cn_stop, cn_weight = task\n            cn_img = HWC3(cn_img)\n\n            # https://github.com/tencent-ailab/IP-Adapter/blob/d580c50a291566bbf9fc7ac0f760506607297e6d/README.md?plain=1#L75\n            cn_img = resize_image(cn_img, width=224, height=224, resize_mode=0)\n\n            task[0] = ip_adapter.preprocess(cn_img, ip_adapter_path=ip_adapter_path)\n            if async_task.debugging_cn_preprocessor:\n                yield_result(async_task, cn_img, current_progress, async_task.black_out_nsfw, do_not_show_finished_images=True)\n        for task in async_task.cn_tasks[flags.cn_ip_face]:\n            cn_img, cn_stop, cn_weight = task\n            cn_img = HWC3(cn_img)\n\n            if not async_task.skipping_cn_preprocessor:\n                cn_img = extras.face_crop.crop_image(cn_img)\n\n            # https://github.com/tencent-ailab/IP-Adapter/blob/d580c50a291566bbf9fc7ac0f760506607297e6d/README.md?plain=1#L75\n            cn_img = resize_image(cn_img, width=224, height=224, resize_mode=0)\n\n            task[0] = ip_adapter.preprocess(cn_img, ip_adapter_path=ip_adapter_face_path)\n            if async_task.debugging_cn_preprocessor:\n                yield_result(async_task, cn_img, current_progress, async_task.black_out_nsfw, do_not_show_finished_images=True)\n        all_ip_tasks = async_task.cn_tasks[flags.cn_ip] + async_task.cn_tasks[flags.cn_ip_face]\n        if len(all_ip_tasks) > 0:\n            pipeline.final_unet = ip_adapter.patch_model(pipeline.final_unet, all_ip_tasks)\n\n    def apply_vary(async_task, uov_method, denoising_strength, uov_input_image, switch, current_progress, advance_progress=False):\n        if 'subtle' in uov_method:\n            denoising_strength = 0.5\n        if 'strong' in uov_method:\n            denoising_strength = 0.85\n        if async_task.overwrite_vary_strength > 0:\n            denoising_strength = async_task.overwrite_vary_strength\n        shape_ceil = get_image_shape_ceil(uov_input_image)\n        if shape_ceil < 1024:\n            print(f'[Vary] Image is resized because it is too small.')\n            shape_ceil = 1024\n        elif shape_ceil > 2048:\n            print(f'[Vary] Image is resized because it is too big.')\n            shape_ceil = 2048\n        uov_input_image = set_image_shape_ceil(uov_input_image, shape_ceil)\n        initial_pixels = core.numpy_to_pytorch(uov_input_image)\n        if advance_progress:\n            current_progress += 1\n        progressbar(async_task, current_progress, 'VAE encoding ...')\n        candidate_vae, _ = pipeline.get_candidate_vae(\n            steps=async_task.steps,\n            switch=switch,\n            denoise=denoising_strength,\n            refiner_swap_method=async_task.refiner_swap_method\n        )\n        initial_latent = core.encode_vae(vae=candidate_vae, pixels=initial_pixels)\n        B, C, H, W = initial_latent['samples'].shape\n        width = W * 8\n        height = H * 8\n        print(f'Final resolution is {str((width, height))}.')\n        return uov_input_image, denoising_strength, initial_latent, width, height, current_progress\n\n    def apply_inpaint(async_task, initial_latent, inpaint_head_model_path, inpaint_image,\n                      inpaint_mask, inpaint_parameterized, denoising_strength, inpaint_respective_field, switch,\n                      inpaint_disable_initial_latent, current_progress, skip_apply_outpaint=False,\n                      advance_progress=False):\n        if not skip_apply_outpaint:\n            inpaint_image, inpaint_mask = apply_outpaint(async_task, inpaint_image, inpaint_mask)\n\n        inpaint_worker.current_task = inpaint_worker.InpaintWorker(\n            image=inpaint_image,\n            mask=inpaint_mask,\n            use_fill=denoising_strength > 0.99,\n            k=inpaint_respective_field\n        )\n        if async_task.debugging_inpaint_preprocessor:\n            yield_result(async_task, inpaint_worker.current_task.visualize_mask_processing(), 100,\n                         async_task.black_out_nsfw, do_not_show_finished_images=True)\n            raise EarlyReturnException\n\n        if advance_progress:\n            current_progress += 1\n        progressbar(async_task, current_progress, 'VAE Inpaint encoding ...')\n        inpaint_pixel_fill = core.numpy_to_pytorch(inpaint_worker.current_task.interested_fill)\n        inpaint_pixel_image = core.numpy_to_pytorch(inpaint_worker.current_task.interested_image)\n        inpaint_pixel_mask = core.numpy_to_pytorch(inpaint_worker.current_task.interested_mask)\n        candidate_vae, candidate_vae_swap = pipeline.get_candidate_vae(\n            steps=async_task.steps,\n            switch=switch,\n            denoise=denoising_strength,\n            refiner_swap_method=async_task.refiner_swap_method\n        )\n        latent_inpaint, latent_mask = core.encode_vae_inpaint(\n            mask=inpaint_pixel_mask,\n            vae=candidate_vae,\n            pixels=inpaint_pixel_image)\n        latent_swap = None\n        if candidate_vae_swap is not None:\n            if advance_progress:\n                current_progress += 1\n            progressbar(async_task, current_progress, 'VAE SD15 encoding ...')\n            latent_swap = core.encode_vae(\n                vae=candidate_vae_swap,\n                pixels=inpaint_pixel_fill)['samples']\n        if advance_progress:\n            current_progress += 1\n        progressbar(async_task, current_progress, 'VAE encoding ...')\n        latent_fill = core.encode_vae(\n            vae=candidate_vae,\n            pixels=inpaint_pixel_fill)['samples']\n        inpaint_worker.current_task.load_latent(\n            latent_fill=latent_fill, latent_mask=latent_mask, latent_swap=latent_swap)\n        if inpaint_parameterized:\n            pipeline.final_unet = inpaint_worker.current_task.patch(\n                inpaint_head_model_path=inpaint_head_model_path,\n                inpaint_latent=latent_inpaint,\n                inpaint_latent_mask=latent_mask,\n                model=pipeline.final_unet\n            )\n        if not inpaint_disable_initial_latent:\n            initial_latent = {'samples': latent_fill}\n        B, C, H, W = latent_fill.shape\n        height, width = H * 8, W * 8\n        final_height, final_width = inpaint_worker.current_task.image.shape[:2]\n        print(f'Final resolution is {str((final_width, final_height))}, latent is {str((width, height))}.')\n\n        return denoising_strength, initial_latent, width, height, current_progress\n\n    def apply_outpaint(async_task, inpaint_image, inpaint_mask):\n        if len(async_task.outpaint_selections) > 0:\n            H, W, C = inpaint_image.shape\n            if 'top' in async_task.outpaint_selections:\n                inpaint_image = np.pad(inpaint_image, [[int(H * 0.3), 0], [0, 0], [0, 0]], mode='edge')\n                inpaint_mask = np.pad(inpaint_mask, [[int(H * 0.3), 0], [0, 0]], mode='constant',\n                                      constant_values=255)\n            if 'bottom' in async_task.outpaint_selections:\n                inpaint_image = np.pad(inpaint_image, [[0, int(H * 0.3)], [0, 0], [0, 0]], mode='edge')\n                inpaint_mask = np.pad(inpaint_mask, [[0, int(H * 0.3)], [0, 0]], mode='constant',\n                                      constant_values=255)\n\n            H, W, C = inpaint_image.shape\n            if 'left' in async_task.outpaint_selections:\n                inpaint_image = np.pad(inpaint_image, [[0, 0], [int(W * 0.3), 0], [0, 0]], mode='edge')\n                inpaint_mask = np.pad(inpaint_mask, [[0, 0], [int(W * 0.3), 0]], mode='constant',\n                                      constant_values=255)\n            if 'right' in async_task.outpaint_selections:\n                inpaint_image = np.pad(inpaint_image, [[0, 0], [0, int(W * 0.3)], [0, 0]], mode='edge')\n                inpaint_mask = np.pad(inpaint_mask, [[0, 0], [0, int(W * 0.3)]], mode='constant',\n                                      constant_values=255)\n\n            inpaint_image = np.ascontiguousarray(inpaint_image.copy())\n            inpaint_mask = np.ascontiguousarray(inpaint_mask.copy())\n            async_task.inpaint_strength = 1.0\n            async_task.inpaint_respective_field = 1.0\n        return inpaint_image, inpaint_mask\n\n    def apply_upscale(async_task, uov_input_image, uov_method, switch, current_progress, advance_progress=False):\n        H, W, C = uov_input_image.shape\n        if advance_progress:\n            current_progress += 1\n        progressbar(async_task, current_progress, f'Upscaling image from {str((W, H))} ...')\n        uov_input_image = perform_upscale(uov_input_image)\n        print(f'Image upscaled.')\n        if '1.5x' in uov_method:\n            f = 1.5\n        elif '2x' in uov_method:\n            f = 2.0\n        else:\n            f = 1.0\n        shape_ceil = get_shape_ceil(H * f, W * f)\n        if shape_ceil < 1024:\n            print(f'[Upscale] Image is resized because it is too small.')\n            uov_input_image = set_image_shape_ceil(uov_input_image, 1024)\n            shape_ceil = 1024\n        else:\n            uov_input_image = resample_image(uov_input_image, width=W * f, height=H * f)\n        image_is_super_large = shape_ceil > 2800\n        if 'fast' in uov_method:\n            direct_return = True\n        elif image_is_super_large:\n            print('Image is too large. Directly returned the SR image. '\n                  'Usually directly return SR image at 4K resolution '\n                  'yields better results than SDXL diffusion.')\n            direct_return = True\n        else:\n            direct_return = False\n        if direct_return:\n            return direct_return, uov_input_image, None, None, None, None, None, current_progress\n\n        tiled = True\n        denoising_strength = 0.382\n        if async_task.overwrite_upscale_strength > 0:\n            denoising_strength = async_task.overwrite_upscale_strength\n        initial_pixels = core.numpy_to_pytorch(uov_input_image)\n        if advance_progress:\n            current_progress += 1\n        progressbar(async_task, current_progress, 'VAE encoding ...')\n        candidate_vae, _ = pipeline.get_candidate_vae(\n            steps=async_task.steps,\n            switch=switch,\n            denoise=denoising_strength,\n            refiner_swap_method=async_task.refiner_swap_method\n        )\n        initial_latent = core.encode_vae(\n            vae=candidate_vae,\n            pixels=initial_pixels, tiled=True)\n        B, C, H, W = initial_latent['samples'].shape\n        width = W * 8\n        height = H * 8\n        print(f'Final resolution is {str((width, height))}.')\n        return direct_return, uov_input_image, denoising_strength, initial_latent, tiled, width, height, current_progress\n\n    def apply_overrides(async_task, steps, height, width):\n        if async_task.overwrite_step > 0:\n            steps = async_task.overwrite_step\n        switch = int(round(async_task.steps * async_task.refiner_switch))\n        if async_task.overwrite_switch > 0:\n            switch = async_task.overwrite_switch\n        if async_task.overwrite_width > 0:\n            width = async_task.overwrite_width\n        if async_task.overwrite_height > 0:\n            height = async_task.overwrite_height\n        return steps, switch, width, height\n\n    def process_prompt(async_task, prompt, negative_prompt, base_model_additional_loras, image_number, disable_seed_increment, use_expansion, use_style,\n                       use_synthetic_refiner, current_progress, advance_progress=False):\n        prompts = remove_empty_str([safe_str(p) for p in prompt.splitlines()], default='')\n        negative_prompts = remove_empty_str([safe_str(p) for p in negative_prompt.splitlines()], default='')\n        prompt = prompts[0]\n        negative_prompt = negative_prompts[0]\n        if prompt == '':\n            # disable expansion when empty since it is not meaningful and influences image prompt\n            use_expansion = False\n        extra_positive_prompts = prompts[1:] if len(prompts) > 1 else []\n        extra_negative_prompts = negative_prompts[1:] if len(negative_prompts) > 1 else []\n        if advance_progress:\n            current_progress += 1\n        progressbar(async_task, current_progress, 'Loading models ...')\n        lora_filenames = modules.util.remove_performance_lora(modules.config.lora_filenames,\n                                                              async_task.performance_selection)\n        loras, prompt = parse_lora_references_from_prompt(prompt, async_task.loras,\n                                                          modules.config.default_max_lora_number,\n                                                          lora_filenames=lora_filenames)\n        loras += async_task.performance_loras\n        pipeline.refresh_everything(refiner_model_name=async_task.refiner_model_name,\n                                    base_model_name=async_task.base_model_name,\n                                    loras=loras, base_model_additional_loras=base_model_additional_loras,\n                                    use_synthetic_refiner=use_synthetic_refiner, vae_name=async_task.vae_name)\n        pipeline.set_clip_skip(async_task.clip_skip)\n        if advance_progress:\n            current_progress += 1\n        progressbar(async_task, current_progress, 'Processing prompts ...')\n        tasks = []\n        for i in range(image_number):\n            if disable_seed_increment:\n                task_seed = async_task.seed % (constants.MAX_SEED + 1)\n            else:\n                task_seed = (async_task.seed + i) % (constants.MAX_SEED + 1)  # randint is inclusive, % is not\n\n            task_rng = random.Random(task_seed)  # may bind to inpaint noise in the future\n            task_prompt = apply_wildcards(prompt, task_rng, i, async_task.read_wildcards_in_order)\n            task_prompt = apply_arrays(task_prompt, i)\n            task_negative_prompt = apply_wildcards(negative_prompt, task_rng, i, async_task.read_wildcards_in_order)\n            task_extra_positive_prompts = [apply_wildcards(pmt, task_rng, i, async_task.read_wildcards_in_order) for pmt\n                                           in\n                                           extra_positive_prompts]\n            task_extra_negative_prompts = [apply_wildcards(pmt, task_rng, i, async_task.read_wildcards_in_order) for pmt\n                                           in\n                                           extra_negative_prompts]\n\n            positive_basic_workloads = []\n            negative_basic_workloads = []\n\n            task_styles = async_task.style_selections.copy()\n            if use_style:\n                placeholder_replaced = False\n\n                for j, s in enumerate(task_styles):\n                    if s == random_style_name:\n                        s = get_random_style(task_rng)\n                        task_styles[j] = s\n                    p, n, style_has_placeholder = apply_style(s, positive=task_prompt)\n                    if style_has_placeholder:\n                        placeholder_replaced = True\n                    positive_basic_workloads = positive_basic_workloads + p\n                    negative_basic_workloads = negative_basic_workloads + n\n\n                if not placeholder_replaced:\n                    positive_basic_workloads = [task_prompt] + positive_basic_workloads\n            else:\n                positive_basic_workloads.append(task_prompt)\n\n            negative_basic_workloads.append(task_negative_prompt)  # Always use independent workload for negative.\n\n            positive_basic_workloads = positive_basic_workloads + task_extra_positive_prompts\n            negative_basic_workloads = negative_basic_workloads + task_extra_negative_prompts\n\n            positive_basic_workloads = remove_empty_str(positive_basic_workloads, default=task_prompt)\n            negative_basic_workloads = remove_empty_str(negative_basic_workloads, default=task_negative_prompt)\n\n            tasks.append(dict(\n                task_seed=task_seed,\n                task_prompt=task_prompt,\n                task_negative_prompt=task_negative_prompt,\n                positive=positive_basic_workloads,\n                negative=negative_basic_workloads,\n                expansion='',\n                c=None,\n                uc=None,\n                positive_top_k=len(positive_basic_workloads),\n                negative_top_k=len(negative_basic_workloads),\n                log_positive_prompt='\\n'.join([task_prompt] + task_extra_positive_prompts),\n                log_negative_prompt='\\n'.join([task_negative_prompt] + task_extra_negative_prompts),\n                styles=task_styles\n            ))\n        if use_expansion:\n            if advance_progress:\n                current_progress += 1\n            for i, t in enumerate(tasks):\n\n                progressbar(async_task, current_progress, f'Preparing Fooocus text #{i + 1} ...')\n                expansion = pipeline.final_expansion(t['task_prompt'], t['task_seed'])\n                print(f'[Prompt Expansion] {expansion}')\n                t['expansion'] = expansion\n                t['positive'] = copy.deepcopy(t['positive']) + [expansion]  # Deep copy.\n        if advance_progress:\n            current_progress += 1\n        for i, t in enumerate(tasks):\n            progressbar(async_task, current_progress, f'Encoding positive #{i + 1} ...')\n            t['c'] = pipeline.clip_encode(texts=t['positive'], pool_top_k=t['positive_top_k'])\n        if advance_progress:\n            current_progress += 1\n        for i, t in enumerate(tasks):\n            if abs(float(async_task.cfg_scale) - 1.0) < 1e-4:\n                t['uc'] = pipeline.clone_cond(t['c'])\n            else:\n                progressbar(async_task, current_progress, f'Encoding negative #{i + 1} ...')\n                t['uc'] = pipeline.clip_encode(texts=t['negative'], pool_top_k=t['negative_top_k'])\n        return tasks, use_expansion, loras, current_progress\n\n    def apply_freeu(async_task):\n        print(f'FreeU is enabled!')\n        pipeline.final_unet = core.apply_freeu(\n            pipeline.final_unet,\n            async_task.freeu_b1,\n            async_task.freeu_b2,\n            async_task.freeu_s1,\n            async_task.freeu_s2\n        )\n\n    def patch_discrete(unet, scheduler_name):\n        return core.opModelSamplingDiscrete.patch(unet, scheduler_name, False)[0]\n\n    def patch_edm(unet, scheduler_name):\n        return core.opModelSamplingContinuousEDM.patch(unet, scheduler_name, 120.0, 0.002)[0]\n\n    def patch_samplers(async_task):\n        final_scheduler_name = async_task.scheduler_name\n\n        if async_task.scheduler_name in ['lcm', 'tcd']:\n            final_scheduler_name = 'sgm_uniform'\n            if pipeline.final_unet is not None:\n                pipeline.final_unet = patch_discrete(pipeline.final_unet, async_task.scheduler_name)\n            if pipeline.final_refiner_unet is not None:\n                pipeline.final_refiner_unet = patch_discrete(pipeline.final_refiner_unet, async_task.scheduler_name)\n\n        elif async_task.scheduler_name == 'edm_playground_v2.5':\n            final_scheduler_name = 'karras'\n            if pipeline.final_unet is not None:\n                pipeline.final_unet = patch_edm(pipeline.final_unet, async_task.scheduler_name)\n            if pipeline.final_refiner_unet is not None:\n                pipeline.final_refiner_unet = patch_edm(pipeline.final_refiner_unet, async_task.scheduler_name)\n\n        return final_scheduler_name\n\n    def set_hyper_sd_defaults(async_task, current_progress, advance_progress=False):\n        print('Enter Hyper-SD mode.')\n        if advance_progress:\n            current_progress += 1\n        progressbar(async_task, current_progress, 'Downloading Hyper-SD components ...')\n        async_task.performance_loras += [(modules.config.downloading_sdxl_hyper_sd_lora(), 0.8)]\n        if async_task.refiner_model_name != 'None':\n            print(f'Refiner disabled in Hyper-SD mode.')\n        async_task.refiner_model_name = 'None'\n        async_task.sampler_name = 'dpmpp_sde_gpu'\n        async_task.scheduler_name = 'karras'\n        async_task.sharpness = 0.0\n        async_task.cfg_scale = 1.0\n        async_task.adaptive_cfg = 1.0\n        async_task.refiner_switch = 1.0\n        async_task.adm_scaler_positive = 1.0\n        async_task.adm_scaler_negative = 1.0\n        async_task.adm_scaler_end = 0.0\n        return current_progress\n\n    def set_lightning_defaults(async_task, current_progress, advance_progress=False):\n        print('Enter Lightning mode.')\n        if advance_progress:\n            current_progress += 1\n        progressbar(async_task, 1, 'Downloading Lightning components ...')\n        async_task.performance_loras += [(modules.config.downloading_sdxl_lightning_lora(), 1.0)]\n        if async_task.refiner_model_name != 'None':\n            print(f'Refiner disabled in Lightning mode.')\n        async_task.refiner_model_name = 'None'\n        async_task.sampler_name = 'euler'\n        async_task.scheduler_name = 'sgm_uniform'\n        async_task.sharpness = 0.0\n        async_task.cfg_scale = 1.0\n        async_task.adaptive_cfg = 1.0\n        async_task.refiner_switch = 1.0\n        async_task.adm_scaler_positive = 1.0\n        async_task.adm_scaler_negative = 1.0\n        async_task.adm_scaler_end = 0.0\n        return current_progress\n\n    def set_lcm_defaults(async_task, current_progress, advance_progress=False):\n        print('Enter LCM mode.')\n        if advance_progress:\n            current_progress += 1\n        progressbar(async_task, 1, 'Downloading LCM components ...')\n        async_task.performance_loras += [(modules.config.downloading_sdxl_lcm_lora(), 1.0)]\n        if async_task.refiner_model_name != 'None':\n            print(f'Refiner disabled in LCM mode.')\n        async_task.refiner_model_name = 'None'\n        async_task.sampler_name = 'lcm'\n        async_task.scheduler_name = 'lcm'\n        async_task.sharpness = 0.0\n        async_task.cfg_scale = 1.0\n        async_task.adaptive_cfg = 1.0\n        async_task.refiner_switch = 1.0\n        async_task.adm_scaler_positive = 1.0\n        async_task.adm_scaler_negative = 1.0\n        async_task.adm_scaler_end = 0.0\n        return current_progress\n\n    def apply_image_input(async_task, base_model_additional_loras, clip_vision_path, controlnet_canny_path,\n                          controlnet_cpds_path, goals, inpaint_head_model_path, inpaint_image, inpaint_mask,\n                          inpaint_parameterized,  ip_adapter_face_path, ip_adapter_path, ip_negative_path,\n                          skip_prompt_processing, use_synthetic_refiner):\n        if (async_task.current_tab == 'uov' or (\n                async_task.current_tab == 'ip' and async_task.mixing_image_prompt_and_vary_upscale)) \\\n                and async_task.uov_method != flags.disabled.casefold() and async_task.uov_input_image is not None:\n            async_task.uov_input_image, skip_prompt_processing, async_task.steps = prepare_upscale(\n                async_task, goals, async_task.uov_input_image, async_task.uov_method, async_task.performance_selection,\n                async_task.steps, 1, skip_prompt_processing=skip_prompt_processing)\n        if (async_task.current_tab == 'inpaint' or (\n                async_task.current_tab == 'ip' and async_task.mixing_image_prompt_and_inpaint)) \\\n                and isinstance(async_task.inpaint_input_image, dict):\n            inpaint_image = async_task.inpaint_input_image['image']\n            inpaint_mask = async_task.inpaint_input_image['mask'][:, :, 0]\n\n            if async_task.inpaint_advanced_masking_checkbox:\n                if isinstance(async_task.inpaint_mask_image_upload, dict):\n                    if (isinstance(async_task.inpaint_mask_image_upload['image'], np.ndarray)\n                            and isinstance(async_task.inpaint_mask_image_upload['mask'], np.ndarray)\n                            and async_task.inpaint_mask_image_upload['image'].ndim == 3):\n                        async_task.inpaint_mask_image_upload = np.maximum(\n                            async_task.inpaint_mask_image_upload['image'],\n                            async_task.inpaint_mask_image_upload['mask'])\n                if isinstance(async_task.inpaint_mask_image_upload,\n                              np.ndarray) and async_task.inpaint_mask_image_upload.ndim == 3:\n                    H, W, C = inpaint_image.shape\n                    async_task.inpaint_mask_image_upload = resample_image(async_task.inpaint_mask_image_upload,\n                                                                          width=W, height=H)\n                    async_task.inpaint_mask_image_upload = np.mean(async_task.inpaint_mask_image_upload, axis=2)\n                    async_task.inpaint_mask_image_upload = (async_task.inpaint_mask_image_upload > 127).astype(\n                        np.uint8) * 255\n                    inpaint_mask = np.maximum(inpaint_mask, async_task.inpaint_mask_image_upload)\n\n            if int(async_task.inpaint_erode_or_dilate) != 0:\n                inpaint_mask = erode_or_dilate(inpaint_mask, async_task.inpaint_erode_or_dilate)\n\n            if async_task.invert_mask_checkbox:\n                inpaint_mask = 255 - inpaint_mask\n\n            inpaint_image = HWC3(inpaint_image)\n            if isinstance(inpaint_image, np.ndarray) and isinstance(inpaint_mask, np.ndarray) \\\n                    and (np.any(inpaint_mask > 127) or len(async_task.outpaint_selections) > 0):\n                progressbar(async_task, 1, 'Downloading upscale models ...')\n                modules.config.downloading_upscale_model()\n                if inpaint_parameterized:\n                    progressbar(async_task, 1, 'Downloading inpainter ...')\n                    inpaint_head_model_path, inpaint_patch_model_path = modules.config.downloading_inpaint_models(\n                        async_task.inpaint_engine)\n                    base_model_additional_loras += [(inpaint_patch_model_path, 1.0)]\n                    print(f'[Inpaint] Current inpaint model is {inpaint_patch_model_path}')\n                    if async_task.refiner_model_name == 'None':\n                        use_synthetic_refiner = True\n                        async_task.refiner_switch = 0.8\n                else:\n                    inpaint_head_model_path, inpaint_patch_model_path = None, None\n                    print(f'[Inpaint] Parameterized inpaint is disabled.')\n                if async_task.inpaint_additional_prompt != '':\n                    if async_task.prompt == '':\n                        async_task.prompt = async_task.inpaint_additional_prompt\n                    else:\n                        async_task.prompt = async_task.inpaint_additional_prompt + '\\n' + async_task.prompt\n                goals.append('inpaint')\n        if async_task.current_tab == 'ip' or \\\n                async_task.mixing_image_prompt_and_vary_upscale or \\\n                async_task.mixing_image_prompt_and_inpaint:\n            goals.append('cn')\n            progressbar(async_task, 1, 'Downloading control models ...')\n            if len(async_task.cn_tasks[flags.cn_canny]) > 0:\n                controlnet_canny_path = modules.config.downloading_controlnet_canny()\n            if len(async_task.cn_tasks[flags.cn_cpds]) > 0:\n                controlnet_cpds_path = modules.config.downloading_controlnet_cpds()\n            if len(async_task.cn_tasks[flags.cn_ip]) > 0:\n                clip_vision_path, ip_negative_path, ip_adapter_path = modules.config.downloading_ip_adapters('ip')\n            if len(async_task.cn_tasks[flags.cn_ip_face]) > 0:\n                clip_vision_path, ip_negative_path, ip_adapter_face_path = modules.config.downloading_ip_adapters(\n                    'face')\n        if async_task.current_tab == 'enhance' and async_task.enhance_input_image is not None:\n            goals.append('enhance')\n            skip_prompt_processing = True\n            async_task.enhance_input_image = HWC3(async_task.enhance_input_image)\n        return base_model_additional_loras, clip_vision_path, controlnet_canny_path, controlnet_cpds_path, inpaint_head_model_path, inpaint_image, inpaint_mask, ip_adapter_face_path, ip_adapter_path, ip_negative_path, skip_prompt_processing, use_synthetic_refiner\n\n    def prepare_upscale(async_task, goals, uov_input_image, uov_method, performance, steps, current_progress,\n                        advance_progress=False, skip_prompt_processing=False):\n        uov_input_image = HWC3(uov_input_image)\n        if 'vary' in uov_method:\n            goals.append('vary')\n        elif 'upscale' in uov_method:\n            goals.append('upscale')\n            if 'fast' in uov_method:\n                skip_prompt_processing = True\n                steps = 0\n            else:\n                steps = performance.steps_uov()\n\n            if advance_progress:\n                current_progress += 1\n            progressbar(async_task, current_progress, 'Downloading upscale models ...')\n            modules.config.downloading_upscale_model()\n        return uov_input_image, skip_prompt_processing, steps\n\n    def prepare_enhance_prompt(prompt: str, fallback_prompt: str):\n        if safe_str(prompt) == '' or len(remove_empty_str([safe_str(p) for p in prompt.splitlines()], default='')) == 0:\n            prompt = fallback_prompt\n\n        return prompt\n\n    def stop_processing(async_task, processing_start_time):\n        async_task.processing = False\n        processing_time = time.perf_counter() - processing_start_time\n        print(f'Processing time (total): {processing_time:.2f} seconds')\n\n    def process_enhance(all_steps, async_task, callback, controlnet_canny_path, controlnet_cpds_path,\n                        current_progress, current_task_id, denoising_strength, inpaint_disable_initial_latent,\n                        inpaint_engine, inpaint_respective_field, inpaint_strength,\n                        prompt, negative_prompt, final_scheduler_name, goals, height, img, mask,\n                        preparation_steps, steps, switch, tiled, total_count, use_expansion, use_style,\n                        use_synthetic_refiner, width, show_intermediate_results=True, persist_image=True):\n        base_model_additional_loras = []\n        inpaint_head_model_path = None\n        inpaint_parameterized = inpaint_engine != 'None'  # inpaint_engine = None, improve detail\n        initial_latent = None\n\n        prompt = prepare_enhance_prompt(prompt, async_task.prompt)\n        negative_prompt = prepare_enhance_prompt(negative_prompt, async_task.negative_prompt)\n\n        if 'vary' in goals:\n            img, denoising_strength, initial_latent, width, height, current_progress = apply_vary(\n                async_task, async_task.enhance_uov_method, denoising_strength, img, switch, current_progress)\n        if 'upscale' in goals:\n            direct_return, img, denoising_strength, initial_latent, tiled, width, height, current_progress = apply_upscale(\n                async_task, img, async_task.enhance_uov_method, switch, current_progress)\n            if direct_return:\n                d = [('Upscale (Fast)', 'upscale_fast', '2x')]\n                if modules.config.default_black_out_nsfw or async_task.black_out_nsfw:\n                    progressbar(async_task, current_progress, 'Checking for NSFW content ...')\n                    img = default_censor(img)\n                progressbar(async_task, current_progress, f'Saving image {current_task_id + 1}/{total_count} to system ...')\n                uov_image_path = log(img, d, output_format=async_task.output_format, persist_image=persist_image)\n                yield_result(async_task, uov_image_path, current_progress, async_task.black_out_nsfw, False,\n                             do_not_show_finished_images=not show_intermediate_results or async_task.disable_intermediate_results)\n                return current_progress, img, prompt, negative_prompt\n\n        if 'inpaint' in goals and inpaint_parameterized:\n            progressbar(async_task, current_progress, 'Downloading inpainter ...')\n            inpaint_head_model_path, inpaint_patch_model_path = modules.config.downloading_inpaint_models(\n                inpaint_engine)\n            if inpaint_patch_model_path not in base_model_additional_loras:\n                base_model_additional_loras += [(inpaint_patch_model_path, 1.0)]\n        progressbar(async_task, current_progress, 'Preparing enhance prompts ...')\n        # positive and negative conditioning aren't available here anymore, process prompt again\n        tasks_enhance, use_expansion, loras, current_progress = process_prompt(\n            async_task, prompt, negative_prompt, base_model_additional_loras, 1, True,\n            use_expansion, use_style, use_synthetic_refiner, current_progress)\n        task_enhance = tasks_enhance[0]\n        # TODO could support vary, upscale and CN in the future\n        # if 'cn' in goals:\n        #     apply_control_nets(async_task, height, ip_adapter_face_path, ip_adapter_path, width)\n        if async_task.freeu_enabled:\n            apply_freeu(async_task)\n        patch_samplers(async_task)\n        if 'inpaint' in goals:\n            denoising_strength, initial_latent, width, height, current_progress = apply_inpaint(\n                async_task, None, inpaint_head_model_path, img, mask,\n                inpaint_parameterized, inpaint_strength,\n                inpaint_respective_field, switch, inpaint_disable_initial_latent,\n                current_progress, True)\n        imgs, img_paths, current_progress = process_task(all_steps, async_task, callback, controlnet_canny_path,\n                                                         controlnet_cpds_path, current_task_id, denoising_strength,\n                                                         final_scheduler_name, goals, initial_latent, steps, switch,\n                                                         task_enhance['c'], task_enhance['uc'], task_enhance, loras,\n                                                         tiled, use_expansion, width, height, current_progress,\n                                                         preparation_steps, total_count, show_intermediate_results,\n                                                         persist_image)\n\n        del task_enhance['c'], task_enhance['uc']  # Save memory\n        return current_progress, imgs[0], prompt, negative_prompt\n\n    def enhance_upscale(all_steps, async_task, base_progress, callback, controlnet_canny_path, controlnet_cpds_path,\n                        current_task_id, denoising_strength, done_steps_inpainting, done_steps_upscaling, enhance_steps,\n                        prompt, negative_prompt, final_scheduler_name, height, img, preparation_steps, switch, tiled,\n                        total_count, use_expansion, use_style, use_synthetic_refiner, width, persist_image=True):\n        # reset inpaint worker to prevent tensor size issues and not mix upscale and inpainting\n        inpaint_worker.current_task = None\n\n        current_progress = int(base_progress + (100 - preparation_steps) / float(all_steps) * (done_steps_upscaling + done_steps_inpainting))\n        goals_enhance = []\n        img, skip_prompt_processing, steps = prepare_upscale(\n            async_task, goals_enhance, img, async_task.enhance_uov_method, async_task.performance_selection,\n            enhance_steps, current_progress)\n        steps, _, _, _ = apply_overrides(async_task, steps, height, width)\n        exception_result = ''\n        if len(goals_enhance) > 0:\n            try:\n                current_progress, img, prompt, negative_prompt = process_enhance(\n                    all_steps, async_task, callback, controlnet_canny_path,\n                    controlnet_cpds_path, current_progress, current_task_id, denoising_strength, False,\n                    'None', 0.0, 0.0, prompt, negative_prompt, final_scheduler_name,\n                    goals_enhance, height, img, None, preparation_steps, steps, switch, tiled, total_count,\n                    use_expansion, use_style, use_synthetic_refiner, width, persist_image=persist_image)\n\n            except ldm_patched.modules.model_management.InterruptProcessingException:\n                if async_task.last_stop == 'skip':\n                    print('User skipped')\n                    async_task.last_stop = False\n                    # also skip all enhance steps for this image, but add the steps to the progress bar\n                    if async_task.enhance_uov_processing_order == flags.enhancement_uov_before:\n                        done_steps_inpainting += len(async_task.enhance_ctrls) * enhance_steps\n                    exception_result = 'continue'\n                else:\n                    print('User stopped')\n                    exception_result = 'break'\n            finally:\n                done_steps_upscaling += steps\n        return current_task_id, done_steps_inpainting, done_steps_upscaling, img, exception_result\n\n    @torch.no_grad()\n    @torch.inference_mode()\n    def handler(async_task: AsyncTask):\n        preparation_start_time = time.perf_counter()\n        async_task.processing = True\n\n        async_task.outpaint_selections = [o.lower() for o in async_task.outpaint_selections]\n        base_model_additional_loras = []\n        async_task.uov_method = async_task.uov_method.casefold()\n        async_task.enhance_uov_method = async_task.enhance_uov_method.casefold()\n\n        if fooocus_expansion in async_task.style_selections:\n            use_expansion = True\n            async_task.style_selections.remove(fooocus_expansion)\n        else:\n            use_expansion = False\n\n        use_style = len(async_task.style_selections) > 0\n\n        if async_task.base_model_name == async_task.refiner_model_name:\n            print(f'Refiner disabled because base model and refiner are same.')\n            async_task.refiner_model_name = 'None'\n\n        current_progress = 0\n        if async_task.performance_selection == Performance.EXTREME_SPEED:\n            set_lcm_defaults(async_task, current_progress, advance_progress=True)\n        elif async_task.performance_selection == Performance.LIGHTNING:\n            set_lightning_defaults(async_task, current_progress, advance_progress=True)\n        elif async_task.performance_selection == Performance.HYPER_SD:\n            set_hyper_sd_defaults(async_task, current_progress, advance_progress=True)\n\n        print(f'[Parameters] Adaptive CFG = {async_task.adaptive_cfg}')\n        print(f'[Parameters] CLIP Skip = {async_task.clip_skip}')\n        print(f'[Parameters] Sharpness = {async_task.sharpness}')\n        print(f'[Parameters] ControlNet Softness = {async_task.controlnet_softness}')\n        print(f'[Parameters] ADM Scale = '\n              f'{async_task.adm_scaler_positive} : '\n              f'{async_task.adm_scaler_negative} : '\n              f'{async_task.adm_scaler_end}')\n        print(f'[Parameters] Seed = {async_task.seed}')\n\n        apply_patch_settings(async_task)\n\n        print(f'[Parameters] CFG = {async_task.cfg_scale}')\n\n        initial_latent = None\n        denoising_strength = 1.0\n        tiled = False\n\n        width, height = async_task.aspect_ratios_selection.replace('×', ' ').split(' ')[:2]\n        width, height = int(width), int(height)\n\n        skip_prompt_processing = False\n\n        inpaint_worker.current_task = None\n        inpaint_parameterized = async_task.inpaint_engine != 'None'\n        inpaint_image = None\n        inpaint_mask = None\n        inpaint_head_model_path = None\n\n        use_synthetic_refiner = False\n\n        controlnet_canny_path = None\n        controlnet_cpds_path = None\n        clip_vision_path, ip_negative_path, ip_adapter_path, ip_adapter_face_path = None, None, None, None\n\n        goals = []\n        tasks = []\n        current_progress = 1\n\n        if async_task.input_image_checkbox:\n            base_model_additional_loras, clip_vision_path, controlnet_canny_path, controlnet_cpds_path, inpaint_head_model_path, inpaint_image, inpaint_mask, ip_adapter_face_path, ip_adapter_path, ip_negative_path, skip_prompt_processing, use_synthetic_refiner = apply_image_input(\n                async_task, base_model_additional_loras, clip_vision_path, controlnet_canny_path, controlnet_cpds_path,\n                goals, inpaint_head_model_path, inpaint_image, inpaint_mask, inpaint_parameterized, ip_adapter_face_path,\n                ip_adapter_path, ip_negative_path, skip_prompt_processing, use_synthetic_refiner)\n\n        # Load or unload CNs\n        progressbar(async_task, current_progress, 'Loading control models ...')\n        pipeline.refresh_controlnets([controlnet_canny_path, controlnet_cpds_path])\n        ip_adapter.load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_path)\n        ip_adapter.load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_face_path)\n\n        async_task.steps, switch, width, height = apply_overrides(async_task, async_task.steps, height, width)\n\n        print(f'[Parameters] Sampler = {async_task.sampler_name} - {async_task.scheduler_name}')\n        print(f'[Parameters] Steps = {async_task.steps} - {switch}')\n\n        progressbar(async_task, current_progress, 'Initializing ...')\n\n        loras = async_task.loras\n        if not skip_prompt_processing:\n            tasks, use_expansion, loras, current_progress = process_prompt(async_task, async_task.prompt, async_task.negative_prompt,\n                                                         base_model_additional_loras, async_task.image_number,\n                                                         async_task.disable_seed_increment, use_expansion, use_style,\n                                                         use_synthetic_refiner, current_progress, advance_progress=True)\n\n        if len(goals) > 0:\n            current_progress += 1\n            progressbar(async_task, current_progress, 'Image processing ...')\n\n        should_enhance = async_task.enhance_checkbox and (async_task.enhance_uov_method != flags.disabled.casefold() or len(async_task.enhance_ctrls) > 0)\n\n        if 'vary' in goals:\n            async_task.uov_input_image, denoising_strength, initial_latent, width, height, current_progress = apply_vary(\n                async_task, async_task.uov_method, denoising_strength, async_task.uov_input_image, switch,\n                current_progress)\n\n        if 'upscale' in goals:\n            direct_return, async_task.uov_input_image, denoising_strength, initial_latent, tiled, width, height, current_progress = apply_upscale(\n                async_task, async_task.uov_input_image, async_task.uov_method, switch, current_progress,\n                advance_progress=True)\n            if direct_return:\n                d = [('Upscale (Fast)', 'upscale_fast', '2x')]\n                if modules.config.default_black_out_nsfw or async_task.black_out_nsfw:\n                    progressbar(async_task, 100, 'Checking for NSFW content ...')\n                    async_task.uov_input_image = default_censor(async_task.uov_input_image)\n                progressbar(async_task, 100, 'Saving image to system ...')\n                uov_input_image_path = log(async_task.uov_input_image, d, output_format=async_task.output_format)\n                yield_result(async_task, uov_input_image_path, 100, async_task.black_out_nsfw, False,\n                             do_not_show_finished_images=True)\n                return\n\n        if 'inpaint' in goals:\n            try:\n                denoising_strength, initial_latent, width, height, current_progress = apply_inpaint(async_task,\n                                                                                                    initial_latent,\n                                                                                                    inpaint_head_model_path,\n                                                                                                    inpaint_image,\n                                                                                                    inpaint_mask,\n                                                                                                    inpaint_parameterized,\n                                                                                                    async_task.inpaint_strength,\n                                                                                                    async_task.inpaint_respective_field,\n                                                                                                    switch,\n                                                                                                    async_task.inpaint_disable_initial_latent,\n                                                                                                    current_progress,\n                                                                                                    advance_progress=True)\n            except EarlyReturnException:\n                return\n\n        if 'cn' in goals:\n            apply_control_nets(async_task, height, ip_adapter_face_path, ip_adapter_path, width, current_progress)\n            if async_task.debugging_cn_preprocessor:\n                return\n\n        if async_task.freeu_enabled:\n            apply_freeu(async_task)\n\n        # async_task.steps can have value of uov steps here when upscale has been applied\n        steps, _, _, _ = apply_overrides(async_task, async_task.steps, height, width)\n\n        images_to_enhance = []\n        if 'enhance' in goals:\n            async_task.image_number = 1\n            images_to_enhance += [async_task.enhance_input_image]\n            height, width, _ = async_task.enhance_input_image.shape\n            # input image already provided, processing is skipped\n            steps = 0\n            yield_result(async_task, async_task.enhance_input_image, current_progress, async_task.black_out_nsfw, False,\n                         async_task.disable_intermediate_results)\n\n        all_steps = steps * async_task.image_number\n\n        if async_task.enhance_checkbox and async_task.enhance_uov_method != flags.disabled.casefold():\n            enhance_upscale_steps = async_task.performance_selection.steps()\n            if 'upscale' in async_task.enhance_uov_method:\n                if 'fast' in async_task.enhance_uov_method:\n                    enhance_upscale_steps = 0\n                else:\n                    enhance_upscale_steps = async_task.performance_selection.steps_uov()\n            enhance_upscale_steps, _, _, _ = apply_overrides(async_task, enhance_upscale_steps, height, width)\n            enhance_upscale_steps_total = async_task.image_number * enhance_upscale_steps\n            all_steps += enhance_upscale_steps_total\n\n        if async_task.enhance_checkbox and len(async_task.enhance_ctrls) != 0:\n            enhance_steps, _, _, _ = apply_overrides(async_task, async_task.original_steps, height, width)\n            all_steps += async_task.image_number * len(async_task.enhance_ctrls) * enhance_steps\n\n        all_steps = max(all_steps, 1)\n\n        print(f'[Parameters] Denoising Strength = {denoising_strength}')\n\n        if isinstance(initial_latent, dict) and 'samples' in initial_latent:\n            log_shape = initial_latent['samples'].shape\n        else:\n            log_shape = f'Image Space {(height, width)}'\n\n        print(f'[Parameters] Initial Latent shape: {log_shape}')\n\n        preparation_time = time.perf_counter() - preparation_start_time\n        print(f'Preparation time: {preparation_time:.2f} seconds')\n\n        final_scheduler_name = patch_samplers(async_task)\n        print(f'Using {final_scheduler_name} scheduler.')\n\n        async_task.yields.append(['preview', (current_progress, 'Moving model to GPU ...', None)])\n\n        processing_start_time = time.perf_counter()\n\n        preparation_steps = current_progress\n        total_count = async_task.image_number\n\n        def callback(step, x0, x, total_steps, y):\n            if step == 0:\n                async_task.callback_steps = 0\n            async_task.callback_steps += (100 - preparation_steps) / float(all_steps)\n            async_task.yields.append(['preview', (\n                int(current_progress + async_task.callback_steps),\n                f'Sampling step {step + 1}/{total_steps}, image {current_task_id + 1}/{total_count} ...', y)])\n\n        show_intermediate_results = len(tasks) > 1 or async_task.should_enhance\n        persist_image = not async_task.should_enhance or not async_task.save_final_enhanced_image_only\n\n        for current_task_id, task in enumerate(tasks):\n            progressbar(async_task, current_progress, f'Preparing task {current_task_id + 1}/{async_task.image_number} ...')\n            execution_start_time = time.perf_counter()\n\n            try:\n                imgs, img_paths, current_progress = process_task(all_steps, async_task, callback, controlnet_canny_path,\n                                                                 controlnet_cpds_path, current_task_id,\n                                                                 denoising_strength, final_scheduler_name, goals,\n                                                                 initial_latent, async_task.steps, switch, task['c'],\n                                                                 task['uc'], task, loras, tiled, use_expansion, width,\n                                                                 height, current_progress, preparation_steps,\n                                                                 async_task.image_number, show_intermediate_results,\n                                                                 persist_image)\n\n                current_progress = int(preparation_steps + (100 - preparation_steps) / float(all_steps) * async_task.steps * (current_task_id + 1))\n                images_to_enhance += imgs\n\n            except ldm_patched.modules.model_management.InterruptProcessingException:\n                if async_task.last_stop == 'skip':\n                    print('User skipped')\n                    async_task.last_stop = False\n                    continue\n                else:\n                    print('User stopped')\n                    break\n\n            del task['c'], task['uc']  # Save memory\n            execution_time = time.perf_counter() - execution_start_time\n            print(f'Generating and saving time: {execution_time:.2f} seconds')\n\n        if not async_task.should_enhance:\n            print(f'[Enhance] Skipping, preconditions aren\\'t met')\n            stop_processing(async_task, processing_start_time)\n            return\n\n        progressbar(async_task, current_progress, 'Processing enhance ...')\n\n        active_enhance_tabs = len(async_task.enhance_ctrls)\n        should_process_enhance_uov = async_task.enhance_uov_method != flags.disabled.casefold()\n        enhance_uov_before = False\n        enhance_uov_after = False\n        if should_process_enhance_uov:\n            active_enhance_tabs += 1\n            enhance_uov_before = async_task.enhance_uov_processing_order == flags.enhancement_uov_before\n            enhance_uov_after = async_task.enhance_uov_processing_order == flags.enhancement_uov_after\n        total_count = len(images_to_enhance) * active_enhance_tabs\n        async_task.images_to_enhance_count = len(images_to_enhance)\n\n        base_progress = current_progress\n        current_task_id = -1\n        done_steps_upscaling = 0\n        done_steps_inpainting = 0\n        enhance_steps, _, _, _ = apply_overrides(async_task, async_task.original_steps, height, width)\n        exception_result = None\n        for index, img in enumerate(images_to_enhance):\n            async_task.enhance_stats[index] = 0\n            enhancement_image_start_time = time.perf_counter()\n\n            last_enhance_prompt = async_task.prompt\n            last_enhance_negative_prompt = async_task.negative_prompt\n\n            if enhance_uov_before:\n                current_task_id += 1\n                persist_image = not async_task.save_final_enhanced_image_only or active_enhance_tabs == 0\n                current_task_id, done_steps_inpainting, done_steps_upscaling, img, exception_result = enhance_upscale(\n                    all_steps, async_task, base_progress, callback, controlnet_canny_path, controlnet_cpds_path,\n                    current_task_id, denoising_strength, done_steps_inpainting, done_steps_upscaling, enhance_steps,\n                    async_task.prompt, async_task.negative_prompt, final_scheduler_name, height, img, preparation_steps,\n                    switch, tiled, total_count, use_expansion, use_style, use_synthetic_refiner, width, persist_image)\n                async_task.enhance_stats[index] += 1\n\n                if exception_result == 'continue':\n                    continue\n                elif exception_result == 'break':\n                    break\n\n            # inpaint for all other tabs\n            for enhance_mask_dino_prompt_text, enhance_prompt, enhance_negative_prompt, enhance_mask_model, enhance_mask_cloth_category, enhance_mask_sam_model, enhance_mask_text_threshold, enhance_mask_box_threshold, enhance_mask_sam_max_detections, enhance_inpaint_disable_initial_latent, enhance_inpaint_engine, enhance_inpaint_strength, enhance_inpaint_respective_field, enhance_inpaint_erode_or_dilate, enhance_mask_invert in async_task.enhance_ctrls:\n                current_task_id += 1\n                current_progress = int(base_progress + (100 - preparation_steps) / float(all_steps) * (done_steps_upscaling + done_steps_inpainting))\n                progressbar(async_task, current_progress, f'Preparing enhancement {current_task_id + 1}/{total_count} ...')\n                enhancement_task_start_time = time.perf_counter()\n                is_last_enhance_for_image = (current_task_id + 1) % active_enhance_tabs == 0 and not enhance_uov_after\n                persist_image = not async_task.save_final_enhanced_image_only or is_last_enhance_for_image\n\n                extras = {}\n                if enhance_mask_model == 'sam':\n                    print(f'[Enhance] Searching for \"{enhance_mask_dino_prompt_text}\"')\n                elif enhance_mask_model == 'u2net_cloth_seg':\n                    extras['cloth_category'] = enhance_mask_cloth_category\n\n                mask, dino_detection_count, sam_detection_count, sam_detection_on_mask_count = generate_mask_from_image(\n                    img, mask_model=enhance_mask_model, extras=extras, sam_options=SAMOptions(\n                        dino_prompt=enhance_mask_dino_prompt_text,\n                        dino_box_threshold=enhance_mask_box_threshold,\n                        dino_text_threshold=enhance_mask_text_threshold,\n                        dino_erode_or_dilate=async_task.dino_erode_or_dilate,\n                        dino_debug=async_task.debugging_dino,\n                        max_detections=enhance_mask_sam_max_detections,\n                        model_type=enhance_mask_sam_model,\n                    ))\n                if len(mask.shape) == 3:\n                    mask = mask[:, :, 0]\n\n                if int(enhance_inpaint_erode_or_dilate) != 0:\n                    mask = erode_or_dilate(mask, enhance_inpaint_erode_or_dilate)\n\n                if enhance_mask_invert:\n                    mask = 255 - mask\n\n                if async_task.debugging_enhance_masks_checkbox:\n                    async_task.yields.append(['preview', (current_progress, 'Loading ...', mask)])\n                    yield_result(async_task, mask, current_progress, async_task.black_out_nsfw, False,\n                                 async_task.disable_intermediate_results)\n                    async_task.enhance_stats[index] += 1\n\n                print(f'[Enhance] {dino_detection_count} boxes detected')\n                print(f'[Enhance] {sam_detection_count} segments detected in boxes')\n                print(f'[Enhance] {sam_detection_on_mask_count} segments applied to mask')\n\n                if enhance_mask_model == 'sam' and (dino_detection_count == 0 or not async_task.debugging_dino and sam_detection_on_mask_count == 0):\n                    print(f'[Enhance] No \"{enhance_mask_dino_prompt_text}\" detected, skipping')\n                    continue\n\n                goals_enhance = ['inpaint']\n\n                try:\n                    current_progress, img, enhance_prompt_processed, enhance_negative_prompt_processed = process_enhance(\n                        all_steps, async_task, callback, controlnet_canny_path, controlnet_cpds_path,\n                        current_progress, current_task_id, denoising_strength, enhance_inpaint_disable_initial_latent,\n                        enhance_inpaint_engine, enhance_inpaint_respective_field, enhance_inpaint_strength,\n                        enhance_prompt, enhance_negative_prompt, final_scheduler_name, goals_enhance, height, img, mask,\n                        preparation_steps, enhance_steps, switch, tiled, total_count, use_expansion, use_style,\n                        use_synthetic_refiner, width, persist_image=persist_image)\n                    async_task.enhance_stats[index] += 1\n\n                    if (should_process_enhance_uov and async_task.enhance_uov_processing_order == flags.enhancement_uov_after\n                            and async_task.enhance_uov_prompt_type == flags.enhancement_uov_prompt_type_last_filled):\n                        if enhance_prompt_processed != '':\n                            last_enhance_prompt = enhance_prompt_processed\n                        if enhance_negative_prompt_processed != '':\n                            last_enhance_negative_prompt = enhance_negative_prompt_processed\n\n                except ldm_patched.modules.model_management.InterruptProcessingException:\n                    if async_task.last_stop == 'skip':\n                        print('User skipped')\n                        async_task.last_stop = False\n                        continue\n                    else:\n                        print('User stopped')\n                        exception_result = 'break'\n                        break\n                finally:\n                    done_steps_inpainting += enhance_steps\n\n                enhancement_task_time = time.perf_counter() - enhancement_task_start_time\n                print(f'Enhancement time: {enhancement_task_time:.2f} seconds')\n\n            if exception_result == 'break':\n                break\n\n            if enhance_uov_after:\n                current_task_id += 1\n                # last step in enhance, always save\n                persist_image = True\n                current_task_id, done_steps_inpainting, done_steps_upscaling, img, exception_result = enhance_upscale(\n                    all_steps, async_task, base_progress, callback, controlnet_canny_path, controlnet_cpds_path,\n                    current_task_id, denoising_strength, done_steps_inpainting, done_steps_upscaling, enhance_steps,\n                    last_enhance_prompt, last_enhance_negative_prompt, final_scheduler_name, height, img,\n                    preparation_steps, switch, tiled, total_count, use_expansion, use_style, use_synthetic_refiner,\n                    width, persist_image)\n                async_task.enhance_stats[index] += 1\n                \n                if exception_result == 'continue':\n                    continue\n                elif exception_result == 'break':\n                    break\n\n            enhancement_image_time = time.perf_counter() - enhancement_image_start_time\n            print(f'Enhancement image time: {enhancement_image_time:.2f} seconds')\n\n        stop_processing(async_task, processing_start_time)\n        return\n\n    while True:\n        time.sleep(0.01)\n        if len(async_tasks) > 0:\n            task = async_tasks.pop(0)\n\n            try:\n                handler(task)\n                if task.generate_image_grid:\n                    build_image_wall(task)\n                task.yields.append(['finish', task.results])\n                pipeline.prepare_text_encoder(async_call=True)\n            except:\n                traceback.print_exc()\n                task.yields.append(['finish', task.results])\n            finally:\n                if pid in modules.patch.patch_settings:\n                    del modules.patch.patch_settings[pid]\n    pass\n\n\nthreading.Thread(target=worker, daemon=True).start()\n"
  },
  {
    "path": "modules/auth.py",
    "content": "import json\nimport hashlib\nimport modules.constants as constants\n\nfrom os.path import exists\n\n\ndef auth_list_to_dict(auth_list):\n    auth_dict = {}\n    for auth_data in auth_list:\n        if 'user' in auth_data:\n            if 'hash' in auth_data:\n                auth_dict |= {auth_data['user']: auth_data['hash']}\n            elif 'pass' in auth_data:\n                auth_dict |= {auth_data['user']: hashlib.sha256(bytes(auth_data['pass'], encoding='utf-8')).hexdigest()}\n    return auth_dict\n\n\ndef load_auth_data(filename=None):\n    auth_dict = None\n    if filename != None and exists(filename):\n        with open(filename, encoding='utf-8') as auth_file:\n            try:\n                auth_obj = json.load(auth_file)\n                if isinstance(auth_obj, list) and len(auth_obj) > 0:\n                    auth_dict = auth_list_to_dict(auth_obj)\n            except Exception as e:\n                print('load_auth_data, e: ' + str(e))\n    return auth_dict\n\n\nauth_dict = load_auth_data(constants.AUTH_FILENAME)\n\nauth_enabled = auth_dict != None\n\n\ndef check_auth(user, password):\n    if user not in auth_dict:\n        return False\n    else:   \n        return hashlib.sha256(bytes(password, encoding='utf-8')).hexdigest() == auth_dict[user]\n"
  },
  {
    "path": "modules/config.py",
    "content": "import os\nimport json\nimport math\nimport numbers\n\nimport args_manager\nimport tempfile\nimport modules.flags\nimport modules.sdxl_styles\n\nfrom modules.model_loader import load_file_from_url\nfrom modules.extra_utils import makedirs_with_log, get_files_from_folder, try_eval_env_var\nfrom modules.flags import OutputFormat, Performance, MetadataScheme\n\n\ndef get_config_path(key, default_value):\n    env = os.getenv(key)\n    if env is not None and isinstance(env, str):\n        print(f\"Environment: {key} = {env}\")\n        return env\n    else:\n        return os.path.abspath(default_value)\n\nwildcards_max_bfs_depth = 64\nconfig_path = get_config_path('config_path', \"./config.txt\")\nconfig_example_path = get_config_path('config_example_path', \"config_modification_tutorial.txt\")\nconfig_dict = {}\nalways_save_keys = []\nvisited_keys = []\n\ntry:\n    with open(os.path.abspath(f'./presets/default.json'), \"r\", encoding=\"utf-8\") as json_file:\n        config_dict.update(json.load(json_file))\nexcept Exception as e:\n    print(f'Load default preset failed.')\n    print(e)\n\ntry:\n    if os.path.exists(config_path):\n        with open(config_path, \"r\", encoding=\"utf-8\") as json_file:\n            config_dict.update(json.load(json_file))\n            always_save_keys = list(config_dict.keys())\nexcept Exception as e:\n    print(f'Failed to load config file \"{config_path}\" . The reason is: {str(e)}')\n    print('Please make sure that:')\n    print(f'1. The file \"{config_path}\" is a valid text file, and you have access to read it.')\n    print('2. Use \"\\\\\\\\\" instead of \"\\\\\" when describing paths.')\n    print('3. There is no \",\" before the last \"}\".')\n    print('4. All key/value formats are correct.')\n\n\ndef try_load_deprecated_user_path_config():\n    global config_dict\n\n    if not os.path.exists('user_path_config.txt'):\n        return\n\n    try:\n        deprecated_config_dict = json.load(open('user_path_config.txt', \"r\", encoding=\"utf-8\"))\n\n        def replace_config(old_key, new_key):\n            if old_key in deprecated_config_dict:\n                config_dict[new_key] = deprecated_config_dict[old_key]\n                del deprecated_config_dict[old_key]\n\n        replace_config('modelfile_path', 'path_checkpoints')\n        replace_config('lorafile_path', 'path_loras')\n        replace_config('embeddings_path', 'path_embeddings')\n        replace_config('vae_approx_path', 'path_vae_approx')\n        replace_config('upscale_models_path', 'path_upscale_models')\n        replace_config('inpaint_models_path', 'path_inpaint')\n        replace_config('controlnet_models_path', 'path_controlnet')\n        replace_config('clip_vision_models_path', 'path_clip_vision')\n        replace_config('fooocus_expansion_path', 'path_fooocus_expansion')\n        replace_config('temp_outputs_path', 'path_outputs')\n\n        if deprecated_config_dict.get(\"default_model\", None) == 'juggernautXL_version6Rundiffusion.safetensors':\n            os.replace('user_path_config.txt', 'user_path_config-deprecated.txt')\n            print('Config updated successfully in silence. '\n                  'A backup of previous config is written to \"user_path_config-deprecated.txt\".')\n            return\n\n        if input(\"Newer models and configs are available. \"\n                 \"Download and update files? [Y/n]:\") in ['n', 'N', 'No', 'no', 'NO']:\n            config_dict.update(deprecated_config_dict)\n            print('Loading using deprecated old models and deprecated old configs.')\n            return\n        else:\n            os.replace('user_path_config.txt', 'user_path_config-deprecated.txt')\n            print('Config updated successfully by user. '\n                  'A backup of previous config is written to \"user_path_config-deprecated.txt\".')\n            return\n    except Exception as e:\n        print('Processing deprecated config failed')\n        print(e)\n    return\n\n\ntry_load_deprecated_user_path_config()\n\ndef get_presets():\n    preset_folder = 'presets'\n    presets = ['initial']\n    if not os.path.exists(preset_folder):\n        print('No presets found.')\n        return presets\n\n    return presets + [f[:f.index(\".json\")] for f in os.listdir(preset_folder) if f.endswith('.json')]\n\ndef update_presets():\n    global available_presets\n    available_presets = get_presets()\n\ndef try_get_preset_content(preset):\n    if isinstance(preset, str):\n        preset_path = os.path.abspath(f'./presets/{preset}.json')\n        try:\n            if os.path.exists(preset_path):\n                with open(preset_path, \"r\", encoding=\"utf-8\") as json_file:\n                    json_content = json.load(json_file)\n                    print(f'Loaded preset: {preset_path}')\n                    return json_content\n            else:\n                raise FileNotFoundError\n        except Exception as e:\n            print(f'Load preset [{preset_path}] failed')\n            print(e)\n    return {}\n\navailable_presets = get_presets()\npreset = args_manager.args.preset\nconfig_dict.update(try_get_preset_content(preset))\n\ndef get_path_output() -> str:\n    \"\"\"\n    Checking output path argument and overriding default path.\n    \"\"\"\n    global config_dict\n    path_output = get_dir_or_set_default('path_outputs', '../outputs/', make_directory=True)\n    if args_manager.args.output_path:\n        print(f'Overriding config value path_outputs with {args_manager.args.output_path}')\n        config_dict['path_outputs'] = path_output = args_manager.args.output_path\n    return path_output\n\n\ndef get_dir_or_set_default(key, default_value, as_array=False, make_directory=False):\n    global config_dict, visited_keys, always_save_keys\n\n    if key not in visited_keys:\n        visited_keys.append(key)\n\n    if key not in always_save_keys:\n        always_save_keys.append(key)\n\n    v = os.getenv(key)\n    if v is not None:\n        print(f\"Environment: {key} = {v}\")\n        config_dict[key] = v\n    else:\n        v = config_dict.get(key, None)\n\n    if isinstance(v, str):\n        if make_directory:\n            makedirs_with_log(v)\n        if os.path.exists(v) and os.path.isdir(v):\n            return v if not as_array else [v]\n    elif isinstance(v, list):\n        if make_directory:\n            for d in v:\n                makedirs_with_log(d)\n        if all([os.path.exists(d) and os.path.isdir(d) for d in v]):\n            return v\n\n    if v is not None:\n        print(f'Failed to load config key: {json.dumps({key:v})} is invalid or does not exist; will use {json.dumps({key:default_value})} instead.')\n    if isinstance(default_value, list):\n        dp = []\n        for path in default_value:\n            abs_path = os.path.abspath(os.path.join(os.path.dirname(__file__), path))\n            dp.append(abs_path)\n            os.makedirs(abs_path, exist_ok=True)\n    else:\n        dp = os.path.abspath(os.path.join(os.path.dirname(__file__), default_value))\n        os.makedirs(dp, exist_ok=True)\n        if as_array:\n            dp = [dp]\n    config_dict[key] = dp\n    return dp\n\n\npaths_checkpoints = get_dir_or_set_default('path_checkpoints', ['../models/checkpoints/'], True)\npaths_loras = get_dir_or_set_default('path_loras', ['../models/loras/'], True)\npath_embeddings = get_dir_or_set_default('path_embeddings', '../models/embeddings/')\npath_vae_approx = get_dir_or_set_default('path_vae_approx', '../models/vae_approx/')\npath_vae = get_dir_or_set_default('path_vae', '../models/vae/')\npath_upscale_models = get_dir_or_set_default('path_upscale_models', '../models/upscale_models/')\npath_inpaint = get_dir_or_set_default('path_inpaint', '../models/inpaint/')\npath_controlnet = get_dir_or_set_default('path_controlnet', '../models/controlnet/')\npath_clip_vision = get_dir_or_set_default('path_clip_vision', '../models/clip_vision/')\npath_fooocus_expansion = get_dir_or_set_default('path_fooocus_expansion', '../models/prompt_expansion/fooocus_expansion')\npath_wildcards = get_dir_or_set_default('path_wildcards', '../wildcards/')\npath_safety_checker = get_dir_or_set_default('path_safety_checker', '../models/safety_checker/')\npath_sam = get_dir_or_set_default('path_sam', '../models/sam/')\npath_outputs = get_path_output()\n\n\ndef get_config_item_or_set_default(key, default_value, validator, disable_empty_as_none=False, expected_type=None):\n    global config_dict, visited_keys\n\n    if key not in visited_keys:\n        visited_keys.append(key)\n    \n    v = os.getenv(key)\n    if v is not None:\n        v = try_eval_env_var(v, expected_type)\n        print(f\"Environment: {key} = {v}\")\n        config_dict[key] = v\n\n    if key not in config_dict:\n        config_dict[key] = default_value\n        return default_value\n\n    v = config_dict.get(key, None)\n    if not disable_empty_as_none:\n        if v is None or v == '':\n            v = 'None'\n    if validator(v):\n        return v\n    else:\n        if v is not None:\n            print(f'Failed to load config key: {json.dumps({key:v})} is invalid; will use {json.dumps({key:default_value})} instead.')\n        config_dict[key] = default_value\n        return default_value\n\n\ndef init_temp_path(path: str | None, default_path: str) -> str:\n    if args_manager.args.temp_path:\n        path = args_manager.args.temp_path\n\n    if path != '' and path != default_path:\n        try:\n            if not os.path.isabs(path):\n                path = os.path.abspath(path)\n            os.makedirs(path, exist_ok=True)\n            print(f'Using temp path {path}')\n            return path\n        except Exception as e:\n            print(f'Could not create temp path {path}. Reason: {e}')\n            print(f'Using default temp path {default_path} instead.')\n\n    os.makedirs(default_path, exist_ok=True)\n    return default_path\n\n\ndefault_temp_path = os.path.join(tempfile.gettempdir(), 'fooocus')\ntemp_path = init_temp_path(get_config_item_or_set_default(\n    key='temp_path',\n    default_value=default_temp_path,\n    validator=lambda x: isinstance(x, str),\n    expected_type=str\n), default_temp_path)\ntemp_path_cleanup_on_launch = get_config_item_or_set_default(\n    key='temp_path_cleanup_on_launch',\n    default_value=True,\n    validator=lambda x: isinstance(x, bool),\n    expected_type=bool\n)\ndefault_base_model_name = default_model = get_config_item_or_set_default(\n    key='default_model',\n    default_value='model.safetensors',\n    validator=lambda x: isinstance(x, str),\n    expected_type=str\n)\nprevious_default_models = get_config_item_or_set_default(\n    key='previous_default_models',\n    default_value=[],\n    validator=lambda x: isinstance(x, list) and all(isinstance(k, str) for k in x),\n    expected_type=list\n)\ndefault_refiner_model_name = default_refiner = get_config_item_or_set_default(\n    key='default_refiner',\n    default_value='None',\n    validator=lambda x: isinstance(x, str),\n    expected_type=str\n)\ndefault_refiner_switch = get_config_item_or_set_default(\n    key='default_refiner_switch',\n    default_value=0.8,\n    validator=lambda x: isinstance(x, numbers.Number) and 0 <= x <= 1,\n    expected_type=numbers.Number\n)\ndefault_loras_min_weight = get_config_item_or_set_default(\n    key='default_loras_min_weight',\n    default_value=-2,\n    validator=lambda x: isinstance(x, numbers.Number) and -10 <= x <= 10,\n    expected_type=numbers.Number\n)\ndefault_loras_max_weight = get_config_item_or_set_default(\n    key='default_loras_max_weight',\n    default_value=2,\n    validator=lambda x: isinstance(x, numbers.Number) and -10 <= x <= 10,\n    expected_type=numbers.Number\n)\ndefault_loras = get_config_item_or_set_default(\n    key='default_loras',\n    default_value=[\n        [\n            True,\n            \"None\",\n            1.0\n        ],\n        [\n            True,\n            \"None\",\n            1.0\n        ],\n        [\n            True,\n            \"None\",\n            1.0\n        ],\n        [\n            True,\n            \"None\",\n            1.0\n        ],\n        [\n            True,\n            \"None\",\n            1.0\n        ]\n    ],\n    validator=lambda x: isinstance(x, list) and all(\n        len(y) == 3 and isinstance(y[0], bool) and isinstance(y[1], str) and isinstance(y[2], numbers.Number)\n        or len(y) == 2 and isinstance(y[0], str) and isinstance(y[1], numbers.Number)\n        for y in x),\n    expected_type=list\n)\ndefault_loras = [(y[0], y[1], y[2]) if len(y) == 3 else (True, y[0], y[1]) for y in default_loras]\ndefault_max_lora_number = get_config_item_or_set_default(\n    key='default_max_lora_number',\n    default_value=len(default_loras) if isinstance(default_loras, list) and len(default_loras) > 0 else 5,\n    validator=lambda x: isinstance(x, int) and x >= 1,\n    expected_type=int\n)\ndefault_cfg_scale = get_config_item_or_set_default(\n    key='default_cfg_scale',\n    default_value=7.0,\n    validator=lambda x: isinstance(x, numbers.Number),\n    expected_type=numbers.Number\n)\ndefault_sample_sharpness = get_config_item_or_set_default(\n    key='default_sample_sharpness',\n    default_value=2.0,\n    validator=lambda x: isinstance(x, numbers.Number),\n    expected_type=numbers.Number\n)\ndefault_sampler = get_config_item_or_set_default(\n    key='default_sampler',\n    default_value='dpmpp_2m_sde_gpu',\n    validator=lambda x: x in modules.flags.sampler_list,\n    expected_type=str\n)\ndefault_scheduler = get_config_item_or_set_default(\n    key='default_scheduler',\n    default_value='karras',\n    validator=lambda x: x in modules.flags.scheduler_list,\n    expected_type=str\n)\ndefault_vae = get_config_item_or_set_default(\n    key='default_vae',\n    default_value=modules.flags.default_vae,\n    validator=lambda x: isinstance(x, str),\n    expected_type=str\n)\ndefault_styles = get_config_item_or_set_default(\n    key='default_styles',\n    default_value=[\n        \"Fooocus V2\",\n        \"Fooocus Enhance\",\n        \"Fooocus Sharp\"\n    ],\n    validator=lambda x: isinstance(x, list) and all(y in modules.sdxl_styles.legal_style_names for y in x),\n    expected_type=list\n)\ndefault_prompt_negative = get_config_item_or_set_default(\n    key='default_prompt_negative',\n    default_value='',\n    validator=lambda x: isinstance(x, str),\n    disable_empty_as_none=True,\n    expected_type=str\n)\ndefault_prompt = get_config_item_or_set_default(\n    key='default_prompt',\n    default_value='',\n    validator=lambda x: isinstance(x, str),\n    disable_empty_as_none=True,\n    expected_type=str\n)\ndefault_performance = get_config_item_or_set_default(\n    key='default_performance',\n    default_value=Performance.SPEED.value,\n    validator=lambda x: x in Performance.values(),\n    expected_type=str\n)\ndefault_image_prompt_checkbox = get_config_item_or_set_default(\n    key='default_image_prompt_checkbox',\n    default_value=False,\n    validator=lambda x: isinstance(x, bool),\n    expected_type=bool\n)\ndefault_enhance_checkbox = get_config_item_or_set_default(\n    key='default_enhance_checkbox',\n    default_value=False,\n    validator=lambda x: isinstance(x, bool),\n    expected_type=bool\n)\ndefault_advanced_checkbox = get_config_item_or_set_default(\n    key='default_advanced_checkbox',\n    default_value=False,\n    validator=lambda x: isinstance(x, bool),\n    expected_type=bool\n)\ndefault_developer_debug_mode_checkbox = get_config_item_or_set_default(\n    key='default_developer_debug_mode_checkbox',\n    default_value=False,\n    validator=lambda x: isinstance(x, bool),\n    expected_type=bool\n)\ndefault_image_prompt_advanced_checkbox = get_config_item_or_set_default(\n    key='default_image_prompt_advanced_checkbox',\n    default_value=False,\n    validator=lambda x: isinstance(x, bool),\n    expected_type=bool\n)\ndefault_max_image_number = get_config_item_or_set_default(\n    key='default_max_image_number',\n    default_value=32,\n    validator=lambda x: isinstance(x, int) and x >= 1,\n    expected_type=int\n)\ndefault_output_format = get_config_item_or_set_default(\n    key='default_output_format',\n    default_value='png',\n    validator=lambda x: x in OutputFormat.list(),\n    expected_type=str\n)\ndefault_image_number = get_config_item_or_set_default(\n    key='default_image_number',\n    default_value=2,\n    validator=lambda x: isinstance(x, int) and 1 <= x <= default_max_image_number,\n    expected_type=int\n)\ncheckpoint_downloads = get_config_item_or_set_default(\n    key='checkpoint_downloads',\n    default_value={},\n    validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items()),\n    expected_type=dict\n)\nlora_downloads = get_config_item_or_set_default(\n    key='lora_downloads',\n    default_value={},\n    validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items()),\n    expected_type=dict\n)\nembeddings_downloads = get_config_item_or_set_default(\n    key='embeddings_downloads',\n    default_value={},\n    validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items()),\n    expected_type=dict\n)\nvae_downloads = get_config_item_or_set_default(\n    key='vae_downloads',\n    default_value={},\n    validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items()),\n    expected_type=dict\n)\navailable_aspect_ratios = get_config_item_or_set_default(\n    key='available_aspect_ratios',\n    default_value=modules.flags.sdxl_aspect_ratios,\n    validator=lambda x: isinstance(x, list) and all('*' in v for v in x) and len(x) > 1,\n    expected_type=list\n)\ndefault_aspect_ratio = get_config_item_or_set_default(\n    key='default_aspect_ratio',\n    default_value='1152*896' if '1152*896' in available_aspect_ratios else available_aspect_ratios[0],\n    validator=lambda x: x in available_aspect_ratios,\n    expected_type=str\n)\ndefault_inpaint_engine_version = get_config_item_or_set_default(\n    key='default_inpaint_engine_version',\n    default_value='v2.6',\n    validator=lambda x: x in modules.flags.inpaint_engine_versions,\n    expected_type=str\n)\ndefault_selected_image_input_tab_id = get_config_item_or_set_default(\n    key='default_selected_image_input_tab_id',\n    default_value=modules.flags.default_input_image_tab,\n    validator=lambda x: x in modules.flags.input_image_tab_ids,\n    expected_type=str\n)\ndefault_uov_method = get_config_item_or_set_default(\n    key='default_uov_method',\n    default_value=modules.flags.disabled,\n    validator=lambda x: x in modules.flags.uov_list,\n    expected_type=str\n)\ndefault_controlnet_image_count = get_config_item_or_set_default(\n    key='default_controlnet_image_count',\n    default_value=4,\n    validator=lambda x: isinstance(x, int) and x > 0,\n    expected_type=int\n)\ndefault_ip_images = {}\ndefault_ip_stop_ats = {}\ndefault_ip_weights = {}\ndefault_ip_types = {}\n\nfor image_count in range(default_controlnet_image_count):\n    image_count += 1\n    default_ip_images[image_count] = get_config_item_or_set_default(\n        key=f'default_ip_image_{image_count}',\n        default_value='None',\n        validator=lambda x: x == 'None' or isinstance(x, str) and os.path.exists(x),\n        expected_type=str\n    )\n\n    if default_ip_images[image_count] == 'None':\n        default_ip_images[image_count] = None\n\n    default_ip_types[image_count] = get_config_item_or_set_default(\n        key=f'default_ip_type_{image_count}',\n        default_value=modules.flags.default_ip,\n        validator=lambda x: x in modules.flags.ip_list,\n        expected_type=str\n    )\n\n    default_end, default_weight = modules.flags.default_parameters[default_ip_types[image_count]]\n\n    default_ip_stop_ats[image_count] = get_config_item_or_set_default(\n        key=f'default_ip_stop_at_{image_count}',\n        default_value=default_end,\n        validator=lambda x: isinstance(x, float) and 0 <= x <= 1,\n        expected_type=float\n    )\n    default_ip_weights[image_count] = get_config_item_or_set_default(\n        key=f'default_ip_weight_{image_count}',\n        default_value=default_weight,\n        validator=lambda x: isinstance(x, float) and 0 <= x <= 2,\n        expected_type=float\n    )\n\ndefault_inpaint_advanced_masking_checkbox = get_config_item_or_set_default(\n    key='default_inpaint_advanced_masking_checkbox',\n    default_value=False,\n    validator=lambda x: isinstance(x, bool),\n    expected_type=bool\n)\ndefault_inpaint_method = get_config_item_or_set_default(\n    key='default_inpaint_method',\n    default_value=modules.flags.inpaint_option_default,\n    validator=lambda x: x in modules.flags.inpaint_options,\n    expected_type=str\n)\ndefault_cfg_tsnr = get_config_item_or_set_default(\n    key='default_cfg_tsnr',\n    default_value=7.0,\n    validator=lambda x: isinstance(x, numbers.Number),\n    expected_type=numbers.Number\n)\ndefault_clip_skip = get_config_item_or_set_default(\n    key='default_clip_skip',\n    default_value=2,\n    validator=lambda x: isinstance(x, int) and 1 <= x <= modules.flags.clip_skip_max,\n    expected_type=int\n)\ndefault_overwrite_step = get_config_item_or_set_default(\n    key='default_overwrite_step',\n    default_value=-1,\n    validator=lambda x: isinstance(x, int),\n    expected_type=int\n)\ndefault_overwrite_switch = get_config_item_or_set_default(\n    key='default_overwrite_switch',\n    default_value=-1,\n    validator=lambda x: isinstance(x, int),\n    expected_type=int\n)\ndefault_overwrite_upscale = get_config_item_or_set_default(\n    key='default_overwrite_upscale',\n    default_value=-1,\n    validator=lambda x: isinstance(x, numbers.Number)\n)\nexample_inpaint_prompts = get_config_item_or_set_default(\n    key='example_inpaint_prompts',\n    default_value=[\n        'highly detailed face', 'detailed girl face', 'detailed man face', 'detailed hand', 'beautiful eyes'\n    ],\n    validator=lambda x: isinstance(x, list) and all(isinstance(v, str) for v in x),\n    expected_type=list\n)\nexample_enhance_detection_prompts = get_config_item_or_set_default(\n    key='example_enhance_detection_prompts',\n    default_value=[\n        'face', 'eye', 'mouth', 'hair', 'hand', 'body'\n    ],\n    validator=lambda x: isinstance(x, list) and all(isinstance(v, str) for v in x),\n    expected_type=list\n)\ndefault_enhance_tabs = get_config_item_or_set_default(\n    key='default_enhance_tabs',\n    default_value=3,\n    validator=lambda x: isinstance(x, int) and 1 <= x <= 5,\n    expected_type=int\n)\ndefault_enhance_uov_method = get_config_item_or_set_default(\n    key='default_enhance_uov_method',\n    default_value=modules.flags.disabled,\n    validator=lambda x: x in modules.flags.uov_list,\n    expected_type=int\n)\ndefault_enhance_uov_processing_order = get_config_item_or_set_default(\n    key='default_enhance_uov_processing_order',\n    default_value=modules.flags.enhancement_uov_before,\n    validator=lambda x: x in modules.flags.enhancement_uov_processing_order,\n    expected_type=int\n)\ndefault_enhance_uov_prompt_type = get_config_item_or_set_default(\n    key='default_enhance_uov_prompt_type',\n    default_value=modules.flags.enhancement_uov_prompt_type_original,\n    validator=lambda x: x in modules.flags.enhancement_uov_prompt_types,\n    expected_type=int\n)\ndefault_sam_max_detections = get_config_item_or_set_default(\n    key='default_sam_max_detections',\n    default_value=0,\n    validator=lambda x: isinstance(x, int) and 0 <= x <= 10,\n    expected_type=int\n)\ndefault_black_out_nsfw = get_config_item_or_set_default(\n    key='default_black_out_nsfw',\n    default_value=False,\n    validator=lambda x: isinstance(x, bool),\n    expected_type=bool\n)\ndefault_save_only_final_enhanced_image = get_config_item_or_set_default(\n    key='default_save_only_final_enhanced_image',\n    default_value=False,\n    validator=lambda x: isinstance(x, bool),\n    expected_type=bool\n)\ndefault_save_metadata_to_images = get_config_item_or_set_default(\n    key='default_save_metadata_to_images',\n    default_value=False,\n    validator=lambda x: isinstance(x, bool),\n    expected_type=bool\n)\ndefault_metadata_scheme = get_config_item_or_set_default(\n    key='default_metadata_scheme',\n    default_value=MetadataScheme.FOOOCUS.value,\n    validator=lambda x: x in [y[1] for y in modules.flags.metadata_scheme if y[1] == x],\n    expected_type=str\n)\nmetadata_created_by = get_config_item_or_set_default(\n    key='metadata_created_by',\n    default_value='',\n    validator=lambda x: isinstance(x, str),\n    expected_type=str\n)\n\nexample_inpaint_prompts = [[x] for x in example_inpaint_prompts]\nexample_enhance_detection_prompts = [[x] for x in example_enhance_detection_prompts]\n\ndefault_invert_mask_checkbox = get_config_item_or_set_default(\n    key='default_invert_mask_checkbox',\n    default_value=False,\n    validator=lambda x: isinstance(x, bool),\n    expected_type=bool\n)\n\ndefault_inpaint_mask_model = get_config_item_or_set_default(\n    key='default_inpaint_mask_model',\n    default_value='isnet-general-use',\n    validator=lambda x: x in modules.flags.inpaint_mask_models,\n    expected_type=str\n)\n\ndefault_enhance_inpaint_mask_model = get_config_item_or_set_default(\n    key='default_enhance_inpaint_mask_model',\n    default_value='sam',\n    validator=lambda x: x in modules.flags.inpaint_mask_models,\n    expected_type=str\n)\n\ndefault_inpaint_mask_cloth_category = get_config_item_or_set_default(\n    key='default_inpaint_mask_cloth_category',\n    default_value='full',\n    validator=lambda x: x in modules.flags.inpaint_mask_cloth_category,\n    expected_type=str\n)\n\ndefault_inpaint_mask_sam_model = get_config_item_or_set_default(\n    key='default_inpaint_mask_sam_model',\n    default_value='vit_b',\n    validator=lambda x: x in modules.flags.inpaint_mask_sam_model,\n    expected_type=str\n)\n\ndefault_describe_apply_prompts_checkbox = get_config_item_or_set_default(\n    key='default_describe_apply_prompts_checkbox',\n    default_value=True,\n    validator=lambda x: isinstance(x, bool),\n    expected_type=bool\n)\ndefault_describe_content_type = get_config_item_or_set_default(\n    key='default_describe_content_type',\n    default_value=[modules.flags.describe_type_photo],\n    validator=lambda x: all(k in modules.flags.describe_types for k in x),\n    expected_type=list\n)\n\nconfig_dict[\"default_loras\"] = default_loras = default_loras[:default_max_lora_number] + [[True, 'None', 1.0] for _ in range(default_max_lora_number - len(default_loras))]\n\n# mapping config to meta parameter\npossible_preset_keys = {\n    \"default_model\": \"base_model\",\n    \"default_refiner\": \"refiner_model\",\n    \"default_refiner_switch\": \"refiner_switch\",\n    \"previous_default_models\": \"previous_default_models\",\n    \"default_loras_min_weight\": \"default_loras_min_weight\",\n    \"default_loras_max_weight\": \"default_loras_max_weight\",\n    \"default_loras\": \"<processed>\",\n    \"default_cfg_scale\": \"guidance_scale\",\n    \"default_sample_sharpness\": \"sharpness\",\n    \"default_cfg_tsnr\": \"adaptive_cfg\",\n    \"default_clip_skip\": \"clip_skip\",\n    \"default_sampler\": \"sampler\",\n    \"default_scheduler\": \"scheduler\",\n    \"default_overwrite_step\": \"steps\",\n    \"default_overwrite_switch\": \"overwrite_switch\",\n    \"default_performance\": \"performance\",\n    \"default_image_number\": \"image_number\",\n    \"default_prompt\": \"prompt\",\n    \"default_prompt_negative\": \"negative_prompt\",\n    \"default_styles\": \"styles\",\n    \"default_aspect_ratio\": \"resolution\",\n    \"default_save_metadata_to_images\": \"default_save_metadata_to_images\",\n    \"checkpoint_downloads\": \"checkpoint_downloads\",\n    \"embeddings_downloads\": \"embeddings_downloads\",\n    \"lora_downloads\": \"lora_downloads\",\n    \"vae_downloads\": \"vae_downloads\",\n    \"default_vae\": \"vae\",\n    # \"default_inpaint_method\": \"inpaint_method\", # disabled so inpaint mode doesn't refresh after every preset change\n    \"default_inpaint_engine_version\": \"inpaint_engine_version\",\n}\n\nREWRITE_PRESET = False\n\nif REWRITE_PRESET and isinstance(args_manager.args.preset, str):\n    save_path = 'presets/' + args_manager.args.preset + '.json'\n    with open(save_path, \"w\", encoding=\"utf-8\") as json_file:\n        json.dump({k: config_dict[k] for k in possible_preset_keys}, json_file, indent=4)\n    print(f'Preset saved to {save_path}. Exiting ...')\n    exit(0)\n\n\ndef add_ratio(x):\n    a, b = x.replace('*', ' ').split(' ')[:2]\n    a, b = int(a), int(b)\n    g = math.gcd(a, b)\n    return f'{a}×{b} <span style=\"color: grey;\"> \\U00002223 {a // g}:{b // g}</span>'\n\n\ndefault_aspect_ratio = add_ratio(default_aspect_ratio)\navailable_aspect_ratios_labels = [add_ratio(x) for x in available_aspect_ratios]\n\n\n# Only write config in the first launch.\nif not os.path.exists(config_path):\n    with open(config_path, \"w\", encoding=\"utf-8\") as json_file:\n        json.dump({k: config_dict[k] for k in always_save_keys}, json_file, indent=4)\n\n\n# Always write tutorials.\nwith open(config_example_path, \"w\", encoding=\"utf-8\") as json_file:\n    cpa = config_path.replace(\"\\\\\", \"\\\\\\\\\")\n    json_file.write(f'You can modify your \"{cpa}\" using the below keys, formats, and examples.\\n'\n                    f'Do not modify this file. Modifications in this file will not take effect.\\n'\n                    f'This file is a tutorial and example. Please edit \"{cpa}\" to really change any settings.\\n'\n                    + 'Remember to split the paths with \"\\\\\\\\\" rather than \"\\\\\", '\n                      'and there is no \",\" before the last \"}\". \\n\\n\\n')\n    json.dump({k: config_dict[k] for k in visited_keys}, json_file, indent=4)\n\nmodel_filenames = []\nlora_filenames = []\nvae_filenames = []\nwildcard_filenames = []\n\n\ndef get_model_filenames(folder_paths, extensions=None, name_filter=None):\n    if extensions is None:\n        extensions = ['.pth', '.ckpt', '.bin', '.safetensors', '.fooocus.patch']\n    files = []\n\n    if not isinstance(folder_paths, list):\n        folder_paths = [folder_paths]\n    for folder in folder_paths:\n        files += get_files_from_folder(folder, extensions, name_filter)\n\n    return files\n\n\ndef update_files():\n    global model_filenames, lora_filenames, vae_filenames, wildcard_filenames, available_presets\n    model_filenames = get_model_filenames(paths_checkpoints)\n    lora_filenames = get_model_filenames(paths_loras)\n    vae_filenames = get_model_filenames(path_vae)\n    wildcard_filenames = get_files_from_folder(path_wildcards, ['.txt'])\n    available_presets = get_presets()\n    return\n\n\ndef downloading_inpaint_models(v):\n    assert v in modules.flags.inpaint_engine_versions\n\n    load_file_from_url(\n        url='https://huggingface.co/lllyasviel/fooocus_inpaint/resolve/main/fooocus_inpaint_head.pth',\n        model_dir=path_inpaint,\n        file_name='fooocus_inpaint_head.pth'\n    )\n    head_file = os.path.join(path_inpaint, 'fooocus_inpaint_head.pth')\n    patch_file = None\n\n    if v == 'v1':\n        load_file_from_url(\n            url='https://huggingface.co/lllyasviel/fooocus_inpaint/resolve/main/inpaint.fooocus.patch',\n            model_dir=path_inpaint,\n            file_name='inpaint.fooocus.patch'\n        )\n        patch_file = os.path.join(path_inpaint, 'inpaint.fooocus.patch')\n\n    if v == 'v2.5':\n        load_file_from_url(\n            url='https://huggingface.co/lllyasviel/fooocus_inpaint/resolve/main/inpaint_v25.fooocus.patch',\n            model_dir=path_inpaint,\n            file_name='inpaint_v25.fooocus.patch'\n        )\n        patch_file = os.path.join(path_inpaint, 'inpaint_v25.fooocus.patch')\n\n    if v == 'v2.6':\n        load_file_from_url(\n            url='https://huggingface.co/lllyasviel/fooocus_inpaint/resolve/main/inpaint_v26.fooocus.patch',\n            model_dir=path_inpaint,\n            file_name='inpaint_v26.fooocus.patch'\n        )\n        patch_file = os.path.join(path_inpaint, 'inpaint_v26.fooocus.patch')\n\n    return head_file, patch_file\n\n\ndef downloading_sdxl_lcm_lora():\n    load_file_from_url(\n        url='https://huggingface.co/lllyasviel/misc/resolve/main/sdxl_lcm_lora.safetensors',\n        model_dir=paths_loras[0],\n        file_name=modules.flags.PerformanceLoRA.EXTREME_SPEED.value\n    )\n    return modules.flags.PerformanceLoRA.EXTREME_SPEED.value\n\n\ndef downloading_sdxl_lightning_lora():\n    load_file_from_url(\n        url='https://huggingface.co/mashb1t/misc/resolve/main/sdxl_lightning_4step_lora.safetensors',\n        model_dir=paths_loras[0],\n        file_name=modules.flags.PerformanceLoRA.LIGHTNING.value\n    )\n    return modules.flags.PerformanceLoRA.LIGHTNING.value\n\n\ndef downloading_sdxl_hyper_sd_lora():\n    load_file_from_url(\n        url='https://huggingface.co/mashb1t/misc/resolve/main/sdxl_hyper_sd_4step_lora.safetensors',\n        model_dir=paths_loras[0],\n        file_name=modules.flags.PerformanceLoRA.HYPER_SD.value\n    )\n    return modules.flags.PerformanceLoRA.HYPER_SD.value\n\n\ndef downloading_controlnet_canny():\n    load_file_from_url(\n        url='https://huggingface.co/lllyasviel/misc/resolve/main/control-lora-canny-rank128.safetensors',\n        model_dir=path_controlnet,\n        file_name='control-lora-canny-rank128.safetensors'\n    )\n    return os.path.join(path_controlnet, 'control-lora-canny-rank128.safetensors')\n\n\ndef downloading_controlnet_cpds():\n    load_file_from_url(\n        url='https://huggingface.co/lllyasviel/misc/resolve/main/fooocus_xl_cpds_128.safetensors',\n        model_dir=path_controlnet,\n        file_name='fooocus_xl_cpds_128.safetensors'\n    )\n    return os.path.join(path_controlnet, 'fooocus_xl_cpds_128.safetensors')\n\n\ndef downloading_ip_adapters(v):\n    assert v in ['ip', 'face']\n\n    results = []\n\n    load_file_from_url(\n        url='https://huggingface.co/lllyasviel/misc/resolve/main/clip_vision_vit_h.safetensors',\n        model_dir=path_clip_vision,\n        file_name='clip_vision_vit_h.safetensors'\n    )\n    results += [os.path.join(path_clip_vision, 'clip_vision_vit_h.safetensors')]\n\n    load_file_from_url(\n        url='https://huggingface.co/lllyasviel/misc/resolve/main/fooocus_ip_negative.safetensors',\n        model_dir=path_controlnet,\n        file_name='fooocus_ip_negative.safetensors'\n    )\n    results += [os.path.join(path_controlnet, 'fooocus_ip_negative.safetensors')]\n\n    if v == 'ip':\n        load_file_from_url(\n            url='https://huggingface.co/lllyasviel/misc/resolve/main/ip-adapter-plus_sdxl_vit-h.bin',\n            model_dir=path_controlnet,\n            file_name='ip-adapter-plus_sdxl_vit-h.bin'\n        )\n        results += [os.path.join(path_controlnet, 'ip-adapter-plus_sdxl_vit-h.bin')]\n\n    if v == 'face':\n        load_file_from_url(\n            url='https://huggingface.co/lllyasviel/misc/resolve/main/ip-adapter-plus-face_sdxl_vit-h.bin',\n            model_dir=path_controlnet,\n            file_name='ip-adapter-plus-face_sdxl_vit-h.bin'\n        )\n        results += [os.path.join(path_controlnet, 'ip-adapter-plus-face_sdxl_vit-h.bin')]\n\n    return results\n\n\ndef downloading_upscale_model():\n    load_file_from_url(\n        url='https://huggingface.co/lllyasviel/misc/resolve/main/fooocus_upscaler_s409985e5.bin',\n        model_dir=path_upscale_models,\n        file_name='fooocus_upscaler_s409985e5.bin'\n    )\n    return os.path.join(path_upscale_models, 'fooocus_upscaler_s409985e5.bin')\n\ndef downloading_safety_checker_model():\n    load_file_from_url(\n        url='https://huggingface.co/mashb1t/misc/resolve/main/stable-diffusion-safety-checker.bin',\n        model_dir=path_safety_checker,\n        file_name='stable-diffusion-safety-checker.bin'\n    )\n    return os.path.join(path_safety_checker, 'stable-diffusion-safety-checker.bin')\n\n\ndef download_sam_model(sam_model: str) -> str:\n    match sam_model:\n        case 'vit_b':\n            return downloading_sam_vit_b()\n        case 'vit_l':\n            return downloading_sam_vit_l()\n        case 'vit_h':\n            return downloading_sam_vit_h()\n        case _:\n            raise ValueError(f\"sam model {sam_model} does not exist.\")\n\n\ndef downloading_sam_vit_b():\n    load_file_from_url(\n        url='https://huggingface.co/mashb1t/misc/resolve/main/sam_vit_b_01ec64.pth',\n        model_dir=path_sam,\n        file_name='sam_vit_b_01ec64.pth'\n    )\n    return os.path.join(path_sam, 'sam_vit_b_01ec64.pth')\n\n\ndef downloading_sam_vit_l():\n    load_file_from_url(\n        url='https://huggingface.co/mashb1t/misc/resolve/main/sam_vit_l_0b3195.pth',\n        model_dir=path_sam,\n        file_name='sam_vit_l_0b3195.pth'\n    )\n    return os.path.join(path_sam, 'sam_vit_l_0b3195.pth')\n\n\ndef downloading_sam_vit_h():\n    load_file_from_url(\n        url='https://huggingface.co/mashb1t/misc/resolve/main/sam_vit_h_4b8939.pth',\n        model_dir=path_sam,\n        file_name='sam_vit_h_4b8939.pth'\n    )\n    return os.path.join(path_sam, 'sam_vit_h_4b8939.pth')\n"
  },
  {
    "path": "modules/constants.py",
    "content": "# as in k-diffusion (sampling.py)\nMIN_SEED = 0\nMAX_SEED = 2**63 - 1\n\nAUTH_FILENAME = 'auth.json'\n"
  },
  {
    "path": "modules/core.py",
    "content": "import os\nimport einops\nimport torch\nimport numpy as np\n\nimport ldm_patched.modules.model_management\nimport ldm_patched.modules.model_detection\nimport ldm_patched.modules.model_patcher\nimport ldm_patched.modules.utils\nimport ldm_patched.modules.controlnet\nimport modules.sample_hijack\nimport ldm_patched.modules.samplers\nimport ldm_patched.modules.latent_formats\n\nfrom ldm_patched.modules.sd import load_checkpoint_guess_config\nfrom ldm_patched.contrib.external import VAEDecode, EmptyLatentImage, VAEEncode, VAEEncodeTiled, VAEDecodeTiled, \\\n    ControlNetApplyAdvanced\nfrom ldm_patched.contrib.external_freelunch import FreeU_V2\nfrom ldm_patched.modules.sample import prepare_mask\nfrom modules.lora import match_lora\nfrom modules.util import get_file_from_folder_list\nfrom ldm_patched.modules.lora import model_lora_keys_unet, model_lora_keys_clip\nfrom modules.config import path_embeddings\nfrom ldm_patched.contrib.external_model_advanced import ModelSamplingDiscrete, ModelSamplingContinuousEDM\n\nopEmptyLatentImage = EmptyLatentImage()\nopVAEDecode = VAEDecode()\nopVAEEncode = VAEEncode()\nopVAEDecodeTiled = VAEDecodeTiled()\nopVAEEncodeTiled = VAEEncodeTiled()\nopControlNetApplyAdvanced = ControlNetApplyAdvanced()\nopFreeU = FreeU_V2()\nopModelSamplingDiscrete = ModelSamplingDiscrete()\nopModelSamplingContinuousEDM = ModelSamplingContinuousEDM()\n\n\nclass StableDiffusionModel:\n    def __init__(self, unet=None, vae=None, clip=None, clip_vision=None, filename=None, vae_filename=None):\n        self.unet = unet\n        self.vae = vae\n        self.clip = clip\n        self.clip_vision = clip_vision\n        self.filename = filename\n        self.vae_filename = vae_filename\n        self.unet_with_lora = unet\n        self.clip_with_lora = clip\n        self.visited_loras = ''\n\n        self.lora_key_map_unet = {}\n        self.lora_key_map_clip = {}\n\n        if self.unet is not None:\n            self.lora_key_map_unet = model_lora_keys_unet(self.unet.model, self.lora_key_map_unet)\n            self.lora_key_map_unet.update({x: x for x in self.unet.model.state_dict().keys()})\n\n        if self.clip is not None:\n            self.lora_key_map_clip = model_lora_keys_clip(self.clip.cond_stage_model, self.lora_key_map_clip)\n            self.lora_key_map_clip.update({x: x for x in self.clip.cond_stage_model.state_dict().keys()})\n\n    @torch.no_grad()\n    @torch.inference_mode()\n    def refresh_loras(self, loras):\n        assert isinstance(loras, list)\n\n        if self.visited_loras == str(loras):\n            return\n\n        self.visited_loras = str(loras)\n\n        if self.unet is None:\n            return\n\n        print(f'Request to load LoRAs {str(loras)} for model [{self.filename}].')\n\n        loras_to_load = []\n\n        for filename, weight in loras:\n            if filename == 'None':\n                continue\n\n            if os.path.exists(filename):\n                lora_filename = filename\n            else:\n                lora_filename = get_file_from_folder_list(filename, modules.config.paths_loras)\n\n            if not os.path.exists(lora_filename):\n                print(f'Lora file not found: {lora_filename}')\n                continue\n\n            loras_to_load.append((lora_filename, weight))\n\n        self.unet_with_lora = self.unet.clone() if self.unet is not None else None\n        self.clip_with_lora = self.clip.clone() if self.clip is not None else None\n\n        for lora_filename, weight in loras_to_load:\n            lora_unmatch = ldm_patched.modules.utils.load_torch_file(lora_filename, safe_load=False)\n            lora_unet, lora_unmatch = match_lora(lora_unmatch, self.lora_key_map_unet)\n            lora_clip, lora_unmatch = match_lora(lora_unmatch, self.lora_key_map_clip)\n\n            if len(lora_unmatch) > 12:\n                # model mismatch\n                continue\n\n            if len(lora_unmatch) > 0:\n                print(f'Loaded LoRA [{lora_filename}] for model [{self.filename}] '\n                      f'with unmatched keys {list(lora_unmatch.keys())}')\n\n            if self.unet_with_lora is not None and len(lora_unet) > 0:\n                loaded_keys = self.unet_with_lora.add_patches(lora_unet, weight)\n                print(f'Loaded LoRA [{lora_filename}] for UNet [{self.filename}] '\n                      f'with {len(loaded_keys)} keys at weight {weight}.')\n                for item in lora_unet:\n                    if item not in loaded_keys:\n                        print(\"UNet LoRA key skipped: \", item)\n\n            if self.clip_with_lora is not None and len(lora_clip) > 0:\n                loaded_keys = self.clip_with_lora.add_patches(lora_clip, weight)\n                print(f'Loaded LoRA [{lora_filename}] for CLIP [{self.filename}] '\n                      f'with {len(loaded_keys)} keys at weight {weight}.')\n                for item in lora_clip:\n                    if item not in loaded_keys:\n                        print(\"CLIP LoRA key skipped: \", item)\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef apply_freeu(model, b1, b2, s1, s2):\n    return opFreeU.patch(model=model, b1=b1, b2=b2, s1=s1, s2=s2)[0]\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef load_controlnet(ckpt_filename):\n    return ldm_patched.modules.controlnet.load_controlnet(ckpt_filename)\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef apply_controlnet(positive, negative, control_net, image, strength, start_percent, end_percent):\n    return opControlNetApplyAdvanced.apply_controlnet(positive=positive, negative=negative, control_net=control_net,\n        image=image, strength=strength, start_percent=start_percent, end_percent=end_percent)\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef load_model(ckpt_filename, vae_filename=None):\n    unet, clip, vae, vae_filename, clip_vision = load_checkpoint_guess_config(ckpt_filename, embedding_directory=path_embeddings,\n                                                                vae_filename_param=vae_filename)\n    return StableDiffusionModel(unet=unet, clip=clip, vae=vae, clip_vision=clip_vision, filename=ckpt_filename, vae_filename=vae_filename)\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef generate_empty_latent(width=1024, height=1024, batch_size=1):\n    return opEmptyLatentImage.generate(width=width, height=height, batch_size=batch_size)[0]\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef decode_vae(vae, latent_image, tiled=False):\n    if tiled:\n        return opVAEDecodeTiled.decode(samples=latent_image, vae=vae, tile_size=512)[0]\n    else:\n        return opVAEDecode.decode(samples=latent_image, vae=vae)[0]\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef encode_vae(vae, pixels, tiled=False):\n    if tiled:\n        return opVAEEncodeTiled.encode(pixels=pixels, vae=vae, tile_size=512)[0]\n    else:\n        return opVAEEncode.encode(pixels=pixels, vae=vae)[0]\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef encode_vae_inpaint(vae, pixels, mask):\n    assert mask.ndim == 3 and pixels.ndim == 4\n    assert mask.shape[-1] == pixels.shape[-2]\n    assert mask.shape[-2] == pixels.shape[-3]\n\n    w = mask.round()[..., None]\n    pixels = pixels * (1 - w) + 0.5 * w\n\n    latent = vae.encode(pixels)\n    B, C, H, W = latent.shape\n\n    latent_mask = mask[:, None, :, :]\n    latent_mask = torch.nn.functional.interpolate(latent_mask, size=(H * 8, W * 8), mode=\"bilinear\").round()\n    latent_mask = torch.nn.functional.max_pool2d(latent_mask, (8, 8)).round().to(latent)\n\n    return latent, latent_mask\n\n\nclass VAEApprox(torch.nn.Module):\n    def __init__(self):\n        super(VAEApprox, self).__init__()\n        self.conv1 = torch.nn.Conv2d(4, 8, (7, 7))\n        self.conv2 = torch.nn.Conv2d(8, 16, (5, 5))\n        self.conv3 = torch.nn.Conv2d(16, 32, (3, 3))\n        self.conv4 = torch.nn.Conv2d(32, 64, (3, 3))\n        self.conv5 = torch.nn.Conv2d(64, 32, (3, 3))\n        self.conv6 = torch.nn.Conv2d(32, 16, (3, 3))\n        self.conv7 = torch.nn.Conv2d(16, 8, (3, 3))\n        self.conv8 = torch.nn.Conv2d(8, 3, (3, 3))\n        self.current_type = None\n\n    def forward(self, x):\n        extra = 11\n        x = torch.nn.functional.interpolate(x, (x.shape[2] * 2, x.shape[3] * 2))\n        x = torch.nn.functional.pad(x, (extra, extra, extra, extra))\n        for layer in [self.conv1, self.conv2, self.conv3, self.conv4, self.conv5, self.conv6, self.conv7, self.conv8]:\n            x = layer(x)\n            x = torch.nn.functional.leaky_relu(x, 0.1)\n        return x\n\n\nVAE_approx_models = {}\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef get_previewer(model):\n    global VAE_approx_models\n\n    from modules.config import path_vae_approx\n    is_sdxl = isinstance(model.model.latent_format, ldm_patched.modules.latent_formats.SDXL)\n    vae_approx_filename = os.path.join(path_vae_approx, 'xlvaeapp.pth' if is_sdxl else 'vaeapp_sd15.pth')\n\n    if vae_approx_filename in VAE_approx_models:\n        VAE_approx_model = VAE_approx_models[vae_approx_filename]\n    else:\n        sd = torch.load(vae_approx_filename, map_location='cpu', weights_only=True)\n        VAE_approx_model = VAEApprox()\n        VAE_approx_model.load_state_dict(sd)\n        del sd\n        VAE_approx_model.eval()\n\n        if ldm_patched.modules.model_management.should_use_fp16():\n            VAE_approx_model.half()\n            VAE_approx_model.current_type = torch.float16\n        else:\n            VAE_approx_model.float()\n            VAE_approx_model.current_type = torch.float32\n\n        VAE_approx_model.to(ldm_patched.modules.model_management.get_torch_device())\n        VAE_approx_models[vae_approx_filename] = VAE_approx_model\n\n    @torch.no_grad()\n    @torch.inference_mode()\n    def preview_function(x0, step, total_steps):\n        with torch.no_grad():\n            x_sample = x0.to(VAE_approx_model.current_type)\n            x_sample = VAE_approx_model(x_sample) * 127.5 + 127.5\n            x_sample = einops.rearrange(x_sample, 'b c h w -> b h w c')[0]\n            x_sample = x_sample.cpu().numpy().clip(0, 255).astype(np.uint8)\n            return x_sample\n\n    return preview_function\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sampler_name='dpmpp_2m_sde_gpu',\n             scheduler='karras', denoise=1.0, disable_noise=False, start_step=None, last_step=None,\n             force_full_denoise=False, callback_function=None, refiner=None, refiner_switch=-1,\n             previewer_start=None, previewer_end=None, sigmas=None, noise_mean=None, disable_preview=False):\n\n    if sigmas is not None:\n        sigmas = sigmas.clone().to(ldm_patched.modules.model_management.get_torch_device())\n\n    latent_image = latent[\"samples\"]\n\n    if disable_noise:\n        noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device=\"cpu\")\n    else:\n        batch_inds = latent[\"batch_index\"] if \"batch_index\" in latent else None\n        noise = ldm_patched.modules.sample.prepare_noise(latent_image, seed, batch_inds)\n\n    if isinstance(noise_mean, torch.Tensor):\n        noise = noise + noise_mean - torch.mean(noise, dim=1, keepdim=True)\n\n    noise_mask = None\n    if \"noise_mask\" in latent:\n        noise_mask = latent[\"noise_mask\"]\n\n    previewer = get_previewer(model)\n\n    if previewer_start is None:\n        previewer_start = 0\n\n    if previewer_end is None:\n        previewer_end = steps\n\n    def callback(step, x0, x, total_steps):\n        ldm_patched.modules.model_management.throw_exception_if_processing_interrupted()\n        y = None\n        if previewer is not None and not disable_preview:\n            y = previewer(x0, previewer_start + step, previewer_end)\n        if callback_function is not None:\n            callback_function(previewer_start + step, x0, x, previewer_end, y)\n\n    disable_pbar = False\n    modules.sample_hijack.current_refiner = refiner\n    modules.sample_hijack.refiner_switch_step = refiner_switch\n    ldm_patched.modules.samplers.sample = modules.sample_hijack.sample_hacked\n\n    try:\n        samples = ldm_patched.modules.sample.sample(model,\n                                                    noise, steps, cfg, sampler_name, scheduler,\n                                                    positive, negative, latent_image,\n                                                    denoise=denoise, disable_noise=disable_noise,\n                                                    start_step=start_step,\n                                                    last_step=last_step,\n                                                    force_full_denoise=force_full_denoise, noise_mask=noise_mask,\n                                                    callback=callback,\n                                                    disable_pbar=disable_pbar, seed=seed, sigmas=sigmas)\n\n        out = latent.copy()\n        out[\"samples\"] = samples\n    finally:\n        modules.sample_hijack.current_refiner = None\n\n    return out\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef pytorch_to_numpy(x):\n    return [np.clip(255. * y.cpu().numpy(), 0, 255).astype(np.uint8) for y in x]\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef numpy_to_pytorch(x):\n    y = x.astype(np.float32) / 255.0\n    y = y[None]\n    y = np.ascontiguousarray(y.copy())\n    y = torch.from_numpy(y).float()\n    return y\n"
  },
  {
    "path": "modules/default_pipeline.py",
    "content": "import modules.core as core\nimport os\nimport torch\nimport modules.patch\nimport modules.config\nimport modules.flags\nimport ldm_patched.modules.model_management\nimport ldm_patched.modules.latent_formats\nimport modules.inpaint_worker\nimport extras.vae_interpose as vae_interpose\nfrom extras.expansion import FooocusExpansion\n\nfrom ldm_patched.modules.model_base import SDXL, SDXLRefiner\nfrom modules.sample_hijack import clip_separate\nfrom modules.util import get_file_from_folder_list, get_enabled_loras\n\n\nmodel_base = core.StableDiffusionModel()\nmodel_refiner = core.StableDiffusionModel()\n\nfinal_expansion = None\nfinal_unet = None\nfinal_clip = None\nfinal_vae = None\nfinal_refiner_unet = None\nfinal_refiner_vae = None\n\nloaded_ControlNets = {}\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef refresh_controlnets(model_paths):\n    global loaded_ControlNets\n    cache = {}\n    for p in model_paths:\n        if p is not None:\n            if p in loaded_ControlNets:\n                cache[p] = loaded_ControlNets[p]\n            else:\n                cache[p] = core.load_controlnet(p)\n    loaded_ControlNets = cache\n    return\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef assert_model_integrity():\n    error_message = None\n\n    if not isinstance(model_base.unet_with_lora.model, SDXL):\n        error_message = 'You have selected base model other than SDXL. This is not supported yet.'\n\n    if error_message is not None:\n        raise NotImplementedError(error_message)\n\n    return True\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef refresh_base_model(name, vae_name=None):\n    global model_base\n\n    filename = get_file_from_folder_list(name, modules.config.paths_checkpoints)\n\n    vae_filename = None\n    if vae_name is not None and vae_name != modules.flags.default_vae:\n        vae_filename = get_file_from_folder_list(vae_name, modules.config.path_vae)\n\n    if model_base.filename == filename and model_base.vae_filename == vae_filename:\n        return\n\n    model_base = core.load_model(filename, vae_filename)\n    print(f'Base model loaded: {model_base.filename}')\n    print(f'VAE loaded: {model_base.vae_filename}')\n    return\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef refresh_refiner_model(name):\n    global model_refiner\n\n    filename = get_file_from_folder_list(name, modules.config.paths_checkpoints)\n\n    if model_refiner.filename == filename:\n        return\n\n    model_refiner = core.StableDiffusionModel()\n\n    if name == 'None':\n        print(f'Refiner unloaded.')\n        return\n\n    model_refiner = core.load_model(filename)\n    print(f'Refiner model loaded: {model_refiner.filename}')\n\n    if isinstance(model_refiner.unet.model, SDXL):\n        model_refiner.clip = None\n        model_refiner.vae = None\n    elif isinstance(model_refiner.unet.model, SDXLRefiner):\n        model_refiner.clip = None\n        model_refiner.vae = None\n    else:\n        model_refiner.clip = None\n\n    return\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef synthesize_refiner_model():\n    global model_base, model_refiner\n\n    print('Synthetic Refiner Activated')\n    model_refiner = core.StableDiffusionModel(\n        unet=model_base.unet,\n        vae=model_base.vae,\n        clip=model_base.clip,\n        clip_vision=model_base.clip_vision,\n        filename=model_base.filename\n    )\n    model_refiner.vae = None\n    model_refiner.clip = None\n    model_refiner.clip_vision = None\n\n    return\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef refresh_loras(loras, base_model_additional_loras=None):\n    global model_base, model_refiner\n\n    if not isinstance(base_model_additional_loras, list):\n        base_model_additional_loras = []\n\n    model_base.refresh_loras(loras + base_model_additional_loras)\n    model_refiner.refresh_loras(loras)\n\n    return\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef clip_encode_single(clip, text, verbose=False):\n    cached = clip.fcs_cond_cache.get(text, None)\n    if cached is not None:\n        if verbose:\n            print(f'[CLIP Cached] {text}')\n        return cached\n    tokens = clip.tokenize(text)\n    result = clip.encode_from_tokens(tokens, return_pooled=True)\n    clip.fcs_cond_cache[text] = result\n    if verbose:\n        print(f'[CLIP Encoded] {text}')\n    return result\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef clone_cond(conds):\n    results = []\n\n    for c, p in conds:\n        p = p[\"pooled_output\"]\n\n        if isinstance(c, torch.Tensor):\n            c = c.clone()\n\n        if isinstance(p, torch.Tensor):\n            p = p.clone()\n\n        results.append([c, {\"pooled_output\": p}])\n\n    return results\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef clip_encode(texts, pool_top_k=1):\n    global final_clip\n\n    if final_clip is None:\n        return None\n    if not isinstance(texts, list):\n        return None\n    if len(texts) == 0:\n        return None\n\n    cond_list = []\n    pooled_acc = 0\n\n    for i, text in enumerate(texts):\n        cond, pooled = clip_encode_single(final_clip, text)\n        cond_list.append(cond)\n        if i < pool_top_k:\n            pooled_acc += pooled\n\n    return [[torch.cat(cond_list, dim=1), {\"pooled_output\": pooled_acc}]]\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef set_clip_skip(clip_skip: int):\n    global final_clip\n\n    if final_clip is None:\n        return\n\n    final_clip.clip_layer(-abs(clip_skip))\n    return\n\n@torch.no_grad()\n@torch.inference_mode()\ndef clear_all_caches():\n    final_clip.fcs_cond_cache = {}\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef prepare_text_encoder(async_call=True):\n    if async_call:\n        # TODO: make sure that this is always called in an async way so that users cannot feel it.\n        pass\n    assert_model_integrity()\n    ldm_patched.modules.model_management.load_models_gpu([final_clip.patcher, final_expansion.patcher])\n    return\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef refresh_everything(refiner_model_name, base_model_name, loras,\n                       base_model_additional_loras=None, use_synthetic_refiner=False, vae_name=None):\n    global final_unet, final_clip, final_vae, final_refiner_unet, final_refiner_vae, final_expansion\n\n    final_unet = None\n    final_clip = None\n    final_vae = None\n    final_refiner_unet = None\n    final_refiner_vae = None\n\n    if use_synthetic_refiner and refiner_model_name == 'None':\n        print('Synthetic Refiner Activated')\n        refresh_base_model(base_model_name, vae_name)\n        synthesize_refiner_model()\n    else:\n        refresh_refiner_model(refiner_model_name)\n        refresh_base_model(base_model_name, vae_name)\n\n    refresh_loras(loras, base_model_additional_loras=base_model_additional_loras)\n    assert_model_integrity()\n\n    final_unet = model_base.unet_with_lora\n    final_clip = model_base.clip_with_lora\n    final_vae = model_base.vae\n\n    final_refiner_unet = model_refiner.unet_with_lora\n    final_refiner_vae = model_refiner.vae\n\n    if final_expansion is None:\n        final_expansion = FooocusExpansion()\n\n    prepare_text_encoder(async_call=True)\n    clear_all_caches()\n    return\n\n\nrefresh_everything(\n    refiner_model_name=modules.config.default_refiner_model_name,\n    base_model_name=modules.config.default_base_model_name,\n    loras=get_enabled_loras(modules.config.default_loras),\n    vae_name=modules.config.default_vae,\n)\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef vae_parse(latent):\n    if final_refiner_vae is None:\n        return latent\n\n    result = vae_interpose.parse(latent[\"samples\"])\n    return {'samples': result}\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef calculate_sigmas_all(sampler, model, scheduler, steps):\n    from ldm_patched.modules.samplers import calculate_sigmas_scheduler\n\n    discard_penultimate_sigma = False\n    if sampler in ['dpm_2', 'dpm_2_ancestral']:\n        steps += 1\n        discard_penultimate_sigma = True\n\n    sigmas = calculate_sigmas_scheduler(model, scheduler, steps)\n\n    if discard_penultimate_sigma:\n        sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])\n    return sigmas\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef calculate_sigmas(sampler, model, scheduler, steps, denoise):\n    if denoise is None or denoise > 0.9999:\n        sigmas = calculate_sigmas_all(sampler, model, scheduler, steps)\n    else:\n        new_steps = int(steps / denoise)\n        sigmas = calculate_sigmas_all(sampler, model, scheduler, new_steps)\n        sigmas = sigmas[-(steps + 1):]\n    return sigmas\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef get_candidate_vae(steps, switch, denoise=1.0, refiner_swap_method='joint'):\n    assert refiner_swap_method in ['joint', 'separate', 'vae']\n\n    if final_refiner_vae is not None and final_refiner_unet is not None:\n        if denoise > 0.9:\n            return final_vae, final_refiner_vae\n        else:\n            if denoise > (float(steps - switch) / float(steps)) ** 0.834:  # karras 0.834\n                return final_vae, None\n            else:\n                return final_refiner_vae, None\n\n    return final_vae, final_refiner_vae\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef process_diffusion(positive_cond, negative_cond, steps, switch, width, height, image_seed, callback, sampler_name, scheduler_name, latent=None, denoise=1.0, tiled=False, cfg_scale=7.0, refiner_swap_method='joint', disable_preview=False):\n    target_unet, target_vae, target_refiner_unet, target_refiner_vae, target_clip \\\n        = final_unet, final_vae, final_refiner_unet, final_refiner_vae, final_clip\n\n    assert refiner_swap_method in ['joint', 'separate', 'vae']\n\n    if final_refiner_vae is not None and final_refiner_unet is not None:\n        # Refiner Use Different VAE (then it is SD15)\n        if denoise > 0.9:\n            refiner_swap_method = 'vae'\n        else:\n            refiner_swap_method = 'joint'\n            if denoise > (float(steps - switch) / float(steps)) ** 0.834:  # karras 0.834\n                target_unet, target_vae, target_refiner_unet, target_refiner_vae \\\n                    = final_unet, final_vae, None, None\n                print(f'[Sampler] only use Base because of partial denoise.')\n            else:\n                positive_cond = clip_separate(positive_cond, target_model=final_refiner_unet.model, target_clip=final_clip)\n                negative_cond = clip_separate(negative_cond, target_model=final_refiner_unet.model, target_clip=final_clip)\n                target_unet, target_vae, target_refiner_unet, target_refiner_vae \\\n                    = final_refiner_unet, final_refiner_vae, None, None\n                print(f'[Sampler] only use Refiner because of partial denoise.')\n\n    print(f'[Sampler] refiner_swap_method = {refiner_swap_method}')\n\n    if latent is None:\n        initial_latent = core.generate_empty_latent(width=width, height=height, batch_size=1)\n    else:\n        initial_latent = latent\n\n    minmax_sigmas = calculate_sigmas(sampler=sampler_name, scheduler=scheduler_name, model=final_unet.model, steps=steps, denoise=denoise)\n    sigma_min, sigma_max = minmax_sigmas[minmax_sigmas > 0].min(), minmax_sigmas.max()\n    sigma_min = float(sigma_min.cpu().numpy())\n    sigma_max = float(sigma_max.cpu().numpy())\n    print(f'[Sampler] sigma_min = {sigma_min}, sigma_max = {sigma_max}')\n\n    modules.patch.BrownianTreeNoiseSamplerPatched.global_init(\n        initial_latent['samples'].to(ldm_patched.modules.model_management.get_torch_device()),\n        sigma_min, sigma_max, seed=image_seed, cpu=False)\n\n    decoded_latent = None\n\n    if refiner_swap_method == 'joint':\n        sampled_latent = core.ksampler(\n            model=target_unet,\n            refiner=target_refiner_unet,\n            positive=positive_cond,\n            negative=negative_cond,\n            latent=initial_latent,\n            steps=steps, start_step=0, last_step=steps, disable_noise=False, force_full_denoise=True,\n            seed=image_seed,\n            denoise=denoise,\n            callback_function=callback,\n            cfg=cfg_scale,\n            sampler_name=sampler_name,\n            scheduler=scheduler_name,\n            refiner_switch=switch,\n            previewer_start=0,\n            previewer_end=steps,\n            disable_preview=disable_preview\n        )\n        decoded_latent = core.decode_vae(vae=target_vae, latent_image=sampled_latent, tiled=tiled)\n\n    if refiner_swap_method == 'separate':\n        sampled_latent = core.ksampler(\n            model=target_unet,\n            positive=positive_cond,\n            negative=negative_cond,\n            latent=initial_latent,\n            steps=steps, start_step=0, last_step=switch, disable_noise=False, force_full_denoise=False,\n            seed=image_seed,\n            denoise=denoise,\n            callback_function=callback,\n            cfg=cfg_scale,\n            sampler_name=sampler_name,\n            scheduler=scheduler_name,\n            previewer_start=0,\n            previewer_end=steps,\n            disable_preview=disable_preview\n        )\n        print('Refiner swapped by changing ksampler. Noise preserved.')\n\n        target_model = target_refiner_unet\n        if target_model is None:\n            target_model = target_unet\n            print('Use base model to refine itself - this may because of developer mode.')\n\n        sampled_latent = core.ksampler(\n            model=target_model,\n            positive=clip_separate(positive_cond, target_model=target_model.model, target_clip=target_clip),\n            negative=clip_separate(negative_cond, target_model=target_model.model, target_clip=target_clip),\n            latent=sampled_latent,\n            steps=steps, start_step=switch, last_step=steps, disable_noise=True, force_full_denoise=True,\n            seed=image_seed,\n            denoise=denoise,\n            callback_function=callback,\n            cfg=cfg_scale,\n            sampler_name=sampler_name,\n            scheduler=scheduler_name,\n            previewer_start=switch,\n            previewer_end=steps,\n            disable_preview=disable_preview\n        )\n\n        target_model = target_refiner_vae\n        if target_model is None:\n            target_model = target_vae\n        decoded_latent = core.decode_vae(vae=target_model, latent_image=sampled_latent, tiled=tiled)\n\n    if refiner_swap_method == 'vae':\n        modules.patch.patch_settings[os.getpid()].eps_record = 'vae'\n\n        if modules.inpaint_worker.current_task is not None:\n            modules.inpaint_worker.current_task.unswap()\n\n        sampled_latent = core.ksampler(\n            model=target_unet,\n            positive=positive_cond,\n            negative=negative_cond,\n            latent=initial_latent,\n            steps=steps, start_step=0, last_step=switch, disable_noise=False, force_full_denoise=True,\n            seed=image_seed,\n            denoise=denoise,\n            callback_function=callback,\n            cfg=cfg_scale,\n            sampler_name=sampler_name,\n            scheduler=scheduler_name,\n            previewer_start=0,\n            previewer_end=steps,\n            disable_preview=disable_preview\n        )\n        print('Fooocus VAE-based swap.')\n\n        target_model = target_refiner_unet\n        if target_model is None:\n            target_model = target_unet\n            print('Use base model to refine itself - this may because of developer mode.')\n\n        sampled_latent = vae_parse(sampled_latent)\n\n        k_sigmas = 1.4\n        sigmas = calculate_sigmas(sampler=sampler_name,\n                                  scheduler=scheduler_name,\n                                  model=target_model.model,\n                                  steps=steps,\n                                  denoise=denoise)[switch:] * k_sigmas\n        len_sigmas = len(sigmas) - 1\n\n        noise_mean = torch.mean(modules.patch.patch_settings[os.getpid()].eps_record, dim=1, keepdim=True)\n\n        if modules.inpaint_worker.current_task is not None:\n            modules.inpaint_worker.current_task.swap()\n\n        sampled_latent = core.ksampler(\n            model=target_model,\n            positive=clip_separate(positive_cond, target_model=target_model.model, target_clip=target_clip),\n            negative=clip_separate(negative_cond, target_model=target_model.model, target_clip=target_clip),\n            latent=sampled_latent,\n            steps=len_sigmas, start_step=0, last_step=len_sigmas, disable_noise=False, force_full_denoise=True,\n            seed=image_seed+1,\n            denoise=denoise,\n            callback_function=callback,\n            cfg=cfg_scale,\n            sampler_name=sampler_name,\n            scheduler=scheduler_name,\n            previewer_start=switch,\n            previewer_end=steps,\n            sigmas=sigmas,\n            noise_mean=noise_mean,\n            disable_preview=disable_preview\n        )\n\n        target_model = target_refiner_vae\n        if target_model is None:\n            target_model = target_vae\n        decoded_latent = core.decode_vae(vae=target_model, latent_image=sampled_latent, tiled=tiled)\n\n    images = core.pytorch_to_numpy(decoded_latent)\n    modules.patch.patch_settings[os.getpid()].eps_record = None\n    return images\n"
  },
  {
    "path": "modules/extra_utils.py",
    "content": "import os\nfrom ast import literal_eval\n\n\ndef makedirs_with_log(path):\n    try:\n        os.makedirs(path, exist_ok=True)\n    except OSError as error:\n        print(f'Directory {path} could not be created, reason: {error}')\n\n\ndef get_files_from_folder(folder_path, extensions=None, name_filter=None):\n    if not os.path.isdir(folder_path):\n        raise ValueError(\"Folder path is not a valid directory.\")\n\n    filenames = []\n\n    for root, _, files in os.walk(folder_path, topdown=False):\n        relative_path = os.path.relpath(root, folder_path)\n        if relative_path == \".\":\n            relative_path = \"\"\n        for filename in sorted(files, key=lambda s: s.casefold()):\n            _, file_extension = os.path.splitext(filename)\n            if (extensions is None or file_extension.lower() in extensions) and (name_filter is None or name_filter in _):\n                path = os.path.join(relative_path, filename)\n                filenames.append(path)\n\n    return filenames\n\n\ndef try_eval_env_var(value: str, expected_type=None):\n    try:\n        value_eval = value\n        if expected_type is bool:\n            value_eval = value.title()\n        value_eval = literal_eval(value_eval)\n        if expected_type is not None and not isinstance(value_eval, expected_type):\n            return value\n        return value_eval\n    except:\n        return value\n"
  },
  {
    "path": "modules/flags.py",
    "content": "from enum import IntEnum, Enum\n\ndisabled = 'Disabled'\nenabled = 'Enabled'\nsubtle_variation = 'Vary (Subtle)'\nstrong_variation = 'Vary (Strong)'\nupscale_15 = 'Upscale (1.5x)'\nupscale_2 = 'Upscale (2x)'\nupscale_fast = 'Upscale (Fast 2x)'\n\nuov_list = [disabled, subtle_variation, strong_variation, upscale_15, upscale_2, upscale_fast]\n\nenhancement_uov_before = \"Before First Enhancement\"\nenhancement_uov_after = \"After Last Enhancement\"\nenhancement_uov_processing_order = [enhancement_uov_before, enhancement_uov_after]\n\nenhancement_uov_prompt_type_original = 'Original Prompts'\nenhancement_uov_prompt_type_last_filled = 'Last Filled Enhancement Prompts'\nenhancement_uov_prompt_types = [enhancement_uov_prompt_type_original, enhancement_uov_prompt_type_last_filled]\n\nCIVITAI_NO_KARRAS = [\"euler\", \"euler_ancestral\", \"heun\", \"dpm_fast\", \"dpm_adaptive\", \"ddim\", \"uni_pc\"]\n\n# fooocus: a1111 (Civitai)\nKSAMPLER = {\n    \"euler\": \"Euler\",\n    \"euler_ancestral\": \"Euler a\",\n    \"heun\": \"Heun\",\n    \"heunpp2\": \"\",\n    \"dpm_2\": \"DPM2\",\n    \"dpm_2_ancestral\": \"DPM2 a\",\n    \"lms\": \"LMS\",\n    \"dpm_fast\": \"DPM fast\",\n    \"dpm_adaptive\": \"DPM adaptive\",\n    \"dpmpp_2s_ancestral\": \"DPM++ 2S a\",\n    \"dpmpp_sde\": \"DPM++ SDE\",\n    \"dpmpp_sde_gpu\": \"DPM++ SDE\",\n    \"dpmpp_2m\": \"DPM++ 2M\",\n    \"dpmpp_2m_sde\": \"DPM++ 2M SDE\",\n    \"dpmpp_2m_sde_gpu\": \"DPM++ 2M SDE\",\n    \"dpmpp_3m_sde\": \"\",\n    \"dpmpp_3m_sde_gpu\": \"\",\n    \"ddpm\": \"\",\n    \"lcm\": \"LCM\",\n    \"tcd\": \"TCD\",\n    \"restart\": \"Restart\"\n}\n\nSAMPLER_EXTRA = {\n    \"ddim\": \"DDIM\",\n    \"uni_pc\": \"UniPC\",\n    \"uni_pc_bh2\": \"\"\n}\n\nSAMPLERS = KSAMPLER | SAMPLER_EXTRA\n\nKSAMPLER_NAMES = list(KSAMPLER.keys())\n\nSCHEDULER_NAMES = [\"normal\", \"karras\", \"exponential\", \"sgm_uniform\", \"simple\", \"ddim_uniform\", \"lcm\", \"turbo\", \"align_your_steps\", \"tcd\", \"edm_playground_v2.5\"]\nSAMPLER_NAMES = KSAMPLER_NAMES + list(SAMPLER_EXTRA.keys())\n\nsampler_list = SAMPLER_NAMES\nscheduler_list = SCHEDULER_NAMES\n\nclip_skip_max = 12\n\ndefault_vae = 'Default (model)'\n\nrefiner_swap_method = 'joint'\n\ndefault_input_image_tab = 'uov_tab'\ninput_image_tab_ids = ['uov_tab', 'ip_tab', 'inpaint_tab', 'describe_tab', 'enhance_tab', 'metadata_tab']\n\ncn_ip = \"ImagePrompt\"\ncn_ip_face = \"FaceSwap\"\ncn_canny = \"PyraCanny\"\ncn_cpds = \"CPDS\"\n\nip_list = [cn_ip, cn_canny, cn_cpds, cn_ip_face]\ndefault_ip = cn_ip\n\ndefault_parameters = {\n    cn_ip: (0.5, 0.6), cn_ip_face: (0.9, 0.75), cn_canny: (0.5, 1.0), cn_cpds: (0.5, 1.0)\n}  # stop, weight\n\noutput_formats = ['png', 'jpeg', 'webp']\n\ninpaint_mask_models = ['u2net', 'u2netp', 'u2net_human_seg', 'u2net_cloth_seg', 'silueta', 'isnet-general-use', 'isnet-anime', 'sam']\ninpaint_mask_cloth_category = ['full', 'upper', 'lower']\ninpaint_mask_sam_model = ['vit_b', 'vit_l', 'vit_h']\n\ninpaint_engine_versions = ['None', 'v1', 'v2.5', 'v2.6']\ninpaint_option_default = 'Inpaint or Outpaint (default)'\ninpaint_option_detail = 'Improve Detail (face, hand, eyes, etc.)'\ninpaint_option_modify = 'Modify Content (add objects, change background, etc.)'\ninpaint_options = [inpaint_option_default, inpaint_option_detail, inpaint_option_modify]\n\ndescribe_type_photo = 'Photograph'\ndescribe_type_anime = 'Art/Anime'\ndescribe_types = [describe_type_photo, describe_type_anime]\n\nsdxl_aspect_ratios = [\n    '704*1408', '704*1344', '768*1344', '768*1280', '832*1216', '832*1152',\n    '896*1152', '896*1088', '960*1088', '960*1024', '1024*1024', '1024*960',\n    '1088*960', '1088*896', '1152*896', '1152*832', '1216*832', '1280*768',\n    '1344*768', '1344*704', '1408*704', '1472*704', '1536*640', '1600*640',\n    '1664*576', '1728*576'\n]\n\n\nclass MetadataScheme(Enum):\n    FOOOCUS = 'fooocus'\n    A1111 = 'a1111'\n\n\nmetadata_scheme = [\n    (f'{MetadataScheme.FOOOCUS.value} (json)', MetadataScheme.FOOOCUS.value),\n    (f'{MetadataScheme.A1111.value} (plain text)', MetadataScheme.A1111.value),\n]\n\n\nclass OutputFormat(Enum):\n    PNG = 'png'\n    JPEG = 'jpeg'\n    WEBP = 'webp'\n\n    @classmethod\n    def list(cls) -> list:\n        return list(map(lambda c: c.value, cls))\n\n\nclass PerformanceLoRA(Enum):\n    QUALITY = None\n    SPEED = None\n    EXTREME_SPEED = 'sdxl_lcm_lora.safetensors'\n    LIGHTNING = 'sdxl_lightning_4step_lora.safetensors'\n    HYPER_SD = 'sdxl_hyper_sd_4step_lora.safetensors'\n\n\nclass Steps(IntEnum):\n    QUALITY = 60\n    SPEED = 30\n    EXTREME_SPEED = 8\n    LIGHTNING = 4\n    HYPER_SD = 4\n\n    @classmethod\n    def keys(cls) -> list:\n        return list(map(lambda c: c, Steps.__members__))\n\n\nclass StepsUOV(IntEnum):\n    QUALITY = 36\n    SPEED = 18\n    EXTREME_SPEED = 8\n    LIGHTNING = 4\n    HYPER_SD = 4\n\n\nclass Performance(Enum):\n    QUALITY = 'Quality'\n    SPEED = 'Speed'\n    EXTREME_SPEED = 'Extreme Speed'\n    LIGHTNING = 'Lightning'\n    HYPER_SD = 'Hyper-SD'\n\n    @classmethod\n    def list(cls) -> list:\n        return list(map(lambda c: (c.name, c.value), cls))\n\n    @classmethod\n    def values(cls) -> list:\n        return list(map(lambda c: c.value, cls))\n\n    @classmethod\n    def by_steps(cls, steps: int | str):\n        return cls[Steps(int(steps)).name]\n\n    @classmethod\n    def has_restricted_features(cls, x) -> bool:\n        if isinstance(x, Performance):\n            x = x.value\n        return x in [cls.EXTREME_SPEED.value, cls.LIGHTNING.value, cls.HYPER_SD.value]\n\n    def steps(self) -> int | None:\n        return Steps[self.name].value if self.name in Steps.__members__ else None\n\n    def steps_uov(self) -> int | None:\n        return StepsUOV[self.name].value if self.name in StepsUOV.__members__ else None\n\n    def lora_filename(self) -> str | None:\n        return PerformanceLoRA[self.name].value if self.name in PerformanceLoRA.__members__ else None\n"
  },
  {
    "path": "modules/gradio_hijack.py",
    "content": "\"\"\"gr.Image() component.\"\"\"\n\nfrom __future__ import annotations\n\nimport warnings\nfrom pathlib import Path\nfrom typing import Any, Literal\n\nimport numpy as np\nimport PIL\nimport PIL.ImageOps\nimport gradio.routes\nimport importlib\n\nfrom gradio_client import utils as client_utils\nfrom gradio_client.documentation import document, set_documentation_group\nfrom gradio_client.serializing import ImgSerializable\nfrom PIL import Image as _Image  # using _ to minimize namespace pollution\n\nfrom gradio import processing_utils, utils, Error\nfrom gradio.components.base import IOComponent, _Keywords, Block\nfrom gradio.deprecation import warn_style_method_deprecation\nfrom gradio.events import (\n    Changeable,\n    Clearable,\n    Editable,\n    EventListenerMethod,\n    Selectable,\n    Streamable,\n    Uploadable,\n)\nfrom gradio.interpretation import TokenInterpretable\n\nset_documentation_group(\"component\")\n_Image.init()  # fixes https://github.com/gradio-app/gradio/issues/2843\n\n\n@document()\nclass Image(\n    Editable,\n    Clearable,\n    Changeable,\n    Streamable,\n    Selectable,\n    Uploadable,\n    IOComponent,\n    ImgSerializable,\n    TokenInterpretable,\n):\n    \"\"\"\n    Creates an image component that can be used to upload/draw images (as an input) or display images (as an output).\n    Preprocessing: passes the uploaded image as a {numpy.array}, {PIL.Image} or {str} filepath depending on `type` -- unless `tool` is `sketch` AND source is one of `upload` or `webcam`. In these cases, a {dict} with keys `image` and `mask` is passed, and the format of the corresponding values depends on `type`.\n    Postprocessing: expects a {numpy.array}, {PIL.Image} or {str} or {pathlib.Path} filepath to an image and displays the image.\n    Examples-format: a {str} filepath to a local file that contains the image.\n    Demos: image_mod, image_mod_default_image\n    Guides: image-classification-in-pytorch, image-classification-in-tensorflow, image-classification-with-vision-transformers, building-a-pictionary_app, create-your-own-friends-with-a-gan\n    \"\"\"\n\n    def __init__(\n        self,\n        value: str | _Image.Image | np.ndarray | None = None,\n        *,\n        shape: tuple[int, int] | None = None,\n        height: int | None = None,\n        width: int | None = None,\n        image_mode: Literal[\n            \"1\", \"L\", \"P\", \"RGB\", \"RGBA\", \"CMYK\", \"YCbCr\", \"LAB\", \"HSV\", \"I\", \"F\"\n        ] = \"RGB\",\n        invert_colors: bool = False,\n        source: Literal[\"upload\", \"webcam\", \"canvas\"] = \"upload\",\n        tool: Literal[\"editor\", \"select\", \"sketch\", \"color-sketch\"] | None = None,\n        type: Literal[\"numpy\", \"pil\", \"filepath\"] = \"numpy\",\n        label: str | None = None,\n        every: float | None = None,\n        show_label: bool | None = None,\n        show_download_button: bool = True,\n        container: bool = True,\n        scale: int | None = None,\n        min_width: int = 160,\n        interactive: bool | None = None,\n        visible: bool = True,\n        streaming: bool = False,\n        elem_id: str | None = None,\n        elem_classes: list[str] | str | None = None,\n        mirror_webcam: bool = True,\n        brush_radius: float | None = None,\n        brush_color: str = \"#000000\",\n        mask_opacity: float = 0.7,\n        show_share_button: bool | None = None,\n        **kwargs,\n    ):\n        \"\"\"\n        Parameters:\n            value: A PIL Image, numpy array, path or URL for the default value that Image component is going to take. If callable, the function will be called whenever the app loads to set the initial value of the component.\n            shape: (width, height) shape to crop and resize image when passed to function. If None, matches input image size. Pass None for either width or height to only crop and resize the other.\n            height: Height of the displayed image in pixels.\n            width: Width of the displayed image in pixels.\n            image_mode: \"RGB\" if color, or \"L\" if black and white. See https://pillow.readthedocs.io/en/stable/handbook/concepts.html for other supported image modes and their meaning.\n            invert_colors: whether to invert the image as a preprocessing step.\n            source: Source of image. \"upload\" creates a box where user can drop an image file, \"webcam\" allows user to take snapshot from their webcam, \"canvas\" defaults to a white image that can be edited and drawn upon with tools.\n            tool: Tools used for editing. \"editor\" allows a full screen editor (and is the default if source is \"upload\" or \"webcam\"), \"select\" provides a cropping and zoom tool, \"sketch\" allows you to create a binary sketch (and is the default if source=\"canvas\"), and \"color-sketch\" allows you to created a sketch in different colors. \"color-sketch\" can be used with source=\"upload\" or \"webcam\" to allow sketching on an image. \"sketch\" can also be used with \"upload\" or \"webcam\" to create a mask over an image and in that case both the image and mask are passed into the function as a dictionary with keys \"image\" and \"mask\" respectively.\n            type: The format the image is converted to before being passed into the prediction function. \"numpy\" converts the image to a numpy array with shape (height, width, 3) and values from 0 to 255, \"pil\" converts the image to a PIL image object, \"filepath\" passes a str path to a temporary file containing the image.\n            label: component name in interface.\n            every: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.\n            show_label: if True, will display label.\n            show_download_button: If True, will display button to download image.\n            container: If True, will place the component in a container - providing some extra padding around the border.\n            scale: relative width compared to adjacent Components in a Row. For example, if Component A has scale=2, and Component B has scale=1, A will be twice as wide as B. Should be an integer.\n            min_width: minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.\n            interactive: if True, will allow users to upload and edit an image; if False, can only be used to display images. If not provided, this is inferred based on whether the component is used as an input or output.\n            visible: If False, component will be hidden.\n            streaming: If True when used in a `live` interface, will automatically stream webcam feed. Only valid is source is 'webcam'.\n            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.\n            elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.\n            mirror_webcam: If True webcam will be mirrored. Default is True.\n            brush_radius: Size of the brush for Sketch. Default is None which chooses a sensible default\n            brush_color: Color of the brush for Sketch as hex string. Default is \"#000000\".\n            mask_opacity: Opacity of mask drawn on image, as a value between 0 and 1.\n            show_share_button: If True, will show a share icon in the corner of the component that allows user to share outputs to Hugging Face Spaces Discussions. If False, icon does not appear. If set to None (default behavior), then the icon appears if this Gradio app is launched on Spaces, but not otherwise.\n        \"\"\"\n        self.brush_radius = brush_radius\n        self.brush_color = brush_color\n        self.mask_opacity = mask_opacity\n        self.mirror_webcam = mirror_webcam\n        valid_types = [\"numpy\", \"pil\", \"filepath\"]\n        if type not in valid_types:\n            raise ValueError(\n                f\"Invalid value for parameter `type`: {type}. Please choose from one of: {valid_types}\"\n            )\n        self.type = type\n        self.shape = shape\n        self.height = height\n        self.width = width\n        self.image_mode = image_mode\n        valid_sources = [\"upload\", \"webcam\", \"canvas\"]\n        if source not in valid_sources:\n            raise ValueError(\n                f\"Invalid value for parameter `source`: {source}. Please choose from one of: {valid_sources}\"\n            )\n        self.source = source\n        if tool is None:\n            self.tool = \"sketch\" if source == \"canvas\" else \"editor\"\n        else:\n            self.tool = tool\n        self.invert_colors = invert_colors\n        self.streaming = streaming\n        self.show_download_button = show_download_button\n        if streaming and source != \"webcam\":\n            raise ValueError(\"Image streaming only available if source is 'webcam'.\")\n        self.select: EventListenerMethod\n        \"\"\"\n        Event listener for when the user clicks on a pixel within the image.\n        Uses event data gradio.SelectData to carry `index` to refer to the [x, y] coordinates of the clicked pixel.\n        See EventData documentation on how to use this event data.\n        \"\"\"\n        self.show_share_button = (\n            (utils.get_space() is not None)\n            if show_share_button is None\n            else show_share_button\n        )\n        IOComponent.__init__(\n            self,\n            label=label,\n            every=every,\n            show_label=show_label,\n            container=container,\n            scale=scale,\n            min_width=min_width,\n            interactive=interactive,\n            visible=visible,\n            elem_id=elem_id,\n            elem_classes=elem_classes,\n            value=value,\n            **kwargs,\n        )\n        TokenInterpretable.__init__(self)\n\n    def get_config(self):\n        return {\n            \"image_mode\": self.image_mode,\n            \"shape\": self.shape,\n            \"height\": self.height,\n            \"width\": self.width,\n            \"source\": self.source,\n            \"tool\": self.tool,\n            \"value\": self.value,\n            \"streaming\": self.streaming,\n            \"mirror_webcam\": self.mirror_webcam,\n            \"brush_radius\": self.brush_radius,\n            \"brush_color\": self.brush_color,\n            \"mask_opacity\": self.mask_opacity,\n            \"selectable\": self.selectable,\n            \"show_share_button\": self.show_share_button,\n            \"show_download_button\": self.show_download_button,\n            **IOComponent.get_config(self),\n        }\n\n    @staticmethod\n    def update(\n        value: Any | Literal[_Keywords.NO_VALUE] | None = _Keywords.NO_VALUE,\n        height: int | None = None,\n        width: int | None = None,\n        label: str | None = None,\n        show_label: bool | None = None,\n        show_download_button: bool | None = None,\n        container: bool | None = None,\n        scale: int | None = None,\n        min_width: int | None = None,\n        interactive: bool | None = None,\n        visible: bool | None = None,\n        brush_radius: float | None = None,\n        brush_color: str | None = None,\n        mask_opacity: float | None = None,\n        show_share_button: bool | None = None,\n    ):\n        return {\n            \"height\": height,\n            \"width\": width,\n            \"label\": label,\n            \"show_label\": show_label,\n            \"show_download_button\": show_download_button,\n            \"container\": container,\n            \"scale\": scale,\n            \"min_width\": min_width,\n            \"interactive\": interactive,\n            \"visible\": visible,\n            \"value\": value,\n            \"brush_radius\": brush_radius,\n            \"brush_color\": brush_color,\n            \"mask_opacity\": mask_opacity,\n            \"show_share_button\": show_share_button,\n            \"__type__\": \"update\",\n        }\n\n    def _format_image(\n        self, im: _Image.Image | None\n    ) -> np.ndarray | _Image.Image | str | None:\n        \"\"\"Helper method to format an image based on self.type\"\"\"\n        if im is None:\n            return im\n        fmt = im.format\n        if self.type == \"pil\":\n            return im\n        elif self.type == \"numpy\":\n            return np.array(im)\n        elif self.type == \"filepath\":\n            path = self.pil_to_temp_file(\n                im, dir=self.DEFAULT_TEMP_DIR, format=fmt or \"png\"\n            )\n            self.temp_files.add(path)\n            return path\n        else:\n            raise ValueError(\n                \"Unknown type: \"\n                + str(self.type)\n                + \". Please choose from: 'numpy', 'pil', 'filepath'.\"\n            )\n\n    def preprocess(\n        self, x: str | dict[str, str]\n    ) -> np.ndarray | _Image.Image | str | dict | None:\n        \"\"\"\n        Parameters:\n            x: base64 url data, or (if tool == \"sketch\") a dict of image and mask base64 url data\n        Returns:\n            image in requested format, or (if tool == \"sketch\") a dict of image and mask in requested format\n        \"\"\"\n        if x is None:\n            return x\n\n        mask = None\n\n        if self.tool == \"sketch\" and self.source in [\"upload\", \"webcam\"]:\n            if isinstance(x, dict):\n                x, mask = x[\"image\"], x[\"mask\"]\n\n        assert isinstance(x, str)\n        try:\n            im = processing_utils.decode_base64_to_image(x)\n        except PIL.UnidentifiedImageError:\n            raise Error(\"Unsupported image type in input\")\n        with warnings.catch_warnings():\n            warnings.simplefilter(\"ignore\")\n            im = im.convert(self.image_mode)\n        if self.shape is not None:\n            im = processing_utils.resize_and_crop(im, self.shape)\n        if self.invert_colors:\n            im = PIL.ImageOps.invert(im)\n        if (\n            self.source == \"webcam\"\n            and self.mirror_webcam is True\n            and self.tool != \"color-sketch\"\n        ):\n            im = PIL.ImageOps.mirror(im)\n\n        if self.tool == \"sketch\" and self.source in [\"upload\", \"webcam\"]:\n            if mask is not None:\n                mask_im = processing_utils.decode_base64_to_image(mask)\n                if mask_im.mode == \"RGBA\":  # whiten any opaque pixels in the mask\n                    alpha_data = mask_im.getchannel(\"A\").convert(\"L\")\n                    mask_im = _Image.merge(\"RGB\", [alpha_data, alpha_data, alpha_data])\n                return {\n                    \"image\": self._format_image(im),\n                    \"mask\": self._format_image(mask_im),\n                }\n            else:\n                return {\n                    \"image\": self._format_image(im),\n                    \"mask\": None,\n                }\n\n        return self._format_image(im)\n\n    def postprocess(\n        self, y: np.ndarray | _Image.Image | str | Path | None\n    ) -> str | None:\n        \"\"\"\n        Parameters:\n            y: image as a numpy array, PIL Image, string/Path filepath, or string URL\n        Returns:\n            base64 url data\n        \"\"\"\n        if y is None:\n            return None\n        if isinstance(y, np.ndarray):\n            return processing_utils.encode_array_to_base64(y)\n        elif isinstance(y, _Image.Image):\n            return processing_utils.encode_pil_to_base64(y)\n        elif isinstance(y, (str, Path)):\n            return client_utils.encode_url_or_file_to_base64(y)\n        else:\n            raise ValueError(\"Cannot process this value as an Image\")\n\n    def set_interpret_parameters(self, segments: int = 16):\n        \"\"\"\n        Calculates interpretation score of image subsections by splitting the image into subsections, then using a \"leave one out\" method to calculate the score of each subsection by whiting out the subsection and measuring the delta of the output value.\n        Parameters:\n            segments: Number of interpretation segments to split image into.\n        \"\"\"\n        self.interpretation_segments = segments\n        return self\n\n    def _segment_by_slic(self, x):\n        \"\"\"\n        Helper method that segments an image into superpixels using slic.\n        Parameters:\n            x: base64 representation of an image\n        \"\"\"\n        x = processing_utils.decode_base64_to_image(x)\n        if self.shape is not None:\n            x = processing_utils.resize_and_crop(x, self.shape)\n        resized_and_cropped_image = np.array(x)\n        try:\n            from skimage.segmentation import slic\n        except (ImportError, ModuleNotFoundError) as err:\n            raise ValueError(\n                \"Error: running this interpretation for images requires scikit-image, please install it first.\"\n            ) from err\n        try:\n            segments_slic = slic(\n                resized_and_cropped_image,\n                self.interpretation_segments,\n                compactness=10,\n                sigma=1,\n                start_label=1,\n            )\n        except TypeError:  # For skimage 0.16 and older\n            segments_slic = slic(\n                resized_and_cropped_image,\n                self.interpretation_segments,\n                compactness=10,\n                sigma=1,\n            )\n        return segments_slic, resized_and_cropped_image\n\n    def tokenize(self, x):\n        \"\"\"\n        Segments image into tokens, masks, and leave-one-out-tokens\n        Parameters:\n            x: base64 representation of an image\n        Returns:\n            tokens: list of tokens, used by the get_masked_input() method\n            leave_one_out_tokens: list of left-out tokens, used by the get_interpretation_neighbors() method\n            masks: list of masks, used by the get_interpretation_neighbors() method\n        \"\"\"\n        segments_slic, resized_and_cropped_image = self._segment_by_slic(x)\n        tokens, masks, leave_one_out_tokens = [], [], []\n        replace_color = np.mean(resized_and_cropped_image, axis=(0, 1))\n        for segment_value in np.unique(segments_slic):\n            mask = segments_slic == segment_value\n            image_screen = np.copy(resized_and_cropped_image)\n            image_screen[segments_slic == segment_value] = replace_color\n            leave_one_out_tokens.append(\n                processing_utils.encode_array_to_base64(image_screen)\n            )\n            token = np.copy(resized_and_cropped_image)\n            token[segments_slic != segment_value] = 0\n            tokens.append(token)\n            masks.append(mask)\n        return tokens, leave_one_out_tokens, masks\n\n    def get_masked_inputs(self, tokens, binary_mask_matrix):\n        masked_inputs = []\n        for binary_mask_vector in binary_mask_matrix:\n            masked_input = np.zeros_like(tokens[0], dtype=int)\n            for token, b in zip(tokens, binary_mask_vector):\n                masked_input = masked_input + token * int(b)\n            masked_inputs.append(processing_utils.encode_array_to_base64(masked_input))\n        return masked_inputs\n\n    def get_interpretation_scores(\n        self, x, neighbors, scores, masks, tokens=None, **kwargs\n    ) -> list[list[float]]:\n        \"\"\"\n        Returns:\n            A 2D array representing the interpretation score of each pixel of the image.\n        \"\"\"\n        x = processing_utils.decode_base64_to_image(x)\n        if self.shape is not None:\n            x = processing_utils.resize_and_crop(x, self.shape)\n        x = np.array(x)\n        output_scores = np.zeros((x.shape[0], x.shape[1]))\n\n        for score, mask in zip(scores, masks):\n            output_scores += score * mask\n\n        max_val, min_val = np.max(output_scores), np.min(output_scores)\n        if max_val > 0:\n            output_scores = (output_scores - min_val) / (max_val - min_val)\n        return output_scores.tolist()\n\n    def style(self, *, height: int | None = None, width: int | None = None, **kwargs):\n        \"\"\"\n        This method is deprecated. Please set these arguments in the constructor instead.\n        \"\"\"\n        warn_style_method_deprecation()\n        if height is not None:\n            self.height = height\n        if width is not None:\n            self.width = width\n        return self\n\n    def check_streamable(self):\n        if self.source != \"webcam\":\n            raise ValueError(\"Image streaming only available if source is 'webcam'.\")\n\n    def as_example(self, input_data: str | None) -> str:\n        if input_data is None:\n            return \"\"\n        elif (\n            self.root_url\n        ):  # If an externally hosted image, don't convert to absolute path\n            return input_data\n        return str(utils.abspath(input_data))\n\n\nall_components = []\n\nif not hasattr(Block, 'original__init__'):\n    Block.original_init = Block.__init__\n\n\ndef blk_ini(self, *args, **kwargs):\n    all_components.append(self)\n    return Block.original_init(self, *args, **kwargs)\n\n\nBlock.__init__ = blk_ini\n\n\ngradio.routes.asyncio = importlib.reload(gradio.routes.asyncio)\n\nif not hasattr(gradio.routes.asyncio, 'original_wait_for'):\n    gradio.routes.asyncio.original_wait_for = gradio.routes.asyncio.wait_for\n\n\ndef patched_wait_for(fut, timeout):\n    del timeout\n    return gradio.routes.asyncio.original_wait_for(fut, timeout=65535)\n\n\ngradio.routes.asyncio.wait_for = patched_wait_for\n\n"
  },
  {
    "path": "modules/hash_cache.py",
    "content": "import json\nimport os\nfrom concurrent.futures import ThreadPoolExecutor\nfrom multiprocessing import cpu_count\n\nimport args_manager\nfrom modules.util import sha256, HASH_SHA256_LENGTH, get_file_from_folder_list\n\nhash_cache_filename = 'hash_cache.txt'\nhash_cache = {}\n\n\ndef sha256_from_cache(filepath):\n    global hash_cache\n    if filepath not in hash_cache:\n        print(f\"[Cache] Calculating sha256 for {filepath}\")\n        hash_value = sha256(filepath)\n        print(f\"[Cache] sha256 for {filepath}: {hash_value}\")\n        hash_cache[filepath] = hash_value\n        save_cache_to_file(filepath, hash_value)\n\n    return hash_cache[filepath]\n\n\ndef load_cache_from_file():\n    global hash_cache\n\n    try:\n        if os.path.exists(hash_cache_filename):\n            with open(hash_cache_filename, 'rt', encoding='utf-8') as fp:\n                for line in fp:\n                    entry = json.loads(line)\n                    for filepath, hash_value in entry.items():\n                        if not os.path.exists(filepath) or not isinstance(hash_value, str) and len(hash_value) != HASH_SHA256_LENGTH:\n                            print(f'[Cache] Skipping invalid cache entry: {filepath}')\n                            continue\n                        hash_cache[filepath] = hash_value\n    except Exception as e:\n        print(f'[Cache] Loading failed: {e}')\n\n\ndef save_cache_to_file(filename=None, hash_value=None):\n    global hash_cache\n\n    if filename is not None and hash_value is not None:\n        items = [(filename, hash_value)]\n        mode = 'at'\n    else:\n        items = sorted(hash_cache.items())\n        mode = 'wt'\n\n    try:\n        with open(hash_cache_filename, mode, encoding='utf-8') as fp:\n            for filepath, hash_value in items:\n                json.dump({filepath: hash_value}, fp)\n                fp.write('\\n')\n    except Exception as e:\n        print(f'[Cache] Saving failed: {e}')\n\n\ndef init_cache(model_filenames, paths_checkpoints, lora_filenames, paths_loras):\n    load_cache_from_file()\n\n    if args_manager.args.rebuild_hash_cache:\n        max_workers = args_manager.args.rebuild_hash_cache if args_manager.args.rebuild_hash_cache > 0 else cpu_count()\n        rebuild_cache(lora_filenames, model_filenames, paths_checkpoints, paths_loras, max_workers)\n\n    # write cache to file again for sorting and cleanup of invalid cache entries\n    save_cache_to_file()\n\n\ndef rebuild_cache(lora_filenames, model_filenames, paths_checkpoints, paths_loras, max_workers=cpu_count()):\n    def thread(filename, paths):\n        filepath = get_file_from_folder_list(filename, paths)\n        sha256_from_cache(filepath)\n\n    print('[Cache] Rebuilding hash cache')\n    with ThreadPoolExecutor(max_workers=max_workers) as executor:\n        for model_filename in model_filenames:\n            executor.submit(thread, model_filename, paths_checkpoints)\n        for lora_filename in lora_filenames:\n            executor.submit(thread, lora_filename, paths_loras)\n    print('[Cache] Done')\n"
  },
  {
    "path": "modules/html.py",
    "content": "progress_html = '''\n<div class=\"loader-container\">\n  <div class=\"loader\"></div>\n  <div class=\"progress-container\">\n    <progress value=\"*number*\" max=\"100\"></progress>\n  </div>\n  <span>*text*</span>\n</div>\n'''\n\n\ndef make_progress_html(number, text):\n    return progress_html.replace('*number*', str(number)).replace('*text*', text)\n"
  },
  {
    "path": "modules/inpaint_worker.py",
    "content": "import torch\nimport numpy as np\n\nfrom PIL import Image, ImageFilter\nfrom modules.util import resample_image, set_image_shape_ceil, get_image_shape_ceil\nfrom modules.upscaler import perform_upscale\nimport cv2\n\n\ninpaint_head_model = None\n\n\nclass InpaintHead(torch.nn.Module):\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.head = torch.nn.Parameter(torch.empty(size=(320, 5, 3, 3), device='cpu'))\n\n    def __call__(self, x):\n        x = torch.nn.functional.pad(x, (1, 1, 1, 1), \"replicate\")\n        return torch.nn.functional.conv2d(input=x, weight=self.head)\n\n\ncurrent_task = None\n\n\ndef box_blur(x, k):\n    x = Image.fromarray(x)\n    x = x.filter(ImageFilter.BoxBlur(k))\n    return np.array(x)\n\n\ndef max_filter_opencv(x, ksize=3):\n    # Use OpenCV maximum filter\n    # Make sure the input type is int16\n    return cv2.dilate(x, np.ones((ksize, ksize), dtype=np.int16))\n\n\ndef morphological_open(x):\n    # Convert array to int16 type via threshold operation\n    x_int16 = np.zeros_like(x, dtype=np.int16)\n    x_int16[x > 127] = 256\n\n    for i in range(32):\n        # Use int16 type to avoid overflow\n        maxed = max_filter_opencv(x_int16, ksize=3) - 8\n        x_int16 = np.maximum(maxed, x_int16)\n\n    # Clip negative values to 0 and convert back to uint8 type\n    x_uint8 = np.clip(x_int16, 0, 255).astype(np.uint8)\n    return x_uint8\n\n\ndef up255(x, t=0):\n    y = np.zeros_like(x).astype(np.uint8)\n    y[x > t] = 255\n    return y\n\n\ndef imsave(x, path):\n    x = Image.fromarray(x)\n    x.save(path)\n\n\ndef regulate_abcd(x, a, b, c, d):\n    H, W = x.shape[:2]\n    if a < 0:\n        a = 0\n    if a > H:\n        a = H\n    if b < 0:\n        b = 0\n    if b > H:\n        b = H\n    if c < 0:\n        c = 0\n    if c > W:\n        c = W\n    if d < 0:\n        d = 0\n    if d > W:\n        d = W\n    return int(a), int(b), int(c), int(d)\n\n\ndef compute_initial_abcd(x):\n    indices = np.where(x)\n    a = np.min(indices[0])\n    b = np.max(indices[0])\n    c = np.min(indices[1])\n    d = np.max(indices[1])\n    abp = (b + a) // 2\n    abm = (b - a) // 2\n    cdp = (d + c) // 2\n    cdm = (d - c) // 2\n    l = int(max(abm, cdm) * 1.15)\n    a = abp - l\n    b = abp + l + 1\n    c = cdp - l\n    d = cdp + l + 1\n    a, b, c, d = regulate_abcd(x, a, b, c, d)\n    return a, b, c, d\n\n\ndef solve_abcd(x, a, b, c, d, k):\n    k = float(k)\n    assert 0.0 <= k <= 1.0\n\n    H, W = x.shape[:2]\n    if k == 1.0:\n        return 0, H, 0, W\n    while True:\n        if b - a >= H * k and d - c >= W * k:\n            break\n\n        add_h = (b - a) < (d - c)\n        add_w = not add_h\n\n        if b - a == H:\n            add_w = True\n\n        if d - c == W:\n            add_h = True\n\n        if add_h:\n            a -= 1\n            b += 1\n\n        if add_w:\n            c -= 1\n            d += 1\n\n        a, b, c, d = regulate_abcd(x, a, b, c, d)\n    return a, b, c, d\n\n\ndef fooocus_fill(image, mask):\n    current_image = image.copy()\n    raw_image = image.copy()\n    area = np.where(mask < 127)\n    store = raw_image[area]\n\n    for k, repeats in [(512, 2), (256, 2), (128, 4), (64, 4), (33, 8), (15, 8), (5, 16), (3, 16)]:\n        for _ in range(repeats):\n            current_image = box_blur(current_image, k)\n            current_image[area] = store\n\n    return current_image\n\n\nclass InpaintWorker:\n    def __init__(self, image, mask, use_fill=True, k=0.618):\n        a, b, c, d = compute_initial_abcd(mask > 0)\n        a, b, c, d = solve_abcd(mask, a, b, c, d, k=k)\n\n        # interested area\n        self.interested_area = (a, b, c, d)\n        self.interested_mask = mask[a:b, c:d]\n        self.interested_image = image[a:b, c:d]\n\n        # super resolution\n        if get_image_shape_ceil(self.interested_image) < 1024:\n            self.interested_image = perform_upscale(self.interested_image)\n\n        # resize to make images ready for diffusion\n        self.interested_image = set_image_shape_ceil(self.interested_image, 1024)\n        self.interested_fill = self.interested_image.copy()\n        H, W, C = self.interested_image.shape\n\n        # process mask\n        self.interested_mask = up255(resample_image(self.interested_mask, W, H), t=127)\n\n        # compute filling\n        if use_fill:\n            self.interested_fill = fooocus_fill(self.interested_image, self.interested_mask)\n\n        # soft pixels\n        self.mask = morphological_open(mask)\n        self.image = image\n\n        # ending\n        self.latent = None\n        self.latent_after_swap = None\n        self.swapped = False\n        self.latent_mask = None\n        self.inpaint_head_feature = None\n        return\n\n    def load_latent(self, latent_fill, latent_mask, latent_swap=None):\n        self.latent = latent_fill\n        self.latent_mask = latent_mask\n        self.latent_after_swap = latent_swap\n        return\n\n    def patch(self, inpaint_head_model_path, inpaint_latent, inpaint_latent_mask, model):\n        global inpaint_head_model\n\n        if inpaint_head_model is None:\n            inpaint_head_model = InpaintHead()\n            sd = torch.load(inpaint_head_model_path, map_location='cpu', weights_only=True)\n            inpaint_head_model.load_state_dict(sd)\n\n        feed = torch.cat([\n            inpaint_latent_mask,\n            model.model.process_latent_in(inpaint_latent)\n        ], dim=1)\n\n        inpaint_head_model.to(device=feed.device, dtype=feed.dtype)\n        inpaint_head_feature = inpaint_head_model(feed)\n\n        def input_block_patch(h, transformer_options):\n            if transformer_options[\"block\"][1] == 0:\n                h = h + inpaint_head_feature.to(h)\n            return h\n\n        m = model.clone()\n        m.set_model_input_block_patch(input_block_patch)\n        return m\n\n    def swap(self):\n        if self.swapped:\n            return\n\n        if self.latent is None:\n            return\n\n        if self.latent_after_swap is None:\n            return\n\n        self.latent, self.latent_after_swap = self.latent_after_swap, self.latent\n        self.swapped = True\n        return\n\n    def unswap(self):\n        if not self.swapped:\n            return\n\n        if self.latent is None:\n            return\n\n        if self.latent_after_swap is None:\n            return\n\n        self.latent, self.latent_after_swap = self.latent_after_swap, self.latent\n        self.swapped = False\n        return\n\n    def color_correction(self, img):\n        fg = img.astype(np.float32)\n        bg = self.image.copy().astype(np.float32)\n        w = self.mask[:, :, None].astype(np.float32) / 255.0\n        y = fg * w + bg * (1 - w)\n        return y.clip(0, 255).astype(np.uint8)\n\n    def post_process(self, img):\n        a, b, c, d = self.interested_area\n        content = resample_image(img, d - c, b - a)\n        result = self.image.copy()\n        result[a:b, c:d] = content\n        result = self.color_correction(result)\n        return result\n\n    def visualize_mask_processing(self):\n        return [self.interested_fill, self.interested_mask, self.interested_image]\n\n"
  },
  {
    "path": "modules/launch_util.py",
    "content": "import os\nimport importlib\nimport importlib.util\nimport shutil\nimport subprocess\nimport sys\nimport re\nimport logging\nimport importlib.metadata\nimport packaging.version\nfrom packaging.requirements import Requirement\n\nlogging.getLogger(\"torch.distributed.nn\").setLevel(logging.ERROR)  # sshh...\nlogging.getLogger(\"xformers\").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())\n\nre_requirement = re.compile(r\"\\s*([-\\w]+)\\s*(?:==\\s*([-+.\\w]+))?\\s*\")\n\npython = sys.executable\ndefault_command_live = (os.environ.get('LAUNCH_LIVE_OUTPUT') == \"1\")\nindex_url = os.environ.get('INDEX_URL', \"\")\n\nmodules_path = os.path.dirname(os.path.realpath(__file__))\nscript_path = os.path.dirname(modules_path)\n\n\ndef is_installed(package):\n    try:\n        spec = importlib.util.find_spec(package)\n    except ModuleNotFoundError:\n        return False\n\n    return spec is not None\n\n\ndef run(command, desc=None, errdesc=None, custom_env=None, live: bool = default_command_live) -> str:\n    if desc is not None:\n        print(desc)\n\n    run_kwargs = {\n        \"args\": command,\n        \"shell\": True,\n        \"env\": os.environ if custom_env is None else custom_env,\n        \"encoding\": 'utf8',\n        \"errors\": 'ignore',\n    }\n\n    if not live:\n        run_kwargs[\"stdout\"] = run_kwargs[\"stderr\"] = subprocess.PIPE\n\n    result = subprocess.run(**run_kwargs)\n\n    if result.returncode != 0:\n        error_bits = [\n            f\"{errdesc or 'Error running command'}.\",\n            f\"Command: {command}\",\n            f\"Error code: {result.returncode}\",\n        ]\n        if result.stdout:\n            error_bits.append(f\"stdout: {result.stdout}\")\n        if result.stderr:\n            error_bits.append(f\"stderr: {result.stderr}\")\n        raise RuntimeError(\"\\n\".join(error_bits))\n\n    return (result.stdout or \"\")\n\n\ndef run_pip(command, desc=None, live=default_command_live):\n    try:\n        index_url_line = f' --index-url {index_url}' if index_url != '' else ''\n        return run(f'\"{python}\" -m pip {command} --prefer-binary{index_url_line}', desc=f\"Installing {desc}\",\n                   errdesc=f\"Couldn't install {desc}\", live=live)\n    except Exception as e:\n        print(e)\n        print(f'CMD Failed {desc}: {command}')\n        return None\n\n\ndef requirements_met(requirements_file):\n    with open(requirements_file, \"r\", encoding=\"utf8\") as file:\n        for line in file:\n            line = line.strip()\n            if line == \"\" or line.startswith('#'):\n                continue\n\n            requirement = Requirement(line)\n            package = requirement.name\n\n            try:\n                version_installed = importlib.metadata.version(package)\n                installed_version = packaging.version.parse(version_installed)\n\n                # Check if the installed version satisfies the requirement\n                if installed_version not in requirement.specifier:\n                    print(f\"Version mismatch for {package}: Installed version {version_installed} does not meet requirement {requirement}\")\n                    return False\n            except Exception as e:\n                print(f\"Error checking version for {package}: {e}\")\n                return False\n\n    return True\n\n\ndef delete_folder_content(folder, prefix=None):\n    result = True\n\n    for filename in os.listdir(folder):\n        file_path = os.path.join(folder, filename)\n        try:\n            if os.path.isfile(file_path) or os.path.islink(file_path):\n                os.unlink(file_path)\n            elif os.path.isdir(file_path):\n                shutil.rmtree(file_path)\n        except Exception as e:\n            print(f'{prefix}Failed to delete {file_path}. Reason: {e}')\n            result = False\n\n    return result"
  },
  {
    "path": "modules/localization.py",
    "content": "import json\nimport os\n\n\ncurrent_translation = {}\nlocalization_root = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'language')\n\n\ndef localization_js(filename):\n    global current_translation\n\n    if isinstance(filename, str):\n        full_name = os.path.abspath(os.path.join(localization_root, filename + '.json'))\n        if os.path.exists(full_name):\n            try:\n                with open(full_name, encoding='utf-8') as f:\n                    current_translation = json.load(f)\n                    assert isinstance(current_translation, dict)\n                    for k, v in current_translation.items():\n                        assert isinstance(k, str)\n                        assert isinstance(v, str)\n            except Exception as e:\n                print(str(e))\n                print(f'Failed to load localization file {full_name}')\n\n    # current_translation = {k: 'XXX' for k in current_translation.keys()}  # use this to see if all texts are covered\n\n    return f\"window.localization = {json.dumps(current_translation)}\"\n\n\ndef dump_english_config(components):\n    all_texts = []\n    for c in components:\n        label = getattr(c, 'label', None)\n        value = getattr(c, 'value', None)\n        choices = getattr(c, 'choices', None)\n        info = getattr(c, 'info', None)\n\n        if isinstance(label, str):\n            all_texts.append(label)\n        if isinstance(value, str):\n            all_texts.append(value)\n        if isinstance(info, str):\n            all_texts.append(info)\n        if isinstance(choices, list):\n            for x in choices:\n                if isinstance(x, str):\n                    all_texts.append(x)\n                if isinstance(x, tuple):\n                    for y in x:\n                        if isinstance(y, str):\n                            all_texts.append(y)\n\n    config_dict = {k: k for k in all_texts if k != \"\" and 'progress-container' not in k}\n    full_name = os.path.abspath(os.path.join(localization_root, 'en.json'))\n\n    with open(full_name, \"w\", encoding=\"utf-8\") as json_file:\n        json.dump(config_dict, json_file, indent=4)\n\n    return\n"
  },
  {
    "path": "modules/lora.py",
    "content": "def match_lora(lora, to_load):\n    patch_dict = {}\n    loaded_keys = set()\n    for x in to_load:\n        real_load_key = to_load[x]\n        if real_load_key in lora:\n            patch_dict[real_load_key] = ('fooocus', lora[real_load_key])\n            loaded_keys.add(real_load_key)\n            continue\n\n        alpha_name = \"{}.alpha\".format(x)\n        alpha = None\n        if alpha_name in lora.keys():\n            alpha = lora[alpha_name].item()\n            loaded_keys.add(alpha_name)\n\n        regular_lora = \"{}.lora_up.weight\".format(x)\n        diffusers_lora = \"{}_lora.up.weight\".format(x)\n        transformers_lora = \"{}.lora_linear_layer.up.weight\".format(x)\n        A_name = None\n\n        if regular_lora in lora.keys():\n            A_name = regular_lora\n            B_name = \"{}.lora_down.weight\".format(x)\n            mid_name = \"{}.lora_mid.weight\".format(x)\n        elif diffusers_lora in lora.keys():\n            A_name = diffusers_lora\n            B_name = \"{}_lora.down.weight\".format(x)\n            mid_name = None\n        elif transformers_lora in lora.keys():\n            A_name = transformers_lora\n            B_name =\"{}.lora_linear_layer.down.weight\".format(x)\n            mid_name = None\n\n        if A_name is not None:\n            mid = None\n            if mid_name is not None and mid_name in lora.keys():\n                mid = lora[mid_name]\n                loaded_keys.add(mid_name)\n            patch_dict[to_load[x]] = (\"lora\", (lora[A_name], lora[B_name], alpha, mid))\n            loaded_keys.add(A_name)\n            loaded_keys.add(B_name)\n\n\n        ######## loha\n        hada_w1_a_name = \"{}.hada_w1_a\".format(x)\n        hada_w1_b_name = \"{}.hada_w1_b\".format(x)\n        hada_w2_a_name = \"{}.hada_w2_a\".format(x)\n        hada_w2_b_name = \"{}.hada_w2_b\".format(x)\n        hada_t1_name = \"{}.hada_t1\".format(x)\n        hada_t2_name = \"{}.hada_t2\".format(x)\n        if hada_w1_a_name in lora.keys():\n            hada_t1 = None\n            hada_t2 = None\n            if hada_t1_name in lora.keys():\n                hada_t1 = lora[hada_t1_name]\n                hada_t2 = lora[hada_t2_name]\n                loaded_keys.add(hada_t1_name)\n                loaded_keys.add(hada_t2_name)\n\n            patch_dict[to_load[x]] = (\"loha\", (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2))\n            loaded_keys.add(hada_w1_a_name)\n            loaded_keys.add(hada_w1_b_name)\n            loaded_keys.add(hada_w2_a_name)\n            loaded_keys.add(hada_w2_b_name)\n\n\n        ######## lokr\n        lokr_w1_name = \"{}.lokr_w1\".format(x)\n        lokr_w2_name = \"{}.lokr_w2\".format(x)\n        lokr_w1_a_name = \"{}.lokr_w1_a\".format(x)\n        lokr_w1_b_name = \"{}.lokr_w1_b\".format(x)\n        lokr_t2_name = \"{}.lokr_t2\".format(x)\n        lokr_w2_a_name = \"{}.lokr_w2_a\".format(x)\n        lokr_w2_b_name = \"{}.lokr_w2_b\".format(x)\n\n        lokr_w1 = None\n        if lokr_w1_name in lora.keys():\n            lokr_w1 = lora[lokr_w1_name]\n            loaded_keys.add(lokr_w1_name)\n\n        lokr_w2 = None\n        if lokr_w2_name in lora.keys():\n            lokr_w2 = lora[lokr_w2_name]\n            loaded_keys.add(lokr_w2_name)\n\n        lokr_w1_a = None\n        if lokr_w1_a_name in lora.keys():\n            lokr_w1_a = lora[lokr_w1_a_name]\n            loaded_keys.add(lokr_w1_a_name)\n\n        lokr_w1_b = None\n        if lokr_w1_b_name in lora.keys():\n            lokr_w1_b = lora[lokr_w1_b_name]\n            loaded_keys.add(lokr_w1_b_name)\n\n        lokr_w2_a = None\n        if lokr_w2_a_name in lora.keys():\n            lokr_w2_a = lora[lokr_w2_a_name]\n            loaded_keys.add(lokr_w2_a_name)\n\n        lokr_w2_b = None\n        if lokr_w2_b_name in lora.keys():\n            lokr_w2_b = lora[lokr_w2_b_name]\n            loaded_keys.add(lokr_w2_b_name)\n\n        lokr_t2 = None\n        if lokr_t2_name in lora.keys():\n            lokr_t2 = lora[lokr_t2_name]\n            loaded_keys.add(lokr_t2_name)\n\n        if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None):\n            patch_dict[to_load[x]] = (\"lokr\", (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2))\n\n        #glora\n        a1_name = \"{}.a1.weight\".format(x)\n        a2_name = \"{}.a2.weight\".format(x)\n        b1_name = \"{}.b1.weight\".format(x)\n        b2_name = \"{}.b2.weight\".format(x)\n        if a1_name in lora:\n            patch_dict[to_load[x]] = (\"glora\", (lora[a1_name], lora[a2_name], lora[b1_name], lora[b2_name], alpha))\n            loaded_keys.add(a1_name)\n            loaded_keys.add(a2_name)\n            loaded_keys.add(b1_name)\n            loaded_keys.add(b2_name)\n\n        w_norm_name = \"{}.w_norm\".format(x)\n        b_norm_name = \"{}.b_norm\".format(x)\n        w_norm = lora.get(w_norm_name, None)\n        b_norm = lora.get(b_norm_name, None)\n\n        if w_norm is not None:\n            loaded_keys.add(w_norm_name)\n            patch_dict[to_load[x]] = (\"diff\", (w_norm,))\n            if b_norm is not None:\n                loaded_keys.add(b_norm_name)\n                patch_dict[\"{}.bias\".format(to_load[x][:-len(\".weight\")])] = (\"diff\", (b_norm,))\n\n        diff_name = \"{}.diff\".format(x)\n        diff_weight = lora.get(diff_name, None)\n        if diff_weight is not None:\n            patch_dict[to_load[x]] = (\"diff\", (diff_weight,))\n            loaded_keys.add(diff_name)\n\n        diff_bias_name = \"{}.diff_b\".format(x)\n        diff_bias = lora.get(diff_bias_name, None)\n        if diff_bias is not None:\n            patch_dict[\"{}.bias\".format(to_load[x][:-len(\".weight\")])] = (\"diff\", (diff_bias,))\n            loaded_keys.add(diff_bias_name)\n\n    remaining_dict = {x: y for x, y in lora.items() if x not in loaded_keys}\n    return patch_dict, remaining_dict\n"
  },
  {
    "path": "modules/meta_parser.py",
    "content": "import json\nimport re\nfrom abc import ABC, abstractmethod\nfrom pathlib import Path\n\nimport gradio as gr\nfrom PIL import Image\n\nimport fooocus_version\nimport modules.config\nimport modules.sdxl_styles\nfrom modules.flags import MetadataScheme, Performance, Steps\nfrom modules.flags import SAMPLERS, CIVITAI_NO_KARRAS\nfrom modules.hash_cache import sha256_from_cache\nfrom modules.util import quote, unquote, extract_styles_from_prompt, is_json, get_file_from_folder_list\n\nre_param_code = r'\\s*(\\w[\\w \\-/]+):\\s*(\"(?:\\\\.|[^\\\\\"])+\"|[^,]*)(?:,|$)'\nre_param = re.compile(re_param_code)\nre_imagesize = re.compile(r\"^(\\d+)x(\\d+)$\")\n\n\ndef load_parameter_button_click(raw_metadata: dict | str, is_generating: bool, inpaint_mode: str):\n    loaded_parameter_dict = raw_metadata\n    if isinstance(raw_metadata, str):\n        loaded_parameter_dict = json.loads(raw_metadata)\n    assert isinstance(loaded_parameter_dict, dict)\n\n    results = [len(loaded_parameter_dict) > 0]\n\n    get_image_number('image_number', 'Image Number', loaded_parameter_dict, results)\n    get_str('prompt', 'Prompt', loaded_parameter_dict, results)\n    get_str('negative_prompt', 'Negative Prompt', loaded_parameter_dict, results)\n    get_list('styles', 'Styles', loaded_parameter_dict, results)\n    performance = get_str('performance', 'Performance', loaded_parameter_dict, results)\n    get_steps('steps', 'Steps', loaded_parameter_dict, results)\n    get_number('overwrite_switch', 'Overwrite Switch', loaded_parameter_dict, results)\n    get_resolution('resolution', 'Resolution', loaded_parameter_dict, results)\n    get_number('guidance_scale', 'Guidance Scale', loaded_parameter_dict, results)\n    get_number('sharpness', 'Sharpness', loaded_parameter_dict, results)\n    get_adm_guidance('adm_guidance', 'ADM Guidance', loaded_parameter_dict, results)\n    get_str('refiner_swap_method', 'Refiner Swap Method', loaded_parameter_dict, results)\n    get_number('adaptive_cfg', 'CFG Mimicking from TSNR', loaded_parameter_dict, results)\n    get_number('clip_skip', 'CLIP Skip', loaded_parameter_dict, results, cast_type=int)\n    get_str('base_model', 'Base Model', loaded_parameter_dict, results)\n    get_str('refiner_model', 'Refiner Model', loaded_parameter_dict, results)\n    get_number('refiner_switch', 'Refiner Switch', loaded_parameter_dict, results)\n    get_str('sampler', 'Sampler', loaded_parameter_dict, results)\n    get_str('scheduler', 'Scheduler', loaded_parameter_dict, results)\n    get_str('vae', 'VAE', loaded_parameter_dict, results)\n    get_seed('seed', 'Seed', loaded_parameter_dict, results)\n    get_inpaint_engine_version('inpaint_engine_version', 'Inpaint Engine Version', loaded_parameter_dict, results, inpaint_mode)\n    get_inpaint_method('inpaint_method', 'Inpaint Mode', loaded_parameter_dict, results)\n\n    if is_generating:\n        results.append(gr.update())\n    else:\n        results.append(gr.update(visible=True))\n\n    results.append(gr.update(visible=False))\n\n    get_freeu('freeu', 'FreeU', loaded_parameter_dict, results)\n\n    # prevent performance LoRAs to be added twice, by performance and by lora\n    performance_filename = None\n    if performance is not None and performance in Performance.values():\n        performance = Performance(performance)\n        performance_filename = performance.lora_filename()\n\n    for i in range(modules.config.default_max_lora_number):\n        get_lora(f'lora_combined_{i + 1}', f'LoRA {i + 1}', loaded_parameter_dict, results, performance_filename)\n\n    return results\n\n\ndef get_str(key: str, fallback: str | None, source_dict: dict, results: list, default=None) -> str | None:\n    try:\n        h = source_dict.get(key, source_dict.get(fallback, default))\n        assert isinstance(h, str)\n        results.append(h)\n        return h\n    except:\n        results.append(gr.update())\n        return None\n\n\ndef get_list(key: str, fallback: str | None, source_dict: dict, results: list, default=None):\n    try:\n        h = source_dict.get(key, source_dict.get(fallback, default))\n        h = eval(h)\n        assert isinstance(h, list)\n        results.append(h)\n    except:\n        results.append(gr.update())\n\n\ndef get_number(key: str, fallback: str | None, source_dict: dict, results: list, default=None, cast_type=float):\n    try:\n        h = source_dict.get(key, source_dict.get(fallback, default))\n        assert h is not None\n        h = cast_type(h)\n        results.append(h)\n    except:\n        results.append(gr.update())\n\n\ndef get_image_number(key: str, fallback: str | None, source_dict: dict, results: list, default=None):\n    try:\n        h = source_dict.get(key, source_dict.get(fallback, default))\n        assert h is not None\n        h = int(h)\n        h = min(h, modules.config.default_max_image_number)\n        results.append(h)\n    except:\n        results.append(1)\n\n\ndef get_steps(key: str, fallback: str | None, source_dict: dict, results: list, default=None):\n    try:\n        h = source_dict.get(key, source_dict.get(fallback, default))\n        assert h is not None\n        h = int(h)\n        # if not in steps or in steps and performance is not the same\n        performance_name = source_dict.get('performance', '').replace(' ', '_').replace('-', '_').casefold()\n        performance_candidates = [key for key in Steps.keys() if key.casefold() == performance_name and Steps[key] == h]\n        if len(performance_candidates) == 0:\n            results.append(h)\n            return\n        results.append(-1)\n    except:\n        results.append(-1)\n\n\ndef get_resolution(key: str, fallback: str | None, source_dict: dict, results: list, default=None):\n    try:\n        h = source_dict.get(key, source_dict.get(fallback, default))\n        width, height = eval(h)\n        formatted = modules.config.add_ratio(f'{width}*{height}')\n        if formatted in modules.config.available_aspect_ratios_labels:\n            results.append(formatted)\n            results.append(-1)\n            results.append(-1)\n        else:\n            results.append(gr.update())\n            results.append(int(width))\n            results.append(int(height))\n    except:\n        results.append(gr.update())\n        results.append(gr.update())\n        results.append(gr.update())\n\n\ndef get_seed(key: str, fallback: str | None, source_dict: dict, results: list, default=None):\n    try:\n        h = source_dict.get(key, source_dict.get(fallback, default))\n        assert h is not None\n        h = int(h)\n        results.append(False)\n        results.append(h)\n    except:\n        results.append(gr.update())\n        results.append(gr.update())\n\n\ndef get_inpaint_engine_version(key: str, fallback: str | None, source_dict: dict, results: list, inpaint_mode: str, default=None) -> str | None:\n    try:\n        h = source_dict.get(key, source_dict.get(fallback, default))\n        assert isinstance(h, str) and h in modules.flags.inpaint_engine_versions\n        if inpaint_mode != modules.flags.inpaint_option_detail:\n            results.append(h)\n        else:\n            results.append(gr.update())\n        results.append(h)\n        return h\n    except:\n        results.append(gr.update())\n        results.append('empty')\n        return None\n\n\ndef get_inpaint_method(key: str, fallback: str | None, source_dict: dict, results: list, default=None) -> str | None:\n    try:\n        h = source_dict.get(key, source_dict.get(fallback, default))\n        assert isinstance(h, str) and h in modules.flags.inpaint_options\n        results.append(h)\n        for i in range(modules.config.default_enhance_tabs):\n            results.append(h)\n        return h\n    except:\n        results.append(gr.update())\n        for i in range(modules.config.default_enhance_tabs):\n            results.append(gr.update())\n\n\ndef get_adm_guidance(key: str, fallback: str | None, source_dict: dict, results: list, default=None):\n    try:\n        h = source_dict.get(key, source_dict.get(fallback, default))\n        p, n, e = eval(h)\n        results.append(float(p))\n        results.append(float(n))\n        results.append(float(e))\n    except:\n        results.append(gr.update())\n        results.append(gr.update())\n        results.append(gr.update())\n\n\ndef get_freeu(key: str, fallback: str | None, source_dict: dict, results: list, default=None):\n    try:\n        h = source_dict.get(key, source_dict.get(fallback, default))\n        b1, b2, s1, s2 = eval(h)\n        results.append(True)\n        results.append(float(b1))\n        results.append(float(b2))\n        results.append(float(s1))\n        results.append(float(s2))\n    except:\n        results.append(False)\n        results.append(gr.update())\n        results.append(gr.update())\n        results.append(gr.update())\n        results.append(gr.update())\n\n\ndef get_lora(key: str, fallback: str | None, source_dict: dict, results: list, performance_filename: str | None):\n    try:\n        split_data = source_dict.get(key, source_dict.get(fallback)).split(' : ')\n        enabled = True\n        name = split_data[0]\n        weight = split_data[1]\n\n        if len(split_data) == 3:\n            enabled = split_data[0] == 'True'\n            name = split_data[1]\n            weight = split_data[2]\n\n        if name == performance_filename:\n            raise Exception\n\n        weight = float(weight)\n        results.append(enabled)\n        results.append(name)\n        results.append(weight)\n    except:\n        results.append(True)\n        results.append('None')\n        results.append(1)\n\n\ndef parse_meta_from_preset(preset_content):\n    assert isinstance(preset_content, dict)\n    preset_prepared = {}\n    items = preset_content\n\n    for settings_key, meta_key in modules.config.possible_preset_keys.items():\n        if settings_key == \"default_loras\":\n            loras = getattr(modules.config, settings_key)\n            if settings_key in items:\n                loras = items[settings_key]\n            for index, lora in enumerate(loras[:modules.config.default_max_lora_number]):\n                preset_prepared[f'lora_combined_{index + 1}'] = ' : '.join(map(str, lora))\n        elif settings_key == \"default_aspect_ratio\":\n            if settings_key in items and items[settings_key] is not None:\n                default_aspect_ratio = items[settings_key]\n                width, height = default_aspect_ratio.split('*')\n            else:\n                default_aspect_ratio = getattr(modules.config, settings_key)\n                width, height = default_aspect_ratio.split('×')\n                height = height[:height.index(\" \")]\n            preset_prepared[meta_key] = (width, height)\n        else:\n            preset_prepared[meta_key] = items[settings_key] if settings_key in items and items[settings_key] is not None else getattr(modules.config, settings_key)\n\n        if settings_key == \"default_styles\" or settings_key == \"default_aspect_ratio\":\n            preset_prepared[meta_key] = str(preset_prepared[meta_key])\n\n    return preset_prepared\n\n\nclass MetadataParser(ABC):\n    def __init__(self):\n        self.raw_prompt: str = ''\n        self.full_prompt: str = ''\n        self.raw_negative_prompt: str = ''\n        self.full_negative_prompt: str = ''\n        self.steps: int = Steps.SPEED.value\n        self.base_model_name: str = ''\n        self.base_model_hash: str = ''\n        self.refiner_model_name: str = ''\n        self.refiner_model_hash: str = ''\n        self.loras: list = []\n        self.vae_name: str = ''\n\n    @abstractmethod\n    def get_scheme(self) -> MetadataScheme:\n        raise NotImplementedError\n\n    @abstractmethod\n    def to_json(self, metadata: dict | str) -> dict:\n        raise NotImplementedError\n\n    @abstractmethod\n    def to_string(self, metadata: dict) -> str:\n        raise NotImplementedError\n\n    def set_data(self, raw_prompt, full_prompt, raw_negative_prompt, full_negative_prompt, steps, base_model_name,\n                 refiner_model_name, loras, vae_name):\n        self.raw_prompt = raw_prompt\n        self.full_prompt = full_prompt\n        self.raw_negative_prompt = raw_negative_prompt\n        self.full_negative_prompt = full_negative_prompt\n        self.steps = steps\n        self.base_model_name = Path(base_model_name).stem\n\n        base_model_path = get_file_from_folder_list(base_model_name, modules.config.paths_checkpoints)\n        self.base_model_hash = sha256_from_cache(base_model_path)\n\n        if refiner_model_name not in ['', 'None']:\n            self.refiner_model_name = Path(refiner_model_name).stem\n            refiner_model_path = get_file_from_folder_list(refiner_model_name, modules.config.paths_checkpoints)\n            self.refiner_model_hash = sha256_from_cache(refiner_model_path)\n\n        self.loras = []\n        for (lora_name, lora_weight) in loras:\n            if lora_name != 'None':\n                lora_path = get_file_from_folder_list(lora_name, modules.config.paths_loras)\n                lora_hash = sha256_from_cache(lora_path)\n                self.loras.append((Path(lora_name).stem, lora_weight, lora_hash))\n        self.vae_name = Path(vae_name).stem\n\n\nclass A1111MetadataParser(MetadataParser):\n    def get_scheme(self) -> MetadataScheme:\n        return MetadataScheme.A1111\n\n    fooocus_to_a1111 = {\n        'raw_prompt': 'Raw prompt',\n        'raw_negative_prompt': 'Raw negative prompt',\n        'negative_prompt': 'Negative prompt',\n        'styles': 'Styles',\n        'performance': 'Performance',\n        'steps': 'Steps',\n        'sampler': 'Sampler',\n        'scheduler': 'Scheduler',\n        'vae': 'VAE',\n        'guidance_scale': 'CFG scale',\n        'seed': 'Seed',\n        'resolution': 'Size',\n        'sharpness': 'Sharpness',\n        'adm_guidance': 'ADM Guidance',\n        'refiner_swap_method': 'Refiner Swap Method',\n        'adaptive_cfg': 'Adaptive CFG',\n        'clip_skip': 'Clip skip',\n        'overwrite_switch': 'Overwrite Switch',\n        'freeu': 'FreeU',\n        'base_model': 'Model',\n        'base_model_hash': 'Model hash',\n        'refiner_model': 'Refiner',\n        'refiner_model_hash': 'Refiner hash',\n        'lora_hashes': 'Lora hashes',\n        'lora_weights': 'Lora weights',\n        'created_by': 'User',\n        'version': 'Version'\n    }\n\n    def to_json(self, metadata: str) -> dict:\n        metadata_prompt = ''\n        metadata_negative_prompt = ''\n\n        done_with_prompt = False\n\n        *lines, lastline = metadata.strip().split(\"\\n\")\n        if len(re_param.findall(lastline)) < 3:\n            lines.append(lastline)\n            lastline = ''\n\n        for line in lines:\n            line = line.strip()\n            if line.startswith(f\"{self.fooocus_to_a1111['negative_prompt']}:\"):\n                done_with_prompt = True\n                line = line[len(f\"{self.fooocus_to_a1111['negative_prompt']}:\"):].strip()\n            if done_with_prompt:\n                metadata_negative_prompt += ('' if metadata_negative_prompt == '' else \"\\n\") + line\n            else:\n                metadata_prompt += ('' if metadata_prompt == '' else \"\\n\") + line\n\n        found_styles, prompt, negative_prompt = extract_styles_from_prompt(metadata_prompt, metadata_negative_prompt)\n\n        data = {\n            'prompt': prompt,\n            'negative_prompt': negative_prompt\n        }\n\n        for k, v in re_param.findall(lastline):\n            try:\n                if v != '' and v[0] == '\"' and v[-1] == '\"':\n                    v = unquote(v)\n\n                m = re_imagesize.match(v)\n                if m is not None:\n                    data['resolution'] = str((m.group(1), m.group(2)))\n                else:\n                    data[list(self.fooocus_to_a1111.keys())[list(self.fooocus_to_a1111.values()).index(k)]] = v\n            except Exception:\n                print(f\"Error parsing \\\"{k}: {v}\\\"\")\n\n        # workaround for multiline prompts\n        if 'raw_prompt' in data:\n            data['prompt'] = data['raw_prompt']\n            raw_prompt = data['raw_prompt'].replace(\"\\n\", ', ')\n            if metadata_prompt != raw_prompt and modules.sdxl_styles.fooocus_expansion not in found_styles:\n                found_styles.append(modules.sdxl_styles.fooocus_expansion)\n\n        if 'raw_negative_prompt' in data:\n            data['negative_prompt'] = data['raw_negative_prompt']\n\n        data['styles'] = str(found_styles)\n\n        # try to load performance based on steps, fallback for direct A1111 imports\n        if 'steps' in data and 'performance' in data is None:\n            try:\n                data['performance'] = Performance.by_steps(data['steps']).value\n            except ValueError | KeyError:\n                pass\n\n        if 'sampler' in data:\n            data['sampler'] = data['sampler'].replace(' Karras', '')\n            # get key\n            for k, v in SAMPLERS.items():\n                if v == data['sampler']:\n                    data['sampler'] = k\n                    break\n\n        for key in ['base_model', 'refiner_model', 'vae']:\n            if key in data:\n                if key == 'vae':\n                    self.add_extension_to_filename(data, modules.config.vae_filenames, 'vae')\n                else:\n                    self.add_extension_to_filename(data, modules.config.model_filenames, key)\n\n        lora_data = ''\n        if 'lora_weights' in data and data['lora_weights'] != '':\n            lora_data = data['lora_weights']\n        elif 'lora_hashes' in data and data['lora_hashes'] != '' and data['lora_hashes'].split(', ')[0].count(':') == 2:\n            lora_data = data['lora_hashes']\n\n        if lora_data != '':\n            for li, lora in enumerate(lora_data.split(', ')):\n                lora_split = lora.split(': ')\n                lora_name = lora_split[0]\n                lora_weight = lora_split[2] if len(lora_split) == 3 else lora_split[1]\n                for filename in modules.config.lora_filenames:\n                    path = Path(filename)\n                    if lora_name == path.stem:\n                        data[f'lora_combined_{li + 1}'] = f'{filename} : {lora_weight}'\n                        break\n\n        return data\n\n    def to_string(self, metadata: dict) -> str:\n        data = {k: v for _, k, v in metadata}\n\n        width, height = eval(data['resolution'])\n\n        sampler = data['sampler']\n        scheduler = data['scheduler']\n\n        if sampler in SAMPLERS and SAMPLERS[sampler] != '':\n            sampler = SAMPLERS[sampler]\n            if sampler not in CIVITAI_NO_KARRAS and scheduler == 'karras':\n                sampler += f' Karras'\n\n        generation_params = {\n            self.fooocus_to_a1111['steps']: self.steps,\n            self.fooocus_to_a1111['sampler']: sampler,\n            self.fooocus_to_a1111['seed']: data['seed'],\n            self.fooocus_to_a1111['resolution']: f'{width}x{height}',\n            self.fooocus_to_a1111['guidance_scale']: data['guidance_scale'],\n            self.fooocus_to_a1111['sharpness']: data['sharpness'],\n            self.fooocus_to_a1111['adm_guidance']: data['adm_guidance'],\n            self.fooocus_to_a1111['base_model']: Path(data['base_model']).stem,\n            self.fooocus_to_a1111['base_model_hash']: self.base_model_hash,\n\n            self.fooocus_to_a1111['performance']: data['performance'],\n            self.fooocus_to_a1111['scheduler']: scheduler,\n            self.fooocus_to_a1111['vae']: Path(data['vae']).stem,\n            # workaround for multiline prompts\n            self.fooocus_to_a1111['raw_prompt']: self.raw_prompt,\n            self.fooocus_to_a1111['raw_negative_prompt']: self.raw_negative_prompt,\n        }\n\n        if self.refiner_model_name not in ['', 'None']:\n            generation_params |= {\n                self.fooocus_to_a1111['refiner_model']: self.refiner_model_name,\n                self.fooocus_to_a1111['refiner_model_hash']: self.refiner_model_hash\n            }\n\n        for key in ['adaptive_cfg', 'clip_skip', 'overwrite_switch', 'refiner_swap_method', 'freeu']:\n            if key in data:\n                generation_params[self.fooocus_to_a1111[key]] = data[key]\n\n        if len(self.loras) > 0:\n            lora_hashes = []\n            lora_weights = []\n            for index, (lora_name, lora_weight, lora_hash) in enumerate(self.loras):\n                # workaround for Fooocus not knowing LoRA name in LoRA metadata\n                lora_hashes.append(f'{lora_name}: {lora_hash}')\n                lora_weights.append(f'{lora_name}: {lora_weight}')\n            lora_hashes_string = ', '.join(lora_hashes)\n            lora_weights_string = ', '.join(lora_weights)\n            generation_params[self.fooocus_to_a1111['lora_hashes']] = lora_hashes_string\n            generation_params[self.fooocus_to_a1111['lora_weights']] = lora_weights_string\n\n        generation_params[self.fooocus_to_a1111['version']] = data['version']\n\n        if modules.config.metadata_created_by != '':\n            generation_params[self.fooocus_to_a1111['created_by']] = modules.config.metadata_created_by\n\n        generation_params_text = \", \".join(\n            [k if k == v else f'{k}: {quote(v)}' for k, v in generation_params.items() if\n             v is not None])\n        positive_prompt_resolved = ', '.join(self.full_prompt)\n        negative_prompt_resolved = ', '.join(self.full_negative_prompt)\n        negative_prompt_text = f\"\\nNegative prompt: {negative_prompt_resolved}\" if negative_prompt_resolved else \"\"\n        return f\"{positive_prompt_resolved}{negative_prompt_text}\\n{generation_params_text}\".strip()\n\n    @staticmethod\n    def add_extension_to_filename(data, filenames, key):\n        for filename in filenames:\n            path = Path(filename)\n            if data[key] == path.stem:\n                data[key] = filename\n                break\n\n\nclass FooocusMetadataParser(MetadataParser):\n    def get_scheme(self) -> MetadataScheme:\n        return MetadataScheme.FOOOCUS\n\n    def to_json(self, metadata: dict) -> dict:\n        for key, value in metadata.items():\n            if value in ['', 'None']:\n                continue\n            if key in ['base_model', 'refiner_model']:\n                metadata[key] = self.replace_value_with_filename(key, value, modules.config.model_filenames)\n            elif key.startswith('lora_combined_'):\n                metadata[key] = self.replace_value_with_filename(key, value, modules.config.lora_filenames)\n            elif key == 'vae':\n                metadata[key] = self.replace_value_with_filename(key, value, modules.config.vae_filenames)\n            else:\n                continue\n\n        return metadata\n\n    def to_string(self, metadata: list) -> str:\n        for li, (label, key, value) in enumerate(metadata):\n            # remove model folder paths from metadata\n            if key.startswith('lora_combined_'):\n                name, weight = value.split(' : ')\n                name = Path(name).stem\n                value = f'{name} : {weight}'\n                metadata[li] = (label, key, value)\n\n        res = {k: v for _, k, v in metadata}\n\n        res['full_prompt'] = self.full_prompt\n        res['full_negative_prompt'] = self.full_negative_prompt\n        res['steps'] = self.steps\n        res['base_model'] = self.base_model_name\n        res['base_model_hash'] = self.base_model_hash\n\n        if self.refiner_model_name not in ['', 'None']:\n            res['refiner_model'] = self.refiner_model_name\n            res['refiner_model_hash'] = self.refiner_model_hash\n\n        res['vae'] = self.vae_name\n        res['loras'] = self.loras\n\n        if modules.config.metadata_created_by != '':\n            res['created_by'] = modules.config.metadata_created_by\n\n        return json.dumps(dict(sorted(res.items())))\n\n    @staticmethod\n    def replace_value_with_filename(key, value, filenames):\n        for filename in filenames:\n            path = Path(filename)\n            if key.startswith('lora_combined_'):\n                name, weight = value.split(' : ')\n                if name == path.stem:\n                    return f'{filename} : {weight}'\n            elif value == path.stem:\n                return filename\n\n        return None\n\n\ndef get_metadata_parser(metadata_scheme: MetadataScheme) -> MetadataParser:\n    match metadata_scheme:\n        case MetadataScheme.FOOOCUS:\n            return FooocusMetadataParser()\n        case MetadataScheme.A1111:\n            return A1111MetadataParser()\n        case _:\n            raise NotImplementedError\n\n\ndef read_info_from_image(file) -> tuple[str | None, MetadataScheme | None]:\n    items = (file.info or {}).copy()\n\n    parameters = items.pop('parameters', None)\n    metadata_scheme = items.pop('fooocus_scheme', None)\n    exif = items.pop('exif', None)\n\n    if parameters is not None and is_json(parameters):\n        parameters = json.loads(parameters)\n    elif exif is not None:\n        exif = file.getexif()\n        # 0x9286 = UserComment\n        parameters = exif.get(0x9286, None)\n        # 0x927C = MakerNote\n        metadata_scheme = exif.get(0x927C, None)\n\n        if is_json(parameters):\n            parameters = json.loads(parameters)\n\n    try:\n        metadata_scheme = MetadataScheme(metadata_scheme)\n    except ValueError:\n        metadata_scheme = None\n\n        # broad fallback\n        if isinstance(parameters, dict):\n            metadata_scheme = MetadataScheme.FOOOCUS\n\n        if isinstance(parameters, str):\n            metadata_scheme = MetadataScheme.A1111\n\n    return parameters, metadata_scheme\n\n\ndef get_exif(metadata: str | None, metadata_scheme: str):\n    exif = Image.Exif()\n    # tags see see https://github.com/python-pillow/Pillow/blob/9.2.x/src/PIL/ExifTags.py\n    # 0x9286 = UserComment\n    exif[0x9286] = metadata\n    # 0x0131 = Software\n    exif[0x0131] = 'Fooocus v' + fooocus_version.version\n    # 0x927C = MakerNote\n    exif[0x927C] = metadata_scheme\n    return exif\n"
  },
  {
    "path": "modules/model_loader.py",
    "content": "import os\nfrom urllib.parse import urlparse\nfrom typing import Optional\n\n\ndef load_file_from_url(\n        url: str,\n        *,\n        model_dir: str,\n        progress: bool = True,\n        file_name: Optional[str] = None,\n) -> str:\n    \"\"\"Download a file from `url` into `model_dir`, using the file present if possible.\n\n    Returns the path to the downloaded file.\n    \"\"\"\n    domain = os.environ.get(\"HF_MIRROR\", \"https://huggingface.co\").rstrip('/')\n    url = str.replace(url, \"https://huggingface.co\", domain, 1)\n    os.makedirs(model_dir, exist_ok=True)\n    if not file_name:\n        parts = urlparse(url)\n        file_name = os.path.basename(parts.path)\n    cached_file = os.path.abspath(os.path.join(model_dir, file_name))\n    if not os.path.exists(cached_file):\n        print(f'Downloading: \"{url}\" to {cached_file}\\n')\n        from torch.hub import download_url_to_file\n        download_url_to_file(url, cached_file, progress=progress)\n    return cached_file\n"
  },
  {
    "path": "modules/ops.py",
    "content": "import torch\nimport contextlib\n\n\n@contextlib.contextmanager\ndef use_patched_ops(operations):\n    op_names = ['Linear', 'Conv2d', 'Conv3d', 'GroupNorm', 'LayerNorm']\n    backups = {op_name: getattr(torch.nn, op_name) for op_name in op_names}\n\n    try:\n        for op_name in op_names:\n            setattr(torch.nn, op_name, getattr(operations, op_name))\n\n        yield\n\n    finally:\n        for op_name in op_names:\n            setattr(torch.nn, op_name, backups[op_name])\n    return\n"
  },
  {
    "path": "modules/patch.py",
    "content": "import os\nimport torch\nimport time\nimport math\nimport ldm_patched.modules.model_base\nimport ldm_patched.ldm.modules.diffusionmodules.openaimodel\nimport ldm_patched.modules.model_management\nimport modules.anisotropic as anisotropic\nimport ldm_patched.ldm.modules.attention\nimport ldm_patched.k_diffusion.sampling\nimport ldm_patched.modules.sd1_clip\nimport modules.inpaint_worker as inpaint_worker\nimport ldm_patched.ldm.modules.diffusionmodules.openaimodel\nimport ldm_patched.ldm.modules.diffusionmodules.model\nimport ldm_patched.modules.sd\nimport ldm_patched.controlnet.cldm\nimport ldm_patched.modules.model_patcher\nimport ldm_patched.modules.samplers\nimport ldm_patched.modules.args_parser\nimport warnings\nimport safetensors.torch\nimport modules.constants as constants\n\nfrom ldm_patched.modules.samplers import calc_cond_uncond_batch\nfrom ldm_patched.k_diffusion.sampling import BatchedBrownianTree\nfrom ldm_patched.ldm.modules.diffusionmodules.openaimodel import forward_timestep_embed, apply_control\nfrom modules.patch_precision import patch_all_precision\nfrom modules.patch_clip import patch_all_clip\n\n\nclass PatchSettings:\n    def __init__(self,\n                 sharpness=2.0,\n                 adm_scaler_end=0.3,\n                 positive_adm_scale=1.5,\n                 negative_adm_scale=0.8,\n                 controlnet_softness=0.25,\n                 adaptive_cfg=7.0):\n        self.sharpness = sharpness\n        self.adm_scaler_end = adm_scaler_end\n        self.positive_adm_scale = positive_adm_scale\n        self.negative_adm_scale = negative_adm_scale\n        self.controlnet_softness = controlnet_softness\n        self.adaptive_cfg = adaptive_cfg\n        self.global_diffusion_progress = 0\n        self.eps_record = None\n\n\npatch_settings = {}\n\n\ndef calculate_weight_patched(self, patches, weight, key):\n    for p in patches:\n        alpha = p[0]\n        v = p[1]\n        strength_model = p[2]\n\n        if strength_model != 1.0:\n            weight *= strength_model\n\n        if isinstance(v, list):\n            v = (self.calculate_weight(v[1:], v[0].clone(), key),)\n\n        if len(v) == 1:\n            patch_type = \"diff\"\n        elif len(v) == 2:\n            patch_type = v[0]\n            v = v[1]\n\n        if patch_type == \"diff\":\n            w1 = v[0]\n            if alpha != 0.0:\n                if w1.shape != weight.shape:\n                    print(\"WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}\".format(key, w1.shape, weight.shape))\n                else:\n                    weight += alpha * ldm_patched.modules.model_management.cast_to_device(w1, weight.device, weight.dtype)\n        elif patch_type == \"lora\":\n            mat1 = ldm_patched.modules.model_management.cast_to_device(v[0], weight.device, torch.float32)\n            mat2 = ldm_patched.modules.model_management.cast_to_device(v[1], weight.device, torch.float32)\n            if v[2] is not None:\n                alpha *= v[2] / mat2.shape[0]\n            if v[3] is not None:\n                mat3 = ldm_patched.modules.model_management.cast_to_device(v[3], weight.device, torch.float32)\n                final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]]\n                mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1),\n                                mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1)\n            try:\n                weight += (alpha * torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1))).reshape(\n                    weight.shape).type(weight.dtype)\n            except Exception as e:\n                print(\"ERROR\", key, e)\n        elif patch_type == \"fooocus\":\n            w1 = ldm_patched.modules.model_management.cast_to_device(v[0], weight.device, torch.float32)\n            w_min = ldm_patched.modules.model_management.cast_to_device(v[1], weight.device, torch.float32)\n            w_max = ldm_patched.modules.model_management.cast_to_device(v[2], weight.device, torch.float32)\n            w1 = (w1 / 255.0) * (w_max - w_min) + w_min\n            if alpha != 0.0:\n                if w1.shape != weight.shape:\n                    print(\"WARNING SHAPE MISMATCH {} FOOOCUS WEIGHT NOT MERGED {} != {}\".format(key, w1.shape, weight.shape))\n                else:\n                    weight += alpha * ldm_patched.modules.model_management.cast_to_device(w1, weight.device, weight.dtype)\n        elif patch_type == \"lokr\":\n            w1 = v[0]\n            w2 = v[1]\n            w1_a = v[3]\n            w1_b = v[4]\n            w2_a = v[5]\n            w2_b = v[6]\n            t2 = v[7]\n            dim = None\n\n            if w1 is None:\n                dim = w1_b.shape[0]\n                w1 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w1_a, weight.device, torch.float32),\n                              ldm_patched.modules.model_management.cast_to_device(w1_b, weight.device, torch.float32))\n            else:\n                w1 = ldm_patched.modules.model_management.cast_to_device(w1, weight.device, torch.float32)\n\n            if w2 is None:\n                dim = w2_b.shape[0]\n                if t2 is None:\n                    w2 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w2_a, weight.device, torch.float32),\n                                  ldm_patched.modules.model_management.cast_to_device(w2_b, weight.device, torch.float32))\n                else:\n                    w2 = torch.einsum('i j k l, j r, i p -> p r k l',\n                                      ldm_patched.modules.model_management.cast_to_device(t2, weight.device, torch.float32),\n                                      ldm_patched.modules.model_management.cast_to_device(w2_b, weight.device, torch.float32),\n                                      ldm_patched.modules.model_management.cast_to_device(w2_a, weight.device, torch.float32))\n            else:\n                w2 = ldm_patched.modules.model_management.cast_to_device(w2, weight.device, torch.float32)\n\n            if len(w2.shape) == 4:\n                w1 = w1.unsqueeze(2).unsqueeze(2)\n            if v[2] is not None and dim is not None:\n                alpha *= v[2] / dim\n\n            try:\n                weight += alpha * torch.kron(w1, w2).reshape(weight.shape).type(weight.dtype)\n            except Exception as e:\n                print(\"ERROR\", key, e)\n        elif patch_type == \"loha\":\n            w1a = v[0]\n            w1b = v[1]\n            if v[2] is not None:\n                alpha *= v[2] / w1b.shape[0]\n            w2a = v[3]\n            w2b = v[4]\n            if v[5] is not None:  # cp decomposition\n                t1 = v[5]\n                t2 = v[6]\n                m1 = torch.einsum('i j k l, j r, i p -> p r k l',\n                                  ldm_patched.modules.model_management.cast_to_device(t1, weight.device, torch.float32),\n                                  ldm_patched.modules.model_management.cast_to_device(w1b, weight.device, torch.float32),\n                                  ldm_patched.modules.model_management.cast_to_device(w1a, weight.device, torch.float32))\n\n                m2 = torch.einsum('i j k l, j r, i p -> p r k l',\n                                  ldm_patched.modules.model_management.cast_to_device(t2, weight.device, torch.float32),\n                                  ldm_patched.modules.model_management.cast_to_device(w2b, weight.device, torch.float32),\n                                  ldm_patched.modules.model_management.cast_to_device(w2a, weight.device, torch.float32))\n            else:\n                m1 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w1a, weight.device, torch.float32),\n                              ldm_patched.modules.model_management.cast_to_device(w1b, weight.device, torch.float32))\n                m2 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w2a, weight.device, torch.float32),\n                              ldm_patched.modules.model_management.cast_to_device(w2b, weight.device, torch.float32))\n\n            try:\n                weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype)\n            except Exception as e:\n                print(\"ERROR\", key, e)\n        elif patch_type == \"glora\":\n            if v[4] is not None:\n                alpha *= v[4] / v[0].shape[0]\n\n            a1 = ldm_patched.modules.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, torch.float32)\n            a2 = ldm_patched.modules.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, torch.float32)\n            b1 = ldm_patched.modules.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, torch.float32)\n            b2 = ldm_patched.modules.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, torch.float32)\n\n            weight += ((torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)) * alpha).reshape(weight.shape).type(weight.dtype)\n        else:\n            print(\"patch type not recognized\", patch_type, key)\n\n    return weight\n\n\nclass BrownianTreeNoiseSamplerPatched:\n    transform = None\n    tree = None\n\n    @staticmethod\n    def global_init(x, sigma_min, sigma_max, seed=None, transform=lambda x: x, cpu=False):\n        if ldm_patched.modules.model_management.directml_enabled:\n            cpu = True\n\n        t0, t1 = transform(torch.as_tensor(sigma_min)), transform(torch.as_tensor(sigma_max))\n\n        BrownianTreeNoiseSamplerPatched.transform = transform\n        BrownianTreeNoiseSamplerPatched.tree = BatchedBrownianTree(x, t0, t1, seed, cpu=cpu)\n\n    def __init__(self, *args, **kwargs):\n        pass\n\n    @staticmethod\n    def __call__(sigma, sigma_next):\n        transform = BrownianTreeNoiseSamplerPatched.transform\n        tree = BrownianTreeNoiseSamplerPatched.tree\n\n        t0, t1 = transform(torch.as_tensor(sigma)), transform(torch.as_tensor(sigma_next))\n        return tree(t0, t1) / (t1 - t0).abs().sqrt()\n\n\ndef compute_cfg(uncond, cond, cfg_scale, t):\n    pid = os.getpid()\n    mimic_cfg = float(patch_settings[pid].adaptive_cfg)\n    real_cfg = float(cfg_scale)\n\n    real_eps = uncond + real_cfg * (cond - uncond)\n\n    if cfg_scale > patch_settings[pid].adaptive_cfg:\n        mimicked_eps = uncond + mimic_cfg * (cond - uncond)\n        return real_eps * t + mimicked_eps * (1 - t)\n    else:\n        return real_eps\n\n\ndef patched_sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options=None, seed=None):\n    pid = os.getpid()\n\n    if math.isclose(cond_scale, 1.0) and not model_options.get(\"disable_cfg1_optimization\", False):\n        final_x0 = calc_cond_uncond_batch(model, cond, None, x, timestep, model_options)[0]\n\n        if patch_settings[pid].eps_record is not None:\n            patch_settings[pid].eps_record = ((x - final_x0) / timestep).cpu()\n\n        return final_x0\n\n    positive_x0, negative_x0 = calc_cond_uncond_batch(model, cond, uncond, x, timestep, model_options)\n\n    positive_eps = x - positive_x0\n    negative_eps = x - negative_x0\n\n    alpha = 0.001 * patch_settings[pid].sharpness * patch_settings[pid].global_diffusion_progress\n\n    positive_eps_degraded = anisotropic.adaptive_anisotropic_filter(x=positive_eps, g=positive_x0)\n    positive_eps_degraded_weighted = positive_eps_degraded * alpha + positive_eps * (1.0 - alpha)\n\n    final_eps = compute_cfg(uncond=negative_eps, cond=positive_eps_degraded_weighted,\n                            cfg_scale=cond_scale, t=patch_settings[pid].global_diffusion_progress)\n\n    if patch_settings[pid].eps_record is not None:\n        patch_settings[pid].eps_record = (final_eps / timestep).cpu()\n\n    return x - final_eps\n\n\ndef round_to_64(x):\n    h = float(x)\n    h = h / 64.0\n    h = round(h)\n    h = int(h)\n    h = h * 64\n    return h\n\n\ndef sdxl_encode_adm_patched(self, **kwargs):\n    clip_pooled = ldm_patched.modules.model_base.sdxl_pooled(kwargs, self.noise_augmentor)\n    width = kwargs.get(\"width\", 1024)\n    height = kwargs.get(\"height\", 1024)\n    target_width = width\n    target_height = height\n    pid = os.getpid()\n\n    if kwargs.get(\"prompt_type\", \"\") == \"negative\":\n        width = float(width) * patch_settings[pid].negative_adm_scale\n        height = float(height) * patch_settings[pid].negative_adm_scale\n    elif kwargs.get(\"prompt_type\", \"\") == \"positive\":\n        width = float(width) * patch_settings[pid].positive_adm_scale\n        height = float(height) * patch_settings[pid].positive_adm_scale\n\n    def embedder(number_list):\n        h = self.embedder(torch.tensor(number_list, dtype=torch.float32))\n        h = torch.flatten(h).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1)\n        return h\n\n    width, height = int(width), int(height)\n    target_width, target_height = round_to_64(target_width), round_to_64(target_height)\n\n    adm_emphasized = embedder([height, width, 0, 0, target_height, target_width])\n    adm_consistent = embedder([target_height, target_width, 0, 0, target_height, target_width])\n\n    clip_pooled = clip_pooled.to(adm_emphasized)\n    final_adm = torch.cat((clip_pooled, adm_emphasized, clip_pooled, adm_consistent), dim=1)\n\n    return final_adm\n\n\ndef patched_KSamplerX0Inpaint_forward(self, x, sigma, uncond, cond, cond_scale, denoise_mask, model_options={}, seed=None):\n    if inpaint_worker.current_task is not None:\n        latent_processor = self.inner_model.inner_model.process_latent_in\n        inpaint_latent = latent_processor(inpaint_worker.current_task.latent).to(x)\n        inpaint_mask = inpaint_worker.current_task.latent_mask.to(x)\n\n        if getattr(self, 'energy_generator', None) is None:\n            # avoid bad results by using different seeds.\n            self.energy_generator = torch.Generator(device='cpu').manual_seed((seed + 1) % constants.MAX_SEED)\n\n        energy_sigma = sigma.reshape([sigma.shape[0]] + [1] * (len(x.shape) - 1))\n        current_energy = torch.randn(\n            x.size(), dtype=x.dtype, generator=self.energy_generator, device=\"cpu\").to(x) * energy_sigma\n        x = x * inpaint_mask + (inpaint_latent + current_energy) * (1.0 - inpaint_mask)\n\n        out = self.inner_model(x, sigma,\n                               cond=cond,\n                               uncond=uncond,\n                               cond_scale=cond_scale,\n                               model_options=model_options,\n                               seed=seed)\n\n        out = out * inpaint_mask + inpaint_latent * (1.0 - inpaint_mask)\n    else:\n        out = self.inner_model(x, sigma,\n                               cond=cond,\n                               uncond=uncond,\n                               cond_scale=cond_scale,\n                               model_options=model_options,\n                               seed=seed)\n    return out\n\n\ndef timed_adm(y, timesteps):\n    if isinstance(y, torch.Tensor) and int(y.dim()) == 2 and int(y.shape[1]) == 5632:\n        y_mask = (timesteps > 999.0 * (1.0 - float(patch_settings[os.getpid()].adm_scaler_end))).to(y)[..., None]\n        y_with_adm = y[..., :2816].clone()\n        y_without_adm = y[..., 2816:].clone()\n        return y_with_adm * y_mask + y_without_adm * (1.0 - y_mask)\n    return y\n\n\ndef patched_cldm_forward(self, x, hint, timesteps, context, y=None, **kwargs):\n    t_emb = ldm_patched.ldm.modules.diffusionmodules.openaimodel.timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)\n    emb = self.time_embed(t_emb)\n    pid = os.getpid()\n\n    guided_hint = self.input_hint_block(hint, emb, context)\n\n    y = timed_adm(y, timesteps)\n\n    outs = []\n\n    hs = []\n    if self.num_classes is not None:\n        assert y.shape[0] == x.shape[0]\n        emb = emb + self.label_emb(y)\n\n    h = x\n    for module, zero_conv in zip(self.input_blocks, self.zero_convs):\n        if guided_hint is not None:\n            h = module(h, emb, context)\n            h += guided_hint\n            guided_hint = None\n        else:\n            h = module(h, emb, context)\n        outs.append(zero_conv(h, emb, context))\n\n    h = self.middle_block(h, emb, context)\n    outs.append(self.middle_block_out(h, emb, context))\n\n    if patch_settings[pid].controlnet_softness > 0:\n        for i in range(10):\n            k = 1.0 - float(i) / 9.0\n            outs[i] = outs[i] * (1.0 - patch_settings[pid].controlnet_softness * k)\n\n    return outs\n\n\ndef patched_unet_forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs):\n    self.current_step = 1.0 - timesteps.to(x) / 999.0\n    patch_settings[os.getpid()].global_diffusion_progress = float(self.current_step.detach().cpu().numpy().tolist()[0])\n\n    y = timed_adm(y, timesteps)\n\n    transformer_options[\"original_shape\"] = list(x.shape)\n    transformer_options[\"transformer_index\"] = 0\n    transformer_patches = transformer_options.get(\"patches\", {})\n\n    num_video_frames = kwargs.get(\"num_video_frames\", self.default_num_video_frames)\n    image_only_indicator = kwargs.get(\"image_only_indicator\", self.default_image_only_indicator)\n    time_context = kwargs.get(\"time_context\", None)\n\n    assert (y is not None) == (\n            self.num_classes is not None\n    ), \"must specify y if and only if the model is class-conditional\"\n    hs = []\n    t_emb = ldm_patched.ldm.modules.diffusionmodules.openaimodel.timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)\n    emb = self.time_embed(t_emb)\n\n    if self.num_classes is not None:\n        assert y.shape[0] == x.shape[0]\n        emb = emb + self.label_emb(y)\n\n    h = x\n    for id, module in enumerate(self.input_blocks):\n        transformer_options[\"block\"] = (\"input\", id)\n        h = forward_timestep_embed(module, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator)\n        h = apply_control(h, control, 'input')\n        if \"input_block_patch\" in transformer_patches:\n            patch = transformer_patches[\"input_block_patch\"]\n            for p in patch:\n                h = p(h, transformer_options)\n\n        hs.append(h)\n        if \"input_block_patch_after_skip\" in transformer_patches:\n            patch = transformer_patches[\"input_block_patch_after_skip\"]\n            for p in patch:\n                h = p(h, transformer_options)\n\n    transformer_options[\"block\"] = (\"middle\", 0)\n    h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator)\n    h = apply_control(h, control, 'middle')\n\n    for id, module in enumerate(self.output_blocks):\n        transformer_options[\"block\"] = (\"output\", id)\n        hsp = hs.pop()\n        hsp = apply_control(hsp, control, 'output')\n\n        if \"output_block_patch\" in transformer_patches:\n            patch = transformer_patches[\"output_block_patch\"]\n            for p in patch:\n                h, hsp = p(h, hsp, transformer_options)\n\n        h = torch.cat([h, hsp], dim=1)\n        del hsp\n        if len(hs) > 0:\n            output_shape = hs[-1].shape\n        else:\n            output_shape = None\n        h = forward_timestep_embed(module, h, emb, context, transformer_options, output_shape, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator)\n    h = h.type(x.dtype)\n    if self.predict_codebook_ids:\n        return self.id_predictor(h)\n    else:\n        return self.out(h)\n\n\ndef patched_load_models_gpu(*args, **kwargs):\n    execution_start_time = time.perf_counter()\n    y = ldm_patched.modules.model_management.load_models_gpu_origin(*args, **kwargs)\n    moving_time = time.perf_counter() - execution_start_time\n    if moving_time > 0.1:\n        print(f'[Fooocus Model Management] Moving model(s) has taken {moving_time:.2f} seconds')\n    return y\n\n\ndef build_loaded(module, loader_name):\n    original_loader_name = loader_name + '_origin'\n\n    if not hasattr(module, original_loader_name):\n        setattr(module, original_loader_name, getattr(module, loader_name))\n\n    original_loader = getattr(module, original_loader_name)\n\n    def loader(*args, **kwargs):\n        result = None\n        try:\n            result = original_loader(*args, **kwargs)\n        except Exception as e:\n            result = None\n            exp = str(e) + '\\n'\n            for path in list(args) + list(kwargs.values()):\n                if isinstance(path, str):\n                    if os.path.exists(path):\n                        exp += f'File corrupted: {path} \\n'\n                        corrupted_backup_file = path + '.corrupted'\n                        if os.path.exists(corrupted_backup_file):\n                            os.remove(corrupted_backup_file)\n                        os.replace(path, corrupted_backup_file)\n                        if os.path.exists(path):\n                            os.remove(path)\n                        exp += f'Fooocus has tried to move the corrupted file to {corrupted_backup_file} \\n'\n                        exp += f'You may try again now and Fooocus will download models again. \\n'\n            raise ValueError(exp)\n        return result\n\n    setattr(module, loader_name, loader)\n    return\n\n\ndef patch_all():\n    if ldm_patched.modules.model_management.directml_enabled:\n        ldm_patched.modules.model_management.lowvram_available = True\n        ldm_patched.modules.model_management.OOM_EXCEPTION = Exception\n\n    patch_all_precision()\n    patch_all_clip()\n\n    if not hasattr(ldm_patched.modules.model_management, 'load_models_gpu_origin'):\n        ldm_patched.modules.model_management.load_models_gpu_origin = ldm_patched.modules.model_management.load_models_gpu\n\n    ldm_patched.modules.model_management.load_models_gpu = patched_load_models_gpu\n    ldm_patched.modules.model_patcher.ModelPatcher.calculate_weight = calculate_weight_patched\n    ldm_patched.controlnet.cldm.ControlNet.forward = patched_cldm_forward\n    ldm_patched.ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = patched_unet_forward\n    ldm_patched.modules.model_base.SDXL.encode_adm = sdxl_encode_adm_patched\n    ldm_patched.modules.samplers.KSamplerX0Inpaint.forward = patched_KSamplerX0Inpaint_forward\n    ldm_patched.k_diffusion.sampling.BrownianTreeNoiseSampler = BrownianTreeNoiseSamplerPatched\n    ldm_patched.modules.samplers.sampling_function = patched_sampling_function\n\n    warnings.filterwarnings(action='ignore', module='torchsde')\n\n    build_loaded(safetensors.torch, 'load_file')\n    build_loaded(torch, 'load')\n\n    return\n"
  },
  {
    "path": "modules/patch_clip.py",
    "content": "# Consistent with Kohya/A1111 to reduce differences between model training and inference.\n\nimport os\nimport torch\nimport ldm_patched.controlnet.cldm\nimport ldm_patched.k_diffusion.sampling\nimport ldm_patched.ldm.modules.attention\nimport ldm_patched.ldm.modules.diffusionmodules.model\nimport ldm_patched.ldm.modules.diffusionmodules.openaimodel\nimport ldm_patched.ldm.modules.diffusionmodules.openaimodel\nimport ldm_patched.modules.args_parser\nimport ldm_patched.modules.model_base\nimport ldm_patched.modules.model_management\nimport ldm_patched.modules.model_patcher\nimport ldm_patched.modules.samplers\nimport ldm_patched.modules.sd\nimport ldm_patched.modules.sd1_clip\nimport ldm_patched.modules.clip_vision\nimport ldm_patched.modules.ops as ops\n\nfrom modules.ops import use_patched_ops\nfrom transformers import CLIPTextModel, CLIPTextConfig, modeling_utils, CLIPVisionConfig, CLIPVisionModelWithProjection\n\n\ndef patched_encode_token_weights(self, token_weight_pairs):\n    to_encode = list()\n    max_token_len = 0\n    has_weights = False\n    for x in token_weight_pairs:\n        tokens = list(map(lambda a: a[0], x))\n        max_token_len = max(len(tokens), max_token_len)\n        has_weights = has_weights or not all(map(lambda a: a[1] == 1.0, x))\n        to_encode.append(tokens)\n\n    sections = len(to_encode)\n    if has_weights or sections == 0:\n        to_encode.append(ldm_patched.modules.sd1_clip.gen_empty_tokens(self.special_tokens, max_token_len))\n\n    out, pooled = self.encode(to_encode)\n    if pooled is not None:\n        first_pooled = pooled[0:1].to(ldm_patched.modules.model_management.intermediate_device())\n    else:\n        first_pooled = pooled\n\n    output = []\n    for k in range(0, sections):\n        z = out[k:k + 1]\n        if has_weights:\n            original_mean = z.mean()\n            z_empty = out[-1]\n            for i in range(len(z)):\n                for j in range(len(z[i])):\n                    weight = token_weight_pairs[k][j][1]\n                    if weight != 1.0:\n                        z[i][j] = (z[i][j] - z_empty[j]) * weight + z_empty[j]\n            new_mean = z.mean()\n            z = z * (original_mean / new_mean)\n        output.append(z)\n\n    if len(output) == 0:\n        return out[-1:].to(ldm_patched.modules.model_management.intermediate_device()), first_pooled\n    return torch.cat(output, dim=-2).to(ldm_patched.modules.model_management.intermediate_device()), first_pooled\n\n\ndef patched_SDClipModel__init__(self, max_length=77, freeze=True, layer=\"last\", layer_idx=None,\n                                textmodel_json_config=None, dtype=None, special_tokens=None,\n                                layer_norm_hidden_state=True, **kwargs):\n    torch.nn.Module.__init__(self)\n    assert layer in self.LAYERS\n\n    if special_tokens is None:\n        special_tokens = {\"start\": 49406, \"end\": 49407, \"pad\": 49407}\n\n    if textmodel_json_config is None:\n        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(ldm_patched.modules.sd1_clip.__file__)),\n                                             \"sd1_clip_config.json\")\n\n    config = CLIPTextConfig.from_json_file(textmodel_json_config)\n    self.num_layers = config.num_hidden_layers\n\n    with use_patched_ops(ops.manual_cast):\n        with modeling_utils.no_init_weights():\n            self.transformer = CLIPTextModel(config)\n\n    if dtype is not None:\n        self.transformer.to(dtype)\n\n    self.transformer.text_model.embeddings.to(torch.float32)\n\n    if freeze:\n        self.freeze()\n\n    self.max_length = max_length\n    self.layer = layer\n    self.layer_idx = None\n    self.special_tokens = special_tokens\n    self.text_projection = torch.nn.Parameter(torch.eye(self.transformer.get_input_embeddings().weight.shape[1]))\n    self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055))\n    self.enable_attention_masks = False\n\n    self.layer_norm_hidden_state = layer_norm_hidden_state\n    if layer == \"hidden\":\n        assert layer_idx is not None\n        assert abs(layer_idx) < self.num_layers\n        self.clip_layer(layer_idx)\n    self.layer_default = (self.layer, self.layer_idx)\n\n\ndef patched_SDClipModel_forward(self, tokens):\n    backup_embeds = self.transformer.get_input_embeddings()\n    device = backup_embeds.weight.device\n    tokens = self.set_up_textual_embeddings(tokens, backup_embeds)\n    tokens = torch.LongTensor(tokens).to(device)\n\n    attention_mask = None\n    if self.enable_attention_masks:\n        attention_mask = torch.zeros_like(tokens)\n        max_token = self.transformer.get_input_embeddings().weight.shape[0] - 1\n        for x in range(attention_mask.shape[0]):\n            for y in range(attention_mask.shape[1]):\n                attention_mask[x, y] = 1\n                if tokens[x, y] == max_token:\n                    break\n\n    outputs = self.transformer(input_ids=tokens, attention_mask=attention_mask,\n                               output_hidden_states=self.layer == \"hidden\")\n    self.transformer.set_input_embeddings(backup_embeds)\n\n    if self.layer == \"last\":\n        z = outputs.last_hidden_state\n    elif self.layer == \"pooled\":\n        z = outputs.pooler_output[:, None, :]\n    else:\n        z = outputs.hidden_states[self.layer_idx]\n        if self.layer_norm_hidden_state:\n            z = self.transformer.text_model.final_layer_norm(z)\n\n    if hasattr(outputs, \"pooler_output\"):\n        pooled_output = outputs.pooler_output.float()\n    else:\n        pooled_output = None\n\n    if self.text_projection is not None and pooled_output is not None:\n        pooled_output = pooled_output.float().to(self.text_projection.device) @ self.text_projection.float()\n\n    return z.float(), pooled_output\n\n\ndef patched_ClipVisionModel__init__(self, json_config):\n    config = CLIPVisionConfig.from_json_file(json_config)\n\n    self.load_device = ldm_patched.modules.model_management.text_encoder_device()\n    self.offload_device = ldm_patched.modules.model_management.text_encoder_offload_device()\n\n    if ldm_patched.modules.model_management.should_use_fp16(self.load_device, prioritize_performance=False):\n        self.dtype = torch.float16\n    else:\n        self.dtype = torch.float32\n\n    with use_patched_ops(ops.manual_cast):\n        with modeling_utils.no_init_weights():\n            self.model = CLIPVisionModelWithProjection(config)\n\n    self.model.to(self.dtype)\n    self.patcher = ldm_patched.modules.model_patcher.ModelPatcher(\n        self.model,\n        load_device=self.load_device,\n        offload_device=self.offload_device\n    )\n\n\ndef patched_ClipVisionModel_encode_image(self, image):\n    ldm_patched.modules.model_management.load_model_gpu(self.patcher)\n    pixel_values = ldm_patched.modules.clip_vision.clip_preprocess(image.to(self.load_device))\n    outputs = self.model(pixel_values=pixel_values, output_hidden_states=True)\n\n    for k in outputs:\n        t = outputs[k]\n        if t is not None:\n            if k == 'hidden_states':\n                outputs[\"penultimate_hidden_states\"] = t[-2].to(ldm_patched.modules.model_management.intermediate_device())\n                outputs[\"hidden_states\"] = None\n            else:\n                outputs[k] = t.to(ldm_patched.modules.model_management.intermediate_device())\n\n    return outputs\n\n\ndef patch_all_clip():\n    ldm_patched.modules.sd1_clip.ClipTokenWeightEncoder.encode_token_weights = patched_encode_token_weights\n    ldm_patched.modules.sd1_clip.SDClipModel.__init__ = patched_SDClipModel__init__\n    ldm_patched.modules.sd1_clip.SDClipModel.forward = patched_SDClipModel_forward\n    ldm_patched.modules.clip_vision.ClipVisionModel.__init__ = patched_ClipVisionModel__init__\n    ldm_patched.modules.clip_vision.ClipVisionModel.encode_image = patched_ClipVisionModel_encode_image\n    return\n"
  },
  {
    "path": "modules/patch_precision.py",
    "content": "# Consistent with Kohya to reduce differences between model training and inference.\n\nimport torch\nimport math\nimport einops\nimport numpy as np\n\nimport ldm_patched.ldm.modules.diffusionmodules.openaimodel\nimport ldm_patched.modules.model_sampling\nimport ldm_patched.modules.sd1_clip\n\nfrom ldm_patched.ldm.modules.diffusionmodules.util import make_beta_schedule\n\n\ndef patched_timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):\n    # Consistent with Kohya to reduce differences between model training and inference.\n\n    if not repeat_only:\n        half = dim // 2\n        freqs = torch.exp(\n            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half\n        ).to(device=timesteps.device)\n        args = timesteps[:, None].float() * freqs[None]\n        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)\n        if dim % 2:\n            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)\n    else:\n        embedding = einops.repeat(timesteps, 'b -> b d', d=dim)\n    return embedding\n\n\ndef patched_register_schedule(self, given_betas=None, beta_schedule=\"linear\", timesteps=1000,\n                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):\n    # Consistent with Kohya to reduce differences between model training and inference.\n\n    if given_betas is not None:\n        betas = given_betas\n    else:\n        betas = make_beta_schedule(\n            beta_schedule,\n            timesteps,\n            linear_start=linear_start,\n            linear_end=linear_end,\n            cosine_s=cosine_s)\n\n    alphas = 1. - betas\n    alphas_cumprod = np.cumprod(alphas, axis=0)\n    timesteps, = betas.shape\n    self.num_timesteps = int(timesteps)\n    self.linear_start = linear_start\n    self.linear_end = linear_end\n    sigmas = torch.tensor(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5, dtype=torch.float32)\n    self.set_sigmas(sigmas)\n    alphas_cumprod = torch.tensor(alphas_cumprod, dtype=torch.float32)\n    self.set_alphas_cumprod(alphas_cumprod)\n    return\n\n\ndef patch_all_precision():\n    ldm_patched.ldm.modules.diffusionmodules.openaimodel.timestep_embedding = patched_timestep_embedding\n    ldm_patched.modules.model_sampling.ModelSamplingDiscrete._register_schedule = patched_register_schedule\n    return\n"
  },
  {
    "path": "modules/private_logger.py",
    "content": "import os\nimport args_manager\nimport modules.config\nimport json\nimport urllib.parse\n\nfrom PIL import Image\nfrom PIL.PngImagePlugin import PngInfo\nfrom modules.flags import OutputFormat\nfrom modules.meta_parser import MetadataParser, get_exif\nfrom modules.util import generate_temp_filename\n\nlog_cache = {}\n\n\ndef get_current_html_path(output_format=None):\n    output_format = output_format if output_format else modules.config.default_output_format\n    date_string, local_temp_filename, only_name = generate_temp_filename(folder=modules.config.path_outputs,\n                                                                         extension=output_format)\n    html_name = os.path.join(os.path.dirname(local_temp_filename), 'log.html')\n    return html_name\n\n\ndef log(img, metadata, metadata_parser: MetadataParser | None = None, output_format=None, task=None, persist_image=True) -> str:\n    path_outputs = modules.config.temp_path if args_manager.args.disable_image_log or not persist_image else modules.config.path_outputs\n    output_format = output_format if output_format else modules.config.default_output_format\n    date_string, local_temp_filename, only_name = generate_temp_filename(folder=path_outputs, extension=output_format)\n    os.makedirs(os.path.dirname(local_temp_filename), exist_ok=True)\n\n    parsed_parameters = metadata_parser.to_string(metadata.copy()) if metadata_parser is not None else ''\n    image = Image.fromarray(img)\n\n    if output_format == OutputFormat.PNG.value:\n        if parsed_parameters != '':\n            pnginfo = PngInfo()\n            pnginfo.add_text('parameters', parsed_parameters)\n            pnginfo.add_text('fooocus_scheme', metadata_parser.get_scheme().value)\n        else:\n            pnginfo = None\n        image.save(local_temp_filename, pnginfo=pnginfo)\n    elif output_format == OutputFormat.JPEG.value:\n        image.save(local_temp_filename, quality=95, optimize=True, progressive=True, exif=get_exif(parsed_parameters, metadata_parser.get_scheme().value) if metadata_parser else Image.Exif())\n    elif output_format == OutputFormat.WEBP.value:\n        image.save(local_temp_filename, quality=95, lossless=False, exif=get_exif(parsed_parameters, metadata_parser.get_scheme().value) if metadata_parser else Image.Exif())\n    else:\n        image.save(local_temp_filename)\n\n    if args_manager.args.disable_image_log:\n        return local_temp_filename\n\n    html_name = os.path.join(os.path.dirname(local_temp_filename), 'log.html')\n\n    css_styles = (\n        \"<style>\"\n        \"body { background-color: #121212; color: #E0E0E0; } \"\n        \"a { color: #BB86FC; } \"\n        \".metadata { border-collapse: collapse; width: 100%; } \"\n        \".metadata .label { width: 15%; } \"\n        \".metadata .value { width: 85%; font-weight: bold; } \"\n        \".metadata th, .metadata td { border: 1px solid #4d4d4d; padding: 4px; } \"\n        \".image-container img { height: auto; max-width: 512px; display: block; padding-right:10px; } \"\n        \".image-container div { text-align: center; padding: 4px; } \"\n        \"hr { border-color: gray; } \"\n        \"button { background-color: black; color: white; border: 1px solid grey; border-radius: 5px; padding: 5px 10px; text-align: center; display: inline-block; font-size: 16px; cursor: pointer; }\"\n        \"button:hover {background-color: grey; color: black;}\"\n        \"</style>\"\n    )\n\n    js = (\n        \"\"\"<script>\n        function to_clipboard(txt) { \n        txt = decodeURIComponent(txt);\n        if (navigator.clipboard && navigator.permissions) {\n            navigator.clipboard.writeText(txt)\n        } else {\n            const textArea = document.createElement('textArea')\n            textArea.value = txt\n            textArea.style.width = 0\n            textArea.style.position = 'fixed'\n            textArea.style.left = '-999px'\n            textArea.style.top = '10px'\n            textArea.setAttribute('readonly', 'readonly')\n            document.body.appendChild(textArea)\n\n            textArea.select()\n            document.execCommand('copy')\n            document.body.removeChild(textArea)\n        }\n        alert('Copied to Clipboard!\\\\nPaste to prompt area to load parameters.\\\\nCurrent clipboard content is:\\\\n\\\\n' + txt);\n        }\n        </script>\"\"\"\n    )\n\n    begin_part = f\"<!DOCTYPE html><html><head><title>Fooocus Log {date_string}</title>{css_styles}</head><body>{js}<p>Fooocus Log {date_string} (private)</p>\\n<p>Metadata is embedded if enabled in the config or developer debug mode. You can find the information for each image in line Metadata Scheme.</p><!--fooocus-log-split-->\\n\\n\"\n    end_part = f'\\n<!--fooocus-log-split--></body></html>'\n\n    middle_part = log_cache.get(html_name, \"\")\n\n    if middle_part == \"\":\n        if os.path.exists(html_name):\n            existing_split = open(html_name, 'r', encoding='utf-8').read().split('<!--fooocus-log-split-->')\n            if len(existing_split) == 3:\n                middle_part = existing_split[1]\n            else:\n                middle_part = existing_split[0]\n\n    div_name = only_name.replace('.', '_')\n    item = f\"<div id=\\\"{div_name}\\\" class=\\\"image-container\\\"><hr><table><tr>\\n\"\n    item += f\"<td><a href=\\\"{only_name}\\\" target=\\\"_blank\\\"><img src='{only_name}' onerror=\\\"this.closest('.image-container').style.display='none';\\\" loading='lazy'/></a><div>{only_name}</div></td>\"\n    item += \"<td><table class='metadata'>\"\n    for label, key, value in metadata:\n        value_txt = str(value).replace('\\n', ' </br> ')\n        item += f\"<tr><td class='label'>{label}</td><td class='value'>{value_txt}</td></tr>\\n\"\n\n    if task is not None and 'positive' in task and 'negative' in task:\n        full_prompt_details = f\"\"\"<details><summary>Positive</summary>{', '.join(task['positive'])}</details>\n        <details><summary>Negative</summary>{', '.join(task['negative'])}</details>\"\"\"\n        item += f\"<tr><td class='label'>Full raw prompt</td><td class='value'>{full_prompt_details}</td></tr>\\n\"\n\n    item += \"</table>\"\n\n    js_txt = urllib.parse.quote(json.dumps({k: v for _, k, v, in metadata}, indent=0), safe='')\n    item += f\"</br><button onclick=\\\"to_clipboard('{js_txt}')\\\">Copy to Clipboard</button>\"\n\n    item += \"</td>\"\n    item += \"</tr></table></div>\\n\\n\"\n\n    middle_part = item + middle_part\n\n    with open(html_name, 'w', encoding='utf-8') as f:\n        f.write(begin_part + middle_part + end_part)\n\n    print(f'Image generated with private log at: {html_name}')\n\n    log_cache[html_name] = middle_part\n\n    return local_temp_filename\n"
  },
  {
    "path": "modules/sample_hijack.py",
    "content": "import torch\nimport ldm_patched.modules.samplers\nimport ldm_patched.modules.model_management\n\nfrom collections import namedtuple\nfrom ldm_patched.contrib.external_align_your_steps import AlignYourStepsScheduler\nfrom ldm_patched.contrib.external_custom_sampler import SDTurboScheduler\nfrom ldm_patched.k_diffusion import sampling as k_diffusion_sampling\nfrom ldm_patched.modules.samplers import normal_scheduler, simple_scheduler, ddim_scheduler\nfrom ldm_patched.modules.model_base import SDXLRefiner, SDXL\nfrom ldm_patched.modules.conds import CONDRegular\nfrom ldm_patched.modules.sample import get_additional_models, get_models_from_cond, cleanup_additional_models\nfrom ldm_patched.modules.samplers import resolve_areas_and_cond_masks, wrap_model, calculate_start_end_timesteps, \\\n    create_cond_with_same_area_if_none, pre_run_control, apply_empty_x_to_equal_area, encode_model_conds\n\n\ncurrent_refiner = None\nrefiner_switch_step = -1\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef clip_separate_inner(c, p, target_model=None, target_clip=None):\n    if target_model is None or isinstance(target_model, SDXLRefiner):\n        c = c[..., -1280:].clone()\n    elif isinstance(target_model, SDXL):\n        c = c.clone()\n    else:\n        p = None\n        c = c[..., :768].clone()\n\n        final_layer_norm = target_clip.cond_stage_model.clip_l.transformer.text_model.final_layer_norm\n\n        final_layer_norm_origin_device = final_layer_norm.weight.device\n        final_layer_norm_origin_dtype = final_layer_norm.weight.dtype\n\n        c_origin_device = c.device\n        c_origin_dtype = c.dtype\n\n        final_layer_norm.to(device='cpu', dtype=torch.float32)\n        c = c.to(device='cpu', dtype=torch.float32)\n\n        c = torch.chunk(c, int(c.size(1)) // 77, 1)\n        c = [final_layer_norm(ci) for ci in c]\n        c = torch.cat(c, dim=1)\n\n        final_layer_norm.to(device=final_layer_norm_origin_device, dtype=final_layer_norm_origin_dtype)\n        c = c.to(device=c_origin_device, dtype=c_origin_dtype)\n    return c, p\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef clip_separate(cond, target_model=None, target_clip=None):\n    results = []\n\n    for c, px in cond:\n        p = px.get('pooled_output', None)\n        c, p = clip_separate_inner(c, p, target_model=target_model, target_clip=target_clip)\n        p = {} if p is None else {'pooled_output': p.clone()}\n        results.append([c, p])\n\n    return results\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef clip_separate_after_preparation(cond, target_model=None, target_clip=None):\n    results = []\n\n    for x in cond:\n        p = x.get('pooled_output', None)\n        c = x['model_conds']['c_crossattn'].cond\n\n        c, p = clip_separate_inner(c, p, target_model=target_model, target_clip=target_clip)\n\n        result = {'model_conds': {'c_crossattn': CONDRegular(c)}}\n\n        if p is not None:\n            result['pooled_output'] = p.clone()\n\n        results.append(result)\n\n    return results\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef sample_hacked(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={}, latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None):\n    global current_refiner\n\n    positive = positive[:]\n    negative = negative[:]\n\n    resolve_areas_and_cond_masks(positive, noise.shape[2], noise.shape[3], device)\n    resolve_areas_and_cond_masks(negative, noise.shape[2], noise.shape[3], device)\n\n    model_wrap = wrap_model(model)\n\n    calculate_start_end_timesteps(model, negative)\n    calculate_start_end_timesteps(model, positive)\n\n    if latent_image is not None:\n        latent_image = model.process_latent_in(latent_image)\n\n    if hasattr(model, 'extra_conds'):\n        positive = encode_model_conds(model.extra_conds, positive, noise, device, \"positive\", latent_image=latent_image, denoise_mask=denoise_mask)\n        negative = encode_model_conds(model.extra_conds, negative, noise, device, \"negative\", latent_image=latent_image, denoise_mask=denoise_mask)\n\n    #make sure each cond area has an opposite one with the same area\n    for c in positive:\n        create_cond_with_same_area_if_none(negative, c)\n    for c in negative:\n        create_cond_with_same_area_if_none(positive, c)\n\n    # pre_run_control(model, negative + positive)\n    pre_run_control(model, positive)  # negative is not necessary in Fooocus, 0.5s faster.\n\n    apply_empty_x_to_equal_area(list(filter(lambda c: c.get('control_apply_to_uncond', False) == True, positive)), negative, 'control', lambda cond_cnets, x: cond_cnets[x])\n    apply_empty_x_to_equal_area(positive, negative, 'gligen', lambda cond_cnets, x: cond_cnets[x])\n\n    extra_args = {\"cond\":positive, \"uncond\":negative, \"cond_scale\": cfg, \"model_options\": model_options, \"seed\":seed}\n\n    if current_refiner is not None and hasattr(current_refiner.model, 'extra_conds'):\n        positive_refiner = clip_separate_after_preparation(positive, target_model=current_refiner.model)\n        negative_refiner = clip_separate_after_preparation(negative, target_model=current_refiner.model)\n\n        positive_refiner = encode_model_conds(current_refiner.model.extra_conds, positive_refiner, noise, device, \"positive\", latent_image=latent_image, denoise_mask=denoise_mask)\n        negative_refiner = encode_model_conds(current_refiner.model.extra_conds, negative_refiner, noise, device, \"negative\", latent_image=latent_image, denoise_mask=denoise_mask)\n\n    def refiner_switch():\n        cleanup_additional_models(set(get_models_from_cond(positive, \"control\") + get_models_from_cond(negative, \"control\")))\n\n        extra_args[\"cond\"] = positive_refiner\n        extra_args[\"uncond\"] = negative_refiner\n\n        # clear ip-adapter for refiner\n        extra_args['model_options'] = {k: {} if k == 'transformer_options' else v for k, v in extra_args['model_options'].items()}\n\n        models, inference_memory = get_additional_models(positive_refiner, negative_refiner, current_refiner.model_dtype())\n        ldm_patched.modules.model_management.load_models_gpu(\n            [current_refiner] + models,\n            model.memory_required([noise.shape[0] * 2] + list(noise.shape[1:])) + inference_memory)\n\n        model_wrap.inner_model = current_refiner.model\n        print('Refiner Swapped')\n        return\n\n    def callback_wrap(step, x0, x, total_steps):\n        if step == refiner_switch_step and current_refiner is not None:\n            refiner_switch()\n        if callback is not None:\n            # residual_noise_preview = x - x0\n            # residual_noise_preview /= residual_noise_preview.std()\n            # residual_noise_preview *= x0.std()\n            callback(step, x0, x, total_steps)\n\n    samples = sampler.sample(model_wrap, sigmas, extra_args, callback_wrap, noise, latent_image, denoise_mask, disable_pbar)\n    return model.process_latent_out(samples.to(torch.float32))\n\n\n@torch.no_grad()\n@torch.inference_mode()\ndef calculate_sigmas_scheduler_hacked(model, scheduler_name, steps):\n    if scheduler_name == \"karras\":\n        sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=float(model.model_sampling.sigma_min), sigma_max=float(model.model_sampling.sigma_max))\n    elif scheduler_name == \"exponential\":\n        sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=float(model.model_sampling.sigma_min), sigma_max=float(model.model_sampling.sigma_max))\n    elif scheduler_name == \"normal\":\n        sigmas = normal_scheduler(model, steps)\n    elif scheduler_name == \"simple\":\n        sigmas = simple_scheduler(model, steps)\n    elif scheduler_name == \"ddim_uniform\":\n        sigmas = ddim_scheduler(model, steps)\n    elif scheduler_name == \"sgm_uniform\":\n        sigmas = normal_scheduler(model, steps, sgm=True)\n    elif scheduler_name == \"turbo\":\n        sigmas = SDTurboScheduler().get_sigmas(model=model, steps=steps, denoise=1.0)[0]\n    elif scheduler_name == \"align_your_steps\":\n        model_type = 'SDXL' if isinstance(model.latent_format, ldm_patched.modules.latent_formats.SDXL) else 'SD1'\n        sigmas = AlignYourStepsScheduler().get_sigmas(model_type=model_type, steps=steps, denoise=1.0)[0]\n    else:\n        raise TypeError(\"error invalid scheduler\")\n    return sigmas\n\n\nldm_patched.modules.samplers.calculate_sigmas_scheduler = calculate_sigmas_scheduler_hacked\nldm_patched.modules.samplers.sample = sample_hacked\n"
  },
  {
    "path": "modules/sdxl_styles.py",
    "content": "import os\nimport re\nimport json\nimport math\n\nfrom modules.extra_utils import get_files_from_folder\nfrom random import Random\n\n# cannot use modules.config - validators causing circular imports\nstyles_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../sdxl_styles/'))\n\n\ndef normalize_key(k):\n    k = k.replace('-', ' ')\n    words = k.split(' ')\n    words = [w[:1].upper() + w[1:].lower() for w in words]\n    k = ' '.join(words)\n    k = k.replace('3d', '3D')\n    k = k.replace('Sai', 'SAI')\n    k = k.replace('Mre', 'MRE')\n    k = k.replace('(s', '(S')\n    return k\n\n\nstyles = {}\nstyles_files = get_files_from_folder(styles_path, ['.json'])\n\nfor x in ['sdxl_styles_fooocus.json',\n          'sdxl_styles_sai.json',\n          'sdxl_styles_mre.json',\n          'sdxl_styles_twri.json',\n          'sdxl_styles_diva.json',\n          'sdxl_styles_marc_k3nt3l.json']:\n    if x in styles_files:\n        styles_files.remove(x)\n        styles_files.append(x)\n\nfor styles_file in styles_files:\n    try:\n        with open(os.path.join(styles_path, styles_file), encoding='utf-8') as f:\n            for entry in json.load(f):\n                name = normalize_key(entry['name'])\n                prompt = entry['prompt'] if 'prompt' in entry else ''\n                negative_prompt = entry['negative_prompt'] if 'negative_prompt' in entry else ''\n                styles[name] = (prompt, negative_prompt)\n    except Exception as e:\n        print(str(e))\n        print(f'Failed to load style file {styles_file}')\n\nstyle_keys = list(styles.keys())\nfooocus_expansion = 'Fooocus V2'\nrandom_style_name = 'Random Style'\nlegal_style_names = [fooocus_expansion, random_style_name] + style_keys\n\n\ndef get_random_style(rng: Random) -> str:\n    return rng.choice(list(styles.items()))[0]\n\n\ndef apply_style(style, positive):\n    p, n = styles[style]\n    return p.replace('{prompt}', positive).splitlines(), n.splitlines(), '{prompt}' in p\n\n\ndef get_words(arrays, total_mult, index):\n    if len(arrays) == 1:\n        return [arrays[0].split(',')[index]]\n    else:\n        words = arrays[0].split(',')\n        word = words[index % len(words)]\n        index -= index % len(words)\n        index /= len(words)\n        index = math.floor(index)\n        return [word] + get_words(arrays[1:], math.floor(total_mult / len(words)), index)\n\n\ndef apply_arrays(text, index):\n    arrays = re.findall(r'\\[\\[(.*?)\\]\\]', text)\n    if len(arrays) == 0:\n        return text\n\n    print(f'[Arrays] processing: {text}')\n    mult = 1\n    for arr in arrays:\n        words = arr.split(',')\n        mult *= len(words)\n    \n    index %= mult\n    chosen_words = get_words(arrays, mult, index)\n    \n    i = 0\n    for arr in arrays:\n        text = text.replace(f'[[{arr}]]', chosen_words[i], 1)   \n        i = i+1\n    \n    return text\n\n"
  },
  {
    "path": "modules/style_sorter.py",
    "content": "import os\nimport gradio as gr\nimport modules.localization as localization\nimport json\n\n\nall_styles = []\n\n\ndef try_load_sorted_styles(style_names, default_selected):\n    global all_styles\n\n    all_styles = style_names\n\n    try:\n        if os.path.exists('sorted_styles.json'):\n            with open('sorted_styles.json', 'rt', encoding='utf-8') as fp:\n                sorted_styles = []\n                for x in json.load(fp):\n                    if x in all_styles:\n                        sorted_styles.append(x)\n                for x in all_styles:\n                    if x not in sorted_styles:\n                        sorted_styles.append(x)\n                all_styles = sorted_styles\n    except Exception as e:\n        print('Load style sorting failed.')\n        print(e)\n\n    unselected = [y for y in all_styles if y not in default_selected]\n    all_styles = default_selected + unselected\n\n    return\n\n\ndef sort_styles(selected):\n    global all_styles\n    unselected = [y for y in all_styles if y not in selected]\n    sorted_styles = selected + unselected\n    try:\n        with open('sorted_styles.json', 'wt', encoding='utf-8') as fp:\n            json.dump(sorted_styles, fp, indent=4)\n    except Exception as e:\n        print('Write style sorting failed.')\n        print(e)\n    all_styles = sorted_styles\n    return gr.CheckboxGroup.update(choices=sorted_styles)\n\n\ndef localization_key(x):\n    return x + localization.current_translation.get(x, '')\n\n\ndef search_styles(selected, query):\n    unselected = [y for y in all_styles if y not in selected]\n    matched = [y for y in unselected if query.lower() in localization_key(y).lower()] if len(query.replace(' ', '')) > 0 else []\n    unmatched = [y for y in unselected if y not in matched]\n    sorted_styles = matched + selected + unmatched\n    return gr.CheckboxGroup.update(choices=sorted_styles)\n"
  },
  {
    "path": "modules/ui_gradio_extensions.py",
    "content": "# based on https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/v1.6.0/modules/ui_gradio_extensions.py\n\nimport os\nimport gradio as gr\nimport args_manager\n\nfrom modules.localization import localization_js\n\n\nGradioTemplateResponseOriginal = gr.routes.templates.TemplateResponse\n\nmodules_path = os.path.dirname(os.path.realpath(__file__))\nscript_path = os.path.dirname(modules_path)\n\n\ndef webpath(fn):\n    if fn.startswith(script_path):\n        web_path = os.path.relpath(fn, script_path).replace('\\\\', '/')\n    else:\n        web_path = os.path.abspath(fn)\n\n    return f'file={web_path}?{os.path.getmtime(fn)}'\n\n\ndef javascript_html():\n    script_js_path = webpath('javascript/script.js')\n    context_menus_js_path = webpath('javascript/contextMenus.js')\n    localization_js_path = webpath('javascript/localization.js')\n    zoom_js_path = webpath('javascript/zoom.js')\n    edit_attention_js_path = webpath('javascript/edit-attention.js')\n    viewer_js_path = webpath('javascript/viewer.js')\n    image_viewer_js_path = webpath('javascript/imageviewer.js')\n    samples_path = webpath(os.path.abspath('./sdxl_styles/samples/fooocus_v2.jpg'))\n    head = f'<script type=\"text/javascript\">{localization_js(args_manager.args.language)}</script>\\n'\n    head += f'<script type=\"text/javascript\" src=\"{script_js_path}\"></script>\\n'\n    head += f'<script type=\"text/javascript\" src=\"{context_menus_js_path}\"></script>\\n'\n    head += f'<script type=\"text/javascript\" src=\"{localization_js_path}\"></script>\\n'\n    head += f'<script type=\"text/javascript\" src=\"{zoom_js_path}\"></script>\\n'\n    head += f'<script type=\"text/javascript\" src=\"{edit_attention_js_path}\"></script>\\n'\n    head += f'<script type=\"text/javascript\" src=\"{viewer_js_path}\"></script>\\n'\n    head += f'<script type=\"text/javascript\" src=\"{image_viewer_js_path}\"></script>\\n'\n    head += f'<meta name=\"samples-path\" content=\"{samples_path}\">\\n'\n\n    if args_manager.args.theme:\n        head += f'<script type=\"text/javascript\">set_theme(\\\"{args_manager.args.theme}\\\");</script>\\n'\n\n    return head\n\n\ndef css_html():\n    style_css_path = webpath('css/style.css')\n    head = f'<link rel=\"stylesheet\" property=\"stylesheet\" href=\"{style_css_path}\">'\n    return head\n\n\ndef reload_javascript():\n    js = javascript_html()\n    css = css_html()\n\n    def template_response(*args, **kwargs):\n        res = GradioTemplateResponseOriginal(*args, **kwargs)\n        res.body = res.body.replace(b'</head>', f'{js}</head>'.encode(\"utf8\"))\n        res.body = res.body.replace(b'</body>', f'{css}</body>'.encode(\"utf8\"))\n        res.init_headers()\n        return res\n\n    gr.routes.templates.TemplateResponse = template_response\n"
  },
  {
    "path": "modules/upscaler.py",
    "content": "from collections import OrderedDict\n\nimport modules.core as core\nimport torch\nfrom ldm_patched.contrib.external_upscale_model import ImageUpscaleWithModel\nfrom ldm_patched.pfn.architecture.RRDB import RRDBNet as ESRGAN\nfrom modules.config import downloading_upscale_model\n\nopImageUpscaleWithModel = ImageUpscaleWithModel()\nmodel = None\n\n\ndef perform_upscale(img):\n    global model\n\n    print(f'Upscaling image with shape {str(img.shape)} ...')\n\n    if model is None:\n        model_filename = downloading_upscale_model()\n        sd = torch.load(model_filename, weights_only=True)\n        sdo = OrderedDict()\n        for k, v in sd.items():\n            sdo[k.replace('residual_block_', 'RDB')] = v\n        del sd\n        model = ESRGAN(sdo)\n        model.cpu()\n        model.eval()\n\n    img = core.numpy_to_pytorch(img)\n    img = opImageUpscaleWithModel.upscale(model, img)[0]\n    img = core.pytorch_to_numpy(img)[0]\n\n    return img\n"
  },
  {
    "path": "modules/util.py",
    "content": "from pathlib import Path\n\nimport numpy as np\nimport datetime\nimport random\nimport math\nimport os\nimport cv2\nimport re\nfrom typing import List, Tuple, AnyStr, NamedTuple\n\nimport json\nimport hashlib\n\nfrom PIL import Image\n\nimport modules.config\nimport modules.sdxl_styles\nfrom modules.flags import Performance\n\nLANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)\n\n# Regexp compiled once. Matches entries with the following pattern:\n# <lora:some_lora:1>\n# <lora:aNotherLora:-1.6>\nLORAS_PROMPT_PATTERN = re.compile(r\"(<lora:([^:]+):([+-]?(?:\\d+(?:\\.\\d*)?|\\.\\d+))>)\", re.X)\n\nHASH_SHA256_LENGTH = 10\n\n\ndef erode_or_dilate(x, k):\n    k = int(k)\n    if k > 0:\n        return cv2.dilate(x, kernel=np.ones(shape=(3, 3), dtype=np.uint8), iterations=k)\n    if k < 0:\n        return cv2.erode(x, kernel=np.ones(shape=(3, 3), dtype=np.uint8), iterations=-k)\n    return x\n\n\ndef resample_image(im, width, height):\n    im = Image.fromarray(im)\n    im = im.resize((int(width), int(height)), resample=LANCZOS)\n    return np.array(im)\n\n\ndef resize_image(im, width, height, resize_mode=1):\n    \"\"\"\n    Resizes an image with the specified resize_mode, width, and height.\n\n    Args:\n        resize_mode: The mode to use when resizing the image.\n            0: Resize the image to the specified width and height.\n            1: Resize the image to fill the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, cropping the excess.\n            2: Resize the image to fit within the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, filling empty with data from image.\n        im: The image to resize.\n        width: The width to resize the image to.\n        height: The height to resize the image to.\n    \"\"\"\n\n    im = Image.fromarray(im)\n\n    def resize(im, w, h):\n        return im.resize((w, h), resample=LANCZOS)\n\n    if resize_mode == 0:\n        res = resize(im, width, height)\n\n    elif resize_mode == 1:\n        ratio = width / height\n        src_ratio = im.width / im.height\n\n        src_w = width if ratio > src_ratio else im.width * height // im.height\n        src_h = height if ratio <= src_ratio else im.height * width // im.width\n\n        resized = resize(im, src_w, src_h)\n        res = Image.new(\"RGB\", (width, height))\n        res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))\n\n    else:\n        ratio = width / height\n        src_ratio = im.width / im.height\n\n        src_w = width if ratio < src_ratio else im.width * height // im.height\n        src_h = height if ratio >= src_ratio else im.height * width // im.width\n\n        resized = resize(im, src_w, src_h)\n        res = Image.new(\"RGB\", (width, height))\n        res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))\n\n        if ratio < src_ratio:\n            fill_height = height // 2 - src_h // 2\n            if fill_height > 0:\n                res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0))\n                res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h))\n        elif ratio > src_ratio:\n            fill_width = width // 2 - src_w // 2\n            if fill_width > 0:\n                res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0))\n                res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0))\n\n    return np.array(res)\n\n\ndef get_shape_ceil(h, w):\n    return math.ceil(((h * w) ** 0.5) / 64.0) * 64.0\n\n\ndef get_image_shape_ceil(im):\n    H, W = im.shape[:2]\n    return get_shape_ceil(H, W)\n\n\ndef set_image_shape_ceil(im, shape_ceil):\n    shape_ceil = float(shape_ceil)\n\n    H_origin, W_origin, _ = im.shape\n    H, W = H_origin, W_origin\n    \n    for _ in range(256):\n        current_shape_ceil = get_shape_ceil(H, W)\n        if abs(current_shape_ceil - shape_ceil) < 0.1:\n            break\n        k = shape_ceil / current_shape_ceil\n        H = int(round(float(H) * k / 64.0) * 64)\n        W = int(round(float(W) * k / 64.0) * 64)\n\n    if H == H_origin and W == W_origin:\n        return im\n\n    return resample_image(im, width=W, height=H)\n\n\ndef HWC3(x):\n    assert x.dtype == np.uint8\n    if x.ndim == 2:\n        x = x[:, :, None]\n    assert x.ndim == 3\n    H, W, C = x.shape\n    assert C == 1 or C == 3 or C == 4\n    if C == 3:\n        return x\n    if C == 1:\n        return np.concatenate([x, x, x], axis=2)\n    if C == 4:\n        color = x[:, :, 0:3].astype(np.float32)\n        alpha = x[:, :, 3:4].astype(np.float32) / 255.0\n        y = color * alpha + 255.0 * (1.0 - alpha)\n        y = y.clip(0, 255).astype(np.uint8)\n        return y\n\n\ndef remove_empty_str(items, default=None):\n    items = [x for x in items if x != \"\"]\n    if len(items) == 0 and default is not None:\n        return [default]\n    return items\n\n\ndef join_prompts(*args, **kwargs):\n    prompts = [str(x) for x in args if str(x) != \"\"]\n    if len(prompts) == 0:\n        return \"\"\n    if len(prompts) == 1:\n        return prompts[0]\n    return ', '.join(prompts)\n\n\ndef generate_temp_filename(folder='./outputs/', extension='png'):\n    current_time = datetime.datetime.now()\n    date_string = current_time.strftime(\"%Y-%m-%d\")\n    time_string = current_time.strftime(\"%Y-%m-%d_%H-%M-%S\")\n    random_number = random.randint(1000, 9999)\n    filename = f\"{time_string}_{random_number}.{extension}\"\n    result = os.path.join(folder, date_string, filename)\n    return date_string, os.path.abspath(result), filename\n\n\ndef sha256(filename, use_addnet_hash=False, length=HASH_SHA256_LENGTH):\n    if use_addnet_hash:\n        with open(filename, \"rb\") as file:\n            sha256_value = addnet_hash_safetensors(file)\n    else:\n        sha256_value = calculate_sha256(filename)\n\n    return sha256_value[:length] if length is not None else sha256_value\n\n\ndef addnet_hash_safetensors(b):\n    \"\"\"kohya-ss hash for safetensors from https://github.com/kohya-ss/sd-scripts/blob/main/library/train_util.py\"\"\"\n    hash_sha256 = hashlib.sha256()\n    blksize = 1024 * 1024\n\n    b.seek(0)\n    header = b.read(8)\n    n = int.from_bytes(header, \"little\")\n\n    offset = n + 8\n    b.seek(offset)\n    for chunk in iter(lambda: b.read(blksize), b\"\"):\n        hash_sha256.update(chunk)\n\n    return hash_sha256.hexdigest()\n\n\ndef calculate_sha256(filename) -> str:\n    hash_sha256 = hashlib.sha256()\n    blksize = 1024 * 1024\n\n    with open(filename, \"rb\") as f:\n        for chunk in iter(lambda: f.read(blksize), b\"\"):\n            hash_sha256.update(chunk)\n\n    return hash_sha256.hexdigest()\n\n\ndef quote(text):\n    if ',' not in str(text) and '\\n' not in str(text) and ':' not in str(text):\n        return text\n\n    return json.dumps(text, ensure_ascii=False)\n\n\ndef unquote(text):\n    if len(text) == 0 or text[0] != '\"' or text[-1] != '\"':\n        return text\n\n    try:\n        return json.loads(text)\n    except Exception:\n        return text\n\n\ndef unwrap_style_text_from_prompt(style_text, prompt):\n    \"\"\"\n    Checks the prompt to see if the style text is wrapped around it. If so,\n    returns True plus the prompt text without the style text. Otherwise, returns\n    False with the original prompt.\n\n    Note that the \"cleaned\" version of the style text is only used for matching\n    purposes here. It isn't returned; the original style text is not modified.\n    \"\"\"\n    stripped_prompt = prompt\n    stripped_style_text = style_text\n    if \"{prompt}\" in stripped_style_text:\n        # Work out whether the prompt is wrapped in the style text. If so, we\n        # return True and the \"inner\" prompt text that isn't part of the style.\n        try:\n            left, right = stripped_style_text.split(\"{prompt}\", 2)\n        except ValueError as e:\n            # If the style text has multple \"{prompt}\"s, we can't split it into\n            # two parts. This is an error, but we can't do anything about it.\n            print(f\"Unable to compare style text to prompt:\\n{style_text}\")\n            print(f\"Error: {e}\")\n            return False, prompt, ''\n\n        left_pos = stripped_prompt.find(left)\n        right_pos = stripped_prompt.find(right)\n        if 0 <= left_pos < right_pos:\n            real_prompt = stripped_prompt[left_pos + len(left):right_pos]\n            prompt = stripped_prompt.replace(left + real_prompt + right, '', 1)\n            if prompt.startswith(\", \"):\n                prompt = prompt[2:]\n            if prompt.endswith(\", \"):\n                prompt = prompt[:-2]\n            return True, prompt, real_prompt\n    else:\n        # Work out whether the given prompt starts with the style text. If so, we\n        # return True and the prompt text up to where the style text starts.\n        if stripped_prompt.endswith(stripped_style_text):\n            prompt = stripped_prompt[: len(stripped_prompt) - len(stripped_style_text)]\n            if prompt.endswith(\", \"):\n                prompt = prompt[:-2]\n            return True, prompt, prompt\n\n    return False, prompt, ''\n\n\ndef extract_original_prompts(style, prompt, negative_prompt):\n    \"\"\"\n    Takes a style and compares it to the prompt and negative prompt. If the style\n    matches, returns True plus the prompt and negative prompt with the style text\n    removed. Otherwise, returns False with the original prompt and negative prompt.\n    \"\"\"\n    if not style.prompt and not style.negative_prompt:\n        return False, prompt, negative_prompt\n\n    match_positive, extracted_positive, real_prompt = unwrap_style_text_from_prompt(\n        style.prompt, prompt\n    )\n    if not match_positive:\n        return False, prompt, negative_prompt, ''\n\n    match_negative, extracted_negative, _ = unwrap_style_text_from_prompt(\n        style.negative_prompt, negative_prompt\n    )\n    if not match_negative:\n        return False, prompt, negative_prompt, ''\n\n    return True, extracted_positive, extracted_negative, real_prompt\n\n\ndef extract_styles_from_prompt(prompt, negative_prompt):\n    extracted = []\n    applicable_styles = []\n\n    for style_name, (style_prompt, style_negative_prompt) in modules.sdxl_styles.styles.items():\n        applicable_styles.append(PromptStyle(name=style_name, prompt=style_prompt, negative_prompt=style_negative_prompt))\n\n    real_prompt = ''\n\n    while True:\n        found_style = None\n\n        for style in applicable_styles:\n            is_match, new_prompt, new_neg_prompt, new_real_prompt = extract_original_prompts(\n                style, prompt, negative_prompt\n            )\n            if is_match:\n                found_style = style\n                prompt = new_prompt\n                negative_prompt = new_neg_prompt\n                if real_prompt == '' and new_real_prompt != '' and new_real_prompt != prompt:\n                    real_prompt = new_real_prompt\n                break\n\n        if not found_style:\n            break\n\n        applicable_styles.remove(found_style)\n        extracted.append(found_style.name)\n\n    # add prompt expansion if not all styles could be resolved\n    if prompt != '':\n        if real_prompt != '':\n            extracted.append(modules.sdxl_styles.fooocus_expansion)\n        else:\n            # find real_prompt when only prompt expansion is selected\n            first_word = prompt.split(', ')[0]\n            first_word_positions = [i for i in range(len(prompt)) if prompt.startswith(first_word, i)]\n            if len(first_word_positions) > 1:\n                real_prompt = prompt[:first_word_positions[-1]]\n                extracted.append(modules.sdxl_styles.fooocus_expansion)\n                if real_prompt.endswith(', '):\n                    real_prompt = real_prompt[:-2]\n\n    return list(reversed(extracted)), real_prompt, negative_prompt\n\n\nclass PromptStyle(NamedTuple):\n    name: str\n    prompt: str\n    negative_prompt: str\n\n\ndef is_json(data: str) -> bool:\n    try:\n        loaded_json = json.loads(data)\n        assert isinstance(loaded_json, dict)\n    except (ValueError, AssertionError):\n        return False\n    return True\n\n\ndef get_filname_by_stem(lora_name, filenames: List[str]) -> str | None:\n    for filename in filenames:\n        path = Path(filename)\n        if lora_name == path.stem:\n            return filename\n    return None\n\n\ndef get_file_from_folder_list(name, folders):\n    if not isinstance(folders, list):\n        folders = [folders]\n\n    for folder in folders:\n        filename = os.path.abspath(os.path.realpath(os.path.join(folder, name)))\n        if os.path.isfile(filename):\n            return filename\n\n    return os.path.abspath(os.path.realpath(os.path.join(folders[0], name)))\n\n\ndef get_enabled_loras(loras: list, remove_none=True) -> list:\n    return [(lora[1], lora[2]) for lora in loras if lora[0] and (lora[1] != 'None' if remove_none else True)]\n\n\ndef parse_lora_references_from_prompt(prompt: str, loras: List[Tuple[AnyStr, float]], loras_limit: int = 5,\n                                      skip_file_check=False, prompt_cleanup=True, deduplicate_loras=True,\n                                      lora_filenames=None) -> tuple[List[Tuple[AnyStr, float]], str]:\n    # prevent unintended side effects when returning without detection\n    loras = loras.copy()\n\n    if lora_filenames is None:\n        lora_filenames = []\n\n    found_loras = []\n    prompt_without_loras = ''\n    cleaned_prompt = ''\n\n    for token in prompt.split(','):\n        matches = LORAS_PROMPT_PATTERN.findall(token)\n\n        if len(matches) == 0:\n            prompt_without_loras += token + ', '\n            continue\n        for match in matches:\n            lora_name = match[1] + '.safetensors'\n            if not skip_file_check:\n                lora_name = get_filname_by_stem(match[1], lora_filenames)\n            if lora_name is not None:\n                found_loras.append((lora_name, float(match[2])))\n            token = token.replace(match[0], '')\n        prompt_without_loras += token + ', '\n\n    if prompt_without_loras != '':\n        cleaned_prompt = prompt_without_loras[:-2]\n\n    if prompt_cleanup:\n        cleaned_prompt = cleanup_prompt(prompt_without_loras)\n\n    new_loras = []\n    lora_names = [lora[0] for lora in loras]\n    for found_lora in found_loras:\n        if deduplicate_loras and (found_lora[0] in lora_names or found_lora in new_loras):\n            continue\n        new_loras.append(found_lora)\n\n    if len(new_loras) == 0:\n        return loras, cleaned_prompt\n\n    updated_loras = []\n    for lora in loras + new_loras:\n        if lora[0] != \"None\":\n            updated_loras.append(lora)\n\n    return updated_loras[:loras_limit], cleaned_prompt\n\n\ndef remove_performance_lora(filenames: list, performance: Performance | None):\n    loras_without_performance = filenames.copy()\n\n    if performance is None:\n        return loras_without_performance\n\n    performance_lora = performance.lora_filename()\n\n    for filename in filenames:\n        path = Path(filename)\n        if performance_lora == path.name:\n            loras_without_performance.remove(filename)\n\n    return loras_without_performance\n\n\ndef cleanup_prompt(prompt):\n    prompt = re.sub(' +', ' ', prompt)\n    prompt = re.sub(',+', ',', prompt)\n    cleaned_prompt = ''\n    for token in prompt.split(','):\n        token = token.strip()\n        if token == '':\n            continue\n        cleaned_prompt += token + ', '\n    return cleaned_prompt[:-2]\n\n\ndef apply_wildcards(wildcard_text, rng, i, read_wildcards_in_order) -> str:\n    for _ in range(modules.config.wildcards_max_bfs_depth):\n        placeholders = re.findall(r'__([\\w-]+)__', wildcard_text)\n        if len(placeholders) == 0:\n            return wildcard_text\n\n        print(f'[Wildcards] processing: {wildcard_text}')\n        for placeholder in placeholders:\n            try:\n                matches = [x for x in modules.config.wildcard_filenames if os.path.splitext(os.path.basename(x))[0] == placeholder]\n                words = open(os.path.join(modules.config.path_wildcards, matches[0]), encoding='utf-8').read().splitlines()\n                words = [x for x in words if x != '']\n                assert len(words) > 0\n                if read_wildcards_in_order:\n                    wildcard_text = wildcard_text.replace(f'__{placeholder}__', words[i % len(words)], 1)\n                else:\n                    wildcard_text = wildcard_text.replace(f'__{placeholder}__', rng.choice(words), 1)\n            except:\n                print(f'[Wildcards] Warning: {placeholder}.txt missing or empty. '\n                      f'Using \"{placeholder}\" as a normal word.')\n                wildcard_text = wildcard_text.replace(f'__{placeholder}__', placeholder)\n            print(f'[Wildcards] {wildcard_text}')\n\n    print(f'[Wildcards] BFS stack overflow. Current text: {wildcard_text}')\n    return wildcard_text\n\n\ndef get_image_size_info(image: np.ndarray, aspect_ratios: list) -> str:\n    try:\n        image = Image.fromarray(np.uint8(image))\n        width, height = image.size\n        ratio = round(width / height, 2)\n        gcd = math.gcd(width, height)\n        lcm_ratio = f'{width // gcd}:{height // gcd}'\n        size_info = f'Image Size: {width} x {height}, Ratio: {ratio}, {lcm_ratio}'\n\n        closest_ratio = min(aspect_ratios, key=lambda x: abs(ratio - float(x.split('*')[0]) / float(x.split('*')[1])))\n        recommended_width, recommended_height = map(int, closest_ratio.split('*'))\n        recommended_ratio = round(recommended_width / recommended_height, 2)\n        recommended_gcd = math.gcd(recommended_width, recommended_height)\n        recommended_lcm_ratio = f'{recommended_width // recommended_gcd}:{recommended_height // recommended_gcd}'\n\n        size_info = f'{width} x {height}, {ratio}, {lcm_ratio}'\n        size_info += f'\\n{recommended_width} x {recommended_height}, {recommended_ratio}, {recommended_lcm_ratio}'\n\n        return size_info\n    except Exception as e:\n        return f'Error reading image: {e}'\n"
  },
  {
    "path": "presets/.gitignore",
    "content": "*.json\n!anime.json\n!default.json\n!lcm.json\n!playground_v2.5.json\n!pony_v6.json\n!realistic.json\n!sai.json"
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    "path": "presets/anime.json",
    "content": "{\n    \"default_model\": \"animaPencilXL_v500.safetensors\",\n    \"default_refiner\": \"None\",\n    \"default_refiner_switch\": 0.5,\n    \"default_loras\": [\n        [\n            true,\n            \"None\",\n            1.0\n        ],\n        [\n            true,\n            \"None\",\n            1.0\n        ],\n        [\n            true,\n            \"None\",\n            1.0\n        ],\n        [\n            true,\n            \"None\",\n            1.0\n        ],\n        [\n            true,\n            \"None\",\n            1.0\n        ]\n    ],\n    \"default_cfg_scale\": 6.0,\n    \"default_sample_sharpness\": 2.0,\n    \"default_sampler\": \"dpmpp_2m_sde_gpu\",\n    \"default_scheduler\": \"karras\",\n    \"default_performance\": \"Speed\",\n    \"default_prompt\": \"\",\n    \"default_prompt_negative\": \"\",\n    \"default_styles\": [\n        \"Fooocus V2\",\n        \"Fooocus Semi Realistic\",\n        \"Fooocus Masterpiece\"\n    ],\n    \"default_aspect_ratio\": \"896*1152\",\n    \"default_overwrite_step\": -1,\n    \"checkpoint_downloads\": {\n        \"animaPencilXL_v500.safetensors\": \"https://huggingface.co/mashb1t/fav_models/resolve/main/fav/animaPencilXL_v500.safetensors\"\n    },\n    \"embeddings_downloads\": {},\n    \"lora_downloads\": {},\n    \"previous_default_models\": [\n        \"animaPencilXL_v400.safetensors\",\n        \"animaPencilXL_v310.safetensors\",\n        \"animaPencilXL_v300.safetensors\",\n        \"animaPencilXL_v260.safetensors\",\n        \"animaPencilXL_v210.safetensors\",\n        \"animaPencilXL_v200.safetensors\",\n        \"animaPencilXL_v100.safetensors\"\n    ]\n}\n"
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    "content": "{\n    \"default_model\": \"juggernautXL_v8Rundiffusion.safetensors\",\n    \"default_refiner\": \"None\",\n    \"default_refiner_switch\": 0.5,\n    \"default_loras\": [\n        [\n            true,\n            \"sd_xl_offset_example-lora_1.0.safetensors\",\n            0.1\n        ],\n        [\n            true,\n            \"None\",\n            1.0\n        ],\n        [\n            true,\n            \"None\",\n            1.0\n        ],\n        [\n            true,\n            \"None\",\n            1.0\n        ],\n        [\n            true,\n            \"None\",\n            1.0\n        ]\n    ],\n    \"default_cfg_scale\": 4.0,\n    \"default_sample_sharpness\": 2.0,\n    \"default_sampler\": \"dpmpp_2m_sde_gpu\",\n    \"default_scheduler\": \"karras\",\n    \"default_performance\": \"Speed\",\n    \"default_prompt\": \"\",\n    \"default_prompt_negative\": \"\",\n    \"default_styles\": [\n        \"Fooocus V2\",\n        \"Fooocus Enhance\",\n        \"Fooocus Sharp\"\n    ],\n    \"default_aspect_ratio\": \"1152*896\",\n    \"default_overwrite_step\": -1,\n    \"checkpoint_downloads\": {\n        \"juggernautXL_v8Rundiffusion.safetensors\": \"https://huggingface.co/lllyasviel/fav_models/resolve/main/fav/juggernautXL_v8Rundiffusion.safetensors\"\n    },\n    \"embeddings_downloads\": {},\n    \"lora_downloads\": {\n        \"sd_xl_offset_example-lora_1.0.safetensors\": \"https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_offset_example-lora_1.0.safetensors\"\n    },\n    \"previous_default_models\": [\n        \"juggernautXL_version8Rundiffusion.safetensors\",\n        \"juggernautXL_version7Rundiffusion.safetensors\",\n        \"juggernautXL_v7Rundiffusion.safetensors\",\n        \"juggernautXL_version6Rundiffusion.safetensors\",\n        \"juggernautXL_v6Rundiffusion.safetensors\"\n    ]\n}"
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    "path": "presets/lcm.json",
    "content": "{\n    \"default_model\": \"juggernautXL_v8Rundiffusion.safetensors\",\n    \"default_refiner\": \"None\",\n    \"default_refiner_switch\": 0.5,\n    \"default_loras\": [\n        [\n            true,\n            \"None\",\n            1.0\n        ],\n        [\n            true,\n            \"None\",\n            1.0\n        ],\n        [\n            true,\n            \"None\",\n            1.0\n        ],\n        [\n            true,\n            \"None\",\n            1.0\n        ],\n        [\n            true,\n            \"None\",\n            1.0\n        ]\n    ],\n    \"default_cfg_scale\": 4.0,\n    \"default_sample_sharpness\": 2.0,\n    \"default_sampler\": \"dpmpp_2m_sde_gpu\",\n    \"default_scheduler\": \"karras\",\n    \"default_performance\": \"Extreme Speed\",\n    \"default_prompt\": \"\",\n    \"default_prompt_negative\": \"\",\n    \"default_styles\": [\n        \"Fooocus V2\",\n        \"Fooocus Enhance\",\n        \"Fooocus Sharp\"\n    ],\n    \"default_aspect_ratio\": \"1152*896\",\n    \"default_overwrite_step\": -1,\n    \"checkpoint_downloads\": {\n        \"juggernautXL_v8Rundiffusion.safetensors\": \"https://huggingface.co/lllyasviel/fav_models/resolve/main/fav/juggernautXL_v8Rundiffusion.safetensors\"\n    },\n    \"embeddings_downloads\": {},\n    \"lora_downloads\": {},\n    \"previous_default_models\": [\n        \"juggernautXL_version8Rundiffusion.safetensors\",\n        \"juggernautXL_version7Rundiffusion.safetensors\",\n        \"juggernautXL_v7Rundiffusion.safetensors\",\n        \"juggernautXL_version6Rundiffusion.safetensors\",\n        \"juggernautXL_v6Rundiffusion.safetensors\"\n    ]\n}"
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    "path": "presets/playground_v2.5.json",
    "content": "{\n    \"default_model\": \"playground-v2.5-1024px-aesthetic.fp16.safetensors\",\n    \"default_refiner\": \"None\",\n    \"default_refiner_switch\": 0.5,\n    \"default_loras\": [\n        [\n            true,\n            \"None\",\n            1.0\n        ],\n        [\n            true,\n            \"None\",\n            1.0\n        ],\n        [\n            true,\n            \"None\",\n            1.0\n        ],\n        [\n            true,\n            \"None\",\n            1.0\n        ],\n        [\n            true,\n            \"None\",\n            1.0\n        ]\n    ],\n    \"default_cfg_scale\": 2.0,\n    \"default_sample_sharpness\": 2.0,\n    \"default_sampler\": \"dpmpp_2m\",\n    \"default_scheduler\": \"edm_playground_v2.5\",\n    \"default_performance\": \"Speed\",\n    \"default_prompt\": \"\",\n    \"default_prompt_negative\": \"\",\n    \"default_styles\": [\n        \"Fooocus V2\"\n    ],\n    \"default_aspect_ratio\": \"1024*1024\",\n    \"default_overwrite_step\": -1,\n    \"default_inpaint_engine_version\": \"None\",\n    \"checkpoint_downloads\": {\n        \"playground-v2.5-1024px-aesthetic.fp16.safetensors\": \"https://huggingface.co/mashb1t/fav_models/resolve/main/fav/playground-v2.5-1024px-aesthetic.fp16.safetensors\"\n    },\n    \"embeddings_downloads\": {},\n    \"lora_downloads\": {},\n    \"previous_default_models\": []\n}"
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    "content": "{\n    \"default_model\": \"ponyDiffusionV6XL.safetensors\",\n    \"default_refiner\": \"None\",\n    \"default_refiner_switch\": 0.5,\n    \"default_vae\": \"ponyDiffusionV6XL_vae.safetensors\",\n    \"default_loras\": [\n        [\n            true,\n            \"None\",\n            1.0\n        ],\n        [\n            true,\n            \"None\",\n            1.0\n        ],\n        [\n            true,\n            \"None\",\n            1.0\n        ],\n        [\n            true,\n            \"None\",\n            1.0\n        ],\n        [\n            true,\n            \"None\",\n            1.0\n        ]\n    ],\n    \"default_cfg_scale\": 7.0,\n    \"default_sample_sharpness\": 2.0,\n    \"default_sampler\": \"dpmpp_2m_sde_gpu\",\n    \"default_scheduler\": \"karras\",\n    \"default_performance\": \"Speed\",\n    \"default_prompt\": \"\",\n    \"default_prompt_negative\": \"\",\n    \"default_styles\": [\n        \"Fooocus Pony\"\n    ],\n    \"default_aspect_ratio\": \"896*1152\",\n    \"default_overwrite_step\": -1,\n    \"default_inpaint_engine_version\": \"None\",\n    \"checkpoint_downloads\": {\n        \"ponyDiffusionV6XL.safetensors\": \"https://huggingface.co/mashb1t/fav_models/resolve/main/fav/ponyDiffusionV6XL.safetensors\"\n    },\n    \"embeddings_downloads\": {},\n    \"lora_downloads\": {},\n    \"vae_downloads\": {\n        \"ponyDiffusionV6XL_vae.safetensors\": \"https://huggingface.co/mashb1t/fav_models/resolve/main/fav/ponyDiffusionV6XL_vae.safetensors\"\n    }\n}\n"
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    "content": "{\n    \"default_model\": \"realisticStockPhoto_v20.safetensors\",\n    \"default_refiner\": \"None\",\n    \"default_refiner_switch\": 0.5,\n    \"default_loras\": [\n        [\n            true,\n            \"SDXL_FILM_PHOTOGRAPHY_STYLE_V1.safetensors\",\n            0.25\n        ],\n        [\n            true,\n            \"None\",\n            1.0\n        ],\n        [\n            true,\n            \"None\",\n            1.0\n        ],\n        [\n            true,\n            \"None\",\n            1.0\n        ],\n        [\n            true,\n            \"None\",\n            1.0\n        ]\n    ],\n    \"default_cfg_scale\": 3.0,\n    \"default_sample_sharpness\": 2.0,\n    \"default_sampler\": \"dpmpp_2m_sde_gpu\",\n    \"default_scheduler\": \"karras\",\n    \"default_performance\": \"Speed\",\n    \"default_prompt\": \"\",\n    \"default_prompt_negative\": \"unrealistic, saturated, high contrast, big nose, painting, drawing, sketch, cartoon, anime, manga, render, CG, 3d, watermark, signature, label\",\n    \"default_styles\": [\n        \"Fooocus V2\",\n        \"Fooocus Photograph\",\n        \"Fooocus Negative\"\n    ],\n    \"default_aspect_ratio\": \"896*1152\",\n    \"default_overwrite_step\": -1,\n    \"checkpoint_downloads\": {\n        \"realisticStockPhoto_v20.safetensors\": \"https://huggingface.co/lllyasviel/fav_models/resolve/main/fav/realisticStockPhoto_v20.safetensors\"\n    },\n    \"embeddings_downloads\": {},\n    \"lora_downloads\": {\n        \"SDXL_FILM_PHOTOGRAPHY_STYLE_V1.safetensors\": \"https://huggingface.co/mashb1t/fav_models/resolve/main/fav/SDXL_FILM_PHOTOGRAPHY_STYLE_V1.safetensors\"\n    },\n    \"previous_default_models\": [\"realisticStockPhoto_v10.safetensors\"]\n}"
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    "content": "{\n    \"default_model\": \"sd_xl_base_1.0_0.9vae.safetensors\",\n    \"default_refiner\": \"sd_xl_refiner_1.0_0.9vae.safetensors\",\n    \"default_refiner_switch\": 0.75,\n    \"default_loras\": [\n        [\n            true,\n            \"sd_xl_offset_example-lora_1.0.safetensors\",\n            0.5\n        ],\n        [\n            true,\n            \"None\",\n            1.0\n        ],\n        [\n            true,\n            \"None\",\n            1.0\n        ],\n        [\n            true,\n            \"None\",\n            1.0\n        ],\n        [\n            true,\n            \"None\",\n            1.0\n        ]\n    ],\n    \"default_cfg_scale\": 7.0,\n    \"default_sample_sharpness\": 2.0,\n    \"default_sampler\": \"dpmpp_2m_sde_gpu\",\n    \"default_scheduler\": \"karras\",\n    \"default_performance\": \"Speed\",\n    \"default_prompt\": \"\",\n    \"default_prompt_negative\": \"\",\n    \"default_styles\": [\n        \"Fooocus V2\",\n        \"Fooocus Cinematic\"\n    ],\n    \"default_aspect_ratio\": \"1152*896\",\n    \"default_overwrite_step\": -1,\n    \"checkpoint_downloads\": {\n        \"sd_xl_base_1.0_0.9vae.safetensors\": \"https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0_0.9vae.safetensors\",\n        \"sd_xl_refiner_1.0_0.9vae.safetensors\": \"https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/resolve/main/sd_xl_refiner_1.0_0.9vae.safetensors\"\n    },\n    \"embeddings_downloads\": {},\n    \"lora_downloads\": {\n        \"sd_xl_offset_example-lora_1.0.safetensors\": \"https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_offset_example-lora_1.0.safetensors\"\n    },\n    \"previous_default_models\": []\n}"
  },
  {
    "path": "readme.md",
    "content": "<div align=center>\n<img src=\"https://github.com/lllyasviel/Fooocus/assets/19834515/483fb86d-c9a2-4c20-997c-46dafc124f25\">\n</div>\n\n# Fooocus\n\n[>>> Click Here to Install Fooocus <<<](#download)\n\nFooocus is an image generating software (based on [Gradio](https://www.gradio.app/) <a href='https://github.com/gradio-app/gradio'><img src='https://img.shields.io/github/stars/gradio-app/gradio'></a>).\n\nFooocus presents a rethinking of image generator designs. The software is offline, open source, and free, while at the same time, similar to many online image generators like Midjourney, the manual tweaking is not needed, and users only need to focus on the prompts and images. Fooocus has also simplified the installation: between pressing \"download\" and generating the first image, the number of needed mouse clicks is strictly limited to less than 3. Minimal GPU memory requirement is 4GB (Nvidia).\n\n**Recently many fake websites exist on Google when you search “fooocus”. Do not trust those – here is the only official source of Fooocus.**\n\n# Project Status: Limited Long-Term Support (LTS) with Bug Fixes Only\n\nThe Fooocus project, built entirely on the **Stable Diffusion XL** architecture, is now in a state of limited long-term support (LTS) with bug fixes only. As the existing functionalities are considered as nearly free of programmartic issues (Thanks to [mashb1t](https://github.com/mashb1t)'s huge efforts), future updates will focus exclusively on addressing any bugs that may arise. \n\n**There are no current plans to migrate to or incorporate newer model architectures.** However, this may change during time with the development of open-source community. For example, if the community converge to one single dominant method for image generation (which may really happen in half or one years given the current status), Fooocus may also migrate to that exact method.\n\nFor those interested in utilizing newer models such as **Flux**, we recommend exploring alternative platforms such as [WebUI Forge](https://github.com/lllyasviel/stable-diffusion-webui-forge) (also from us), [ComfyUI/SwarmUI](https://github.com/comfyanonymous/ComfyUI). Additionally, several [excellent forks of Fooocus](https://github.com/lllyasviel/Fooocus?tab=readme-ov-file#forks) are available for experimentation.\n\nAgain, recently many fake websites exist on Google when you search “fooocus”. Do **NOT** get Fooocus from those websites – this page is the only official source of Fooocus. We never have any website like such as “fooocus.com”, “fooocus.net”, “fooocus.co”, “fooocus.ai”, “fooocus.org”, “fooocus.pro”, “fooocus.one”. Those websites are ALL FAKE. **They have ABSOLUTLY no relationship to us. Fooocus is a 100% non-commercial offline open-source software.**\n\n# Features\n\nBelow is a quick list using Midjourney's examples:\n\n| Midjourney | Fooocus |\n| - | - |\n| High-quality text-to-image without needing much prompt engineering or parameter tuning. <br> (Unknown method) | High-quality text-to-image without needing much prompt engineering or parameter tuning. <br> (Fooocus has an offline GPT-2 based prompt processing engine and lots of sampling improvements so that results are always beautiful, no matter if your prompt is as short as “house in garden” or as long as 1000 words) |\n| V1 V2 V3 V4 | Input Image -> Upscale or Variation -> Vary (Subtle) / Vary (Strong)|\n| U1 U2 U3 U4 | Input Image -> Upscale or Variation -> Upscale (1.5x) / Upscale (2x) |\n| Inpaint / Up / Down / Left / Right (Pan) | Input Image -> Inpaint or Outpaint -> Inpaint / Up / Down / Left / Right <br> (Fooocus uses its own inpaint algorithm and inpaint models so that results are more satisfying than all other software that uses standard SDXL inpaint method/model) |\n| Image Prompt | Input Image -> Image Prompt <br> (Fooocus uses its own image prompt algorithm so that result quality and prompt understanding are more satisfying than all other software that uses standard SDXL methods like standard IP-Adapters or Revisions) |\n| --style | Advanced -> Style |\n| --stylize | Advanced -> Advanced -> Guidance |\n| --niji | [Multiple launchers: \"run.bat\", \"run_anime.bat\", and \"run_realistic.bat\".](https://github.com/lllyasviel/Fooocus/discussions/679) <br> Fooocus support SDXL models on Civitai <br> (You can google search “Civitai” if you do not know about it) |\n| --quality | Advanced -> Quality |\n| --repeat | Advanced -> Image Number |\n| Multi Prompts (::) | Just use multiple lines of prompts |\n| Prompt Weights | You can use \" I am (happy:1.5)\". <br> Fooocus uses A1111's reweighting algorithm so that results are better than ComfyUI if users directly copy prompts from Civitai. (Because if prompts are written in ComfyUI's reweighting, users are less likely to copy prompt texts as they prefer dragging files) <br> To use embedding, you can use \"(embedding:file_name:1.1)\" |\n| --no | Advanced -> Negative Prompt |\n| --ar | Advanced -> Aspect Ratios |\n| InsightFace | Input Image -> Image Prompt -> Advanced -> FaceSwap |\n| Describe | Input Image -> Describe |\n\nBelow is a quick list using LeonardoAI's examples:\n\n| LeonardoAI | Fooocus |\n| - | - |\n| Prompt Magic | Advanced -> Style -> Fooocus V2 |\n| Advanced Sampler Parameters (like Contrast/Sharpness/etc) | Advanced -> Advanced -> Sampling Sharpness / etc |\n| User-friendly ControlNets | Input Image -> Image Prompt -> Advanced |\n\nAlso, [click here to browse the advanced features.](https://github.com/lllyasviel/Fooocus/discussions/117)\n\n# Download\n\n### Windows\n\nYou can directly download Fooocus with:\n\n**[>>> Click here to download <<<](https://github.com/lllyasviel/Fooocus/releases/download/v2.5.0/Fooocus_win64_2-5-0.7z)**\n\nAfter you download the file, please uncompress it and then run the \"run.bat\".\n\n![image](https://github.com/lllyasviel/Fooocus/assets/19834515/c49269c4-c274-4893-b368-047c401cc58c)\n\nThe first time you launch the software, it will automatically download models:\n\n1. It will download [default models](#models) to the folder \"Fooocus\\models\\checkpoints\" given different presets. You can download them in advance if you do not want automatic download.\n2. Note that if you use inpaint, at the first time you inpaint an image, it will download [Fooocus's own inpaint control model from here](https://huggingface.co/lllyasviel/fooocus_inpaint/resolve/main/inpaint_v26.fooocus.patch) as the file \"Fooocus\\models\\inpaint\\inpaint_v26.fooocus.patch\" (the size of this file is 1.28GB).\n\nAfter Fooocus 2.1.60, you will also have `run_anime.bat` and `run_realistic.bat`. They are different model presets (and require different models, but they will be automatically downloaded). [Check here for more details](https://github.com/lllyasviel/Fooocus/discussions/679).\n\nAfter Fooocus 2.3.0 you can also switch presets directly in the browser. Keep in mind to add these arguments if you want to change the default behavior:\n* Use `--disable-preset-selection` to disable preset selection in the browser.\n* Use `--always-download-new-model` to download missing models on preset switch. Default is fallback to `previous_default_models` defined in the corresponding preset, also see terminal output.\n\n![image](https://github.com/lllyasviel/Fooocus/assets/19834515/d386f817-4bd7-490c-ad89-c1e228c23447)\n\nIf you already have these files, you can copy them to the above locations to speed up installation.\n\nNote that if you see **\"MetadataIncompleteBuffer\" or \"PytorchStreamReader\"**, then your model files are corrupted. Please download models again.\n\nBelow is a test on a relatively low-end laptop with **16GB System RAM** and **6GB VRAM** (Nvidia 3060 laptop). The speed on this machine is about 1.35 seconds per iteration. Pretty impressive – nowadays laptops with 3060 are usually at very acceptable price.\n\n![image](https://github.com/lllyasviel/Fooocus/assets/19834515/938737a5-b105-4f19-b051-81356cb7c495)\n\nBesides, recently many other software report that Nvidia driver above 532 is sometimes 10x slower than Nvidia driver 531. If your generation time is very long, consider download [Nvidia Driver 531 Laptop](https://www.nvidia.com/download/driverResults.aspx/199991/en-us/) or [Nvidia Driver 531 Desktop](https://www.nvidia.com/download/driverResults.aspx/199990/en-us/).\n\nNote that the minimal requirement is **4GB Nvidia GPU memory (4GB VRAM)** and **8GB system memory (8GB RAM)**. This requires using Microsoft’s Virtual Swap technique, which is automatically enabled by your Windows installation in most cases, so you often do not need to do anything about it. However, if you are not sure, or if you manually turned it off (would anyone really do that?), or **if you see any \"RuntimeError: CPUAllocator\"**, you can enable it here:\n\n<details>\n<summary>Click here to see the image instructions. </summary>\n\n![image](https://github.com/lllyasviel/Fooocus/assets/19834515/2a06b130-fe9b-4504-94f1-2763be4476e9)\n\n**And make sure that you have at least 40GB free space on each drive if you still see \"RuntimeError: CPUAllocator\" !**\n\n</details>\n\nPlease open an issue if you use similar devices but still cannot achieve acceptable performances.\n\nNote that the [minimal requirement](#minimal-requirement) for different platforms is different.\n\nSee also the common problems and troubleshoots [here](troubleshoot.md).\n\n### Colab\n\n(Last tested - 2024 Aug 12 by [mashb1t](https://github.com/mashb1t))\n\n| Colab | Info\n| --- | --- |\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lllyasviel/Fooocus/blob/main/fooocus_colab.ipynb) | Fooocus Official\n\nIn Colab, you can modify the last line to `!python entry_with_update.py --share --always-high-vram` or `!python entry_with_update.py --share --always-high-vram --preset anime` or `!python entry_with_update.py --share --always-high-vram --preset realistic` for Fooocus Default/Anime/Realistic Edition.\n\nYou can also change the preset in the UI. Please be aware that this may lead to timeouts after 60 seconds. If this is the case, please wait until the download has finished, change the preset to initial and back to the one you've selected or reload the page.\n\nNote that this Colab will disable refiner by default because Colab free's resources are relatively limited (and some \"big\" features like image prompt may cause free-tier Colab to disconnect). We make sure that basic text-to-image is always working on free-tier Colab.\n\nUsing `--always-high-vram` shifts resource allocation from RAM to VRAM and achieves the overall best balance between performance, flexibility and stability on the default T4 instance. Please find more information [here](https://github.com/lllyasviel/Fooocus/pull/1710#issuecomment-1989185346).\n\nThanks to [camenduru](https://github.com/camenduru) for the template!\n\n### Linux (Using Anaconda)\n\nIf you want to use Anaconda/Miniconda, you can\n\n    git clone https://github.com/lllyasviel/Fooocus.git\n    cd Fooocus\n    conda env create -f environment.yaml\n    conda activate fooocus\n    pip install -r requirements_versions.txt\n\nThen download the models: download [default models](#models) to the folder \"Fooocus\\models\\checkpoints\". **Or let Fooocus automatically download the models** using the launcher:\n\n    conda activate fooocus\n    python entry_with_update.py\n\nOr, if you want to open a remote port, use\n\n    conda activate fooocus\n    python entry_with_update.py --listen\n\nUse `python entry_with_update.py --preset anime` or `python entry_with_update.py --preset realistic` for Fooocus Anime/Realistic Edition.\n\n### Linux (Using Python Venv)\n\nYour Linux needs to have **Python 3.10** installed, and let's say your Python can be called with the command **python3** with your venv system working; you can\n\n    git clone https://github.com/lllyasviel/Fooocus.git\n    cd Fooocus\n    python3 -m venv fooocus_env\n    source fooocus_env/bin/activate\n    pip install -r requirements_versions.txt\n\nSee the above sections for model downloads. You can launch the software with:\n\n    source fooocus_env/bin/activate\n    python entry_with_update.py\n\nOr, if you want to open a remote port, use\n\n    source fooocus_env/bin/activate\n    python entry_with_update.py --listen\n\nUse `python entry_with_update.py --preset anime` or `python entry_with_update.py --preset realistic` for Fooocus Anime/Realistic Edition.\n\n### Linux (Using native system Python)\n\nIf you know what you are doing, and your Linux already has **Python 3.10** installed, and your Python can be called with the command **python3** (and Pip with **pip3**), you can\n\n    git clone https://github.com/lllyasviel/Fooocus.git\n    cd Fooocus\n    pip3 install -r requirements_versions.txt\n\nSee the above sections for model downloads. You can launch the software with:\n\n    python3 entry_with_update.py\n\nOr, if you want to open a remote port, use\n\n    python3 entry_with_update.py --listen\n\nUse `python entry_with_update.py --preset anime` or `python entry_with_update.py --preset realistic` for Fooocus Anime/Realistic Edition.\n\n### Linux (AMD GPUs)\n\nNote that the [minimal requirement](#minimal-requirement) for different platforms is different.\n\nSame with the above instructions. You need to change torch to the AMD version\n\n    pip uninstall torch torchvision torchaudio torchtext functorch xformers \n    pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.6\n\nAMD is not intensively tested, however. The AMD support is in beta.\n\nUse `python entry_with_update.py --preset anime` or `python entry_with_update.py --preset realistic` for Fooocus Anime/Realistic Edition.\n\n### Windows (AMD GPUs)\n\nNote that the [minimal requirement](#minimal-requirement) for different platforms is different.\n\nSame with Windows. Download the software and edit the content of `run.bat` as:\n\n    .\\python_embeded\\python.exe -m pip uninstall torch torchvision torchaudio torchtext functorch xformers -y\n    .\\python_embeded\\python.exe -m pip install torch-directml\n    .\\python_embeded\\python.exe -s Fooocus\\entry_with_update.py --directml\n    pause\n\nThen run the `run.bat`.\n\nAMD is not intensively tested, however. The AMD support is in beta.\n\nFor AMD, use `.\\python_embeded\\python.exe Fooocus\\entry_with_update.py --directml --preset anime` or `.\\python_embeded\\python.exe Fooocus\\entry_with_update.py --directml --preset realistic` for Fooocus Anime/Realistic Edition.\n\n### Mac\n\nNote that the [minimal requirement](#minimal-requirement) for different platforms is different.\n\nMac is not intensively tested. Below is an unofficial guideline for using Mac. You can discuss problems [here](https://github.com/lllyasviel/Fooocus/pull/129).\n\nYou can install Fooocus on Apple Mac silicon (M1 or M2) with macOS 'Catalina' or a newer version. Fooocus runs on Apple silicon computers via [PyTorch](https://pytorch.org/get-started/locally/) MPS device acceleration. Mac Silicon computers don't come with a dedicated graphics card, resulting in significantly longer image processing times compared to computers with dedicated graphics cards.\n\n1. Install the conda package manager and pytorch nightly. Read the [Accelerated PyTorch training on Mac](https://developer.apple.com/metal/pytorch/) Apple Developer guide for instructions. Make sure pytorch recognizes your MPS device.\n1. Open the macOS Terminal app and clone this repository with `git clone https://github.com/lllyasviel/Fooocus.git`.\n1. Change to the new Fooocus directory, `cd Fooocus`.\n1. Create a new conda environment, `conda env create -f environment.yaml`.\n1. Activate your new conda environment, `conda activate fooocus`.\n1. Install the packages required by Fooocus, `pip install -r requirements_versions.txt`.\n1. Launch Fooocus by running `python entry_with_update.py`. (Some Mac M2 users may need `python entry_with_update.py --disable-offload-from-vram` to speed up model loading/unloading.) The first time you run Fooocus, it will automatically download the Stable Diffusion SDXL models and will take a significant amount of time, depending on your internet connection.\n\nUse `python entry_with_update.py --preset anime` or `python entry_with_update.py --preset realistic` for Fooocus Anime/Realistic Edition.\n\n### Docker\n\nSee [docker.md](docker.md)\n\n### Download Previous Version\n\nSee the guidelines [here](https://github.com/lllyasviel/Fooocus/discussions/1405).\n\n## Minimal Requirement\n\nBelow is the minimal requirement for running Fooocus locally. If your device capability is lower than this spec, you may not be able to use Fooocus locally. (Please let us know, in any case, if your device capability is lower but Fooocus still works.)\n\n| Operating System  | GPU                          | Minimal GPU Memory           | Minimal System Memory     | [System Swap](troubleshoot.md) | Note                                                                       |\n|-------------------|------------------------------|------------------------------|---------------------------|--------------------------------|----------------------------------------------------------------------------|\n| Windows/Linux     | Nvidia RTX 4XXX              | 4GB                          | 8GB                       | Required                       | fastest                                                                    |\n| Windows/Linux     | Nvidia RTX 3XXX              | 4GB                          | 8GB                       | Required                       | usually faster than RTX 2XXX                                               |\n| Windows/Linux     | Nvidia RTX 2XXX              | 4GB                          | 8GB                       | Required                       | usually faster than GTX 1XXX                                               |\n| Windows/Linux     | Nvidia GTX 1XXX              | 8GB (&ast; 6GB uncertain)    | 8GB                       | Required                       | only marginally faster than CPU                                            |\n| Windows/Linux     | Nvidia GTX 9XX               | 8GB                          | 8GB                       | Required                       | faster or slower than CPU                                                  |\n| Windows/Linux     | Nvidia GTX < 9XX             | Not supported                | /                         | /                              | /                                                                          |\n| Windows           | AMD GPU                      | 8GB    (updated 2023 Dec 30) | 8GB                       | Required                       | via DirectML (&ast; ROCm is on hold), about 3x slower than Nvidia RTX 3XXX |\n| Linux             | AMD GPU                      | 8GB                          | 8GB                       | Required                       | via ROCm, about 1.5x slower than Nvidia RTX 3XXX                           |\n| Mac               | M1/M2 MPS                    | Shared                       | Shared                    | Shared                         | about 9x slower than Nvidia RTX 3XXX                                       |\n| Windows/Linux/Mac | only use CPU                 | 0GB                          | 32GB                      | Required                       | about 17x slower than Nvidia RTX 3XXX                                      |\n\n&ast; AMD GPU ROCm (on hold): The AMD is still working on supporting ROCm on Windows.\n\n&ast; Nvidia GTX 1XXX 6GB uncertain: Some people report 6GB success on GTX 10XX, but some other people report failure cases.\n\n*Note that Fooocus is only for extremely high quality image generating. We will not support smaller models to reduce the requirement and sacrifice result quality.*\n\n## Troubleshoot\n\nSee the common problems [here](troubleshoot.md).\n\n## Default Models\n<a name=\"models\"></a>\n\nGiven different goals, the default models and configs of Fooocus are different:\n\n| Task      | Windows | Linux args | Main Model                  | Refiner | Config                                                                         |\n|-----------| --- | --- |-----------------------------| --- |--------------------------------------------------------------------------------|\n| General   | run.bat |  | juggernautXL_v8Rundiffusion | not used | [here](https://github.com/lllyasviel/Fooocus/blob/main/presets/default.json)   |\n| Realistic | run_realistic.bat | --preset realistic | realisticStockPhoto_v20     | not used | [here](https://github.com/lllyasviel/Fooocus/blob/main/presets/realistic.json) |\n| Anime     | run_anime.bat | --preset anime | animaPencilXL_v500          | not used | [here](https://github.com/lllyasviel/Fooocus/blob/main/presets/anime.json)     |\n\nNote that the download is **automatic** - you do not need to do anything if the internet connection is okay. However, you can download them manually if you (or move them from somewhere else) have your own preparation.\n\n## UI Access and Authentication\nIn addition to running on localhost, Fooocus can also expose its UI in two ways: \n* Local UI listener: use `--listen` (specify port e.g. with `--port 8888`). \n* API access: use `--share` (registers an endpoint at `.gradio.live`).\n\nIn both ways the access is unauthenticated by default. You can add basic authentication by creating a file called `auth.json` in the main directory, which contains a list of JSON objects with the keys `user` and `pass` (see example in [auth-example.json](./auth-example.json)).\n\n## List of \"Hidden\" Tricks\n<a name=\"tech_list\"></a>\n\n<details>\n<summary>Click to see a list of tricks. Those are based on SDXL and are not very up-to-date with latest models.</summary>\n\n1. GPT2-based [prompt expansion as a dynamic style \"Fooocus V2\".](https://github.com/lllyasviel/Fooocus/discussions/117#raw) (similar to Midjourney's hidden pre-processing and \"raw\" mode, or the LeonardoAI's Prompt Magic).\n2. Native refiner swap inside one single k-sampler. The advantage is that the refiner model can now reuse the base model's momentum (or ODE's history parameters) collected from k-sampling to achieve more coherent sampling. In Automatic1111's high-res fix and ComfyUI's node system, the base model and refiner use two independent k-samplers, which means the momentum is largely wasted, and the sampling continuity is broken. Fooocus uses its own advanced k-diffusion sampling that ensures seamless, native, and continuous swap in a refiner setup. (Update Aug 13: Actually, I discussed this with Automatic1111 several days ago, and it seems that the “native refiner swap inside one single k-sampler” is [merged]( https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12371) into the dev branch of webui. Great!)\n3. Negative ADM guidance. Because the highest resolution level of XL Base does not have cross attentions, the positive and negative signals for XL's highest resolution level cannot receive enough contrasts during the CFG sampling, causing the results to look a bit plastic or overly smooth in certain cases. Fortunately, since the XL's highest resolution level is still conditioned on image aspect ratios (ADM), we can modify the adm on the positive/negative side to compensate for the lack of CFG contrast in the highest resolution level. (Update Aug 16, the IOS App [Draw Things](https://apps.apple.com/us/app/draw-things-ai-generation/id6444050820) will support Negative ADM Guidance. Great!)\n4. We implemented a carefully tuned variation of Section 5.1 of [\"Improving Sample Quality of Diffusion Models Using Self-Attention Guidance\"](https://arxiv.org/pdf/2210.00939.pdf). The weight is set to very low, but this is Fooocus's final guarantee to make sure that the XL will never yield an overly smooth or plastic appearance (examples [here](https://github.com/lllyasviel/Fooocus/discussions/117#sharpness)). This can almost eliminate all cases for which XL still occasionally produces overly smooth results, even with negative ADM guidance. (Update 2023 Aug 18, the Gaussian kernel of SAG is changed to an anisotropic kernel for better structure preservation and fewer artifacts.)\n5. We modified the style templates a bit and added the \"cinematic-default\".\n6. We tested the \"sd_xl_offset_example-lora_1.0.safetensors\" and it seems that when the lora weight is below 0.5, the results are always better than XL without lora.\n7. The parameters of samplers are carefully tuned.\n8. Because XL uses positional encoding for generation resolution, images generated by several fixed resolutions look a bit better than those from arbitrary resolutions (because the positional encoding is not very good at handling int numbers that are unseen during training). This suggests that the resolutions in UI may be hard coded for best results.\n9. Separated prompts for two different text encoders seem unnecessary. Separated prompts for the base model and refiner may work, but the effects are random, and we refrain from implementing this.\n10. The DPM family seems well-suited for XL since XL sometimes generates overly smooth texture, but the DPM family sometimes generates overly dense detail in texture. Their joint effect looks neutral and appealing to human perception.\n11. A carefully designed system for balancing multiple styles as well as prompt expansion.\n12. Using automatic1111's method to normalize prompt emphasizing. This significantly improves results when users directly copy prompts from civitai.\n13. The joint swap system of the refiner now also supports img2img and upscale in a seamless way.\n14. CFG Scale and TSNR correction (tuned for SDXL) when CFG is bigger than 10.\n</details>\n\n## Customization\n\nAfter the first time you run Fooocus, a config file will be generated at `Fooocus\\config.txt`. This file can be edited to change the model path or default parameters.\n\nFor example, an edited `Fooocus\\config.txt` (this file will be generated after the first launch) may look like this:\n\n```json\n{\n    \"path_checkpoints\": \"D:\\\\Fooocus\\\\models\\\\checkpoints\",\n    \"path_loras\": \"D:\\\\Fooocus\\\\models\\\\loras\",\n    \"path_embeddings\": \"D:\\\\Fooocus\\\\models\\\\embeddings\",\n    \"path_vae_approx\": \"D:\\\\Fooocus\\\\models\\\\vae_approx\",\n    \"path_upscale_models\": \"D:\\\\Fooocus\\\\models\\\\upscale_models\",\n    \"path_inpaint\": \"D:\\\\Fooocus\\\\models\\\\inpaint\",\n    \"path_controlnet\": \"D:\\\\Fooocus\\\\models\\\\controlnet\",\n    \"path_clip_vision\": \"D:\\\\Fooocus\\\\models\\\\clip_vision\",\n    \"path_fooocus_expansion\": \"D:\\\\Fooocus\\\\models\\\\prompt_expansion\\\\fooocus_expansion\",\n    \"path_outputs\": \"D:\\\\Fooocus\\\\outputs\",\n    \"default_model\": \"realisticStockPhoto_v10.safetensors\",\n    \"default_refiner\": \"\",\n    \"default_loras\": [[\"lora_filename_1.safetensors\", 0.5], [\"lora_filename_2.safetensors\", 0.5]],\n    \"default_cfg_scale\": 3.0,\n    \"default_sampler\": \"dpmpp_2m\",\n    \"default_scheduler\": \"karras\",\n    \"default_negative_prompt\": \"low quality\",\n    \"default_positive_prompt\": \"\",\n    \"default_styles\": [\n        \"Fooocus V2\",\n        \"Fooocus Photograph\",\n        \"Fooocus Negative\"\n    ]\n}\n```\n\nMany other keys, formats, and examples are in `Fooocus\\config_modification_tutorial.txt` (this file will be generated after the first launch).\n\nConsider twice before you really change the config. If you find yourself breaking things, just delete `Fooocus\\config.txt`. Fooocus will go back to default.\n\nA safer way is just to try \"run_anime.bat\" or \"run_realistic.bat\" - they should already be good enough for different tasks.\n\n~Note that `user_path_config.txt` is deprecated and will be removed soon.~ (Edit: it is already removed.)\n\n### All CMD Flags\n\n```\nentry_with_update.py  [-h] [--listen [IP]] [--port PORT]\n                      [--disable-header-check [ORIGIN]]\n                      [--web-upload-size WEB_UPLOAD_SIZE]\n                      [--hf-mirror HF_MIRROR]\n                      [--external-working-path PATH [PATH ...]]\n                      [--output-path OUTPUT_PATH]\n                      [--temp-path TEMP_PATH] [--cache-path CACHE_PATH]\n                      [--in-browser] [--disable-in-browser]\n                      [--gpu-device-id DEVICE_ID]\n                      [--async-cuda-allocation | --disable-async-cuda-allocation]\n                      [--disable-attention-upcast]\n                      [--all-in-fp32 | --all-in-fp16]\n                      [--unet-in-bf16 | --unet-in-fp16 | --unet-in-fp8-e4m3fn | --unet-in-fp8-e5m2]\n                      [--vae-in-fp16 | --vae-in-fp32 | --vae-in-bf16]\n                      [--vae-in-cpu]\n                      [--clip-in-fp8-e4m3fn | --clip-in-fp8-e5m2 | --clip-in-fp16 | --clip-in-fp32]\n                      [--directml [DIRECTML_DEVICE]]\n                      [--disable-ipex-hijack]\n                      [--preview-option [none,auto,fast,taesd]]\n                      [--attention-split | --attention-quad | --attention-pytorch]\n                      [--disable-xformers]\n                      [--always-gpu | --always-high-vram | --always-normal-vram | --always-low-vram | --always-no-vram | --always-cpu [CPU_NUM_THREADS]]\n                      [--always-offload-from-vram]\n                      [--pytorch-deterministic] [--disable-server-log]\n                      [--debug-mode] [--is-windows-embedded-python]\n                      [--disable-server-info] [--multi-user] [--share]\n                      [--preset PRESET] [--disable-preset-selection]\n                      [--language LANGUAGE]\n                      [--disable-offload-from-vram] [--theme THEME]\n                      [--disable-image-log] [--disable-analytics]\n                      [--disable-metadata] [--disable-preset-download]\n                      [--disable-enhance-output-sorting]\n                      [--enable-auto-describe-image]\n                      [--always-download-new-model]\n                      [--rebuild-hash-cache [CPU_NUM_THREADS]]\n```\n\n## Inline Prompt Features\n\n### Wildcards\n\nExample prompt: `__color__ flower`\n\nProcessed for positive and negative prompt.\n\nSelects a random wildcard from a predefined list of options, in this case the `wildcards/color.txt` file. \nThe wildcard will be replaced with a random color (randomness based on seed). \nYou can also disable randomness and process a wildcard file from top to bottom by enabling the checkbox `Read wildcards in order` in Developer Debug Mode.\n\nWildcards can be nested and combined, and multiple wildcards can be used in the same prompt (example see `wildcards/color_flower.txt`).\n\n### Array Processing\n\nExample prompt: `[[red, green, blue]] flower`\n\nProcessed only for positive prompt.\n\nProcesses the array from left to right, generating a separate image for each element in the array. In this case 3 images would be generated, one for each color.\nIncrease the image number to 3 to generate all 3 variants.\n\nArrays can not be nested, but multiple arrays can be used in the same prompt.\nDoes support inline LoRAs as array elements!\n\n### Inline LoRAs\n\nExample prompt: `flower <lora:sunflowers:1.2>`\n\nProcessed only for positive prompt.\n\nApplies a LoRA to the prompt. The LoRA file must be located in the `models/loras` directory.\n\n## Advanced Features\n\n[Click here to browse the advanced features.](https://github.com/lllyasviel/Fooocus/discussions/117)\n\n## Forks\n\nBelow are some Forks to Fooocus:\n\n| Fooocus' forks |\n| - |\n| [fenneishi/Fooocus-Control](https://github.com/fenneishi/Fooocus-Control) </br>[runew0lf/RuinedFooocus](https://github.com/runew0lf/RuinedFooocus) </br> [MoonRide303/Fooocus-MRE](https://github.com/MoonRide303/Fooocus-MRE) </br> [mashb1t/Fooocus](https://github.com/mashb1t/Fooocus) </br> and so on ... |\n\n## Thanks\n\nMany thanks to [twri](https://github.com/twri) and [3Diva](https://github.com/3Diva) and [Marc K3nt3L](https://github.com/K3nt3L) for creating additional SDXL styles available in Fooocus. \n\nThe project starts from a mixture of [Stable Diffusion WebUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui) and [ComfyUI](https://github.com/comfyanonymous/ComfyUI) codebases.\n\nAlso, thanks [daswer123](https://github.com/daswer123) for contributing the Canvas Zoom!\n\n## Update Log\n\nThe log is [here](update_log.md).\n\n## Localization/Translation/I18N\n\nYou can put json files in the `language` folder to translate the user interface.\n\nFor example, below is the content of `Fooocus/language/example.json`:\n\n```json\n{\n  \"Generate\": \"生成\",\n  \"Input Image\": \"入力画像\",\n  \"Advanced\": \"고급\",\n  \"SAI 3D Model\": \"SAI 3D Modèle\"\n}\n```\n\nIf you add `--language example` arg, Fooocus will read `Fooocus/language/example.json` to translate the UI.\n\nFor example, you can edit the ending line of Windows `run.bat` as\n\n    .\\python_embeded\\python.exe -s Fooocus\\entry_with_update.py --language example\n\nOr `run_anime.bat` as\n\n    .\\python_embeded\\python.exe -s Fooocus\\entry_with_update.py --language example --preset anime\n\nOr `run_realistic.bat` as\n\n    .\\python_embeded\\python.exe -s Fooocus\\entry_with_update.py --language example --preset realistic\n\nFor practical translation, you may create your own file like `Fooocus/language/jp.json` or `Fooocus/language/cn.json` and then use flag `--language jp` or `--language cn`. Apparently, these files do not exist now. **We need your help to create these files!**\n\nNote that if no `--language` is given and at the same time `Fooocus/language/default.json` exists, Fooocus will always load `Fooocus/language/default.json` for translation. By default, the file `Fooocus/language/default.json` does not exist.\n"
  },
  {
    "path": "requirements_docker.txt",
    "content": "torch==2.1.0\ntorchvision==0.16.0\n"
  },
  {
    "path": "requirements_versions.txt",
    "content": "torchsde==0.2.6\neinops==0.8.0\ntransformers==4.42.4\nsafetensors==0.4.3\naccelerate==0.32.1\npyyaml==6.0.1\npillow==10.4.0\nscipy==1.14.0\ntqdm==4.66.4\npsutil==6.0.0\npytorch_lightning==2.3.3\nomegaconf==2.3.0\ngradio==3.41.2\npygit2==1.15.1\nopencv-contrib-python-headless==4.10.0.84\nhttpx==0.27.0\nonnxruntime==1.18.1\ntimm==1.0.7\nnumpy==1.26.4\ntokenizers==0.19.1\npackaging==24.1\nrembg==2.0.57\ngroundingdino-py==0.4.0\nsegment_anything==1.0"
  },
  {
    "path": "sdxl_styles/sdxl_styles_diva.json",
    "content": "[\n    {\n        \"name\": \"cinematic-diva\",\n        \"prompt\": \"UHD, 8K, ultra detailed, a cinematic photograph of {prompt}, beautiful lighting, great composition\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, NSFW\"\n    },\n    {\n        \"name\": \"Abstract Expressionism\",\n        \"prompt\": \"Abstract Expressionism Art, {prompt}, High contrast, minimalistic, colorful, stark, dramatic, expressionism\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, realism, photorealistic\"\n    },\n    {\n        \"name\": \"Academia\",\n        \"prompt\": \"Academia, {prompt}, preppy Ivy League style, stark, dramatic, chic boarding school, academia\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, grunge, sloppy, unkempt\"\n    },\n    {\n        \"name\": \"Action Figure\",\n        \"prompt\": \"Action Figure, {prompt}, plastic collectable action figure, collectable toy action figure\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Adorable 3D Character\",\n        \"prompt\": \"Adorable 3D Character, {prompt}, 3D render, adorable character, 3D art\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, grunge, sloppy, unkempt, photograph, photo, realistic\"\n    },\n    {\n        \"name\": \"Adorable Kawaii\",\n        \"prompt\": \"Adorable Kawaii, {prompt}, pretty, cute, adorable, kawaii\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, gothic, dark, moody, monochromatic\"\n    },\n    {\n        \"name\": \"Art Deco\",\n        \"prompt\": \"Art Deco, {prompt}, sleek, geometric forms, art deco style\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Art Nouveau\",\n        \"prompt\": \"Art Nouveau, beautiful art, {prompt}, sleek, organic forms, long, sinuous, art nouveau style\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, industrial, mechanical\"\n    },\n    {\n        \"name\": \"Astral Aura\",\n        \"prompt\": \"Astral Aura, {prompt}, astral, colorful aura, vibrant energy\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Avant-garde\",\n        \"prompt\": \"Avant-garde, {prompt}, unusual, experimental, avant-garde art\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Baroque\",\n        \"prompt\": \"Baroque, {prompt}, dramatic, exuberant, grandeur, baroque art\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Bauhaus-Style Poster\",\n        \"prompt\": \"Bauhaus-Style Poster, {prompt}, simple geometric shapes, clean lines, primary colors, Bauhaus-Style Poster\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Blueprint Schematic Drawing\",\n        \"prompt\": \"Blueprint Schematic Drawing, {prompt}, technical drawing, blueprint, schematic\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Caricature\",\n        \"prompt\": \"Caricature, {prompt}, exaggerated, comical, caricature\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, realistic\"\n    },\n    {\n        \"name\": \"Cel Shaded Art\",\n        \"prompt\": \"Cel Shaded Art, {prompt}, 2D, flat color, toon shading, cel shaded style\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Character Design Sheet\",\n        \"prompt\": \"Character Design Sheet, {prompt}, character reference sheet, character turn around\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Classicism Art\",\n        \"prompt\": \"Classicism Art, {prompt}, inspired by Roman and Greek culture, clarity, harmonious, classicism art\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Color Field Painting\",\n        \"prompt\": \"Color Field Painting, {prompt}, abstract, simple, geometic, color field painting style\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Colored Pencil Art\",\n        \"prompt\": \"Colored Pencil Art, {prompt}, colored pencil strokes, light color, visible paper texture, colored pencil art\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Conceptual Art\",\n        \"prompt\": \"Conceptual Art, {prompt}, concept art\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Constructivism\",\n        \"prompt\": \"Constructivism Art, {prompt}, minimalistic, geometric forms, constructivism art\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Cubism\",\n        \"prompt\": \"Cubism Art, {prompt}, flat geometric forms, cubism art\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Dadaism\",\n        \"prompt\": \"Dadaism Art, {prompt}, satirical, nonsensical, dadaism art\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Dark Fantasy\",\n        \"prompt\": \"Dark Fantasy Art, {prompt}, dark, moody, dark fantasy style\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, bright, sunny\"\n    },\n    {\n        \"name\": \"Dark Moody Atmosphere\",\n        \"prompt\": \"Dark Moody Atmosphere, {prompt}, dramatic, mysterious, dark moody atmosphere\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, vibrant, colorful, bright\"\n    },\n    {\n        \"name\": \"DMT Art Style\",\n        \"prompt\": \"DMT Art Style, {prompt}, bright colors, surreal visuals, swirling patterns, DMT art style\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Doodle Art\",\n        \"prompt\": \"Doodle Art Style, {prompt}, drawing, freeform, swirling patterns, doodle art style\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Double Exposure\",\n        \"prompt\": \"Double Exposure Style, {prompt}, double image ghost effect, image combination, double exposure style\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Dripping Paint Splatter Art\",\n        \"prompt\": \"Dripping Paint Splatter Art, {prompt}, dramatic, paint drips, splatters, dripping paint\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Expressionism\",\n        \"prompt\": \"Expressionism Art Style, {prompt}, movement, contrast, emotional, exaggerated forms, expressionism art style\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Faded Polaroid Photo\",\n        \"prompt\": \"Faded Polaroid Photo, {prompt}, analog, old faded photo, old polaroid\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, vibrant, colorful\"\n    },\n    {\n        \"name\": \"Fauvism\",\n        \"prompt\": \"Fauvism Art, {prompt}, painterly, bold colors, textured brushwork, fauvism art\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Flat 2D Art\",\n        \"prompt\": \"Flat 2D Art, {prompt}, simple flat color, 2-dimensional, Flat 2D Art Style\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, 3D, photo, realistic\"\n    },\n    {\n        \"name\": \"Fortnite Art Style\",\n        \"prompt\": \"Fortnite Art Style, {prompt}, 3D cartoon, colorful, Fortnite Art Style\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, photo, realistic\"\n    },\n    {\n        \"name\": \"Futurism\",\n        \"prompt\": \"Futurism Art Style, {prompt}, dynamic, dramatic, Futurism Art Style\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Glitchcore\",\n        \"prompt\": \"Glitchcore Art Style, {prompt}, dynamic, dramatic, distorted, vibrant colors, glitchcore art style\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Glo-fi\",\n        \"prompt\": \"Glo-fi Art Style, {prompt}, dynamic, dramatic, vibrant colors, glo-fi art style\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Googie Art Style\",\n        \"prompt\": \"Googie Art Style, {prompt}, dynamic, dramatic, 1950's futurism, bold boomerang angles, Googie art style\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Graffiti Art\",\n        \"prompt\": \"Graffiti Art Style, {prompt}, dynamic, dramatic, vibrant colors, graffiti art style\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Harlem Renaissance Art\",\n        \"prompt\": \"Harlem Renaissance Art Style, {prompt}, dynamic, dramatic, 1920s African American culture, Harlem Renaissance art style\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"High Fashion\",\n        \"prompt\": \"High Fashion, {prompt}, dynamic, dramatic, haute couture, elegant, ornate clothing, High Fashion\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Idyllic\",\n        \"prompt\": \"Idyllic, {prompt}, peaceful, happy, pleasant, happy, harmonious, picturesque, charming\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Impressionism\",\n        \"prompt\": \"Impressionism, {prompt}, painterly, small brushstrokes, visible brushstrokes, impressionistic style\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Infographic Drawing\",\n        \"prompt\": \"Infographic Drawing, {prompt}, diagram, infographic\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Ink Dripping Drawing\",\n        \"prompt\": \"Ink Dripping Drawing, {prompt}, ink drawing, dripping ink\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, colorful, vibrant\"\n    },\n    {\n        \"name\": \"Japanese Ink Drawing\",\n        \"prompt\": \"Japanese Ink Drawing, {prompt}, ink drawing, inkwash, Japanese Ink Drawing\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, colorful, vibrant\"\n    },\n    {\n        \"name\": \"Knolling Photography\",\n        \"prompt\": \"Knolling Photography, {prompt}, flat lay photography, object arrangment, knolling photography\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Light Cheery Atmosphere\",\n        \"prompt\": \"Light Cheery Atmosphere, {prompt}, happy, joyful, cheerful, carefree, gleeful, lighthearted, pleasant atmosphere\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, monochromatic, dark, moody\"\n    },\n    {\n        \"name\": \"Logo Design\",\n        \"prompt\": \"Logo Design, {prompt}, dynamic graphic art, vector art, minimalist, professional logo design\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Luxurious Elegance\",\n        \"prompt\": \"Luxurious Elegance, {prompt}, extravagant, ornate, designer, opulent, picturesque, lavish\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Macro Photography\",\n        \"prompt\": \"Macro Photography, {prompt}, close-up, macro 100mm, macro photography\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Mandola Art\",\n        \"prompt\": \"Mandola art style, {prompt}, complex, circular design, mandola\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Marker Drawing\",\n        \"prompt\": \"Marker Drawing, {prompt}, bold marker lines, visibile paper texture, marker drawing\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, photograph, realistic\"\n    },\n    {\n        \"name\": \"Medievalism\",\n        \"prompt\": \"Medievalism, {prompt}, inspired by The Middle Ages, medieval art, elaborate patterns and decoration, Medievalism\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Minimalism\",\n        \"prompt\": \"Minimalism, {prompt}, abstract, simple geometic shapes, hard edges, sleek contours, Minimalism\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Neo-Baroque\",\n        \"prompt\": \"Neo-Baroque, {prompt}, ornate and elaborate, dynaimc, Neo-Baroque\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Neo-Byzantine\",\n        \"prompt\": \"Neo-Byzantine, {prompt}, grand decorative religious style, Orthodox Christian inspired, Neo-Byzantine\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Neo-Futurism\",\n        \"prompt\": \"Neo-Futurism, {prompt}, high-tech, curves, spirals, flowing lines, idealistic future, Neo-Futurism\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Neo-Impressionism\",\n        \"prompt\": \"Neo-Impressionism, {prompt}, tiny dabs of color, Pointillism, painterly, Neo-Impressionism\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, photograph, realistic\"\n    },\n    {\n        \"name\": \"Neo-Rococo\",\n        \"prompt\": \"Neo-Rococo, {prompt}, curved forms, naturalistic ornamentation, elaborate, decorative, gaudy, Neo-Rococo\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Neoclassicism\",\n        \"prompt\": \"Neoclassicism, {prompt}, ancient Rome and Greece inspired, idealic, sober colors, Neoclassicism\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Op Art\",\n        \"prompt\": \"Op Art, {prompt}, optical illusion, abstract, geometric pattern, impression of movement, Op Art\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Ornate and Intricate\",\n        \"prompt\": \"Ornate and Intricate, {prompt}, decorative, highly detailed, elaborate, ornate, intricate\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Pencil Sketch Drawing\",\n        \"prompt\": \"Pencil Sketch Drawing, {prompt}, black and white drawing, graphite drawing\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Pop Art 2\",\n        \"prompt\": \"Pop Art, {prompt}, vivid colors, flat color, 2D, strong lines, Pop Art\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, photo, realistic\"\n    },\n    {\n        \"name\": \"Rococo\",\n        \"prompt\": \"Rococo, {prompt}, flamboyant, pastel colors, curved lines, elaborate detail, Rococo\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Silhouette Art\",\n        \"prompt\": \"Silhouette Art, {prompt}, high contrast, well defined, Silhouette Art\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Simple Vector Art\",\n        \"prompt\": \"Simple Vector Art, {prompt}, 2D flat, simple shapes, minimalistic, professional graphic, flat color, high contrast, Simple Vector Art\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, 3D, photo, realistic\"\n    },\n    {\n        \"name\": \"Sketchup\",\n        \"prompt\": \"Sketchup, {prompt}, CAD, professional design, Sketchup\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, photo, photograph\"\n    },\n    {\n        \"name\": \"Steampunk 2\",\n        \"prompt\": \"Steampunk, {prompt}, retrofuturistic science fantasy, steam-powered tech, vintage industry, gears, neo-victorian, steampunk\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Surrealism\",\n        \"prompt\": \"Surrealism, {prompt}, expressive, dramatic, organic lines and forms, dreamlike and mysterious, Surrealism\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, realistic\"\n    },\n    {\n        \"name\": \"Suprematism\",\n        \"prompt\": \"Suprematism, {prompt}, abstract, limited color palette, geometric forms, Suprematism\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, realistic\"\n    },\n    {\n        \"name\": \"Terragen\",\n        \"prompt\": \"Terragen, {prompt}, beautiful massive landscape, epic scenery, Terragen\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Tranquil Relaxing Atmosphere\",\n        \"prompt\": \"Tranquil Relaxing Atmosphere, {prompt}, calming style, soothing colors, peaceful, idealic, Tranquil Relaxing Atmosphere\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, oversaturated\"\n    },\n    {\n        \"name\": \"Sticker Designs\",\n        \"prompt\": \"Vector Art Stickers, {prompt}, professional vector design, sticker designs, Sticker Sheet\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Vibrant Rim Light\",\n        \"prompt\": \"Vibrant Rim Light, {prompt}, bright rim light, high contrast, bold edge light\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Volumetric Lighting\",\n        \"prompt\": \"Volumetric Lighting, {prompt}, light depth, dramatic atmospheric lighting, Volumetric Lighting\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast\"\n    },\n    {\n        \"name\": \"Watercolor 2\",\n        \"prompt\": \"Watercolor style painting, {prompt}, visible paper texture, colorwash, watercolor\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, photo, realistic\"\n    },\n    {\n        \"name\": \"Whimsical and Playful\",\n        \"prompt\": \"Whimsical and Playful, {prompt}, imaginative, fantastical, bight colors, stylized, happy, Whimsical and Playful\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, drab, boring, moody\"\n    }\n]\n"
  },
  {
    "path": "sdxl_styles/sdxl_styles_fooocus.json",
    "content": "[\n    {\n        \"name\": \"Fooocus Enhance\",\n        \"negative_prompt\": \"(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic, cgi, render, blender, digital art, manga, amateur:1.3), (3D ,3D Game, 3D Game Scene, 3D Character:1.1), (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)\"\n    },\n    {\n        \"name\": \"Fooocus Semi Realistic\",\n        \"negative_prompt\": \"(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)\"\n    },\n    {\n        \"name\": \"Fooocus Sharp\",\n        \"prompt\": \"cinematic still {prompt} . emotional, harmonious, vignette, 4k epic detailed, shot on kodak, 35mm photo, sharp focus, high budget, cinemascope, moody, epic, gorgeous, film grain, grainy\",\n        \"negative_prompt\": \"anime, cartoon, graphic, (blur, blurry, bokeh), text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured\"\n    },\n    {\n        \"name\": \"Fooocus Masterpiece\",\n        \"prompt\": \"(masterpiece), (best quality), (ultra-detailed), {prompt}, illustration, disheveled hair, detailed eyes, perfect composition, moist skin, intricate details, earrings\",\n        \"negative_prompt\": \"longbody, lowres, bad anatomy, bad hands, missing fingers, pubic hair,extra digit, fewer digits, cropped, worst quality, low quality\"\n    },\n    {\n        \"name\": \"Fooocus Photograph\",\n        \"prompt\": \"photograph {prompt}, 50mm . cinematic 4k epic detailed 4k epic detailed photograph shot on kodak detailed cinematic hbo dark moody, 35mm photo, grainy, vignette, vintage, Kodachrome, Lomography, stained, highly detailed, found footage\",\n        \"negative_prompt\": \"Brad Pitt, bokeh, depth of field, blurry, cropped, regular face, saturated, contrast, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, text, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck\"\n    },\n    {\n        \"name\": \"Fooocus Negative\",\n        \"negative_prompt\": \"deformed, bad anatomy, disfigured, poorly drawn face, mutated, extra limb, ugly, poorly drawn hands, missing limb, floating limbs, disconnected limbs, disconnected head, malformed hands, long neck, mutated hands and fingers, bad hands, missing fingers, cropped, worst quality, low quality, mutation, poorly drawn, huge calf, bad hands, fused hand, missing hand, disappearing arms, disappearing thigh, disappearing calf, disappearing legs, missing fingers, fused fingers, abnormal eye proportion, Abnormal hands, abnormal legs, abnormal feet, abnormal fingers, drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly, anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch\"\n    },\n    {\n        \"name\": \"Fooocus Cinematic\",\n        \"prompt\": \"cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy\",\n        \"negative_prompt\": \"anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured\"\n    },\n    {\n        \"name\": \"Fooocus Pony\",\n        \"prompt\": \"score_9, score_8_up, score_7_up, {prompt}\",\n        \"negative_prompt\": \"score_6, score_5, score_4\"\n    }\n]\n"
  },
  {
    "path": "sdxl_styles/sdxl_styles_marc_k3nt3l.json",
    "content": "[\n  {\n    \"name\": \"MK Chromolithography\",\n    \"prompt\": \"Chromolithograph {prompt}. Vibrant colors, intricate details, rich color saturation, meticulous registration, multi-layered printing, decorative elements, historical charm, artistic reproductions, commercial posters, nostalgic, ornate compositions.\",\n    \"negative_prompt\": \"monochromatic, simple designs, limited color palette, imprecise registration, minimalistic, modern aesthetic, digital appearance.\"\n  },\n  {\n    \"name\": \"MK Cross Processing Print\",\n    \"prompt\": \"Cross processing print {prompt}. Experimental color shifts, unconventional tonalities, vibrant and surreal hues, heightened contrasts, unpredictable results, artistic unpredictability, retro and vintage feel, dynamic color interplay, abstract and dreamlike.\",\n    \"negative_prompt\": \"predictable color tones, traditional processing, realistic color representation, subdued contrasts, standard photographic aesthetics.\"\n  },\n  {\n    \"name\": \"MK Dufaycolor Photograph\",\n    \"prompt\": \"Dufaycolor photograph {prompt}. Vintage color palette, distinctive color rendering, soft and dreamy atmosphere, historical charm, unique color process, grainy texture, evocative mood, nostalgic aesthetic, hand-tinted appearance, artistic patina.\",\n    \"negative_prompt\": \"modern color reproduction, hyperrealistic tones, sharp and clear details, digital precision, contemporary aesthetic.\"\n  },\n  {\n    \"name\": \"MK Herbarium\",\n    \"prompt\": \"Herbarium drawing{prompt}. Botanical accuracy, old botanical book illustration, detailed illustrations, pressed plants, delicate and precise linework, scientific documentation, meticulous presentation, educational purpose, organic compositions, timeless aesthetic, naturalistic beauty.\",\n    \"negative_prompt\": \"abstract representation, vibrant colors, artistic interpretation, chaotic compositions, fantastical elements, digital appearance.\"\n  },\n  {\n    \"name\": \"MK Punk Collage\",\n    \"prompt\": \"punk collage style {prompt} . mixed media, papercut,textured paper, overlapping, ripped posters, safety pins, chaotic layers, graffiti-style elements, anarchy symbols, vintage photos, cut-and-paste aesthetic, bold typography, distorted images, political messages, urban decay, distressed textures, newspaper clippings, spray paint, rebellious icons, DIY spirit, vivid colors, punk band logos, edgy and raw compositions, \",\n    \"negative_prompt\": \"conventional,blurry, noisy, low contrast\"\n  },\n  {\n    \"name\": \"MK mosaic\",\n    \"prompt\": \"mosaic style {prompt} . fragmented, assembled, colorful, highly detailed\",\n    \"negative_prompt\": \"whole, unbroken, monochrome\"\n  },\n  {\n    \"name\": \"MK Van Gogh\",\n    \"prompt\": \"Oil painting by Van Gogh {prompt} . Expressive, impasto, swirling brushwork, vibrant, brush strokes, Brushstroke-heavy, Textured, Impasto, Colorful, Dynamic, Bold, Distinctive, Vibrant, Whirling, Expressive, Dramatic, Swirling, Layered, Intense, Contrastive, Atmospheric, Luminous, Textural, Evocative, SpiraledVan Gogh style\",\n    \"negative_prompt\": \"realistic, photorealistic, calm, straight lines, signature, frame, text, watermark\"\n  },\n  {\n    \"name\": \"MK Coloring Book\",\n    \"prompt\": \"centered black and white high contrast line drawing, coloring book style,{prompt} . monochrome, blank white background\",\n    \"negative_prompt\": \"greyscale, gradients,shadows,shadow, colored, Red, Blue, Yellow, Green, Orange, Purple, Pink, Brown, Gray, Beige, Turquoise, Lavender, Cyan, Magenta, Olive, Indigo, black background\"\n  },\n  {\n    \"name\": \"MK Singer Sargent\",\n    \"prompt\": \"Oil painting by John Singer Sargent {prompt}. Elegant, refined, masterful technique,realistic portrayal, subtle play of light, captivating expression, rich details, harmonious colors, skillful composition, brush strokes, chiaroscuro.\",\n    \"negative_prompt\": \"realistic, photorealistic, abstract, overly stylized, excessive contrasts, distorted,bright colors,disorder.\"\n  },\n  {\n    \"name\": \"MK Pollock\",\n    \"prompt\": \"Oil painting by Jackson Pollock {prompt}. Abstract expressionism, drip painting, chaotic composition, energetic, spontaneous, unconventional technique, dynamic, bold, distinctive, vibrant, intense, expressive, energetic, layered, non-representational, gestural.\",\n    \"negative_prompt\": \"(realistic:1.5), (photorealistic:1.5), representational, calm, ordered composition, precise lines, detailed forms, subdued colors, quiet, static, traditional, figurative.\"\n  },\n  {\n    \"name\": \"MK Basquiat\",\n    \"prompt\": \"Artwork by Jean-Michel Basquiat {prompt}. Neo-expressionism, street art influence, graffiti-inspired, raw, energetic, bold colors, dynamic composition, chaotic, layered, textural, expressive, spontaneous, distinctive, symbolic,energetic brushstrokes.\",\n    \"negative_prompt\": \"(realistic:1.5), (photorealistic:1.5), calm, precise lines, conventional composition, subdued\"\n  },\n  {\n    \"name\": \"MK Andy Warhol\",\n    \"prompt\": \"Artwork in the style of Andy Warhol {prompt}. Pop art, vibrant colors, bold compositions, repetition of iconic imagery, celebrity culture, commercial aesthetics, mass production influence, stylized simplicity, cultural commentary, graphical elements, distinctive portraits.\",\n    \"negative_prompt\": \"subdued colors, realistic, lack of repetition, minimalistic.\"\n  },\n  {\n    \"name\": \"MK Halftone print\",\n    \"prompt\": \"Halftone print of {prompt}. Dot matrix pattern, grayscale tones, vintage aesthetic, newspaper print vibe, stylized dots, visual texture, black and white contrasts, retro appearance, artistic pointillism,pop culture, (Roy Lichtenstein style:1.5).\",\n    \"negative_prompt\": \"smooth gradients, continuous tones, vibrant colors.\"\n  },\n  {\n    \"name\": \"MK Gond Painting\",\n    \"prompt\": \"Gond painting {prompt}. Intricate patterns, vibrant colors, detailed motifs, nature-inspired themes, tribal folklore, fine lines, intricate detailing, storytelling compositions, mystical and folkloric, cultural richness.\",\n    \"negative_prompt\": \"monochromatic, abstract shapes, minimalistic.\"\n  },\n  {\n    \"name\": \"MK Albumen Print\",\n    \"prompt\": \"Albumen print {prompt}. Sepia tones, fine details, subtle tonal gradations, delicate highlights, vintage aesthetic, soft and muted atmosphere, historical charm, rich textures, meticulous craftsmanship, classic photographic technique, vignetting.\",\n    \"negative_prompt\": \"vibrant colors, high contrast, modern, digital appearance, sharp details, contemporary style.\"\n  },\n  {\n    \"name\": \"MK Aquatint Print\",\n    \"prompt\": \"Aquatint print {prompt}. Soft tonal gradations, atmospheric effects, velvety textures, rich contrasts, fine details, etching process, delicate lines, nuanced shading, expressive and moody atmosphere, artistic depth.\",\n    \"negative_prompt\": \"sharp contrasts, bold lines, minimalistic.\"\n  },\n  {\n    \"name\": \"MK Anthotype Print\",\n    \"prompt\": \"Anthotype print {prompt}. Monochrome dye, soft and muted colors, organic textures, ephemeral and delicate appearance, low details, watercolor canvas, low contrast, overexposed, silhouette, textured paper.\",\n    \"negative_prompt\": \"vibrant synthetic dyes, bold and saturated colors.\"\n  },\n  {\n    \"name\": \"MK Inuit Carving\",\n    \"prompt\": \"A sculpture made of ivory, {prompt} made of . Sculptures, Inuit art style, intricate carvings, natural materials, storytelling motifs, arctic wildlife themes, symbolic representations, cultural traditions, earthy tones, harmonious compositions, spiritual and mythological elements.\",\n    \"negative_prompt\": \"abstract, vibrant colors.\"\n  },\n  {\n    \"name\": \"MK Bromoil Print\",\n    \"prompt\": \"Bromoil print {prompt}. Painterly effects, sepia tones, textured surfaces, rich contrasts, expressive brushwork, tonal variations, vintage aesthetic, atmospheric mood, handmade quality, artistic experimentation, darkroom craftsmanship, vignetting.\",\n    \"negative_prompt\": \"smooth surfaces, minimal brushwork, contemporary digital appearance.\"\n  },\n  {\n    \"name\": \"MK Calotype Print\",\n    \"prompt\": \"Calotype print {prompt}. Soft focus, subtle tonal range, paper negative process, fine details, vintage aesthetic, artistic experimentation, atmospheric mood, early photographic charm, handmade quality, vignetting.\",\n    \"negative_prompt\": \"sharp focus, bold contrasts, modern aesthetic, digital photography.\"\n  },\n  {\n    \"name\": \"MK Color Sketchnote\",\n    \"prompt\": \"Color sketchnote {prompt}. Hand-drawn elements, vibrant colors, visual hierarchy, playful illustrations, varied typography, graphic icons, organic and dynamic layout, personalized touches, creative expression, engaging storytelling.\",\n    \"negative_prompt\": \"monochromatic, geometric layout.\"\n  },\n  {\n    \"name\": \"MK Cibulak Porcelain\",\n    \"prompt\": \"A sculpture made of blue pattern porcelain of {prompt}. Classic design, blue and white color scheme, intricate detailing, floral motifs, onion-shaped elements, historical charm, rococo, white ware, cobalt blue, underglaze pattern, fine craftsmanship, traditional elegance, delicate patterns, vintage aesthetic, Meissen, Blue Onion pattern, Cibulak.\",\n    \"negative_prompt\": \"tea, teapot, cup, teacup,bright colors, bold and modern design, absence of intricate detailing, lack of floral motifs, non-traditional shapes.\"\n  },\n  {\n    \"name\": \"MK Alcohol Ink Art\",\n    \"prompt\": \"Alcohol ink art {prompt}. Fluid and vibrant colors, unpredictable patterns, organic textures, translucent layers, abstract compositions, ethereal and dreamy effects, free-flowing movement, expressive brushstrokes, contemporary aesthetic, wet textured paper.\",\n    \"negative_prompt\": \"monochromatic, controlled patterns.\"\n  },\n  {\n    \"name\": \"MK One Line Art\",\n    \"prompt\": \"One line art {prompt}. Continuous and unbroken black line, minimalistic, simplicity, economical use of space, flowing and dynamic, symbolic representations, contemporary aesthetic, evocative and abstract, white background.\",\n    \"negative_prompt\": \"disjointed lines, complexity, complex detailing.\"\n  },\n  {\n    \"name\": \"MK Blacklight Paint\",\n    \"prompt\": \"Blacklight paint {prompt}. Fluorescent pigments, vibrant and surreal colors, ethereal glow, otherworldly effects, dynamic and psychedelic compositions, neon aesthetics, transformative in ultraviolet light, contemporary and experimental.\",\n    \"negative_prompt\": \"muted colors, traditional and realistic compositions.\"\n  },\n  {\n    \"name\": \"MK Carnival Glass\",\n    \"prompt\": \"A sculpture made of Carnival glass, {prompt}. Iridescent surfaces, vibrant colors, intricate patterns, opalescent hues, reflective and prismatic effects, Art Nouveau and Art Deco influences, vintage charm, intricate detailing, lustrous and luminous appearance, Carnival Glass style.\",\n    \"negative_prompt\": \"non-iridescent surfaces, muted colors, absence of intricate patterns, lack of opalescent hues, modern and minimalist aesthetic.\"\n  },\n  {\n    \"name\": \"MK Cyanotype Print\",\n    \"prompt\": \"Cyanotype print {prompt}. Prussian blue tones, distinctive coloration, high contrast, blueprint aesthetics, atmospheric mood, sun-exposed paper, silhouette effects, delicate details, historical charm, handmade and experimental quality.\",\n    \"negative_prompt\": \"vibrant colors, low contrast, modern and polished appearance.\"\n  },\n  {\n    \"name\": \"MK Cross-Stitching\",\n    \"prompt\": \"Cross-stitching {prompt}. Intricate patterns, embroidery thread, sewing, fine details, precise stitches, textile artistry, symmetrical designs, varied color palette, traditional and contemporary motifs, handmade and crafted,canvas, nostalgic charm.\",\n    \"negative_prompt\": \"paper, paint, ink, photography.\"\n  },\n  {\n    \"name\": \"MK Encaustic Paint\",\n    \"prompt\": \"Encaustic paint {prompt}. Textured surfaces, translucent layers, luminous quality, wax medium, rich color saturation, fluid and organic shapes, contemporary and historical influences, mixed media elements, atmospheric depth.\",\n    \"negative_prompt\": \"flat surfaces, opaque layers, lack of wax medium, muted color palette, absence of textured surfaces, non-mixed media.\"\n  },\n  {\n    \"name\": \"MK Embroidery\",\n    \"prompt\": \"Embroidery {prompt}. Intricate stitching, embroidery thread, fine details, varied thread textures, textile artistry, embellished surfaces, diverse color palette, traditional and contemporary motifs, handmade and crafted, tactile and ornate.\",\n    \"negative_prompt\": \"minimalist, monochromatic.\"\n  },\n  {\n    \"name\": \"MK Gyotaku\",\n    \"prompt\": \"Gyotaku {prompt}. Fish impressions, realistic details, ink rubbings, textured surfaces, traditional Japanese art form, nature-inspired compositions, artistic representation of marine life, black and white contrasts, cultural significance.\",\n    \"negative_prompt\": \"photography.\"\n  },\n  {\n    \"name\": \"MK Luminogram\",\n    \"prompt\": \"Luminogram {prompt}. Photogram technique, ethereal and abstract effects, light and shadow interplay, luminous quality, experimental process, direct light exposure, unique and unpredictable results, artistic experimentation.\",\n    \"negative_prompt\": \"\"\n  },\n  {\n    \"name\": \"MK Lite Brite Art\",\n    \"prompt\": \"Lite Brite art {prompt}. Luminous and colorful designs, pixelated compositions, retro aesthetic, glowing effects, creative patterns, interactive and playful, nostalgic charm, vibrant and dynamic arrangements.\",\n    \"negative_prompt\": \"monochromatic.\"\n  },\n  {\n    \"name\": \"MK Mokume-gane\",\n    \"prompt\": \"Mokume-gane {prompt}. Wood-grain patterns, mixed metal layers, intricate and organic designs, traditional Japanese metalwork, harmonious color combinations, artisanal craftsmanship, unique and layered textures, cultural and historical significance.\",\n    \"negative_prompt\": \"uniform metal surfaces.\"\n  },\n  {\n    \"name\": \"Pebble Art\",\n    \"prompt\": \"a sculpture made of peebles, {prompt}. Pebble art style,natural materials, textured surfaces, balanced compositions, organic forms, harmonious arrangements, tactile and 3D effects, beach-inspired aesthetic, creative storytelling, artisanal craftsmanship.\",\n    \"negative_prompt\": \"non-natural materials, lack of textured surfaces, imbalanced compositions, absence of organic forms, non-tactile appearance.\"\n  },\n  {\n    \"name\": \"MK Palekh\",\n    \"prompt\": \"Palekh art {prompt}. Miniature paintings, intricate details, vivid colors, folkloric themes, lacquer finish, storytelling compositions, symbolic elements, Russian folklore influence, cultural and historical significance.\",\n    \"negative_prompt\": \"large-scale paintings.\"\n  },\n  {\n    \"name\": \"MK Suminagashi\",\n    \"prompt\": \"Suminagashi {prompt}. Floating ink patterns, marbled effects, delicate and ethereal designs, water-based ink, fluid and unpredictable compositions, meditative process, monochromatic or subtle color palette, Japanese artistic tradition.\",\n    \"negative_prompt\": \"vibrant and bold color palette.\"\n  },\n  {\n    \"name\": \"MK Scrimshaw\",\n    \"prompt\": \"A Scrimshaw engraving of {prompt}. Intricate engravings on a spermwhale's teeth, marine motifs, detailed scenes, nautical themes, black and white contrasts, historical craftsmanship, artisanal carving, storytelling compositions, maritime heritage.\",\n    \"negative_prompt\": \"colorful, modern.\"\n  },\n  {\n    \"name\": \"MK Shibori\",\n    \"prompt\": \"Shibori {prompt}. Textured fabric, intricate patterns, resist-dyeing technique, indigo or vibrant colors, organic and flowing designs, Japanese textile art, cultural tradition, tactile and visual interest.\",\n    \"negative_prompt\": \"monochromatic.\"\n  },\n  {\n    \"name\": \"MK Vitreous Enamel\",\n    \"prompt\": \"A sculpture made of Vitreous enamel {prompt}. Smooth and glossy surfaces, vibrant colors, glass-like finish, durable and resilient, intricate detailing, traditional and contemporary applications, artistic craftsmanship, jewelry and decorative objects, , Vitreous enamel, colored glass.\",\n    \"negative_prompt\": \"rough surfaces, muted colors.\"\n  },\n  {\n    \"name\": \"MK Ukiyo-e\",\n    \"prompt\": \"Ukiyo-e {prompt}. Woodblock prints, vibrant colors, intricate details, depictions of landscapes, kabuki actors, beautiful women, cultural scenes, traditional Japanese art, artistic craftsmanship, historical significance.\",\n    \"negative_prompt\": \"absence of woodblock prints, muted colors, lack of intricate details, non-traditional Japanese themes, absence of cultural scenes.\"\n  },\n  {\n    \"name\": \"MK vintage-airline-poster\",\n    \"prompt\": \"vintage airline poster {prompt} . classic aviation fonts, pastel colors, elegant aircraft illustrations, scenic destinations, distressed textures, retro travel allure\",\n    \"negative_prompt\": \"modern fonts, bold colors, hyper-realistic, sleek design\"\n  },\n  {\n    \"name\": \"MK vintage-travel-poster\",\n    \"prompt\": \"vintage travel poster {prompt} . retro fonts, muted colors, scenic illustrations, iconic landmarks, distressed textures, nostalgic vibes\",\n    \"negative_prompt\": \"modern fonts, vibrant colors, hyper-realistic, sleek design\"\n  },\n  {\n    \"name\": \"MK bauhaus-style\",\n    \"prompt\": \"Bauhaus-inspired {prompt} . minimalism, geometric precision, primary colors, sans-serif typography, asymmetry, functional design\",\n    \"negative_prompt\": \"ornate, intricate, excessive detail, complex patterns, serif typography\"\n  },\n  {\n    \"name\": \"MK afrofuturism\",\n    \"prompt\": \"Afrofuturism illustration {prompt} . vibrant colors, futuristic elements, cultural symbolism, cosmic imagery, dynamic patterns, empowering narratives\",\n    \"negative_prompt\": \"monochromatic\"\n  },\n  {\n    \"name\": \"MK atompunk\",\n    \"prompt\": \"Atompunk illustation, {prompt} . retro-futuristic, atomic age aesthetics, sleek lines, metallic textures, futuristic technology, optimism, energy\",\n    \"negative_prompt\": \"organic, natural textures, rustic, dystopian\"\n  },\n  {\n    \"name\": \"MK constructivism\",\n    \"prompt\": \"Constructivism {prompt} . geometric abstraction, bold colors, industrial aesthetics, dynamic compositions, utilitarian design, revolutionary spirit\",\n    \"negative_prompt\": \"organic shapes, muted colors, ornate elements, traditional\"\n  },\n  {\n    \"name\": \"MK chicano-art\",\n    \"prompt\": \"Chicano art {prompt} . bold colors, cultural symbolism, muralism, lowrider aesthetics, barrio life, political messages, social activism, Mexico\",\n    \"negative_prompt\": \"monochromatic, minimalist, mainstream aesthetics\"\n  },\n  {\n    \"name\": \"MK de-stijl\",\n    \"prompt\": \"De Stijl Art {prompt} . neoplasticism, primary colors, geometric abstraction, horizontal and vertical lines, simplicity, harmony, utopian ideals\",\n    \"negative_prompt\": \"complex patterns, muted colors, ornate elements, asymmetry\"\n  },\n  {\n    \"name\": \"MK dayak-art\",\n    \"prompt\": \"Dayak art sculpture of {prompt} . intricate patterns, nature-inspired motifs, vibrant colors, traditional craftsmanship, cultural symbolism, storytelling\",\n    \"negative_prompt\": \"minimalist, monochromatic, modern\"\n  },\n  {\n    \"name\": \"MK fayum-portrait\",\n    \"prompt\": \"Fayum portrait {prompt} . encaustic painting, realistic facial features, warm earth tones, serene expressions, ancient Egyptian influences\",\n    \"negative_prompt\": \"abstract, vibrant colors, exaggerated features, modern\"\n  },\n  {\n    \"name\": \"MK illuminated-manuscript\",\n    \"prompt\": \"Illuminated manuscript {prompt} . intricate calligraphy, rich colors, detailed illustrations, gold leaf accents, ornate borders, religious, historical, medieval\",\n    \"negative_prompt\": \"modern typography, minimalist design, monochromatic, abstract themes\"\n  },\n  {\n    \"name\": \"MK kalighat-painting\",\n    \"prompt\": \"Kalighat painting {prompt} . bold lines, vibrant colors, narrative storytelling, cultural motifs, flat compositions, expressive characters\",\n    \"negative_prompt\": \"subdued colors, intricate details, realistic portrayal, modern aesthetics\"\n  },\n  {\n    \"name\": \"MK madhubani-painting\",\n    \"prompt\": \"Madhubani painting {prompt} . intricate patterns, vibrant colors, nature-inspired motifs, cultural storytelling, symmetry, folk art aesthetics\",\n    \"negative_prompt\": \"abstract, muted colors, minimalistic design, modern aesthetics\"\n  },\n  {\n    \"name\": \"MK pictorialism\",\n    \"prompt\": \"Pictorialism illustration{prompt} . soft focus, atmospheric effects, artistic interpretation, tonality, muted colors, evocative storytelling\",\n    \"negative_prompt\": \"sharp focus, high contrast, realistic depiction, vivid colors\"\n  },\n  {\n    \"name\": \"MK pichwai-painting\",\n    \"prompt\": \"Pichwai painting {prompt} . intricate detailing, vibrant colors, religious themes, nature motifs, devotional storytelling, gold leaf accents\",\n    \"negative_prompt\": \"minimalist, subdued colors, abstract design\"\n  },\n  {\n    \"name\": \"MK patachitra-painting\",\n    \"prompt\": \"Patachitra painting {prompt} . bold outlines, vibrant colors, intricate detailing, mythological themes, storytelling, traditional craftsmanship\",\n    \"negative_prompt\": \"subdued colors, minimalistic, abstract, modern aesthetics\"\n  },\n  {\n    \"name\": \"MK samoan-art-inspired\",\n    \"prompt\": \"Samoan art-inspired wooden sculpture {prompt} . traditional motifs, natural elements, bold colors, cultural symbolism, storytelling, craftsmanship\",\n    \"negative_prompt\": \"modern aesthetics, minimalist, abstract\"\n  },\n  {\n    \"name\": \"MK tlingit-art\",\n    \"prompt\": \"Tlingit art {prompt} . formline design, natural elements, animal motifs, bold colors, cultural storytelling, traditional craftsmanship, Alaska traditional art, (totem:1.5)\",\n    \"negative_prompt\": \"\"\n  },\n  {\n    \"name\": \"MK adnate-style\",\n    \"prompt\": \"Painting by Adnate {prompt} . realistic portraits, street art, large-scale murals, subdued color palette, social narratives\",\n    \"negative_prompt\": \"abstract, vibrant colors, small-scale art\"\n  },\n  {\n    \"name\": \"MK ron-english-style\",\n    \"prompt\": \"Painting by Ron English {prompt} . pop-surrealism, cultural subversion, iconic mash-ups, vibrant and bold colors, satirical commentary\",\n    \"negative_prompt\": \"traditional, monochromatic\"\n  },\n  {\n    \"name\": \"MK shepard-fairey-style\",\n    \"prompt\": \"Painting by Shepard Fairey {prompt} . street art, political activism, iconic stencils, bold typography, high contrast, red, black, and white color palette\",\n    \"negative_prompt\": \"traditional, muted colors\"\n  }\n]\n"
  },
  {
    "path": "sdxl_styles/sdxl_styles_mre.json",
    "content": "[\n    {\n        \"name\": \"mre-cinematic-dynamic\",\n        \"prompt\": \"epic cinematic shot of dynamic {prompt} in motion. main subject of high budget action movie. raw photo, motion blur. best quality, high resolution\",\n        \"negative_prompt\": \"static, still, motionless, sluggish. drawing, painting, illustration, rendered. low budget. low quality, low resolution\"\n    },\n    {\n        \"name\": \"mre-spontaneous-picture\",\n        \"prompt\": \"spontaneous picture of {prompt}, taken by talented amateur. best quality, high resolution. magical moment, natural look. simple but good looking\",\n        \"negative_prompt\": \"overthinked. low quality, low resolution\"\n    },\n    {\n        \"name\": \"mre-artistic-vision\",\n        \"prompt\": \"powerful artistic vision of {prompt}. breathtaking masterpiece made by great artist. best quality, high resolution\",\n        \"negative_prompt\": \"insignificant, flawed, made by bad artist. low quality, low resolution\"\n    },\n    {\n        \"name\": \"mre-dark-dream\",\n        \"prompt\": \"dark and unsettling dream showing {prompt}. best quality, high resolution. created by genius but depressed mad artist. grim beauty\",\n        \"negative_prompt\": \"naive, cheerful. comfortable, casual, boring, cliche. low quality, low resolution\"\n    },\n    {\n        \"name\": \"mre-gloomy-art\",\n        \"prompt\": \"astonishing gloomy art made mainly of shadows and lighting, forming {prompt}. masterful usage of lighting, shadows and chiaroscuro. made by black-hearted artist, drawing from darkness. best quality, high resolution\",\n        \"negative_prompt\": \"low quality, low resolution\"\n    },\n    {\n        \"name\": \"mre-bad-dream\",\n        \"prompt\": \"picture from really bad dream about terrifying {prompt}, true horror. bone-chilling vision. mad world that shouldn't exist. best quality, high resolution\",\n        \"negative_prompt\": \"nice dream, pleasant experience. low quality, low resolution\"\n    },\n    {\n        \"name\": \"mre-underground\",\n        \"prompt\": \"uncanny caliginous vision of {prompt}, created by remarkable underground artist. best quality, high resolution. raw and brutal art, careless but impressive style. inspired by darkness and chaos\",\n        \"negative_prompt\": \"photography, mainstream, civilized. low quality, low resolution\"\n    },\n    {\n        \"name\": \"mre-surreal-painting\",\n        \"prompt\": \"surreal painting representing strange vision of {prompt}. harmonious madness, synergy with chance. unique artstyle, mindbending art, magical surrealism. best quality, high resolution\",\n        \"negative_prompt\": \"photography, illustration, drawing. realistic, possible. logical, sane. low quality, low resolution\"\n    },\n    {\n        \"name\": \"mre-dynamic-illustration\",\n        \"prompt\": \"insanely dynamic illustration of {prompt}. best quality, high resolution. crazy artstyle, careless brushstrokes, emotional and fun\",\n        \"negative_prompt\": \"photography, realistic. static, still, slow, boring. low quality, low resolution\"\n    },\n    {\n        \"name\": \"mre-undead-art\",\n        \"prompt\": \"long forgotten art created by undead artist illustrating {prompt}, tribute to the death and decay. miserable art of the damned. wretched and decaying world. best quality, high resolution\",\n        \"negative_prompt\": \"alive, playful, living. low quality, low resolution\"\n    },\n    {\n        \"name\": \"mre-elemental-art\",\n        \"prompt\": \"art illustrating insane amounts of raging elemental energy turning into {prompt}, avatar of elements. magical surrealism, wizardry. best quality, high resolution\",\n        \"negative_prompt\": \"photography, realistic, real. low quality, low resolution\"\n    },\n    {\n        \"name\": \"mre-space-art\",\n        \"prompt\": \"winner of inter-galactic art contest illustrating {prompt}, symbol of the interstellar singularity. best quality, high resolution. artstyle previously unseen in the whole galaxy\",\n        \"negative_prompt\": \"created by human race, low quality, low resolution\"\n    },\n    {\n        \"name\": \"mre-ancient-illustration\",\n        \"prompt\": \"sublime ancient illustration of {prompt}, predating human civilization. crude and simple, but also surprisingly beautiful artwork, made by genius primeval artist. best quality, high resolution\",\n        \"negative_prompt\": \"low quality, low resolution\"\n    },\n    {\n        \"name\": \"mre-brave-art\",\n        \"prompt\": \"brave, shocking, and brutally true art showing {prompt}. inspired by courage and unlimited creativity. truth found in chaos. best quality, high resolution\",\n        \"negative_prompt\": \"low quality, low resolution\"\n    },\n    {\n        \"name\": \"mre-heroic-fantasy\",\n        \"prompt\": \"heroic fantasy painting of {prompt}, in the dangerous fantasy world. airbrush over oil on canvas. best quality, high resolution\",\n        \"negative_prompt\": \"low quality, low resolution\"\n    },\n    {\n        \"name\": \"mre-dark-cyberpunk\",\n        \"prompt\": \"dark cyberpunk illustration of brutal {prompt} in a world without hope, ruled by ruthless criminal corporations. best quality, high resolution\",\n        \"negative_prompt\": \"low quality, low resolution\"\n    },\n    {\n        \"name\": \"mre-lyrical-geometry\",\n        \"prompt\": \"geometric and lyrical abstraction painting presenting {prompt}. oil on metal. best quality, high resolution\",\n        \"negative_prompt\": \"photography, realistic, drawing, rendered. low quality, low resolution\"\n    },\n    {\n        \"name\": \"mre-sumi-e-symbolic\",\n        \"prompt\": \"big long brushstrokes of deep black sumi-e turning into symbolic painting of {prompt}. master level raw art. best quality, high resolution\",\n        \"negative_prompt\": \"photography, rendered. low quality, low resolution\"\n    },\n    {\n        \"name\": \"mre-sumi-e-detailed\",\n        \"prompt\": \"highly detailed black sumi-e painting of {prompt}. in-depth study of perfection, created by a master. best quality, high resolution\",\n        \"negative_prompt\": \"low quality, low resolution\"\n    },\n    {\n        \"name\": \"mre-manga\",\n        \"prompt\": \"manga artwork presenting {prompt}. created by japanese manga artist. highly emotional. best quality, high resolution\",\n        \"negative_prompt\": \"low quality, low resolution\"\n    },\n    {\n        \"name\": \"mre-anime\",\n        \"prompt\": \"anime artwork illustrating {prompt}. created by japanese anime studio. highly emotional. best quality, high resolution\",\n        \"negative_prompt\": \"low quality, low resolution\"\n    },\n    {\n        \"name\": \"mre-comic\",\n        \"prompt\": \"breathtaking illustration from adult comic book presenting {prompt}. fabulous artwork. best quality, high resolution\",\n        \"negative_prompt\": \"deformed, ugly, low quality, low resolution\"\n    }\n]\n"
  },
  {
    "path": "sdxl_styles/sdxl_styles_sai.json",
    "content": "[\n    {\n        \"name\": \"sai-3d-model\",\n        \"prompt\": \"professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting\",\n        \"negative_prompt\": \"ugly, deformed, noisy, low poly, blurry, painting\"\n    },\n    {\n        \"name\": \"sai-analog film\",\n        \"prompt\": \"analog film photo {prompt} . faded film, desaturated, 35mm photo, grainy, vignette, vintage, Kodachrome, Lomography, stained, highly detailed, found footage\",\n        \"negative_prompt\": \"painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured\"\n    },\n    {\n        \"name\": \"sai-anime\",\n        \"prompt\": \"anime artwork {prompt} . anime style, key visual, vibrant, studio anime, highly detailed\",\n        \"negative_prompt\": \"photo, deformed, black and white, realism, disfigured, low contrast\"\n    },\n    {\n        \"name\": \"sai-cinematic\",\n        \"prompt\": \"cinematic film still {prompt} . shallow depth of field, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy\",\n        \"negative_prompt\": \"anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured\"\n    },\n    {\n        \"name\": \"sai-comic book\",\n        \"prompt\": \"comic {prompt} . graphic illustration, comic art, graphic novel art, vibrant, highly detailed\",\n        \"negative_prompt\": \"photograph, deformed, glitch, noisy, realistic, stock photo\"\n    },\n    {\n        \"name\": \"sai-craft clay\",\n        \"prompt\": \"play-doh style {prompt} . sculpture, clay art, centered composition, Claymation\",\n        \"negative_prompt\": \"sloppy, messy, grainy, highly detailed, ultra textured, photo\"\n    },\n    {\n        \"name\": \"sai-digital art\",\n        \"prompt\": \"concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed\",\n        \"negative_prompt\": \"photo, photorealistic, realism, ugly\"\n    },\n    {\n        \"name\": \"sai-enhance\",\n        \"prompt\": \"breathtaking {prompt} . award-winning, professional, highly detailed\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, distorted, grainy\"\n    },\n    {\n        \"name\": \"sai-fantasy art\",\n        \"prompt\": \"ethereal fantasy concept art of  {prompt} . magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy\",\n        \"negative_prompt\": \"photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white\"\n    },\n    {\n        \"name\": \"sai-isometric\",\n        \"prompt\": \"isometric style {prompt} . vibrant, beautiful, crisp, detailed, ultra detailed, intricate\",\n        \"negative_prompt\": \"deformed, mutated, ugly, disfigured, blur, blurry, noise, noisy, realistic, photographic\"\n    },\n    {\n        \"name\": \"sai-line art\",\n        \"prompt\": \"line art drawing {prompt} . professional, sleek, modern, minimalist, graphic, line art, vector graphics\",\n        \"negative_prompt\": \"anime, photorealistic, 35mm film, deformed, glitch, blurry, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, mutated, realism, realistic, impressionism, expressionism, oil, acrylic\"\n    },\n    {\n        \"name\": \"sai-lowpoly\",\n        \"prompt\": \"low-poly style {prompt} . low-poly game art, polygon mesh, jagged, blocky, wireframe edges, centered composition\",\n        \"negative_prompt\": \"noisy, sloppy, messy, grainy, highly detailed, ultra textured, photo\"\n    },\n    {\n        \"name\": \"sai-neonpunk\",\n        \"prompt\": \"neonpunk style {prompt} . cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, ultra detailed, intricate, professional\",\n        \"negative_prompt\": \"painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured\"\n    },\n    {\n        \"name\": \"sai-origami\",\n        \"prompt\": \"origami style {prompt} . paper art, pleated paper, folded, origami art, pleats, cut and fold, centered composition\",\n        \"negative_prompt\": \"noisy, sloppy, messy, grainy, highly detailed, ultra textured, photo\"\n    },\n    {\n        \"name\": \"sai-photographic\",\n        \"prompt\": \"cinematic photo {prompt} . 35mm photograph, film, bokeh, professional, 4k, highly detailed\",\n        \"negative_prompt\": \"drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly\"\n    },\n    {\n        \"name\": \"sai-pixel art\",\n        \"prompt\": \"pixel-art {prompt} . low-res, blocky, pixel art style, 8-bit graphics\",\n        \"negative_prompt\": \"sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic\"\n    },\n    {\n        \"name\": \"sai-texture\",\n        \"prompt\": \"texture {prompt} top down close-up\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry\"\n    }\n]"
  },
  {
    "path": "sdxl_styles/sdxl_styles_twri.json",
    "content": "[\n    {\n        \"name\": \"ads-advertising\",\n        \"prompt\": \"advertising poster style {prompt} . Professional, modern, product-focused, commercial, eye-catching, highly detailed\",\n        \"negative_prompt\": \"noisy, blurry, amateurish, sloppy, unattractive\"\n    },\n    {\n        \"name\": \"ads-automotive\",\n        \"prompt\": \"automotive advertisement style {prompt} . sleek, dynamic, professional, commercial, vehicle-focused, high-resolution, highly detailed\",\n        \"negative_prompt\": \"noisy, blurry, unattractive, sloppy, unprofessional\"\n    },\n    {\n        \"name\": \"ads-corporate\",\n        \"prompt\": \"corporate branding style {prompt} . professional, clean, modern, sleek, minimalist, business-oriented, highly detailed\",\n        \"negative_prompt\": \"noisy, blurry, grungy, sloppy, cluttered, disorganized\"\n    },\n    {\n        \"name\": \"ads-fashion editorial\",\n        \"prompt\": \"fashion editorial style {prompt} . high fashion, trendy, stylish, editorial, magazine style, professional, highly detailed\",\n        \"negative_prompt\": \"outdated, blurry, noisy, unattractive, sloppy\"\n    },\n    {\n        \"name\": \"ads-food photography\",\n        \"prompt\": \"food photography style {prompt} . appetizing, professional, culinary, high-resolution, commercial, highly detailed\",\n        \"negative_prompt\": \"unappetizing, sloppy, unprofessional, noisy, blurry\"\n    },\n    {\n        \"name\": \"ads-gourmet food photography\",\n        \"prompt\": \"gourmet food photo of {prompt} . soft natural lighting, macro details, vibrant colors, fresh ingredients, glistening textures, bokeh background, styled plating, wooden tabletop, garnished, tantalizing, editorial quality\",\n        \"negative_prompt\": \"cartoon, anime, sketch, grayscale, dull, overexposed, cluttered, messy plate, deformed\"\n    },\n    {\n        \"name\": \"ads-luxury\",\n        \"prompt\": \"luxury product style {prompt} . elegant, sophisticated, high-end, luxurious, professional, highly detailed\",\n        \"negative_prompt\": \"cheap, noisy, blurry, unattractive, amateurish\"\n    },\n    {\n        \"name\": \"ads-real estate\",\n        \"prompt\": \"real estate photography style {prompt} . professional, inviting, well-lit, high-resolution, property-focused, commercial, highly detailed\",\n        \"negative_prompt\": \"dark, blurry, unappealing, noisy, unprofessional\"\n    },\n    {\n        \"name\": \"ads-retail\",\n        \"prompt\": \"retail packaging style {prompt} . vibrant, enticing, commercial, product-focused, eye-catching, professional, highly detailed\",\n        \"negative_prompt\": \"noisy, blurry, amateurish, sloppy, unattractive\"\n    },\n    {\n        \"name\": \"artstyle-abstract\",\n        \"prompt\": \"abstract style {prompt} . non-representational, colors and shapes, expression of feelings, imaginative, highly detailed\",\n        \"negative_prompt\": \"realistic, photographic, figurative, concrete\"\n    },\n    {\n        \"name\": \"artstyle-abstract expressionism\",\n        \"prompt\": \"abstract expressionist painting {prompt} . energetic brushwork, bold colors, abstract forms, expressive, emotional\",\n        \"negative_prompt\": \"realistic, photorealistic, low contrast, plain, simple, monochrome\"\n    },\n    {\n        \"name\": \"artstyle-art deco\",\n        \"prompt\": \"art deco style {prompt} . geometric shapes, bold colors, luxurious, elegant, decorative, symmetrical, ornate, detailed\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, modernist, minimalist\"\n    },\n    {\n        \"name\": \"artstyle-art nouveau\",\n        \"prompt\": \"art nouveau style {prompt} . elegant, decorative, curvilinear forms, nature-inspired, ornate, detailed\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, modernist, minimalist\"\n    },\n    {\n        \"name\": \"artstyle-constructivist\",\n        \"prompt\": \"constructivist style {prompt} . geometric shapes, bold colors, dynamic composition, propaganda art style\",\n        \"negative_prompt\": \"realistic, photorealistic, low contrast, plain, simple, abstract expressionism\"\n    },\n    {\n        \"name\": \"artstyle-cubist\",\n        \"prompt\": \"cubist artwork {prompt} . geometric shapes, abstract, innovative, revolutionary\",\n        \"negative_prompt\": \"anime, photorealistic, 35mm film, deformed, glitch, low contrast, noisy\"\n    },\n    {\n        \"name\": \"artstyle-expressionist\",\n        \"prompt\": \"expressionist {prompt} . raw, emotional, dynamic, distortion for emotional effect, vibrant, use of unusual colors, detailed\",\n        \"negative_prompt\": \"realism, symmetry, quiet, calm, photo\"\n    },\n    {\n        \"name\": \"artstyle-graffiti\",\n        \"prompt\": \"graffiti style {prompt} . street art, vibrant, urban, detailed, tag, mural\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, realism, photorealistic\"\n    },\n    {\n        \"name\": \"artstyle-hyperrealism\",\n        \"prompt\": \"hyperrealistic art {prompt} . extremely high-resolution details, photographic, realism pushed to extreme, fine texture, incredibly lifelike\",\n        \"negative_prompt\": \"simplified, abstract, unrealistic, impressionistic, low resolution\"\n    },\n    {\n        \"name\": \"artstyle-impressionist\",\n        \"prompt\": \"impressionist painting {prompt} . loose brushwork, vibrant color, light and shadow play, captures feeling over form\",\n        \"negative_prompt\": \"anime, photorealistic, 35mm film, deformed, glitch, low contrast, noisy\"\n    },\n    {\n        \"name\": \"artstyle-pointillism\",\n        \"prompt\": \"pointillism style {prompt} . composed entirely of small, distinct dots of color, vibrant, highly detailed\",\n        \"negative_prompt\": \"line drawing, smooth shading, large color fields, simplistic\"\n    },\n    {\n        \"name\": \"artstyle-pop art\",\n        \"prompt\": \"pop Art style {prompt} . bright colors, bold outlines, popular culture themes, ironic or kitsch\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, minimalist\"\n    },\n    {\n        \"name\": \"artstyle-psychedelic\",\n        \"prompt\": \"psychedelic style {prompt} . vibrant colors, swirling patterns, abstract forms, surreal, trippy\",\n        \"negative_prompt\": \"monochrome, black and white, low contrast, realistic, photorealistic, plain, simple\"\n    },\n    {\n        \"name\": \"artstyle-renaissance\",\n        \"prompt\": \"renaissance style {prompt} . realistic, perspective, light and shadow, religious or mythological themes, highly detailed\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, modernist, minimalist, abstract\"\n    },\n    {\n        \"name\": \"artstyle-steampunk\",\n        \"prompt\": \"steampunk style {prompt} . antique, mechanical, brass and copper tones, gears, intricate, detailed\",\n        \"negative_prompt\": \"deformed, glitch, noisy, low contrast, anime, photorealistic\"\n    },\n    {\n        \"name\": \"artstyle-surrealist\",\n        \"prompt\": \"surrealist art {prompt} . dreamlike, mysterious, provocative, symbolic, intricate, detailed\",\n        \"negative_prompt\": \"anime, photorealistic, realistic, deformed, glitch, noisy, low contrast\"\n    },\n    {\n        \"name\": \"artstyle-typography\",\n        \"prompt\": \"typographic art {prompt} . stylized, intricate, detailed, artistic, text-based\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, realism, photorealistic\"\n    },\n    {\n        \"name\": \"artstyle-watercolor\",\n        \"prompt\": \"watercolor painting {prompt} . vibrant, beautiful, painterly, detailed, textural, artistic\",\n        \"negative_prompt\": \"anime, photorealistic, 35mm film, deformed, glitch, low contrast, noisy\"\n    },\n    {\n        \"name\": \"futuristic-biomechanical\",\n        \"prompt\": \"biomechanical style {prompt} . blend of organic and mechanical elements, futuristic, cybernetic, detailed, intricate\",\n        \"negative_prompt\": \"natural, rustic, primitive, organic, simplistic\"\n    },\n    {\n        \"name\": \"futuristic-biomechanical cyberpunk\",\n        \"prompt\": \"biomechanical cyberpunk {prompt} . cybernetics, human-machine fusion, dystopian, organic meets artificial, dark, intricate, highly detailed\",\n        \"negative_prompt\": \"natural, colorful, deformed, sketch, low contrast, watercolor\"\n    },\n    {\n        \"name\": \"futuristic-cybernetic\",\n        \"prompt\": \"cybernetic style {prompt} . futuristic, technological, cybernetic enhancements, robotics, artificial intelligence themes\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, historical, medieval\"\n    },\n    {\n        \"name\": \"futuristic-cybernetic robot\",\n        \"prompt\": \"cybernetic robot {prompt} . android, AI, machine, metal, wires, tech, futuristic, highly detailed\",\n        \"negative_prompt\": \"organic, natural, human, sketch, watercolor, low contrast\"\n    },\n    {\n        \"name\": \"futuristic-cyberpunk cityscape\",\n        \"prompt\": \"cyberpunk cityscape {prompt} . neon lights, dark alleys, skyscrapers, futuristic, vibrant colors, high contrast, highly detailed\",\n        \"negative_prompt\": \"natural, rural, deformed, low contrast, black and white, sketch, watercolor\"\n    },\n    {\n        \"name\": \"futuristic-futuristic\",\n        \"prompt\": \"futuristic style {prompt} . sleek, modern, ultramodern, high tech, detailed\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, vintage, antique\"\n    },\n    {\n        \"name\": \"futuristic-retro cyberpunk\",\n        \"prompt\": \"retro cyberpunk {prompt} . 80's inspired, synthwave, neon, vibrant, detailed, retro futurism\",\n        \"negative_prompt\": \"modern, desaturated, black and white, realism, low contrast\"\n    },\n    {\n        \"name\": \"futuristic-retro futurism\",\n        \"prompt\": \"retro-futuristic {prompt} . vintage sci-fi, 50s and 60s style, atomic age, vibrant, highly detailed\",\n        \"negative_prompt\": \"contemporary, realistic, rustic, primitive\"\n    },\n    {\n        \"name\": \"futuristic-sci-fi\",\n        \"prompt\": \"sci-fi style {prompt} . futuristic, technological, alien worlds, space themes, advanced civilizations\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, historical, medieval\"\n    },\n    {\n        \"name\": \"futuristic-vaporwave\",\n        \"prompt\": \"vaporwave style {prompt} . retro aesthetic, cyberpunk, vibrant, neon colors, vintage 80s and 90s style, highly detailed\",\n        \"negative_prompt\": \"monochrome, muted colors, realism, rustic, minimalist, dark\"\n    },\n    {\n        \"name\": \"game-bubble bobble\",\n        \"prompt\": \"Bubble Bobble style {prompt} . 8-bit, cute, pixelated, fantasy, vibrant, reminiscent of Bubble Bobble game\",\n        \"negative_prompt\": \"realistic, modern, photorealistic, violent, horror\"\n    },\n    {\n        \"name\": \"game-cyberpunk game\",\n        \"prompt\": \"cyberpunk game style {prompt} . neon, dystopian, futuristic, digital, vibrant, detailed, high contrast, reminiscent of cyberpunk genre video games\",\n        \"negative_prompt\": \"historical, natural, rustic, low detailed\"\n    },\n    {\n        \"name\": \"game-fighting game\",\n        \"prompt\": \"fighting game style {prompt} . dynamic, vibrant, action-packed, detailed character design, reminiscent of fighting video games\",\n        \"negative_prompt\": \"peaceful, calm, minimalist, photorealistic\"\n    },\n    {\n        \"name\": \"game-gta\",\n        \"prompt\": \"GTA-style artwork {prompt} . satirical, exaggerated, pop art style, vibrant colors, iconic characters, action-packed\",\n        \"negative_prompt\": \"realistic, black and white, low contrast, impressionist, cubist, noisy, blurry, deformed\"\n    },\n    {\n        \"name\": \"game-mario\",\n        \"prompt\": \"Super Mario style {prompt} . vibrant, cute, cartoony, fantasy, playful, reminiscent of Super Mario series\",\n        \"negative_prompt\": \"realistic, modern, horror, dystopian, violent\"\n    },\n    {\n        \"name\": \"game-minecraft\",\n        \"prompt\": \"Minecraft style {prompt} . blocky, pixelated, vibrant colors, recognizable characters and objects, game assets\",\n        \"negative_prompt\": \"smooth, realistic, detailed, photorealistic, noise, blurry, deformed\"\n    },\n    {\n        \"name\": \"game-pokemon\",\n        \"prompt\": \"Pokémon style {prompt} . vibrant, cute, anime, fantasy, reminiscent of Pokémon series\",\n        \"negative_prompt\": \"realistic, modern, horror, dystopian, violent\"\n    },\n    {\n        \"name\": \"game-retro arcade\",\n        \"prompt\": \"retro arcade style {prompt} . 8-bit, pixelated, vibrant, classic video game, old school gaming, reminiscent of 80s and 90s arcade games\",\n        \"negative_prompt\": \"modern, ultra-high resolution, photorealistic, 3D\"\n    },\n    {\n        \"name\": \"game-retro game\",\n        \"prompt\": \"retro game art {prompt} . 16-bit, vibrant colors, pixelated, nostalgic, charming, fun\",\n        \"negative_prompt\": \"realistic, photorealistic, 35mm film, deformed, glitch, low contrast, noisy\"\n    },\n    {\n        \"name\": \"game-rpg fantasy game\",\n        \"prompt\": \"role-playing game (RPG) style fantasy {prompt} . detailed, vibrant, immersive, reminiscent of high fantasy RPG games\",\n        \"negative_prompt\": \"sci-fi, modern, urban, futuristic, low detailed\"\n    },\n    {\n        \"name\": \"game-strategy game\",\n        \"prompt\": \"strategy game style {prompt} . overhead view, detailed map, units, reminiscent of real-time strategy video games\",\n        \"negative_prompt\": \"first-person view, modern, photorealistic\"\n    },\n    {\n        \"name\": \"game-streetfighter\",\n        \"prompt\": \"Street Fighter style {prompt} . vibrant, dynamic, arcade, 2D fighting game, highly detailed, reminiscent of Street Fighter series\",\n        \"negative_prompt\": \"3D, realistic, modern, photorealistic, turn-based strategy\"\n    },\n    {\n        \"name\": \"game-zelda\",\n        \"prompt\": \"Legend of Zelda style {prompt} . vibrant, fantasy, detailed, epic, heroic, reminiscent of The Legend of Zelda series\",\n        \"negative_prompt\": \"sci-fi, modern, realistic, horror\"\n    },\n    {\n        \"name\": \"misc-architectural\",\n        \"prompt\": \"architectural style {prompt} . clean lines, geometric shapes, minimalist, modern, architectural drawing, highly detailed\",\n        \"negative_prompt\": \"curved lines, ornate, baroque, abstract, grunge\"\n    },\n    {\n        \"name\": \"misc-disco\",\n        \"prompt\": \"disco-themed {prompt} . vibrant, groovy, retro 70s style, shiny disco balls, neon lights, dance floor, highly detailed\",\n        \"negative_prompt\": \"minimalist, rustic, monochrome, contemporary, simplistic\"\n    },\n    {\n        \"name\": \"misc-dreamscape\",\n        \"prompt\": \"dreamscape {prompt} . surreal, ethereal, dreamy, mysterious, fantasy, highly detailed\",\n        \"negative_prompt\": \"realistic, concrete, ordinary, mundane\"\n    },\n    {\n        \"name\": \"misc-dystopian\",\n        \"prompt\": \"dystopian style {prompt} . bleak, post-apocalyptic, somber, dramatic, highly detailed\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, cheerful, optimistic, vibrant, colorful\"\n    },\n    {\n        \"name\": \"misc-fairy tale\",\n        \"prompt\": \"fairy tale {prompt} . magical, fantastical, enchanting, storybook style, highly detailed\",\n        \"negative_prompt\": \"realistic, modern, ordinary, mundane\"\n    },\n    {\n        \"name\": \"misc-gothic\",\n        \"prompt\": \"gothic style {prompt} . dark, mysterious, haunting, dramatic, ornate, detailed\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, cheerful, optimistic\"\n    },\n    {\n        \"name\": \"misc-grunge\",\n        \"prompt\": \"grunge style {prompt} . textured, distressed, vintage, edgy, punk rock vibe, dirty, noisy\",\n        \"negative_prompt\": \"smooth, clean, minimalist, sleek, modern, photorealistic\"\n    },\n    {\n        \"name\": \"misc-horror\",\n        \"prompt\": \"horror-themed {prompt} . eerie, unsettling, dark, spooky, suspenseful, grim, highly detailed\",\n        \"negative_prompt\": \"cheerful, bright, vibrant, light-hearted, cute\"\n    },\n    {\n        \"name\": \"misc-kawaii\",\n        \"prompt\": \"kawaii style {prompt} . cute, adorable, brightly colored, cheerful, anime influence, highly detailed\",\n        \"negative_prompt\": \"dark, scary, realistic, monochrome, abstract\"\n    },\n    {\n        \"name\": \"misc-lovecraftian\",\n        \"prompt\": \"lovecraftian horror {prompt} . eldritch, cosmic horror, unknown, mysterious, surreal, highly detailed\",\n        \"negative_prompt\": \"light-hearted, mundane, familiar, simplistic, realistic\"\n    },\n    {\n        \"name\": \"misc-macabre\",\n        \"prompt\": \"macabre style {prompt} . dark, gothic, grim, haunting, highly detailed\",\n        \"negative_prompt\": \"bright, cheerful, light-hearted, cartoonish, cute\"\n    },\n    {\n        \"name\": \"misc-manga\",\n        \"prompt\": \"manga style {prompt} . vibrant, high-energy, detailed, iconic, Japanese comic style\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style\"\n    },\n    {\n        \"name\": \"misc-metropolis\",\n        \"prompt\": \"metropolis-themed {prompt} . urban, cityscape, skyscrapers, modern, futuristic, highly detailed\",\n        \"negative_prompt\": \"rural, natural, rustic, historical, simple\"\n    },\n    {\n        \"name\": \"misc-minimalist\",\n        \"prompt\": \"minimalist style {prompt} . simple, clean, uncluttered, modern, elegant\",\n        \"negative_prompt\": \"ornate, complicated, highly detailed, cluttered, disordered, messy, noisy\"\n    },\n    {\n        \"name\": \"misc-monochrome\",\n        \"prompt\": \"monochrome {prompt} . black and white, contrast, tone, texture, detailed\",\n        \"negative_prompt\": \"colorful, vibrant, noisy, blurry, deformed\"\n    },\n    {\n        \"name\": \"misc-nautical\",\n        \"prompt\": \"nautical-themed {prompt} . sea, ocean, ships, maritime, beach, marine life, highly detailed\",\n        \"negative_prompt\": \"landlocked, desert, mountains, urban, rustic\"\n    },\n    {\n        \"name\": \"misc-space\",\n        \"prompt\": \"space-themed {prompt} . cosmic, celestial, stars, galaxies, nebulas, planets, science fiction, highly detailed\",\n        \"negative_prompt\": \"earthly, mundane, ground-based, realism\"\n    },\n    {\n        \"name\": \"misc-stained glass\",\n        \"prompt\": \"stained glass style {prompt} . vibrant, beautiful, translucent, intricate, detailed\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, realism, photorealistic\"\n    },\n    {\n        \"name\": \"misc-techwear fashion\",\n        \"prompt\": \"techwear fashion {prompt} . futuristic, cyberpunk, urban, tactical, sleek, dark, highly detailed\",\n        \"negative_prompt\": \"vintage, rural, colorful, low contrast, realism, sketch, watercolor\"\n    },\n    {\n        \"name\": \"misc-tribal\",\n        \"prompt\": \"tribal style {prompt} . indigenous, ethnic, traditional patterns, bold, natural colors, highly detailed\",\n        \"negative_prompt\": \"modern, futuristic, minimalist, pastel\"\n    },\n    {\n        \"name\": \"misc-zentangle\",\n        \"prompt\": \"zentangle {prompt} . intricate, abstract, monochrome, patterns, meditative, highly detailed\",\n        \"negative_prompt\": \"colorful, representative, simplistic, large fields of color\"\n    },\n    {\n        \"name\": \"papercraft-collage\",\n        \"prompt\": \"collage style {prompt} . mixed media, layered, textural, detailed, artistic\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, realism, photorealistic\"\n    },\n    {\n        \"name\": \"papercraft-flat papercut\",\n        \"prompt\": \"flat papercut style {prompt} . silhouette, clean cuts, paper, sharp edges, minimalist, color block\",\n        \"negative_prompt\": \"3D, high detail, noise, grainy, blurry, painting, drawing, photo, disfigured\"\n    },\n    {\n        \"name\": \"papercraft-kirigami\",\n        \"prompt\": \"kirigami representation of {prompt} . 3D, paper folding, paper cutting, Japanese, intricate, symmetrical, precision, clean lines\",\n        \"negative_prompt\": \"painting, drawing, 2D, noisy, blurry, deformed\"\n    },\n    {\n        \"name\": \"papercraft-paper mache\",\n        \"prompt\": \"paper mache representation of {prompt} . 3D, sculptural, textured, handmade, vibrant, fun\",\n        \"negative_prompt\": \"2D, flat, photo, sketch, digital art, deformed, noisy, blurry\"\n    },\n    {\n        \"name\": \"papercraft-paper quilling\",\n        \"prompt\": \"paper quilling art of {prompt} . intricate, delicate, curling, rolling, shaping, coiling, loops, 3D, dimensional, ornamental\",\n        \"negative_prompt\": \"photo, painting, drawing, 2D, flat, deformed, noisy, blurry\"\n    },\n    {\n        \"name\": \"papercraft-papercut collage\",\n        \"prompt\": \"papercut collage of {prompt} . mixed media, textured paper, overlapping, asymmetrical, abstract, vibrant\",\n        \"negative_prompt\": \"photo, 3D, realistic, drawing, painting, high detail, disfigured\"\n    },\n    {\n        \"name\": \"papercraft-papercut shadow box\",\n        \"prompt\": \"3D papercut shadow box of {prompt} . layered, dimensional, depth, silhouette, shadow, papercut, handmade, high contrast\",\n        \"negative_prompt\": \"painting, drawing, photo, 2D, flat, high detail, blurry, noisy, disfigured\"\n    },\n    {\n        \"name\": \"papercraft-stacked papercut\",\n        \"prompt\": \"stacked papercut art of {prompt} . 3D, layered, dimensional, depth, precision cut, stacked layers, papercut, high contrast\",\n        \"negative_prompt\": \"2D, flat, noisy, blurry, painting, drawing, photo, deformed\"\n    },\n    {\n        \"name\": \"papercraft-thick layered papercut\",\n        \"prompt\": \"thick layered papercut art of {prompt} . deep 3D, volumetric, dimensional, depth, thick paper, high stack, heavy texture, tangible layers\",\n        \"negative_prompt\": \"2D, flat, thin paper, low stack, smooth texture, painting, drawing, photo, deformed\"\n    },\n    {\n        \"name\": \"photo-alien\",\n        \"prompt\": \"alien-themed {prompt} . extraterrestrial, cosmic, otherworldly, mysterious, sci-fi, highly detailed\",\n        \"negative_prompt\": \"earthly, mundane, common, realistic, simple\"\n    },\n    {\n        \"name\": \"photo-film noir\",\n        \"prompt\": \"film noir style {prompt} . monochrome, high contrast, dramatic shadows, 1940s style, mysterious, cinematic\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, vibrant, colorful\"\n    },\n    {\n        \"name\": \"photo-glamour\",\n        \"prompt\": \"glamorous photo {prompt} . high fashion, luxurious, extravagant, stylish, sensual, opulent, elegance, stunning beauty, professional, high contrast, detailed\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, distorted, grainy, sketch, low contrast, dull, plain, modest\"\n    },\n    {\n        \"name\": \"photo-hdr\",\n        \"prompt\": \"HDR photo of {prompt} . High dynamic range, vivid, rich details, clear shadows and highlights, realistic, intense, enhanced contrast, highly detailed\",\n        \"negative_prompt\": \"flat, low contrast, oversaturated, underexposed, overexposed, blurred, noisy\"\n    },\n    {\n        \"name\": \"photo-iphone photographic\",\n        \"prompt\": \"iphone photo {prompt} . large depth of field, deep depth of field, highly detailed\",\n        \"negative_prompt\": \"drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly, shallow depth of field, bokeh\"\n    },\n    {\n        \"name\": \"photo-long exposure\",\n        \"prompt\": \"long exposure photo of {prompt} . Blurred motion, streaks of light, surreal, dreamy, ghosting effect, highly detailed\",\n        \"negative_prompt\": \"static, noisy, deformed, shaky, abrupt, flat, low contrast\"\n    },\n    {\n        \"name\": \"photo-neon noir\",\n        \"prompt\": \"neon noir {prompt} . cyberpunk, dark, rainy streets, neon signs, high contrast, low light, vibrant, highly detailed\",\n        \"negative_prompt\": \"bright, sunny, daytime, low contrast, black and white, sketch, watercolor\"\n    },\n    {\n        \"name\": \"photo-silhouette\",\n        \"prompt\": \"silhouette style {prompt} . high contrast, minimalistic, black and white, stark, dramatic\",\n        \"negative_prompt\": \"ugly, deformed, noisy, blurry, low contrast, color, realism, photorealistic\"\n    },\n    {\n        \"name\": \"photo-tilt-shift\",\n        \"prompt\": \"tilt-shift photo of {prompt} . selective focus, miniature effect, blurred background, highly detailed, vibrant, perspective control\",\n        \"negative_prompt\": \"blurry, noisy, deformed, flat, low contrast, unrealistic, oversaturated, underexposed\"\n    }\n]"
  },
  {
    "path": "shared.py",
    "content": "gradio_root = None"
  },
  {
    "path": "tests/__init__.py",
    "content": "import sys\nimport pathlib\n\nsys.path.append(pathlib.Path(f'{__file__}/../modules').parent.resolve())\n"
  },
  {
    "path": "tests/test_extra_utils.py",
    "content": "import numbers\nimport os\nimport unittest\n\nimport modules.flags\nfrom modules import extra_utils\n\n\nclass TestUtils(unittest.TestCase):\n    def test_try_eval_env_var(self):\n        test_cases = [\n            {\n                \"input\": (\"foo\", str),\n                \"output\": \"foo\"\n            },\n            {\n                \"input\": (\"1\", int),\n                \"output\": 1\n            },\n            {\n                \"input\": (\"1.0\", float),\n                \"output\": 1.0\n            },\n            {\n                \"input\": (\"1\", numbers.Number),\n                \"output\": 1\n            },\n            {\n                \"input\": (\"1.0\", numbers.Number),\n                \"output\": 1.0\n            },\n            {\n                \"input\": (\"true\", bool),\n                \"output\": True\n            },\n            {\n                \"input\": (\"True\", bool),\n                \"output\": True\n            },\n            {\n                \"input\": (\"false\", bool),\n                \"output\": False\n            },\n            {\n                \"input\": (\"False\", bool),\n                \"output\": False\n            },\n            {\n                \"input\": (\"True\", str),\n                \"output\": \"True\"\n            },\n            {\n                \"input\": (\"False\", str),\n                \"output\": \"False\"\n            },\n            {\n                \"input\": (\"['a', 'b', 'c']\", list),\n                \"output\": ['a', 'b', 'c']\n            },\n            {\n                \"input\": (\"{'a':1}\", dict),\n                \"output\": {'a': 1}\n            },\n            {\n                \"input\": (\"('foo', 1)\", tuple),\n                \"output\": ('foo', 1)\n            }\n        ]\n\n        for test in test_cases:\n            value, expected_type = test[\"input\"]\n            expected = test[\"output\"]\n            actual = extra_utils.try_eval_env_var(value, expected_type)\n            self.assertEqual(expected, actual)\n"
  },
  {
    "path": "tests/test_utils.py",
    "content": "import os\nimport unittest\n\nimport modules.flags\nfrom modules import util\n\n\nclass TestUtils(unittest.TestCase):\n    def test_can_parse_tokens_with_lora(self):\n        test_cases = [\n            {\n                \"input\": (\"some prompt, very cool, <lora:hey-lora:0.4>,  cool   <lora:you-lora:0.2>\", [], 5, True),\n                \"output\": (\n                    [('hey-lora.safetensors', 0.4), ('you-lora.safetensors', 0.2)], 'some prompt, very cool, cool'),\n            },\n            # Test can not exceed limit\n            {\n                \"input\": (\"some prompt, very cool, <lora:hey-lora:0.4>,  cool   <lora:you-lora:0.2>\", [], 1, True),\n                \"output\": (\n                    [('hey-lora.safetensors', 0.4)],\n                    'some prompt, very cool, cool'\n                ),\n            },\n            # test Loras from UI take precedence over prompt\n            {\n                \"input\": (\n                    \"some prompt, very cool, <lora:l1:0.4>, <lora:l2:-0.2>, <lora:l3:0.3>, <lora:l4:0.5>, <lora:l6:0.24>, <lora:l7:0.1>\",\n                    [(\"hey-lora.safetensors\", 0.4)],\n                    5,\n                    True\n                ),\n                \"output\": (\n                    [\n                        ('hey-lora.safetensors', 0.4),\n                        ('l1.safetensors', 0.4),\n                        ('l2.safetensors', -0.2),\n                        ('l3.safetensors', 0.3),\n                        ('l4.safetensors', 0.5)\n                    ],\n                    'some prompt, very cool'\n                )\n            },\n            # test correct matching even if there is no space separating loras in the same token\n            {\n                \"input\": (\"some prompt, very cool, <lora:hey-lora:0.4><lora:you-lora:0.2>\", [], 3, True),\n                \"output\": (\n                    [\n                        ('hey-lora.safetensors', 0.4),\n                        ('you-lora.safetensors', 0.2)\n                    ],\n                    'some prompt, very cool'\n                ),\n            },\n            # test deduplication, also selected loras are never overridden with loras in prompt\n            {\n                \"input\": (\n                    \"some prompt, very cool, <lora:hey-lora:0.4><lora:hey-lora:0.4><lora:you-lora:0.2>\",\n                    [('you-lora.safetensors', 0.3)],\n                    3,\n                    True\n                ),\n                \"output\": (\n                    [\n                        ('you-lora.safetensors', 0.3),\n                        ('hey-lora.safetensors', 0.4)\n                    ],\n                    'some prompt, very cool'\n                ),\n            },\n            {\n                \"input\": (\"<lora:foo:1..2>, <lora:bar:.>, <test:1.0>, <lora:baz:+> and <lora:quux:>\", [], 6, True),\n                \"output\": (\n                    [],\n                    '<lora:foo:1..2>, <lora:bar:.>, <test:1.0>, <lora:baz:+> and <lora:quux:>'\n                )\n            }\n        ]\n\n        for test in test_cases:\n            prompt, loras, loras_limit, skip_file_check = test[\"input\"]\n            expected = test[\"output\"]\n            actual = util.parse_lora_references_from_prompt(prompt, loras, loras_limit=loras_limit,\n                                                            skip_file_check=skip_file_check)\n            self.assertEqual(expected, actual)\n\n    def test_can_parse_tokens_and_strip_performance_lora(self):\n        lora_filenames = [\n            'hey-lora.safetensors',\n            modules.flags.PerformanceLoRA.EXTREME_SPEED.value,\n            modules.flags.PerformanceLoRA.LIGHTNING.value,\n            os.path.join('subfolder', modules.flags.PerformanceLoRA.HYPER_SD.value)\n        ]\n\n        test_cases = [\n            {\n                \"input\": (\"some prompt, <lora:hey-lora:0.4>\", [], 5, True, modules.flags.Performance.QUALITY),\n                \"output\": (\n                    [('hey-lora.safetensors', 0.4)],\n                    'some prompt'\n                ),\n            },\n            {\n                \"input\": (\"some prompt, <lora:hey-lora:0.4>\", [], 5, True, modules.flags.Performance.SPEED),\n                \"output\": (\n                    [('hey-lora.safetensors', 0.4)],\n                    'some prompt'\n                ),\n            },\n            {\n                \"input\": (\"some prompt, <lora:sdxl_lcm_lora:1>, <lora:hey-lora:0.4>\", [], 5, True, modules.flags.Performance.EXTREME_SPEED),\n                \"output\": (\n                    [('hey-lora.safetensors', 0.4)],\n                    'some prompt'\n                ),\n            },\n            {\n                \"input\": (\"some prompt, <lora:sdxl_lightning_4step_lora:1>, <lora:hey-lora:0.4>\", [], 5, True, modules.flags.Performance.LIGHTNING),\n                \"output\": (\n                    [('hey-lora.safetensors', 0.4)],\n                    'some prompt'\n                ),\n            },\n            {\n                \"input\": (\"some prompt, <lora:sdxl_hyper_sd_4step_lora:1>, <lora:hey-lora:0.4>\", [], 5, True, modules.flags.Performance.HYPER_SD),\n                \"output\": (\n                    [('hey-lora.safetensors', 0.4)],\n                    'some prompt'\n                ),\n            }\n        ]\n\n        for test in test_cases:\n            prompt, loras, loras_limit, skip_file_check, performance = test[\"input\"]\n            lora_filenames = modules.util.remove_performance_lora(lora_filenames, performance)\n            expected = test[\"output\"]\n            actual = util.parse_lora_references_from_prompt(prompt, loras, loras_limit=loras_limit, lora_filenames=lora_filenames)\n            self.assertEqual(expected, actual)\n"
  },
  {
    "path": "troubleshoot.md",
    "content": "Below are many common problems that people encountered:\n\n### RuntimeError: CPUAllocator\n\nSee also the section: **System Swap**\n\n### Model loaded, then paused, then nothing happens\n\nSee also the section: **System Swap**\n\n### Segmentation Fault\n\nSee also the section: **System Swap**\n\n### Aborted\n\nSee also the section: **System Swap**\n\n### core dumped\n\nSee also the section: **System Swap**\n\n### Killed\n\nSee also the section: **System Swap**\n\n### ^C, then quit\n\nSee also the section: **System Swap**\n\n### adm 2816, then stuck\n\nSee also the section: **System Swap**\n\n### Connection errored out\n\nSee also the section: **System Swap**\n\n### Error 1006\n\nSee also the section: **System Swap**\n\n### WinError 10060\n\nSee also the section: **System Swap**\n\n### Read timed out\n\nSee also the section: **System Swap**\n\n### No error, but the console close in a flash. Cannot find any error.\n\nSee also the section: **System Swap**\n\n### Model loading is extremely slow (more than 1 minute)\n\nSee also the section: **System Swap**\n\n### System Swap\n\nAll above problems are caused by the fact that you do not have enough System Swap.\n\nPlease make sure that you have at least 40GB System Swap. In fact, it does not need so much Swap, but 40Gb should be safe for you to run Fooocus in 100% success.\n\n(If you have more than 64GB RAM, then *perhaps* you do not need any System Swap, but we are not exactly sure about this.)\n\nAlso, if your system swap is on HDD, the speed of model loading will be very slow. Please try best to put system swap on SSD.\n\nIf you are using Linux/Mac, please follow your provider's instructions to set Swap Space. Herein, the \"provider\" refers to Ubuntu official, CentOS official, Mac official, etc.\n\nIf you are using Windows, you can set Swap here:\n\n![swap](https://github.com/lllyasviel/Fooocus/assets/19834515/2a06b130-fe9b-4504-94f1-2763be4476e9)\n\nIf you use both HDD and SSD, you *may* test some settings on the above step 7 to try best to put swap area on SSD, so that the speed of model loading will be faster.\n\n**Important: Microsoft Windows 10/11 by default automate system swap for you so that you do not need to touch this dangerous setting. If you do not have enough system swap, just make sure that you have at least 40GB free space on each disk.** The Microsoft Windows 10/11 will automatically make swap areas for you.\n\nAlso, if you obtain Microsoft Windows 10/11 from some unofficial Chinese or Russian provider, they may have modified the default setting of system swap to advertise some \"Enhanced Windows 10/11\" (but actually they are just making things worse rather than improve things). In those cases, you may need to manually check if your system swap setting is consistent to the above screenshot.\n\nFinally, note that you need to restart computer to activate any changes in system swap.\n\n### MetadataIncompleteBuffer\n\nSee also the section: **Model corrupted**\n\n### PytorchStreamReader failed\n\nSee also the section: **Model corrupted**\n\n### Model corrupted\n\nIf you see Model Corrupted, then your model is corrupted. Fooocus will re-download corrupted models for you if your internet connection is good. Otherwise, you may also manually download models. You can find model url and their local location in the console each time a model download is requested.\n\n### UserWarning: The operator 'aten::std_mean.correction' is not currently supported on the DML\n\nThis is a warning that you can ignore.\n\n### Torch not compiled with CUDA enabled\n\nYou are not following the official installation guide. \n\nPlease do not trust those wrong tutorials on the internet, and please only trust the official installation guide. \n\n### subprocess-exited-with-error\n\nPlease use python 3.10\n\nAlso, you are not following the official installation guide. \n\nPlease do not trust those wrong tutorials on the internet, and please only trust the official installation guide. \n\n### SSL: CERTIFICATE_VERIFY_FAILED\n\nAre you living in China? If yes, please consider turn off VPN, and/or try to download models manually.\n\nIf you get this error elsewhere in the world, then you may need to look at [this search](https://www.google.com/search?q=SSL+Certificate+Error). We cannot give very specific guide to fix this since the cause can vary a lot.\n\n### CUDA kernel errors might be asynchronously reported at some other API call\n\nA very small amount of devices does have this problem. The cause can be complicated but usually can be resolved after following these steps:\n\n1. Make sure that you are using official version and latest version installed from [here](https://github.com/lllyasviel/Fooocus#download). (Some forks and other versions are more likely to cause this problem.)\n2. Upgrade your Nvidia driver to the latest version. (Usually the version of your Nvidia driver should be 53X, not 3XX or 4XX.)\n3. If things still do not work, then perhaps it is a problem with CUDA 12. You can use CUDA 11 and Xformers to try to solve this problem. We have prepared all files for you, and please do NOT install any CUDA or other environment on you own. The only one official way to do this is: (1) Backup and delete your `python_embeded` folder (near the `run.bat`); (2) Download the \"previous_old_xformers_env.7z\" from the [release page](https://github.com/lllyasviel/Fooocus/releases/tag/release), decompress it, and put the newly extracted `python_embeded` folder near your `run.bat`; (3) run Fooocus.\n4. If it still does not work, please open an issue for us to take a look.\n\n### Found no NVIDIA driver on your system\n\nPlease upgrade your Nvidia Driver. \n\nIf you are using AMD, please follow official installation guide.\n\n### NVIDIA driver too old\n\nPlease upgrade your Nvidia Driver.\n\n### I am using Mac, the speed is very slow.\n\nSome MAC users may need `--disable-offload-from-vram` to speed up model loading.\n\nBesides, the current support for MAC is very experimental, and we encourage users to also try Diffusionbee or Drawingthings: they are developed only for MAC.\n\n### I am using Nvidia with 8GB VRAM, I get CUDA Out Of Memory\n\nIt is a BUG. Please let us know as soon as possible. Please make an issue. See also [minimal requirements](https://github.com/lllyasviel/Fooocus/tree/main?tab=readme-ov-file#minimal-requirement).\n\n### I am using Nvidia with 6GB VRAM, I get CUDA Out Of Memory\n\nIt is very likely a BUG. Please let us know as soon as possible. Please make an issue. See also [minimal requirements](https://github.com/lllyasviel/Fooocus/tree/main?tab=readme-ov-file#minimal-requirement).\n\n### I am using Nvidia with 4GB VRAM with Float16 support, like RTX 3050, I get CUDA Out Of Memory\n\nIt is a BUG. Please let us know as soon as possible. Please make an issue. See also [minimal requirements](https://github.com/lllyasviel/Fooocus/tree/main?tab=readme-ov-file#minimal-requirement).\n\n### I am using Nvidia with 4GB VRAM without Float16 support, like GTX 960, I get CUDA Out Of Memory\n\nSupporting GPU with 4GB VRAM without fp16 is extremely difficult, and you may not be able to use SDXL. However, you may still make an issue and let us know. You may try SD1.5 in Automatic1111 or other software for your device. See also [minimal requirements](https://github.com/lllyasviel/Fooocus/tree/main?tab=readme-ov-file#minimal-requirement).\n\n### I am using AMD GPU on Windows, I get CUDA Out Of Memory\n\nCurrent AMD support is very experimental for Windows. If you see this, then perhaps you cannot use Fooocus on this device on Windows.\n\nHowever, if you re able to run SDXL on this same device on any other software, please let us know immediately, and we will support it as soon as possible. If no other software can enable your device to run SDXL on Windows, then we also do not have much to help.\n\nBesides, the AMD support on Linux is slightly better because it will use ROCM. You may also try it if you are willing to change OS to linux. See also [minimal requirements](https://github.com/lllyasviel/Fooocus/tree/main?tab=readme-ov-file#minimal-requirement).\n\n### I am using AMD GPU on Linux, I get CUDA Out Of Memory\n\nCurrent AMD support for Linux is better than that for Windows, but still, very experimental. However, if you re able to run SDXL on this same device on any other software, please let us know immediately, and we will support it as soon as possible. If no other software can enable your device to run SDXL on Windows, then we also do not have much to help. See also [minimal requirements](https://github.com/lllyasviel/Fooocus/tree/main?tab=readme-ov-file#minimal-requirement).\n\n### I tried flags like --lowvram or --gpu-only or --bf16 or so on, and things are not getting any better?\n\nPlease remove these flags if you are mislead by some wrong tutorials. In most cases these flags are making things worse and introducing more problems.\n\n### Fooocus suddenly becomes very slow and I have not changed anything\n\nAre you accidentally running two Fooocus at the same time?\n"
  },
  {
    "path": "update_log.md",
    "content": "# [2.5.5](https://github.com/lllyasviel/Fooocus/releases/tag/v2.5.5)\n\n* Fix colab inpaint issue by moving an import statement\n\n# [2.5.4](https://github.com/lllyasviel/Fooocus/releases/tag/v2.5.4)\n\n* Fix validation for default_ip_image_* and default_inpaint_mask_sam_model\n* Fix enhance mask debugging in combination with image sorting\n* Fix loading of checkpoints and LoRAs when using multiple directories in config and then switching presets\n\n# [2.5.3](https://github.com/lllyasviel/Fooocus/releases/tag/v2.5.3)\n\n* Only load weights from non-safetensors files, preventing harmful code injection\n* Add checkbox for applying/resetting styles when describing images, also allowing multiple describe content types\n\n# [2.5.2](https://github.com/lllyasviel/Fooocus/releases/tag/v2.5.2)\n\n* Fix not adding positive prompt when styles didn't have a {prompt} placeholder in the positive prompt\n* Extend config settings for input image, see list in [PR](https://github.com/lllyasviel/Fooocus/pull/3382)\n\n# [2.5.1](https://github.com/lllyasviel/Fooocus/releases/tag/v2.5.1)\n\n* Update download URL in readme\n* Increase speed of metadata loading\n* Fix reading of metadata from jpeg, jpg and webp (exif)\n* Fix debug preprocessor\n* Update attributes and add inline prompt features section to readme\n* Add checkbox, config and handling for saving only the final enhanced image. Use config `default_save_only_final_enhanced_image`, default False.\n* Add sorting of final images when enhanced is enabled. Use argument `--disable-enhance-output-sorting` to disable.\n\n# [2.5.0](https://github.com/lllyasviel/Fooocus/releases/tag/v2.5.0)\n\nThis version includes various package updates. If the auto-update doesn't work you can do one of the following:\n1. Open a terminal in the Fooocus folder (location of config.txt) and run `git pull`\n2. Update packages\n   - Windows (installation through zip file): open a terminal in the Fooocus folder (location of config.txt) `..\\python_embeded\\python.exe -m pip install -r .\\requirements_versions.txt` (Windows using embedded python, installation method zip file) or download Fooocus again (zip file attached to this release)\n   - other: manually update the packages using `python.exe -m pip install -r requirements_versions.txt` or use the docker image\n\n---\n\n* Update python dependencies, add segment_anything\n* Add enhance feature, which offers easy image refinement steps (similar to adetailer, but based on dynamic image detection instead of specific mask detection models). See [documentation](https://github.com/lllyasviel/Fooocus/discussions/3281).\n* Rewrite async worker code, make code much more reusable to allow iterations and improve reusability\n* Improve GroundingDINO and SAM image masking\n* Fix inference tensor version counter tracking issue for GroundingDINO after using Enhance (see [discussion](https://github.com/lllyasviel/Fooocus/discussions/3213))\n* Move checkboxes Enable Mask Upload and Invert Mask When Generating from Developer Debug Mode to Inpaint Or Outpaint\n* Add persistent model cache for metadata. Use `--rebuild-hash-cache X` (X = int, number of CPU cores, default all) to manually rebuild the cache for all non-cached hashes\n* Rename `--enable-describe-uov-image` to `--enable-auto-describe-image`, now also works for enhance image upload\n* Rename checkbox `Enable Mask Upload` to `Enable Advanced Masking Features` to better hint to mask auto-generation feature\n* Get upscale model filepath by calling downloading_upscale_model() to ensure the model exists\n* Rename tab titles and translations from singular to plural\n* Rename document to documentation\n* Update default models to latest versions\n  * animaPencilXL_v400 => animaPencilXL_v500\n  * DreamShaperXL_Turbo_dpmppSdeKarras => DreamShaperXL_Turbo_v2_1\n  * SDXL_FILM_PHOTOGRAPHY_STYLE_BetaV0.4 => SDXL_FILM_PHOTOGRAPHY_STYLE_V1\n* Add preset for pony_v6 (using ponyDiffusionV6XL)\n* Add style `Fooocus Pony`\n* Add restart sampler ([paper](https://arxiv.org/abs/2306.14878))\n* Add config option for default_inpaint_engine_version, sets inpaint engine for pony_v6 and playground_v2.5 to None for improved results (incompatible with inpaint engine)\n* Add image editor functionality to mask upload (same as for inpaint, now correctly resizes and allows more detailed mask creation)\n\n# [2.4.3](https://github.com/lllyasviel/Fooocus/releases/tag/v2.4.3)\n\n* Fix alphas_cumprod setter for TCD sampler\n* Add parser for env var strings to expected config value types to allow override of all non-path config keys \n\n# [2.4.2](https://github.com/lllyasviel/Fooocus/releases/tag/v2.4.2)\n\n* Fix some small bugs (tcd scheduler when gamma is 0, chown in Dockerfile, update cmd args in readme, translation for aspect ratios, vae default after file reload)\n* Fix performance LoRA replacement when data is loaded from history log and inline prompt\n* Add support and preset for playground v2.5 (only works with performance Quality or Speed, use with scheduler edm_playground_v2)\n* Make textboxes (incl. positive prompt) resizable\n* Hide intermediate images when performance of Gradio would bottleneck the generation process (Extreme Speed, Lightning, Hyper-SD)\n\n# [2.4.1](https://github.com/lllyasviel/Fooocus/releases/tag/v2.4.1)\n\n* Fix some small bugs (e.g. adjust clip skip default value from 1 to 2, add type check to aspect ratios js update function)\n* Add automated docker build on push to main, tagged with `edge`. See [available docker images](https://github.com/lllyasviel/Fooocus/pkgs/container/fooocus).\n\n# [2.4.0](https://github.com/lllyasviel/Fooocus/releases/tag/v2.4.0)\n\n* Change settings tab elements to be more compact\n* Add clip skip slider\n* Add select for custom VAE\n* Add new style \"Random Style\"\n* Update default anime model to animaPencilXL_v310\n* Add button to reconnect the UI after Fooocus crashed without having to configure everything again (no page reload required)\n* Add performance \"hyper-sd\" (based on [Hyper-SDXL 4 step LoRA](https://huggingface.co/ByteDance/Hyper-SD/blob/main/Hyper-SDXL-4steps-lora.safetensors))\n* Add [AlignYourSteps](https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/) scheduler by Nvidia, see \n* Add [TCD](https://github.com/jabir-zheng/TCD) sampler and scheduler (based on sgm_uniform)\n* Add NSFW image censoring (disables intermediate image preview while generating). Set config value `default_black_out_nsfw` to True to always enable.\n* Add argument `--enable-describe-uov-image` to automatically describe uploaded images for upscaling\n* Add inline lora prompt references with subfolder support, example prompt: `colorful bird <lora:toucan:1.2>`\n* Add size and aspect ratio recommendation on image describe\n* Add inpaint brush color picker, helpful when image and mask brush have the same color\n* Add automated Docker image build using Github Actions on each release.\n* Add full raw prompts to history logs\n* Change code ownership from @lllyasviel to @mashb1t for automated issue / MR notification\n\n# [2.3.1](https://github.com/lllyasviel/Fooocus/releases/tag/2.3.1)\n\n* Remove positive prompt from anime prefix to not reset prompt after switching presets\n* Fix image number being reset to 1 when switching preset, now doesn't reset anymore\n* Fix outpainting dimension calculation when extending left/right\n* Fix LoRA compatibility for LoRAs in a1111 metadata scheme\n\n# [2.3.0](https://github.com/lllyasviel/Fooocus/releases/tag/2.3.0)\n\n* Add performance \"lightning\" (based on [SDXL-Lightning 4 step LoRA](https://huggingface.co/ByteDance/SDXL-Lightning/blob/main/sdxl_lightning_4step_lora.safetensors))\n* Add preset selection to UI, disable with argument `--disable-preset-selection`. Use `--always-download-new-model` to download missing models on preset switch.\n* Improve face swap consistency by switching later in the process to (synthetic) refiner\n* Add temp path cleanup on startup\n* Add support for wildcard subdirectories\n* Add scrollable 2 column layout for styles for better structure\n* Improve Colab resource needs for T4 instances (default), positively tested with all image prompt features\n* Improve anime preset, now uses style `Fooocus Semi Realistic` instead of `Fooocus Negative` (less wet look images)\n\n# [2.2.1](https://github.com/lllyasviel/Fooocus/releases/tag/2.2.1)\n\n* Fix some small bugs (e.g. image grid, upscale fast 2x, LoRA weight width in Firefox)\n* Allow prompt weights in array syntax\n* Add steps override and metadata scheme to history log\n\n# [2.2.0](https://github.com/lllyasviel/Fooocus/releases/tag/2.2.0)\n\n* Isolate every image generation to truly allow multi-user usage\n* Add array support, changes the main prompt when increasing the image number. Syntax: `[[red, green, blue]] flower` \n* Add optional metadata to images, allowing you to regenerate and modify them later with the same parameters \n* Now supports native PNG, JPG and WEBP image generation\n* Add Docker support\n\n# [2.1.865](https://github.com/lllyasviel/Fooocus/releases/tag/2.1.865)\n\n* Various bugfixes\n* Add authentication to --listen\n\n# 2.1.864\n\n* New model list. See also discussions.\n\n# 2.1.861 (requested update)\n\n(2023 Dec 21) Hi all, the feature updating of Fooocus will be paused for about two or three weeks because we have some other workloads. See you soon and we will come back in mid or late Jan. However, you may still see updates if other collaborators are fixing bugs or solving problems.\n\n* Show image preview in Style when mouse hover.\n\n# 2.1.860 (requested update)\n\n* Allow upload inpaint mask in developer mode.\n\n# 2.1.857 (requested update)\n\n* Begin to support 8GB AMD GPU on Windows.\n\n# 2.1.854\n\n* Add a button to copy parameters to clipboard in log.\n* Allow users to load parameters directly by pasting parameters to prompt.\n\n# 2.1.853\n\n* Add Marc K3nt3L's styles. Thanks [Marc K3nt3L](https://github.com/K3nt3L)!\n\n# 2.1.852\n\n* New Log System: Log system now uses tables. If this is breaking some other browser extension or javascript developments, see also [use previous version](https://github.com/lllyasviel/Fooocus/discussions/1405).\n\n# 2.1.846\n\n* Many users reported that image quality is different from 2.1.824. We reviewed all codes and fixed several precision problems in 2.1.846.\n\n# 2.1.843\n\n* Many improvements to Canvas. Thanks CanvasZoom author!\n\n# 2.1.841\n\n* Backend maintain.\n* Fix some potential frozen after model mismatch.\n* Fix crash when cfg=1 when using anime preset.\n* Added some guidelines for troubleshoot the \"CUDA kernel errors asynchronously\" problem.\n* Fix inpaint device problem in `--always-gpu` mode.\n\n# 2.1.839\n\n* Maintained some computation codes in backend for efficiency.\n* Added a note about Seed Breaking Change.\n\n**Seed Breaking Change**: Note that 2.1.825-2.1.839 is seed breaking change. The computation float point is changed and some seeds may give slightly different results. The minor change in 2.1.825-2.1.839 do not influence image quality. See also [use previous version](https://github.com/lllyasviel/Fooocus/discussions/1405).\n\n# 2.1.837\n\n* Fix some precision-related problems.\n\n# 2.1.836\n\n* Avoid blip tokenizer download from torch hub\n\n# 2.1.831\n\n* Input Image -> Describe (Midjourney Describe)\n\n# 2.1.830\n\n* SegmindVega support.\n\n# 2.1.829\n\n* Change SDE tree to CPU on AMD/DirectMl to avoid potential problems.\n\n# 2.1.828\n\n* Allow to disable gradio analytics.\n* Use html table in log.\n* fix some SSL problems.\n\n# 2.1.826\n\n* New backend.\n* FP8 support (see also the new cmd flag list in Readme, eg, --unet-in-fp8-e4m3fn and --unet-in-fp8-e5m2).\n* Fix some MPS problems.\n* GLoRA support.\n* Turbo scheduler.\n\n# 2.1.823\n\n(2023 Nov 26) Hi all, the feature updating of Fooocus will be paused for about two or three weeks because we have some other workloads. See you soon and we will come back in mid December. However, you may still see updates if other collaborators are fixing bugs or solving problems.\n\n* Fix some potential problem when LoRAs has clip keys and user want to load those LoRAs to refiners.\n\n# 2.1.822\n\n* New inpaint system (inpaint beta test ends).\n\n# 2.1.821\n\n* New UI for LoRAs.\n* Improved preset system: normalized preset keys and file names.\n* Improved session system: now multiple users can use one Fooocus at the same time without seeing others' results.\n* Improved some computation related to model precision.\n* Improved config loading system with user-friendly prints.\n\n# 2.1.820\n\n* support \"--disable-image-log\" to prevent writing images and logs to hard drive.\n\n# 2.1.819\n\n* Allow disabling preview in dev tools.\n\n# 2.1.818\n\n* Fix preset lora failed to load when the weight is exactly one.\n\n# 2.1.817\n\n* support \"--theme dark\" and \"--theme light\".\n* added preset files \"default\" and \"lcm\", these presets exist but will not create launcher files (will not be exposed to users) to keep entry clean. The \"--preset lcm\" is equivalent to select \"Extreme Speed\" in UI, but will likely to make some online service deploying easier.\n\n# 2.1.815\n\n* Multiple loras in preset.\n\n# 2.1.814\n\n* Allow using previous preset of official SAI SDXL by modify the args to '--preset sai'. ~Note that this preset will set inpaint engine back to previous v1 to get same results like before. To change the inpaint engine to v2.6, use the dev tools -> inpaint engine -> v2.6.~ (update: it is not needed now after some tests.)\n\n# 2.1.813\n\n* Allow preset to set default inpaint engine.\n\n# 2.1.812\n\n* Allow preset to set default performance.\n* heunpp2 sampler.\n\n# 2.1.810\n\n* Added hints to config_modification_tutorial.txt\n* Removed user hacked aspect ratios in I18N english templates, but it will still be read like before.\n* fix some style sorting problem again (perhaps should try Gradio 4.0 later).\n* Refreshed I18N english templates with more keys.\n\n# 2.1.809\n\n* fix some sorting problem.\n\n# 2.1.808\n\n* Aspect ratios now show aspect ratios.\n* Added style search.\n* Added style sorting/ordering/favorites.\n\n# 2.1.807\n\n* Click on image to see it in full screen.\n\n# 2.1.806\n\n* Fix some lora problems related to clip.\n\n# 2.1.805\n\n* Responsive UI for small screens.\n* Added skip preprocessor in dev tools.\n\n# 2.1.802\n\n* Default inpaint engine changed to v2.6. You can still use inpaint engine v1 in dev tools.\n* Fix some VRAM problems.\n\n# 2.1.799\n\n* Added 'Extreme Speed' performance mode (based on LCM). The previous complicated settings are not needed now.\n\n# 2.1.798\n\n* added lcm scheduler - LCM may need to set both sampler and scheduler to \"lcm\". Other than that, see the description in 2.1.782 logs.\n\n# 2.1.797\n\n* fixed some dependency problems with facexlib and filterpy.\n\n# 2.1.793\n\n* Added many javascripts to improve user experience. Now users with small screen will always see full canvas without needing to scroll.\n\n# 2.1.790\n\n* Face swap (in line with Midjourney InsightFace): Input Image -> Image Prompt -> Advanced -> FaceSwap\n* The performance is super high. Use it carefully and never use it in any illegal things!\n* This implementation will crop faces for you and you do NOT need to crop faces before feeding images into Fooocus. (If you previously manually crop faces from images for other software, you do not need to do that now in Fooocus.)\n\n# 2.1.788\n\n* Fixed some math problems in previous versions.\n* Inpaint engine v2.6 join the beta test of Fooocus inpaint models. Use it in dev tools -> inpaint engine -> v2.6 .\n\n# 2.1.785\n\n* The `user_path_config.txt` is deprecated since 2.1.785. If you are using it right now, please use the new `config.txt` instead. See also the new documentation in the Readme.\n* The paths in `user_path_config.txt` will still be loaded in recent versions, but it will be removed soon.\n* We use very user-friendly method to automatically transfer your path settings from `user_path_config.txt` to `config.txt` and usually you do not need to do anything.\n* The new `config.txt` will never save default values so the default value changes in scripts will not be prevented by old config files.\n\n# 2.1.782\n\n2.1.782 is mainly an update for a new LoRA system that supports both SDXL loras and SD1.5 loras.\n\nNow when you load a lora, the following things will happen:\n\n1. try to load the lora to the base model, if failed (model mismatch), then try to load the lora to refiner.\n2. try to load the lora to refiner, if failed (model mismatch) then do nothing.\n\nIn this way, Fooocus 2.1.782 can benefit from all models and loras from CivitAI with both SDXL and SD1.5 ecosystem, using the unique Fooocus swap algorithm, to achieve extremely high quality results (although the default setting is already very high quality), especially in some anime use cases, if users really want to play with all these things.\n\nRecently the community also developed LCM loras. Users can use it by setting the sampler as 'LCM', scheduler as 'sgm_uniform' (Update in 2.1.798: scheduler should also be \"lcm\"), the forced overwrite of sampling step as 4 to 8, and CFG guidance as 1.0, in dev tools. Do not forget to change the LCM lora weight to 1.0 (many people forget this and report failure cases). Also, set refiner to None. If LCM's feedback in the artists community is good (not the feedback in the programmer community of Stable Diffusion), Fooocus may add some other shortcuts in the future.\n\n# 2.1.781\n\n(2023 Oct 26) Hi all, the feature updating of Fooocus will (really, really, this time) be paused for about two or three weeks because we really have some other workloads. Thanks for the passion of you all (and we in fact have kept updating even after last pausing announcement a week ago, because of many great feedbacks)  - see you soon and we will come back in mid November. However, you may still see updates if other collaborators are fixing bugs or solving problems.\n\n* Disable refiner to speed up when new users mistakenly set same model to base and refiner.\n\n# 2.1.779\n\n* Disable image grid by default because many users reports performance issues. For example, https://github.com/lllyasviel/Fooocus/issues/829 and so on. The image grid will cause problem when user hard drive is not super fast, or when user internet connection is not very good (eg, run in remote). The option is moved to dev tools if users want to use it. We will take a look at it later.\n\n# 2.1.776\n\n* Support Ctrl+Up/Down Arrow to change prompt emphasizing weights.\n\n# 2.1.750\n\n* New UI: now you can get each image during generating.\n\n# 2.1.743\n\n* Improved GPT2 by removing some tokens that may corrupt styles.\n\n# 2.1.741\n\nStyle Updates:\n\n* \"Default (Slightly Cinematic)\" as renamed to \"Fooocus Cinematic\".\n* \"Default (Slightly Cinematic)\" is canceled from default style selections. \n* Added \"Fooocus Sharp\". This style combines many CivitAI prompts that reduces SDXL blurry and improves sharpness in a relatively natural way.\n* Added \"Fooocus Enhance\". This style mainly use the very popular [default negative prompts from JuggernautXL](https://civitai.com/models/133005) and some other enhancing words. JuggernautXL's negative prompt has been proved to be very effective in many recent image posts on CivitAI to improve JuggernautXL and many other models.\n* \"Fooocus Sharp\" and \"Fooocus Enhance\" and \"Fooocus V2\" becomes the new default set of styles.\n* Removed the default text in the \"negative prompt\" input area since it is not necessary now.\n* You can reproduce previous results by using \"Fooocus Cinematic\".\n* \"Fooocus Sharp\" and \"Fooocus Enhance\" may undergo minor revision in future updates.\n\n# 2.1.739\n\n* Added support for authentication in --share mode (via auth.json).\n\n# 2.1.737\n\n* Allowed customizing resolutions in config. \n\nModifying this will make results worse if you do not understand how Positional Encoding works. \n\nYou have been warned.\n\nIf you do not know why numbers must be transformed with many Sin and Cos functions (yes, those Trigonometric functions that you learn in junior high school) before they are fed to SDXL, we do not encourage you to change this - you will become a victim of Positional Encoding. You are likely to suffer from an easy-to-fail tool, rather than getting more control.\n\nYour knowledge gained from SD1.5 (for example, resolution numbers divided by 8 or 64 are good enough for UNet) does not work in SDXL. The SDXL uses Positional Encoding. The SD1.5 does not use Positional Encoding. They are completely different. \n\nYour knowledge gained from other resources (for example, resolutions around 1024 are good enough for SDXL) is wrong. The SDXL uses Positional Encoding. People who say \"all resolutions around 1024 are good\" do not understand what is Positional Encoding. They are not intentionally misleading. They are just not aware of the fact that SDXL is using Positional Encoding. \n\nThe number 1152 must be exactly 1152, not 1152-1, not 1152+1, not 1152-8, not 1152+8. The number 1152 must be exactly 1152. Just Google what is a Positional Encoding.\n\nAgain, if you do not understand how Positional Encoding works, just do not change the resolution numbers.\n\n# 2.1.735\n\n* Fixed many problems related to torch autocast.\n\n# 2.1.733\n\n* Increased allowed random seed range.\n\n# 2.1.728\n\n* Fixed some potential numerical problems since 2.1.723\n\n# 2.1.723\n\n* Improve Fooocus Anime a bit by using better SD1.5 refining formulation.\n\n# 2.1.722\n\n* Now it is possible to translate 100% all texts in the UI.\n\n# 2.1.721\n\n* Added language/en.json to make translation easier.\n\n# 2.1.720\n\n* Added Canvas Zoom to inpaint canvas\n* Fixed the problem that image will be cropped in UI when the uploaded image is too wide.\n\n# 2.1.719\n\n* I18N\n\n# 2.1.718\n\n* Corrected handling dash in wildcard names, more wildcards (extended-color).\n\n# 2.1.717\n\n* Corrected displaying multi-line prompts in Private Log.\n\n# 2.1.716\n\n* Added support for nested wildcards, more wildcards (flower, color_flower).\n\n# 2.1.714\n\n* Fixed resolution problems.\n\n# 2.1.712\n\n* Cleaned up Private Log (most users won't need information about raw prompts).\n\n# 2.1.711\n\n* Added more information about prompts in Private Log.\n* Made wildcards in negative prompt use different seed.\n\n# 2.1.710\n\n* Added information about wildcards usage in console log.\n\n# 2.1.709\n\n* Allowed changing default values of advanced checkbox and image number.\n\n# 2.1.707\n\n* Updated Gradio to v3.41.2.\n\n# 2.1.703\n\n* Fixed many previous problems related to inpaint.\n\n# 2.1.702\n\n* Corrected reading empty negative prompt from config (it shouldn't turn into None).\n\n# 2.1.701\n\n* Updated FreeU node to v2 (gives less overcooked results).\n\n# 2.1.699\n\n* Disabled smart memory management (solves some memory issues).\n\n# 2.1.698\n\n* Added support for loading model files from subfolders.\n\n# 2.1.696\n\n* Improved wildcards implementation (using same wildcard multiple times will now return different values).\n\n**(2023 Oct 18) Again, the feature updating of Fooocus will be paused for about two or three weeks because we have some other workloads - we will come back in early or mid November. However, you may still see updates if other collaborators are fixing bugs or solving problems.**\n\n# 2.1.695 (requested emergency bug fix)\n\n* Reduced 3.4GB RAM use when swapping base model.\n* Reduced 372MB VRAM use in VAE decoding after using control model in image prompt.\n* Note that Official ComfyUI (d44a2de) will run out of VRAM when using sdxl and control-lora on 2060 6GB that does not support float16 at resolution 1024. Fooocus 2.1.695 succeeded in outputting images without OOM using exactly same devices.\n\n(2023 Oct 17) Announcement of update being paused.\n\n# 2.1.693\n\n* Putting custom styles before pre-defined styles.\n* Avoided the consusion between Fooocus Anime preset and Fooocus Anime style (Fooocus Anime style is renamed to Fooocus Masterpiece because it does not make images Anime-looking if not using with Fooocus Anime preset).\n* Fixed some minor bugs in Fooocus Anime preset's prompt emphasizing of commas.\n* Supported and documented embedding grammar (and wildcards grammar). \n* This release is a relative stable version and many features are determined now.\n\n# 2.1.687\n\n* Added support for wildcards (using files from wildcards folder - try prompts like `__color__ sports car` with different seeds).\n\n# 2.1.682\n\n* Added support for custom styles (loaded from JSON files placed in sdxl_styles folder).\n\n# 2.1.681\n\n* Added support for generate hotkey (CTRL+ENTER).\n* Added support for generate forever (RMB on Generate button).\n* Added support for playing sound when generation is finished ('notification.ogg' or 'notification.mp3').\n\n# 2.1.62\n\n* Preset system. Added anime and realistic support.\n\n# 2.1.52\n\n* removed pygit2 dependency (expect auto update) so that people will never have permission denied problems.\n\n# 2.1.50\n\n* Begin to support sd1.5 as refiner. This method scale sigmas given SD15/Xl latent scale and is probably the most correct way to do it. I am going to write a discussion soon.\n\n# 2.1.25\n\nAMD support on Linux and Windows.\n\n# 2.1.0\n\n* Image Prompt\n* Finished the \"Moving from Midjourney\" Table\n\n# 2.0.85\n\n* Speed Up Again\n\n# 2.0.80\n\n* Improved the scheduling of ADM guidance and CFG mimicking for better visual quality in high frequency domain and small objects.\n\n# 2.0.80\n\n* Rework many patches and some UI details.\n* Speed up processing.\n* Move Colab to independent branch.\n* Implemented CFG Scale and TSNR correction when CFG is bigger than 10.\n* Implemented Developer Mode with more options to debug.\n\n### 2.0.72\n\n(2023 sep 21) The feature updating of Fooocus will be paused for about two or three weeks because we have some events and travelling - we will come back in early or mid October. \n\n### 2.0.72\n\n* Allow users to choose path of models.\n\n### 2.0.65\n\n* Inpaint model released.\n\n### 2.0.50\n\n* Variation/Upscale (Midjourney Toolbar) implemented.\n\n### 2.0.16\n\n* Virtual memory system implemented. Now Colab can run both base model and refiner model with 7.8GB RAM + 5.3GB VRAM, and it never crashes.\n* If you are lucky enough to read this line, keep in mind that ComfyUI cannot do this. This is very reasonable that Fooocus is more optimized because it only need to handle a fixed pipeline, but ComfyUI need to consider arbitrary pipelines. \n* But if we just consider the optimization of this fixed workload, after 2.0.16, Fooocus has become the most optimized SDXL app, outperforming ComfyUI.\n\n### 2.0.0\n\n* V2 released.\n* completely rewrite text processing pipeline (higher image quality and prompt understanding).\n* support multi-style.\n* In 100 tests (prompts written by ChatGPT), V2 default results outperform V1 default results in 87 cases, evaluated by two human.\n* In 100 tests (prompts written by ChatGPT), V2 prompt understanding outperform V1 prompt understanding in 81 cases, evaluated by two human, in both default setting and multi/single style mode.\n* Because the above number is above 80%, we view this as a major update and directly jump to 2.0.0.\n* Some other things are renamed.\n\n### 1.0.67\n\n* Use dynamic weighting and lower weights for prompt expansion.\n\n### 1.0.64\n\n* Fixed a small OOM problem.\n\n### 1.0.62\n\n* Change prompt expansion to suffix mode for better balance of semantic and style (and debugging).\n\n### 1.0.60\n\n* Tune the balance between style and Prompt Expansion.\n\n### 1.0.56\n\n* Begin to use magic split.\n\n### 1.0.55\n\n* Minor changes of Prompt Expansion.\n\n### 1.0.52\n\n* Reduce the semantic corruption of Prompt Expansion.\n\n### 1.0.51\n\n* Speed up Prompt Expansion a bit.\n\n### 1.0.50\n\n* Prompt expansion and a \"Raw mode\" to turn it off (similar to Midjourney's \"raw\").\n\n### 1.0.45\n\n* Reworked SAG, removed unnecessary patch\n* Reworked anisotropic filters for faster compute.\n* Replaced with guided anisotropic filter for less distortion.\n\n### 1.0.41\n\n(The update of Fooocus will be paused for a period of time for AUTOMATIC1111 sd-webui 1.6.X, and some features will also be implemented as webui extensions)\n\n### 1.0.40\n\n* Behaviors reverted to 1.0.36 again (refiner steps). The 1.0.36 is too perfect and too typical; beating 1.0.36 is just impossible.\n\n### 1.0.39\n\n* Reverted unstable changes between 1.0.37 and 1.0.38 .\n* Increased refiner steps to half of sampling steps.\n\n### 1.0.36\n\n* Change gaussian kernel to anisotropic kernel.\n\n### 1.0.34\n\n* Random seed restoring.\n\n### 1.0.33\n\n* Hide items in log when images are removed.\n\n### 1.0.32\n\n* Fooocus private log\n\n### 1.0.31\n\n* Fix typo and UI.\n\n### 1.0.29\n\n* Added \"Advanced->Advanced->Advanced\" block for future development.\n\n### 1.0.29\n\n* Fix overcook problem in 1.0.28\n\n### 1.0.28\n\n* SAG implemented\n\n### 1.0.27\n\n* Fix small problem in textbox css \n\n### 1.0.25\n\n* support sys.argv --listen --share --port\n\n### 1.0.24\n\n* Taller input textbox.\n\n### 1.0.23\n\n* Added some hints on linux after UI start so users know the App does not fail.\n\n### 1.0.20\n\n* Support linux.\n\n### 1.0.20\n\n* Speed-up text encoder.\n\n### 1.0.20\n\n* Re-write UI to use async codes: (1) for faster start, and (2) for better live preview.\n* Removed opencv dependency\n* Plan to support Linux soon\n\n### 1.0.19\n\n* Unlock to allow changing model.\n\n### 1.0.17\n\n* Change default model to SDXL-1.0-vae-0.9. (This means the models will be downloaded again, but we should do it as early as possible so that all new users only need to download once. Really sorry for day-0 users. But frankly this is not too late considering that the project is just publicly available in less than 24 hours - if it has been a week then we will prefer more lightweight tricks to update.)\n\n### 1.0.16\n\n* Implemented \"Fooocus/outputs\" folder for saving user results.\n* Ignored cv2 errors when preview fails.\n* Mentioned future AMD support in Readme.\n* Created this log.\n\n### 1.0.15\n\nPublicly available.\n\n### 1.0.0\n\nInitial Version.\n"
  },
  {
    "path": "webui.py",
    "content": "import gradio as gr\nimport random\nimport os\nimport json\nimport time\nimport shared\nimport modules.config\nimport fooocus_version\nimport modules.html\nimport modules.async_worker as worker\nimport modules.constants as constants\nimport modules.flags as flags\nimport modules.gradio_hijack as grh\nimport modules.style_sorter as style_sorter\nimport modules.meta_parser\nimport args_manager\nimport copy\nimport launch\nfrom extras.inpaint_mask import SAMOptions\n\nfrom modules.sdxl_styles import legal_style_names\nfrom modules.private_logger import get_current_html_path\nfrom modules.ui_gradio_extensions import reload_javascript\nfrom modules.auth import auth_enabled, check_auth\nfrom modules.util import is_json\n\ndef get_task(*args):\n    args = list(args)\n    args.pop(0)\n\n    return worker.AsyncTask(args=args)\n\ndef generate_clicked(task: worker.AsyncTask):\n    import ldm_patched.modules.model_management as model_management\n\n    with model_management.interrupt_processing_mutex:\n        model_management.interrupt_processing = False\n    # outputs=[progress_html, progress_window, progress_gallery, gallery]\n\n    if len(task.args) == 0:\n        return\n\n    execution_start_time = time.perf_counter()\n    finished = False\n\n    yield gr.update(visible=True, value=modules.html.make_progress_html(1, 'Waiting for task to start ...')), \\\n        gr.update(visible=True, value=None), \\\n        gr.update(visible=False, value=None), \\\n        gr.update(visible=False)\n\n    worker.async_tasks.append(task)\n\n    while not finished:\n        time.sleep(0.01)\n        if len(task.yields) > 0:\n            flag, product = task.yields.pop(0)\n            if flag == 'preview':\n\n                # help bad internet connection by skipping duplicated preview\n                if len(task.yields) > 0:  # if we have the next item\n                    if task.yields[0][0] == 'preview':   # if the next item is also a preview\n                        # print('Skipped one preview for better internet connection.')\n                        continue\n\n                percentage, title, image = product\n                yield gr.update(visible=True, value=modules.html.make_progress_html(percentage, title)), \\\n                    gr.update(visible=True, value=image) if image is not None else gr.update(), \\\n                    gr.update(), \\\n                    gr.update(visible=False)\n            if flag == 'results':\n                yield gr.update(visible=True), \\\n                    gr.update(visible=True), \\\n                    gr.update(visible=True, value=product), \\\n                    gr.update(visible=False)\n            if flag == 'finish':\n                if not args_manager.args.disable_enhance_output_sorting:\n                    product = sort_enhance_images(product, task)\n\n                yield gr.update(visible=False), \\\n                    gr.update(visible=False), \\\n                    gr.update(visible=False), \\\n                    gr.update(visible=True, value=product)\n                finished = True\n\n                # delete Fooocus temp images, only keep gradio temp images\n                if args_manager.args.disable_image_log:\n                    for filepath in product:\n                        if isinstance(filepath, str) and os.path.exists(filepath):\n                            os.remove(filepath)\n\n    execution_time = time.perf_counter() - execution_start_time\n    print(f'Total time: {execution_time:.2f} seconds')\n    return\n\n\ndef sort_enhance_images(images, task):\n    if not task.should_enhance or len(images) <= task.images_to_enhance_count:\n        return images\n\n    sorted_images = []\n    walk_index = task.images_to_enhance_count\n\n    for index, enhanced_img in enumerate(images[:task.images_to_enhance_count]):\n        sorted_images.append(enhanced_img)\n        if index not in task.enhance_stats:\n            continue\n        target_index = walk_index + task.enhance_stats[index]\n        if walk_index < len(images) and target_index <= len(images):\n            sorted_images += images[walk_index:target_index]\n        walk_index += task.enhance_stats[index]\n\n    return sorted_images\n\n\ndef inpaint_mode_change(mode, inpaint_engine_version):\n    assert mode in modules.flags.inpaint_options\n\n    # inpaint_additional_prompt, outpaint_selections, example_inpaint_prompts,\n    # inpaint_disable_initial_latent, inpaint_engine,\n    # inpaint_strength, inpaint_respective_field\n\n    if mode == modules.flags.inpaint_option_detail:\n        return [\n            gr.update(visible=True), gr.update(visible=False, value=[]),\n            gr.Dataset.update(visible=True, samples=modules.config.example_inpaint_prompts),\n            False, 'None', 0.5, 0.0\n        ]\n\n    if inpaint_engine_version == 'empty':\n        inpaint_engine_version = modules.config.default_inpaint_engine_version\n\n    if mode == modules.flags.inpaint_option_modify:\n        return [\n            gr.update(visible=True), gr.update(visible=False, value=[]),\n            gr.Dataset.update(visible=False, samples=modules.config.example_inpaint_prompts),\n            True, inpaint_engine_version, 1.0, 0.0\n        ]\n\n    return [\n        gr.update(visible=False, value=''), gr.update(visible=True),\n        gr.Dataset.update(visible=False, samples=modules.config.example_inpaint_prompts),\n        False, inpaint_engine_version, 1.0, 0.618\n    ]\n\n\nreload_javascript()\n\ntitle = f'Fooocus {fooocus_version.version}'\n\nif isinstance(args_manager.args.preset, str):\n    title += ' ' + args_manager.args.preset\n\nshared.gradio_root = gr.Blocks(title=title).queue()\n\nwith shared.gradio_root:\n    currentTask = gr.State(worker.AsyncTask(args=[]))\n    inpaint_engine_state = gr.State('empty')\n    with gr.Row():\n        with gr.Column(scale=2):\n            with gr.Row():\n                progress_window = grh.Image(label='Preview', show_label=True, visible=False, height=768,\n                                            elem_classes=['main_view'])\n                progress_gallery = gr.Gallery(label='Finished Images', show_label=True, object_fit='contain',\n                                              height=768, visible=False, elem_classes=['main_view', 'image_gallery'])\n            progress_html = gr.HTML(value=modules.html.make_progress_html(32, 'Progress 32%'), visible=False,\n                                    elem_id='progress-bar', elem_classes='progress-bar')\n            gallery = gr.Gallery(label='Gallery', show_label=False, object_fit='contain', visible=True, height=768,\n                                 elem_classes=['resizable_area', 'main_view', 'final_gallery', 'image_gallery'],\n                                 elem_id='final_gallery')\n            with gr.Row():\n                with gr.Column(scale=17):\n                    prompt = gr.Textbox(show_label=False, placeholder=\"Type prompt here or paste parameters.\", elem_id='positive_prompt',\n                                        autofocus=True, lines=3)\n\n                    default_prompt = modules.config.default_prompt\n                    if isinstance(default_prompt, str) and default_prompt != '':\n                        shared.gradio_root.load(lambda: default_prompt, outputs=prompt)\n\n                with gr.Column(scale=3, min_width=0):\n                    generate_button = gr.Button(label=\"Generate\", value=\"Generate\", elem_classes='type_row', elem_id='generate_button', visible=True)\n                    reset_button = gr.Button(label=\"Reconnect\", value=\"Reconnect\", elem_classes='type_row', elem_id='reset_button', visible=False)\n                    load_parameter_button = gr.Button(label=\"Load Parameters\", value=\"Load Parameters\", elem_classes='type_row', elem_id='load_parameter_button', visible=False)\n                    skip_button = gr.Button(label=\"Skip\", value=\"Skip\", elem_classes='type_row_half', elem_id='skip_button', visible=False)\n                    stop_button = gr.Button(label=\"Stop\", value=\"Stop\", elem_classes='type_row_half', elem_id='stop_button', visible=False)\n\n                    def stop_clicked(currentTask):\n                        import ldm_patched.modules.model_management as model_management\n                        currentTask.last_stop = 'stop'\n                        if (currentTask.processing):\n                            model_management.interrupt_current_processing()\n                        return currentTask\n\n                    def skip_clicked(currentTask):\n                        import ldm_patched.modules.model_management as model_management\n                        currentTask.last_stop = 'skip'\n                        if (currentTask.processing):\n                            model_management.interrupt_current_processing()\n                        return currentTask\n\n                    stop_button.click(stop_clicked, inputs=currentTask, outputs=currentTask, queue=False, show_progress=False, _js='cancelGenerateForever')\n                    skip_button.click(skip_clicked, inputs=currentTask, outputs=currentTask, queue=False, show_progress=False)\n            with gr.Row(elem_classes='advanced_check_row'):\n                input_image_checkbox = gr.Checkbox(label='Input Image', value=modules.config.default_image_prompt_checkbox, container=False, elem_classes='min_check')\n                enhance_checkbox = gr.Checkbox(label='Enhance', value=modules.config.default_enhance_checkbox, container=False, elem_classes='min_check')\n                advanced_checkbox = gr.Checkbox(label='Advanced', value=modules.config.default_advanced_checkbox, container=False, elem_classes='min_check')\n            with gr.Row(visible=modules.config.default_image_prompt_checkbox) as image_input_panel:\n                with gr.Tabs(selected=modules.config.default_selected_image_input_tab_id):\n                    with gr.Tab(label='Upscale or Variation', id='uov_tab') as uov_tab:\n                        with gr.Row():\n                            with gr.Column():\n                                uov_input_image = grh.Image(label='Image', source='upload', type='numpy', show_label=False)\n                            with gr.Column():\n                                uov_method = gr.Radio(label='Upscale or Variation:', choices=flags.uov_list, value=modules.config.default_uov_method)\n                                gr.HTML('<a href=\"https://github.com/lllyasviel/Fooocus/discussions/390\" target=\"_blank\">\\U0001F4D4 Documentation</a>')\n                    with gr.Tab(label='Image Prompt', id='ip_tab') as ip_tab:\n                        with gr.Row():\n                            ip_images = []\n                            ip_types = []\n                            ip_stops = []\n                            ip_weights = []\n                            ip_ctrls = []\n                            ip_ad_cols = []\n                            for image_count in range(modules.config.default_controlnet_image_count):\n                                image_count += 1\n                                with gr.Column():\n                                    ip_image = grh.Image(label='Image', source='upload', type='numpy', show_label=False, height=300, value=modules.config.default_ip_images[image_count])\n                                    ip_images.append(ip_image)\n                                    ip_ctrls.append(ip_image)\n                                    with gr.Column(visible=modules.config.default_image_prompt_advanced_checkbox) as ad_col:\n                                        with gr.Row():\n                                            ip_stop = gr.Slider(label='Stop At', minimum=0.0, maximum=1.0, step=0.001, value=modules.config.default_ip_stop_ats[image_count])\n                                            ip_stops.append(ip_stop)\n                                            ip_ctrls.append(ip_stop)\n\n                                            ip_weight = gr.Slider(label='Weight', minimum=0.0, maximum=2.0, step=0.001, value=modules.config.default_ip_weights[image_count])\n                                            ip_weights.append(ip_weight)\n                                            ip_ctrls.append(ip_weight)\n\n                                        ip_type = gr.Radio(label='Type', choices=flags.ip_list, value=modules.config.default_ip_types[image_count], container=False)\n                                        ip_types.append(ip_type)\n                                        ip_ctrls.append(ip_type)\n\n                                        ip_type.change(lambda x: flags.default_parameters[x], inputs=[ip_type], outputs=[ip_stop, ip_weight], queue=False, show_progress=False)\n                                    ip_ad_cols.append(ad_col)\n                        ip_advanced = gr.Checkbox(label='Advanced', value=modules.config.default_image_prompt_advanced_checkbox, container=False)\n                        gr.HTML('* \\\"Image Prompt\\\" is powered by Fooocus Image Mixture Engine (v1.0.1). <a href=\"https://github.com/lllyasviel/Fooocus/discussions/557\" target=\"_blank\">\\U0001F4D4 Documentation</a>')\n\n                        def ip_advance_checked(x):\n                            return [gr.update(visible=x)] * len(ip_ad_cols) + \\\n                                [flags.default_ip] * len(ip_types) + \\\n                                [flags.default_parameters[flags.default_ip][0]] * len(ip_stops) + \\\n                                [flags.default_parameters[flags.default_ip][1]] * len(ip_weights)\n\n                        ip_advanced.change(ip_advance_checked, inputs=ip_advanced,\n                                           outputs=ip_ad_cols + ip_types + ip_stops + ip_weights,\n                                           queue=False, show_progress=False)\n\n                    with gr.Tab(label='Inpaint or Outpaint', id='inpaint_tab') as inpaint_tab:\n                        with gr.Row():\n                            with gr.Column():\n                                inpaint_input_image = grh.Image(label='Image', source='upload', type='numpy', tool='sketch', height=500, brush_color=\"#FFFFFF\", elem_id='inpaint_canvas', show_label=False)\n                                inpaint_advanced_masking_checkbox = gr.Checkbox(label='Enable Advanced Masking Features', value=modules.config.default_inpaint_advanced_masking_checkbox)\n                                inpaint_mode = gr.Dropdown(choices=modules.flags.inpaint_options, value=modules.config.default_inpaint_method, label='Method')\n                                inpaint_additional_prompt = gr.Textbox(placeholder=\"Describe what you want to inpaint.\", elem_id='inpaint_additional_prompt', label='Inpaint Additional Prompt', visible=False)\n                                outpaint_selections = gr.CheckboxGroup(choices=['Left', 'Right', 'Top', 'Bottom'], value=[], label='Outpaint Direction')\n                                example_inpaint_prompts = gr.Dataset(samples=modules.config.example_inpaint_prompts,\n                                                                     label='Additional Prompt Quick List',\n                                                                     components=[inpaint_additional_prompt],\n                                                                     visible=False)\n                                gr.HTML('* Powered by Fooocus Inpaint Engine <a href=\"https://github.com/lllyasviel/Fooocus/discussions/414\" target=\"_blank\">\\U0001F4D4 Documentation</a>')\n                                example_inpaint_prompts.click(lambda x: x[0], inputs=example_inpaint_prompts, outputs=inpaint_additional_prompt, show_progress=False, queue=False)\n\n                            with gr.Column(visible=modules.config.default_inpaint_advanced_masking_checkbox) as inpaint_mask_generation_col:\n                                inpaint_mask_image = grh.Image(label='Mask Upload', source='upload', type='numpy', tool='sketch', height=500, brush_color=\"#FFFFFF\", mask_opacity=1, elem_id='inpaint_mask_canvas')\n                                invert_mask_checkbox = gr.Checkbox(label='Invert Mask When Generating', value=modules.config.default_invert_mask_checkbox)\n                                inpaint_mask_model = gr.Dropdown(label='Mask generation model',\n                                                                 choices=flags.inpaint_mask_models,\n                                                                 value=modules.config.default_inpaint_mask_model)\n                                inpaint_mask_cloth_category = gr.Dropdown(label='Cloth category',\n                                                             choices=flags.inpaint_mask_cloth_category,\n                                                             value=modules.config.default_inpaint_mask_cloth_category,\n                                                             visible=False)\n                                inpaint_mask_dino_prompt_text = gr.Textbox(label='Detection prompt', value='', visible=False, info='Use singular whenever possible', placeholder='Describe what you want to detect.')\n                                example_inpaint_mask_dino_prompt_text = gr.Dataset(\n                                    samples=modules.config.example_enhance_detection_prompts,\n                                    label='Detection Prompt Quick List',\n                                    components=[inpaint_mask_dino_prompt_text],\n                                    visible=modules.config.default_inpaint_mask_model == 'sam')\n                                example_inpaint_mask_dino_prompt_text.click(lambda x: x[0],\n                                                                            inputs=example_inpaint_mask_dino_prompt_text,\n                                                                            outputs=inpaint_mask_dino_prompt_text,\n                                                                            show_progress=False, queue=False)\n\n                                with gr.Accordion(\"Advanced options\", visible=False, open=False) as inpaint_mask_advanced_options:\n                                    inpaint_mask_sam_model = gr.Dropdown(label='SAM model', choices=flags.inpaint_mask_sam_model, value=modules.config.default_inpaint_mask_sam_model)\n                                    inpaint_mask_box_threshold = gr.Slider(label=\"Box Threshold\", minimum=0.0, maximum=1.0, value=0.3, step=0.05)\n                                    inpaint_mask_text_threshold = gr.Slider(label=\"Text Threshold\", minimum=0.0, maximum=1.0, value=0.25, step=0.05)\n                                    inpaint_mask_sam_max_detections = gr.Slider(label=\"Maximum number of detections\", info=\"Set to 0 to detect all\", minimum=0, maximum=10, value=modules.config.default_sam_max_detections, step=1, interactive=True)\n                                generate_mask_button = gr.Button(value='Generate mask from image')\n\n                                def generate_mask(image, mask_model, cloth_category, dino_prompt_text, sam_model, box_threshold, text_threshold, sam_max_detections, dino_erode_or_dilate, dino_debug):\n                                    from extras.inpaint_mask import generate_mask_from_image\n\n                                    extras = {}\n                                    sam_options = None\n                                    if mask_model == 'u2net_cloth_seg':\n                                        extras['cloth_category'] = cloth_category\n                                    elif mask_model == 'sam':\n                                        sam_options = SAMOptions(\n                                            dino_prompt=dino_prompt_text,\n                                            dino_box_threshold=box_threshold,\n                                            dino_text_threshold=text_threshold,\n                                            dino_erode_or_dilate=dino_erode_or_dilate,\n                                            dino_debug=dino_debug,\n                                            max_detections=sam_max_detections,\n                                            model_type=sam_model\n                                        )\n\n                                    mask, _, _, _ = generate_mask_from_image(image, mask_model, extras, sam_options)\n\n                                    return mask\n\n\n                                inpaint_mask_model.change(lambda x: [gr.update(visible=x == 'u2net_cloth_seg')] +\n                                                                    [gr.update(visible=x == 'sam')] * 2 +\n                                                                    [gr.Dataset.update(visible=x == 'sam',\n                                                                                       samples=modules.config.example_enhance_detection_prompts)],\n                                                          inputs=inpaint_mask_model,\n                                                          outputs=[inpaint_mask_cloth_category,\n                                                                   inpaint_mask_dino_prompt_text,\n                                                                   inpaint_mask_advanced_options,\n                                                                   example_inpaint_mask_dino_prompt_text],\n                                                          queue=False, show_progress=False)\n\n                    with gr.Tab(label='Describe', id='describe_tab') as describe_tab:\n                        with gr.Row():\n                            with gr.Column():\n                                describe_input_image = grh.Image(label='Image', source='upload', type='numpy', show_label=False)\n                            with gr.Column():\n                                describe_methods = gr.CheckboxGroup(\n                                    label='Content Type',\n                                    choices=flags.describe_types,\n                                    value=modules.config.default_describe_content_type)\n                                describe_apply_styles = gr.Checkbox(label='Apply Styles', value=modules.config.default_describe_apply_prompts_checkbox)\n                                describe_btn = gr.Button(value='Describe this Image into Prompt')\n                                describe_image_size = gr.Textbox(label='Image Size and Recommended Size', elem_id='describe_image_size', visible=False)\n                                gr.HTML('<a href=\"https://github.com/lllyasviel/Fooocus/discussions/1363\" target=\"_blank\">\\U0001F4D4 Documentation</a>')\n\n                                def trigger_show_image_properties(image):\n                                    value = modules.util.get_image_size_info(image, modules.flags.sdxl_aspect_ratios)\n                                    return gr.update(value=value, visible=True)\n\n                                describe_input_image.upload(trigger_show_image_properties, inputs=describe_input_image,\n                                                            outputs=describe_image_size, show_progress=False, queue=False)\n\n                    with gr.Tab(label='Enhance', id='enhance_tab') as enhance_tab:\n                        with gr.Row():\n                            with gr.Column():\n                                enhance_input_image = grh.Image(label='Use with Enhance, skips image generation', source='upload', type='numpy')\n                                gr.HTML('<a href=\"https://github.com/lllyasviel/Fooocus/discussions/3281\" target=\"_blank\">\\U0001F4D4 Documentation</a>')\n\n                    with gr.Tab(label='Metadata', id='metadata_tab') as metadata_tab:\n                        with gr.Column():\n                            metadata_input_image = grh.Image(label='For images created by Fooocus', source='upload', type='pil')\n                            metadata_json = gr.JSON(label='Metadata')\n                            metadata_import_button = gr.Button(value='Apply Metadata')\n\n                        def trigger_metadata_preview(file):\n                            parameters, metadata_scheme = modules.meta_parser.read_info_from_image(file)\n\n                            results = {}\n                            if parameters is not None:\n                                results['parameters'] = parameters\n\n                            if isinstance(metadata_scheme, flags.MetadataScheme):\n                                results['metadata_scheme'] = metadata_scheme.value\n\n                            return results\n\n                        metadata_input_image.upload(trigger_metadata_preview, inputs=metadata_input_image,\n                                                    outputs=metadata_json, queue=False, show_progress=True)\n\n            with gr.Row(visible=modules.config.default_enhance_checkbox) as enhance_input_panel:\n                with gr.Tabs():\n                    with gr.Tab(label='Upscale or Variation'):\n                        with gr.Row():\n                            with gr.Column():\n                                enhance_uov_method = gr.Radio(label='Upscale or Variation:', choices=flags.uov_list,\n                                                              value=modules.config.default_enhance_uov_method)\n                                enhance_uov_processing_order = gr.Radio(label='Order of Processing',\n                                                                        info='Use before to enhance small details and after to enhance large areas.',\n                                                                        choices=flags.enhancement_uov_processing_order,\n                                                                        value=modules.config.default_enhance_uov_processing_order)\n                                enhance_uov_prompt_type = gr.Radio(label='Prompt',\n                                                                   info='Choose which prompt to use for Upscale or Variation.',\n                                                                   choices=flags.enhancement_uov_prompt_types,\n                                                                   value=modules.config.default_enhance_uov_prompt_type,\n                                                                   visible=modules.config.default_enhance_uov_processing_order == flags.enhancement_uov_after)\n\n                                enhance_uov_processing_order.change(lambda x: gr.update(visible=x == flags.enhancement_uov_after),\n                                                                    inputs=enhance_uov_processing_order,\n                                                                    outputs=enhance_uov_prompt_type,\n                                                                    queue=False, show_progress=False)\n                                gr.HTML('<a href=\"https://github.com/lllyasviel/Fooocus/discussions/3281\" target=\"_blank\">\\U0001F4D4 Documentation</a>')\n                    enhance_ctrls = []\n                    enhance_inpaint_mode_ctrls = []\n                    enhance_inpaint_engine_ctrls = []\n                    enhance_inpaint_update_ctrls = []\n                    for index in range(modules.config.default_enhance_tabs):\n                        with gr.Tab(label=f'#{index + 1}') as enhance_tab_item:\n                            enhance_enabled = gr.Checkbox(label='Enable', value=False, elem_classes='min_check',\n                                                          container=False)\n\n                            enhance_mask_dino_prompt_text = gr.Textbox(label='Detection prompt',\n                                                                       info='Use singular whenever possible',\n                                                                       placeholder='Describe what you want to detect.',\n                                                                       interactive=True,\n                                                                       visible=modules.config.default_enhance_inpaint_mask_model == 'sam')\n                            example_enhance_mask_dino_prompt_text = gr.Dataset(\n                                samples=modules.config.example_enhance_detection_prompts,\n                                label='Detection Prompt Quick List',\n                                components=[enhance_mask_dino_prompt_text],\n                                visible=modules.config.default_enhance_inpaint_mask_model == 'sam')\n                            example_enhance_mask_dino_prompt_text.click(lambda x: x[0],\n                                                                        inputs=example_enhance_mask_dino_prompt_text,\n                                                                        outputs=enhance_mask_dino_prompt_text,\n                                                                        show_progress=False, queue=False)\n\n                            enhance_prompt = gr.Textbox(label=\"Enhancement positive prompt\",\n                                                        placeholder=\"Uses original prompt instead if empty.\",\n                                                        elem_id='enhance_prompt')\n                            enhance_negative_prompt = gr.Textbox(label=\"Enhancement negative prompt\",\n                                                                 placeholder=\"Uses original negative prompt instead if empty.\",\n                                                                 elem_id='enhance_negative_prompt')\n\n                            with gr.Accordion(\"Detection\", open=False):\n                                enhance_mask_model = gr.Dropdown(label='Mask generation model',\n                                                                 choices=flags.inpaint_mask_models,\n                                                                 value=modules.config.default_enhance_inpaint_mask_model)\n                                enhance_mask_cloth_category = gr.Dropdown(label='Cloth category',\n                                                                          choices=flags.inpaint_mask_cloth_category,\n                                                                          value=modules.config.default_inpaint_mask_cloth_category,\n                                                                          visible=modules.config.default_enhance_inpaint_mask_model == 'u2net_cloth_seg',\n                                                                          interactive=True)\n\n                                with gr.Accordion(\"SAM Options\",\n                                                  visible=modules.config.default_enhance_inpaint_mask_model == 'sam',\n                                                  open=False) as sam_options:\n                                    enhance_mask_sam_model = gr.Dropdown(label='SAM model',\n                                                                         choices=flags.inpaint_mask_sam_model,\n                                                                         value=modules.config.default_inpaint_mask_sam_model,\n                                                                         interactive=True)\n                                    enhance_mask_box_threshold = gr.Slider(label=\"Box Threshold\", minimum=0.0,\n                                                                           maximum=1.0, value=0.3, step=0.05,\n                                                                           interactive=True)\n                                    enhance_mask_text_threshold = gr.Slider(label=\"Text Threshold\", minimum=0.0,\n                                                                            maximum=1.0, value=0.25, step=0.05,\n                                                                            interactive=True)\n                                    enhance_mask_sam_max_detections = gr.Slider(label=\"Maximum number of detections\",\n                                                                                info=\"Set to 0 to detect all\",\n                                                                                minimum=0, maximum=10,\n                                                                                value=modules.config.default_sam_max_detections,\n                                                                                step=1, interactive=True)\n\n                            with gr.Accordion(\"Inpaint\", visible=True, open=False):\n                                enhance_inpaint_mode = gr.Dropdown(choices=modules.flags.inpaint_options,\n                                                                   value=modules.config.default_inpaint_method,\n                                                                   label='Method', interactive=True)\n                                enhance_inpaint_disable_initial_latent = gr.Checkbox(\n                                    label='Disable initial latent in inpaint', value=False)\n                                enhance_inpaint_engine = gr.Dropdown(label='Inpaint Engine',\n                                                                     value=modules.config.default_inpaint_engine_version,\n                                                                     choices=flags.inpaint_engine_versions,\n                                                                     info='Version of Fooocus inpaint model. If set, use performance Quality or Speed (no performance LoRAs) for best results.')\n                                enhance_inpaint_strength = gr.Slider(label='Inpaint Denoising Strength',\n                                                                     minimum=0.0, maximum=1.0, step=0.001,\n                                                                     value=1.0,\n                                                                     info='Same as the denoising strength in A1111 inpaint. '\n                                                                          'Only used in inpaint, not used in outpaint. '\n                                                                          '(Outpaint always use 1.0)')\n                                enhance_inpaint_respective_field = gr.Slider(label='Inpaint Respective Field',\n                                                                             minimum=0.0, maximum=1.0, step=0.001,\n                                                                             value=0.618,\n                                                                             info='The area to inpaint. '\n                                                                                  'Value 0 is same as \"Only Masked\" in A1111. '\n                                                                                  'Value 1 is same as \"Whole Image\" in A1111. '\n                                                                                  'Only used in inpaint, not used in outpaint. '\n                                                                                  '(Outpaint always use 1.0)')\n                                enhance_inpaint_erode_or_dilate = gr.Slider(label='Mask Erode or Dilate',\n                                                                            minimum=-64, maximum=64, step=1, value=0,\n                                                                            info='Positive value will make white area in the mask larger, '\n                                                                                 'negative value will make white area smaller. '\n                                                                                 '(default is 0, always processed before any mask invert)')\n                                enhance_mask_invert = gr.Checkbox(label='Invert Mask', value=False)\n\n                            gr.HTML('<a href=\"https://github.com/lllyasviel/Fooocus/discussions/3281\" target=\"_blank\">\\U0001F4D4 Documentation</a>')\n\n                        enhance_ctrls += [\n                            enhance_enabled,\n                            enhance_mask_dino_prompt_text,\n                            enhance_prompt,\n                            enhance_negative_prompt,\n                            enhance_mask_model,\n                            enhance_mask_cloth_category,\n                            enhance_mask_sam_model,\n                            enhance_mask_text_threshold,\n                            enhance_mask_box_threshold,\n                            enhance_mask_sam_max_detections,\n                            enhance_inpaint_disable_initial_latent,\n                            enhance_inpaint_engine,\n                            enhance_inpaint_strength,\n                            enhance_inpaint_respective_field,\n                            enhance_inpaint_erode_or_dilate,\n                            enhance_mask_invert\n                        ]\n\n                        enhance_inpaint_mode_ctrls += [enhance_inpaint_mode]\n                        enhance_inpaint_engine_ctrls += [enhance_inpaint_engine]\n\n                        enhance_inpaint_update_ctrls += [[\n                            enhance_inpaint_mode, enhance_inpaint_disable_initial_latent, enhance_inpaint_engine,\n                            enhance_inpaint_strength, enhance_inpaint_respective_field\n                        ]]\n\n                        enhance_inpaint_mode.change(inpaint_mode_change, inputs=[enhance_inpaint_mode, inpaint_engine_state], outputs=[\n                            inpaint_additional_prompt, outpaint_selections, example_inpaint_prompts,\n                            enhance_inpaint_disable_initial_latent, enhance_inpaint_engine,\n                            enhance_inpaint_strength, enhance_inpaint_respective_field\n                        ], show_progress=False, queue=False)\n\n                        enhance_mask_model.change(\n                            lambda x: [gr.update(visible=x == 'u2net_cloth_seg')] +\n                                      [gr.update(visible=x == 'sam')] * 2 +\n                                      [gr.Dataset.update(visible=x == 'sam',\n                                                         samples=modules.config.example_enhance_detection_prompts)],\n                            inputs=enhance_mask_model,\n                            outputs=[enhance_mask_cloth_category, enhance_mask_dino_prompt_text, sam_options,\n                                     example_enhance_mask_dino_prompt_text],\n                            queue=False, show_progress=False)\n\n            switch_js = \"(x) => {if(x){viewer_to_bottom(100);viewer_to_bottom(500);}else{viewer_to_top();} return x;}\"\n            down_js = \"() => {viewer_to_bottom();}\"\n\n            input_image_checkbox.change(lambda x: gr.update(visible=x), inputs=input_image_checkbox,\n                                        outputs=image_input_panel, queue=False, show_progress=False, _js=switch_js)\n            ip_advanced.change(lambda: None, queue=False, show_progress=False, _js=down_js)\n\n            current_tab = gr.Textbox(value='uov', visible=False)\n            uov_tab.select(lambda: 'uov', outputs=current_tab, queue=False, _js=down_js, show_progress=False)\n            inpaint_tab.select(lambda: 'inpaint', outputs=current_tab, queue=False, _js=down_js, show_progress=False)\n            ip_tab.select(lambda: 'ip', outputs=current_tab, queue=False, _js=down_js, show_progress=False)\n            describe_tab.select(lambda: 'desc', outputs=current_tab, queue=False, _js=down_js, show_progress=False)\n            enhance_tab.select(lambda: 'enhance', outputs=current_tab, queue=False, _js=down_js, show_progress=False)\n            metadata_tab.select(lambda: 'metadata', outputs=current_tab, queue=False, _js=down_js, show_progress=False)\n            enhance_checkbox.change(lambda x: gr.update(visible=x), inputs=enhance_checkbox,\n                                        outputs=enhance_input_panel, queue=False, show_progress=False, _js=switch_js)\n\n        with gr.Column(scale=1, visible=modules.config.default_advanced_checkbox) as advanced_column:\n            with gr.Tab(label='Settings'):\n                if not args_manager.args.disable_preset_selection:\n                    preset_selection = gr.Dropdown(label='Preset',\n                                                   choices=modules.config.available_presets,\n                                                   value=args_manager.args.preset if args_manager.args.preset else \"initial\",\n                                                   interactive=True)\n\n                performance_selection = gr.Radio(label='Performance',\n                                                 choices=flags.Performance.values(),\n                                                 value=modules.config.default_performance,\n                                                 elem_classes=['performance_selection'])\n\n                with gr.Accordion(label='Aspect Ratios', open=False, elem_id='aspect_ratios_accordion') as aspect_ratios_accordion:\n                    aspect_ratios_selection = gr.Radio(label='Aspect Ratios', show_label=False,\n                                                       choices=modules.config.available_aspect_ratios_labels,\n                                                       value=modules.config.default_aspect_ratio,\n                                                       info='width × height',\n                                                       elem_classes='aspect_ratios')\n\n                    aspect_ratios_selection.change(lambda x: None, inputs=aspect_ratios_selection, queue=False, show_progress=False, _js='(x)=>{refresh_aspect_ratios_label(x);}')\n                    shared.gradio_root.load(lambda x: None, inputs=aspect_ratios_selection, queue=False, show_progress=False, _js='(x)=>{refresh_aspect_ratios_label(x);}')\n\n                image_number = gr.Slider(label='Image Number', minimum=1, maximum=modules.config.default_max_image_number, step=1, value=modules.config.default_image_number)\n\n                output_format = gr.Radio(label='Output Format',\n                                         choices=flags.OutputFormat.list(),\n                                         value=modules.config.default_output_format)\n\n                negative_prompt = gr.Textbox(label='Negative Prompt', show_label=True, placeholder=\"Type prompt here.\",\n                                             info='Describing what you do not want to see.', lines=2,\n                                             elem_id='negative_prompt',\n                                             value=modules.config.default_prompt_negative)\n                seed_random = gr.Checkbox(label='Random', value=True)\n                image_seed = gr.Textbox(label='Seed', value=0, max_lines=1, visible=False) # workaround for https://github.com/gradio-app/gradio/issues/5354\n\n                def random_checked(r):\n                    return gr.update(visible=not r)\n\n                def refresh_seed(r, seed_string):\n                    if r:\n                        return random.randint(constants.MIN_SEED, constants.MAX_SEED)\n                    else:\n                        try:\n                            seed_value = int(seed_string)\n                            if constants.MIN_SEED <= seed_value <= constants.MAX_SEED:\n                                return seed_value\n                        except ValueError:\n                            pass\n                        return random.randint(constants.MIN_SEED, constants.MAX_SEED)\n\n                seed_random.change(random_checked, inputs=[seed_random], outputs=[image_seed],\n                                   queue=False, show_progress=False)\n\n                def update_history_link():\n                    if args_manager.args.disable_image_log:\n                        return gr.update(value='')\n\n                    return gr.update(value=f'<a href=\"file={get_current_html_path(output_format)}\" target=\"_blank\">\\U0001F4DA History Log</a>')\n\n                history_link = gr.HTML()\n                shared.gradio_root.load(update_history_link, outputs=history_link, queue=False, show_progress=False)\n\n            with gr.Tab(label='Styles', elem_classes=['style_selections_tab']):\n                style_sorter.try_load_sorted_styles(\n                    style_names=legal_style_names,\n                    default_selected=modules.config.default_styles)\n\n                style_search_bar = gr.Textbox(show_label=False, container=False,\n                                              placeholder=\"\\U0001F50E Type here to search styles ...\",\n                                              value=\"\",\n                                              label='Search Styles')\n                style_selections = gr.CheckboxGroup(show_label=False, container=False,\n                                                    choices=copy.deepcopy(style_sorter.all_styles),\n                                                    value=copy.deepcopy(modules.config.default_styles),\n                                                    label='Selected Styles',\n                                                    elem_classes=['style_selections'])\n                gradio_receiver_style_selections = gr.Textbox(elem_id='gradio_receiver_style_selections', visible=False)\n\n                shared.gradio_root.load(lambda: gr.update(choices=copy.deepcopy(style_sorter.all_styles)),\n                                        outputs=style_selections)\n\n                style_search_bar.change(style_sorter.search_styles,\n                                        inputs=[style_selections, style_search_bar],\n                                        outputs=style_selections,\n                                        queue=False,\n                                        show_progress=False).then(\n                    lambda: None, _js='()=>{refresh_style_localization();}')\n\n                gradio_receiver_style_selections.input(style_sorter.sort_styles,\n                                                       inputs=style_selections,\n                                                       outputs=style_selections,\n                                                       queue=False,\n                                                       show_progress=False).then(\n                    lambda: None, _js='()=>{refresh_style_localization();}')\n\n            with gr.Tab(label='Models'):\n                with gr.Group():\n                    with gr.Row():\n                        base_model = gr.Dropdown(label='Base Model (SDXL only)', choices=modules.config.model_filenames, value=modules.config.default_base_model_name, show_label=True)\n                        refiner_model = gr.Dropdown(label='Refiner (SDXL or SD 1.5)', choices=['None'] + modules.config.model_filenames, value=modules.config.default_refiner_model_name, show_label=True)\n\n                    refiner_switch = gr.Slider(label='Refiner Switch At', minimum=0.1, maximum=1.0, step=0.0001,\n                                               info='Use 0.4 for SD1.5 realistic models; '\n                                                    'or 0.667 for SD1.5 anime models; '\n                                                    'or 0.8 for XL-refiners; '\n                                                    'or any value for switching two SDXL models.',\n                                               value=modules.config.default_refiner_switch,\n                                               visible=modules.config.default_refiner_model_name != 'None')\n\n                    refiner_model.change(lambda x: gr.update(visible=x != 'None'),\n                                         inputs=refiner_model, outputs=refiner_switch, show_progress=False, queue=False)\n\n                with gr.Group():\n                    lora_ctrls = []\n\n                    for i, (enabled, filename, weight) in enumerate(modules.config.default_loras):\n                        with gr.Row():\n                            lora_enabled = gr.Checkbox(label='Enable', value=enabled,\n                                                       elem_classes=['lora_enable', 'min_check'], scale=1)\n                            lora_model = gr.Dropdown(label=f'LoRA {i + 1}',\n                                                     choices=['None'] + modules.config.lora_filenames, value=filename,\n                                                     elem_classes='lora_model', scale=5)\n                            lora_weight = gr.Slider(label='Weight', minimum=modules.config.default_loras_min_weight,\n                                                    maximum=modules.config.default_loras_max_weight, step=0.01, value=weight,\n                                                    elem_classes='lora_weight', scale=5)\n                            lora_ctrls += [lora_enabled, lora_model, lora_weight]\n\n                with gr.Row():\n                    refresh_files = gr.Button(label='Refresh', value='\\U0001f504 Refresh All Files', variant='secondary', elem_classes='refresh_button')\n            with gr.Tab(label='Advanced'):\n                guidance_scale = gr.Slider(label='Guidance Scale', minimum=1.0, maximum=30.0, step=0.01,\n                                           value=modules.config.default_cfg_scale,\n                                           info='Higher value means style is cleaner, vivider, and more artistic.')\n                sharpness = gr.Slider(label='Image Sharpness', minimum=0.0, maximum=30.0, step=0.001,\n                                      value=modules.config.default_sample_sharpness,\n                                      info='Higher value means image and texture are sharper.')\n                gr.HTML('<a href=\"https://github.com/lllyasviel/Fooocus/discussions/117\" target=\"_blank\">\\U0001F4D4 Documentation</a>')\n                dev_mode = gr.Checkbox(label='Developer Debug Mode', value=modules.config.default_developer_debug_mode_checkbox, container=False)\n\n                with gr.Column(visible=modules.config.default_developer_debug_mode_checkbox) as dev_tools:\n                    with gr.Tab(label='Debug Tools'):\n                        adm_scaler_positive = gr.Slider(label='Positive ADM Guidance Scaler', minimum=0.1, maximum=3.0,\n                                                        step=0.001, value=1.5, info='The scaler multiplied to positive ADM (use 1.0 to disable). ')\n                        adm_scaler_negative = gr.Slider(label='Negative ADM Guidance Scaler', minimum=0.1, maximum=3.0,\n                                                        step=0.001, value=0.8, info='The scaler multiplied to negative ADM (use 1.0 to disable). ')\n                        adm_scaler_end = gr.Slider(label='ADM Guidance End At Step', minimum=0.0, maximum=1.0,\n                                                   step=0.001, value=0.3,\n                                                   info='When to end the guidance from positive/negative ADM. ')\n\n                        refiner_swap_method = gr.Dropdown(label='Refiner swap method', value=flags.refiner_swap_method,\n                                                          choices=['joint', 'separate', 'vae'])\n\n                        adaptive_cfg = gr.Slider(label='CFG Mimicking from TSNR', minimum=1.0, maximum=30.0, step=0.01,\n                                                 value=modules.config.default_cfg_tsnr,\n                                                 info='Enabling Fooocus\\'s implementation of CFG mimicking for TSNR '\n                                                      '(effective when real CFG > mimicked CFG).')\n                        clip_skip = gr.Slider(label='CLIP Skip', minimum=1, maximum=flags.clip_skip_max, step=1,\n                                                 value=modules.config.default_clip_skip,\n                                                 info='Bypass CLIP layers to avoid overfitting (use 1 to not skip any layers, 2 is recommended).')\n                        sampler_name = gr.Dropdown(label='Sampler', choices=flags.sampler_list,\n                                                   value=modules.config.default_sampler)\n                        scheduler_name = gr.Dropdown(label='Scheduler', choices=flags.scheduler_list,\n                                                     value=modules.config.default_scheduler)\n                        vae_name = gr.Dropdown(label='VAE', choices=[modules.flags.default_vae] + modules.config.vae_filenames,\n                                                     value=modules.config.default_vae, show_label=True)\n\n                        generate_image_grid = gr.Checkbox(label='Generate Image Grid for Each Batch',\n                                                          info='(Experimental) This may cause performance problems on some computers and certain internet conditions.',\n                                                          value=False)\n\n                        overwrite_step = gr.Slider(label='Forced Overwrite of Sampling Step',\n                                                   minimum=-1, maximum=200, step=1,\n                                                   value=modules.config.default_overwrite_step,\n                                                   info='Set as -1 to disable. For developer debugging.')\n                        overwrite_switch = gr.Slider(label='Forced Overwrite of Refiner Switch Step',\n                                                     minimum=-1, maximum=200, step=1,\n                                                     value=modules.config.default_overwrite_switch,\n                                                     info='Set as -1 to disable. For developer debugging.')\n                        overwrite_width = gr.Slider(label='Forced Overwrite of Generating Width',\n                                                    minimum=-1, maximum=2048, step=1, value=-1,\n                                                    info='Set as -1 to disable. For developer debugging. '\n                                                         'Results will be worse for non-standard numbers that SDXL is not trained on.')\n                        overwrite_height = gr.Slider(label='Forced Overwrite of Generating Height',\n                                                     minimum=-1, maximum=2048, step=1, value=-1,\n                                                     info='Set as -1 to disable. For developer debugging. '\n                                                          'Results will be worse for non-standard numbers that SDXL is not trained on.')\n                        overwrite_vary_strength = gr.Slider(label='Forced Overwrite of Denoising Strength of \"Vary\"',\n                                                            minimum=-1, maximum=1.0, step=0.001, value=-1,\n                                                            info='Set as negative number to disable. For developer debugging.')\n                        overwrite_upscale_strength = gr.Slider(label='Forced Overwrite of Denoising Strength of \"Upscale\"',\n                                                               minimum=-1, maximum=1.0, step=0.001,\n                                                               value=modules.config.default_overwrite_upscale,\n                                                               info='Set as negative number to disable. For developer debugging.')\n\n                        disable_preview = gr.Checkbox(label='Disable Preview', value=modules.config.default_black_out_nsfw,\n                                                      interactive=not modules.config.default_black_out_nsfw,\n                                                      info='Disable preview during generation.')\n                        disable_intermediate_results = gr.Checkbox(label='Disable Intermediate Results',\n                                                      value=flags.Performance.has_restricted_features(modules.config.default_performance),\n                                                      info='Disable intermediate results during generation, only show final gallery.')\n\n                        disable_seed_increment = gr.Checkbox(label='Disable seed increment',\n                                                             info='Disable automatic seed increment when image number is > 1.',\n                                                             value=False)\n                        read_wildcards_in_order = gr.Checkbox(label=\"Read wildcards in order\", value=False)\n\n                        black_out_nsfw = gr.Checkbox(label='Black Out NSFW', value=modules.config.default_black_out_nsfw,\n                                                     interactive=not modules.config.default_black_out_nsfw,\n                                                     info='Use black image if NSFW is detected.')\n\n                        black_out_nsfw.change(lambda x: gr.update(value=x, interactive=not x),\n                                              inputs=black_out_nsfw, outputs=disable_preview, queue=False,\n                                              show_progress=False)\n\n                        if not args_manager.args.disable_image_log:\n                            save_final_enhanced_image_only = gr.Checkbox(label='Save only final enhanced image',\n                                                                         value=modules.config.default_save_only_final_enhanced_image)\n\n                        if not args_manager.args.disable_metadata:\n                            save_metadata_to_images = gr.Checkbox(label='Save Metadata to Images', value=modules.config.default_save_metadata_to_images,\n                                                                  info='Adds parameters to generated images allowing manual regeneration.')\n                            metadata_scheme = gr.Radio(label='Metadata Scheme', choices=flags.metadata_scheme, value=modules.config.default_metadata_scheme,\n                                                       info='Image Prompt parameters are not included. Use png and a1111 for compatibility with Civitai.',\n                                                       visible=modules.config.default_save_metadata_to_images)\n\n                            save_metadata_to_images.change(lambda x: gr.update(visible=x), inputs=[save_metadata_to_images], outputs=[metadata_scheme],\n                                                           queue=False, show_progress=False)\n\n                    with gr.Tab(label='Control'):\n                        debugging_cn_preprocessor = gr.Checkbox(label='Debug Preprocessors', value=False,\n                                                                info='See the results from preprocessors.')\n                        skipping_cn_preprocessor = gr.Checkbox(label='Skip Preprocessors', value=False,\n                                                               info='Do not preprocess images. (Inputs are already canny/depth/cropped-face/etc.)')\n\n                        mixing_image_prompt_and_vary_upscale = gr.Checkbox(label='Mixing Image Prompt and Vary/Upscale',\n                                                                           value=False)\n                        mixing_image_prompt_and_inpaint = gr.Checkbox(label='Mixing Image Prompt and Inpaint',\n                                                                      value=False)\n\n                        controlnet_softness = gr.Slider(label='Softness of ControlNet', minimum=0.0, maximum=1.0,\n                                                        step=0.001, value=0.25,\n                                                        info='Similar to the Control Mode in A1111 (use 0.0 to disable). ')\n\n                        with gr.Tab(label='Canny'):\n                            canny_low_threshold = gr.Slider(label='Canny Low Threshold', minimum=1, maximum=255,\n                                                            step=1, value=64)\n                            canny_high_threshold = gr.Slider(label='Canny High Threshold', minimum=1, maximum=255,\n                                                             step=1, value=128)\n\n                    with gr.Tab(label='Inpaint'):\n                        debugging_inpaint_preprocessor = gr.Checkbox(label='Debug Inpaint Preprocessing', value=False)\n                        debugging_enhance_masks_checkbox = gr.Checkbox(label='Debug Enhance Masks', value=False,\n                                                                       info='Show enhance masks in preview and final results')\n                        debugging_dino = gr.Checkbox(label='Debug GroundingDINO', value=False,\n                                                     info='Use GroundingDINO boxes instead of more detailed SAM masks')\n                        inpaint_disable_initial_latent = gr.Checkbox(label='Disable initial latent in inpaint', value=False)\n                        inpaint_engine = gr.Dropdown(label='Inpaint Engine',\n                                                     value=modules.config.default_inpaint_engine_version,\n                                                     choices=flags.inpaint_engine_versions,\n                                                     info='Version of Fooocus inpaint model. If set, use performance Quality or Speed (no performance LoRAs) for best results.')\n                        inpaint_strength = gr.Slider(label='Inpaint Denoising Strength',\n                                                     minimum=0.0, maximum=1.0, step=0.001, value=1.0,\n                                                     info='Same as the denoising strength in A1111 inpaint. '\n                                                          'Only used in inpaint, not used in outpaint. '\n                                                          '(Outpaint always use 1.0)')\n                        inpaint_respective_field = gr.Slider(label='Inpaint Respective Field',\n                                                             minimum=0.0, maximum=1.0, step=0.001, value=0.618,\n                                                             info='The area to inpaint. '\n                                                                  'Value 0 is same as \"Only Masked\" in A1111. '\n                                                                  'Value 1 is same as \"Whole Image\" in A1111. '\n                                                                  'Only used in inpaint, not used in outpaint. '\n                                                                  '(Outpaint always use 1.0)')\n                        inpaint_erode_or_dilate = gr.Slider(label='Mask Erode or Dilate',\n                                                            minimum=-64, maximum=64, step=1, value=0,\n                                                            info='Positive value will make white area in the mask larger, '\n                                                                 'negative value will make white area smaller. '\n                                                                 '(default is 0, always processed before any mask invert)')\n                        dino_erode_or_dilate = gr.Slider(label='GroundingDINO Box Erode or Dilate',\n                                                         minimum=-64, maximum=64, step=1, value=0,\n                                                         info='Positive value will make white area in the mask larger, '\n                                                              'negative value will make white area smaller. '\n                                                              '(default is 0, processed before SAM)')\n\n                        inpaint_mask_color = gr.ColorPicker(label='Inpaint brush color', value='#FFFFFF', elem_id='inpaint_brush_color')\n\n                        inpaint_ctrls = [debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine,\n                                         inpaint_strength, inpaint_respective_field,\n                                         inpaint_advanced_masking_checkbox, invert_mask_checkbox, inpaint_erode_or_dilate]\n\n                        inpaint_advanced_masking_checkbox.change(lambda x: [gr.update(visible=x)] * 2,\n                                                                 inputs=inpaint_advanced_masking_checkbox,\n                                                                 outputs=[inpaint_mask_image, inpaint_mask_generation_col],\n                                                                 queue=False, show_progress=False)\n\n                        inpaint_mask_color.change(lambda x: gr.update(brush_color=x), inputs=inpaint_mask_color,\n                                                  outputs=inpaint_input_image,\n                                                  queue=False, show_progress=False)\n\n                    with gr.Tab(label='FreeU'):\n                        freeu_enabled = gr.Checkbox(label='Enabled', value=False)\n                        freeu_b1 = gr.Slider(label='B1', minimum=0, maximum=2, step=0.01, value=1.01)\n                        freeu_b2 = gr.Slider(label='B2', minimum=0, maximum=2, step=0.01, value=1.02)\n                        freeu_s1 = gr.Slider(label='S1', minimum=0, maximum=4, step=0.01, value=0.99)\n                        freeu_s2 = gr.Slider(label='S2', minimum=0, maximum=4, step=0.01, value=0.95)\n                        freeu_ctrls = [freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2]\n\n                def dev_mode_checked(r):\n                    return gr.update(visible=r)\n\n                dev_mode.change(dev_mode_checked, inputs=[dev_mode], outputs=[dev_tools],\n                                queue=False, show_progress=False)\n\n                def refresh_files_clicked():\n                    modules.config.update_files()\n                    results = [gr.update(choices=modules.config.model_filenames)]\n                    results += [gr.update(choices=['None'] + modules.config.model_filenames)]\n                    results += [gr.update(choices=[flags.default_vae] + modules.config.vae_filenames)]\n                    if not args_manager.args.disable_preset_selection:\n                        results += [gr.update(choices=modules.config.available_presets)]\n                    for i in range(modules.config.default_max_lora_number):\n                        results += [gr.update(interactive=True),\n                                    gr.update(choices=['None'] + modules.config.lora_filenames), gr.update()]\n                    return results\n\n                refresh_files_output = [base_model, refiner_model, vae_name]\n                if not args_manager.args.disable_preset_selection:\n                    refresh_files_output += [preset_selection]\n                refresh_files.click(refresh_files_clicked, [], refresh_files_output + lora_ctrls,\n                                    queue=False, show_progress=False)\n\n        state_is_generating = gr.State(False)\n\n        load_data_outputs = [advanced_checkbox, image_number, prompt, negative_prompt, style_selections,\n                             performance_selection, overwrite_step, overwrite_switch, aspect_ratios_selection,\n                             overwrite_width, overwrite_height, guidance_scale, sharpness, adm_scaler_positive,\n                             adm_scaler_negative, adm_scaler_end, refiner_swap_method, adaptive_cfg, clip_skip,\n                             base_model, refiner_model, refiner_switch, sampler_name, scheduler_name, vae_name,\n                             seed_random, image_seed, inpaint_engine, inpaint_engine_state,\n                             inpaint_mode] + enhance_inpaint_mode_ctrls + [generate_button,\n                             load_parameter_button] + freeu_ctrls + lora_ctrls\n\n        if not args_manager.args.disable_preset_selection:\n            def preset_selection_change(preset, is_generating, inpaint_mode):\n                preset_content = modules.config.try_get_preset_content(preset) if preset != 'initial' else {}\n                preset_prepared = modules.meta_parser.parse_meta_from_preset(preset_content)\n\n                default_model = preset_prepared.get('base_model')\n                previous_default_models = preset_prepared.get('previous_default_models', [])\n                checkpoint_downloads = preset_prepared.get('checkpoint_downloads', {})\n                embeddings_downloads = preset_prepared.get('embeddings_downloads', {})\n                lora_downloads = preset_prepared.get('lora_downloads', {})\n                vae_downloads = preset_prepared.get('vae_downloads', {})\n\n                preset_prepared['base_model'], preset_prepared['checkpoint_downloads'] = launch.download_models(\n                    default_model, previous_default_models, checkpoint_downloads, embeddings_downloads, lora_downloads,\n                    vae_downloads)\n\n                if 'prompt' in preset_prepared and preset_prepared.get('prompt') == '':\n                    del preset_prepared['prompt']\n\n                return modules.meta_parser.load_parameter_button_click(json.dumps(preset_prepared), is_generating, inpaint_mode)\n\n\n            def inpaint_engine_state_change(inpaint_engine_version, *args):\n                if inpaint_engine_version == 'empty':\n                    inpaint_engine_version = modules.config.default_inpaint_engine_version\n\n                result = []\n                for inpaint_mode in args:\n                    if inpaint_mode != modules.flags.inpaint_option_detail:\n                        result.append(gr.update(value=inpaint_engine_version))\n                    else:\n                        result.append(gr.update())\n\n                return result\n\n            preset_selection.change(preset_selection_change, inputs=[preset_selection, state_is_generating, inpaint_mode], outputs=load_data_outputs, queue=False, show_progress=True) \\\n                .then(fn=style_sorter.sort_styles, inputs=style_selections, outputs=style_selections, queue=False, show_progress=False) \\\n                .then(lambda: None, _js='()=>{refresh_style_localization();}') \\\n                .then(inpaint_engine_state_change, inputs=[inpaint_engine_state] + enhance_inpaint_mode_ctrls, outputs=enhance_inpaint_engine_ctrls, queue=False, show_progress=False)\n\n        performance_selection.change(lambda x: [gr.update(interactive=not flags.Performance.has_restricted_features(x))] * 11 +\n                                               [gr.update(visible=not flags.Performance.has_restricted_features(x))] * 1 +\n                                               [gr.update(value=flags.Performance.has_restricted_features(x))] * 1,\n                                     inputs=performance_selection,\n                                     outputs=[\n                                         guidance_scale, sharpness, adm_scaler_end, adm_scaler_positive,\n                                         adm_scaler_negative, refiner_switch, refiner_model, sampler_name,\n                                         scheduler_name, adaptive_cfg, refiner_swap_method, negative_prompt, disable_intermediate_results\n                                     ], queue=False, show_progress=False)\n\n        output_format.input(lambda x: gr.update(output_format=x), inputs=output_format)\n\n        advanced_checkbox.change(lambda x: gr.update(visible=x), advanced_checkbox, advanced_column,\n                                 queue=False, show_progress=False) \\\n            .then(fn=lambda: None, _js='refresh_grid_delayed', queue=False, show_progress=False)\n\n        inpaint_mode.change(inpaint_mode_change, inputs=[inpaint_mode, inpaint_engine_state], outputs=[\n            inpaint_additional_prompt, outpaint_selections, example_inpaint_prompts,\n            inpaint_disable_initial_latent, inpaint_engine,\n            inpaint_strength, inpaint_respective_field\n        ], show_progress=False, queue=False)\n\n        # load configured default_inpaint_method\n        default_inpaint_ctrls = [inpaint_mode, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field]\n        for mode, disable_initial_latent, engine, strength, respective_field in [default_inpaint_ctrls] + enhance_inpaint_update_ctrls:\n            shared.gradio_root.load(inpaint_mode_change, inputs=[mode, inpaint_engine_state], outputs=[\n                inpaint_additional_prompt, outpaint_selections, example_inpaint_prompts, disable_initial_latent,\n                engine, strength, respective_field\n            ], show_progress=False, queue=False)\n\n        generate_mask_button.click(fn=generate_mask,\n                                   inputs=[inpaint_input_image, inpaint_mask_model, inpaint_mask_cloth_category,\n                                           inpaint_mask_dino_prompt_text, inpaint_mask_sam_model,\n                                           inpaint_mask_box_threshold, inpaint_mask_text_threshold,\n                                           inpaint_mask_sam_max_detections, dino_erode_or_dilate, debugging_dino],\n                                   outputs=inpaint_mask_image, show_progress=True, queue=True)\n\n        ctrls = [currentTask, generate_image_grid]\n        ctrls += [\n            prompt, negative_prompt, style_selections,\n            performance_selection, aspect_ratios_selection, image_number, output_format, image_seed,\n            read_wildcards_in_order, sharpness, guidance_scale\n        ]\n\n        ctrls += [base_model, refiner_model, refiner_switch] + lora_ctrls\n        ctrls += [input_image_checkbox, current_tab]\n        ctrls += [uov_method, uov_input_image]\n        ctrls += [outpaint_selections, inpaint_input_image, inpaint_additional_prompt, inpaint_mask_image]\n        ctrls += [disable_preview, disable_intermediate_results, disable_seed_increment, black_out_nsfw]\n        ctrls += [adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg, clip_skip]\n        ctrls += [sampler_name, scheduler_name, vae_name]\n        ctrls += [overwrite_step, overwrite_switch, overwrite_width, overwrite_height, overwrite_vary_strength]\n        ctrls += [overwrite_upscale_strength, mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint]\n        ctrls += [debugging_cn_preprocessor, skipping_cn_preprocessor, canny_low_threshold, canny_high_threshold]\n        ctrls += [refiner_swap_method, controlnet_softness]\n        ctrls += freeu_ctrls\n        ctrls += inpaint_ctrls\n\n        if not args_manager.args.disable_image_log:\n            ctrls += [save_final_enhanced_image_only]\n\n        if not args_manager.args.disable_metadata:\n            ctrls += [save_metadata_to_images, metadata_scheme]\n\n        ctrls += ip_ctrls\n        ctrls += [debugging_dino, dino_erode_or_dilate, debugging_enhance_masks_checkbox,\n                  enhance_input_image, enhance_checkbox, enhance_uov_method, enhance_uov_processing_order,\n                  enhance_uov_prompt_type]\n        ctrls += enhance_ctrls\n\n        def parse_meta(raw_prompt_txt, is_generating):\n            loaded_json = None\n            if is_json(raw_prompt_txt):\n                loaded_json = json.loads(raw_prompt_txt)\n\n            if loaded_json is None:\n                if is_generating:\n                    return gr.update(), gr.update(), gr.update()\n                else:\n                    return gr.update(), gr.update(visible=True), gr.update(visible=False)\n\n            return json.dumps(loaded_json), gr.update(visible=False), gr.update(visible=True)\n\n        prompt.input(parse_meta, inputs=[prompt, state_is_generating], outputs=[prompt, generate_button, load_parameter_button], queue=False, show_progress=False)\n\n        load_parameter_button.click(modules.meta_parser.load_parameter_button_click, inputs=[prompt, state_is_generating, inpaint_mode], outputs=load_data_outputs, queue=False, show_progress=False)\n\n        def trigger_metadata_import(file, state_is_generating):\n            parameters, metadata_scheme = modules.meta_parser.read_info_from_image(file)\n            if parameters is None:\n                print('Could not find metadata in the image!')\n                parsed_parameters = {}\n            else:\n                metadata_parser = modules.meta_parser.get_metadata_parser(metadata_scheme)\n                parsed_parameters = metadata_parser.to_json(parameters)\n\n            return modules.meta_parser.load_parameter_button_click(parsed_parameters, state_is_generating, inpaint_mode)\n\n        metadata_import_button.click(trigger_metadata_import, inputs=[metadata_input_image, state_is_generating], outputs=load_data_outputs, queue=False, show_progress=True) \\\n            .then(style_sorter.sort_styles, inputs=style_selections, outputs=style_selections, queue=False, show_progress=False)\n\n        generate_button.click(lambda: (gr.update(visible=True, interactive=True), gr.update(visible=True, interactive=True), gr.update(visible=False, interactive=False), [], True),\n                              outputs=[stop_button, skip_button, generate_button, gallery, state_is_generating]) \\\n            .then(fn=refresh_seed, inputs=[seed_random, image_seed], outputs=image_seed) \\\n            .then(fn=get_task, inputs=ctrls, outputs=currentTask) \\\n            .then(fn=generate_clicked, inputs=currentTask, outputs=[progress_html, progress_window, progress_gallery, gallery]) \\\n            .then(lambda: (gr.update(visible=True, interactive=True), gr.update(visible=False, interactive=False), gr.update(visible=False, interactive=False), False),\n                  outputs=[generate_button, stop_button, skip_button, state_is_generating]) \\\n            .then(fn=update_history_link, outputs=history_link) \\\n            .then(fn=lambda: None, _js='playNotification').then(fn=lambda: None, _js='refresh_grid_delayed')\n\n        reset_button.click(lambda: [worker.AsyncTask(args=[]), False, gr.update(visible=True, interactive=True)] +\n                                   [gr.update(visible=False)] * 6 +\n                                   [gr.update(visible=True, value=[])],\n                           outputs=[currentTask, state_is_generating, generate_button,\n                                    reset_button, stop_button, skip_button,\n                                    progress_html, progress_window, progress_gallery, gallery],\n                           queue=False)\n\n        for notification_file in ['notification.ogg', 'notification.mp3']:\n            if os.path.exists(notification_file):\n                gr.Audio(interactive=False, value=notification_file, elem_id='audio_notification', visible=False)\n                break\n\n        def trigger_describe(modes, img, apply_styles):\n            describe_prompts = []\n            styles = set()\n\n            if flags.describe_type_photo in modes:\n                from extras.interrogate import default_interrogator as default_interrogator_photo\n                describe_prompts.append(default_interrogator_photo(img))\n                styles.update([\"Fooocus V2\", \"Fooocus Enhance\", \"Fooocus Sharp\"])\n\n            if flags.describe_type_anime in modes:\n                from extras.wd14tagger import default_interrogator as default_interrogator_anime\n                describe_prompts.append(default_interrogator_anime(img))\n                styles.update([\"Fooocus V2\", \"Fooocus Masterpiece\"])\n\n            if len(styles) == 0 or not apply_styles:\n                styles = gr.update()\n            else:\n                styles = list(styles)\n\n            if len(describe_prompts) == 0:\n                describe_prompt = gr.update()\n            else:\n                describe_prompt = ', '.join(describe_prompts)\n\n            return describe_prompt, styles\n\n        describe_btn.click(trigger_describe, inputs=[describe_methods, describe_input_image, describe_apply_styles],\n                           outputs=[prompt, style_selections], show_progress=True, queue=True) \\\n            .then(fn=style_sorter.sort_styles, inputs=style_selections, outputs=style_selections, queue=False, show_progress=False) \\\n            .then(lambda: None, _js='()=>{refresh_style_localization();}')\n\n        if args_manager.args.enable_auto_describe_image:\n            def trigger_auto_describe(mode, img, prompt, apply_styles):\n                # keep prompt if not empty\n                if prompt == '':\n                    return trigger_describe(mode, img, apply_styles)\n                return gr.update(), gr.update()\n\n            uov_input_image.upload(trigger_auto_describe, inputs=[describe_methods, uov_input_image, prompt, describe_apply_styles],\n                                   outputs=[prompt, style_selections], show_progress=True, queue=True) \\\n                .then(fn=style_sorter.sort_styles, inputs=style_selections, outputs=style_selections, queue=False, show_progress=False) \\\n                .then(lambda: None, _js='()=>{refresh_style_localization();}')\n\n            enhance_input_image.upload(lambda: gr.update(value=True), outputs=enhance_checkbox, queue=False, show_progress=False) \\\n                .then(trigger_auto_describe, inputs=[describe_methods, enhance_input_image, prompt, describe_apply_styles],\n                      outputs=[prompt, style_selections], show_progress=True, queue=True) \\\n                .then(fn=style_sorter.sort_styles, inputs=style_selections, outputs=style_selections, queue=False, show_progress=False) \\\n                .then(lambda: None, _js='()=>{refresh_style_localization();}')\n\ndef dump_default_english_config():\n    from modules.localization import dump_english_config\n    dump_english_config(grh.all_components)\n\n\n# dump_default_english_config()\n\nshared.gradio_root.launch(\n    inbrowser=args_manager.args.in_browser,\n    server_name=args_manager.args.listen,\n    server_port=args_manager.args.port,\n    share=args_manager.args.share,\n    auth=check_auth if (args_manager.args.share or args_manager.args.listen) and auth_enabled else None,\n    allowed_paths=[modules.config.path_outputs],\n    blocked_paths=[constants.AUTH_FILENAME]\n)\n"
  },
  {
    "path": "wildcards/.gitignore",
    "content": "*.txt\n!animal.txt\n!artist.txt\n!color.txt\n!color_flower.txt\n!extended-color.txt\n!flower.txt\n!nationality.txt"
  },
  {
    "path": "wildcards/animal.txt",
    "content": "Alligator\nAnt\nAntelope\nArmadillo\nBadger\nBat\nBear\nBeaver\nBison\nBoar\nBobcat\nBull\nCamel\nChameleon\nCheetah\nChicken\nChihuahua\nChimpanzee\nChinchilla\nChipmunk\nKomodo Dragon\nCow\nCoyote\nCrocodile\nCrow\nDeer\nDinosaur\nDolphin\nDonkey\nDuck\nEagle\nEel\nElephant\nElk\nEmu\nFalcon\nFerret\nFlamingo\nFlying Squirrel\nGiraffe\nGoose\nGuinea pig\nHawk\nHedgehog\nHippopotamus\nHorse\nHummingbird\nHyena\nJackal\nJaguar\nJellyfish\nKangaroo\nKing Cobra\nKoala bear\nLeopard\nLion\nLizard\nMagpie\nMarten\nMeerkat\nMole\nMonkey\nMoose\nMouse\nOctopus\nOkapi\nOrangutan\nOstrich\nOtter\nOwl\nPanda\nPangolin\nPanther\nPenguin\nPig\nPorcupine\nPossum\nPuma\nQuokka\nRabbit\nRaccoon\nRaven\nReindeer\nRhinoceros\nSeal\nShark\nSheep\nSnail\nSnake\nSparrow\nSpider\nSquirrel\nSwallow\nTiger\nWalrus\nWhale\nWolf\nWombat\nYak\nZebra\n"
  },
  {
    "path": "wildcards/artist.txt",
    "content": "A. J. Casson\nAaron Douglas\nAaron Horkey\nAaron Jasinski\nAaron Siskind\nAbbott Fuller Graves\nAbbott Handerson Thayer\nAbigail Larson\nAbraham Pether\nAbram Efimovich Arkhipov\nAdam Elsheimer\nAdam Hughes\nAdam Martinakis\nAdi Granov\nAdolf Hirémy-Hirschl\nAdolph Gottlieb\nAdolph Menzel\nAdonna Khare\nAdriaen van Ostade\nAdriaen van Utrecht\nAdrian Donoghue\nAdrian Ghenie\nAdrian Smith\nAdrian Tomine\nAdrianus Eversen\nAfarin Sajedi\nAgnes Cecile\nAgnes Lawrence Pelton\nAgnes Martin\nAgostino Arrivabene\nAkihiko Yoshida\nAkira Toriyama\nAkos Major\nAl Capp\nAl Feldstein\nAl Williamson\nAlain Laboile\nAlan Bean\nAlan Kenny\nAlan Lee\nAlan Moore\nAlan Parry\nAlasdair McLellan\nAlbert Benois\nAlbert Bierstadt\nAlbert Dubois-Pillet\nAlbert Edelfelt\nAlbert Gleizes\nAlbert Goodwin\nAlbert Joseph Moore\nAlbert Lynch\nAlbert Marquet\nAlbert Pinkham Ryder\nAlberto Biasi\nAlberto Burri\nAlberto Giacometti\nAlberto Seveso\nAlberto Sughi\nAlbrecht Anker\nAlbrecht Dürer\nAlec Soth\nAlejandro Burdisio\nAleksey Savrasov\nAleksi Briclot\nAlena Aenami\nAlessandro Allori\nAlessandro Gottardo\nAlessio Albi\nAlex Alemany\nAlex Andreev\nAlex Colville\nAlex Garant\nAlex Grey\nAlex Gross\nAlex Hirsch\nAlex Horley\nAlex Howitt\nAlex Katz\nAlex Maleev\nAlex Prager\nAlex Ross\nAlex Russell Flint\nAlex Timmermans\nAlex Toth\nAlexander Archipenko\nAlexander Jansson\nAlexander McQueen\nAlexander Millar\nAlexandr Averin\nAlexandre Benois\nAlexandre Cabanel\nAlexandre Calame\nAlexandre-Évariste Fragonard\nAlexej von Jawlensky\nAlfred Augustus Glendening\nAlfred Cheney Johnston\nAlfred Eisenstaedt\nAlfred Guillou\nAlfred Kubin\nAlfred Munnings\nAlfred Parsons\nAlfred Sisley\nAlfred Stevens\nAlfredo Jaar\nAlgernon Blackwood\nAlice Bailly\nAlice Neel\nAlice Pasquini\nAlice Rahon\nAlison Bechdel\nAliza Razell\nAllie Brosh\nAlma Woodsey Thomas\nAlois Arnegger\nAlphonse Mucha\nAlphonse Osbert\nAlson Skinner Clark\nAlvar Aalto\nÁlvaro Siza\nAlvin Langdon Coburn\nAlyssa Monks\nAmanda Clark\nAmanda Sage\nAmandine van Ray\nAmbrosius Benson\nAmbrosius Bosschaert\nAmedeo Modigliani\nAmy Earles\nAmy Judd\nAmy Sillman\nAnato Finnstark\nAnatoly Metlan\nAnders Zorn\nAndo Fuchs\nAndre de Dienes\nAndre Derain\nAndré Kertész\nAndre Kohn\nAndré Lhote\nAndré Masson\nAndre Norton\nAndre-Charles Boulle\nAndrea Kowch\nAndrea Mantegna\nAndreas Achenbach\nAndreas Franke\nAndreas Levers\nAndreas Rocha\nAndreas Vesalius\nAndrei Markin\nAndrew Atroshenko\nAndrew Ferez\nAndrew Macara\nAndrew Robinson\nAndrew Wyeth\nAndrey Remnev\nAndroid Jones\nAndy Fairhurst\nAndy Goldsworthy\nAndy Kehoe\nAndy Singer\nAndy Warhol\nAngela Barrett\nAngelina Wrona\nAngus McKie\nAnja Millen\nAnja Percival\nAnka Zhuravleva\nAnn Stookey\nAnna Ancher\nAnna Bocek\nAnna Dittmann\nAnne Bachelier\nAnne Brigman\nAnne Dewailly\nAnne Geddes\nAnne McCaffrey\nAnne Packard\nAnne Rothenstein\nAnne Stokes\nAnne Truitt\nAnne-Louis Girodet\nAnni Albers\nAnnibale Carracci\nAnnick Bouvattier\nAnnie Leibovitz\nAnnie Soudain\nAnnie Swynnerton\nAnsel Adams\nAnselm Kiefer\nAntanas Sutkus\nAnthony Thieme\nAnthony van Dyck\nAntoine Blanchard\nAnton Fadeev\nAnton Mauve\nAnton Otto Fischer\nAnton Pieck\nAnton Raphael Mengs\nAnton Semenov\nAntonello da Messina\nAntoni Gaudí\nAntonio Canova\nAntonio J. Manzanedo\nAntonio Mancini\nAntonio Mora\nAntony Gormley\nApollinary Vasnetsov\nApollonia Saintclair\nAquirax Uno\nArchibald Thorburn\nAries Moross\nAristarkh Lentulov\nAristide Maillol\nArmand Guillaumin\nArmand Point\nArnold Böcklin\nAron Demetz\nAron Wiesenfeld\nArshile Gorky\nArt Frahm\nArt Spiegelman\nArtemisia Gentileschi\nArthur Adams\nArthur Boyd\nArthur Dove\nArthur Elgort\nArthur Hacker\nArthur Hughes\nArthur Lismer\nArthur Rackham\nArthur Sarnoff\nArthur Stanley Wilkinson\nArthur Streeton\nArthur Tress\nArthur Wardle\nArtur Bordalo\nArturo Souto\nAry Scheffer\nAsaf Hanuka\nAsger Jorn\nAsher Brown Durand\nAshley Wood\nAtelier Olschinsky\nAtey Ghailan\nAubrey Beardsley\nAudrey Kawasaki\nAugust Friedrich Schenck\nAugust Macke\nAugust Sander\nAuguste Herbin\nAuguste Toulmouche\nAugustus Edwin Mulready\nAugustus John\nAustin Briggs\nAustin Osman Spare\nAyami Kojima\nBarbara Hepworth\nBarbara Kruger\nBarbara Stauffacher Solomon\nBarbara Takenaga\nBarclay Shaw\nBarkley L. Hendricks\nBarnett Newman\nBarry McGee\nBarry Windsor Smith\nBart Sears\nBartolomé Esteban Murillo\nBasil Gogos\nBastien Lecouffe-Deharme\nBayard Wu\nBeatrix Potter\nBeauford Delaney\nBecky Cloonan\nBella Kotak\nBen Aronson\nBen Goossens\nBen Hatke\nBen Nicholson\nBen Quilty\nBen Shahn\nBen Templesmith\nBenedick Bana\nBenoit B. Mandelbrot\nBernard Buffet\nBernardo Bellotto\nBernardo Strozzi\nBerndnaut Smilde\nBernie Wrightson\nBert Hardy\nBert Stern\nBerthe Morisot\nBertil Nilsson\nBeth Conklin\nBettina Rheims\nBhupen Khakhar\nBill Brandt\nBill Brauer\nBill Carman\nBill Durgin\nBill Gekas\nBill Henson\nBill Jacklin\nBill Medcalf\nBill Sienkiewicz\nBill Traylor\nBill Viola\nBill Ward\nBill Watterson\nBilly Childish\nBjarke Ingels\nBo Bartlett\nBo Chen\nBob Byerley\nBob Clampett\nBob Eggleton\nBob Ross\nBojan Jevtic\nBoris Grigoriev\nBoris Groh\nBoris Kustodiev\nBoris Vallejo\nBrad Kunkle\nBrad Rigney\nBrandon Mably\nBrandon Woelfel\nBrent Heighton\nBrett Weston\nBrett Whiteley\nBrian Bolland\nBrian Despain\nBrian Froud\nBrian K. Vaughan\nBrian Kesinger\nBrian M. Viveros\nBrian Mashburn\nBrian Stelfreeze\nBrian Sum\nBriana Mora\nBrice Marden\nBridget Bate Tichenor\nBridget Riley\nBriton Rivière\nBrooke DiDonato\nBrooke Shaden\nBrothers Grimm\nBrothers Hildebrandt\nBruce Davidson\nBruce Munro\nBruce Nauman\nBruce Pennington\nBruce Timm\nBruno Catalano\nBruno Munari\nBruno Walpoth\nBryan Hitch\nBuckminster Fuller\nBurt Glinn\nButcher Billy\nC. R. W. Nevinson\nCai Guo-Qiang\nCaia Koopman\nCallie Fink\nCamilla d'Errico\nCamille Claudel\nCamille Corot\nCamille Pissarro\nCamille Walala\nCandido Portinari\nCaras Ionut\nCarel Willink\nCarl Barks\nCarl Gustav Carus\nCarl Holsoe\nCarl Kleiner\nCarl Larsson\nCarl Moll\nCarl Spitzweg\nCarlo Crivelli\nCarlo Dolci\nCarlo Scarpa\nCarlos Cruz-Diez\nCarlos Schwabe\nCarne Griffiths\nCarolina Herrera\nCarolyn Blish\nCarrie Ann Baade\nCarrie Graber\nCarrie Mae Weems\nCarsten Holler\nCarsten Meyerdierks\nCasey Baugh\nCasey Childs\nCasey Weldon\nCaspar David Friedrich\nCatherine Hyde\nCatherine Nolin\nCathy Wilkes\nCatrin Welz-Stein\nCecil Beaton\nCecilia Beaux\nCecily Brown\nCedric Seaut\nCerith Wyn Evans\nCFA Voysey\nChad Knight\nChaïm Soutine\nChantal Joffe\nCharles Addams\nCharles Angrand\nCharles Blackman\nCharles Burns\nCharles Camoin\nCharles Courtney Curran\nCharles Demuth\nCharles Dwyer\nCharles E. Burchfield\nCharles Ginner\nCharles Gwathmey\nCharles Le Brun\nCharles Maurice Detmold\nCharles Mellin\nCharles Reiffel\nCharles Rennie Mackintosh\nCharles Robinson\nCharles Schulz\nCharles Sheeler\nCharles Spencelayh\nCharles Tunnicliffe\nCharles Vess\nCharles Willson Peale\nCharles Wysocki\nCharles-Amable Lenoir\nCharles-Francois Daubigny\nCharley Harper\nCharlie Bowater\nCharline von Heyl\nChen Zhen\nChesley Bonestell\nChiara Bautista\nChie Yoshii\nChiharu Shiota\nChiho Aoshima\nChilde Hassam\nChip Zdarsky\nChris Bachalo\nChris Claremont\nChris Cunningham\nChris Dyer\nChris Foss\nChris Friel\nChris Leib\nChris Mars\nChris Moore\nChris Ofili\nChris Riddell\nChris Samnee\nChris Uminga\nChris Van Allsburg\nChris Ware\nChristian Boltanski\nChristian Schad\nChristian Schloe\nChristine Ellger\nChristoffer Relander\nChristophe Jacrot\nChristophe Staelens\nChristophe Vacher\nChristopher Balaskas\nChristopher Ryan McKenney\nChristopher Wool\nChuck Close\nChung Shek\nCicely Mary Barker\nCildo Meireles\nCindy Sherman\nClaes Oldenburg\nClara Peeters\nClarence Gagnon\nClaude Cahun\nClaude Lorrain\nClaude Monet\nClaudia Tremblay\nClay Mann\nClayton Crain\nClemens Ascher\nCleon Peterson\nCliff Chiang\nClifford Coffin\nClint Langley\nClive Barker\nClive Madgwick\nClyde Caldwell\nClyfford Still\nCoby Whitmore\nColes Phillips\nColin Campbell Cooper\nConrad Roset\nConrad Shawcross\nConstant Permeke\nConstantin Brancusi\nConstantin Joffe\nCornelia Parker\nCornelis Springer\nCory Arcangel\nCory Loftis\nCraig Davison\nCraig McCracken\nCraig Mullins\nCraigie Aitchison\nCuno Amiet\nCyril Rolando\nDaan Roosegaarde\nDaido Moriyama\nDain Yoon\nDale Chihuly\nDamien Hirst\nDan Colen\nDan Flavin\nDan Hillier\nDan Matutina\nDan McPharlin\nDan Mumford\nDan Piraro\nDan Witz\nDana Schutz\nDanh Võ\nDaniel Arsham\nDaniel Buren\nDaniel Clowes\nDaniel F. Gerhartz\nDaniel Garber\nDaniel Jaems\nDaniel Libeskind\nDaniel Merriam\nDaniel Ridgway Knight\nDaniela Uhlig\nDanny Lyon\nDanny Roberts\nDante Gabriel Rossetti\nDaria Endresen\nDaria Petrilli\nDariusz Klimczak\nDarwyn Cooke\nDave Coverly\nDave Dorman\nDave Gibbons\nDave McKean\nDave Stevens\nDavid A. Hardy\nDavid Aja\nDavid Alfaro Siqueiros\nDavid B. Mattingly\nDavid Bailey\nDavid Bates\nDavid Bomberg\nDavid Brayne\nDavid Brown Milne\nDavid Burdeny\nDavid Burliuk\nDavid Chipperfield\nDavid Choe\nDavid Downton\nDavid Driskell\nDavid Finch\nDavid Goldblatt\nDavid Hammons\nDavid Hettinger\nDavid Hockney\nDavid Inshaw\nDavid LaChapelle\nDavid Ligare\nDavid Lynch\nDavid Mann\nDavid Michael Bowers\nDavid Mould\nDavid Nordahl\nDavid Palumbo\nDavid Plowden\nDavid Renshaw\nDavid Sims\nDavid Spriggs\nDavid Teniers the Younger\nDavid Tindle\nDavid Tutwiler\nDavid Walker\nDavid Welker\nDavid Wiesner\nDavid Wojnarowicz\nDavid Yarrow\nDavide Sorrenti\nDean Cornwell\nDean Ellis\nDebbie Criswell\nDebbie Fleming Caffery\nDeborah Azzopardi\nDeborah Turbeville\nDee Nickerson\nDeirdre Sullivan-Beeman\nDelphin Enjolras\nDenis Sarazhin\nDennis Stock\nDenys Lasdun\nDerek Gores\nDesmond Morris\nDiane Arbus\nDidier Lourenço\nDiego Dayer\nDiego Rivera\nDiego Velázquez\nDima Dmitriev\nDimitra Milan\nDimitry Roulland\nDino Valls\nDmitri Danish\nDmitry Kustanovich\nDmitry Spiros\nDo Ho Suh\nDod Procter\nDon Bergland\nDon Blanding\nDon Bluth\nDon Lawrence\nDon Maitz\nDonald Judd\nDonato Giancola\nDonna Huanca\nDora Carrington\nDora Maar\nDorina Costras\nDorothea Lange\nDorothea Sharp\nDorothea Tanning\nDorothy Johnstone\nDorothy Lathrop\nDoug Aitken\nDoug Hyde\nDouglas Smith\nDr. Seuss\nDrew Struzan\nDuffy Sheridan\nDustin Nguyen\nDuy Huynh\nE. H. Shepard\nEadweard Muybridge\nEarl Norem\nEastman Johnson\nEbru Sidar\nEd Binkley\nEd Brubaker\nEd Emshwiller\nEd Freeman\nEd Mell\nEd Piskor\nEd Roth\nEddie Campbell\nEddie Colla\nEddie Mendoza\nEdgar Degas\nEdgar Maxence\nEdmondo Senatore\nEdmund Dulac\nEdmund Leighton\nEdmund Tarbell\nEdoardo Tresoldi\nÉdouard Manet\nÉdouard Vuillard\nEduard Gaertner\nEduard Veith\nEdvard Munch\nEdward Atkinson Hornel\nEdward Bawden\nEdward Blair Wilkins\nEdward Burne-Jones\nEdward Cucuel\nEdward Gorey\nEdward Henry Potthast\nEdward Hopper\nEdward Julius Detmold\nEdward Lear\nEdward Moran\nEdward Okuń\nEdward Poynter\nEdward Robert Hughes\nEdward Seago\nEdward Steichen\nEdward Wadsworth\nEdward Weston\nEdwin Austin Abbey\nEdwin Henry Landseer\nEdwin Lord Weeks\nEero Saarinen\nEgon Schiele\nEiichiro Oda\nEiko Ojala\nEileen Agar\nEileen Gray\nEilif Peterssen\nEl Anatsui\nEl Lissitzky\nElaine de Kooning\nEleanor Fortescue-Brickdale\nEleanor Vere Boyle\nElena Paraskeva\nElihu Vedder\nElisabeth Sonrel\nÉlisabeth Vigée Le Brun\nElizabeth Catlett\nElizabeth Gadd\nElke Vogelsang\nEllen Jewett\nEllen von Unwerth\nElliott Erwitt\nEllsworth Kelly\nElly Smallwood\nElsa Beskow\nElsa Bleda\nEmek Golan\nEmerico Imre Toth\nEmil Alzamora\nEmil Carlsen\nEmil Ferris\nEmil Melmoth\nEmil Nolde\nÉmile Bernard\nEmile Claus\nÉmile Gallé\nEmilia Wilk\nEmiliano Ponzi\nEmily Balivet\nEmily Carr\nEmily Kame Kngwarreye\nEmma Geary\nEmma Ríos\nEmmanuelle Moureaux\nEnki Bilal\nEnoch Bolles\nEphraim Moses Lilien\nEric Canete\nEric Carle\nEric Fischl\nEric Ravilious\nEric Wallis\nEric Zener\nErica Hopper\nErich Heckel\nErik Johansson\nErik Jones\nErin Hanson\nErnest Lawson\nErnest Meissonier\nErnest Zacharevic\nErnesto Neto\nErnie Barnes\nErnst Barlach\nErnst Fuchs\nErnst Haas\nErnst Haeckel\nErnst Ludwig Kirchner\nErnst Wilhelm Nay\nErwin Blumenfeld\nEsaias van de Velde\nEsao Andrews\nEtam Cru\nEtel Adnan\nEthan Van Sciver\nÉtienne Adolphe Piot\nEttore Sottsass\nEttore Tito\nEuan Uglow\nEugène Atget\nEugène Boudin\nEugene Delacroix\nEugene Galien-Laloue\nEugène Girardet\nEugène Giraud\nEugène Grasset\nEugene von Guerard\nEustache Le Sueur\nEva Rothschild\nEve Arnold\nEve Ventrue\nEvelyn De Morgan\nEvelyn Dunbar\nEverett Shinn\nEvgeni Gordiets\nEvgeny Lushpin\nEyvind Earle\nEzra Stoller\nFabian Perez\nFabio Hurtado\nFairfield Porter\nFaith Ringgold\nFan Ho\nFang Lijun\nFarel Dalrymple\nFatima Ronquillo\nFay Godwin\nFelice Casorati\nFelicia Simion\nFelicien Rops\nFelipe Pantone\nFelix Gonzalez-Torres\nFelix Vallotton\nFenghua Zhong\nFerdinand Hodler\nFerdinand Keller\nFerdinand Knab\nFerenc Pinter\nFern Isabel Coppedge\nFernand Cormon\nFernand Fonssagrives\nFernand Khnopff\nFernand Leger\nFernand Toussaint\nFernando Amorsolo\nFernando Botero\nFerris Plock\nFilip Hodas\nFilippino Lippi\nFilippo Brunelleschi\nFintan Magee\nFirmin Baes\nFletcher Sibthorp\nFlora Borsi\nFlorence Harrison\nFlorian Nicolle\nFloris Arntzenius\nFord Madox Brown\nFra Angelico\nFrances MacDonald\nFrancesca Woodman\nFrancesco Borromini\nFrancesco Clemente\nFrancesco Guardi\nFrancesco Hayez\nFrancesco Solimena\nFrancine Van Hove\nFrancis Bacon\nFrancis Coates Jones\nFrancis Davis Millet\nFrancis Picabia\nFrancisco de Zurbaran\nFrancisco Goya\nFranco Fontana\nFrançois Boucher\nFrancois Schuiten\nFrank Auerbach\nFrank Bramley\nFrank Cadogan Cowper\nFrank Cho\nFrank Frazetta\nFrank Gehry\nFrank Holl\nFrank Lloyd Wright\nFrank McCarthy\nFrank Miller\nFrank Montague Moore\nFrank Quitely\nFrank Stella\nFrank Thorne\nFrank Weston Benson\nFrank Xavier Leyendecker\nFranklin Booth\nFranklin Carmichael\nFrans Floris\nFrans Francken the Younger\nFrans Hals\nFrans Snyders\nFrantisek Kupka\nFranz Kline\nFranz Marc\nFranz Sedlacek\nFranz Stuck\nFranz West\nFranz Xaver Winterhalter\nFred Calleri\nFred Stein\nFred Tomaselli\nFrederic Bazille\nFrederic Edwin Church\nFrederic Remington\nFrederick Arthur Bridgman\nFrederick Carl Frieseke\nFrederick Cayley Robinson\nFrederick Goodall\nFrederick Judd Waugh\nFrederick Lord Leighton\nFrederick McCubbin\nFrederick Sandys\nFrida Kahlo\nFriedensreich Regentag Dunkelbunt Hundertwasser\nFrieke Janssens\nFrits Thaulow\nFrits Van den Berghe\nFritz Henle\nFritz Scholder\nGabriel Dawe\nGabriel Metsu\nGabriel Pacheco\nGabriel von Max\nGabriele Dell'otto\nGabriele Münter\nGabriele Viertel\nGaetano Pesce\nGail Potocki\nGail Simone\nGareth Pugh\nGari Melchers\nGarry Winogrand\nGary Baseman\nGary Bunt\nGary Hume\nGary Larson\nGary Panter\nGaston Bussière\nGaston Lachaise\nGediminas Pranckevicius\nGemma Correll\nGene Luen Yang\nGenndy Tartakovsky\nGeof Darrow\nGeof Kern\nGeoff Johns\nGeorg Baselitz\nGeorg Jensen\nGeorge Ault\nGeorge Barbier\nGeorge Bellows\nGeorge Birrell\nGeorge Caleb Bingham\nGeorge Callaghan\nGeorge Catlin\nGeorge Christakis\nGeorge Clausen\nGeorge Condo\nGeorge Cruikshank\nGeorge Digalakis\nGeorge Elgar Hicks\nGeorge Frederic Watts\nGeorge Goodwin Kilburne\nGeorge Grosz\nGeorge Henry Boughton\nGeorge Herriman\nGeorge Hillyard Swinstead\nGeorge Hurrell\nGeorge Inness\nGeorge Lucas\nGeorge Luks\nGeorge Morland\nGeorge Pemba\nGeorge Perez\nGeorge Platt Lynes\nGeorge Romney\nGeorge Segal\nGeorge Stefanescu\nGeorge Stubbs\nGeorge Tice\nGeorge Tooker\nGeorge Underwood\nGeorges Braque\nGeorges Clairin\nGeorges de La Tour\nGeorges Lacombe\nGeorges Ribemont-Dessaignes\nGeorges Rouault\nGeorges Rousse\nGeorges Seurat\nGeorgia O'Keeffe\nGeorgy Kurasov\nGerald Brom\nGerald Harvey Jones\nGerard David\nGerard ter Borch\nGerard van Honthorst\nGerd Arntz\nGerda Taro\nGerda Wegener\nGerhard Gluck\nGerhard Munthe\nGerhard Richter\nGermaine Krull\nGertrude Abercrombie\nGertrude Käsebier\nGhada Amer\nGiacomo Balla\nGian Lorenzo Bernini\nGianni Strino\nGifford Beal\nGil Elvgren\nGilbert Garcin\nGilbert Williams\nGino Severini\nGiorgio Barbarelli da Castelfranco\nGiorgio de Chirico\nGiorgio Morandi\nGiorgio Vasari\nGiovanni Battista Gaulli\nGiovanni Battista Piranesi\nGiovanni Battista Tiepolo\nGiovanni Battista Venanzi\nGiovanni Boldini\nGiovanni Domenico Tiepolo\nGiovanni Segantini\nGiuseppe Arcimboldo\nGiuseppe de Nittis\nGjon Mili\nGlen Keane\nGlenn Fabry\nGo Nagai\nGodfrey Kneller\nGodfried Schalcken\nGordon Parks\nGoro Fujita\nGottfried Helnwein\nGovaert Flinck\nGrace Cossington Smith\nGraham Sutherland\nGrandma Moses\nGrant Morrison\nGrant Wood\nGranville Redmond\nGrayson Perry\nGreg Girard\nGreg Hildebrandt\nGreg Land\nGreg Olsen\nGreg Rucka\nGreg Rutkowski\nGreg Simkins\nGreg Tocchini\nGrégoire Guillemin\nGregory Colbert\nGregory Crewdson\nGrete Stern\nGrigory Gluckmann\nGris Grimly\nGuido Borelli da Caluso\nGuido Crepax\nGuido Reni\nGuido van Helten\nGuillaume Seignac\nGuillem H. Pongiluppi\nGuo Pei\nGustav Klimt\nGustave Baumann\nGustave Buchet\nGustave Caillebotte\nGustave Courbet\nGustave Doré\nGustave Loiseau\nGustave Moreau\nGustave Van de Woestijne\nGuy Aroch\nGuy Billout\nGuy Carleton Wiggins\nGuy Denning\nGuy Rose\nGwen John\nGwenda Morgan\nGyörgy Kepes\nH. R. (Hans Ruedi) Giger\nH.P. Lovecraft\nHajime Sorayama\nHal Foster\nHale Woodruff\nHannah Hoch\nHans Andersen Brendekilde\nHans Baldung\nHans Baluschek\nHans Bellmer\nHans Christian Andersen\nHans Haacke\nHans Hartung\nHans Hofmann\nHans Holbein the Elder\nHans Holbein the Younger\nHans Makart\nHans Memling\nHans Thoma\nHans Zatzka\nHarald Sohlberg\nHarold Cazneaux\nHarold Edgerton\nHarold Gilman\nHarold Harvey\nHaroon Mirza\nHarriet Backer\nHarriet Lee-Merrion\nHarry Bertoia\nHarry Callahan\nHarry Clarke\nHarry Watrous\nHarumi Hironaka\nHarvey Kurtzman\nHarvey Stein\nHassan Hajjaj\nHasui Kawase\nHayao Miyazaki\nHayv Kahraman\nHe Jiaying\nHeather Theurer\nHector Guimard\nHedi Slimane\nHein Gorny\nHeiner Luepke\nHeinrich Kley\nHeinrich Lefler\nHelen Allingham\nHelen Frankenthaler\nHelen Levitt\nHelene Knoop\nHelene Schjerfbeck\nHelio Oiticica\nHelmut Newton\nHendrick Avercamp\nHendrick Cornelisz Vroom\nHendrick Goltzius\nHendrick ter Brugghen\nHendrik Kerstens\nHenri Cartier-Bresson\nHenri de Toulouse-Lautrec\nHenri Fantin-Latour\nHenri Le Fauconnier\nHenri Le Sidaner\nHenri Lebasque\nHenri Manguin\nHenri Matisse\nHenri Rousseau\nHenri-Edmond Cross\nHenrietta Harris\nHenriette Grindat\nHenriëtte Ronner-Knip\nHenry Asencio\nHenry Darger\nHenry Fuseli\nHenry Justice Ford\nHenry Moore\nHenry Moret\nHenry Ossawa Tanner\nHenry Raeburn\nHenry Scott Tuke\nHerb Lubalin\nHerb Ritts\nHerb Trimpe\nHerbert Bayer\nHerbert List\nHerman Brood\nHervé Guibert\nHeywood Hardy\nHideyuki Kikuchi\nHieronymus Bosch\nHikari Shimoda\nHilma af Klint\nHippolyte Flandrin\nHirohiko Araki\nHiroshi Nagai\nHiroshi Sugimoto\nHiroshi Yoshida\nHiroyuki-Mitsume Takahashi\nHonoré Daumier\nHope Gangloff\nHorace Pippin\nHorace Vernet\nHorst P. Horst\nHoward Arkley\nHoward Chaykin\nHoward Finster\nHoward Hodgkin\nHoward Pyle\nHoward Schatz\nHoward Terpning\nHowardena Pindell\nHsiao-Ron Cheng\nHubert Robert\nHugh Ferriss\nHugh Kretschmer\nHugues Merle\nHyacinthe Rigaud\nIain Faulkner\nIan Davenport\nIan Howorth\nIan McQue\nIan Miller\nIda Rentoul Outhwaite\nIgor Morski\nIgor Zenin\nIkenaga Yasunari\nIldiko Neer\nIlse Bing\nIlya Kuvshinov\nIlya Mashkov\nIlya Repin\nInessa Garmash\nIngrid Baars\nIngrid Endel\nInio Asano\nInna Mosina\nIrene Sheri\nIris van Herpen\nIrma Stern\nIrving Penn\nIryna Yermolova\nIsaac Cordal\nIsaac Julien\nIsaac Levitan\nIsaac Maimon\nIsaiah Zagar\nIsamu Noguchi\nIsmail Inceoglu\nIto Shinsui\nIvan Aivazovsky\nIvan Albright\nIvan Bilibin\nIvan Fedorovich Choultse\nIvan Marchuk\nIvan Shishkin\nIwona Lifsches\nJ. J. Grandville\nJ. Scott Campbell\nJ.C. Leyendecker\nJ.M.W. Turner\nJacek Malczewski\nJacek Yerka\nJack Butler Yeats\nJack Davis\nJack Gaughan\nJack Hughes\nJack Kirby\nJack Ohman\nJack Spencer\nJack Vettriano\nJack Whitten\nJackson Pollock\nJacob Hashimoto\nJacob Jordaens\nJacob Lawrence\nJacob van Ruisdael\nJacopo Bassano\nJacques Henri Lartigue\nJacques Tardi\nJacques Villon\nJacques-Firmin Beauvarlet\nJacques-Laurent Agasse\nJacques-Louis David\nJake Wood-Evans\nJakub Różalski\nJakub Schikaneder\nJames Abbott McNeill Whistler\nJames Bullough\nJames C. Christensen\nJames Ensor\nJames Gilleard\nJames Gillray\nJames Gurney\nJames Jean\nJames Lee Byars\nJames McIntosh Patrick\nJames Nares\nJames Paick\nJames Pradier\nJames Rosenquist\nJames Stokoe\nJames Thomas Watts\nJames Tissot\nJames Turrell\nJames Wilson Morrice\nJamie Baldridge\nJamie Hawkesworth\nJamie Heiden\nJamie Hewlett\nJamie McKelvie\nJan Brueghel the Elder\nJan Davidsz de Heem\nJan Ditlev\nJan Frans van Bloemen\nJan Mankes\nJan Matejko\nJan Pietersz Saenredam\nJan Saudek\nJan Steen\nJan Toorop\nJan Urschel\nJan van Eyck\nJan van Goyen\nJan van Kessel the Elder\nJan van Scorel\nJane Crowther\nJane Graverol\nJane Newland\nJanek Sedlar\nJanet Delaney\nJanet Echelman\nJanice Sung\nJanine Antoni\nJanne Kahila\nJanuz Miralles\nJarek Kubicki\nJasmine Becket-Griffith\nJason A. Engle\nJason deCaires Taylor\nJason Edmiston\nJason Middlebrook\nJason Pearson\nJasper Francis Cropsey\nJasper Johns\nJaume Plensa\nJay Anacleto\nJay DeFeo\nJean Arp\nJean Auguste Dominique Ingres\nJean Cocteau\nJean Delville\nJean Dubuffet\nJean Fouquet\nJean Giraud\nJean Jullien\nJean Metzinger\nJean Nouvel\nJean Restout the Younger\nJean-Antoine Watteau\nJean-Baptiste Carpeaux\nJean-Baptiste Monge\nJean-François Millet\nJean-Gabriel Domergue\nJean-Honoré Fragonard\nJean-Joseph Benjamin-Constant\nJean-Léon Gérôme\nJean-Louis Forain\nJean-Michel Basquiat\nJean-Paul Riopelle\nJean-Sebastien Rossbach\nJeanloup Sieff\nJeannette Guichard-Bunel\nJeeYoung Lee\nJeff Danziger\nJeff Easley\nJeff Kinney\nJeff Koons\nJeff Legg\nJeff Lemire\nJeff Rowland\nJeff Simpson\nJeff Smith\nJeff Soto\nJeff Wall\nJeffrey Catherine Jones\nJeffrey Smart\nJeffrey Smith\nJeffrey T. Larson\nJennifer Rubell\nJenny Saville\nJeppe Hein\nJeremiah Ketner\nJeremy Geddes\nJeremy Lipking\nJeremy Mann\nJerry Pinkney\nJerry Schatzberg\nJerry Siegel\nJesper Ejsing\nJessica Drossin\nJessica Rossier\nJessie Willcox Smith\nJhonen Vasquez\nJillian Tamaki\nJim Burns\nJim Davis\nJim Dine\nJim Holland\nJim Lee\nJim Lively\nJim Mahfood\nJim Woodring\nJimmy Lawlor\nJindrich Styrsky\nJo Ann Callis\nJoachim Beuckelaer\nJoachim Brohm\nJoachim Patinir\nJoachim Wtewael\nJoan Eardley\nJoan Miró\nJoan Mitchell\nJoana Vasconcelos\nJoao Ruas\nJoaquín Sorolla\nJocelyn Hobbie\nJody Bergsma\nJoe Fenton\nJoe Jusko\nJoe Kubert\nJoe Madureira\nJoe Quesada\nJoe Shuster\nJoel Meyerowitz\nJoel Rea\nJoel Robison\nJoel Sternfeld\nJohan Christian Dahl\nJohan Messely\nJohannes Itten\nJohannes Jan Schoonhoven\nJohannes Vermeer\nJohannes Voss\nJohfra Bosschart\nJohn Anster Fitzgerald\nJohn Atkinson Grimshaw\nJohn Batho\nJohn Bauer\nJohn Berkey\nJohn Blanche\nJohn Brack\nJohn Bratby\nJohn Buscema\nJohn Butler Yeats\nJohn Cassaday\nJohn Chamberlain\nJohn Closterman\nJohn Collier\nJohn Constable\nJohn Crome\nJohn Currin\nJohn Duncan\nJohn Everett Millais\nJohn Frederick Kensett\nJohn French Sloan\nJohn Harris\nJohn Hejduk\nJohn Henry Twachtman\nJohn Holcroft\nJohn Howe\nJohn Hoyland\nJohn James Audubon\nJohn Kenn Mortensen\nJohn La Farge\nJohn Larriva\nJohn Lavery\nJohn Lowrie Morrison\nJohn Lurie\nJohn Martin\nJohn Mckinstry\nJohn Melhuish Strudwick\nJohn Pawson\nJohn Perceval\nJohn Philip Falter\nJohn Piper\nJohn Pitre\nJohn Reuss\nJohn Roddam Spencer Stanhope\nJohn Ruskin\nJohn Salminen\nJohn Sell Cotman\nJohn Singer Sargent\nJohn Sloane\nJohn T. Biggers\nJohn Tenniel\nJohn Trumbull\nJohn Watkiss\nJohn Wayne Gacy\nJohn White Alexander\nJohn Wilhelm\nJohn William Godward\nJohn William Waterhouse\nJohnson Tsang\nJon Burgerman\nJon Foster\nJon J Muth\nJon Klassen\nJon McNaught\nJonas Burgert\nJonas Lie\nJonathan Lasker\nJonathan Meese\nJonathan Wolstenholme\nJoram Roukes\nJosan Gonzalez\nJosé Clemente Orozco\nJoseba Elorza\nJosef Albers\nJosef Capek\nJosef Kote\nJoseph Beuys\nJoseph Clement Coll\nJoseph Cornell\nJoseph Ducreux\nJoseph Farquharson\nJoseph Lorusso\nJoseph Stella\nJosephine Wall\nJosh Adamski\nJosh Keyes\nJoshua Reynolds\nJoyce Kozloff\nJózsef Rippl-Rónai\nJuan Gris\nJudith Leyster\nJudy Chicago\nJuergen Teller\nJules Bastien-Lepage\nJules Breton\nJules Cheret\nJules Pascin\nJules Tavernier\nJulian Opie\nJulian Schnabel\nJuliana Nan\nJulie Bell\nJulie Blackmon\nJulie Mehretu\nJulio Larraz\nJulio Le Parc\nJun Kaneko\nJunji Ito\nJunko Mizuno\nJusepe de Ribera\nJustin Gaffrey\nJustin Gerard\nJustin Roiland\nKadir Nelson\nKaethe Butcher\nKaja Foglio\nKara Walker\nKarel Thole\nKaren Knorr\nKaren Wallis\nKarl Blossfeldt\nKarl Friedrich Schinkel\nKarl Gerstner\nKarl Knaths\nKarl Schmidt-Rottluff\nKarol Bak\nKate Beaton\nKate Greenaway\nKaterina Belkina\nKäthe Kollwitz\nKathrin Longhurst\nKathryn Morris Trotter\nKati Horna\nKatia Chausheva\nKatsuhiro Otomo\nKatsushika Hokusai\nKawanabe Kyōsai\nKay Nielsen\nKay Sage\nKazimir Malevich\nKazuki Takamatsu\nKazuo Koike\nKazuo Shiraga\nKees Scherer\nKees van Dongen\nKehinde Wiley\nKeith Haring\nKeith Mallett\nKeith Negley\nKeith Parkinson\nKelly Freas\nKelly McKernan\nKelly Sue Deconnick\nKelly Vivanco\nKen Kelly\nKen Sugimori\nKengo Kuma\nKenne Gregoire\nKenneth Noland\nKenneth Rocafort\nKenny Scharf\nKenro Izu\nKent Monkman\nKentaro Miura\nKerby Rosanes\nKerry James Marshall\nKestutis Kasparavicius\nKevin McNeal\nKevin Sloan\nKieron Gillen\nKiki Smith\nKilian Eng\nKim Jung Gi\nKim Keever\nKirsty Mitchell\nKishin Shinoyama\nKitagawa Utamaro\nKitty Lange Kielland\nKlaus Janson\nKlaus Pillon\nKlaus Wittmann\nKoloman Moser\nKonstantin Korovin\nKonstantin Somov\nKonstantin Yuon\nKoson Ohara\nKrenz Cushart\nKris Knight\nKuno Veeber\nKurt Hutton\nKurt Schwitters\nKurt Wenner\nKuzma Petrov-Vodkin\nKyffin Williams\nKylli Sparre\nL. Birge Harrison\nL. S. Lowry\nLarry Carlson\nLarry Elmore\nLarry Fink\nLarry Poons\nLarry Sultan\nLászló Moholy-Nagy\nLaura Makabresku\nLaure Albin Guillot\nLaurel Burch\nLauren Faust\nLaurent Baheux\nLaurent Grasso\nLaurie Greasley\nLaurie Lipton\nLavinia Fontana\nLawren Harris\nLawrence Alma-Tadema\nLawrence Weiner\nLeandro Erlich\nLeanne Surfleet\nLee Bogle\nLee Bontecou\nLee Jeffries\nLee Krasner\nLee Madgwick\nLeiji Matsumoto\nLeo Putz\nLéon Bakst\nLeon Kossoff\nLeon Spilliaert\nLeonardo da Vinci\nLeonetto Cappiello\nLeonid Afremov\nLeonor Fini\nLeonora Carrington\nLeRoy Neiman\nLes Edwards\nLesser Ury\nLeszek Bujnowski\nLeticia Gillett\nLev Lagorio\nLewis Morley\nLi Wei\nLiam Gillick\nLiam Sharp\nLiam Wong\nLilia Alvarado\nLilla Cabot Perry\nLillian Bassman\nLinnea Strid\nLisa Frank\nLisa Holloway\nLise Deharme\nLisette Model\nLiu Ye\nLois Greenfield\nLois van Baarle\nLorenz Hideyoshi\nLoretta Lux\nLori Earley\nLorser Feitelson\nLoui Jover\nLouis Anquetin\nLouis Aston Knight\nLouis Comfort Tiffany\nLouis Faurer\nLouis Icart\nLouis Janmot\nLouis Kahn\nLouis Majorelle\nLouis Marcoussis\nLouis Rhead\nLouis Stettner\nLouis Valtat\nLouis Wain\nLouis Weldon Hawkins\nLouise Bourgeois\nLouise Dahl-Wolfe\nLovis Corinth\nLowell Herrero\nLuca della Robbia\nLuca Giordano\nLucas Cranach the Elder\nLucas Cranach the Younger\nLucian Freud\nLucien Clergue\nLucien Pissarro\nLucio Fontana\nLucy Glendinning\nLucy Grossmith\nLucy Madox Brown\nLudwig Mies van der Rohe\nLuigi Loir\nLuis Ricardo Falero\nLuis Royo\nLuke Fildes\nLygia Clark\nLynd Ward\nLynda Barry\nLynda Benglis\nLynette Yiadom-Boakye\nLyonel Feininger\nLyubov Popova\nM.C. Escher\nM.F. Husain\nM.W. Kaluta\nMab Graves\nMagali Villeneuve\nMaggi Hambling\nMagnus Enckell\nMaia Flore\nMakoto Shinkai\nMakoto Shinkhai\nMalcolm Howie\nMalcolm Liepke\nMalcolm Teasdale\nMalick Sidibé\nMamoru Oshii\nMan Ray\nMandy Disher\nMandy Jurgens\nMao Hamaguchi\nMarat Safin\nMarc Chagall\nMarc Davis\nMarc Lagrange\nMarc Quinn\nMarc Silvestri\nMarc Simonetti\nMarcel Breuer\nMarcel Duchamp\nMarcel Mouly\nMarcin Sobas\nMarco Mazzoni\nMarek Okon\nMargaret Keane\nMargaret Macdonald Mackintosh\nMargaret Modlin\nMargaret Olley\nMargaret Preston\nMaria Kreyn\nMaria Lassnig\nMaria Sibylla Merian\nMarianna Rothen\nMarianne Breslauer\nMarianne North\nMarianne Stokes\nMarianne von Werefkin\nMarie Laurencin\nMarie Severin\nMarie Spartali Stillman\nMarilyn Minter\nMarina Abramović\nMario Sorrenti\nMario Testino\nMarius Borgeaud\nMarjane Satrapi\nMarjorie Miller\nMark Arian\nMark Briscoe\nMark Brooks\nMark Catesby\nMark Demsteader\nMark Gertler\nMark Henson\nMark Keathley\nMark Kostabi\nMark Lague\nMark Lovett\nMark Rothko\nMark Ryden\nMark Seliger\nMark Steinmetz\nMark Tobey\nMarko Manev\nMarlene Dumas\nMarsden Hartley\nMarta Bevacqua\nMartin Ansin\nMartin Creed\nMartin Deschambault\nMartin Grelle\nMartin Johnson Heade\nMartin Kippenberger\nMartin Parr\nMartin Puryear\nMartin Rak\nMartin Schongauer\nMartin Stranka\nMartin Whatson\nMartin Wittfooth\nMartina Hoffman\nMartine Johanna\nMartiros Saryan\nMary Anning\nMary Beale\nMary Blair\nMary Bradish Titcomb\nMary Cassatt\nMary Ellen Mark\nMary Fedden\nMary Heilmann\nMary Jane Ansell\nMasaaki Sasamoto\nMasamune Shirow\nMat Collishaw\nMati Klarwein\nMatt Bors\nMatt Fraction\nMatt Groening\nMatthew Barney\nMatthias Grünewald\nMatthias Jung\nMatti Suuronen\nMattias Adolfsson\nMaurice de Vlaminck\nMaurice Denis\nMaurice Prendergast\nMaurice Sapiro\nMaurice Sendak\nMaurice Utrillo\nMaurizio Cattelan\nMax Beckmann\nMax Dupain\nMax Ernst\nMax Fleischer\nMax Pechstein\nMax Rive\nMax Weber\nMaxfield Parrish\nMaxime Maufra\nMaximilian Pirner\nMaximilien Luce\nMaya Lin\nMaynard Dixon\nMedardo Rosso\nMeghan Howland\nMehmed Siyah-Kalem\nMelissa Launay\nMelvin Sokolsky\nMeredith Marsone\nMéret Oppenheim\nMeryl McMaster\nMichael Ancher\nMichael Borremans\nMichael Carson\nMichael Cheval\nMichael Cho\nMichael Craig-Martin\nMichael Creese\nMichael Deforge\nMichael Eastman\nMichael Garmash\nMichael Heizer\nMichael Hussar\nMichael Hutter\nMichael Kenna\nMichael Malm\nMichael Page\nMichael Parkes\nMichael Shainblum\nMichael Shapcott\nMichael Sowa\nMichael Sweerts\nMichael Vincent Manalo\nMichael Wesely\nMichael Whelan\nMichal Karcz\nMichal Lisowski\nMichelangelo Pistoletto\nMickalene Thomas\nMiho Hirano\nMikalojus Konstantinas Ciurlionis\nMike Allred\nMike Campau\nMike Dargas\nMike Deodato\nMike Judge\nMike Kelley\nMike Mayhew\nMike Mignola\nMike Ploog\nMike Winkelmann\nMike Worrall\nMikhail Larionov\nMikhail Nesterov\nMikhail Vrubel\nMiki Asai\nMikko Lagerstedt\nMiles Aldridge\nMilo Manara\nMilton Avery\nMilton Glaser\nMimmo Rotella\nMinjae Lee\nMiriam Schapiro\nMiroslav Tichý\nMisha Gordin\nMiss Aniela\nMoise Kisling\nMona Hatoum\nMonia Merlo\nMordecai Ardon\nMorris Hirshfield\nMorris Louis\nMort Künstler\nMoses Soyer\nMoshe Safdie\nMyles Birket Foster\nMyoung Ho Lee\nN.C. Wyeth\nN/A Affandi\nN/A Archillect\nN/A Artgerm\nN/A Balthus\nN/A Banksy\nN/A Beeple\nN/A Bordalo II\nN/A Brassaï\nN/A Bronzino\nN/A Byam Shaw\nN/A Canaletto\nN/A Caravaggio\nN/A Craola\nN/A Domenichino\nN/A Duccio\nN/A El Greco\nN/A Giorgione\nN/A Giotto\nN/A Guercino\nN/A Hergé\nN/A Hiroshige\nN/A HUSH\nN/A Kunisada\nN/A Kurzgesagt\nN/A Masaccio\nN/A Michelangelo\nN/A Moebius\nN/A OSGEMEOS\nN/A Parmigianino\nN/A Pinturicchio\nN/A Pontormo\nN/A Raphael\nN/A RHADS\nN/A ROA\nN/A Rone\nN/A Sardax\nN/A Sparth\nN/A teamLab\nN/A theCHAMBA\nN/A Tintoretto\nN/A Titian\nN/A Tom of Finland\nN/A Yuumei\nNadav Kander\nNam June Paik\nNan Goldin\nNaoki Urasawa\nNaoko Takeuchi\nNaomi Okubo\nNaomi Tydeman\nNaoto Hattori\nNatalia Drepina\nNatalia Goncharova\nNatalia Rak\nNatalie Shau\nNathan Coley\nNathan Spoor\nNathan Wirth\nND Stevenson\nNeal Adams\nNeil Gaiman\nNeil Welliver\nNele Zirnite\nNell Dorr\nNelleke Pieters\nNicholas Hely Hutchinson\nNicholas Hilliard\nNicholas Hughes\nNicholas Roerich\nNick Alm\nNick Knight\nNick Veasey\nNick Walker\nNickolas Muray\nNicola Samori\nNicolaes Maes\nNicolaes van Verendael\nNicolas Bruno\nNicolas de Stael\nNicolas Delort\nNicolas Mignard\nNicolas Poussin\nNicole Eisenman\nNicoletta Ceccoli\nNigel van Wieck\nNike Savvas\nNikolai Ge\nNikolai Lockertsen\nNikolay Makovsky\nNikolina Petolas\nNina Leen\nNjideka Akunyili Crosby\nNoah Bradley\nNobuyoshi Araki\nNoell Oszvald\nNora Heysen\nNoriyoshi Ohrai\nNorman Ackroyd\nNorman Bluhm\nNorman Cornish\nNorman Foster\nNorman Lindsay\nNorman Parkinson\nNorman Rockwell\nOctavio Ocampo\nOdd Nerdrum\nOdilon Redon\nOhara Koson\nOkuda San Miguel\nOlafur Eliasson\nOleg Oprisco\nOleg Shuplyak\nOlive Cotton\nOliver Jeffers\nOliver Wetter\nOlivia Locher\nOlivier Bonhomme\nOlivier Valsecchi\nOrazio Gentileschi\nOsamu Tezuka\nOscar Niemeyer\nOskar Fischinger\nOskar Kokoschka\nOskar Schlemmer\nOssip Zadkine\nOswaldo Guayasamin\nOtto Dix\nOtto Marseus van Schrieck\nOtto Piene\nPablo Picasso\nPam Hawkes\nPamela Colman Smith\nPaolo Roversi\nPaolo Soleri\nPaolo Uccello\nPaolo Veronese\nPascale Campion\nPat Steir\nPatrice Murciano\nPatricia Piccinini\nPatricia Polacco\nPatrick Brown\nPatrick Caulfield\nPatrick Demarchelier\nPatrick Dougherty\nPatrick Heron\nPatrick McHale\nPatrick Nagel\nPatrick Woodroffe\nPatty Maher\nPaul Barson\nPaul Cadmus\nPaul Catherall\nPaul Cézanne\nPaul Chabas\nPaul Corfield\nPaul Delvaux\nPaul Fusco\nPaul Gauguin\nPaul Gustav Fischer\nPaul Hedley\nPaul Henry\nPaul Klee\nPaul Laffoley\nPaul Lehr\nPaul Lovering\nPaul Nash\nPaul Pelletier\nPaul Poiret\nPaul Rader\nPaul Rand\nPaul Ranson\nPaul Rudolph\nPaul Sérusier\nPaul Signac\nPaul Strand\nPaul Wonner\nPaula Modersohn-Becker\nPaula Rego\nPaula Scher\nPaulus Potter\nPawel Kuczynski\nPeder Balke\nPeder Mork Monsted\nPegi Nicol MacLeod\nPendleton Ward\nPercy Tarrant\nPeregrine Heathcote\nPerry Rhodan\nPete Turner\nPeter Andrew Jones\nPeter Bagge\nPeter Basch\nPeter Blake\nPeter Coulson\nPeter Doig\nPeter Eisenman\nPeter Elson\nPeter Gric\nPeter Holme III\nPeter Howson\nPeter Kemp\nPeter Lely\nPeter Lindbergh\nPeter Lippmann\nPeter Max\nPeter Milligan\nPeter Mitchev\nPeter Mohrbacher\nPeter Paul Rubens\nPeter Saville\nPeter Sculthorpe\nPeter Sedgley\nPeter Wileman\nPeter Zumthor\nPetra Cortright\nPetrina Hicks\nPhil Foglio\nPhil Jimenez\nPhil Koch\nPhil Noto\nPhilip Guston\nPhilip McKay\nPhilip Pearlstein\nPhilip Taaffe\nPhilip Treacy\nPhilip Wilson Steer\nPhilip-Lorca diCorcia\nPhilipp Otto Runge\nPhilippe Buchet\nPhilippe de Champaigne\nPhilippe Druillet\nPhilippe Halsman\nPhilippe Parreno\nPhoebe Anna Traquair\nPhyllida Barlow\nPiero della Francesca\nPiero di Cosimo\nPierre Bonnard\nPierre Huyghe\nPierre Pellegrini\nPierre Puvis de Chavannes\nPierre Soulages\nPierre-Auguste Renoir\nPiet Hein Eek\nPiet Mondrian\nPieter Aertsen\nPieter Bruegel the Elder\nPieter Brueghel the Younger\nPieter Claesz\nPieter de Hooch\nPieter Hugo\nPieter Jansz Saenredam\nPieter Nason\nPieter-Jansz van Asch\nPietro Antonio Rotari\nPietro da Cortona\nPietro da Rimini\nPino Daeni\nPipilotti Rist\nPJ Crook\nPol Ledent\nPolixeni Papapetrou\nPompeo Batoni\nPosuka Demizu\nPrudence Heward\nQ Hayashida\nQian Xuan\nQuentin Blake\nQuint Buchholz\nR. Kenton Nelson\nRachel Maclean\nRachel Ruysch\nRachel Whiteread\nRafael Albuquerque\nRafael Zabaleta\nRagnar Kjartansson\nRaimonds Staprans\nRaimundo de Madrazo y Garreta\nRaina Telgemeier\nRalph Bakshi\nRalph Blakelock\nRalph Horsley\nRalph McQuarrie\nRalph Steadman\nRamon Casas\nRandolph Caldecott\nRandolph Stanley Hewton\nRange Murata\nRaoul De Keyser\nRaoul Dufy\nRaphael Kirchner\nRaphael Lacoste\nRaphael Soyer\nRaphaelle Peale\nRashad Alakbarov\nRavi Zupa\nRay Caesar\nRay Collins\nRay Donley\nRay Eames\nRay Metzker\nRaymond Briggs\nRaymond Duchamp-Villon\nRaymond Leech\nRaymond Pettibon\nRaymond Swanland\nRaynald Leclerc\nRebeca Saray\nRebecca Guay\nRebecca Louise Law\nRebecca Sugar\nRegina Valluzzi\nReginald Marsh\nRembrandt van Rijn\nRemedios Varo\nRen Hang\nRenato Guttuso\nRene Burri\nRené Lalique\nRene Magritte\nReylia Slaby\nRicardo Bofill\nRichard Anderson\nRichard Bergh\nRichard Billingham\nRichard Burlet\nRichard Corben\nRichard Dadd\nRichard Deacon\nRichard Diebenkorn\nRichard Doyle\nRichard E. Miller\nRichard Eurich\nRichard Hamilton\nRichard Lindner\nRichard Long\nRichard McGuire\nRichard Meier\nRichard Misrach\nRichard Mosse\nRichard Parkes Bonington\nRichard Phillips\nRichard Pousette-Dart\nRichard S. Johnson\nRichard Scarry\nRichard Schmid\nRichard Serra\nRichard Tuttle\nRick Amor\nRick Guidice\nRick Owens\nRimel Neffati\nRinko Kawauchi\nRob Browning\nRob Gonsalves\nRob Guillory\nRob Hefferan\nRob Liefeld\nRobby Cavanaugh\nRobert Antoine Pinchon\nRobert Bateman\nRobert Bissell\nRobert Campin\nRobert Crumb\nRobert Delaunay\nRobert Gillmor\nRobert Hagan\nRobert Henri\nRobert Indiana\nRobert Irwin\nRobert Mapplethorpe\nRobert McCall\nRobert McGinnis\nRobert Motherwell\nRobert Rauschenberg\nRobert S. Duncanson\nRobert Smithson\nRobert Stivers\nRobert Vonnoh\nRobert Williams\nRoberto Ferri\nRoberto Matta\nRockwell Kent\nRodney Matthews\nRodney Smith\nRoger de La Fresnaye\nRoger Deakins\nRoger Dean\nRogier van der Weyden\nRoland Topor\nRolf Armstrong\nRomaine Brooks\nRoman Vishniac\nRomare Bearden\nRomero Britto\nRomina Ressia\nRon Arad\nRon English\nRon Mueck\nRon Walotsky\nRonald Balfour\nRonald Searle\nRonald Wimberly\nRoni Horn\nRosa Bonheur\nRosalba Carriera\nRose Wylie\nRoss Tran\nRoxy Paine\nRoy DeCarava\nRoy Lichtenstein\nRoz Chast\nRuan Jia\nRudolf Ernst\nRudy Siswanto\nRufino Tamayo\nRui Palha\nRumiko Takahashi\nRupert Bunny\nRupert Vandervell\nRupi Kaur\nRuslan Lobanov\nRuss Mills\nRussell Dauterman\nRuth Bernhard\nRuth Orkin\nRyan Hewett\nRyan McGinley\nRyan Ottley\nRyan Stegman\nRyohei Fuke\nRyohei Hase\nRyoji Ikeda\nSabbas Apterus\nSacha Goldberger\nSailor Moon\nSally Mann\nSally Storch\nSalomon van Ruysdael\nSalvador Dalí\nSalvator Rosa\nSam Bosma\nSam Francis\nSam Gilliam\nSam Guay\nSam Kieth\nSam Spratt\nSam Toft\nSamantha Keely Smith\nSamuel Melton Fisher\nSamuel Peploe\nSamuel van Hoogstraten\nSandra Chevrier\nSandra Dieckmann\nSandro Botticelli\nSandy Skoglund\nSanford Robinson Gifford\nSantiago Calatrava\nSantiago Caruso\nSantiago Rusinol\nSarah Andersen\nSarah Lucas\nSarah Morris\nSarah Sze\nSatoshi Kon\nSaturno Butto\nSaul Leiter\nSaul Steinberg\nScarlett Hooft Graafland\nScott Kolins\nScott Listfield\nScott McCloud\nScott Naismith\nScott Rohlfs\nSean Scully\nSean Yoro\nSebastian Errazuriz\nSebastiano del Piombo\nSerge Attukwei Clottey\nSerge Marshennikov\nSerge Najjar\nSergey Musin\nSergio Aragonés\nSergio Larraín\nSergio Toppi\nSeth Globepainter\nShaun Tan\nSheila Hicks\nShepard Fairey\nSherree Valentine Daines\nSherry Akrami\nShigenori Soejima\nShilin Huang\nShinji Aramaki\nShintaro Kago\nShirin Neshat\nShohei Otomo\nShotaro Ishinomori\nShozo Shimamoto\nShuzo Oshimi\nSidney Nolan\nSidney Sime\nSigne Vilstrup\nSilvestro Lega\nSimeon Solomon\nSimon Birch\nSimon Bisley\nSimon Prades\nSimon Stalenhag\nSimon Vouet\nSimone Bianchi\nSir James Guthrie\nSiya Oum\nSkottie Young\nSlawomir Maniak\nSlim Aarons\nSofonisba Anguissola\nSol LeWitt\nSonia Delaunay\nSopheap Pich\nSophie Anderson\nSou Fujimoto\nSpencer Tunick\nSqueak Carnwath\nStan Berenstain\nStanhope Forbes\nStanisław Szukalski\nStanisław Wyspiański\nStanley Donwood\nStanley Kubrick\nStanley Spencer\nStanley William Hayter\nStasia Burrington\nStefan Gesell\nStephan Martinière\nStephanie Rew\nStephen Darbishire\nStephen Gammell\nStephen Hillenburg\nStephen Mackey\nStephen Ormandy\nStephen Shore\nStephen Shortridge\nStephen Wiltshire\nStephen Youll\nSteve Argyle\nSteve Dillon\nSteve Ditko\nSteve Epting\nSteve Hanks\nSteve Henderson\nSteve Hillier\nSteve Lieber\nSteve McCurry\nSteve Sack\nSteven Holl\nSteven Klein\nSteven Meisel\nSteven Outram\nStorm Thorgerson\nStuart Davis\nStuart Haygarth\nStuart Immonen\nSubodh Gupta\nSudersan Pattnaik\nSui Ishida\nSusan Seddon Boulet\nSuzanne Valadon\nSven Nordqvist\nSverre Fehn\nSyd Mead\nSydney Prior Hall\nTadao Ando\nTaiyō Matsumoto\nTakashi Murakami\nTakato Yamamoto\nTakeshi Obata\nTalbot Hughes\nTamara de Lempicka\nTami Bone\nTanya Shatseva\nTara McPherson\nTaras Loboda\nTarsila do Amaral\nTatiana Suarez\nTatsuo Miyajima\nTatsuro Kiuchi\nTaylor Wessing\nTed Nasmith\nTemmie Chang\nTerada Katsuya\nTerry Dodson\nTerry O'Neill\nTerry Oakes\nTerry Redlin\nTetsuya Nomura\nTeun Hocks\nTex Avery\nTheo van Doesburg\nTheo van Rysselberghe\nTheodor Kittelsen\nThéodore Chassériau\nThéodore Géricault\nTheodore Robinson\nThéophile Steinlen\nThiago Valdi\nThom Mayne\nThomas Barbey\nThomas Benjamin Kennington\nThomas Birch\nThomas Blackshear\nThomas Cole\nThomas Dewing\nThomas Dodd\nThomas Eakins\nThomas Edwin Mostyn\nThomas Gainsborough\nThomas Hart Benton\nThomas Heatherwick\nThomas Kinkade\nThomas Lawrence\nThomas Leuthard\nThomas Moran\nThomas Nast\nThomas Saliot\nThomas Sully\nThomas W Schaller\nThomas Wrede\nThurston Hopkins\nTiago Hoisel\nTibor Nagy\nTiffany Bozic\nTim Burton\nTim Doyle\nTim Etchells\nTim Hildebrandt\nTim Holtz\nTim Okamura\nTim Shumate\nTim Walker\nTim White\nTimothy Easton\nTitus Kaphar\nTJ Drysdale\nToby Fox\nTodd Hido\nTodd McFarlane\nTodd Nauck\nTodd Schorr\nTokujin Yoshioka\nTom Bagshaw\nTom Chambers\nTom Everhart\nTom Fruin\nTom Grummett\nTom Hammick\nTom Killion\nTom Roberts\nTom Thomson\nTom Wesselmann\nTom Whalen\nTomás Saraceno\nTomasz Alen Kopera\nTomer Hanuka\nTomma Abts\nTomokazu Matsuyama\nTon Dubbeldam\nToni Frissell\nTony Conrad\nTony Cragg\nTony DiTerlizzi\nTony Fitzpatrick\nTony Moore\nTony Orrico\nTony Oursler\nTony Sart\nTooth Wu\nToshiko Okanoue\nToshio Saeki\nTove Jansson\nToyo Ito\nTracey Emin\nTracie Grimwood\nTran Nguyen\nTravis Louie\nTrevor Brown\nTrish Mistric\nTristan Eaton\nTroy Brooks\nTruls Espedal\nTsutomu Nihei\nTyler Edlin\nTyler Rayburn\nTyler Shields\nUb Iwerks\nUemura Shoen\nUmberto Boccioni\nUrsula von Rydingsvard\nUtagawa Kuniyoshi\nValentin de Boulogne\nValentin Rekunenko\nValentin Serov\nValerie Hegarty\nValerio Olgiati\nVanessa Beecroft\nVanessa Bell\nVania Zouravliov\nVasily Vereshchagin\nVerner Panton\nVeronika Pinke\nVicente Romero Redondo\nVicto Ngai\nVictor Brauner\nVictor Enrich\nVictor Horta\nVictor Moscoso\nVictor Nizovtsev\nVictor Pasmore\nVictor Vasarely\nVictoria Crowe\nVictoria Selbach\nVija Celmins\nVik Muniz\nViktor Vasnetsov\nVilhelm Hammershoi\nVincent Callebaut\nVincent Desiderio\nVincent Di Fate\nVincent van Gogh\nVirgil Finlay\nVirginia Frances Sterrett\nVito Acconci\nVittorio Matteo Corcos\nVittorio Reggianini\nVivian Maier\nViviane Sassen\nVivienne Westwood\nVladimir Kush\nVladimir Tatlin\nVladimir Volegov\nVytautas Kairiukstis\nW. Eugene Smith\nW. Heath Robinson\nWade Guyton\nWadim Kashin\nWalker Evans\nWalt Disney\nWalt Kelly\nWalter Crane\nWalter Ernest Webster\nWalter Gropius\nWalter Langley\nWalter Launt Palmer\nWalter Percy Day\nWalter Sickert\nWangechi Mutu\nWarren Ellis\nWarwick Goble\nWassily Kandinsky\nWayne Barlowe\nWayne Thiebaud\nWendy Froud\nWes Anderson\nWifredo Lam\nWilhelmina Barns-Graham\nWill Barnet\nWill Eisner\nWillard Metcalf\nWillem Basse\nWillem Claesz. Heda\nWillem de Kooning\nWillem Haenraets\nWillem Kalf\nWillem van Aelst\nWillem van Haecht\nWilli Baumeister\nWilliam Blake\nWilliam Dyce\nWilliam Eggleston\nWilliam Etty\nWilliam Gropper\nWilliam Henry Hunt\nWilliam Henry Margetson\nWilliam Hogarth\nWilliam Holman Hunt\nWilliam James Glackens\nWilliam Kay Blacklock\nWilliam Kentridge\nWilliam Klein\nWilliam Langson Lathrop\nWilliam Larkin\nWilliam Morris\nWilliam Nicholson\nWilliam Oxer\nWilliam Powell Frith\nWilliam Russell Flint\nWilliam S. Burroughs\nWilliam Stanley Haseltine\nWilliam Steig\nWilliam Stout\nWilliam Timlin\nWilliam Trost Richards\nWilliam Turner\nWilliam Wegman\nWilliam Wendt\nWilliam Whitaker\nWilliam Wray\nWilliam Zorach\nWilliam-Adolphe Bouguereau\nWim Delvoye\nWim Wenders\nWinifred Knights\nWinslow Homer\nWinsor McCay\nWlad Safronow\nWolfgang Suschitzky\nWong Kar-wai\nWorthington Whittredge\nWu Guanzhong\nXiaofei Yue\nXu Bing\nYaacov Agam\nYanjun Cheng\nYasuo Kuniyoshi\nYasushi Nirasawa\nYayoi Kusama\nYiannis Moralis\nYigal Ozeri\nYinka Shonibare\nYoann Lossel\nYoh Nagao\nYoji Shinkawa\nYoshitaka Amano\nYoshiyuki Tomino\nYosuke Ueno\nYuko Shimizu\nYuri Shwedoff\nYusuke Murata\nYves Klein\nYves Tanguy\nYvonne Coomber\nZack Snyder\nZaha Hadid\nZdzisław Beksiński\nZena Holloway\nZhang Jingna\nZhang Kechun\nZhichao Cai\nZinaida Serebriakova\nZoe Buckman\n"
  },
  {
    "path": "wildcards/color.txt",
    "content": "aqua\nblack\nblue\nfuchsia\ngray\ngreen\nlime\nmaroon\nnavy\nolive\norange\npurple\nred\nsilver\nteal\nwhite\nyellow\n"
  },
  {
    "path": "wildcards/color_flower.txt",
    "content": "__color__ __flower__\n"
  },
  {
    "path": "wildcards/extended-color.txt",
    "content": "aliceblue\nantiquewhite\naqua\naquamarine\nazure\nbeige\nbisque\nblack\nblanchedalmond\nblue\nblueviolet\nbrown\nburlywood\ncadetblue\nchartreuse\nchocolate\ncoral\ncornflowerblue\ncornsilk\ncrimson\ncyan\ndarkblue\ndarkcyan\ndarkgoldenrod\ndarkgray\ndarkgreen\ndarkgrey\ndarkkhaki\ndarkmagenta\ndarkolivegreen\ndarkorange\ndarkorchid\ndarkred\ndarksalmon\ndarkseagreen\ndarkslateblue\ndarkslategray\ndarkslategrey\ndarkturquoise\ndarkviolet\ndeeppink\ndeepskyblue\ndimgray\ndimgrey\ndodgerblue\nfirebrick\nfloralwhite\nforestgreen\nfuchsia\ngainsboro\nghostwhite\ngold\ngoldenrod\ngray\ngreen\ngreenyellow\ngrey\nhoneydew\nhotpink\nindianred\nindigo\nivory\nkhaki\nlavender\nlavenderblush\nlawngreen\nlemonchiffon\nlightblue\nlightcoral\nlightcyan\nlightgoldenrodyellow\nlightgray\nlightgreen\nlightgrey\nlightpink\nlightsalmon\nlightseagreen\nlightskyblue\nlightslategray\nlightslategrey\nlightsteelblue\nlightyellow\nlime\nlimegreen\nlinen\nmagenta\nmaroon\nmediumaquamarine\nmediumblue\nmediumorchid\nmediumpurple\nmediumseagreen\nmediumslateblue\nmediumspringgreen\nmediumturquoise\nmediumvioletred\nmidnightblue\nmintcream\nmistyrose\nmoccasin\nnavajowhite\nnavy\noldlace\nolive\nolivedrab\norange\norangered\norchid\npalegoldenrod\npalegreen\npaleturquoise\npalevioletred\npapayawhip\npeachpuff\nperu\npink\nplum\npowderblue\npurple\nred\nrosybrown\nroyalblue\nsaddlebrown\nsalmon\nsandybrown\nseagreen\nseashell\nsienna\nsilver\nskyblue\nslateblue\nslategray\nslategrey\nsnow\nspringgreen\nsteelblue\ntan\nteal\nthistle\ntomato\nturquoise\nviolet\nwheat\nwhite\nwhitesmoke\nyellow\nyellowgreen\n"
  },
  {
    "path": "wildcards/flower.txt",
    "content": "Acacia\nAchillea\nAdam's-needle\nAfrican Boxwood\nAfrican Lily\nAgapanthus\nAgeratum\nAgeratum houstonim\nAllium\nAlpina\nAlstroemeria\nAmaranthus hypochondriacus\nAmaryllis\nAmmi majus\nAnconitum\nAnemone\nAnigozanthus\nAnnual Delphinium\nAnthurium\nAntirrhinum majus\nArtichoke thistle\nAsparagus\nAster\nAstilbe\nBaby's Breath\nBachelor's Button\nBanksia\nBellflower\nBig Flax\nBighead Knapweed\nBilly Buttons\nBird of Paradise\nBlazing Star\nBlue Lace Flower\nBoronia\nBouvardia\nBoxwood African\nDiosma\nBuckthorn Variegated\nBuddleia\nBupleurum\nButterfly Bush\nButterfly Orchid\nCalla Lily\nCampanula\nCandytuft\nCanterbury Bells\nCarnation\nCarthamus\nCasa Blanca\nCaspia\nCattleya\nCelosia\nCelosia argenta\nCentaurea cyanus\nChamelaucium\nChimney Bells\nChrysanthemum\nChrysanthemum x morifolium\nClarkia\nCockscomb Crested\nCoffee Bean Berry\nCommon Myrtle\nCommon Yarrow\nCone Flower\nConsolida ambigua\nConvallaria\nCordyline\nCosmos\nCornflower\nCraspedia\nCurly Willow\nCymbidium\nCymbidium Orchid\nDaffodil\nDahlia\nDaisy Mums\nDelphinium Belladonna\nDelphinium Pacific Giant\nDendrobium\nDendrobium Orchid\nDianthus barbatus\nDianthus caryophyllus\nDianthus caryophyllus nana\nErica spp\nEucalyptus seeded\nEucalyptus silver dollar\nEustoma grandiflorum\nFalse Bird of Paradise\nFalse Spirea\nFarewell-To-Spring\nFernleaf Yarrow\nFeverfew\nFlamingo Flower\nFlax New Zealand\nFloss Flower\nFoxtail Fern\nFreesia\nFreesia x hybrida\nFuji Mums\nGardenia\nGay Feather\nGenista\nGerbera\nGerbera Ruby Red\nGinger\nGladiolus\nGladiolus hybrid nanus\nGoat's Beard\nGodetia\nGolden Rod\nGuersney Lily\nGyp\nGypsophila paniculata\nHanging Helicona\nHeath\nHeather\nHelianthus annuus\nHeliconia spp.\nHippeastrum\nHydrangea\nIberis amara\nInca Lily\nIris\nJaphette Orchid\nJonquil\nKnapweed\nLace fern\nLarkspur\nLathyrus odoratus\nLavandula\nLavender\nLiatris\nLilac\nLily\nLilly-of-the-Valley\nLily Casa Blanca\nLily of the Field\nLily of the Nile\nLily Stargazer\nLimonium\nLisianthus\nMarguerite daisy\nMattholia incana\nMelaleuca\nMemosa\nMisty Blue Limonium\nMoluccella laevis\nMonkshood\nMontbretia\nMonte Cassino\nMoon orchid\nMusa\nMyrsine\nMyrtle\nMyrtus\nNephrolepis\nNerine\nNerine Lily\nNigella\nOrnithogalum\nPaeonia\nPainted Tongue\nPaper Reed\nPapyrus lion's head\nPeony\nPeruvian Lily\nPhalaenopsis\nPhilodendron\nPhlox\nPincushion Flower\nPink Mink\nPitt\nPittosporum\nPixie Carnation\nPolianthes tuberosa\nPompon Chrysanthemum\nPoppy Anemone\nPorium\nPussy Willow\nQueen Anne's Lace\nRanunculus\nRed Ribbons\nRice flower\nRose\nRose Bridal Pink\nRose Bridal White\nRose Champagne\nRose Diadem\nRose Emblem\nRose Kardinal\nRose Lady Liberty\nRose Lavanda\nRose Osiana\nRose Royalty\nSafari Sunset\nSafflower\nSage Perennial\nSalix\nSalmon Reagan\nSansevieria\nSaponaria\nSatin Flowers\nSaxicola\nScabiosa\nSchinus\nSea lavender\nShell Flowers\nSnake Plant\nSnapdragon\nSolidago\nSolidaster\nSpeedwell\nSpider Lily\nSpider Mums\nSpray Carnation\nSprengeri Fern\nStar of Bethlehem\nStatice\nStenamezon\nStephanotis\nStrawberry banksia\nStrawflower\nSummer poinsettia\nSummer's Darling\nSunflower\nSweet Pea\nSweet William\nSword Fern\nSyringa vulgaris\nTailflowers\nTassel flower\nThouroughwax\nThroatwort\nTracelium\nTree Fern\nTrumpet Lily\nTuberose\nTulip\nTulipa\nVeronica\nWattle\nWaxflower\nWild Plantain\nWillow curly\nWindflower\nWolfsbane\nZantedeschia\nZinna\nZinnia elegans\n"
  },
  {
    "path": "wildcards/nationality.txt",
    "content": "Afghan\nAlbanian\nAlgerian\nAmerican\nAndorran\nAngolan\nAntiguans\nArgentine\nArmenian\nAustralian\nAustrian\nAzerbaijani\nBahamian\nBahraini\nBangladeshi\nBarbadian\nBarbudans\nBatswana\nBelarusian\nBelgian\nBelizean\nBeninese\nBhutanese\nBolivian\nBosnian\nBrazilian\nBritish\nBruneian\nBulgarian\nBurkinabe\nBurmese\nBurundian\nCambodian\nCameroonian\nCanadian\nCape Verdean\nCentral African\nChadian\nChilean\nChinese\nColombian\nComoran\nCongolese\nCosta Rican\nCroatian\nCuban\nCypriot\nCzech\nDanish\nDjibouti\nDominican\nDutch\nEast Timorese\nEcuadorean\nEgyptian\nEmirati\nEquatorial Guinean\nEritrean\nEstonian\nEthiopian\nFijian\nFilipino\nFinnish\nFrench\nGabonese\nGambian\nGeorgian\nGerman\nGhanaian\nGreek\nGrenadian\nGuatemalan\nGuinea-Bissauan\nGuinean\nGuyanese\nHaitian\nHerzegovinian\nHonduran\nHungarian\nIcelander\nIndian\nIndonesian\nIranian\nIraqi\nIrish\nIsraeli\nItalian\nIvorian\nJamaican\nJapanese\nJordanian\nKazakhstani\nKenyan\nKittian and Nevisian\nKuwaiti\nKyrgyz\nLaotian\nLatvian\nLebanese\nLiberian\nLibyan\nLiechtensteiner\nLithuanian\nLuxembourger\nMacedonian\nMalagasy\nMalawian\nMalaysian\nMaldivan\nMalian\nMaltese\nMarshallese\nMauritanian\nMauritian\nMexican\nMicronesian\nMoldovan\nMonacan\nMongolian\nMontenegrin\nMoroccan\nMosotho\nMotswana\nMozambican\nNamibian\nNauruan\nNepalese\nNew Zealander\nNicaraguan\nNigerian\nNigerien\nNorth Korean\nNorthern Irish\nNorwegian\nOmani\nPakistani\nPalauan\nPalestinian\nPanamanian\nPapua New Guinean\nParaguayan\nPeruvian\nPolish\nPortuguese\nQatari\nRomanian\nRussian\nRwandan\nSaint Lucian\nSalvadoran\nSamoan\nSan Marinese\nSao Tomean\nSaudi\nScottish\nSenegalese\nSerbian\nSeychellois\nSierra Leonean\nSingaporean\nSlovakian\nSlovenian\nSolomon Islander\nSomali\nSouth African\nSouth Korean\nSpanish\nSri Lankan\nSudanese\nSurinamer\nSwazi\nSwedish\nSwiss\nSyrian\nTaiwanese\nTajik\nTanzanian\nThai\nTogolese\nTongan\nTrinidadian or Tobagonian\nTunisian\nTurkish\nTuvaluan\nUgandan\nUkrainian\nUruguayan\nUzbekistani\nVanuatuan\nVenezuelan\nVietnamese\nWelsh\nYemenite\nZambian\nZimbabwean\n"
  }
]